From b005567e30af432396ea1cb5b1404487091aec55 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 23 Sep 2020 15:39:33 -0300
Subject: [PATCH] remove current docs

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-<span class="no">G</span> <span class="kw">&lt;-</span> <span class="fu">generator</span>(<span class="kw">latent_dim</span> <span class="kw">=</span> <span class="fl">100</span>, <span class="kw">out_channels</span> <span class="kw">=</span> <span class="fl">1</span>)
-<span class="no">D</span> <span class="kw">&lt;-</span> <span class="fu">discriminator</span>(<span class="kw">in_channels</span> <span class="kw">=</span> <span class="fl">1</span>)
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-  <span class="kw">if</span> (<span class="fu"><a href="https://rdrr.io/r/base/grep.html">grepl</a></span>(<span class="st">"conv"</span>, <span class="no">m</span>$<span class="no">.classes</span><span class="kw">[[</span><span class="fl">1</span>]])) {
-    <span class="fu"><a href="../../reference/nn_init_normal_.html">nn_init_normal_</a></span>(<span class="no">m</span>$<span class="no">weight</span>$<span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span>(), <span class="fl">0.0</span>, <span class="fl">0.02</span>)
-  } <span class="kw">else</span> <span class="kw">if</span> (<span class="fu"><a href="https://rdrr.io/r/base/grep.html">grepl</a></span>(<span class="st">"batch_norm"</span>, <span class="no">m</span>$<span class="no">.classes</span><span class="kw">[[</span><span class="fl">1</span>]])) {
-    <span class="fu"><a href="../../reference/nn_init_normal_.html">nn_init_normal_</a></span>(<span class="no">m</span>$<span class="no">weight</span>$<span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span>(), <span class="fl">1.0</span>, <span class="fl">0.02</span>)
-    <span class="fu"><a href="../../reference/nn_init_constant_.html">nn_init_constant_</a></span>(<span class="no">m</span>$<span class="no">bias</span>$<span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span>(), <span class="fl">0</span>)
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-<span class="no">G</span>$<span class="fu">to</span>(<span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
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-<span class="no">G_optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_adam.html">optim_adam</a></span>(<span class="no">G</span>$<span class="no">parameters</span>, <span class="kw">lr</span> <span class="kw">=</span> <span class="fl">2</span> * <span class="fl">1e-4</span>, <span class="kw">betas</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.999</span>))
-<span class="no">D_optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_adam.html">optim_adam</a></span>(<span class="no">D</span>$<span class="no">parameters</span>, <span class="kw">lr</span> <span class="kw">=</span> <span class="fl">2</span> * <span class="fl">1e-4</span>, <span class="kw">betas</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.999</span>))
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-<span class="no">fixed_noise</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, <span class="fl">100</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
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-<span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_bce_loss.html">nn_bce_loss</a></span>()
-
-<span class="kw">for</span> (<span class="no">epoch</span> <span class="kw">in</span> <span class="fl">1</span>:<span class="fl">10</span>) {
-
-  <span class="no">pb</span> <span class="kw">&lt;-</span> <span class="kw pkg">progress</span><span class="kw ns">::</span><span class="no">progress_bar</span>$<span class="fu">new</span>(
-    <span class="kw">total</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span>(<span class="no">dl</span>),
-    <span class="kw">format</span> <span class="kw">=</span> <span class="st">"[:bar] :eta Loss D: :lossd Loss G: :lossg"</span>
-  )
-  <span class="no">lossg</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
-  <span class="no">lossd</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
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-  <span class="kw">for</span> (<span class="no">b</span> <span class="kw">in</span> <span class="fu"><a href="../../reference/enumerate.html">enumerate</a></span>(<span class="no">dl</span>)) {
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-    <span class="no">y_fake</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">32</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
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-    <span class="no">noise</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">32</span>, <span class="fl">100</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-    <span class="no">fake</span> <span class="kw">&lt;-</span> <span class="fu">G</span>(<span class="no">noise</span>)
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-    <span class="no">img</span> <span class="kw">&lt;-</span> <span class="no">b</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="fu">to</span>(<span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-
-    <span class="co"># train the discriminator ---</span>
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-
-    <span class="no">D_optimizer</span>$<span class="fu">zero_grad</span>()
-    <span class="no">D_loss</span>$<span class="fu">backward</span>()
-    <span class="no">D_optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-
-    <span class="co"># train the generator ---</span>
-
-    <span class="no">G_loss</span> <span class="kw">&lt;-</span> <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">fake</span>), <span class="no">y_real</span>)
-
-    <span class="no">G_optimizer</span>$<span class="fu">zero_grad</span>()
-    <span class="no">G_loss</span>$<span class="fu">backward</span>()
-    <span class="no">G_optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-
-    <span class="no">lossd</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">lossd</span>, <span class="no">D_loss</span>$<span class="fu">item</span>())
-    <span class="no">lossg</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">lossg</span>, <span class="no">G_loss</span>$<span class="fu">item</span>())
-    <span class="no">pb</span>$<span class="fu">tick</span>(<span class="kw">tokens</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">lossd</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossd</span>), <span class="kw">lossg</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossg</span>)))
-  }
-  <span class="fu">plot_gen</span>(<span class="no">fixed_noise</span>)
-
-  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Epoch %d - Loss D: %3f Loss G: %3f\n"</span>, <span class="no">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossd</span>), <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossg</span>)))
-}</pre></body></html></div>
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diff --git a/static/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js
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index ca349fd6a..000000000
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+++ /dev/null
@@ -1,15 +0,0 @@
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diff --git a/static/docs/articles/examples/mnist-mlp.html b/static/docs/articles/examples/mnist-mlp.html
deleted file mode 100644
index df53285e9..000000000
--- a/static/docs/articles/examples/mnist-mlp.html
+++ /dev/null
@@ -1,247 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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-<title>mnist-mlp • torch</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/united/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
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-      <h1 data-toc-skip>mnist-mlp</h1>
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-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-mlp.Rmd"><code>vignettes/examples/mnist-mlp.Rmd</code></a></small>
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-      <span class="fu"><a href="../../reference/torch_flatten.html">torch_flatten</a></span>(<span class="kw">start_dim</span> <span class="kw">=</span> <span class="fl">2</span>) <span class="kw">%&gt;%</span>
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-    <span class="kw">total</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span>(<span class="no">dl</span>),
-    <span class="kw">format</span> <span class="kw">=</span> <span class="st">"[:bar] :eta Loss: :loss"</span>
-  )
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-  <span class="kw">for</span> (<span class="no">b</span> <span class="kw">in</span> <span class="fu"><a href="../../reference/enumerate.html">enumerate</a></span>(<span class="no">dl</span>)) {
-    <span class="no">optimizer</span>$<span class="fu">zero_grad</span>()
-    <span class="no">output</span> <span class="kw">&lt;-</span> <span class="fu">model</span>(<span class="no">b</span><span class="kw">[[</span><span class="fl">1</span>]])
-    <span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_nll_loss.html">nnf_nll_loss</a></span>(<span class="no">output</span>, <span class="no">b</span><span class="kw">[[</span><span class="fl">2</span>]])
-    <span class="no">loss</span>$<span class="fu">backward</span>()
-    <span class="no">optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-    <span class="no">l</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">l</span>, <span class="no">loss</span>$<span class="fu">item</span>())
-    <span class="no">pb</span>$<span class="fu">tick</span>(<span class="kw">tokens</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">loss</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">l</span>)))
-  }
-
-  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="no">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">l</span>)))
-}</pre></body></html></div>
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diff --git a/static/docs/articles/extending-autograd.html b/static/docs/articles/extending-autograd.html
deleted file mode 100644
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--- a/static/docs/articles/extending-autograd.html
+++ /dev/null
@@ -1,251 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Extending Autograd</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/extending-autograd.Rmd"><code>vignettes/extending-autograd.Rmd</code></a></small>
-      <div class="hidden name"><code>extending-autograd.Rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>Adding operations to autograd requires implementing a new <code>autograd_function</code> for each operation. Recall that <code>autograd_functions</code>s are what <code>autograd</code> uses to compute the results and gradients, and encode the operation history. Every new function requires you to implement 2 methods:</p>
-<ul>
-<li><p><code>forward()</code> - the code that performs the operation. It can take as many arguments as you want, with some of them being optional, if you specify the default values. All kinds of R objects are accepted here. Tensor arguments that track history (i.e., with <code>requires_grad=TRUE</code>) will be converted to ones that don’t track history before the call, and their use will be registered in the graph. Note that this logic won’t traverse lists or any other data structures and will only consider Tensor’s that are direct arguments to the call. You can return either a single Tensor output, or a list of <code>Tensor</code>s if there are multiple outputs. Also, please refer to the docs of <code>autograd_function</code> to find descriptions of useful methods that can be called only from <code>forward()</code>.</p></li>
-<li><p><code>backward()</code> - gradient formula. It will be given as many Tensor arguments as there were outputs, with each of them representing gradient w.r.t. that output. It should return as many <code>Tensor</code>s as there were <code>Tensor's</code> that required gradients in <code>forward</code>, with each of them containing the gradient w.r.t. its corresponding input.</p></li>
-</ul>
-<div id="note" class="section level2">
-<h2 class="hasAnchor">
-<a href="#note" class="anchor"></a>Note</h2>
-<p>It’s the user’s responsibility to use the special functions in the forward’s <code>ctx</code> properly in order to ensure that the new <code>autograd_function</code> works properly with the autograd engine.</p>
-<ul>
-<li><p><code>save_for_backward()</code> must be used when saving input or ouput of the forward to be used later in the backward.</p></li>
-<li><p><code>mark_dirty()</code> must be used to mark any input that is modified inplace by the forward function.</p></li>
-<li><p><code>mark_non_differentiable()</code> must be used to tell the engine if an output is not differentiable.</p></li>
-</ul>
-</div>
-<div id="examples" class="section level2">
-<h2 class="hasAnchor">
-<a href="#examples" class="anchor"></a>Examples</h2>
-<p>Below you can find code for a linear function:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">linear</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/autograd_function.html">autograd_function</a></span>(
-  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">ctx</span>, <span class="no">input</span>, <span class="no">weight</span>, <span class="no">bias</span> <span class="kw">=</span> <span class="kw">NULL</span>) {
-    <span class="no">ctx</span>$<span class="fu">save_for_backward</span>(<span class="kw">input</span> <span class="kw">=</span> <span class="no">input</span>, <span class="kw">weight</span> <span class="kw">=</span> <span class="no">weight</span>, <span class="kw">bias</span> <span class="kw">=</span> <span class="no">bias</span>)
-    <span class="no">output</span> <span class="kw">&lt;-</span> <span class="no">input</span>$<span class="fu">mm</span>(<span class="no">weight</span>$<span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>())
-    <span class="kw">if</span> (!<span class="fu"><a href="https://rdrr.io/r/base/NULL.html">is.null</a></span>(<span class="no">bias</span>))
-      <span class="no">output</span> <span class="kw">&lt;-</span> <span class="no">output</span> + <span class="no">bias</span>$<span class="fu">unsqueeze</span>(<span class="fl">0</span>)$<span class="fu">expand_as</span>(<span class="no">output</span>)
-
-    <span class="no">output</span>
-  },
-  <span class="kw">backward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">ctx</span>, <span class="no">grad_output</span>) {
-
-    <span class="no">s</span> <span class="kw">&lt;-</span> <span class="no">ctx</span>$<span class="no">saved_variables</span>
-
-    <span class="no">grads</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(
-      <span class="kw">input</span> <span class="kw">=</span> <span class="kw">NULL</span>,
-      <span class="kw">weight</span> <span class="kw">=</span> <span class="kw">NULL</span>,
-      <span class="kw">bias</span> <span class="kw">=</span> <span class="kw">NULL</span>
-    )
-
-    <span class="kw">if</span> (<span class="no">ctx</span>$<span class="no">needs_input_grad</span>$<span class="no">input</span>)
-      <span class="no">grads</span>$<span class="no">input</span> <span class="kw">&lt;-</span> <span class="no">grad_output</span>$<span class="fu">mm</span>(<span class="no">s</span>$<span class="no">weight</span>)
-
-    <span class="kw">if</span> (<span class="no">ctx</span>$<span class="no">needs_input_grad</span>$<span class="no">weight</span>)
-      <span class="no">grads</span>$<span class="no">weight</span> <span class="kw">&lt;-</span> <span class="no">grad_output</span>$<span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>()$<span class="fu">mm</span>(<span class="no">s</span>$<span class="no">input</span>)
-
-    <span class="kw">if</span> (!<span class="fu"><a href="https://rdrr.io/r/base/NULL.html">is.null</a></span>(<span class="no">s</span>$<span class="no">bias</span>) <span class="kw">&amp;&amp;</span> <span class="no">ctx</span>$<span class="no">needs_input_grad</span>$<span class="no">bias</span>)
-      <span class="no">grads</span>$<span class="no">bias</span> <span class="kw">&lt;-</span> <span class="no">grad_output</span>$<span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>(<span class="kw">dim</span> <span class="kw">=</span> <span class="fl">0</span>)
-
-    <span class="no">grads</span>
-  }
-)</pre></body></html></div>
-<p>Here, we give an additional example of a function that is parametrized by non-Tensor arguments:</p>
-<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">mul_constant</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/autograd_function.html">autograd_function</a></span>(
-  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">ctx</span>, <span class="no">tensor</span>, <span class="no">constant</span>) {
-    <span class="no">ctx</span>$<span class="fu">save_for_backward</span>(<span class="kw">constant</span> <span class="kw">=</span> <span class="no">constant</span>)
-    <span class="no">tensor</span> * <span class="no">constant</span>
-  },
-  <span class="kw">backward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">ctx</span>, <span class="no">grad_output</span>) {
-    <span class="no">v</span> <span class="kw">&lt;-</span> <span class="no">ctx</span>$<span class="no">saved_variables</span>
-    <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(
-      <span class="kw">tensor</span> <span class="kw">=</span> <span class="no">grad_output</span> * <span class="no">v</span>$<span class="no">constant</span>
-    )
-  }
-)</pre></body></html></div>
-<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>, <span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-<span class="no">o</span> <span class="kw">&lt;-</span> <span class="fu">mul_constant</span>(<span class="no">x</span>, <span class="fl">2</span>)
-<span class="no">o</span>$<span class="fu">backward</span>()
-<span class="no">x</span>$<span class="no">grad</span></pre></body></html></div>
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-    </nav>
-</div>
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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diff --git a/static/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6a..000000000
--- a/static/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
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-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/static/docs/articles/getting-started/assets/mnist.png b/static/docs/articles/getting-started/assets/mnist.png
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-      <h1 data-toc-skip>Autograd: automatic differentiation</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/autograd.Rmd"><code>vignettes/getting-started/autograd.Rmd</code></a></small>
-      <div class="hidden name"><code>autograd.Rmd</code></div>
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-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>Central to all neural networks in torch is the autograd functionality. Let’s first briefly visit this, and we will then go to training our first neural network.</p>
-<p>Autograd provides automatic differentiation for all operations on Tensors. It is a define-by-run framework, which means that your backprop is defined by how your code is run, and that every single iteration can be different.</p>
-<p>Let us see this in more simple terms with some examples.</p>
-<div id="tensor" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tensor" class="anchor"></a>Tensor</h2>
-<p><code>torch_tensor</code> is the central class of the package. If you set its attribute <code>$requires_grad</code> as <code>TRUE</code>, it starts to track all operations on it. When you finish your computation you can call <code>$backward()</code> and have all the gradients computed automatically. The gradient for this tensor will be accumulated into <code>$grad</code> attribute.</p>
-<p>To stop a tensor from tracking history, you can call <code>$detach()</code> to detach it from the computation history, and to prevent future computation from being tracked.</p>
-<p>To prevent tracking history (and using memory), you can also wrap the code block in <code>with_no_grad({&lt;code&gt;})</code>. This can be particularly helpful when evaluating a model because the model may have trainable parameters with <code>requires_grad=TRUE</code>, but for which we don’t need the gradients.</p>
-<p>There’s one more class which is very important for autograd implementation - a <code>autograd_function</code>.</p>
-<p>Tensor and Function are interconnected and build up an acyclic graph, that encodes a complete history of computation. Each tensor has a <code>$grad_fn</code> attribute that references an <code>autograd_function</code> that has created the Tensor (except for Tensors created by the user - their <code>grad_fn</code> is <code>NULL</code>).</p>
-<p>If you want to compute the derivatives, you can call <code>$backward()</code> on a Tensor. If Tensor is a scalar (i.e. it holds a one element data), you don’t need to specify any arguments to <code>backward()</code>, however if it has more elements, you need to specify a gradient argument that is a tensor of matching shape.</p>
-<p>Create a tensor and set <code>requires_grad=TRUE</code> to track computation with it:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>, <span class="fl">2</span>, <span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-<span class="no">x</span></pre></body></html></div>
-<p>Do a tensor operation:</p>
-<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">y</span> <span class="kw">&lt;-</span> <span class="no">x</span> + <span class="fl">2</span>
-<span class="no">y</span></pre></body></html></div>
-<p><code>y</code> was created as a result of an operation, so it has a <code>grad_fn</code>.</p>
-<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="no">y</span>$<span class="no">grad_fn</span></pre></body></html></div>
-<p>Do more operations on <code>y</code></p>
-<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="no">z</span> <span class="kw">&lt;-</span> <span class="no">y</span> * <span class="no">y</span> * <span class="fl">3</span>
-<span class="no">z</span>
-<span class="no">out</span> <span class="kw">&lt;-</span> <span class="no">z</span>$<span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
-<span class="no">out</span></pre></body></html></div>
-<p><code>$requires_grad_( ... )</code> changes an existing Tensor’s <code>requires_grad</code> flag in-place. The input flag defaults to <code>FALSE</code> if not given.</p>
-<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">a</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">2</span>)
-<span class="no">a</span> <span class="kw">&lt;-</span> (<span class="no">a</span> * <span class="fl">3</span>) / (<span class="no">a</span> - <span class="fl">1</span>)
-<span class="no">a</span>$<span class="no">requires_grad</span>
-<span class="no">a</span>$<span class="fu">requires_grad_</span>(<span class="fl">TRUE</span>)
-<span class="no">a</span>$<span class="no">requires_grad</span>
-<span class="no">b</span> <span class="kw">&lt;-</span> (<span class="no">a</span> * <span class="no">a</span>)$<span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
-<span class="no">b</span>$<span class="no">grad_fn</span></pre></body></html></div>
-</div>
-<div id="gradients" class="section level2">
-<h2 class="hasAnchor">
-<a href="#gradients" class="anchor"></a>Gradients</h2>
-<p>Let’s backprop now. Because out contains a single scalar, <code>out$backward()</code> is equivalent to <code>out$backward(torch.tensor(1.))</code>.</p>
-<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="no">out</span>$<span class="fu">backward</span>()</pre></body></html></div>
-<p>Print gradients d(out)/dx</p>
-<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="no">x</span>$<span class="no">grad</span></pre></body></html></div>
-<p>You should have got a matrix of <code>4.5</code>. Let’s call the <code>out</code> <em>Tensor</em> <span class="math inline">\(o\)</span>.</p>
-<p>We have that <span class="math inline">\(o = \frac{1}{4}\sum_i z_i\)</span>, <span class="math inline">\(z_i = 3(x_i+2)^2\)</span> and <span class="math inline">\(z_i\bigr\rvert_{x_i=1} = 27\)</span>. Therefore, <span class="math inline">\(\frac{\partial o}{\partial x_i} = \frac{3}{2}(x_i+2)\)</span>, hence <span class="math inline">\(\frac{\partial o}{\partial x_i}\bigr\rvert_{x_i=1} = \frac{9}{2} = 4.5\)</span>.</p>
-<p>Mathematically, if you have a vector valued function <span class="math inline">\(\vec{y}=f(\vec{x})\)</span>, then the gradient of <span class="math inline">\(\vec{y}\)</span> with respect to <span class="math inline">\(\vec{x}\)</span> is a Jacobian matrix:</p>
-<p><span class="math display">\[
-  J=\left(\begin{array}{ccc}
-   \frac{\partial y_{1}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{1}}{\partial x_{n}}\\
-   \vdots &amp; \ddots &amp; \vdots\\
-   \frac{\partial y_{m}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{n}}
-   \end{array}\right)
-\]</span></p>
-<p>Generally speaking, <code>autograd</code> is an engine for computing vector-Jacobian product. That is, given any vector <span class="math inline">\(v=\left(\begin{array}{cccc} v_{1} &amp; v_{2} &amp; \cdots &amp; v_{m}\end{array}\right)^{T}\)</span>, compute the product <span class="math inline">\(v^{T}\cdot J\)</span>. If <span class="math inline">\(v\)</span> happens to be the gradient of a scalar function <span class="math inline">\(l=g\left(\vec{y}\right)\)</span>, that is, <span class="math inline">\(v=\left(\begin{array}{ccc}\frac{\partial l}{\partial y_{1}} &amp; \cdots &amp; \frac{\partial l}{\partial y_{m}}\end{array}\right)^{T}\)</span>, then by the chain rule, the vector-Jacobian product would be the gradient of <span class="math inline">\(l\)</span> with respect to <span class="math inline">\(\vec{x}\)</span>:</p>
-<p><span class="math display">\[
-  J^{T}\cdot v=\left(\begin{array}{ccc}
-   \frac{\partial y_{1}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{1}}\\
-   \vdots &amp; \ddots &amp; \vdots\\
-   \frac{\partial y_{1}}{\partial x_{n}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{n}}
-   \end{array}\right)\left(\begin{array}{c}
-   \frac{\partial l}{\partial y_{1}}\\
-   \vdots\\
-   \frac{\partial l}{\partial y_{m}}
-   \end{array}\right)=\left(\begin{array}{c}
-   \frac{\partial l}{\partial x_{1}}\\
-   \vdots\\
-   \frac{\partial l}{\partial x_{n}}
-   \end{array}\right)
-\]</span></p>
-<p>(Note that <span class="math inline">\(v^{T}\cdot J\)</span> gives a row vector which can be treated as a column vector by taking <span class="math inline">\(J^{T}\cdot v\)</span>.)</p>
-<p>This characteristic of vector-Jacobian product makes it very convenient to feed external gradients into a model that has non-scalar output.</p>
-<p>Now let’s take a look at an example of vector-Jacobian product:</p>
-<div class="sourceCode" id="cb9"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">3</span>, <span class="kw">requires_grad</span><span class="kw">=</span><span class="fl">TRUE</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="fl">100</span> * <span class="no">x</span>
-<span class="no">y</span></pre></body></html></div>
-<p>Now in this case y is no longer a scalar. <code>autograd</code> could not compute the full Jacobian directly, but if we just want the vector-Jacobian product, simply pass the vector to backward as argument:</p>
-<div class="sourceCode" id="cb10"><html><body><pre class="r"><span class="no">v</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.1</span>, <span class="fl">1.0</span>, <span class="fl">0.0001</span>))
-<span class="no">y</span>$<span class="fu">backward</span>(<span class="no">v</span>)
-
-<span class="no">x</span>$<span class="no">grad</span></pre></body></html></div>
-<p>You can also stop autograd from tracking history on Tensors with <code>$requires_grad=TRUE</code> either by wrapping the code block in with <code><a href="../../reference/with_no_grad.html">with_no_grad()</a></code>:</p>
-<div class="sourceCode" id="cb11"><html><body><pre class="r"><span class="no">x</span>$<span class="no">requires_grad</span>
-(<span class="no">x</span> ** <span class="fl">2</span>)$<span class="no">requires_grad</span>
-
-<span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
-  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>((<span class="no">x</span> ** <span class="fl">2</span>)$<span class="no">requires_grad</span>)
-})</pre></body></html></div>
-<div class="sourceCode" id="cb12"><html><body><pre class="r"><span class="no">x</span>$<span class="no">requires_grad</span>
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu"><a href="https://rdrr.io/r/base/detach.html">detach</a></span>()
-<span class="no">y</span>$<span class="no">requires_grad</span>
-<span class="no">x</span>$<span class="fu">eq</span>(<span class="no">y</span>)$<span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>()</pre></body></html></div>
-<p>Read Later:</p>
-<p>Document about <code><a href="../../reference/autograd_function.html">help(autograd_function)</a></code>, <code><a href="../../articles/using-autograd.html">vignette("using-autograd")</a></code>, <code><a href="../../articles/extending-autograd.html">vignette("extending-autograd")</a></code>.</p>
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diff --git a/static/docs/articles/getting-started/control-flow-and-weight-sharing.html b/static/docs/articles/getting-started/control-flow-and-weight-sharing.html
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-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Control flow &amp; Weight sharing</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/control-flow-and-weight-sharing.Rmd"><code>vignettes/getting-started/control-flow-and-weight-sharing.Rmd</code></a></small>
-      <div class="hidden name"><code>control-flow-and-weight-sharing.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>As an example of dynamic graphs and weight sharing, we implement a very strange model: a fully-connected ReLU network that on each forward pass chooses a random number between 1 and 4 and uses that many hidden layers, reusing the same weights multiple times to compute the innermost hidden layers.</p>
-<p>For this model we can use normal R flow control to implement the loop, and we can implement weight sharing among the innermost layers by simply reusing the same Module multiple times when defining the forward pass.</p>
-<p>We can easily implement this model using <code>nn_module</code>:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">dynamic_net</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
-   <span class="st">"dynamic_net"</span>,
-   <span class="co"># In the constructor we construct three nn_linear instances that we will use</span>
-   <span class="co"># in the forward pass.</span>
-   <span class="kw">initialize</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">D_in</span>, <span class="no">H</span>, <span class="no">D_out</span>) {
-      <span class="no">self</span>$<span class="no">input_linear</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="no">D_in</span>, <span class="no">H</span>)
-      <span class="no">self</span>$<span class="no">middle_linear</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="no">H</span>, <span class="no">H</span>)
-      <span class="no">self</span>$<span class="no">output_linear</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="no">H</span>, <span class="no">D_out</span>)
-   },
-   <span class="co"># For the forward pass of the model, we randomly choose either 0, 1, 2, or 3</span>
-   <span class="co"># and reuse the middle_linear Module that many times to compute hidden layer</span>
-   <span class="co"># representations.</span>
-   <span class="co"># </span>
-   <span class="co"># Since each forward pass builds a dynamic computation graph, we can use normal</span>
-   <span class="co"># R control-flow operators like loops or conditional statements when</span>
-   <span class="co"># defining the forward pass of the model.</span>
-   <span class="co"># </span>
-   <span class="co"># Here we also see that it is perfectly safe to reuse the same Module many</span>
-   <span class="co"># times when defining a computational graph. This is a big improvement from Lua</span>
-   <span class="co"># Torch, where each Module could be used only once.</span>
-   <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
-      <span class="no">h_relu</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">input_linear</span>(<span class="no">x</span>)$<span class="fu">clamp</span>(<span class="kw">min</span> <span class="kw">=</span> <span class="fl">0</span>)
-      <span class="kw">for</span> (<span class="no">i</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample.int</a></span>(<span class="fl">4</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="fl">1</span>))) {
-         <span class="no">h_relu</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">middle_linear</span>(<span class="no">h_relu</span>)$<span class="fu">clamp</span>(<span class="kw">min</span><span class="kw">=</span><span class="fl">0</span>)
-      }
-      <span class="no">y_pred</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">output_linear</span>(<span class="no">h_relu</span>)
-      <span class="no">y_pred</span>
-   }
-)
-
-
-<span class="kw">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="kw">else</span> {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="no">N</span> <span class="kw">&lt;-</span> <span class="fl">64</span>
-<span class="no">D_in</span> <span class="kw">&lt;-</span> <span class="fl">1000</span>
-<span class="no">H</span> <span class="kw">&lt;-</span> <span class="fl">100</span>
-<span class="no">D_out</span> <span class="kw">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_in</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_out</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-
-<span class="co"># Construct our model by instantiating the class defined above</span>
-<span class="no">model</span> <span class="kw">&lt;-</span> <span class="fu">dynamic_net</span>(<span class="no">D_in</span>, <span class="no">H</span>, <span class="no">D_out</span>)
-
-<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
-<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
-<span class="no">loss_fn</span> <span class="kw">&lt;-</span> <span class="no">nnf_mse_loss</span>
-
-<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
-<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
-<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
-<span class="co"># optimizer which Tensors it should update.</span>
-<span class="no">learning_rate</span> <span class="kw">&lt;-</span> <span class="fl">1e-4</span>
-<span class="no">optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="no">model</span>$<span class="no">parameters</span>, <span class="kw">lr</span><span class="kw">=</span><span class="no">learning_rate</span>, <span class="kw">momentum</span> <span class="kw">=</span> <span class="fl">0.9</span>)
-
-<span class="kw">for</span> (<span class="no">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
-   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
-   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
-   <span class="no">y_pred</span> <span class="kw">&lt;-</span> <span class="fu">model</span>(<span class="no">x</span>)
-
-   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
-   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
-   <span class="co"># loss.</span>
-   <span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu">loss_fn</span>(<span class="no">y_pred</span>, <span class="no">y</span>)
-   <span class="kw">if</span> (<span class="no">t</span> <span class="kw">%%</span> <span class="fl">100</span> <span class="kw">==</span> <span class="fl">0</span> <span class="kw">||</span> <span class="no">t</span> <span class="kw">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="no">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="no">loss</span>), <span class="st">"\n"</span>)
-
-   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
-   <span class="co"># gradients for the variables it will update (which are the learnable</span>
-   <span class="co"># weights of the model). This is because by default, gradients are</span>
-   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
-   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
-   <span class="no">optimizer</span>$<span class="fu">zero_grad</span>()
-
-   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
-   <span class="co"># parameters</span>
-   <span class="no">loss</span>$<span class="fu">backward</span>()
-
-   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
-   <span class="co"># parameters</span>
-   <span class="no">optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-}</pre></body></html></div>
-  </div>
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diff --git a/static/docs/articles/getting-started/control-flow-and-weight-sharing_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/getting-started/control-flow-and-weight-sharing_files/accessible-code-block-0.0.1/empty-anchor.js
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@@ -1,15 +0,0 @@
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diff --git a/static/docs/articles/getting-started/custom-nn.html b/static/docs/articles/getting-started/custom-nn.html
deleted file mode 100644
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+++ /dev/null
@@ -1,263 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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-      <h1 data-toc-skip>Custom nn modules</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/custom-nn.Rmd"><code>vignettes/getting-started/custom-nn.Rmd</code></a></small>
-      <div class="hidden name"><code>custom-nn.Rmd</code></div>
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-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>Sometimes you will want to specify models that are more complex than a sequence of existing Modules; for these cases you can define your own Modules by using <code>nn_module</code> function and defining a forward which receives input Tensors and produces output Tensors using other modules or other autograd operations on Tensors.</p>
-<p>In this example we implement our two-layer network as a custom Module subclass:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">two_layer_net</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
-   <span class="st">"two_layer_net"</span>,
-   <span class="kw">initialize</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">D_in</span>, <span class="no">H</span>, <span class="no">D_out</span>) {
-      <span class="no">self</span>$<span class="no">linear1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="no">D_in</span>, <span class="no">H</span>)
-      <span class="no">self</span>$<span class="no">linear2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="no">H</span>, <span class="no">D_out</span>)
-   },
-   <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
-      <span class="no">x</span> <span class="kw">%&gt;%</span>
-         <span class="no">self</span>$<span class="fu">linear1</span>() <span class="kw">%&gt;%</span>
-         <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="kw">%&gt;%</span>
-         <span class="no">self</span>$<span class="fu">linear2</span>()
-   }
-)
-
-
-<span class="kw">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="kw">else</span> {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="no">N</span> <span class="kw">&lt;-</span> <span class="fl">64</span>
-<span class="no">D_in</span> <span class="kw">&lt;-</span> <span class="fl">1000</span>
-<span class="no">H</span> <span class="kw">&lt;-</span> <span class="fl">100</span>
-<span class="no">D_out</span> <span class="kw">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_in</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_out</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-
-<span class="co"># Construct our model by instantiating the class defined above</span>
-<span class="no">model</span> <span class="kw">&lt;-</span> <span class="fu">two_layer_net</span>(<span class="no">D_in</span>, <span class="no">H</span>, <span class="no">D_out</span>)
-
-<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
-<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
-<span class="no">loss_fn</span> <span class="kw">&lt;-</span> <span class="no">nnf_mse_loss</span>
-
-<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
-<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
-<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
-<span class="co"># optimizer which Tensors it should update.</span>
-<span class="no">learning_rate</span> <span class="kw">&lt;-</span> <span class="fl">1e-4</span>
-<span class="no">optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="no">model</span>$<span class="no">parameters</span>, <span class="kw">lr</span><span class="kw">=</span><span class="no">learning_rate</span>)
-
-<span class="kw">for</span> (<span class="no">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
-   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
-   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
-   <span class="no">y_pred</span> <span class="kw">&lt;-</span> <span class="fu">model</span>(<span class="no">x</span>)
-
-   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
-   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
-   <span class="co"># loss.</span>
-   <span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu">loss_fn</span>(<span class="no">y_pred</span>, <span class="no">y</span>)
-   <span class="kw">if</span> (<span class="no">t</span> <span class="kw">%%</span> <span class="fl">100</span> <span class="kw">==</span> <span class="fl">0</span> <span class="kw">||</span> <span class="no">t</span> <span class="kw">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="no">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="no">loss</span>), <span class="st">"\n"</span>)
-
-   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
-   <span class="co"># gradients for the variables it will update (which are the learnable</span>
-   <span class="co"># weights of the model). This is because by default, gradients are</span>
-   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
-   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
-   <span class="no">optimizer</span>$<span class="fu">zero_grad</span>()
-
-   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
-   <span class="co"># parameters</span>
-   <span class="no">loss</span>$<span class="fu">backward</span>()
-
-   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
-   <span class="co"># parameters</span>
-   <span class="no">optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-}</pre></body></html></div>
-<p>In the <a href="control-flow-and-weight-sharing.html">next example</a> we will about dynamic graphs in torch.</p>
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-      <h1 data-toc-skip>Neural networks</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/neural-networks.Rmd"><code>vignettes/getting-started/neural-networks.Rmd</code></a></small>
-      <div class="hidden name"><code>neural-networks.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>Neural networks can be constructed using the <code>nn</code> functionality.</p>
-<p>Now that you had a glimpse of <code>autograd</code>, <code>nn</code> depends on <code>autograd</code> to define models and differentiate them. An nn.Module contains layers, and a method <code>forward(input)</code> that returns the output.</p>
-<p>For example, look at this network that classifies digit images:</p>
-<div class="figure">
-<img src="assets/mnist.png" alt=""><p class="caption">Convnet for mnist classification</p>
-</div>
-<p>It is a simple feed-forward network. It takes the input, feeds it through several layers one after the other, and then finally gives the output.</p>
-<p>A typical training procedure for a neural network is as follows:</p>
-<ul>
-<li>Define the neural network that has some learnable parameters (or weights)</li>
-<li>Iterate over a dataset of inputs</li>
-<li>Process input through the network</li>
-<li>Compute the loss (how far is the output from being correct)</li>
-<li>Propagate gradients back into the network’s parameters</li>
-<li>Update the weights of the network, typically using a simple update rule: <code>weight = weight - learning_rate * gradient</code>.</li>
-</ul>
-<div id="define-the-network" class="section level2">
-<h2 class="hasAnchor">
-<a href="#define-the-network" class="anchor"></a>Define the network</h2>
-<p>Let’s define this network:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">Net</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
-  <span class="kw">initialize</span> <span class="kw">=</span> <span class="kw">function</span>() {
-    <span class="no">self</span>$<span class="no">conv1</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">1</span>, <span class="fl">6</span>, <span class="fl">3</span>)
-    <span class="no">self</span>$<span class="no">conv2</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">6</span>, <span class="fl">16</span>, <span class="fl">3</span>)
-    <span class="co"># an affine operation: y = Wx + b</span>
-    <span class="no">self</span>$<span class="no">fc1</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">16</span> * <span class="fl">6</span> * <span class="fl">6</span>, <span class="fl">120</span>)  <span class="co"># 6*6 from image dimension</span>
-    <span class="no">self</span>$<span class="no">fc2</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">120</span>, <span class="fl">84</span>)
-    <span class="no">self</span>$<span class="no">fc3</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">84</span>, <span class="fl">10</span>)
-  },
-  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
-    <span class="no">x</span> <span class="kw">%&gt;%</span>
-
-      <span class="no">self</span>$<span class="fu">conv1</span>() <span class="kw">%&gt;%</span>
-      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="kw">%&gt;%</span>
-      <span class="fu"><a href="../../reference/nnf_max_pool2d.html">nnf_max_pool2d</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>,<span class="fl">2</span>)) <span class="kw">%&gt;%</span>
-
-      <span class="no">self</span>$<span class="fu">conv2</span>() <span class="kw">%&gt;%</span>
-      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="kw">%&gt;%</span>
-      <span class="fu"><a href="../../reference/nnf_max_pool2d.html">nnf_max_pool2d</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>,<span class="fl">2</span>)) <span class="kw">%&gt;%</span>
-
-      <span class="fu"><a href="../../reference/torch_flatten.html">torch_flatten</a></span>(<span class="kw">start_dim</span> <span class="kw">=</span> <span class="fl">2</span>) <span class="kw">%&gt;%</span>
-
-      <span class="no">self</span>$<span class="fu">fc1</span>() <span class="kw">%&gt;%</span>
-      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="kw">%&gt;%</span>
-
-      <span class="no">self</span>$<span class="fu">fc2</span>() <span class="kw">%&gt;%</span>
-      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="kw">%&gt;%</span>
-
-      <span class="no">self</span>$<span class="fu">fc3</span>()
-  }
-)
-
-<span class="no">net</span> <span class="kw">&lt;-</span> <span class="fu">Net</span>()</pre></body></html></div>
-<p>You just have to define the <code>forward</code> function, and the <code>backward</code> function (where gradients are computed) is automatically defined for you using <code>autograd.</code> You can use any of the Tensor operations in the <code>forward</code> function.</p>
-<p>The learnable parameters of a model are returned by <code>net$parameters</code>.</p>
-<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/utils/str.html">str</a></span>(<span class="no">net</span>$<span class="no">parameters</span>)</pre></body></html></div>
-<p>Let’s try a random 32x32 input. Note: expected input size of this net (LeNet) is 32x32. To use this net on the MNIST dataset, please resize the images from the dataset to 32x32.</p>
-<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="no">input</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">32</span>, <span class="fl">32</span>)
-<span class="no">out</span> <span class="kw">&lt;-</span> <span class="fu">net</span>(<span class="no">input</span>)
-<span class="no">out</span></pre></body></html></div>
-<p>Zero the gradient buffers of all parameters and backprops with random gradients:</p>
-<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="no">net</span>$<span class="fu">zero_grad</span>()
-<span class="no">out</span>$<span class="fu">backward</span>(<span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, <span class="fl">10</span>))</pre></body></html></div>
-<blockquote>
-<p><strong>Note</strong>: <code>nn</code> only supports mini-batches. The entire torch.nn package only supports inputs that are a mini-batch of samples, and not a single sample. For example, <code>nn_conv2d</code> will take in a 4D Tensor of nSamples x nChannels x Height x Width. If you have a single sample, just use <code>input$unsqueeze(1)</code> to add a fake batch dimension.</p>
-</blockquote>
-<p>Before proceeding further, let’s recap all the classes you’ve seen so far.</p>
-<div id="recap" class="section level3">
-<h3 class="hasAnchor">
-<a href="#recap" class="anchor"></a>Recap</h3>
-<ul>
-<li><p><code>torch_tensor</code> - A multi-dimensional array with support for autograd operations like <code>backward()</code>. Also holds the gradient w.r.t. the tensor.</p></li>
-<li><p><code>nn_module</code> - Neural network module. Convenient way of encapsulating parameters, with helpers for moving them to GPU, exporting, loading, etc.</p></li>
-<li><p><code>nn_parameter</code> - A kind of Tensor, that is automatically registered as a parameter when assigned as an attribute to a Module.</p></li>
-<li><p><code>autograd_function</code> - Implements forward and backward definitions of an autograd operation. Every Tensor operation creates at least a single Function node that connects to functions that created a Tensor and encodes its history.</p></li>
-</ul>
-</div>
-<div id="at-this-point-we-covered" class="section level3">
-<h3 class="hasAnchor">
-<a href="#at-this-point-we-covered" class="anchor"></a>At this point, we covered</h3>
-<ul>
-<li>Defining a neural network</li>
-<li>Processing inputs and calling backward</li>
-</ul>
-</div>
-<div id="still-left" class="section level3">
-<h3 class="hasAnchor">
-<a href="#still-left" class="anchor"></a>Still left</h3>
-<ul>
-<li>Computing the loss</li>
-<li>Updating the weights of the network</li>
-</ul>
-</div>
-</div>
-<div id="loss-function" class="section level2">
-<h2 class="hasAnchor">
-<a href="#loss-function" class="anchor"></a>Loss function</h2>
-<p>A loss function takes the (output, target) pair of inputs, and computes a value that estimates how far away the output is from the target.</p>
-<p>There are several different loss functions under the nn package . A simple loss is: <code>nnf_mse_loss</code> which computes the mean-squared error between the input and the target.</p>
-<p>For example:</p>
-<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">output</span> <span class="kw">&lt;-</span> <span class="fu">net</span>(<span class="no">input</span>)
-<span class="no">target</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">10</span>)  <span class="co"># a dummy target, for example</span>
-<span class="no">target</span> <span class="kw">&lt;-</span> <span class="no">target</span>$<span class="fu">view</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, -<span class="fl">1</span>))  <span class="co"># make it the same shape as output</span>
-
-<span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_mse_loss.html">nnf_mse_loss</a></span>(<span class="no">output</span>, <span class="no">target</span>)
-<span class="no">loss</span></pre></body></html></div>
-<p>Now, if you follow loss in the backward direction, using its <code>$grad_fn</code> attribute, you will see a graph of computations that looks like this:</p>
-<pre><code>input -&gt; conv2d -&gt; relu -&gt; maxpool2d -&gt; conv2d -&gt; relu -&gt; maxpool2d
-      -&gt; view -&gt; linear -&gt; relu -&gt; linear -&gt; relu -&gt; linear
-      -&gt; MSELoss
-      -&gt; loss</code></pre>
-<p>So, when we call <code>loss$backward()</code>, the whole graph is differentiated w.r.t. the loss, and all Tensors in the graph that has requires_grad=True will have their <code>#grad</code> Tensor accumulated with the gradient.</p>
-<p>For illustration, let us follow a few steps backward:</p>
-<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="no">loss</span>$<span class="no">grad_fn</span>
-<span class="no">loss</span>$<span class="no">grad_fn</span>$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">1</span>]]
-<span class="no">loss</span>$<span class="no">grad_fn</span>$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">1</span>]]</pre></body></html></div>
-</div>
-<div id="backprop" class="section level2">
-<h2 class="hasAnchor">
-<a href="#backprop" class="anchor"></a>Backprop</h2>
-<p>To backpropagate the error all we have to do is to <code>loss$backward()</code>. You need to clear the existing gradients though, else gradients will be accumulated to existing gradients.</p>
-<p>Now we shall call <code>loss$backward()</code>, and have a look at conv1’s bias gradients before and after the backward.</p>
-<div class="sourceCode" id="cb9"><html><body><pre class="r"><span class="no">net</span>$<span class="fu">zero_grad</span>()     <span class="co"># zeroes the gradient buffers of all parameters</span>
-
-<span class="co"># conv1.bias.grad before backward</span>
-<span class="no">net</span>$<span class="no">conv1</span>$<span class="no">bias</span>$<span class="no">grad</span>
-
-<span class="no">loss</span>$<span class="fu">backward</span>()
-
-<span class="co"># conv1.bias.grad after backward</span>
-<span class="no">net</span>$<span class="no">conv1</span>$<span class="no">bias</span>$<span class="no">grad</span></pre></body></html></div>
-<p>Now, we have seen how to use loss functions.</p>
-</div>
-<div id="update-the-weights" class="section level2">
-<h2 class="hasAnchor">
-<a href="#update-the-weights" class="anchor"></a>Update the weights</h2>
-<p>The simplest update rule used in practice is the Stochastic Gradient Descent (SGD):</p>
-<p><span class="math display">\[weight = weight - learning_rate * gradient\]</span></p>
-<p>We can implement this using simple R code:</p>
-<div class="sourceCode" id="cb10"><html><body><pre class="r"><span class="no">learning_rate</span> <span class="kw">&lt;-</span> <span class="fl">0.01</span>
-<span class="kw">for</span> (<span class="no">f</span> <span class="kw">in</span> <span class="no">net</span>$<span class="no">parameters</span>) {
-  <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
-    <span class="no">f</span>$<span class="fu">sub_</span>(<span class="no">f</span>$<span class="no">grad</span> * <span class="no">learning_rate</span>)
-  })
-}</pre></body></html></div>
-<blockquote>
-<p><strong>Note:</strong> Weight updates here is wraped around <code>with_no_grad</code> as we don’t the updates to be tracked by the autograd engine.</p>
-</blockquote>
-<p>However, as you use neural networks, you want to use various different update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc.</p>
-<div class="sourceCode" id="cb11"><html><body><pre class="r"><span class="co"># create your optimizer</span>
-<span class="no">optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="no">net</span>$<span class="no">parameters</span>, <span class="kw">lr</span> <span class="kw">=</span> <span class="fl">0.01</span>)
-
-<span class="co"># in your training loop:</span>
-<span class="no">optimizer</span>$<span class="fu">zero_grad</span>()   <span class="co"># zero the gradient buffers</span>
-<span class="no">output</span> <span class="kw">&lt;-</span> <span class="fu">net</span>(<span class="no">input</span>)
-<span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_mse_loss.html">nnf_mse_loss</a></span>(<span class="no">output</span>, <span class="no">target</span>)
-<span class="no">loss</span>$<span class="fu">backward</span>()
-<span class="no">optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()    <span class="co"># Does the update</span></pre></body></html></div>
-<blockquote>
-<p><strong>Note:</strong> Observe how gradient buffers had to be manually set to zero using <code>optimizer$zero_grad()</code>. This is because gradients are accumulated as explained in the Backprop section.</p>
-</blockquote>
-</div>
-  </div>
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-      <h1 data-toc-skip>Defining new autograd functions</h1>
-            
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/new-autograd-functions.Rmd"><code>vignettes/getting-started/new-autograd-functions.Rmd</code></a></small>
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-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>Under the hood, each primitive autograd operator is really two functions that operate on Tensors. The forward function computes output Tensors from input Tensors. The backward function receives the gradient of the output Tensors with respect to some scalar value, and computes the gradient of the input Tensors with respect to that same scalar value.</p>
-<p>In torch we can easily define our own autograd operator by defining a subclass of <code>autograd_function</code> and implementing the forward and backward functions. We can then use our new autograd operator by constructing an instance and calling it like a function, passing Tensors containing input data.</p>
-<p>In this example we define our own custom autograd function for performing the ReLU nonlinearity, and use it to implement our two-layer network:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="co"># We can implement our own custom autograd Functions by subclassing</span>
-<span class="co"># autograd_functioon and implementing the forward and backward passes</span>
-<span class="co"># which operate on Tensors.</span>
-<span class="no">my_relu</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/autograd_function.html">autograd_function</a></span>(
-   <span class="co"># In the forward pass we receive a Tensor containing the input and return</span>
-   <span class="co"># a Tensor containing the output. ctx is a context object that can be used</span>
-   <span class="co"># to stash information for backward computation. You can cache arbitrary</span>
-   <span class="co"># objects for use in the backward pass using the ctx$save_for_backward method.</span>
-   <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">ctx</span>, <span class="no">input</span>) {
-      <span class="no">ctx</span>$<span class="fu">save_for_backward</span>(<span class="kw">input</span> <span class="kw">=</span> <span class="no">input</span>)
-      <span class="no">input</span>$<span class="fu">clamp</span>(<span class="kw">min</span> <span class="kw">=</span> <span class="fl">0</span>)
-   },
-   <span class="co"># In the backward pass we receive a Tensor containing the gradient of the loss</span>
-   <span class="co"># with respect to the output, and we need to compute the gradient of the loss</span>
-   <span class="co"># with respect to the input.</span>
-   <span class="kw">backward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">ctx</span>, <span class="no">grad_output</span>) {
-      <span class="no">v</span> <span class="kw">&lt;-</span> <span class="no">ctx</span>$<span class="no">saved_variables</span>
-      <span class="no">grad_input</span> <span class="kw">&lt;-</span> <span class="no">grad_output</span>$<span class="fu">clone</span>()
-      <span class="no">grad_input</span>[<span class="no">v</span>$<span class="no">input</span> <span class="kw">&lt;</span> <span class="fl">0</span>] <span class="kw">&lt;-</span> <span class="fl">0</span>
-      <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">input</span> <span class="kw">=</span> <span class="no">grad_input</span>)
-   }
-)
-
-<span class="kw">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="kw">else</span> {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="no">N</span> <span class="kw">&lt;-</span> <span class="fl">64</span>
-<span class="no">D_in</span> <span class="kw">&lt;-</span> <span class="fl">1000</span>
-<span class="no">H</span> <span class="kw">&lt;-</span> <span class="fl">100</span>
-<span class="no">D_out</span> <span class="kw">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_in</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_out</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-
-<span class="co"># Randomly initialize weights</span>
-<span class="co"># Setting requires_grad=TRUE indicates that we want to compute gradients with</span>
-<span class="co"># respect to these Tensors during the backward pass.</span>
-<span class="no">w1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">D_in</span>, <span class="no">H</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>, <span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-<span class="no">w2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">H</span>, <span class="no">D_out</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>, <span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-
-<span class="no">learning_rate</span> <span class="kw">&lt;-</span> <span class="fl">1e-6</span>
-<span class="kw">for</span> (<span class="no">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y using operations on Tensors; these</span>
-   <span class="co"># are exactly the same operations we used to compute the forward pass using</span>
-   <span class="co"># Tensors, but we do not need to keep references to intermediate values since</span>
-   <span class="co"># we are not implementing the backward pass by hand.</span>
-   <span class="no">y_pred</span> <span class="kw">&lt;-</span> <span class="fu">my_relu</span>(<span class="no">x</span>$<span class="fu">mm</span>(<span class="no">w1</span>))$<span class="fu">mm</span>(<span class="no">w2</span>)
-
-   <span class="co"># Compute and print loss using operations on Tensors.</span>
-   <span class="co"># Now loss is a Tensor of shape (1,)</span>
-   <span class="no">loss</span> <span class="kw">&lt;-</span> (<span class="no">y_pred</span> - <span class="no">y</span>)$<span class="fu">pow</span>(<span class="fl">2</span>)$<span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
-   <span class="kw">if</span> (<span class="no">t</span> <span class="kw">%%</span> <span class="fl">100</span> <span class="kw">==</span> <span class="fl">0</span> <span class="kw">||</span> <span class="no">t</span> <span class="kw">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="no">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="no">loss</span>), <span class="st">"\n"</span>)
-
-   <span class="co"># Use autograd to compute the backward pass. This call will compute the</span>
-   <span class="co"># gradient of loss with respect to all Tensors with requires_grad=True.</span>
-   <span class="co"># After this call w1$grad and w2$grad will be Tensors holding the gradient</span>
-   <span class="co"># of the loss with respect to w1 and w2 respectively.</span>
-   <span class="no">loss</span>$<span class="fu">backward</span>()
-
-   <span class="co"># Manually update weights using gradient descent. Wrap in `with_no_grad`</span>
-   <span class="co"># because weights have requires_grad=TRUE, but we don't need to track this</span>
-   <span class="co"># in autograd.</span>
-   <span class="co"># You can also use optim_sgd to achieve this.</span>
-   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
-
-      <span class="co"># operations suffixed with an `_` operates on in-place on the tensor.</span>
-      <span class="no">w1</span>$<span class="fu">sub_</span>(<span class="no">learning_rate</span> * <span class="no">w1</span>$<span class="no">grad</span>)
-      <span class="no">w2</span>$<span class="fu">sub_</span>(<span class="no">learning_rate</span> * <span class="no">w2</span>$<span class="no">grad</span>)
-
-      <span class="co"># Manually zero the gradients after updating weights</span>
-      <span class="no">w1</span>$<span class="no">grad</span>$<span class="fu">zero_</span>()
-      <span class="no">w2</span>$<span class="no">grad</span>$<span class="fu">zero_</span>()
-   })
-}</pre></body></html></div>
-<p>In the <a href="nn.html">next example</a> we will learn how to use the neural networks abstractions in torch.</p>
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-      <h1 data-toc-skip>nn: neural networks with torch</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/nn.Rmd"><code>vignettes/getting-started/nn.Rmd</code></a></small>
-      <div class="hidden name"><code>nn.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>Computational graphs and autograd are a very powerful paradigm for defining complex operators and automatically taking derivatives; however for large neural networks raw autograd can be a bit too low-level.</p>
-<p>When building neural networks we frequently think of arranging the computation into layers, some of which have learnable parameters which will be optimized during learning.</p>
-<p>In TensorFlow, packages like Keras, TensorFlow-Slim, and TFLearn provide higher-level abstractions over raw computational graphs that are useful for building neural networks.</p>
-<p>In torch, the nn functionality serves this same purpose. The nn feature defines a set of Modules, which are roughly equivalent to neural network layers. A Module receives input Tensors and computes output Tensors, but may also hold internal state such as Tensors containing learnable parameters. The nn collection also defines a set of useful loss functions that are commonly used when training neural networks.</p>
-<p>In this example we use nn to implement our two-layer network:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="kw">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="kw">else</span> {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="no">N</span> <span class="kw">&lt;-</span> <span class="fl">64</span>
-<span class="no">D_in</span> <span class="kw">&lt;-</span> <span class="fl">1000</span>
-<span class="no">H</span> <span class="kw">&lt;-</span> <span class="fl">100</span>
-<span class="no">D_out</span> <span class="kw">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_in</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_out</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-
-<span class="co"># Use the nn package to define our model as a sequence of layers. nn_sequential</span>
-<span class="co"># is a Module which contains other Modules, and applies them in sequence to</span>
-<span class="co"># produce its output. Each Linear Module computes output from input using a</span>
-<span class="co"># linear function, and holds internal Tensors for its weight and bias.</span>
-<span class="no">model</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_sequential.html">nn_sequential</a></span>(
-    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="no">D_in</span>, <span class="no">H</span>),
-    <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span>(),
-    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="no">H</span>, <span class="no">D_out</span>)
-)
-
-<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
-<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
-<span class="no">loss_fn</span> <span class="kw">&lt;-</span> <span class="no">nnf_mse_loss</span>
-
-<span class="no">learning_rate</span> <span class="kw">&lt;-</span> <span class="fl">1e-6</span>
-<span class="kw">for</span> (<span class="no">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
-   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
-   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
-   <span class="no">y_pred</span> <span class="kw">&lt;-</span> <span class="fu">model</span>(<span class="no">x</span>)
-
-   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
-   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
-   <span class="co"># loss.</span>
-   <span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu">loss_fn</span>(<span class="no">y_pred</span>, <span class="no">y</span>)
-   <span class="kw">if</span> (<span class="no">t</span> <span class="kw">%%</span> <span class="fl">100</span> <span class="kw">==</span> <span class="fl">0</span> <span class="kw">||</span> <span class="no">t</span> <span class="kw">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="no">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="no">loss</span>), <span class="st">"\n"</span>)
-
-   <span class="co"># Zero the gradients before running the backward pass.</span>
-   <span class="no">model</span>$<span class="fu">zero_grad</span>()
-
-   <span class="co"># Backward pass: compute gradient of the loss with respect to all the learnable</span>
-   <span class="co"># parameters of the model. Internally, the parameters of each Module are stored</span>
-   <span class="co"># in Tensors with requires_grad=TRUE, so this call will compute gradients for</span>
-   <span class="co"># all learnable parameters in the model.</span>
-   <span class="no">loss</span>$<span class="fu">backward</span>()
-
-   <span class="co"># Update the weights using gradient descent. Each parameter is a Tensor, so</span>
-   <span class="co"># we can access its gradients like we did before.</span>
-   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
-      <span class="kw">for</span> (<span class="no">param</span> <span class="kw">in</span> <span class="no">model</span>$<span class="no">parameters</span>) {
-         <span class="no">param</span>$<span class="fu">sub_</span>(<span class="no">learning_rate</span> * <span class="no">param</span>$<span class="no">grad</span>)
-      }
-   })
-}</pre></body></html></div>
-<p>In the <a href="optim.html">next example</a> we will learn how to use optimizers implemented in torch.</p>
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diff --git a/static/docs/articles/getting-started/optim.html b/static/docs/articles/getting-started/optim.html
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-      <h1 data-toc-skip>optim: optimizers in torch</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/optim.Rmd"><code>vignettes/getting-started/optim.Rmd</code></a></small>
-      <div class="hidden name"><code>optim.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>Up to this point we have updated the weights of our models by manually mutating the Tensors holding learnable parameters (with <code>with_no_grad</code> to avoid tracking history in autograd). This is not a huge burden for simple optimization algorithms like stochastic gradient descent, but in practice we often train neural networks using more sophisticated optimizers like AdaGrad, RMSProp, Adam, etc.</p>
-<p>The optim package in torch abstracts the idea of an optimization algorithm and provides implementations of commonly used optimization algorithms.</p>
-<p>In this example we will use the nn package to define our model as before, but we will optimize the model using the Adam algorithm provided by <code>optim</code>:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="kw">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="kw">else</span> {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="no">N</span> <span class="kw">&lt;-</span> <span class="fl">64</span>
-<span class="no">D_in</span> <span class="kw">&lt;-</span> <span class="fl">1000</span>
-<span class="no">H</span> <span class="kw">&lt;-</span> <span class="fl">100</span>
-<span class="no">D_out</span> <span class="kw">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_in</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_out</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-
-<span class="co"># Use the nn package to define our model as a sequence of layers. nn_sequential</span>
-<span class="co"># is a Module which contains other Modules, and applies them in sequence to</span>
-<span class="co"># produce its output. Each Linear Module computes output from input using a</span>
-<span class="co"># linear function, and holds internal Tensors for its weight and bias.</span>
-<span class="no">model</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_sequential.html">nn_sequential</a></span>(
-    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="no">D_in</span>, <span class="no">H</span>),
-    <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span>(),
-    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="no">H</span>, <span class="no">D_out</span>)
-)
-
-<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
-<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
-<span class="no">loss_fn</span> <span class="kw">&lt;-</span> <span class="no">nnf_mse_loss</span>
-
-<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
-<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
-<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
-<span class="co"># optimizer which Tensors it should update.</span>
-<span class="no">learning_rate</span> <span class="kw">&lt;-</span> <span class="fl">1e-4</span>
-<span class="no">optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_adam.html">optim_adam</a></span>(<span class="no">model</span>$<span class="no">parameters</span>, <span class="kw">lr</span><span class="kw">=</span><span class="no">learning_rate</span>)
-
-<span class="kw">for</span> (<span class="no">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
-   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
-   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
-   <span class="no">y_pred</span> <span class="kw">&lt;-</span> <span class="fu">model</span>(<span class="no">x</span>)
-
-   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
-   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
-   <span class="co"># loss.</span>
-   <span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu">loss_fn</span>(<span class="no">y_pred</span>, <span class="no">y</span>)
-   <span class="kw">if</span> (<span class="no">t</span> <span class="kw">%%</span> <span class="fl">100</span> <span class="kw">==</span> <span class="fl">0</span> <span class="kw">||</span> <span class="no">t</span> <span class="kw">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="no">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="no">loss</span>), <span class="st">"\n"</span>)
-
-   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
-   <span class="co"># gradients for the variables it will update (which are the learnable</span>
-   <span class="co"># weights of the model). This is because by default, gradients are</span>
-   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
-   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
-   <span class="no">optimizer</span>$<span class="fu">zero_grad</span>()
-
-   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
-   <span class="co"># parameters</span>
-   <span class="no">loss</span>$<span class="fu">backward</span>()
-
-   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
-   <span class="co"># parameters</span>
-   <span class="no">optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-}</pre></body></html></div>
-<p>In the <a href="custom-nn.html">next example</a> we will learn how to create custom <code>nn_modules</code>.</p>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/articles/getting-started/optim_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/getting-started/optim_files/accessible-code-block-0.0.1/empty-anchor.js
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diff --git a/static/docs/articles/getting-started/tensors-and-autograd.html b/static/docs/articles/getting-started/tensors-and-autograd.html
deleted file mode 100644
index d288c12e4..000000000
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-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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-<li class="dropdown-header">Tensors</li>
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-      <h1 data-toc-skip>Tensors and autograd</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/tensors-and-autograd.Rmd"><code>vignettes/getting-started/tensors-and-autograd.Rmd</code></a></small>
-      <div class="hidden name"><code>tensors-and-autograd.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>In the previous examples, we had to manually implement both the forward and backward passes of our neural network. Manually implementing the backward pass is not a big deal for a small two-layer network, but can quickly get very hairy for large complex networks.</p>
-<p>Thankfully, we can use automatic differentiation to automate the computation of backward passes in neural networks. The autograd feature in torch provides exactly this functionality. When using autograd, the forward pass of your network will define a computational graph; nodes in the graph will be Tensors, and edges will be functions that produce output Tensors from input Tensors. Backpropagating through this graph then allows you to easily compute gradients.</p>
-<p>This sounds complicated, it’s pretty simple to use in practice. Each Tensor represents a node in a computational graph. If x is a Tensor that has <code>x$requires_grad=TRUE</code> then <code>x$grad</code> is another Tensor holding the gradient of x with respect to some scalar value.</p>
-<p>Here we use torch Tensors and autograd to implement our two-layer network; now we no longer need to manually implement the backward pass through the network:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="kw">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="kw">else</span> {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="no">N</span> <span class="kw">&lt;-</span> <span class="fl">64</span>
-<span class="no">D_in</span> <span class="kw">&lt;-</span> <span class="fl">1000</span>
-<span class="no">H</span> <span class="kw">&lt;-</span> <span class="fl">100</span>
-<span class="no">D_out</span> <span class="kw">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_in</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_out</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-
-<span class="co"># Randomly initialize weights</span>
-<span class="co"># Setting requires_grad=TRUE indicates that we want to compute gradients with</span>
-<span class="co"># respect to these Tensors during the backward pass.</span>
-<span class="no">w1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">D_in</span>, <span class="no">H</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>, <span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-<span class="no">w2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">H</span>, <span class="no">D_out</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>, <span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-
-<span class="no">learning_rate</span> <span class="kw">&lt;-</span> <span class="fl">1e-6</span>
-<span class="kw">for</span> (<span class="no">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y using operations on Tensors; these</span>
-   <span class="co"># are exactly the same operations we used to compute the forward pass using</span>
-   <span class="co"># Tensors, but we do not need to keep references to intermediate values since</span>
-   <span class="co"># we are not implementing the backward pass by hand.</span>
-   <span class="no">y_pred</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu">mm</span>(<span class="no">w1</span>)$<span class="fu">clamp</span>(<span class="kw">min</span><span class="kw">=</span><span class="fl">0</span>)$<span class="fu">mm</span>(<span class="no">w2</span>)
-
-   <span class="co"># Compute and print loss using operations on Tensors.</span>
-   <span class="co"># Now loss is a Tensor of shape (1,)</span>
-   <span class="no">loss</span> <span class="kw">&lt;-</span> (<span class="no">y_pred</span> - <span class="no">y</span>)$<span class="fu">pow</span>(<span class="fl">2</span>)$<span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
-   <span class="kw">if</span> (<span class="no">t</span> <span class="kw">%%</span> <span class="fl">100</span> <span class="kw">==</span> <span class="fl">0</span> <span class="kw">||</span> <span class="no">t</span> <span class="kw">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="no">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="no">loss</span>), <span class="st">"\n"</span>)
-
-   <span class="co"># Use autograd to compute the backward pass. This call will compute the</span>
-   <span class="co"># gradient of loss with respect to all Tensors with requires_grad=True.</span>
-   <span class="co"># After this call w1$grad and w2$grad will be Tensors holding the gradient</span>
-   <span class="co"># of the loss with respect to w1 and w2 respectively.</span>
-   <span class="no">loss</span>$<span class="fu">backward</span>()
-
-   <span class="co"># Manually update weights using gradient descent. Wrap in `with_no_grad`</span>
-   <span class="co"># because weights have requires_grad=TRUE, but we don't need to track this</span>
-   <span class="co"># in autograd.</span>
-   <span class="co"># You can also use optim_sgd to achieve this.</span>
-   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
-
-      <span class="co"># operations suffixed with an `_` operates on in-place on the tensor.</span>
-      <span class="no">w1</span>$<span class="fu">sub_</span>(<span class="no">learning_rate</span> * <span class="no">w1</span>$<span class="no">grad</span>)
-      <span class="no">w2</span>$<span class="fu">sub_</span>(<span class="no">learning_rate</span> * <span class="no">w2</span>$<span class="no">grad</span>)
-
-      <span class="co"># Manually zero the gradients after updating weights</span>
-      <span class="no">w1</span>$<span class="no">grad</span>$<span class="fu">zero_</span>()
-      <span class="no">w2</span>$<span class="no">grad</span>$<span class="fu">zero_</span>()
-   })
-}</pre></body></html></div>
-<p>In the <a href="new-autograd-functions.html">next example</a> we will learn how to create new autograd functions.</p>
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diff --git a/static/docs/articles/getting-started/tensors-and-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/getting-started/tensors-and-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
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diff --git a/static/docs/articles/getting-started/tensors.html b/static/docs/articles/getting-started/tensors.html
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-      <h1 data-toc-skip>torch Tensors</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/tensors.Rmd"><code>vignettes/getting-started/tensors.Rmd</code></a></small>
-      <div class="hidden name"><code>tensors.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>R arrays are great, but they cannot utilize GPUs to accelerate its numerical computations. For modern deep neural networks, GPUs often provide speedups of 50x or greater, so unfortunately pure R won’t be enough for modern deep learning.</p>
-<p>Here we introduce the most fundamental torch concept: the Tensor. A torch Tensor is conceptually similar to an R array: a Tensor is an n-dimensional array, and torch provides many functions for operating on these Tensors. Behind the scenes, Tensors can keep track of a computational graph and gradients, but they’re also useful as a generic tool for scientific computing.</p>
-<p>Also unlike R, torch Tensors can utilize GPUs to accelerate their numeric computations. To run a torch Tensor on GPU, you simply need to cast it to a new datatype.</p>
-<p>Here we use torch Tensors to fit a two-layer network to random data. Like the R before we need to manually implement the forward and backward passes through the network:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="kw">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="kw">else</span> {
-   <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="no">N</span> <span class="kw">&lt;-</span> <span class="fl">64</span>
-<span class="no">D_in</span> <span class="kw">&lt;-</span> <span class="fl">1000</span>
-<span class="no">H</span> <span class="kw">&lt;-</span> <span class="fl">100</span>
-<span class="no">D_out</span> <span class="kw">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_in</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">N</span>, <span class="no">D_out</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-
-<span class="co"># Randomly initialize weights</span>
-<span class="no">w1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">D_in</span>, <span class="no">H</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-<span class="no">w2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="no">H</span>, <span class="no">D_out</span>, <span class="kw">device</span><span class="kw">=</span><span class="no">device</span>)
-
-<span class="no">learning_rate</span> <span class="kw">&lt;-</span> <span class="fl">1e-6</span>
-<span class="kw">for</span> (<span class="no">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y</span>
-   <span class="no">h</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu">mm</span>(<span class="no">w1</span>)
-   <span class="no">h_relu</span> <span class="kw">&lt;-</span> <span class="no">h</span>$<span class="fu">clamp</span>(<span class="kw">min</span><span class="kw">=</span><span class="fl">0</span>)
-   <span class="no">y_pred</span> <span class="kw">&lt;-</span> <span class="no">h_relu</span>$<span class="fu">mm</span>(<span class="no">w2</span>)
-
-   <span class="co"># Compute and print loss</span>
-   <span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>((<span class="no">y_pred</span> - <span class="no">y</span>)$<span class="fu">pow</span>(<span class="fl">2</span>)$<span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>())
-   <span class="kw">if</span> (<span class="no">t</span> <span class="kw">%%</span> <span class="fl">100</span> <span class="kw">==</span> <span class="fl">0</span> <span class="kw">||</span> <span class="no">t</span> <span class="kw">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="no">t</span>, <span class="st">":"</span>, <span class="no">loss</span>, <span class="st">"\n"</span>)
-
-   <span class="co"># Backprop to compute gradients of w1 and w2 with respect to loss</span>
-   <span class="no">grad_y_pred</span> <span class="kw">&lt;-</span> <span class="fl">2.0</span> * (<span class="no">y_pred</span> - <span class="no">y</span>)
-   <span class="no">grad_w2</span> <span class="kw">&lt;-</span> <span class="no">h_relu</span>$<span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>()$<span class="fu">mm</span>(<span class="no">grad_y_pred</span>)
-   <span class="no">grad_h_relu</span> <span class="kw">&lt;-</span> <span class="no">grad_y_pred</span>$<span class="fu">mm</span>(<span class="no">w2</span>$<span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>())
-   <span class="no">grad_h</span> <span class="kw">&lt;-</span> <span class="no">grad_h_relu</span>$<span class="fu">clone</span>()
-   <span class="no">grad_h</span>[<span class="no">h</span> <span class="kw">&lt;</span> <span class="fl">0</span>] <span class="kw">&lt;-</span> <span class="fl">0</span>
-   <span class="no">grad_w1</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>()$<span class="fu">mm</span>(<span class="no">grad_h</span>)
-
-   <span class="co"># Update weights using gradient descent</span>
-   <span class="no">w1</span> <span class="kw">&lt;-</span> <span class="no">w1</span> - <span class="no">learning_rate</span> * <span class="no">grad_w1</span>
-   <span class="no">w2</span> <span class="kw">&lt;-</span> <span class="no">w2</span> - <span class="no">learning_rate</span> * <span class="no">grad_w2</span>
-}</pre></body></html></div>
-<p>In the <a href="tensors-and-autograd.html">next example</a> we will use autograd instead of computing the gradients manually.</p>
-  </div>
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diff --git a/static/docs/articles/getting-started/tensors_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/getting-started/tensors_files/accessible-code-block-0.0.1/empty-anchor.js
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-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/warmup.Rmd"><code>vignettes/getting-started/warmup.Rmd</code></a></small>
-      <div class="hidden name"><code>warmup.Rmd</code></div>
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-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>A fully-connected ReLU network with one hidden layer and no biases, trained to predict y from x using Euclidean error.</p>
-<p>This implementation uses pure R to manually compute the forward pass, loss, and backward pass.</p>
-<p>An R array is a generic n-dimensional array; it does not know anything about deep learning or gradients or computational graphs, and is just a way to perform generic numeric computations.</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="no">N</span> <span class="kw">&lt;-</span> <span class="fl">64</span>
-<span class="no">D_in</span> <span class="kw">&lt;-</span> <span class="fl">1000</span>
-<span class="no">H</span> <span class="kw">&lt;-</span> <span class="fl">100</span>
-<span class="no">D_out</span> <span class="kw">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="no">N</span>*<span class="no">D_in</span>), <span class="kw">dim</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">N</span>, <span class="no">D_in</span>))
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="no">N</span>*<span class="no">D_out</span>), <span class="kw">dim</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">N</span>, <span class="no">D_out</span>))
-
-<span class="co"># Randomly initialize weights</span>
-<span class="no">w1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="no">D_in</span>*<span class="no">H</span>), <span class="kw">dim</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">D_in</span>, <span class="no">H</span>))
-<span class="no">w2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="no">H</span>*<span class="no">D_out</span>), <span class="kw">dim</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">H</span>, <span class="no">D_out</span>))
-
-<span class="no">learning_rate</span> <span class="kw">&lt;-</span> <span class="fl">1e-6</span>
-<span class="kw">for</span> (<span class="no">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y</span>
-   <span class="no">h</span> <span class="kw">&lt;-</span> <span class="no">x</span> <span class="kw">%*%</span> <span class="no">w1</span>
-   <span class="no">h_relu</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span>(<span class="no">h</span> <span class="kw">&lt;</span> <span class="fl">0</span>, <span class="fl">0</span>, <span class="no">h</span>)
-   <span class="no">y_pred</span> <span class="kw">&lt;-</span> <span class="no">h_relu</span> <span class="kw">%*%</span> <span class="no">w2</span>
-
-   <span class="co"># Compute and print loss</span>
-   <span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>((<span class="no">y_pred</span> - <span class="no">y</span>)^<span class="fl">2</span>)
-   <span class="kw">if</span> (<span class="no">t</span> <span class="kw">%%</span> <span class="fl">100</span> <span class="kw">==</span> <span class="fl">0</span> <span class="kw">||</span> <span class="no">t</span> <span class="kw">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="no">t</span>, <span class="st">":"</span>, <span class="no">loss</span>, <span class="st">"\n"</span>)
-
-   <span class="co"># Backprop to compute gradients of w1 and w2 with respect to loss</span>
-   <span class="no">grad_y_pred</span> <span class="kw">&lt;-</span> <span class="fl">2</span> * (<span class="no">y_pred</span> - <span class="no">y</span>)
-   <span class="no">grad_w2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>(<span class="no">h_relu</span>) <span class="kw">%*%</span> <span class="no">grad_y_pred</span>
-   <span class="no">grad_h_relu</span> <span class="kw">&lt;-</span> <span class="no">grad_y_pred</span> <span class="kw">%*%</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>(<span class="no">w2</span>)
-   <span class="no">grad_h</span> <span class="kw">&lt;-</span> <span class="no">grad_h_relu</span>
-   <span class="no">grad_h</span>[<span class="no">h</span> <span class="kw">&lt;</span> <span class="fl">0</span>] <span class="kw">&lt;-</span> <span class="fl">0</span>
-   <span class="no">grad_w1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>(<span class="no">x</span>) <span class="kw">%*%</span> <span class="no">grad_h</span>
-
-   <span class="co"># Update weights</span>
-   <span class="no">w1</span> <span class="kw">&lt;-</span> <span class="no">w1</span> - <span class="no">learning_rate</span> * <span class="no">grad_w1</span>
-   <span class="no">w2</span> <span class="kw">&lt;-</span> <span class="no">w2</span> - <span class="no">learning_rate</span> * <span class="no">grad_w2</span>
-}</pre></body></html></div>
-<p>In the <a href="tensors.html">next example</a> we will replace the R array for a torch Tensor.</p>
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-      <h1 data-toc-skip>What is torch?</h1>
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/what-is-torch.Rmd"><code>vignettes/getting-started/what-is-torch.Rmd</code></a></small>
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-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>It’s a scientific computing package targeted at two sets of audiences:</p>
-<ul>
-<li>An array library to use the power of GPUs</li>
-<li>a deep learning research platform that provides maximum flexibility and speed</li>
-</ul>
-<div id="getting-started" class="section level2">
-<h2 class="hasAnchor">
-<a href="#getting-started" class="anchor"></a>Getting started</h2>
-<div id="tensors" class="section level3">
-<h3 class="hasAnchor">
-<a href="#tensors" class="anchor"></a>Tensors</h3>
-<p>Tensors are similar to R arrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.</p>
-<blockquote>
-<p>Note: An uninitialized matrix is declared, but does not contain definite known values before it is used. When an uninitialized matrix is created, whatever values were in the allocated memory at the time will appear as the initial values.</p>
-</blockquote>
-<p>Construct a 5x3 matrix, uninitialized:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_empty.html">torch_empty</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
-<span class="no">x</span></pre></body></html></div>
-<p>Construct a randomly initialized matrix:</p>
-<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
-<span class="no">x</span></pre></body></html></div>
-<p>Construct a matrix filled zeros and of dtype long:</p>
-<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">5</span>, <span class="fl">3</span>, <span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_long</a></span>())
-<span class="no">x</span></pre></body></html></div>
-<p>Construct a tensor directly from data:</p>
-<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">5.5</span>, <span class="fl">3</span>))
-<span class="no">x</span></pre></body></html></div>
-<p>or create a tensor based on an existing tensor. These methods will reuse properties of the input tensor, e.g. dtype, unless new values are provided by user</p>
-<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu">new_ones</span>(<span class="fl">5</span>, <span class="fl">3</span>, <span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_double</a></span>())   <span class="co"># new_* methods take in sizes</span>
-<span class="no">x</span>
-
-<span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn_like.html">torch_randn_like</a></span>(<span class="no">x</span>, <span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>()) <span class="co"># override dtype!</span>
-<span class="no">x</span>                                               <span class="co"># result has the same size</span></pre></body></html></div>
-<p>Get its size:</p>
-<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="no">x</span>$<span class="no">size</span></pre></body></html></div>
-</div>
-<div id="operations" class="section level3">
-<h3 class="hasAnchor">
-<a href="#operations" class="anchor"></a>Operations</h3>
-<p>There are multiple syntaxes for operations. In the following example, we will take a look at the addition operation.</p>
-<p>Addition: syntax 1</p>
-<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="no">y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
-<span class="no">x</span> + <span class="no">y</span></pre></body></html></div>
-<p>Addition: syntax 2</p>
-<div class="sourceCode" id="cb9"><html><body><pre class="r"><span class="fu"><a href="../../reference/torch_add.html">torch_add</a></span>(<span class="no">x</span>, <span class="no">y</span>)</pre></body></html></div>
-<p>Addition: in-place</p>
-<div class="sourceCode" id="cb10"><html><body><pre class="r"><span class="no">y</span>$<span class="fu">add_</span>(<span class="no">x</span>)
-<span class="no">y</span></pre></body></html></div>
-<blockquote>
-<p>Note: Any operation that mutates a tensor in-place is post-fixed with an <code>_</code>. For example: <code>x$copy_(y)</code>, <code>x$t_()</code>, will change x.</p>
-</blockquote>
-<p>You can use standard R-like indexing with all bells and whistles! See more about indexing with <code><a href="../../articles/indexing.html">vignette("indexing")</a></code>.</p>
-<div class="sourceCode" id="cb11"><html><body><pre class="r"><span class="no">x</span>[, <span class="fl">1</span>]</pre></body></html></div>
-<p>Resizing: If you want to resize/reshape tensor, you can use <code>torch_view</code>:</p>
-<div class="sourceCode" id="cb12"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, <span class="fl">4</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu">view</span>(<span class="fl">16</span>)
-<span class="no">z</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu">view</span>(<span class="kw">size</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(-<span class="fl">1</span>, <span class="fl">8</span>))  <span class="co"># the size -1 is inferred from other dimensions</span>
-<span class="no">x</span>$<span class="fu">size</span>()
-<span class="no">y</span>$<span class="fu">size</span>()
-<span class="no">z</span>$<span class="fu">size</span>()</pre></body></html></div>
-<p>If you have a one element tensor, use <code>$item()</code> to get the value as an R number</p>
-<div class="sourceCode" id="cb13"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>)
-<span class="no">x</span>
-<span class="no">x</span>$<span class="fu">item</span>()</pre></body></html></div>
-<p>You can find a complete list of operations in the reference page.</p>
-</div>
-</div>
-<div id="r-bridge" class="section level2">
-<h2 class="hasAnchor">
-<a href="#r-bridge" class="anchor"></a>R bridge</h2>
-<p>Converting a Torch Tensor to an R array and vice versa is a breeze.</p>
-<div id="converting-a-torch-tensor-into-an-r-array" class="section level3">
-<h3 class="hasAnchor">
-<a href="#converting-a-torch-tensor-into-an-r-array" class="anchor"></a>Converting a torch tensor into an R array</h3>
-<div class="sourceCode" id="cb14"><html><body><pre class="r"><span class="no">a</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">5</span>)
-<span class="no">a</span></pre></body></html></div>
-<div class="sourceCode" id="cb15"><html><body><pre class="r"><span class="no">b</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/as_array.html">as_array</a></span>(<span class="no">a</span>)
-<span class="no">b</span></pre></body></html></div>
-</div>
-<div id="converting-r-arrays-to-torch-tensors" class="section level3">
-<h3 class="hasAnchor">
-<a href="#converting-r-arrays-to-torch-tensors" class="anchor"></a>Converting R arrays to torch tensors</h3>
-<div class="sourceCode" id="cb16"><html><body><pre class="r"><span class="no">a</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span>(<span class="fl">1</span>, <span class="fl">5</span>)
-<span class="no">a</span>
-<span class="no">b</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="no">a</span>)
-<span class="no">b</span></pre></body></html></div>
-<p>Currently supported types are numerics and boolean types.</p>
-</div>
-</div>
-<div id="cuda-tensors" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cuda-tensors" class="anchor"></a>CUDA tensors</h2>
-<p>Tensors can be moved onto any device using the <code>$to</code> method.</p>
-<div class="sourceCode" id="cb17"><html><body><pre class="r"><span class="kw">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-  <span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-  <span class="no">y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones_like.html">torch_ones_like</a></span>(<span class="no">x</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)  <span class="co"># directly create a tensor on GPU</span>
-  <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu">to</span>(<span class="no">device</span>)                       <span class="co"># or just use strings ``.to("cuda")``</span>
-  <span class="no">z</span> <span class="kw">&lt;-</span> <span class="no">x</span> + <span class="no">y</span>
-  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">z</span>)
-  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">z</span>$<span class="fu">to</span>(<span class="kw">device</span> <span class="kw">=</span> <span class="st">"cpu"</span>, <span class="fu"><a href="../../reference/torch_dtype.html">torch_double</a></span>())) <span class="co"># `$to` can also change dtype together!</span>
-}</pre></body></html></div>
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/indexing.Rmd"><code>vignettes/indexing.Rmd</code></a></small>
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-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>In this article we describe the indexing operator for torch tensors and how it compares to the R indexing operator for arrays.</p>
-<p>Torch’s indexing semantics are closer to numpy’s semantics than R’s. You will find a lot of similarities between this article and the <code>numpy</code> indexing article available <a href="https://docs.scipy.org/doc/numpy-1.10.0/user/basics.indexing.html">here</a>.</p>
-<div id="single-element-indexing" class="section level2">
-<h2 class="hasAnchor">
-<a href="#single-element-indexing" class="anchor"></a>Single element indexing</h2>
-<p>Single element indexing for a 1-D tensors works mostly as expected. Like R, it is 1-based. Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.)</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>:<span class="fl">10</span>)
-<span class="no">x</span>[<span class="fl">1</span>]
-<span class="no">x</span>[-<span class="fl">1</span>]</pre></body></html></div>
-<p>You can also subset matrices and higher dimensions arrays using the same syntax:</p>
-<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu"><a href="https://rdrr.io/r/stats/reshape.html">reshape</a></span>(<span class="kw">shape</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>,<span class="fl">5</span>))
-<span class="no">x</span>
-<span class="no">x</span>[<span class="fl">1</span>,<span class="fl">3</span>]
-<span class="no">x</span>[<span class="fl">1</span>,-<span class="fl">1</span>]</pre></body></html></div>
-<p>Note that if one indexes a multidimensional tensor with fewer indices than dimensions, one gets an error, unlike in R that would flatten the array. For example:</p>
-<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="no">x</span>[<span class="fl">1</span>]</pre></body></html></div>
-</div>
-<div id="slicing-and-striding" class="section level2">
-<h2 class="hasAnchor">
-<a href="#slicing-and-striding" class="anchor"></a>Slicing and striding</h2>
-<p>It is possible to slice and stride arrays to extract sub-arrays of the same number of dimensions, but of different sizes than the original. This is best illustrated by a few examples:</p>
-<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>:<span class="fl">10</span>)
-<span class="no">x</span>
-<span class="no">x</span>[<span class="fl">2</span>:<span class="fl">5</span>]
-<span class="no">x</span>[<span class="fl">1</span>:(-<span class="fl">7</span>)]</pre></body></html></div>
-<p>You can also use the <code>1:10:2</code> syntax which means: In the range from 1 to 10, take every second item. For example:</p>
-<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">x</span>[<span class="fl">1</span>:<span class="fl">5</span>:<span class="fl">2</span>]</pre></body></html></div>
-<p>Another special syntax is the <code>N</code>, meaning the size of the specified dimension.</p>
-<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="no">x</span>[<span class="fl">5</span>:<span class="no">N</span>]</pre></body></html></div>
-</div>
-<div id="getting-the-complete-dimension" class="section level2">
-<h2 class="hasAnchor">
-<a href="#getting-the-complete-dimension" class="anchor"></a>Getting the complete dimension</h2>
-<p>Like in R, you can take all elements in a dimension by leaving an index empty.</p>
-<p>Consider a matrix:</p>
-<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">3</span>)
-<span class="no">x</span></pre></body></html></div>
-<p>The following syntax will give you the first row:</p>
-<div class="sourceCode" id="cb9"><html><body><pre class="r"><span class="no">x</span>[<span class="fl">1</span>,]</pre></body></html></div>
-<p>And this would give you the first 2 columns:</p>
-<div class="sourceCode" id="cb10"><html><body><pre class="r"><span class="no">x</span>[,<span class="fl">1</span>:<span class="fl">2</span>]</pre></body></html></div>
-</div>
-<div id="dropping-dimensions" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dropping-dimensions" class="anchor"></a>Dropping dimensions</h2>
-<p>By default, when indexing by a single integer, this dimension will be dropped to avoid the singleton dimension:</p>
-<div class="sourceCode" id="cb11"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">3</span>)
-<span class="no">x</span>[<span class="fl">1</span>,]$<span class="no">shape</span></pre></body></html></div>
-<p>You can optionally use the <code>drop = FALSE</code> argument to avoid dropping the dimension.</p>
-<div class="sourceCode" id="cb12"><html><body><pre class="r"><span class="no">x</span>[<span class="fl">1</span>,,<span class="kw">drop</span> <span class="kw">=</span> <span class="fl">FALSE</span>]$<span class="no">shape</span></pre></body></html></div>
-</div>
-<div id="adding-a-new-dimension" class="section level2">
-<h2 class="hasAnchor">
-<a href="#adding-a-new-dimension" class="anchor"></a>Adding a new dimension</h2>
-<p>It’s possible to add a new dimension to a tensor using index-like syntax:</p>
-<div class="sourceCode" id="cb13"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">10</span>))
-<span class="no">x</span>$<span class="no">shape</span>
-<span class="no">x</span>[, <span class="no">newaxis</span>]$<span class="no">shape</span>
-<span class="no">x</span>[, <span class="no">newaxis</span>, <span class="no">newaxis</span>]$<span class="no">shape</span></pre></body></html></div>
-<p>You can also use <code>NULL</code> instead of <code>newaxis</code>:</p>
-<div class="sourceCode" id="cb14"><html><body><pre class="r"><span class="no">x</span>[,<span class="kw">NULL</span>]$<span class="no">shape</span></pre></body></html></div>
-</div>
-<div id="dealing-with-variable-number-of-indices" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dealing-with-variable-number-of-indices" class="anchor"></a>Dealing with variable number of indices</h2>
-<p>Sometimes we don’t know how many dimensions a tensor has, but we do know what to do with the last available dimension, or the first one. To subsume all others, we can use <code>..</code>:</p>
-<div class="sourceCode" id="cb15"><html><body><pre class="r"><span class="no">z</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>:<span class="fl">125</span>)$<span class="fu"><a href="https://rdrr.io/r/stats/reshape.html">reshape</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">5</span>,<span class="fl">5</span>,<span class="fl">5</span>))
-<span class="no">z</span>[<span class="fl">1</span>,<span class="no">..</span>]
-<span class="no">z</span>[<span class="no">..</span>,<span class="fl">1</span>]</pre></body></html></div>
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-      <div class="hidden name"><code>loading-data.Rmd</code></div>
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-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<div id="datasets-and-data-loaders" class="section level2">
-<h2 class="hasAnchor">
-<a href="#datasets-and-data-loaders" class="anchor"></a>Datasets and data loaders</h2>
-<p>Central to data ingestion and preprocessing are datasets and data loaders.</p>
-<p><code>torch</code> comes equipped with a bag of datasets related to, mostly, image recognition and natural language processing (e.g., <code>mnist_dataset()</code>), which can be iterated over by means of <code>dataloader</code>s:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"># ...
-ds &lt;- mnist_dataset(
-  dir, 
-  download = TRUE, 
-  transform = function(x) {
-    x &lt;- x$to(dtype = torch_float())/256
-    x[newaxis,..]
-  }
-)
-
-dl &lt;- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-for (b in enumerate(dl)) {
-  # ...</pre></body></html></div>
-<p>Cf. <code>vignettes/examples/mnist-cnn.R</code> for a complete example.</p>
-<p>What if you want to train on a different dataset? In these cases, you subclass <code>Dataset</code>, an abstract container that needs to know how to iterate over the given data. To that purpose, your subclass needs to implement <code>.getitem()</code>, and say what should be returned when the data loader is asking for the next batch.</p>
-<p>In <code>.getitem()</code>, you can implement whatever preprocessing you require. Additionally, you should implement <code>.length()</code>, so users can find out how many items there are in the dataset.</p>
-<p>While this may sound complicated, it is not at all. The base logic is straightforward – complexity will, naturally, correlate with how involved your preprocessing is. To provide you with a simple but functional prototype, here we show how to create your own <code>dataset</code> to train on <a href="https://github.com/allisonhorst/palmerpenguins">Allison Horst's penguins</a>.</p>
-</div>
-<div id="a-custom-dataset" class="section level2">
-<h2 class="hasAnchor">
-<a href="#a-custom-dataset" class="anchor"></a>A custom dataset</h2>
-<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">palmerpenguins</span>)
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">magrittr</span>)
-
-<span class="no">penguins</span></pre></body></html></div>
-<p><code>Dataset</code>s are R6 classes created using the <code><a href="../reference/dataset.html">dataset()</a></code> constructor. You can pass a <code>name</code> and various member functions. Among those should be <code>initialize()</code>, to create instance variables, <code>.getitem()</code>, to indicate how the data should be returned, and <code>.length()</code>, to say how many items we have.</p>
-<p>In addition, any number of helper functions can be defined.</p>
-<p>Here, we assume the <code>penguins</code> have already been loaded, and all preprocessing consists in removing lines with <code>NA</code> values, transforming <code>factor</code>s to numbers starting from 0, and converting from R data types to <code>torch</code> tensors.</p>
-<p>In <code>.getitem</code>, we essentially decide how this data is going to be used: All variables besides <code>species</code> go into <code>x</code>, the predictor, and <code>species</code> will constitute <code>y</code>, the target. Predictor and target are returned in a list, to be accessed as <code>batch[[1]]</code> and <code>batch[[2]]</code> during training.</p>
-<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="no">penguins_dataset</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/dataset.html">dataset</a></span>(
-
-  <span class="kw">name</span> <span class="kw">=</span> <span class="st">"penguins_dataset"</span>,
-
-  <span class="kw">initialize</span> <span class="kw">=</span> <span class="kw">function</span>() {
-    <span class="no">self</span>$<span class="no">data</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">prepare_penguin_data</span>()
-  },
-
-  <span class="kw">.getitem</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">index</span>) {
-
-    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="no">data</span>[<span class="no">index</span>, <span class="fl">2</span>:-<span class="fl">1</span>]
-    <span class="no">y</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="no">data</span>[<span class="no">index</span>, <span class="fl">1</span>]$<span class="fu">to</span>(<span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span>())
-
-    <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">x</span>, <span class="no">y</span>)
-  },
-
-  <span class="kw">.length</span> <span class="kw">=</span> <span class="kw">function</span>() {
-    <span class="no">self</span>$<span class="no">data</span>$<span class="fu">size</span>()<span class="kw">[[</span><span class="fl">1</span>]]
-  },
-
-  <span class="kw">prepare_penguin_data</span> <span class="kw">=</span> <span class="kw">function</span>() {
-
-    <span class="no">input</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/na.fail.html">na.omit</a></span>(<span class="no">penguins</span>)
-    <span class="co"># conveniently, the categorical data are already factors</span>
-    <span class="no">input</span>$<span class="no">species</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="no">input</span>$<span class="no">species</span>)
-    <span class="no">input</span>$<span class="no">island</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="no">input</span>$<span class="no">island</span>)
-    <span class="no">input</span>$<span class="no">sex</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="no">input</span>$<span class="no">sex</span>)
-
-    <span class="no">input</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html">as.matrix</a></span>(<span class="no">input</span>)
-    <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="no">input</span>)
-  }
-)</pre></body></html></div>
-<p>Let’s create the dataset , query for it’s length, and look at its first item:</p>
-<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="no">tuxes</span> <span class="kw">&lt;-</span> <span class="fu">penguins_dataset</span>()
-<span class="no">tuxes</span>$<span class="fu">.length</span>()
-<span class="no">tuxes</span>$<span class="fu">.getitem</span>(<span class="fl">1</span>)</pre></body></html></div>
-<p>To be able to iterate over <code>tuxes</code>, we need a data loader (we override the default batch size of <code>1</code>):</p>
-<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">dl</span> <span class="kw">&lt;-</span><span class="no">tuxes</span> <span class="kw">%&gt;%</span> <span class="fu"><a href="../reference/dataloader.html">dataloader</a></span>(<span class="kw">batch_size</span> <span class="kw">=</span> <span class="fl">8</span>)</pre></body></html></div>
-<p>Calling <code>.length()</code> on a data loader (as opposed to a dataset) will return the number of <code>batches</code> we have:</p>
-<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="no">dl</span>$<span class="fu">.length</span>()</pre></body></html></div>
-<p>And we can create an iterator to inspect the first batch:</p>
-<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="no">iter</span> <span class="kw">&lt;-</span> <span class="no">dl</span>$<span class="fu">.iter</span>()
-<span class="no">b</span> <span class="kw">&lt;-</span> <span class="no">iter</span>$<span class="fu">.next</span>()
-<span class="no">b</span></pre></body></html></div>
-<p>To train a network, we can use <code>enumerate</code> to iterate over batches.</p>
-</div>
-<div id="training-with-data-loaders" class="section level2">
-<h2 class="hasAnchor">
-<a href="#training-with-data-loaders" class="anchor"></a>Training with data loaders</h2>
-<p>Our example network is very simple. (In reality, we would want to treat <code>island</code> as the categorical variable it is, and either one-hot-encode or embed it.)</p>
-<div class="sourceCode" id="cb9"><html><body><pre class="r"><span class="no">net</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/nn_module.html">nn_module</a></span>(
-  <span class="st">"PenguinNet"</span>,
-  <span class="kw">initialize</span> <span class="kw">=</span> <span class="kw">function</span>() {
-    <span class="no">self</span>$<span class="no">fc1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">6</span>, <span class="fl">32</span>)
-    <span class="no">self</span>$<span class="no">fc2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">32</span>, <span class="fl">3</span>)
-  },
-  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
-    <span class="no">x</span> <span class="kw">%&gt;%</span>
-      <span class="no">self</span>$<span class="fu">fc1</span>() <span class="kw">%&gt;%</span>
-      <span class="fu"><a href="../reference/nnf_relu.html">nnf_relu</a></span>() <span class="kw">%&gt;%</span>
-      <span class="no">self</span>$<span class="fu">fc2</span>() <span class="kw">%&gt;%</span>
-      <span class="fu"><a href="../reference/nnf_log_softmax.html">nnf_log_softmax</a></span>(<span class="kw">dim</span> <span class="kw">=</span> <span class="fl">1</span>)
-  }
-)
-
-<span class="no">model</span> <span class="kw">&lt;-</span> <span class="fu">net</span>()</pre></body></html></div>
-<p>We still need an optimizer:</p>
-<div class="sourceCode" id="cb10"><html><body><pre class="r"><span class="no">optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/optim_sgd.html">optim_sgd</a></span>(<span class="no">model</span>$<span class="no">parameters</span>, <span class="kw">lr</span> <span class="kw">=</span> <span class="fl">0.01</span>)</pre></body></html></div>
-<p>And we’re ready to train:</p>
-<div class="sourceCode" id="cb11"><html><body><pre class="r"><span class="kw">for</span> (<span class="no">epoch</span> <span class="kw">in</span> <span class="fl">1</span>:<span class="fl">10</span>) {
-
-  <span class="no">l</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
-
-  <span class="kw">for</span> (<span class="no">b</span> <span class="kw">in</span> <span class="fu"><a href="../reference/enumerate.html">enumerate</a></span>(<span class="no">dl</span>)) {
-    <span class="no">optimizer</span>$<span class="fu">zero_grad</span>()
-    <span class="no">output</span> <span class="kw">&lt;-</span> <span class="fu">model</span>(<span class="no">b</span><span class="kw">[[</span><span class="fl">1</span>]])
-    <span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/nnf_nll_loss.html">nnf_nll_loss</a></span>(<span class="no">output</span>, <span class="no">b</span><span class="kw">[[</span><span class="fl">2</span>]])
-    <span class="no">loss</span>$<span class="fu">backward</span>()
-    <span class="no">optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-    <span class="no">l</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">l</span>, <span class="no">loss</span>$<span class="fu">item</span>())
-  }
-
-  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="no">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">l</span>)))
-}</pre></body></html></div>
-</div>
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-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
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-document.addEventListener('DOMContentLoaded', function() {
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-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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-<title>Creating tensors • torch</title>
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/tensor-creation.Rmd"><code>vignettes/tensor-creation.Rmd</code></a></small>
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-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>In this article we describe various ways of creating <code>torch</code> tensors in R.</p>
-<div id="from-r-objects" class="section level2">
-<h2 class="hasAnchor">
-<a href="#from-r-objects" class="anchor"></a>From R objects</h2>
-<p>You can create tensors from R objects using the <code>torch_tensor</code> function. The <code>torch_tensor</code> function takes an R vector, matrix or array and creates an equivalent <code>torch_tensor</code>.</p>
-<p>You can see a few examples below:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span>))
-
-<span class="co"># conform to row-major indexing used in torch</span>
-<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span>:<span class="fl">10</span>, <span class="kw">ncol</span> <span class="kw">=</span> <span class="fl">5</span>, <span class="kw">nrow</span> <span class="kw">=</span> <span class="fl">2</span>, <span class="kw">byrow</span> <span class="kw">=</span> <span class="fl">TRUE</span>))
-<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html">runif</a></span>(<span class="fl">12</span>), <span class="kw">dim</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">3</span>)))</pre></body></html></div>
-<p>By default, we will create tensors in the <code>cpu</code> device, converting their R datatype to the corresponding torch <code>dtype</code>.</p>
-<blockquote>
-<p><strong>Note</strong> currently, only numeric and boolean types are supported.</p>
-</blockquote>
-<p>You can always modify <code>dtype</code> and <code>device</code> when converting an R object to a torch tensor. For example:</p>
-<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>, <span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span>())
-<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="st">"cpu"</span>, <span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../reference/torch_dtype.html">torch_float64</a></span>())</pre></body></html></div>
-<p>Other options available when creating a tensor are:</p>
-<ul>
-<li>
-<code>requires_grad</code>: boolean indicating if you want <code>autograd</code> to record operations on them for automatic differentiation.</li>
-<li>
-<code>pin_memory</code>: – If set, the tensor returned would be allocated in pinned memory. Works only for CPU tensors.</li>
-</ul>
-<p>These options are available for all functions that can be used to create new tensors, including the factory functions listed in the next section.</p>
-</div>
-<div id="using-creation-functions" class="section level2">
-<h2 class="hasAnchor">
-<a href="#using-creation-functions" class="anchor"></a>Using creation functions</h2>
-<p>You can also use the <code>torch_*</code> functions listed below to create torch tensors using some algorithm.</p>
-<p>For example, the <code>torch_randn</code> function will create tensors using the normal distribution with mean 0 and standard deviation 1. You can use the <code>...</code> argument to pass the size of the dimensions. For example, the code below will create a normally distributed tensor with shape 5x3.</p>
-<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
-<span class="no">x</span></pre></body></html></div>
-<p>Another example is <code>torch_ones</code>, which creates a tensor filled with ones.</p>
-<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>, <span class="fl">4</span>, <span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../reference/torch_dtype.html">torch_int64</a></span>(), <span class="kw">device</span> <span class="kw">=</span> <span class="st">"cpu"</span>)
-<span class="no">x</span></pre></body></html></div>
-<p>Here is the full list of functions that can be used to bulk-create tensors in torch:</p>
-<ul>
-<li>
-<code>torch_arange</code>: Returns a tensor with a sequence of integers,</li>
-<li>
-<code>torch_empty</code>: Returns a tensor with uninitialized values,</li>
-<li>
-<code>torch_eye</code>: Returns an identity matrix,</li>
-<li>
-<code>torch_full</code>: Returns a tensor filled with a single value,</li>
-<li>
-<code>torch_linspace</code>: Returns a tensor with values linearly spaced in some interval,</li>
-<li>
-<code>torch_logspace</code>: Returns a tensor with values logarithmically spaced in some interval,</li>
-<li>
-<code>torch_ones</code>: Returns a tensor filled with all ones,</li>
-<li>
-<code>torch_rand</code>: Returns a tensor filled with values drawn from a uniform distribution on [0, 1).</li>
-<li>
-<code>torch_randint</code>: Returns a tensor with integers randomly drawn from an interval,</li>
-<li>
-<code>torch_randn</code>: Returns a tensor filled with values drawn from a unit normal distribution,</li>
-<li>
-<code>torch_randperm</code>: Returns a tensor filled with a random permutation of integers in some interval,</li>
-<li>
-<code>torch_zeros</code>: Returns a tensor filled with all zeros.</li>
-</ul>
-</div>
-<div id="conversion" class="section level2">
-<h2 class="hasAnchor">
-<a href="#conversion" class="anchor"></a>Conversion</h2>
-<p>Once a tensor exists you can convert between <code>dtype</code>s and move to a different device with <code>to</code> method. For example:</p>
-<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu">to</span>(<span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../reference/torch_dtype.html">torch_int32</a></span>())
-<span class="no">x</span>
-<span class="no">y</span></pre></body></html></div>
-<p>You can also copy a tensor to the GPU using:</p>
-<div class="sourceCode" id="cb7"><html><body><pre class="r">x &lt;- torch_tensor(1)
-y &lt;- x$cuda())</pre></body></html></div>
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-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)</pre></body></html></div>
-<p>So far, all we’ve been using from torch is <em>tensors</em>, but we’ve been performing all calculations ourselves – the computing the predictions, the loss, the gradients (and thus, the necessary updates to the weights), and the new weight values. In this chapter, we’ll make a significant change: Namely, we spare ourselves the cumbersome calculation of gradients, and have torch do it for us.</p>
-<p>Before we see that in action, let’s get some more background.</p>
-<div id="automatic-differentiation-with-autograd" class="section level2">
-<h2 class="hasAnchor">
-<a href="#automatic-differentiation-with-autograd" class="anchor"></a>Automatic differentiation with autograd</h2>
-<p>Torch uses a module called <em>autograd</em> to record operations performed on tensors, and store what has to be done to obtain the respective gradients. These actions are stored as functions, and those functions are applied in order when the gradient of the output (normally, the loss) with respect to those tensors is calculated: starting from the output node and <em>propagating</em> gradients <em>back</em> through the network. This is a form of <em>reverse mode automatic differentiation</em>.</p>
-<p>As users, we can see a bit of this implementation. As a prerequisite for this “recording” to happen, tensors have to be created with <code>requires_grad = TRUE</code>. E.g.</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>,<span class="fl">2</span>, <span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)</pre></body></html></div>
-<p>To be clear, this is a tensor <em>with respect to which</em> gradients have to be calculated – normally, a tensor representing a weight or a bias, not the input data <a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a>. If we now perform some operation on that tensor, assigning the result to <code>y</code></p>
-<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">y</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()</pre></body></html></div>
-<p>we find that <code>y</code> now has a non-empty <code>grad_fn</code> that tells torch how to compute the gradient of <code>y</code> with respect to <code>x</code>:</p>
-<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="no">y</span>$<span class="no">grad_fn</span></pre></body></html></div>
-<p>Actual computation of gradients is triggered by calling <code>backward()</code> on the output tensor.</p>
-<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="no">y</span>$<span class="fu">backward</span>()</pre></body></html></div>
-<p>That executed, <code>x</code> now has a non-empty field <code>grad</code> that stores the gradient of <code>y</code> with respect to <code>x</code>:</p>
-<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">x</span>$<span class="no">grad</span></pre></body></html></div>
-<p>With a longer chain of computations, we can peek at how torch builds up a graph of backward operations.</p>
-<p>Here is a slightly more complex example. We call <code>retain_grad()</code> on <code>y</code> and <code>z</code> just for demonstration purposes; by default, intermediate gradients – while of course they have to be computed – aren’t stored, in order to save memory.</p>
-<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="no">x1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>,<span class="fl">2</span>, <span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-<span class="no">x2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1.1</span>, <span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="no">x1</span> * (<span class="no">x2</span> + <span class="fl">2</span>)
-<span class="no">y</span>$<span class="fu">retain_grad</span>()
-<span class="no">z</span> <span class="kw">&lt;-</span> <span class="no">y</span>$<span class="fu">pow</span>(<span class="fl">2</span>) * <span class="fl">3</span>
-<span class="no">z</span>$<span class="fu">retain_grad</span>()
-<span class="no">out</span> <span class="kw">&lt;-</span> <span class="no">z</span>$<span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()</pre></body></html></div>
-<p>Starting from <code>out$grad_fn</code>, we can follow the graph all back to the leaf nodes:</p>
-<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="co"># how to compute the gradient for mean, the last operation executed</span>
-<span class="no">out</span>$<span class="no">grad_fn</span>
-<span class="co"># how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3</span>
-<span class="no">out</span>$<span class="no">grad_fn</span>$<span class="no">next_functions</span>
-<span class="co"># how to compute the gradient for pow in z = y.pow(2) * 3</span>
-<span class="no">out</span>$<span class="no">grad_fn</span>$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="no">next_functions</span>
-<span class="co"># how to compute the gradient for the multiplication in y = x * (x + 2)</span>
-<span class="no">out</span>$<span class="no">grad_fn</span>$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="no">next_functions</span>
-<span class="co"># how to compute the gradient for the two branches of y = x * (x + 2),</span>
-<span class="co"># where the left branch is a leaf node (AccumulateGrad for x1)</span>
-<span class="no">out</span>$<span class="no">grad_fn</span>$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="no">next_functions</span>
-<span class="co"># here we arrive at the other leaf node (AccumulateGrad for x2)</span>
-<span class="no">out</span>$<span class="no">grad_fn</span>$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="no">next_functions</span><span class="kw">[[</span><span class="fl">2</span>]]$<span class="no">next_functions</span></pre></body></html></div>
-<p>After calling <code>out$backward()</code>, all tensors in the graph will have their respective gradients created. Without our calls to <code>retain_grad</code> above, <code>z$grad</code> and <code>y$grad</code> would be empty:</p>
-<div class="sourceCode" id="cb9"><html><body><pre class="r"><span class="no">out</span>$<span class="fu">backward</span>()
-<span class="no">z</span>$<span class="no">grad</span>
-<span class="no">y</span>$<span class="no">grad</span>
-<span class="no">x2</span>$<span class="no">grad</span>
-<span class="no">x1</span>$<span class="no">grad</span></pre></body></html></div>
-<p>Thus acquainted with autograd, we’re ready to modify our example.</p>
-</div>
-<div id="the-simple-network-now-using-autograd" class="section level2">
-<h2 class="hasAnchor">
-<a href="#the-simple-network-now-using-autograd" class="anchor"></a>The simple network, now using autograd</h2>
-<p>For a single new line calling <code>loss$backward()</code>, now a number of lines (that did manual backprop) are gone:</p>
-<div class="sourceCode" id="cb10"><html><body><pre class="r"><span class="co">### generate training data -----------------------------------------------------</span>
-<span class="co"># input dimensionality (number of input features)</span>
-<span class="no">d_in</span> <span class="kw">&lt;-</span> <span class="fl">3</span>
-<span class="co"># output dimensionality (number of predicted features)</span>
-<span class="no">d_out</span> <span class="kw">&lt;-</span> <span class="fl">1</span>
-<span class="co"># number of observations in training set</span>
-<span class="no">n</span> <span class="kw">&lt;-</span> <span class="fl">100</span>
-<span class="co"># create random data</span>
-<span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="no">n</span>, <span class="no">d_in</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="no">x</span>[,<span class="fl">1</span>]*<span class="fl">0.2</span> - <span class="no">x</span>[<span class="no">..</span>,<span class="fl">2</span>]*<span class="fl">1.3</span> - <span class="no">x</span>[<span class="no">..</span>,<span class="fl">3</span>]*<span class="fl">0.5</span> + <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="no">n</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="no">y</span>$<span class="fu">unsqueeze</span>(<span class="kw">dim</span> <span class="kw">=</span> <span class="fl">1</span>)
-<span class="co">### initialize weights ---------------------------------------------------------</span>
-<span class="co"># dimensionality of hidden layer</span>
-<span class="no">d_hidden</span> <span class="kw">&lt;-</span> <span class="fl">32</span>
-<span class="co"># weights connecting input to hidden layer</span>
-<span class="no">w1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="no">d_in</span>, <span class="no">d_hidden</span>, <span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-<span class="co"># weights connecting hidden to output layer</span>
-<span class="no">w2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="no">d_hidden</span>, <span class="no">d_out</span>, <span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-<span class="co"># hidden layer bias</span>
-<span class="no">b1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">1</span>, <span class="no">d_hidden</span>, <span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-<span class="co"># output layer bias</span>
-<span class="no">b2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">1</span>, <span class="no">d_out</span>,<span class="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-<span class="co">### network parameters ---------------------------------------------------------</span>
-<span class="no">learning_rate</span> <span class="kw">&lt;-</span> <span class="fl">1e-4</span>
-<span class="co">### training loop --------------------------------------------------------------</span>
-<span class="kw">for</span> (<span class="no">t</span> <span class="kw">in</span> <span class="fl">1</span>:<span class="fl">200</span>) {
-
-    <span class="co">### -------- Forward pass -------- </span>
-    <span class="no">y_pred</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu">mm</span>(<span class="no">w1</span>)$<span class="fu">add</span>(<span class="no">b1</span>)$<span class="fu">clamp</span>(<span class="kw">min</span> <span class="kw">=</span> <span class="fl">0</span>)$<span class="fu">mm</span>(<span class="no">w2</span>)$<span class="fu">add</span>(<span class="no">b2</span>)
-    <span class="co">### -------- compute loss -------- </span>
-    <span class="no">loss</span> <span class="kw">&lt;-</span> (<span class="no">y_pred</span> - <span class="no">y</span>)$<span class="fu">pow</span>(<span class="fl">2</span>)$<span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
-    <span class="kw">if</span> (<span class="no">t</span> <span class="kw">%%</span> <span class="fl">10</span> <span class="kw">==</span> <span class="fl">0</span>) <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="no">t</span>, <span class="fu"><a href="../reference/as_array.html">as_array</a></span>(<span class="no">loss</span>), <span class="st">"\n"</span>)
-    <span class="co">### -------- Backpropagation -------- </span>
-    <span class="co"># compute the gradient of loss with respect to all tensors with requires_grad = True.</span>
-    <span class="no">loss</span>$<span class="fu">backward</span>()
-
-    <span class="co">### -------- Update weights -------- </span>
-
-    <span class="co"># Wrap in torch.no_grad() because this is a part we DON'T want to record for automatic gradient computation</span>
-    <span class="fu"><a href="../reference/with_no_grad.html">with_no_grad</a></span>({
-
-      <span class="no">w1</span>$<span class="fu">sub_</span>(<span class="no">learning_rate</span> * <span class="no">w1</span>$<span class="no">grad</span>)
-      <span class="no">w2</span>$<span class="fu">sub_</span>(<span class="no">learning_rate</span> * <span class="no">w2</span>$<span class="no">grad</span>)
-      <span class="no">b1</span>$<span class="fu">sub_</span>(<span class="no">learning_rate</span> * <span class="no">b1</span>$<span class="no">grad</span>)
-      <span class="no">b2</span>$<span class="fu">sub_</span>(<span class="no">learning_rate</span> * <span class="no">b2</span>$<span class="no">grad</span>)
-
-      <span class="co"># Zero the gradients after every pass, because they'd accumulate otherwise</span>
-      <span class="no">w1</span>$<span class="no">grad</span>$<span class="fu">zero_</span>()
-      <span class="no">w2</span>$<span class="no">grad</span>$<span class="fu">zero_</span>()
-      <span class="no">b1</span>$<span class="no">grad</span>$<span class="fu">zero_</span>()
-      <span class="no">b2</span>$<span class="no">grad</span>$<span class="fu">zero_</span>()
-
-    })
-
-}</pre></body></html></div>
-<p>We still manually compute the forward pass, and we still manually update the weights. In the last two chapters of this section, we’ll see how these parts of the logic can be made more modular and reusable, as well.</p>
-</div>
-<div class="footnotes">
-<hr>
-<ol>
-<li id="fn1"><p>Unless we <em>want</em> to change the data, as in adversarial example generation<a href="#fnref1" class="footnote-back">↩︎</a></p></li>
-</ol>
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-      <span class="fu"><a href="../../reference/nn_leaky_relu.html">nn_leaky_relu</a></span>(<span class="fl">0.2</span>, <span class="kw">inplace</span> <span class="kw">=</span> <span class="fl">TRUE</span>),
-      <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">64</span>, <span class="fl">128</span>, <span class="kw">kernel_size</span> <span class="kw">=</span> <span class="fl">4</span>, <span class="kw">stride</span> <span class="kw">=</span> <span class="fl">2</span>, <span class="kw">padding</span> <span class="kw">=</span> <span class="fl">1</span>, <span class="kw">bias</span> <span class="kw">=</span> <span class="fl">FALSE</span>),
-      <span class="fu"><a href="../../reference/nn_leaky_relu.html">nn_leaky_relu</a></span>(<span class="fl">0.2</span>, <span class="kw">inplace</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-    )
-    <span class="no">self</span>$<span class="no">linear</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">128</span>, <span class="fl">1</span>)
-    <span class="no">self</span>$<span class="no">sigmoid</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_sigmoid.html">nn_sigmoid</a></span>()
-  },
-  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">input</span>) {
-    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">main</span>(<span class="no">input</span>)
-    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_flatten.html">torch_flatten</a></span>(<span class="no">x</span>, <span class="kw">start_dim</span> <span class="kw">=</span> <span class="fl">2</span>)
-    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">linear</span>(<span class="no">x</span>)
-    <span class="no">self</span>$<span class="fu">sigmoid</span>(<span class="no">x</span>)
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-)
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-<span class="no">plot_gen</span> <span class="kw">&lt;-</span> <span class="kw">function</span>(<span class="no">noise</span>) {
-  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="fu">G</span>(<span class="no">noise</span>)
-  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="no">img</span>$<span class="fu">cpu</span>()
-  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="no">img</span>[<span class="fl">1</span>,<span class="fl">1</span>,,,<span class="no">newaxis</span>]/<span class="fl">2</span> + <span class="fl">0.5</span>
-  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_stack.html">torch_stack</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">img</span>, <span class="no">img</span>, <span class="no">img</span>), <span class="kw">dim</span> <span class="kw">=</span> <span class="fl">2</span>)[<span class="no">..</span>,<span class="fl">1</span>]
-  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/grDevices/as.raster.html">as.raster</a></span>(<span class="fu"><a href="../../reference/as_array.html">as_array</a></span>(<span class="no">img</span>))
-  <span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">img</span>)
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-<span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span>(<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>(),  <span class="st">"cuda"</span>, <span class="st">"cpu"</span>))
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-<span class="no">G</span> <span class="kw">&lt;-</span> <span class="fu">generator</span>(<span class="kw">latent_dim</span> <span class="kw">=</span> <span class="fl">100</span>, <span class="kw">out_channels</span> <span class="kw">=</span> <span class="fl">1</span>)
-<span class="no">D</span> <span class="kw">&lt;-</span> <span class="fu">discriminator</span>(<span class="kw">in_channels</span> <span class="kw">=</span> <span class="fl">1</span>)
-
-<span class="no">init_weights</span> <span class="kw">&lt;-</span> <span class="kw">function</span>(<span class="no">m</span>) {
-  <span class="kw">if</span> (<span class="fu"><a href="https://rdrr.io/r/base/grep.html">grepl</a></span>(<span class="st">"conv"</span>, <span class="no">m</span>$<span class="no">.classes</span><span class="kw">[[</span><span class="fl">1</span>]])) {
-    <span class="fu"><a href="../../reference/nn_init_normal_.html">nn_init_normal_</a></span>(<span class="no">m</span>$<span class="no">weight</span>$<span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span>(), <span class="fl">0.0</span>, <span class="fl">0.02</span>)
-  } <span class="kw">else</span> <span class="kw">if</span> (<span class="fu"><a href="https://rdrr.io/r/base/grep.html">grepl</a></span>(<span class="st">"batch_norm"</span>, <span class="no">m</span>$<span class="no">.classes</span><span class="kw">[[</span><span class="fl">1</span>]])) {
-    <span class="fu"><a href="../../reference/nn_init_normal_.html">nn_init_normal_</a></span>(<span class="no">m</span>$<span class="no">weight</span>$<span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span>(), <span class="fl">1.0</span>, <span class="fl">0.02</span>)
-    <span class="fu"><a href="../../reference/nn_init_constant_.html">nn_init_constant_</a></span>(<span class="no">m</span>$<span class="no">bias</span>$<span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span>(), <span class="fl">0</span>)
-  }
-}
-
-<span class="no">G</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="fu"><a href="https://rdrr.io/r/base/apply.html">apply</a></span>(<span class="no">init_weights</span>)
-<span class="no">D</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="fu"><a href="https://rdrr.io/r/base/apply.html">apply</a></span>(<span class="no">init_weights</span>)
-
-<span class="no">G</span>$<span class="fu">to</span>(<span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-<span class="no">D</span>$<span class="fu">to</span>(<span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-
-<span class="no">G_optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_adam.html">optim_adam</a></span>(<span class="no">G</span>$<span class="no">parameters</span>, <span class="kw">lr</span> <span class="kw">=</span> <span class="fl">2</span> * <span class="fl">1e-4</span>, <span class="kw">betas</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.999</span>))
-<span class="no">D_optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_adam.html">optim_adam</a></span>(<span class="no">D</span>$<span class="no">parameters</span>, <span class="kw">lr</span> <span class="kw">=</span> <span class="fl">2</span> * <span class="fl">1e-4</span>, <span class="kw">betas</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.999</span>))
-
-<span class="no">fixed_noise</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, <span class="fl">100</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-
-<span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_bce_loss.html">nn_bce_loss</a></span>()
-
-<span class="kw">for</span> (<span class="no">epoch</span> <span class="kw">in</span> <span class="fl">1</span>:<span class="fl">10</span>) {
-
-  <span class="no">pb</span> <span class="kw">&lt;-</span> <span class="kw pkg">progress</span><span class="kw ns">::</span><span class="no">progress_bar</span>$<span class="fu">new</span>(
-    <span class="kw">total</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span>(<span class="no">dl</span>),
-    <span class="kw">format</span> <span class="kw">=</span> <span class="st">"[:bar] :eta Loss D: :lossd Loss G: :lossg"</span>
-  )
-  <span class="no">lossg</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
-  <span class="no">lossd</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
-
-  <span class="kw">for</span> (<span class="no">b</span> <span class="kw">in</span> <span class="fu"><a href="../../reference/enumerate.html">enumerate</a></span>(<span class="no">dl</span>)) {
-
-    <span class="no">y_real</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">32</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-    <span class="no">y_fake</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">32</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-
-    <span class="no">noise</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">32</span>, <span class="fl">100</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-    <span class="no">fake</span> <span class="kw">&lt;-</span> <span class="fu">G</span>(<span class="no">noise</span>)
-
-    <span class="no">img</span> <span class="kw">&lt;-</span> <span class="no">b</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="fu">to</span>(<span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-
-    <span class="co"># train the discriminator ---</span>
-    <span class="no">D_loss</span> <span class="kw">&lt;-</span> <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">img</span>), <span class="no">y_real</span>) + <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">fake</span>$<span class="fu"><a href="https://rdrr.io/r/base/detach.html">detach</a></span>()), <span class="no">y_fake</span>)
-
-    <span class="no">D_optimizer</span>$<span class="fu">zero_grad</span>()
-    <span class="no">D_loss</span>$<span class="fu">backward</span>()
-    <span class="no">D_optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-
-    <span class="co"># train the generator ---</span>
-
-    <span class="no">G_loss</span> <span class="kw">&lt;-</span> <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">fake</span>), <span class="no">y_real</span>)
-
-    <span class="no">G_optimizer</span>$<span class="fu">zero_grad</span>()
-    <span class="no">G_loss</span>$<span class="fu">backward</span>()
-    <span class="no">G_optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-
-    <span class="no">lossd</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">lossd</span>, <span class="no">D_loss</span>$<span class="fu">item</span>())
-    <span class="no">lossg</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">lossg</span>, <span class="no">G_loss</span>$<span class="fu">item</span>())
-    <span class="no">pb</span>$<span class="fu">tick</span>(<span class="kw">tokens</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">lossd</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossd</span>), <span class="kw">lossg</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossg</span>)))
-  }
-  <span class="fu">plot_gen</span>(<span class="no">fixed_noise</span>)
-
-  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Epoch %d - Loss D: %3f Loss G: %3f\n"</span>, <span class="no">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossd</span>), <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossg</span>)))
-}</pre></body></html></div>
-  </div>
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
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diff --git a/static/docs/dev/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6a..000000000
--- a/static/docs/dev/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
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-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
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diff --git a/static/docs/dev/articles/examples/mnist-mlp.html b/static/docs/dev/articles/examples/mnist-mlp.html
deleted file mode 100644
index b13de836a..000000000
--- a/static/docs/dev/articles/examples/mnist-mlp.html
+++ /dev/null
@@ -1,248 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
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-<title>mnist-mlp • torch</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/united/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
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-<li class="dropdown-header">Beginers guide</li>
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diff --git a/static/docs/dev/articles/extending-autograd.html b/static/docs/dev/articles/extending-autograd.html
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-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Extending Autograd</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/extending-autograd.Rmd"><code>vignettes/extending-autograd.Rmd</code></a></small>
-      <div class="hidden name"><code>extending-autograd.Rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>Adding operations to autograd requires implementing a new <code>autograd_function</code> for each operation. Recall that <code>autograd_functions</code>s are what <code>autograd</code> uses to compute the results and gradients, and encode the operation history. Every new function requires you to implement 2 methods:</p>
-<ul>
-<li><p><code>forward()</code> - the code that performs the operation. It can take as many arguments as you want, with some of them being optional, if you specify the default values. All kinds of R objects are accepted here. Tensor arguments that track history (i.e., with <code>requires_grad=TRUE</code>) will be converted to ones that don’t track history before the call, and their use will be registered in the graph. Note that this logic won’t traverse lists or any other data structures and will only consider Tensor’s that are direct arguments to the call. You can return either a single Tensor output, or a list of <code>Tensor</code>s if there are multiple outputs. Also, please refer to the docs of <code>autograd_function</code> to find descriptions of useful methods that can be called only from <code>forward()</code>.</p></li>
-<li><p><code>backward()</code> - gradient formula. It will be given as many Tensor arguments as there were outputs, with each of them representing gradient w.r.t. that output. It should return as many <code>Tensor</code>s as there were <code>Tensor's</code> that required gradients in <code>forward</code>, with each of them containing the gradient w.r.t. its corresponding input.</p></li>
-</ul>
-<div id="note" class="section level2">
-<h2 class="hasAnchor">
-<a href="#note" class="anchor"></a>Note</h2>
-<p>It’s the user’s responsibility to use the special functions in the forward’s <code>ctx</code> properly in order to ensure that the new <code>autograd_function</code> works properly with the autograd engine.</p>
-<ul>
-<li><p><code>save_for_backward()</code> must be used when saving input or ouput of the forward to be used later in the backward.</p></li>
-<li><p><code>mark_dirty()</code> must be used to mark any input that is modified inplace by the forward function.</p></li>
-<li><p><code>mark_non_differentiable()</code> must be used to tell the engine if an output is not differentiable.</p></li>
-</ul>
-</div>
-<div id="examples" class="section level2">
-<h2 class="hasAnchor">
-<a href="#examples" class="anchor"></a>Examples</h2>
-<p>Below you can find code for a linear function:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="kw">linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/autograd_function.html">autograd_function</a></span>(
-  forward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">input</span>, <span class="kw">weight</span>, <span class="kw">bias</span> = <span class="kw">NULL</span>) {
-    <span class="kw">ctx</span><span class="op">$</span><span class="fu">save_for_backward</span>(input = <span class="kw">input</span>, weight = <span class="kw">weight</span>, bias = <span class="kw">bias</span>)
-    <span class="kw">output</span> <span class="op">&lt;-</span> <span class="kw">input</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">weight</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>())
-    <span class="co">if</span> (<span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NULL.html">is.null</a></span>(<span class="kw">bias</span>))
-      <span class="kw">output</span> <span class="op">&lt;-</span> <span class="kw">output</span> <span class="op">+</span> <span class="kw">bias</span><span class="op">$</span><span class="fu">unsqueeze</span>(<span class="fl">0</span>)<span class="op">$</span><span class="fu">expand_as</span>(<span class="kw">output</span>)
-    
-    <span class="kw">output</span>
-  },
-  backward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">grad_output</span>) {
-    
-    <span class="kw">s</span> <span class="op">&lt;-</span> <span class="kw">ctx</span><span class="op">$</span><span class="kw">saved_variables</span>
-    
-    <span class="kw">grads</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(
-      input = <span class="kw">NULL</span>,
-      weight = <span class="kw">NULL</span>,
-      bias = <span class="kw">NULL</span>
-    )
-    
-    <span class="co">if</span> (<span class="kw">ctx</span><span class="op">$</span><span class="kw">needs_input_grad</span><span class="op">$</span><span class="kw">input</span>)
-      <span class="kw">grads</span><span class="op">$</span><span class="kw">input</span> <span class="op">&lt;-</span> <span class="kw">grad_output</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">s</span><span class="op">$</span><span class="kw">weight</span>)
-    
-    <span class="co">if</span> (<span class="kw">ctx</span><span class="op">$</span><span class="kw">needs_input_grad</span><span class="op">$</span><span class="kw">weight</span>)
-      <span class="kw">grads</span><span class="op">$</span><span class="kw">weight</span> <span class="op">&lt;-</span> <span class="kw">grad_output</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>()<span class="op">$</span><span class="fu">mm</span>(<span class="kw">s</span><span class="op">$</span><span class="kw">input</span>)
-    
-    <span class="co">if</span> (<span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NULL.html">is.null</a></span>(<span class="kw">s</span><span class="op">$</span><span class="kw">bias</span>) <span class="op">&amp;&amp;</span> <span class="kw">ctx</span><span class="op">$</span><span class="kw">needs_input_grad</span><span class="op">$</span><span class="kw">bias</span>)
-      <span class="kw">grads</span><span class="op">$</span><span class="kw">bias</span> <span class="op">&lt;-</span> <span class="kw">grad_output</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>(dim = <span class="fl">0</span>)
-    
-    <span class="kw">grads</span>
-  }
-)
-</pre></div>
-<p>Here, we give an additional example of a function that is parametrized by non-Tensor arguments:</p>
-<div class="sourceCode" id="cb3"><pre class="downlit">
-<span class="kw">mul_constant</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/autograd_function.html">autograd_function</a></span>(
-  forward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">tensor</span>, <span class="kw">constant</span>) {
-    <span class="kw">ctx</span><span class="op">$</span><span class="fu">save_for_backward</span>(constant = <span class="kw">constant</span>)
-    <span class="kw">tensor</span> <span class="op">*</span> <span class="kw">constant</span>
-  },
-  backward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">grad_output</span>) {
-    <span class="kw">v</span> <span class="op">&lt;-</span> <span class="kw">ctx</span><span class="op">$</span><span class="kw">saved_variables</span>
-    <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(
-      tensor = <span class="kw">grad_output</span> <span class="op">*</span> <span class="kw">v</span><span class="op">$</span><span class="kw">constant</span>
-    )
-  }
-)
-</pre></div>
-<div class="sourceCode" id="cb4"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>, requires_grad = <span class="fl">TRUE</span>)
-<span class="kw">o</span> <span class="op">&lt;-</span> <span class="fu">mul_constant</span>(<span class="kw">x</span>, <span class="fl">2</span>)
-<span class="kw">o</span><span class="op">$</span><span class="fu">backward</span>()
-<span class="kw">x</span><span class="op">$</span><span class="kw">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  2</span>
-<span class="co">#&gt; [ CPUFloatType{1} ]</span>
-</pre></div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
-
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
-</div>
-
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-</div>
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diff --git a/static/docs/dev/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6a..000000000
--- a/static/docs/dev/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/static/docs/dev/articles/getting-started/assets/mnist.png b/static/docs/dev/articles/getting-started/assets/mnist.png
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-      <h1 data-toc-skip>Autograd: automatic differentiation</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/autograd.Rmd"><code>vignettes/getting-started/autograd.Rmd</code></a></small>
-      <div class="hidden name"><code>autograd.Rmd</code></div>
-
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-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>Central to all neural networks in torch is the autograd functionality. Let’s first briefly visit this, and we will then go to training our first neural network.</p>
-<p>Autograd provides automatic differentiation for all operations on Tensors. It is a define-by-run framework, which means that your backprop is defined by how your code is run, and that every single iteration can be different.</p>
-<p>Let us see this in more simple terms with some examples.</p>
-<div id="tensor" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tensor" class="anchor"></a>Tensor</h2>
-<p><code>torch_tensor</code> is the central class of the package. If you set its attribute <code>$requires_grad</code> as <code>TRUE</code>, it starts to track all operations on it. When you finish your computation you can call <code>$backward()</code> and have all the gradients computed automatically. The gradient for this tensor will be accumulated into <code>$grad</code> attribute.</p>
-<p>To stop a tensor from tracking history, you can call <code>$detach()</code> to detach it from the computation history, and to prevent future computation from being tracked.</p>
-<p>To prevent tracking history (and using memory), you can also wrap the code block in <code>with_no_grad({&lt;code&gt;})</code>. This can be particularly helpful when evaluating a model because the model may have trainable parameters with <code>requires_grad=TRUE</code>, but for which we don’t need the gradients.</p>
-<p>There’s one more class which is very important for autograd implementation - a <code>autograd_function</code>.</p>
-<p>Tensor and Function are interconnected and build up an acyclic graph, that encodes a complete history of computation. Each tensor has a <code>$grad_fn</code> attribute that references an <code>autograd_function</code> that has created the Tensor (except for Tensors created by the user - their <code>grad_fn</code> is <code>NULL</code>).</p>
-<p>If you want to compute the derivatives, you can call <code>$backward()</code> on a Tensor. If Tensor is a scalar (i.e. it holds a one element data), you don’t need to specify any arguments to <code>backward()</code>, however if it has more elements, you need to specify a gradient argument that is a tensor of matching shape.</p>
-<p>Create a tensor and set <code>requires_grad=TRUE</code> to track computation with it:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>, <span class="fl">2</span>, requires_grad = <span class="fl">TRUE</span>)
-<span class="kw">x</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1  1</span>
-<span class="co">#&gt;  1  1</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
-</pre></div>
-<p>Do a tensor operation:</p>
-<div class="sourceCode" id="cb3"><pre class="downlit">
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span> <span class="op">+</span> <span class="fl">2</span>
-<span class="kw">y</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  3  3</span>
-<span class="co">#&gt;  3  3</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
-</pre></div>
-<p><code>y</code> was created as a result of an operation, so it has a <code>grad_fn</code>.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit">
-<span class="kw">y</span><span class="op">$</span><span class="kw">grad_fn</span>
-<span class="co">#&gt; AddBackward1</span>
-</pre></div>
-<p>Do more operations on <code>y</code></p>
-<div class="sourceCode" id="cb5"><pre class="downlit">
-<span class="kw">z</span> <span class="op">&lt;-</span> <span class="kw">y</span> <span class="op">*</span> <span class="kw">y</span> <span class="op">*</span> <span class="fl">3</span>
-<span class="kw">z</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  27  27</span>
-<span class="co">#&gt;  27  27</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
-<span class="kw">out</span> <span class="op">&lt;-</span> <span class="kw">z</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
-<span class="kw">out</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; 27</span>
-<span class="co">#&gt; [ CPUFloatType{} ]</span>
-</pre></div>
-<p><code>$requires_grad_( ... )</code> changes an existing Tensor’s <code>requires_grad</code> flag in-place. The input flag defaults to <code>FALSE</code> if not given.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit">
-<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">2</span>)
-<span class="kw">a</span> <span class="op">&lt;-</span> (<span class="kw">a</span> <span class="op">*</span> <span class="fl">3</span>) <span class="op">/</span> (<span class="kw">a</span> <span class="op">-</span> <span class="fl">1</span>)
-<span class="kw">a</span><span class="op">$</span><span class="kw">requires_grad</span>
-<span class="co">#&gt; [1] FALSE</span>
-<span class="kw">a</span><span class="op">$</span><span class="fu">requires_grad_</span>(<span class="fl">TRUE</span>)
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;   0.9187  -1.2351</span>
-<span class="co">#&gt;  15.7528   1.9586</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
-<span class="kw">a</span><span class="op">$</span><span class="kw">requires_grad</span>
-<span class="co">#&gt; [1] TRUE</span>
-<span class="kw">b</span> <span class="op">&lt;-</span> (<span class="kw">a</span> <span class="op">*</span> <span class="kw">a</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
-<span class="kw">b</span><span class="op">$</span><span class="kw">grad_fn</span>
-<span class="co">#&gt; SumBackward0</span>
-</pre></div>
-</div>
-<div id="gradients" class="section level2">
-<h2 class="hasAnchor">
-<a href="#gradients" class="anchor"></a>Gradients</h2>
-<p>Let’s backprop now. Because out contains a single scalar, <code>out$backward()</code> is equivalent to <code>out$backward(torch.tensor(1.))</code>.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit">
-<span class="kw">out</span><span class="op">$</span><span class="fu">backward</span>()
-</pre></div>
-<p>Print gradients d(out)/dx</p>
-<div class="sourceCode" id="cb8"><pre class="downlit">
-<span class="kw">x</span><span class="op">$</span><span class="kw">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  4.5000  4.5000</span>
-<span class="co">#&gt;  4.5000  4.5000</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
-</pre></div>
-<p>You should have got a matrix of <code>4.5</code>. Let’s call the <code>out</code> <em>Tensor</em> <span class="math inline">\(o\)</span>.</p>
-<p>We have that <span class="math inline">\(o = \frac{1}{4}\sum_i z_i\)</span>, <span class="math inline">\(z_i = 3(x_i+2)^2\)</span> and <span class="math inline">\(z_i\bigr\rvert_{x_i=1} = 27\)</span>. Therefore, <span class="math inline">\(\frac{\partial o}{\partial x_i} = \frac{3}{2}(x_i+2)\)</span>, hence <span class="math inline">\(\frac{\partial o}{\partial x_i}\bigr\rvert_{x_i=1} = \frac{9}{2} = 4.5\)</span>.</p>
-<p>Mathematically, if you have a vector valued function <span class="math inline">\(\vec{y}=f(\vec{x})\)</span>, then the gradient of <span class="math inline">\(\vec{y}\)</span> with respect to <span class="math inline">\(\vec{x}\)</span> is a Jacobian matrix:</p>
-<p><span class="math display">\[
-  J=\left(\begin{array}{ccc}
-   \frac{\partial y_{1}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{1}}{\partial x_{n}}\\
-   \vdots &amp; \ddots &amp; \vdots\\
-   \frac{\partial y_{m}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{n}}
-   \end{array}\right)
-\]</span></p>
-<p>Generally speaking, <code>autograd</code> is an engine for computing vector-Jacobian product. That is, given any vector <span class="math inline">\(v=\left(\begin{array}{cccc} v_{1} &amp; v_{2} &amp; \cdots &amp; v_{m}\end{array}\right)^{T}\)</span>, compute the product <span class="math inline">\(v^{T}\cdot J\)</span>. If <span class="math inline">\(v\)</span> happens to be the gradient of a scalar function <span class="math inline">\(l=g\left(\vec{y}\right)\)</span>, that is, <span class="math inline">\(v=\left(\begin{array}{ccc}\frac{\partial l}{\partial y_{1}} &amp; \cdots &amp; \frac{\partial l}{\partial y_{m}}\end{array}\right)^{T}\)</span>, then by the chain rule, the vector-Jacobian product would be the gradient of <span class="math inline">\(l\)</span> with respect to <span class="math inline">\(\vec{x}\)</span>:</p>
-<p><span class="math display">\[
-  J^{T}\cdot v=\left(\begin{array}{ccc}
-   \frac{\partial y_{1}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{1}}\\
-   \vdots &amp; \ddots &amp; \vdots\\
-   \frac{\partial y_{1}}{\partial x_{n}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{n}}
-   \end{array}\right)\left(\begin{array}{c}
-   \frac{\partial l}{\partial y_{1}}\\
-   \vdots\\
-   \frac{\partial l}{\partial y_{m}}
-   \end{array}\right)=\left(\begin{array}{c}
-   \frac{\partial l}{\partial x_{1}}\\
-   \vdots\\
-   \frac{\partial l}{\partial x_{n}}
-   \end{array}\right)
-\]</span></p>
-<p>(Note that <span class="math inline">\(v^{T}\cdot J\)</span> gives a row vector which can be treated as a column vector by taking <span class="math inline">\(J^{T}\cdot v\)</span>.)</p>
-<p>This characteristic of vector-Jacobian product makes it very convenient to feed external gradients into a model that has non-scalar output.</p>
-<p>Now let’s take a look at an example of vector-Jacobian product:</p>
-<div class="sourceCode" id="cb9"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">3</span>, requires_grad=<span class="fl">TRUE</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fl">100</span> <span class="op">*</span> <span class="kw">x</span>
-<span class="kw">y</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; -94.3323</span>
-<span class="co">#&gt; -47.4986</span>
-<span class="co">#&gt;  -3.8605</span>
-<span class="co">#&gt; [ CPUFloatType{3} ]</span>
-</pre></div>
-<p>Now in this case y is no longer a scalar. <code>autograd</code> could not compute the full Jacobian directly, but if we just want the vector-Jacobian product, simply pass the vector to backward as argument:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit">
-<span class="kw">v</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.1</span>, <span class="fl">1.0</span>, <span class="fl">0.0001</span>))
-<span class="kw">y</span><span class="op">$</span><span class="fu">backward</span>(<span class="kw">v</span>)
-
-<span class="kw">x</span><span class="op">$</span><span class="kw">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1.0000e+01</span>
-<span class="co">#&gt;  1.0000e+02</span>
-<span class="co">#&gt;  1.0000e-02</span>
-<span class="co">#&gt; [ CPUFloatType{3} ]</span>
-</pre></div>
-<p>You can also stop autograd from tracking history on Tensors with <code>$requires_grad=TRUE</code> either by wrapping the code block in with <code><a href="../../reference/with_no_grad.html">with_no_grad()</a></code>:</p>
-<div class="sourceCode" id="cb11"><pre class="downlit">
-<span class="kw">x</span><span class="op">$</span><span class="kw">requires_grad</span>
-<span class="co">#&gt; [1] TRUE</span>
-(<span class="kw">x</span> <span class="op">**</span> <span class="fl">2</span>)<span class="op">$</span><span class="kw">requires_grad</span>
-<span class="co">#&gt; [1] TRUE</span>
-
-<span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
-  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>((<span class="kw">x</span> <span class="op">**</span> <span class="fl">2</span>)<span class="op">$</span><span class="kw">requires_grad</span>)
-})
-<span class="co">#&gt; [1] FALSE</span>
-</pre></div>
-<div class="sourceCode" id="cb12"><pre class="downlit">
-<span class="kw">x</span><span class="op">$</span><span class="kw">requires_grad</span>
-<span class="co">#&gt; [1] TRUE</span>
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/detach.html">detach</a></span>()
-<span class="kw">y</span><span class="op">$</span><span class="kw">requires_grad</span>
-<span class="co">#&gt; [1] FALSE</span>
-<span class="kw">x</span><span class="op">$</span><span class="fu">eq</span>(<span class="kw">y</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>()
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; 1</span>
-<span class="co">#&gt; [ CPUBoolType{} ]</span>
-</pre></div>
-<p>Read Later:</p>
-<p>Document about <code><a href="../../reference/autograd_function.html">help(autograd_function)</a></code>, <code><a href="../../articles/using-autograd.html">vignette("using-autograd")</a></code>, <code><a href="../../articles/extending-autograd.html">vignette("extending-autograd")</a></code>.</p>
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diff --git a/static/docs/dev/articles/getting-started/autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/getting-started/autograd_files/accessible-code-block-0.0.1/empty-anchor.js
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-document.addEventListener('DOMContentLoaded', function() {
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diff --git a/static/docs/dev/articles/getting-started/control-flow-and-weight-sharing.html b/static/docs/dev/articles/getting-started/control-flow-and-weight-sharing.html
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-<!DOCTYPE html>
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-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Control flow &amp; Weight sharing</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/control-flow-and-weight-sharing.Rmd"><code>vignettes/getting-started/control-flow-and-weight-sharing.Rmd</code></a></small>
-      <div class="hidden name"><code>control-flow-and-weight-sharing.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>As an example of dynamic graphs and weight sharing, we implement a very strange model: a fully-connected ReLU network that on each forward pass chooses a random number between 1 and 4 and uses that many hidden layers, reusing the same weights multiple times to compute the innermost hidden layers.</p>
-<p>For this model we can use normal R flow control to implement the loop, and we can implement weight sharing among the innermost layers by simply reusing the same Module multiple times when defining the forward pass.</p>
-<p>We can easily implement this model using <code>nn_module</code>:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="kw">dynamic_net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
-   <span class="st">"dynamic_net"</span>,
-   <span class="co"># In the constructor we construct three nn_linear instances that we will use</span>
-   <span class="co"># in the forward pass.</span>
-   initialize = <span class="fu">function</span>(<span class="kw">D_in</span>, <span class="kw">H</span>, <span class="kw">D_out</span>) {
-      <span class="kw">self</span><span class="op">$</span><span class="kw">input_linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>)
-      <span class="kw">self</span><span class="op">$</span><span class="kw">middle_linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">H</span>)
-      <span class="kw">self</span><span class="op">$</span><span class="kw">output_linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>)
-   },
-   <span class="co"># For the forward pass of the model, we randomly choose either 0, 1, 2, or 3</span>
-   <span class="co"># and reuse the middle_linear Module that many times to compute hidden layer</span>
-   <span class="co"># representations.</span>
-   <span class="co"># </span>
-   <span class="co"># Since each forward pass builds a dynamic computation graph, we can use normal</span>
-   <span class="co"># R control-flow operators like loops or conditional statements when</span>
-   <span class="co"># defining the forward pass of the model.</span>
-   <span class="co"># </span>
-   <span class="co"># Here we also see that it is perfectly safe to reuse the same Module many</span>
-   <span class="co"># times when defining a computational graph. This is a big improvement from Lua</span>
-   <span class="co"># Torch, where each Module could be used only once.</span>
-   forward = <span class="fu">function</span>(<span class="kw">x</span>) {
-      <span class="kw">h_relu</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="fu">input_linear</span>(<span class="kw">x</span>)<span class="op">$</span><span class="fu">clamp</span>(min = <span class="fl">0</span>)
-      <span class="co">for</span> (<span class="kw">i</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample.int</a></span>(<span class="fl">4</span>, size = <span class="fl">1</span>))) {
-         <span class="kw">h_relu</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="fu">middle_linear</span>(<span class="kw">h_relu</span>)<span class="op">$</span><span class="fu">clamp</span>(min=<span class="fl">0</span>)
-      }
-      <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="fu">output_linear</span>(<span class="kw">h_relu</span>)
-      <span class="kw">y_pred</span>
-   }
-)
-
-
-<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="co">else</span> {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
-
-<span class="co"># Construct our model by instantiating the class defined above</span>
-<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu">dynamic_net</span>(<span class="kw">D_in</span>, <span class="kw">H</span>, <span class="kw">D_out</span>)
-
-<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
-<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
-<span class="kw">loss_fn</span> <span class="op">&lt;-</span> <span class="kw">nnf_mse_loss</span>
-
-<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
-<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
-<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
-<span class="co"># optimizer which Tensors it should update.</span>
-<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
-<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>, lr=<span class="kw">learning_rate</span>, momentum = <span class="fl">0.9</span>)
-
-<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
-   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
-   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
-   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">x</span>)
-   
-   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
-   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
-   <span class="co"># loss.</span>
-   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span>(<span class="kw">y_pred</span>, <span class="kw">y</span>)
-   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
-   
-   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
-   <span class="co"># gradients for the variables it will update (which are the learnable</span>
-   <span class="co"># weights of the model). This is because by default, gradients are</span>
-   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
-   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
-   <span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()
-
-   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
-   <span class="co"># parameters</span>
-   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
-
-   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
-   <span class="co"># parameters</span>
-   <span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-}
-<span class="co">#&gt; Step: 1 : 1.117174 </span>
-<span class="co">#&gt; Step: 100 : 1.114698 </span>
-<span class="co">#&gt; Step: 200 : 1.105049 </span>
-<span class="co">#&gt; Step: 300 : 1.106878 </span>
-<span class="co">#&gt; Step: 400 : 1.105931 </span>
-<span class="co">#&gt; Step: 500 : 1.106624</span>
-</pre></div>
-  </div>
-
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diff --git a/static/docs/dev/articles/getting-started/custom-nn.html b/static/docs/dev/articles/getting-started/custom-nn.html
deleted file mode 100644
index 49dc54b1c..000000000
--- a/static/docs/dev/articles/getting-started/custom-nn.html
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-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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-      <h1 data-toc-skip>Custom nn modules</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/custom-nn.Rmd"><code>vignettes/getting-started/custom-nn.Rmd</code></a></small>
-      <div class="hidden name"><code>custom-nn.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>Sometimes you will want to specify models that are more complex than a sequence of existing Modules; for these cases you can define your own Modules by using <code>nn_module</code> function and defining a forward which receives input Tensors and produces output Tensors using other modules or other autograd operations on Tensors.</p>
-<p>In this example we implement our two-layer network as a custom Module subclass:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="kw">two_layer_net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
-   <span class="st">"two_layer_net"</span>,
-   initialize = <span class="fu">function</span>(<span class="kw">D_in</span>, <span class="kw">H</span>, <span class="kw">D_out</span>) {
-      <span class="kw">self</span><span class="op">$</span><span class="kw">linear1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>)
-      <span class="kw">self</span><span class="op">$</span><span class="kw">linear2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>)
-   },
-   forward = <span class="fu">function</span>(<span class="kw">x</span>) {
-      <span class="kw">x</span> <span class="op">%&gt;%</span> 
-         <span class="kw">self</span><span class="op">$</span><span class="fu">linear1</span>() <span class="op">%&gt;%</span> 
-         <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
-         <span class="kw">self</span><span class="op">$</span><span class="fu">linear2</span>()
-   }
-)
-
-
-<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="co">else</span> {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
-
-<span class="co"># Construct our model by instantiating the class defined above</span>
-<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu">two_layer_net</span>(<span class="kw">D_in</span>, <span class="kw">H</span>, <span class="kw">D_out</span>)
-
-<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
-<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
-<span class="kw">loss_fn</span> <span class="op">&lt;-</span> <span class="kw">nnf_mse_loss</span>
-
-<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
-<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
-<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
-<span class="co"># optimizer which Tensors it should update.</span>
-<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
-<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>, lr=<span class="kw">learning_rate</span>)
-
-<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
-   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
-   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
-   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">x</span>)
-   
-   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
-   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
-   <span class="co"># loss.</span>
-   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span>(<span class="kw">y_pred</span>, <span class="kw">y</span>)
-   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
-   
-   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
-   <span class="co"># gradients for the variables it will update (which are the learnable</span>
-   <span class="co"># weights of the model). This is because by default, gradients are</span>
-   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
-   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
-   <span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()
-
-   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
-   <span class="co"># parameters</span>
-   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
-
-   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
-   <span class="co"># parameters</span>
-   <span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-}
-<span class="co">#&gt; Step: 1 : 1.126243 </span>
-<span class="co">#&gt; Step: 100 : 1.112885 </span>
-<span class="co">#&gt; Step: 200 : 1.099683 </span>
-<span class="co">#&gt; Step: 300 : 1.086815 </span>
-<span class="co">#&gt; Step: 400 : 1.074228 </span>
-<span class="co">#&gt; Step: 500 : 1.061901</span>
-</pre></div>
-<p>In the <a href="control-flow-and-weight-sharing.html">next example</a> we will about dynamic graphs in torch.</p>
-  </div>
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diff --git a/static/docs/dev/articles/getting-started/custom-nn_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/getting-started/custom-nn_files/accessible-code-block-0.0.1/empty-anchor.js
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diff --git a/static/docs/dev/articles/getting-started/neural-networks.html b/static/docs/dev/articles/getting-started/neural-networks.html
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-      <h1 data-toc-skip>Neural networks</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/neural-networks.Rmd"><code>vignettes/getting-started/neural-networks.Rmd</code></a></small>
-      <div class="hidden name"><code>neural-networks.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>Neural networks can be constructed using the <code>nn</code> functionality.</p>
-<p>Now that you had a glimpse of <code>autograd</code>, <code>nn</code> depends on <code>autograd</code> to define models and differentiate them. An nn.Module contains layers, and a method <code>forward(input)</code> that returns the output.</p>
-<p>For example, look at this network that classifies digit images:</p>
-<div class="figure">
-<img src="assets/mnist.png" alt=""><p class="caption">Convnet for mnist classification</p>
-</div>
-<p>It is a simple feed-forward network. It takes the input, feeds it through several layers one after the other, and then finally gives the output.</p>
-<p>A typical training procedure for a neural network is as follows:</p>
-<ul>
-<li>Define the neural network that has some learnable parameters (or weights)</li>
-<li>Iterate over a dataset of inputs</li>
-<li>Process input through the network</li>
-<li>Compute the loss (how far is the output from being correct)</li>
-<li>Propagate gradients back into the network’s parameters</li>
-<li>Update the weights of the network, typically using a simple update rule: <code>weight = weight - learning_rate * gradient</code>.</li>
-</ul>
-<div id="define-the-network" class="section level2">
-<h2 class="hasAnchor">
-<a href="#define-the-network" class="anchor"></a>Define the network</h2>
-<p>Let’s define this network:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="kw">Net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
-  initialize = <span class="fu">function</span>() {
-    <span class="kw">self</span><span class="op">$</span><span class="kw">conv1</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">1</span>, <span class="fl">6</span>, <span class="fl">3</span>)
-    <span class="kw">self</span><span class="op">$</span><span class="kw">conv2</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">6</span>, <span class="fl">16</span>, <span class="fl">3</span>)
-    <span class="co"># an affine operation: y = Wx + b</span>
-    <span class="kw">self</span><span class="op">$</span><span class="kw">fc1</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">16</span> <span class="op">*</span> <span class="fl">6</span> <span class="op">*</span> <span class="fl">6</span>, <span class="fl">120</span>)  <span class="co"># 6*6 from image dimension</span>
-    <span class="kw">self</span><span class="op">$</span><span class="kw">fc2</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">120</span>, <span class="fl">84</span>)
-    <span class="kw">self</span><span class="op">$</span><span class="kw">fc3</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">84</span>, <span class="fl">10</span>)
-  },
-  forward = <span class="fu">function</span>(<span class="kw">x</span>) {
-    <span class="kw">x</span> <span class="op">%&gt;%</span> 
-      
-      <span class="kw">self</span><span class="op">$</span><span class="fu">conv1</span>() <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../../reference/nnf_max_pool2d.html">nnf_max_pool2d</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>,<span class="fl">2</span>)) <span class="op">%&gt;%</span> 
-      
-      <span class="kw">self</span><span class="op">$</span><span class="fu">conv2</span>() <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../../reference/nnf_max_pool2d.html">nnf_max_pool2d</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>,<span class="fl">2</span>)) <span class="op">%&gt;%</span> 
-      
-      <span class="fu"><a href="../../reference/torch_flatten.html">torch_flatten</a></span>(start_dim = <span class="fl">2</span>) <span class="op">%&gt;%</span> 
-      
-      <span class="kw">self</span><span class="op">$</span><span class="fu">fc1</span>() <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
-      
-      <span class="kw">self</span><span class="op">$</span><span class="fu">fc2</span>() <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
-      
-      <span class="kw">self</span><span class="op">$</span><span class="fu">fc3</span>()
-  }
-)
-
-<span class="kw">net</span> <span class="op">&lt;-</span> <span class="fu">Net</span>()
-</pre></div>
-<p>You just have to define the <code>forward</code> function, and the <code>backward</code> function (where gradients are computed) is automatically defined for you using <code>autograd.</code> You can use any of the Tensor operations in the <code>forward</code> function.</p>
-<p>The learnable parameters of a model are returned by <code>net$parameters</code>.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/utils/str.html">str</a></span>(<span class="kw">net</span><span class="op">$</span><span class="kw">parameters</span>)
-<span class="co">#&gt; List of 10</span>
-<span class="co">#&gt;  $ conv1.weight:Float [1:6, 1:1, 1:3, 1:3]</span>
-<span class="co">#&gt;  $ conv1.bias  :Float [1:6]</span>
-<span class="co">#&gt;  $ conv2.weight:Float [1:16, 1:6, 1:3, 1:3]</span>
-<span class="co">#&gt;  $ conv2.bias  :Float [1:16]</span>
-<span class="co">#&gt;  $ fc1.weight  :Float [1:120, 1:576]</span>
-<span class="co">#&gt;  $ fc1.bias    :Float [1:120]</span>
-<span class="co">#&gt;  $ fc2.weight  :Float [1:84, 1:120]</span>
-<span class="co">#&gt;  $ fc2.bias    :Float [1:84]</span>
-<span class="co">#&gt;  $ fc3.weight  :Float [1:10, 1:84]</span>
-<span class="co">#&gt;  $ fc3.bias    :Float [1:10]</span>
-</pre></div>
-<p>Let’s try a random 32x32 input. Note: expected input size of this net (LeNet) is 32x32. To use this net on the MNIST dataset, please resize the images from the dataset to 32x32.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit">
-<span class="kw">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">32</span>, <span class="fl">32</span>)
-<span class="kw">out</span> <span class="op">&lt;-</span> <span class="fu">net</span>(<span class="kw">input</span>)
-<span class="kw">out</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; -0.0509  0.1097  0.0126 -0.1096 -0.0251 -0.0692  0.1237 -0.0674  0.0031  0.0142</span>
-<span class="co">#&gt; [ CPUFloatType{1,10} ]</span>
-</pre></div>
-<p>Zero the gradient buffers of all parameters and backprops with random gradients:</p>
-<div class="sourceCode" id="cb5"><pre class="downlit">
-<span class="kw">net</span><span class="op">$</span><span class="fu">zero_grad</span>()
-<span class="kw">out</span><span class="op">$</span><span class="fu">backward</span>(<span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, <span class="fl">10</span>))
-</pre></div>
-<blockquote>
-<p><strong>Note</strong>: <code>nn</code> only supports mini-batches. The entire torch.nn package only supports inputs that are a mini-batch of samples, and not a single sample. For example, <code>nn_conv2d</code> will take in a 4D Tensor of nSamples x nChannels x Height x Width. If you have a single sample, just use <code>input$unsqueeze(1)</code> to add a fake batch dimension.</p>
-</blockquote>
-<p>Before proceeding further, let’s recap all the classes you’ve seen so far.</p>
-<div id="recap" class="section level3">
-<h3 class="hasAnchor">
-<a href="#recap" class="anchor"></a>Recap</h3>
-<ul>
-<li><p><code>torch_tensor</code> - A multi-dimensional array with support for autograd operations like <code>backward()</code>. Also holds the gradient w.r.t. the tensor.</p></li>
-<li><p><code>nn_module</code> - Neural network module. Convenient way of encapsulating parameters, with helpers for moving them to GPU, exporting, loading, etc.</p></li>
-<li><p><code>nn_parameter</code> - A kind of Tensor, that is automatically registered as a parameter when assigned as an attribute to a Module.</p></li>
-<li><p><code>autograd_function</code> - Implements forward and backward definitions of an autograd operation. Every Tensor operation creates at least a single Function node that connects to functions that created a Tensor and encodes its history.</p></li>
-</ul>
-</div>
-<div id="at-this-point-we-covered" class="section level3">
-<h3 class="hasAnchor">
-<a href="#at-this-point-we-covered" class="anchor"></a>At this point, we covered</h3>
-<ul>
-<li>Defining a neural network</li>
-<li>Processing inputs and calling backward</li>
-</ul>
-</div>
-<div id="still-left" class="section level3">
-<h3 class="hasAnchor">
-<a href="#still-left" class="anchor"></a>Still left</h3>
-<ul>
-<li>Computing the loss</li>
-<li>Updating the weights of the network</li>
-</ul>
-</div>
-</div>
-<div id="loss-function" class="section level2">
-<h2 class="hasAnchor">
-<a href="#loss-function" class="anchor"></a>Loss function</h2>
-<p>A loss function takes the (output, target) pair of inputs, and computes a value that estimates how far away the output is from the target.</p>
-<p>There are several different loss functions under the nn package . A simple loss is: <code>nnf_mse_loss</code> which computes the mean-squared error between the input and the target.</p>
-<p>For example:</p>
-<div class="sourceCode" id="cb6"><pre class="downlit">
-<span class="kw">output</span> <span class="op">&lt;-</span> <span class="fu">net</span>(<span class="kw">input</span>)
-<span class="kw">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">10</span>)  <span class="co"># a dummy target, for example</span>
-<span class="kw">target</span> <span class="op">&lt;-</span> <span class="kw">target</span><span class="op">$</span><span class="fu">view</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="op">-</span><span class="fl">1</span>))  <span class="co"># make it the same shape as output</span>
-
-<span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nnf_mse_loss.html">nnf_mse_loss</a></span>(<span class="kw">output</span>, <span class="kw">target</span>)
-<span class="kw">loss</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; 0.877831</span>
-<span class="co">#&gt; [ CPUFloatType{} ]</span>
-</pre></div>
-<p>Now, if you follow loss in the backward direction, using its <code>$grad_fn</code> attribute, you will see a graph of computations that looks like this:</p>
-<pre><code>input -&gt; conv2d -&gt; relu -&gt; maxpool2d -&gt; conv2d -&gt; relu -&gt; maxpool2d
-      -&gt; view -&gt; linear -&gt; relu -&gt; linear -&gt; relu -&gt; linear
-      -&gt; MSELoss
-      -&gt; loss</code></pre>
-<p>So, when we call <code>loss$backward()</code>, the whole graph is differentiated w.r.t. the loss, and all Tensors in the graph that has requires_grad=True will have their <code>#grad</code> Tensor accumulated with the gradient.</p>
-<p>For illustration, let us follow a few steps backward:</p>
-<div class="sourceCode" id="cb8"><pre class="downlit">
-<span class="kw">loss</span><span class="op">$</span><span class="kw">grad_fn</span>
-<span class="co">#&gt; MseLossBackward</span>
-<span class="kw">loss</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]
-<span class="co">#&gt; AddmmBackward</span>
-<span class="kw">loss</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]
-<span class="co">#&gt; torch::autograd::AccumulateGrad</span>
-</pre></div>
-</div>
-<div id="backprop" class="section level2">
-<h2 class="hasAnchor">
-<a href="#backprop" class="anchor"></a>Backprop</h2>
-<p>To backpropagate the error all we have to do is to <code>loss$backward()</code>. You need to clear the existing gradients though, else gradients will be accumulated to existing gradients.</p>
-<p>Now we shall call <code>loss$backward()</code>, and have a look at conv1’s bias gradients before and after the backward.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit">
-<span class="kw">net</span><span class="op">$</span><span class="fu">zero_grad</span>()     <span class="co"># zeroes the gradient buffers of all parameters</span>
-
-<span class="co"># conv1.bias.grad before backward</span>
-<span class="kw">net</span><span class="op">$</span><span class="kw">conv1</span><span class="op">$</span><span class="kw">bias</span><span class="op">$</span><span class="kw">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  0</span>
-<span class="co">#&gt;  0</span>
-<span class="co">#&gt;  0</span>
-<span class="co">#&gt;  0</span>
-<span class="co">#&gt;  0</span>
-<span class="co">#&gt;  0</span>
-<span class="co">#&gt; [ CPUFloatType{6} ]</span>
-
-<span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
-
-<span class="co"># conv1.bias.grad after backward</span>
-<span class="kw">net</span><span class="op">$</span><span class="kw">conv1</span><span class="op">$</span><span class="kw">bias</span><span class="op">$</span><span class="kw">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; 0.01 *</span>
-<span class="co">#&gt; -1.3819</span>
-<span class="co">#&gt; -0.0162</span>
-<span class="co">#&gt;  0.0680</span>
-<span class="co">#&gt; -0.1130</span>
-<span class="co">#&gt; -0.8646</span>
-<span class="co">#&gt;  0.6219</span>
-<span class="co">#&gt; [ CPUFloatType{6} ]</span>
-</pre></div>
-<p>Now, we have seen how to use loss functions.</p>
-</div>
-<div id="update-the-weights" class="section level2">
-<h2 class="hasAnchor">
-<a href="#update-the-weights" class="anchor"></a>Update the weights</h2>
-<p>The simplest update rule used in practice is the Stochastic Gradient Descent (SGD):</p>
-<p><span class="math display">\[weight = weight - learning_rate * gradient\]</span></p>
-<p>We can implement this using simple R code:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit">
-<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">0.01</span>
-<span class="co">for</span> (<span class="kw">f</span> <span class="co">in</span> <span class="kw">net</span><span class="op">$</span><span class="kw">parameters</span>) {
-  <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
-    <span class="kw">f</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">f</span><span class="op">$</span><span class="kw">grad</span> <span class="op">*</span> <span class="kw">learning_rate</span>)
-  })
-}
-</pre></div>
-<blockquote>
-<p><strong>Note:</strong> Weight updates here is wraped around <code>with_no_grad</code> as we don’t the updates to be tracked by the autograd engine.</p>
-</blockquote>
-<p>However, as you use neural networks, you want to use various different update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc.</p>
-<div class="sourceCode" id="cb11"><pre class="downlit">
-<span class="co"># create your optimizer</span>
-<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="kw">net</span><span class="op">$</span><span class="kw">parameters</span>, lr = <span class="fl">0.01</span>)
-
-<span class="co"># in your training loop:</span>
-<span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()   <span class="co"># zero the gradient buffers</span>
-<span class="kw">output</span> <span class="op">&lt;-</span> <span class="fu">net</span>(<span class="kw">input</span>)
-<span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nnf_mse_loss.html">nnf_mse_loss</a></span>(<span class="kw">output</span>, <span class="kw">target</span>)
-<span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
-<span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()    <span class="co"># Does the update</span>
-<span class="co">#&gt; NULL</span>
-</pre></div>
-<blockquote>
-<p><strong>Note:</strong> Observe how gradient buffers had to be manually set to zero using <code>optimizer$zero_grad()</code>. This is because gradients are accumulated as explained in the Backprop section.</p>
-</blockquote>
-</div>
-  </div>
-
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diff --git a/static/docs/dev/articles/getting-started/new-autograd-functions.html b/static/docs/dev/articles/getting-started/new-autograd-functions.html
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-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Defining new autograd functions</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/new-autograd-functions.Rmd"><code>vignettes/getting-started/new-autograd-functions.Rmd</code></a></small>
-      <div class="hidden name"><code>new-autograd-functions.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>Under the hood, each primitive autograd operator is really two functions that operate on Tensors. The forward function computes output Tensors from input Tensors. The backward function receives the gradient of the output Tensors with respect to some scalar value, and computes the gradient of the input Tensors with respect to that same scalar value.</p>
-<p>In torch we can easily define our own autograd operator by defining a subclass of <code>autograd_function</code> and implementing the forward and backward functions. We can then use our new autograd operator by constructing an instance and calling it like a function, passing Tensors containing input data.</p>
-<p>In this example we define our own custom autograd function for performing the ReLU nonlinearity, and use it to implement our two-layer network:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="co"># We can implement our own custom autograd Functions by subclassing</span>
-<span class="co"># autograd_functioon and implementing the forward and backward passes</span>
-<span class="co"># which operate on Tensors.</span>
-<span class="kw">my_relu</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/autograd_function.html">autograd_function</a></span>(
-   <span class="co"># In the forward pass we receive a Tensor containing the input and return</span>
-   <span class="co"># a Tensor containing the output. ctx is a context object that can be used</span>
-   <span class="co"># to stash information for backward computation. You can cache arbitrary</span>
-   <span class="co"># objects for use in the backward pass using the ctx$save_for_backward method.</span>
-   forward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">input</span>) {
-      <span class="kw">ctx</span><span class="op">$</span><span class="fu">save_for_backward</span>(input = <span class="kw">input</span>)
-      <span class="kw">input</span><span class="op">$</span><span class="fu">clamp</span>(min = <span class="fl">0</span>)
-   },
-   <span class="co"># In the backward pass we receive a Tensor containing the gradient of the loss</span>
-   <span class="co"># with respect to the output, and we need to compute the gradient of the loss</span>
-   <span class="co"># with respect to the input.</span>
-   backward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">grad_output</span>) {
-      <span class="kw">v</span> <span class="op">&lt;-</span> <span class="kw">ctx</span><span class="op">$</span><span class="kw">saved_variables</span>
-      <span class="kw">grad_input</span> <span class="op">&lt;-</span> <span class="kw">grad_output</span><span class="op">$</span><span class="fu">clone</span>()
-      <span class="kw">grad_input</span>[<span class="kw">v</span><span class="op">$</span><span class="kw">input</span> <span class="op">&lt;</span> <span class="fl">0</span>] <span class="op">&lt;-</span> <span class="fl">0</span>
-      <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(input = <span class="kw">grad_input</span>)
-   }
-)
-
-<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="co">else</span> {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
-
-<span class="co"># Randomly initialize weights</span>
-<span class="co"># Setting requires_grad=TRUE indicates that we want to compute gradients with</span>
-<span class="co"># respect to these Tensors during the backward pass.</span>
-<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>, device=<span class="kw">device</span>, requires_grad = <span class="fl">TRUE</span>)
-<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>, requires_grad = <span class="fl">TRUE</span>)
-
-<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
-<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y using operations on Tensors; these</span>
-   <span class="co"># are exactly the same operations we used to compute the forward pass using</span>
-   <span class="co"># Tensors, but we do not need to keep references to intermediate values since</span>
-   <span class="co"># we are not implementing the backward pass by hand.</span>
-   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">my_relu</span>(<span class="kw">x</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w1</span>))<span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span>)
-   
-   <span class="co"># Compute and print loss using operations on Tensors.</span>
-   <span class="co"># Now loss is a Tensor of shape (1,)</span>
-   <span class="kw">loss</span> <span class="op">&lt;-</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
-   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
-   
-   <span class="co"># Use autograd to compute the backward pass. This call will compute the</span>
-   <span class="co"># gradient of loss with respect to all Tensors with requires_grad=True.</span>
-   <span class="co"># After this call w1$grad and w2$grad will be Tensors holding the gradient</span>
-   <span class="co"># of the loss with respect to w1 and w2 respectively.</span>
-   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
-   
-   <span class="co"># Manually update weights using gradient descent. Wrap in `with_no_grad`</span>
-   <span class="co"># because weights have requires_grad=TRUE, but we don't need to track this</span>
-   <span class="co"># in autograd.</span>
-   <span class="co"># You can also use optim_sgd to achieve this.</span>
-   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
-      
-      <span class="co"># operations suffixed with an `_` operates on in-place on the tensor.</span>
-      <span class="kw">w1</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span>)
-      <span class="kw">w2</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span>)
-      
-      <span class="co"># Manually zero the gradients after updating weights</span>
-      <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
-      <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
-   })
-}
-<span class="co">#&gt; Step: 1 : 46539592 </span>
-<span class="co">#&gt; Step: 100 : 854.5317 </span>
-<span class="co">#&gt; Step: 200 : 6.996433 </span>
-<span class="co">#&gt; Step: 300 : 0.09385251 </span>
-<span class="co">#&gt; Step: 400 : 0.001854649 </span>
-<span class="co">#&gt; Step: 500 : 0.0001512455</span>
-</pre></div>
-<p>In the <a href="nn.html">next example</a> we will learn how to use the neural networks abstractions in torch.</p>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/articles/getting-started/new-autograd-functions_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/getting-started/new-autograd-functions_files/accessible-code-block-0.0.1/empty-anchor.js
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diff --git a/static/docs/dev/articles/getting-started/nn.html b/static/docs/dev/articles/getting-started/nn.html
deleted file mode 100644
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-<!DOCTYPE html>
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-      <h1 data-toc-skip>nn: neural networks with torch</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/nn.Rmd"><code>vignettes/getting-started/nn.Rmd</code></a></small>
-      <div class="hidden name"><code>nn.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>Computational graphs and autograd are a very powerful paradigm for defining complex operators and automatically taking derivatives; however for large neural networks raw autograd can be a bit too low-level.</p>
-<p>When building neural networks we frequently think of arranging the computation into layers, some of which have learnable parameters which will be optimized during learning.</p>
-<p>In TensorFlow, packages like Keras, TensorFlow-Slim, and TFLearn provide higher-level abstractions over raw computational graphs that are useful for building neural networks.</p>
-<p>In torch, the nn functionality serves this same purpose. The nn feature defines a set of Modules, which are roughly equivalent to neural network layers. A Module receives input Tensors and computes output Tensors, but may also hold internal state such as Tensors containing learnable parameters. The nn collection also defines a set of useful loss functions that are commonly used when training neural networks.</p>
-<p>In this example we use nn to implement our two-layer network:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="co">else</span> {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
-
-<span class="co"># Use the nn package to define our model as a sequence of layers. nn_sequential</span>
-<span class="co"># is a Module which contains other Modules, and applies them in sequence to</span>
-<span class="co"># produce its output. Each Linear Module computes output from input using a</span>
-<span class="co"># linear function, and holds internal Tensors for its weight and bias.</span>
-<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_sequential.html">nn_sequential</a></span>(
-    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>),
-    <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span>(),
-    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>)
-)
-
-<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
-<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
-<span class="kw">loss_fn</span> <span class="op">&lt;-</span> <span class="kw">nnf_mse_loss</span>
-
-<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
-<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
-   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
-   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
-   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">x</span>)
-   
-   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
-   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
-   <span class="co"># loss.</span>
-   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span>(<span class="kw">y_pred</span>, <span class="kw">y</span>)
-   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
-   
-   <span class="co"># Zero the gradients before running the backward pass.</span>
-   <span class="kw">model</span><span class="op">$</span><span class="fu">zero_grad</span>()
-
-   <span class="co"># Backward pass: compute gradient of the loss with respect to all the learnable</span>
-   <span class="co"># parameters of the model. Internally, the parameters of each Module are stored</span>
-   <span class="co"># in Tensors with requires_grad=TRUE, so this call will compute gradients for</span>
-   <span class="co"># all learnable parameters in the model.</span>
-   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
-   
-   <span class="co"># Update the weights using gradient descent. Each parameter is a Tensor, so</span>
-   <span class="co"># we can access its gradients like we did before.</span>
-   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
-      <span class="co">for</span> (<span class="kw">param</span> <span class="co">in</span> <span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>) {
-         <span class="kw">param</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">param</span><span class="op">$</span><span class="kw">grad</span>)
-      }
-   })
-}
-<span class="co">#&gt; Step: 1 : 1.080564 </span>
-<span class="co">#&gt; Step: 100 : 1.080428 </span>
-<span class="co">#&gt; Step: 200 : 1.080291 </span>
-<span class="co">#&gt; Step: 300 : 1.080154 </span>
-<span class="co">#&gt; Step: 400 : 1.080016 </span>
-<span class="co">#&gt; Step: 500 : 1.079879</span>
-</pre></div>
-<p>In the <a href="optim.html">next example</a> we will learn how to use optimizers implemented in torch.</p>
-  </div>
-
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diff --git a/static/docs/dev/articles/getting-started/nn_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/getting-started/nn_files/accessible-code-block-0.0.1/empty-anchor.js
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diff --git a/static/docs/dev/articles/getting-started/optim.html b/static/docs/dev/articles/getting-started/optim.html
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-      <h1 data-toc-skip>optim: optimizers in torch</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/optim.Rmd"><code>vignettes/getting-started/optim.Rmd</code></a></small>
-      <div class="hidden name"><code>optim.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>Up to this point we have updated the weights of our models by manually mutating the Tensors holding learnable parameters (with <code>with_no_grad</code> to avoid tracking history in autograd). This is not a huge burden for simple optimization algorithms like stochastic gradient descent, but in practice we often train neural networks using more sophisticated optimizers like AdaGrad, RMSProp, Adam, etc.</p>
-<p>The optim package in torch abstracts the idea of an optimization algorithm and provides implementations of commonly used optimization algorithms.</p>
-<p>In this example we will use the nn package to define our model as before, but we will optimize the model using the Adam algorithm provided by <code>optim</code>:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="co">else</span> {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
-
-<span class="co"># Use the nn package to define our model as a sequence of layers. nn_sequential</span>
-<span class="co"># is a Module which contains other Modules, and applies them in sequence to</span>
-<span class="co"># produce its output. Each Linear Module computes output from input using a</span>
-<span class="co"># linear function, and holds internal Tensors for its weight and bias.</span>
-<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_sequential.html">nn_sequential</a></span>(
-    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>),
-    <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span>(),
-    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>)
-)
-
-<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
-<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
-<span class="kw">loss_fn</span> <span class="op">&lt;-</span> <span class="kw">nnf_mse_loss</span>
-
-<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
-<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
-<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
-<span class="co"># optimizer which Tensors it should update.</span>
-<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
-<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_adam.html">optim_adam</a></span>(<span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>, lr=<span class="kw">learning_rate</span>)
-
-<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
-   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
-   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
-   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">x</span>)
-   
-   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
-   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
-   <span class="co"># loss.</span>
-   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span>(<span class="kw">y_pred</span>, <span class="kw">y</span>)
-   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
-   
-   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
-   <span class="co"># gradients for the variables it will update (which are the learnable</span>
-   <span class="co"># weights of the model). This is because by default, gradients are</span>
-   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
-   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
-   <span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()
-
-   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
-   <span class="co"># parameters</span>
-   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
-
-   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
-   <span class="co"># parameters</span>
-   <span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-}
-<span class="co">#&gt; Step: 1 : 0.9856436 </span>
-<span class="co">#&gt; Step: 100 : 0.06738149 </span>
-<span class="co">#&gt; Step: 200 : 0.00114631 </span>
-<span class="co">#&gt; Step: 300 : 2.798474e-05 </span>
-<span class="co">#&gt; Step: 400 : 4.493992e-07 </span>
-<span class="co">#&gt; Step: 500 : 2.513527e-09</span>
-</pre></div>
-<p>In the <a href="custom-nn.html">next example</a> we will learn how to create custom <code>nn_modules</code>.</p>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
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-
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-
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-      <footer><div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
-
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
-</div>
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diff --git a/static/docs/dev/articles/getting-started/optim_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/getting-started/optim_files/accessible-code-block-0.0.1/empty-anchor.js
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-<!DOCTYPE html>
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-      <h1 data-toc-skip>Tensors and autograd</h1>
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/tensors-and-autograd.Rmd"><code>vignettes/getting-started/tensors-and-autograd.Rmd</code></a></small>
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-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>In the previous examples, we had to manually implement both the forward and backward passes of our neural network. Manually implementing the backward pass is not a big deal for a small two-layer network, but can quickly get very hairy for large complex networks.</p>
-<p>Thankfully, we can use automatic differentiation to automate the computation of backward passes in neural networks. The autograd feature in torch provides exactly this functionality. When using autograd, the forward pass of your network will define a computational graph; nodes in the graph will be Tensors, and edges will be functions that produce output Tensors from input Tensors. Backpropagating through this graph then allows you to easily compute gradients.</p>
-<p>This sounds complicated, it’s pretty simple to use in practice. Each Tensor represents a node in a computational graph. If x is a Tensor that has <code>x$requires_grad=TRUE</code> then <code>x$grad</code> is another Tensor holding the gradient of x with respect to some scalar value.</p>
-<p>Here we use torch Tensors and autograd to implement our two-layer network; now we no longer need to manually implement the backward pass through the network:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="co">else</span> {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
-
-<span class="co"># Randomly initialize weights</span>
-<span class="co"># Setting requires_grad=TRUE indicates that we want to compute gradients with</span>
-<span class="co"># respect to these Tensors during the backward pass.</span>
-<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>, device=<span class="kw">device</span>, requires_grad = <span class="fl">TRUE</span>)
-<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>, requires_grad = <span class="fl">TRUE</span>)
-
-<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
-<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y using operations on Tensors; these</span>
-   <span class="co"># are exactly the same operations we used to compute the forward pass using</span>
-   <span class="co"># Tensors, but we do not need to keep references to intermediate values since</span>
-   <span class="co"># we are not implementing the backward pass by hand.</span>
-   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w1</span>)<span class="op">$</span><span class="fu">clamp</span>(min=<span class="fl">0</span>)<span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span>)
-   
-   <span class="co"># Compute and print loss using operations on Tensors.</span>
-   <span class="co"># Now loss is a Tensor of shape (1,)</span>
-   <span class="kw">loss</span> <span class="op">&lt;-</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
-   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
-   
-   <span class="co"># Use autograd to compute the backward pass. This call will compute the</span>
-   <span class="co"># gradient of loss with respect to all Tensors with requires_grad=True.</span>
-   <span class="co"># After this call w1$grad and w2$grad will be Tensors holding the gradient</span>
-   <span class="co"># of the loss with respect to w1 and w2 respectively.</span>
-   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
-   
-   <span class="co"># Manually update weights using gradient descent. Wrap in `with_no_grad`</span>
-   <span class="co"># because weights have requires_grad=TRUE, but we don't need to track this</span>
-   <span class="co"># in autograd.</span>
-   <span class="co"># You can also use optim_sgd to achieve this.</span>
-   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
-      
-      <span class="co"># operations suffixed with an `_` operates on in-place on the tensor.</span>
-      <span class="kw">w1</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span>)
-      <span class="kw">w2</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span>)
-      
-      <span class="co"># Manually zero the gradients after updating weights</span>
-      <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
-      <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
-   })
-}
-<span class="co">#&gt; Step: 1 : 31495954 </span>
-<span class="co">#&gt; Step: 100 : 386.1584 </span>
-<span class="co">#&gt; Step: 200 : 2.09977 </span>
-<span class="co">#&gt; Step: 300 : 0.02291569 </span>
-<span class="co">#&gt; Step: 400 : 0.0005358134 </span>
-<span class="co">#&gt; Step: 500 : 7.358506e-05</span>
-</pre></div>
-<p>In the <a href="new-autograd-functions.html">next example</a> we will learn how to create new autograd functions.</p>
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-      <h1 data-toc-skip>torch Tensors</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/tensors.Rmd"><code>vignettes/getting-started/tensors.Rmd</code></a></small>
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-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>R arrays are great, but they cannot utilize GPUs to accelerate its numerical computations. For modern deep neural networks, GPUs often provide speedups of 50x or greater, so unfortunately pure R won’t be enough for modern deep learning.</p>
-<p>Here we introduce the most fundamental torch concept: the Tensor. A torch Tensor is conceptually similar to an R array: a Tensor is an n-dimensional array, and torch provides many functions for operating on these Tensors. Behind the scenes, Tensors can keep track of a computational graph and gradients, but they’re also useful as a generic tool for scientific computing.</p>
-<p>Also unlike R, torch Tensors can utilize GPUs to accelerate their numeric computations. To run a torch Tensor on GPU, you simply need to cast it to a new datatype.</p>
-<p>Here we use torch Tensors to fit a two-layer network to random data. Like the R before we need to manually implement the forward and backward passes through the network:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-} <span class="co">else</span> {
-   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
-}
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
-
-<span class="co"># Randomly initialize weights</span>
-<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>, device=<span class="kw">device</span>)
-<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
-
-<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
-<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y</span>
-   <span class="kw">h</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w1</span>)
-   <span class="kw">h_relu</span> <span class="op">&lt;-</span> <span class="kw">h</span><span class="op">$</span><span class="fu">clamp</span>(min=<span class="fl">0</span>)
-   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">h_relu</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span>)
-   
-   <span class="co"># Compute and print loss</span>
-   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>((<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>())
-   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="kw">loss</span>, <span class="st">"\n"</span>)
-   
-   <span class="co"># Backprop to compute gradients of w1 and w2 with respect to loss</span>
-   <span class="kw">grad_y_pred</span> <span class="op">&lt;-</span> <span class="fl">2.0</span> <span class="op">*</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)
-   <span class="kw">grad_w2</span> <span class="op">&lt;-</span> <span class="kw">h_relu</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>()<span class="op">$</span><span class="fu">mm</span>(<span class="kw">grad_y_pred</span>)
-   <span class="kw">grad_h_relu</span> <span class="op">&lt;-</span> <span class="kw">grad_y_pred</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>())
-   <span class="kw">grad_h</span> <span class="op">&lt;-</span> <span class="kw">grad_h_relu</span><span class="op">$</span><span class="fu">clone</span>()
-   <span class="kw">grad_h</span>[<span class="kw">h</span> <span class="op">&lt;</span> <span class="fl">0</span>] <span class="op">&lt;-</span> <span class="fl">0</span>
-   <span class="kw">grad_w1</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>()<span class="op">$</span><span class="fu">mm</span>(<span class="kw">grad_h</span>)
-   
-   <span class="co"># Update weights using gradient descent</span>
-   <span class="kw">w1</span> <span class="op">&lt;-</span> <span class="kw">w1</span> <span class="op">-</span> <span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">grad_w1</span>
-   <span class="kw">w2</span> <span class="op">&lt;-</span> <span class="kw">w2</span> <span class="op">-</span> <span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">grad_w2</span>
-}
-<span class="co">#&gt; Step: 1 : 30791944 </span>
-<span class="co">#&gt; Step: 100 : 1032.709 </span>
-<span class="co">#&gt; Step: 200 : 16.02864 </span>
-<span class="co">#&gt; Step: 300 : 0.3686208 </span>
-<span class="co">#&gt; Step: 400 : 0.009427477 </span>
-<span class="co">#&gt; Step: 500 : 0.0004871621</span>
-</pre></div>
-<p>In the <a href="tensors-and-autograd.html">next example</a> we will use autograd instead of computing the gradients manually.</p>
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-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>A fully-connected ReLU network with one hidden layer and no biases, trained to predict y from x using Euclidean error.</p>
-<p>This implementation uses pure R to manually compute the forward pass, loss, and backward pass.</p>
-<p>An R array is a generic n-dimensional array; it does not know anything about deep learning or gradients or computational graphs, and is just a way to perform generic numeric computations.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="kw">N</span><span class="op">*</span><span class="kw">D_in</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>))
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="kw">N</span><span class="op">*</span><span class="kw">D_out</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>))
-
-<span class="co"># Randomly initialize weights</span>
-<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="kw">D_in</span><span class="op">*</span><span class="kw">H</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>))
-<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="kw">H</span><span class="op">*</span><span class="kw">D_out</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>))
-
-<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
-<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
-   <span class="co"># Forward pass: compute predicted y</span>
-   <span class="kw">h</span> <span class="op">&lt;-</span> <span class="kw">x</span> <span class="op">%*%</span> <span class="kw">w1</span>
-   <span class="kw">h_relu</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span>(<span class="kw">h</span> <span class="op">&lt;</span> <span class="fl">0</span>, <span class="fl">0</span>, <span class="kw">h</span>)
-   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">h_relu</span> <span class="op">%*%</span> <span class="kw">w2</span>
-   
-   <span class="co"># Compute and print loss</span>
-   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>((<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">^</span><span class="fl">2</span>)
-   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="kw">loss</span>, <span class="st">"\n"</span>)
-   
-   <span class="co"># Backprop to compute gradients of w1 and w2 with respect to loss</span>
-   <span class="kw">grad_y_pred</span> <span class="op">&lt;-</span> <span class="fl">2</span> <span class="op">*</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)
-   <span class="kw">grad_w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>(<span class="kw">h_relu</span>) <span class="op">%*%</span> <span class="kw">grad_y_pred</span>
-   <span class="kw">grad_h_relu</span> <span class="op">&lt;-</span> <span class="kw">grad_y_pred</span> <span class="op">%*%</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>(<span class="kw">w2</span>)
-   <span class="kw">grad_h</span> <span class="op">&lt;-</span> <span class="kw">grad_h_relu</span>
-   <span class="kw">grad_h</span>[<span class="kw">h</span> <span class="op">&lt;</span> <span class="fl">0</span>] <span class="op">&lt;-</span> <span class="fl">0</span>
-   <span class="kw">grad_w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>(<span class="kw">x</span>) <span class="op">%*%</span> <span class="kw">grad_h</span>
-   
-   <span class="co"># Update weights</span>
-   <span class="kw">w1</span> <span class="op">&lt;-</span> <span class="kw">w1</span> <span class="op">-</span> <span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">grad_w1</span>
-   <span class="kw">w2</span> <span class="op">&lt;-</span> <span class="kw">w2</span> <span class="op">-</span> <span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">grad_w2</span>
-}
-<span class="co">#&gt; Step: 1 : 34209181 </span>
-<span class="co">#&gt; Step: 100 : 443.2724 </span>
-<span class="co">#&gt; Step: 200 : 1.557637 </span>
-<span class="co">#&gt; Step: 300 : 0.01012985 </span>
-<span class="co">#&gt; Step: 400 : 8.622682e-05 </span>
-<span class="co">#&gt; Step: 500 : 8.824223e-07</span>
-</pre></div>
-<p>In the <a href="tensors.html">next example</a> we will replace the R array for a torch Tensor.</p>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/articles/getting-started/warmup_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/getting-started/warmup_files/accessible-code-block-0.0.1/empty-anchor.js
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diff --git a/static/docs/dev/articles/getting-started/what-is-torch.html b/static/docs/dev/articles/getting-started/what-is-torch.html
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@@ -1,412 +0,0 @@
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-      <h1 data-toc-skip>What is torch?</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/what-is-torch.Rmd"><code>vignettes/getting-started/what-is-torch.Rmd</code></a></small>
-      <div class="hidden name"><code>what-is-torch.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>It’s a scientific computing package targeted at two sets of audiences:</p>
-<ul>
-<li>An array library to use the power of GPUs</li>
-<li>a deep learning research platform that provides maximum flexibility and speed</li>
-</ul>
-<div id="getting-started" class="section level2">
-<h2 class="hasAnchor">
-<a href="#getting-started" class="anchor"></a>Getting started</h2>
-<div id="tensors" class="section level3">
-<h3 class="hasAnchor">
-<a href="#tensors" class="anchor"></a>Tensors</h3>
-<p>Tensors are similar to R arrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.</p>
-<blockquote>
-<p>Note: An uninitialized matrix is declared, but does not contain definite known values before it is used. When an uninitialized matrix is created, whatever values were in the allocated memory at the time will appear as the initial values.</p>
-</blockquote>
-<p>Construct a 5x3 matrix, uninitialized:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_empty.html">torch_empty</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
-<span class="kw">x</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  2.7473e+25  4.5842e-41  2.7476e+25</span>
-<span class="co">#&gt;  4.5842e-41  2.7476e+25  4.5842e-41</span>
-<span class="co">#&gt;  3.3181e+24  4.5842e-41  5.0526e+23</span>
-<span class="co">#&gt;  4.5842e-41  3.3181e+24  4.5842e-41</span>
-<span class="co">#&gt;  5.0523e+23  4.5842e-41  2.7474e+25</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
-</pre></div>
-<p>Construct a randomly initialized matrix:</p>
-<div class="sourceCode" id="cb3"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
-<span class="kw">x</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  0.1108  0.8282  0.0169</span>
-<span class="co">#&gt;  0.7220  0.7744  0.5732</span>
-<span class="co">#&gt;  0.8460  0.6761  0.3581</span>
-<span class="co">#&gt;  0.1799  0.6390  0.2520</span>
-<span class="co">#&gt;  0.8121  0.5954  0.2059</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
-</pre></div>
-<p>Construct a matrix filled zeros and of dtype long:</p>
-<div class="sourceCode" id="cb4"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">5</span>, <span class="fl">3</span>, dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_long</a></span>())
-<span class="kw">x</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  0  0  0</span>
-<span class="co">#&gt;  0  0  0</span>
-<span class="co">#&gt;  0  0  0</span>
-<span class="co">#&gt;  0  0  0</span>
-<span class="co">#&gt;  0  0  0</span>
-<span class="co">#&gt; [ CPULongType{5,3} ]</span>
-</pre></div>
-<p>Construct a tensor directly from data:</p>
-<div class="sourceCode" id="cb5"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">5.5</span>, <span class="fl">3</span>))
-<span class="kw">x</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  5.5000</span>
-<span class="co">#&gt;  3.0000</span>
-<span class="co">#&gt; [ CPUFloatType{2} ]</span>
-</pre></div>
-<p>or create a tensor based on an existing tensor. These methods will reuse properties of the input tensor, e.g. dtype, unless new values are provided by user</p>
-<div class="sourceCode" id="cb6"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn_like.html">torch_randn_like</a></span>(<span class="kw">x</span>, dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>()) <span class="co"># override dtype!</span>
-<span class="kw">x</span>                                               <span class="co"># result has the same size</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; -0.5465</span>
-<span class="co">#&gt; -0.6609</span>
-<span class="co">#&gt; [ CPUFloatType{2} ]</span>
-</pre></div>
-<p>Get its size:</p>
-<div class="sourceCode" id="cb7"><pre class="downlit">
-<span class="kw">x</span><span class="op">$</span><span class="fu">size</span>()
-<span class="co">#&gt; [1] 2</span>
-</pre></div>
-</div>
-<div id="operations" class="section level3">
-<h3 class="hasAnchor">
-<a href="#operations" class="anchor"></a>Operations</h3>
-<p>There are multiple syntaxes for operations. In the following example, we will take a look at the addition operation.</p>
-<p>Addition: syntax 1</p>
-<div class="sourceCode" id="cb8"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
-<span class="kw">x</span> <span class="op">+</span> <span class="kw">y</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  0.3940  0.9454  1.6608</span>
-<span class="co">#&gt;  1.0273  1.5459  1.5228</span>
-<span class="co">#&gt;  0.8092  0.8404  1.6997</span>
-<span class="co">#&gt;  0.9045  0.7972  0.9618</span>
-<span class="co">#&gt;  0.7584  0.3400  0.9863</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
-</pre></div>
-<p>Addition: syntax 2</p>
-<div class="sourceCode" id="cb9"><pre class="downlit">
-<span class="fu"><a href="../../reference/torch_add.html">torch_add</a></span>(<span class="kw">x</span>, <span class="kw">y</span>)
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  0.3940  0.9454  1.6608</span>
-<span class="co">#&gt;  1.0273  1.5459  1.5228</span>
-<span class="co">#&gt;  0.8092  0.8404  1.6997</span>
-<span class="co">#&gt;  0.9045  0.7972  0.9618</span>
-<span class="co">#&gt;  0.7584  0.3400  0.9863</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
-</pre></div>
-<p>Addition: in-place</p>
-<div class="sourceCode" id="cb10"><pre class="downlit">
-<span class="kw">y</span><span class="op">$</span><span class="fu">add_</span>(<span class="kw">x</span>)
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  0.3940  0.9454  1.6608</span>
-<span class="co">#&gt;  1.0273  1.5459  1.5228</span>
-<span class="co">#&gt;  0.8092  0.8404  1.6997</span>
-<span class="co">#&gt;  0.9045  0.7972  0.9618</span>
-<span class="co">#&gt;  0.7584  0.3400  0.9863</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
-<span class="kw">y</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  0.3940  0.9454  1.6608</span>
-<span class="co">#&gt;  1.0273  1.5459  1.5228</span>
-<span class="co">#&gt;  0.8092  0.8404  1.6997</span>
-<span class="co">#&gt;  0.9045  0.7972  0.9618</span>
-<span class="co">#&gt;  0.7584  0.3400  0.9863</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
-</pre></div>
-<blockquote>
-<p>Note: Any operation that mutates a tensor in-place is post-fixed with an <code>_</code>. For example: <code>x$copy_(y)</code>, <code>x$t_()</code>, will change x.</p>
-</blockquote>
-<p>You can use standard R-like indexing with all bells and whistles! See more about indexing with <code><a href="../../articles/indexing.html">vignette("indexing")</a></code>.</p>
-<div class="sourceCode" id="cb11"><pre class="downlit">
-<span class="kw">x</span>[, <span class="fl">1</span>]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  0.1133</span>
-<span class="co">#&gt;  0.7501</span>
-<span class="co">#&gt;  0.7795</span>
-<span class="co">#&gt;  0.8314</span>
-<span class="co">#&gt;  0.4004</span>
-<span class="co">#&gt; [ CPUFloatType{5} ]</span>
-</pre></div>
-<p>Resizing: If you want to resize/reshape tensor, you can use <code>torch_view</code>:</p>
-<div class="sourceCode" id="cb12"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, <span class="fl">4</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">view</span>(<span class="fl">16</span>)
-<span class="kw">z</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">view</span>(size = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="op">-</span><span class="fl">1</span>, <span class="fl">8</span>))  <span class="co"># the size -1 is inferred from other dimensions</span>
-<span class="kw">x</span><span class="op">$</span><span class="fu">size</span>()
-<span class="co">#&gt; [1] 4 4</span>
-<span class="kw">y</span><span class="op">$</span><span class="fu">size</span>()
-<span class="co">#&gt; [1] 16</span>
-<span class="kw">z</span><span class="op">$</span><span class="fu">size</span>()
-<span class="co">#&gt; [1] 2 8</span>
-</pre></div>
-<p>If you have a one element tensor, use <code>$item()</code> to get the value as an R number</p>
-<div class="sourceCode" id="cb13"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>)
-<span class="kw">x</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  0.1671</span>
-<span class="co">#&gt; [ CPUFloatType{1} ]</span>
-<span class="kw">x</span><span class="op">$</span><span class="fu">item</span>()
-<span class="co">#&gt; [1] 0.1671343</span>
-</pre></div>
-<p>You can find a complete list of operations in the reference page.</p>
-</div>
-</div>
-<div id="r-bridge" class="section level2">
-<h2 class="hasAnchor">
-<a href="#r-bridge" class="anchor"></a>R bridge</h2>
-<p>Converting a Torch Tensor to an R array and vice versa is a breeze.</p>
-<div id="converting-a-torch-tensor-into-an-r-array" class="section level3">
-<h3 class="hasAnchor">
-<a href="#converting-a-torch-tensor-into-an-r-array" class="anchor"></a>Converting a torch tensor into an R array</h3>
-<div class="sourceCode" id="cb14"><pre class="downlit">
-<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">5</span>)
-<span class="kw">a</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt; [ CPUFloatType{5} ]</span>
-</pre></div>
-<div class="sourceCode" id="cb15"><pre class="downlit">
-<span class="kw">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/as_array.html">as_array</a></span>(<span class="kw">a</span>)
-<span class="kw">b</span>
-<span class="co">#&gt; [1] 1 1 1 1 1</span>
-</pre></div>
-</div>
-<div id="converting-r-arrays-to-torch-tensors" class="section level3">
-<h3 class="hasAnchor">
-<a href="#converting-r-arrays-to-torch-tensors" class="anchor"></a>Converting R arrays to torch tensors</h3>
-<div class="sourceCode" id="cb16"><pre class="downlit">
-<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span>(<span class="fl">1</span>, <span class="fl">5</span>)
-<span class="kw">a</span>
-<span class="co">#&gt; [1] 1 1 1 1 1</span>
-<span class="kw">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="kw">a</span>)
-<span class="kw">b</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt; [ CPUFloatType{5} ]</span>
-</pre></div>
-<p>Currently supported types are numerics and boolean types.</p>
-</div>
-</div>
-<div id="cuda-tensors" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cuda-tensors" class="anchor"></a>CUDA tensors</h2>
-<p>Tensors can be moved onto any device using the <code>$to</code> method.</p>
-<div class="sourceCode" id="cb17"><pre class="downlit">
-<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
-  <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
-  <span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones_like.html">torch_ones_like</a></span>(<span class="kw">x</span>, device = <span class="kw">device</span>)  <span class="co"># directly create a tensor on GPU</span>
-  <span class="kw">x</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">to</span>(<span class="kw">device</span>)                       <span class="co"># or just use strings ``.to("cuda")``</span>
-  <span class="kw">z</span> <span class="op">&lt;-</span> <span class="kw">x</span> <span class="op">+</span> <span class="kw">y</span>
-  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="kw">z</span>)
-  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="kw">z</span><span class="op">$</span><span class="fu">to</span>(device = <span class="st">"cpu"</span>, <span class="fu"><a href="../../reference/torch_dtype.html">torch_double</a></span>())) <span class="co"># `$to` can also change dtype together!</span>
-}
-</pre></div>
-</div>
-  </div>
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diff --git a/static/docs/dev/articles/getting-started/what-is-torch_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/getting-started/what-is-torch_files/accessible-code-block-0.0.1/empty-anchor.js
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deleted file mode 100644
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-      <h1 data-toc-skip>Indexing tensors</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/indexing.Rmd"><code>vignettes/indexing.Rmd</code></a></small>
-      <div class="hidden name"><code>indexing.Rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>In this article we describe the indexing operator for torch tensors and how it compares to the R indexing operator for arrays.</p>
-<p>Torch’s indexing semantics are closer to numpy’s semantics than R’s. You will find a lot of similarities between this article and the <code>numpy</code> indexing article available <a href="https://docs.scipy.org/doc/numpy-1.10.0/user/basics.indexing.html">here</a>.</p>
-<div id="single-element-indexing" class="section level2">
-<h2 class="hasAnchor">
-<a href="#single-element-indexing" class="anchor"></a>Single element indexing</h2>
-<p>Single element indexing for a 1-D tensors works mostly as expected. Like R, it is 1-based. Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.)</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>)
-<span class="kw">x</span>[<span class="fl">1</span>]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; 1</span>
-<span class="co">#&gt; [ CPULongType{} ]</span>
-<span class="kw">x</span>[<span class="op">-</span><span class="fl">1</span>]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; 10</span>
-<span class="co">#&gt; [ CPULongType{} ]</span>
-</pre></div>
-<p>You can also subset matrices and higher dimensions arrays using the same syntax:</p>
-<div class="sourceCode" id="cb3"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/reshape.html">reshape</a></span>(shape = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>,<span class="fl">5</span>))
-<span class="kw">x</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;   1   2   3   4   5</span>
-<span class="co">#&gt;   6   7   8   9  10</span>
-<span class="co">#&gt; [ CPULongType{2,5} ]</span>
-<span class="kw">x</span>[<span class="fl">1</span>,<span class="fl">3</span>]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; 3</span>
-<span class="co">#&gt; [ CPULongType{} ]</span>
-<span class="kw">x</span>[<span class="fl">1</span>,<span class="op">-</span><span class="fl">1</span>]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; 5</span>
-<span class="co">#&gt; [ CPULongType{} ]</span>
-</pre></div>
-<p>Note that if one indexes a multidimensional tensor with fewer indices than dimensions, one gets an error, unlike in R that would flatten the array. For example:</p>
-<div class="sourceCode" id="cb4"><pre class="downlit">
-<span class="kw">x</span>[<span class="fl">1</span>]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  2</span>
-<span class="co">#&gt;  3</span>
-<span class="co">#&gt;  4</span>
-<span class="co">#&gt;  5</span>
-<span class="co">#&gt; [ CPULongType{5} ]</span>
-</pre></div>
-</div>
-<div id="slicing-and-striding" class="section level2">
-<h2 class="hasAnchor">
-<a href="#slicing-and-striding" class="anchor"></a>Slicing and striding</h2>
-<p>It is possible to slice and stride arrays to extract sub-arrays of the same number of dimensions, but of different sizes than the original. This is best illustrated by a few examples:</p>
-<div class="sourceCode" id="cb5"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>)
-<span class="kw">x</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;   1</span>
-<span class="co">#&gt;   2</span>
-<span class="co">#&gt;   3</span>
-<span class="co">#&gt;   4</span>
-<span class="co">#&gt;   5</span>
-<span class="co">#&gt;   6</span>
-<span class="co">#&gt;   7</span>
-<span class="co">#&gt;   8</span>
-<span class="co">#&gt;   9</span>
-<span class="co">#&gt;  10</span>
-<span class="co">#&gt; [ CPULongType{10} ]</span>
-<span class="kw">x</span>[<span class="fl">2</span><span class="op">:</span><span class="fl">5</span>]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  2</span>
-<span class="co">#&gt;  3</span>
-<span class="co">#&gt;  4</span>
-<span class="co">#&gt;  5</span>
-<span class="co">#&gt; [ CPULongType{4} ]</span>
-<span class="kw">x</span>[<span class="fl">1</span><span class="op">:</span>(<span class="op">-</span><span class="fl">7</span>)]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  2</span>
-<span class="co">#&gt;  3</span>
-<span class="co">#&gt;  4</span>
-<span class="co">#&gt; [ CPULongType{4} ]</span>
-</pre></div>
-<p>You can also use the <code>1:10:2</code> syntax which means: In the range from 1 to 10, take every second item. For example:</p>
-<div class="sourceCode" id="cb6"><pre class="downlit">
-<span class="kw">x</span>[<span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">:</span><span class="fl">2</span>]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  3</span>
-<span class="co">#&gt;  5</span>
-<span class="co">#&gt; [ CPULongType{3} ]</span>
-</pre></div>
-<p>Another special syntax is the <code>N</code>, meaning the size of the specified dimension.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit">
-<span class="kw">x</span>[<span class="fl">5</span><span class="op">:</span><span class="kw">N</span>]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;   5</span>
-<span class="co">#&gt;   6</span>
-<span class="co">#&gt;   7</span>
-<span class="co">#&gt;   8</span>
-<span class="co">#&gt;   9</span>
-<span class="co">#&gt;  10</span>
-<span class="co">#&gt; [ CPULongType{6} ]</span>
-</pre></div>
-</div>
-<div id="getting-the-complete-dimension" class="section level2">
-<h2 class="hasAnchor">
-<a href="#getting-the-complete-dimension" class="anchor"></a>Getting the complete dimension</h2>
-<p>Like in R, you can take all elements in a dimension by leaving an index empty.</p>
-<p>Consider a matrix:</p>
-<div class="sourceCode" id="cb8"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">3</span>)
-<span class="kw">x</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; -0.0284  1.6234 -0.4115</span>
-<span class="co">#&gt; -1.4234 -1.6813 -1.4158</span>
-<span class="co">#&gt; [ CPUFloatType{2,3} ]</span>
-</pre></div>
-<p>The following syntax will give you the first row:</p>
-<div class="sourceCode" id="cb9"><pre class="downlit">
-<span class="kw">x</span>[<span class="fl">1</span>,]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; -0.0284</span>
-<span class="co">#&gt;  1.6234</span>
-<span class="co">#&gt; -0.4115</span>
-<span class="co">#&gt; [ CPUFloatType{3} ]</span>
-</pre></div>
-<p>And this would give you the first 2 columns:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit">
-<span class="kw">x</span>[,<span class="fl">1</span><span class="op">:</span><span class="fl">2</span>]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; -0.0284  1.6234</span>
-<span class="co">#&gt; -1.4234 -1.6813</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
-</pre></div>
-</div>
-<div id="dropping-dimensions" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dropping-dimensions" class="anchor"></a>Dropping dimensions</h2>
-<p>By default, when indexing by a single integer, this dimension will be dropped to avoid the singleton dimension:</p>
-<div class="sourceCode" id="cb11"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">3</span>)
-<span class="kw">x</span>[<span class="fl">1</span>,]<span class="op">$</span><span class="kw">shape</span>
-<span class="co">#&gt; [1] 3</span>
-</pre></div>
-<p>You can optionally use the <code>drop = FALSE</code> argument to avoid dropping the dimension.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit">
-<span class="kw">x</span>[<span class="fl">1</span>,,drop = <span class="fl">FALSE</span>]<span class="op">$</span><span class="kw">shape</span>
-<span class="co">#&gt; [1] 1 3</span>
-</pre></div>
-</div>
-<div id="adding-a-new-dimension" class="section level2">
-<h2 class="hasAnchor">
-<a href="#adding-a-new-dimension" class="anchor"></a>Adding a new dimension</h2>
-<p>It’s possible to add a new dimension to a tensor using index-like syntax:</p>
-<div class="sourceCode" id="cb13"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">10</span>))
-<span class="kw">x</span><span class="op">$</span><span class="kw">shape</span>
-<span class="co">#&gt; [1] 1</span>
-<span class="kw">x</span>[, <span class="kw">newaxis</span>]<span class="op">$</span><span class="kw">shape</span>
-<span class="co">#&gt; [1] 1 1</span>
-<span class="kw">x</span>[, <span class="kw">newaxis</span>, <span class="kw">newaxis</span>]<span class="op">$</span><span class="kw">shape</span>
-<span class="co">#&gt; [1] 1 1 1</span>
-</pre></div>
-<p>You can also use <code>NULL</code> instead of <code>newaxis</code>:</p>
-<div class="sourceCode" id="cb14"><pre class="downlit">
-<span class="kw">x</span>[,<span class="kw">NULL</span>]<span class="op">$</span><span class="kw">shape</span>
-<span class="co">#&gt; [1] 1 1</span>
-</pre></div>
-</div>
-<div id="dealing-with-variable-number-of-indices" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dealing-with-variable-number-of-indices" class="anchor"></a>Dealing with variable number of indices</h2>
-<p>Sometimes we don’t know how many dimensions a tensor has, but we do know what to do with the last available dimension, or the first one. To subsume all others, we can use <code>..</code>:</p>
-<div class="sourceCode" id="cb15"><pre class="downlit">
-<span class="kw">z</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">125</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/reshape.html">reshape</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">5</span>,<span class="fl">5</span>,<span class="fl">5</span>))
-<span class="kw">z</span>[<span class="fl">1</span>,<span class="kw">..</span>]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;   1   2   3   4   5</span>
-<span class="co">#&gt;   6   7   8   9  10</span>
-<span class="co">#&gt;  11  12  13  14  15</span>
-<span class="co">#&gt;  16  17  18  19  20</span>
-<span class="co">#&gt;  21  22  23  24  25</span>
-<span class="co">#&gt; [ CPULongType{5,5} ]</span>
-<span class="kw">z</span>[<span class="kw">..</span>,<span class="fl">1</span>]
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;    1    6   11   16   21</span>
-<span class="co">#&gt;   26   31   36   41   46</span>
-<span class="co">#&gt;   51   56   61   66   71</span>
-<span class="co">#&gt;   76   81   86   91   96</span>
-<span class="co">#&gt;  101  106  111  116  121</span>
-<span class="co">#&gt; [ CPULongType{5,5} ]</span>
-</pre></div>
-</div>
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diff --git a/static/docs/dev/articles/indexing_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/indexing_files/accessible-code-block-0.0.1/empty-anchor.js
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diff --git a/static/docs/dev/articles/loading-data.html b/static/docs/dev/articles/loading-data.html
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+++ /dev/null
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-<!DOCTYPE html>
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-      <h1 data-toc-skip>Loading data</h1>
-            
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/loading-data.Rmd"><code>vignettes/loading-data.Rmd</code></a></small>
-      <div class="hidden name"><code>loading-data.Rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<div id="datasets-and-data-loaders" class="section level2">
-<h2 class="hasAnchor">
-<a href="#datasets-and-data-loaders" class="anchor"></a>Datasets and data loaders</h2>
-<p>Central to data ingestion and preprocessing are datasets and data loaders.</p>
-<p><code>torch</code> comes equipped with a bag of datasets related to, mostly, image recognition and natural language processing (e.g., <code>mnist_dataset()</code>), which can be iterated over by means of <code>dataloader</code>s:</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="co"># ...</span></span>
-<span id="cb2-2"><a href="#cb2-2"></a>ds &lt;-<span class="st"> </span><span class="kw">mnist_dataset</span>(</span>
-<span id="cb2-3"><a href="#cb2-3"></a>  dir, </span>
-<span id="cb2-4"><a href="#cb2-4"></a>  <span class="dt">download =</span> <span class="ot">TRUE</span>, </span>
-<span id="cb2-5"><a href="#cb2-5"></a>  <span class="dt">transform =</span> <span class="cf">function</span>(x) {</span>
-<span id="cb2-6"><a href="#cb2-6"></a>    x &lt;-<span class="st"> </span>x<span class="op">$</span><span class="kw">to</span>(<span class="dt">dtype =</span> <span class="kw">torch_float</span>())<span class="op">/</span><span class="dv">256</span></span>
-<span id="cb2-7"><a href="#cb2-7"></a>    x[newaxis,..]</span>
-<span id="cb2-8"><a href="#cb2-8"></a>  }</span>
-<span id="cb2-9"><a href="#cb2-9"></a>)</span>
-<span id="cb2-10"><a href="#cb2-10"></a></span>
-<span id="cb2-11"><a href="#cb2-11"></a>dl &lt;-<span class="st"> </span><span class="kw">dataloader</span>(ds, <span class="dt">batch_size =</span> <span class="dv">32</span>, <span class="dt">shuffle =</span> <span class="ot">TRUE</span>)</span>
-<span id="cb2-12"><a href="#cb2-12"></a></span>
-<span id="cb2-13"><a href="#cb2-13"></a><span class="cf">for</span> (b <span class="cf">in</span> <span class="kw">enumerate</span>(dl)) {</span>
-<span id="cb2-14"><a href="#cb2-14"></a>  <span class="co"># ...</span></span></code></pre></div>
-<p>Cf. <code>vignettes/examples/mnist-cnn.R</code> for a complete example.</p>
-<p>What if you want to train on a different dataset? In these cases, you subclass <code>Dataset</code>, an abstract container that needs to know how to iterate over the given data. To that purpose, your subclass needs to implement <code>.getitem()</code>, and say what should be returned when the data loader is asking for the next batch.</p>
-<p>In <code>.getitem()</code>, you can implement whatever preprocessing you require. Additionally, you should implement <code>.length()</code>, so users can find out how many items there are in the dataset.</p>
-<p>While this may sound complicated, it is not at all. The base logic is straightforward – complexity will, naturally, correlate with how involved your preprocessing is. To provide you with a simple but functional prototype, here we show how to create your own <code>dataset</code> to train on <a href="https://github.com/allisonhorst/palmerpenguins">Allison Horst's penguins</a>.</p>
-</div>
-<div id="a-custom-dataset" class="section level2">
-<h2 class="hasAnchor">
-<a href="#a-custom-dataset" class="anchor"></a>A custom dataset</h2>
-<div class="sourceCode" id="cb3"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://allisonhorst.github.io/palmerpenguins">palmerpenguins</a></span>)
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw">magrittr</span>)
-
-<span class="kw">penguins</span>
-<span class="co">#&gt; # A tibble: 344 x 8</span>
-<span class="co">#&gt;    species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g</span>
-<span class="co">#&gt;    &lt;fct&gt;   &lt;fct&gt;           &lt;dbl&gt;         &lt;dbl&gt;            &lt;int&gt;       &lt;int&gt;</span>
-<span class="co">#&gt;  1 Adelie  Torge…           39.1          18.7              181        3750</span>
-<span class="co">#&gt;  2 Adelie  Torge…           39.5          17.4              186        3800</span>
-<span class="co">#&gt;  3 Adelie  Torge…           40.3          18                195        3250</span>
-<span class="co">#&gt;  4 Adelie  Torge…           NA            NA                 NA          NA</span>
-<span class="co">#&gt;  5 Adelie  Torge…           36.7          19.3              193        3450</span>
-<span class="co">#&gt;  6 Adelie  Torge…           39.3          20.6              190        3650</span>
-<span class="co">#&gt;  7 Adelie  Torge…           38.9          17.8              181        3625</span>
-<span class="co">#&gt;  8 Adelie  Torge…           39.2          19.6              195        4675</span>
-<span class="co">#&gt;  9 Adelie  Torge…           34.1          18.1              193        3475</span>
-<span class="co">#&gt; 10 Adelie  Torge…           42            20.2              190        4250</span>
-<span class="co">#&gt; # … with 334 more rows, and 2 more variables: sex &lt;fct&gt;, year &lt;int&gt;</span>
-</pre></div>
-<p><code>Dataset</code>s are R6 classes created using the <code><a href="../reference/dataset.html">dataset()</a></code> constructor. You can pass a <code>name</code> and various member functions. Among those should be <code>initialize()</code>, to create instance variables, <code>.getitem()</code>, to indicate how the data should be returned, and <code>.length()</code>, to say how many items we have.</p>
-<p>In addition, any number of helper functions can be defined.</p>
-<p>Here, we assume the <code>penguins</code> have already been loaded, and all preprocessing consists in removing lines with <code>NA</code> values, transforming <code>factor</code>s to numbers starting from 0, and converting from R data types to <code>torch</code> tensors.</p>
-<p>In <code>.getitem</code>, we essentially decide how this data is going to be used: All variables besides <code>species</code> go into <code>x</code>, the predictor, and <code>species</code> will constitute <code>y</code>, the target. Predictor and target are returned in a list, to be accessed as <code>batch[[1]]</code> and <code>batch[[2]]</code> during training.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit">
-<span class="kw">penguins_dataset</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/dataset.html">dataset</a></span>(
-  
-  name = <span class="st">"penguins_dataset"</span>,
-  
-  initialize = <span class="fu">function</span>() {
-    <span class="kw">self</span><span class="op">$</span><span class="kw">data</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="fu">prepare_penguin_data</span>()
-  },
-  
-  .getitem = <span class="fu">function</span>(<span class="kw">index</span>) {
-    
-    <span class="kw">x</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="kw">data</span>[<span class="kw">index</span>, <span class="fl">2</span><span class="op">:</span><span class="op">-</span><span class="fl">1</span>]
-    <span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="kw">data</span>[<span class="kw">index</span>, <span class="fl">1</span>]<span class="op">$</span><span class="fu">to</span>(<span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span>())
-    
-    <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">x</span>, <span class="kw">y</span>)
-  },
-  
-  .length = <span class="fu">function</span>() {
-    <span class="kw">self</span><span class="op">$</span><span class="kw">data</span><span class="op">$</span><span class="fu">size</span>()[[<span class="fl">1</span>]]
-  },
-  
-  prepare_penguin_data = <span class="fu">function</span>() {
-    
-    <span class="kw">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/na.fail.html">na.omit</a></span>(<span class="kw">penguins</span>) 
-    <span class="co"># conveniently, the categorical data are already factors</span>
-    <span class="kw">input</span><span class="op">$</span><span class="kw">species</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">input</span><span class="op">$</span><span class="kw">species</span>)
-    <span class="kw">input</span><span class="op">$</span><span class="kw">island</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">input</span><span class="op">$</span><span class="kw">island</span>)
-    <span class="kw">input</span><span class="op">$</span><span class="kw">sex</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">input</span><span class="op">$</span><span class="kw">sex</span>)
-    
-    <span class="kw">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html">as.matrix</a></span>(<span class="kw">input</span>)
-    <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="kw">input</span>)
-  }
-)
-</pre></div>
-<p>Let’s create the dataset , query for it’s length, and look at its first item:</p>
-<div class="sourceCode" id="cb5"><pre class="downlit">
-<span class="kw">tuxes</span> <span class="op">&lt;-</span> <span class="fu">penguins_dataset</span>()
-<span class="kw">tuxes</span><span class="op">$</span><span class="fu">.length</span>()
-<span class="co">#&gt; [1] 333</span>
-<span class="kw">tuxes</span><span class="op">$</span><span class="fu">.getitem</span>(<span class="fl">1</span>)
-<span class="co">#&gt; [[1]]</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;     3.0000</span>
-<span class="co">#&gt;    39.1000</span>
-<span class="co">#&gt;    18.7000</span>
-<span class="co">#&gt;   181.0000</span>
-<span class="co">#&gt;  3750.0000</span>
-<span class="co">#&gt;     2.0000</span>
-<span class="co">#&gt;  2007.0000</span>
-<span class="co">#&gt; [ CPUFloatType{7} ]</span>
-<span class="co">#&gt; </span>
-<span class="co">#&gt; [[2]]</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; 1</span>
-<span class="co">#&gt; [ CPULongType{} ]</span>
-</pre></div>
-<p>To be able to iterate over <code>tuxes</code>, we need a data loader (we override the default batch size of <code>1</code>):</p>
-<div class="sourceCode" id="cb6"><pre class="downlit">
-<span class="kw">dl</span> <span class="op">&lt;-</span><span class="kw">tuxes</span> <span class="op">%&gt;%</span> <span class="fu"><a href="../reference/dataloader.html">dataloader</a></span>(batch_size = <span class="fl">8</span>)
-</pre></div>
-<p>Calling <code>.length()</code> on a data loader (as opposed to a dataset) will return the number of <code>batches</code> we have:</p>
-<div class="sourceCode" id="cb7"><pre class="downlit">
-<span class="kw">dl</span><span class="op">$</span><span class="fu">.length</span>()
-<span class="co">#&gt; [1] 42</span>
-</pre></div>
-<p>And we can create an iterator to inspect the first batch:</p>
-<div class="sourceCode" id="cb8"><pre class="downlit">
-<span class="kw">iter</span> <span class="op">&lt;-</span> <span class="kw">dl</span><span class="op">$</span><span class="fu">.iter</span>()
-<span class="kw">b</span> <span class="op">&lt;-</span> <span class="kw">iter</span><span class="op">$</span><span class="fu">.next</span>()
-<span class="kw">b</span>
-<span class="co">#&gt; [[1]]</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;     3.0000    39.1000    18.7000   181.0000  3750.0000     2.0000  2007.0000</span>
-<span class="co">#&gt;     3.0000    39.5000    17.4000   186.0000  3800.0000     1.0000  2007.0000</span>
-<span class="co">#&gt;     3.0000    40.3000    18.0000   195.0000  3250.0000     1.0000  2007.0000</span>
-<span class="co">#&gt;     3.0000    36.7000    19.3000   193.0000  3450.0000     1.0000  2007.0000</span>
-<span class="co">#&gt;     3.0000    39.3000    20.6000   190.0000  3650.0000     2.0000  2007.0000</span>
-<span class="co">#&gt;     3.0000    38.9000    17.8000   181.0000  3625.0000     1.0000  2007.0000</span>
-<span class="co">#&gt;     3.0000    39.2000    19.6000   195.0000  4675.0000     2.0000  2007.0000</span>
-<span class="co">#&gt;     3.0000    41.1000    17.6000   182.0000  3200.0000     1.0000  2007.0000</span>
-<span class="co">#&gt; [ CPUFloatType{8,7} ]</span>
-<span class="co">#&gt; </span>
-<span class="co">#&gt; [[2]]</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt; [ CPULongType{8} ]</span>
-</pre></div>
-<p>To train a network, we can use <code>enumerate</code> to iterate over batches.</p>
-</div>
-<div id="training-with-data-loaders" class="section level2">
-<h2 class="hasAnchor">
-<a href="#training-with-data-loaders" class="anchor"></a>Training with data loaders</h2>
-<p>Our example network is very simple. (In reality, we would want to treat <code>island</code> as the categorical variable it is, and either one-hot-encode or embed it.)</p>
-<div class="sourceCode" id="cb9"><pre class="downlit">
-<span class="kw">net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_module.html">nn_module</a></span>(
-  <span class="st">"PenguinNet"</span>,
-  initialize = <span class="fu">function</span>() {
-    <span class="kw">self</span><span class="op">$</span><span class="kw">fc1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">7</span>, <span class="fl">32</span>)
-    <span class="kw">self</span><span class="op">$</span><span class="kw">fc2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">32</span>, <span class="fl">3</span>)
-  },
-  forward = <span class="fu">function</span>(<span class="kw">x</span>) {
-    <span class="kw">x</span> <span class="op">%&gt;%</span> 
-      <span class="kw">self</span><span class="op">$</span><span class="fu">fc1</span>() <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
-      <span class="kw">self</span><span class="op">$</span><span class="fu">fc2</span>() <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../reference/nnf_log_softmax.html">nnf_log_softmax</a></span>(dim = <span class="fl">1</span>)
-  }
-)
-
-<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu">net</span>()
-</pre></div>
-<p>We still need an optimizer:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit">
-<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/optim_sgd.html">optim_sgd</a></span>(<span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>, lr = <span class="fl">0.01</span>)
-</pre></div>
-<p>And we’re ready to train:</p>
-<div class="sourceCode" id="cb11"><pre class="downlit">
-<span class="co">for</span> (<span class="kw">epoch</span> <span class="co">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">10</span>) {
-  
-  <span class="kw">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
-  
-  <span class="co">for</span> (<span class="kw">b</span> <span class="co">in</span> <span class="fu"><a href="../reference/enumerate.html">enumerate</a></span>(<span class="kw">dl</span>)) {
-    <span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()
-    <span class="kw">output</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">b</span>[[<span class="fl">1</span>]])
-    <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nnf_nll_loss.html">nnf_nll_loss</a></span>(<span class="kw">output</span>, <span class="kw">b</span>[[<span class="fl">2</span>]])
-    <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
-    <span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-    <span class="kw">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">l</span>, <span class="kw">loss</span><span class="op">$</span><span class="fu">item</span>())
-  }
-  
-  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="kw">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="kw">l</span>)))
-}
-<span class="co">#&gt; Loss at epoch 1: 51.747068</span>
-<span class="co">#&gt; Loss at epoch 2: 2.068251</span>
-<span class="co">#&gt; Loss at epoch 3: 2.068251</span>
-<span class="co">#&gt; Loss at epoch 4: 2.068251</span>
-<span class="co">#&gt; Loss at epoch 5: 2.068251</span>
-<span class="co">#&gt; Loss at epoch 6: 2.068251</span>
-<span class="co">#&gt; Loss at epoch 7: 2.068251</span>
-<span class="co">#&gt; Loss at epoch 8: 2.068251</span>
-<span class="co">#&gt; Loss at epoch 9: 2.068251</span>
-<span class="co">#&gt; Loss at epoch 10: 2.068251</span>
-</pre></div>
-</div>
-  </div>
-
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diff --git a/static/docs/dev/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js
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diff --git a/static/docs/dev/articles/tensor-creation.html b/static/docs/dev/articles/tensor-creation.html
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-      <h1 data-toc-skip>Creating tensors</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/tensor-creation.Rmd"><code>vignettes/tensor-creation.Rmd</code></a></small>
-      <div class="hidden name"><code>tensor-creation.Rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>In this article we describe various ways of creating <code>torch</code> tensors in R.</p>
-<div id="from-r-objects" class="section level2">
-<h2 class="hasAnchor">
-<a href="#from-r-objects" class="anchor"></a>From R objects</h2>
-<p>You can create tensors from R objects using the <code>torch_tensor</code> function. The <code>torch_tensor</code> function takes an R vector, matrix or array and creates an equivalent <code>torch_tensor</code>.</p>
-<p>You can see a few examples below:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span>))
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  2</span>
-<span class="co">#&gt;  3</span>
-<span class="co">#&gt; [ CPUFloatType{3} ]</span>
-
-<span class="co"># conform to row-major indexing used in torch</span>
-<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, ncol = <span class="fl">5</span>, nrow = <span class="fl">2</span>, byrow = <span class="fl">TRUE</span>))
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;   1   2   3   4   5</span>
-<span class="co">#&gt;   6   7   8   9  10</span>
-<span class="co">#&gt; [ CPULongType{2,5} ]</span>
-<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html">runif</a></span>(<span class="fl">12</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">3</span>)))
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; (1,.,.) = </span>
-<span class="co">#&gt;   0.3703  0.9455  0.7631</span>
-<span class="co">#&gt;   0.1020  0.4338  0.2885</span>
-<span class="co">#&gt; </span>
-<span class="co">#&gt; (2,.,.) = </span>
-<span class="co">#&gt;   0.6926  0.4379  0.2797</span>
-<span class="co">#&gt;   0.3698  0.7940  0.9781</span>
-<span class="co">#&gt; [ CPUFloatType{2,2,3} ]</span>
-</pre></div>
-<p>By default, we will create tensors in the <code>cpu</code> device, converting their R datatype to the corresponding torch <code>dtype</code>.</p>
-<blockquote>
-<p><strong>Note</strong> currently, only numeric and boolean types are supported.</p>
-</blockquote>
-<p>You can always modify <code>dtype</code> and <code>device</code> when converting an R object to a torch tensor. For example:</p>
-<div class="sourceCode" id="cb3"><pre class="downlit">
-<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>, dtype = <span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span>())
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt; [ CPULongType{1} ]</span>
-<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>, device = <span class="st">"cpu"</span>, dtype = <span class="fu"><a href="../reference/torch_dtype.html">torch_float64</a></span>())
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt; [ CPUDoubleType{1} ]</span>
-</pre></div>
-<p>Other options available when creating a tensor are:</p>
-<ul>
-<li>
-<code>requires_grad</code>: boolean indicating if you want <code>autograd</code> to record operations on them for automatic differentiation.</li>
-<li>
-<code>pin_memory</code>: – If set, the tensor returned would be allocated in pinned memory. Works only for CPU tensors.</li>
-</ul>
-<p>These options are available for all functions that can be used to create new tensors, including the factory functions listed in the next section.</p>
-</div>
-<div id="using-creation-functions" class="section level2">
-<h2 class="hasAnchor">
-<a href="#using-creation-functions" class="anchor"></a>Using creation functions</h2>
-<p>You can also use the <code>torch_*</code> functions listed below to create torch tensors using some algorithm.</p>
-<p>For example, the <code>torch_randn</code> function will create tensors using the normal distribution with mean 0 and standard deviation 1. You can use the <code>...</code> argument to pass the size of the dimensions. For example, the code below will create a normally distributed tensor with shape 5x3.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
-<span class="kw">x</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  0.6255 -0.2503 -0.8021</span>
-<span class="co">#&gt;  0.6556  0.3303 -2.1184</span>
-<span class="co">#&gt;  0.1929  1.5096  0.2727</span>
-<span class="co">#&gt; -0.8007 -0.0475  0.4077</span>
-<span class="co">#&gt;  0.6121  0.8919  0.8326</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
-</pre></div>
-<p>Another example is <code>torch_ones</code>, which creates a tensor filled with ones.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>, <span class="fl">4</span>, dtype = <span class="fu"><a href="../reference/torch_dtype.html">torch_int64</a></span>(), device = <span class="st">"cpu"</span>)
-<span class="kw">x</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1  1  1  1</span>
-<span class="co">#&gt;  1  1  1  1</span>
-<span class="co">#&gt; [ CPULongType{2,4} ]</span>
-</pre></div>
-<p>Here is the full list of functions that can be used to bulk-create tensors in torch:</p>
-<ul>
-<li>
-<code>torch_arange</code>: Returns a tensor with a sequence of integers,</li>
-<li>
-<code>torch_empty</code>: Returns a tensor with uninitialized values,</li>
-<li>
-<code>torch_eye</code>: Returns an identity matrix,</li>
-<li>
-<code>torch_full</code>: Returns a tensor filled with a single value,</li>
-<li>
-<code>torch_linspace</code>: Returns a tensor with values linearly spaced in some interval,</li>
-<li>
-<code>torch_logspace</code>: Returns a tensor with values logarithmically spaced in some interval,</li>
-<li>
-<code>torch_ones</code>: Returns a tensor filled with all ones,</li>
-<li>
-<code>torch_rand</code>: Returns a tensor filled with values drawn from a uniform distribution on [0, 1).</li>
-<li>
-<code>torch_randint</code>: Returns a tensor with integers randomly drawn from an interval,</li>
-<li>
-<code>torch_randn</code>: Returns a tensor filled with values drawn from a unit normal distribution,</li>
-<li>
-<code>torch_randperm</code>: Returns a tensor filled with a random permutation of integers in some interval,</li>
-<li>
-<code>torch_zeros</code>: Returns a tensor filled with all zeros.</li>
-</ul>
-</div>
-<div id="conversion" class="section level2">
-<h2 class="hasAnchor">
-<a href="#conversion" class="anchor"></a>Conversion</h2>
-<p>Once a tensor exists you can convert between <code>dtype</code>s and move to a different device with <code>to</code> method. For example:</p>
-<div class="sourceCode" id="cb6"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../reference/torch_dtype.html">torch_int32</a></span>())
-<span class="kw">x</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt; [ CPUFloatType{1} ]</span>
-<span class="kw">y</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt; [ CPUIntType{1} ]</span>
-</pre></div>
-<p>You can also copy a tensor to the GPU using:</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>x &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">1</span>)</span>
-<span id="cb7-2"><a href="#cb7-2"></a>y &lt;-<span class="st"> </span>x<span class="op">$</span><span class="kw">cuda</span>()<span class="er">)</span></span></code></pre></div>
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-      <h1 data-toc-skip>Tensor objects</h1>
-            
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/tensor/index.Rmd"><code>vignettes/tensor/index.Rmd</code></a></small>
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-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>Central to torch is the <code>torch_tensor</code> objects. <code>torch_tensor</code>’s are R objects very similar to R6 instances. Tensors have a large amount of methods that can be called using the <code><a href="https://rdrr.io/r/base/Extract.html">$</a></code> operator.</p>
-<p>Following is a list of all methods that can be called by tensor objects and their documentation. You can also look at <a href="https://pytorch.org/docs/stable/tensors.html">PyTorch’s documentation</a> for additional details.</p>
-<div id="t" class="section level2">
-<h2 class="hasAnchor">
-<a href="#t" class="anchor"></a>T</h2>
-<p>Is this Tensor with its dimensions reversed.</p>
-<p>If <code>n</code> is the number of dimensions in <code>x</code>, <code>x$T</code> is equivalent to <code>x$permute(n-1, n-2, ..., 0)</code>.</p>
-</div>
-<div id="abs" class="section level2">
-<h2 class="hasAnchor">
-<a href="#abs" class="anchor"></a>abs</h2>
-<p>abs() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_abs.html">?torch_abs</a></code></p>
-</div>
-<div id="abs_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#abs_" class="anchor"></a>abs_</h2>
-<p>abs_() -&gt; Tensor</p>
-<p>In-place version of <code>$abs</code></p>
-</div>
-<div id="absolute" class="section level2">
-<h2 class="hasAnchor">
-<a href="#absolute" class="anchor"></a>absolute</h2>
-<p>absolute() -&gt; Tensor</p>
-<p>Alias for [$abs()]</p>
-</div>
-<div id="absolute_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#absolute_" class="anchor"></a>absolute_</h2>
-<p>absolute_() -&gt; Tensor</p>
-<p>In-place version of <code>$absolute</code> Alias for [$abs_()]</p>
-</div>
-<div id="acos" class="section level2">
-<h2 class="hasAnchor">
-<a href="#acos" class="anchor"></a>acos</h2>
-<p>acos() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_acos.html">?torch_acos</a></code></p>
-</div>
-<div id="acos_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#acos_" class="anchor"></a>acos_</h2>
-<p>acos_() -&gt; Tensor</p>
-<p>In-place version of <code>$acos</code></p>
-</div>
-<div id="acosh" class="section level2">
-<h2 class="hasAnchor">
-<a href="#acosh" class="anchor"></a>acosh</h2>
-<p>acosh() -&gt; Tensor</p>
-<p>See <code>?torch_acosh</code></p>
-</div>
-<div id="acosh_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#acosh_" class="anchor"></a>acosh_</h2>
-<p>acosh_() -&gt; Tensor</p>
-<p>In-place version of <code>$acosh</code></p>
-</div>
-<div id="add" class="section level2">
-<h2 class="hasAnchor">
-<a href="#add" class="anchor"></a>add</h2>
-<p>add(other, *, alpha=1) -&gt; Tensor</p>
-<p>Add a scalar or tensor to <code>self</code> tensor. If both <code>alpha</code> and <code>other</code> are specified, each element of <code>other</code> is scaled by <code>alpha</code> before being used.</p>
-<p>When <code>other</code> is a tensor, the shape of <code>other</code> must be broadcastable with the shape of the underlying tensor</p>
-<p>See <code><a href="../../reference/torch_add.html">?torch_add</a></code></p>
-</div>
-<div id="add_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#add_" class="anchor"></a>add_</h2>
-<p>add_(other, *, alpha=1) -&gt; Tensor</p>
-<p>In-place version of <code>$add</code></p>
-</div>
-<div id="addbmm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addbmm" class="anchor"></a>addbmm</h2>
-<p>addbmm(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_addbmm.html">?torch_addbmm</a></code></p>
-</div>
-<div id="addbmm_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addbmm_" class="anchor"></a>addbmm_</h2>
-<p>addbmm_(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
-<p>In-place version of <code>$addbmm</code></p>
-</div>
-<div id="addcdiv" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addcdiv" class="anchor"></a>addcdiv</h2>
-<p>addcdiv(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_addcdiv.html">?torch_addcdiv</a></code></p>
-</div>
-<div id="addcdiv_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addcdiv_" class="anchor"></a>addcdiv_</h2>
-<p>addcdiv_(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
-<p>In-place version of <code>$addcdiv</code></p>
-</div>
-<div id="addcmul" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addcmul" class="anchor"></a>addcmul</h2>
-<p>addcmul(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_addcmul.html">?torch_addcmul</a></code></p>
-</div>
-<div id="addcmul_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addcmul_" class="anchor"></a>addcmul_</h2>
-<p>addcmul_(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
-<p>In-place version of <code>$addcmul</code></p>
-</div>
-<div id="addmm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addmm" class="anchor"></a>addmm</h2>
-<p>addmm(mat1, mat2, *, beta=1, alpha=1) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_addmm.html">?torch_addmm</a></code></p>
-</div>
-<div id="addmm_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addmm_" class="anchor"></a>addmm_</h2>
-<p>addmm_(mat1, mat2, *, beta=1, alpha=1) -&gt; Tensor</p>
-<p>In-place version of <code>$addmm</code></p>
-</div>
-<div id="addmv" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addmv" class="anchor"></a>addmv</h2>
-<p>addmv(mat, vec, *, beta=1, alpha=1) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_addmv.html">?torch_addmv</a></code></p>
-</div>
-<div id="addmv_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addmv_" class="anchor"></a>addmv_</h2>
-<p>addmv_(mat, vec, *, beta=1, alpha=1) -&gt; Tensor</p>
-<p>In-place version of <code>$addmv</code></p>
-</div>
-<div id="addr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addr" class="anchor"></a>addr</h2>
-<p>addr(vec1, vec2, *, beta=1, alpha=1) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_addr.html">?torch_addr</a></code></p>
-</div>
-<div id="addr_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addr_" class="anchor"></a>addr_</h2>
-<p>addr_(vec1, vec2, *, beta=1, alpha=1) -&gt; Tensor</p>
-<p>In-place version of <code>$addr</code></p>
-</div>
-<div id="align_as" class="section level2">
-<h2 class="hasAnchor">
-<a href="#align_as" class="anchor"></a>align_as</h2>
-<p>align_as(other) -&gt; Tensor</p>
-<p>Permutes the dimensions of the <code>self</code> tensor to match the dimension order in the <code>other</code> tensor, adding size-one dims for any new names.</p>
-<p>This operation is useful for explicit broadcasting by names (see examples).</p>
-<p>All of the dims of <code>self</code> must be named in order to use this method. The resulting tensor is a view on the original tensor.</p>
-<p>All dimension names of <code>self</code> must be present in <code>other$names</code>. <code>other</code> may contain named dimensions that are not in <code>self$names</code>; the output tensor has a size-one dimension for each of those new names.</p>
-<p>To align a tensor to a specific order, use <code>$align_to</code>.</p>
-<div id="examples" class="section level3">
-<h3 class="hasAnchor">
-<a href="#examples" class="anchor"></a>Examples:</h3>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="co"># Example 1: Applying a mask</span>
-<span class="kw">mask</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randint.html">torch_randint</a></span>(low = <span class="fl">0</span>, high = <span class="fl">2</span>, size = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">127</span>, <span class="fl">128</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span>())<span class="op">$</span><span class="fu">refine_names</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'W'</span>, <span class="st">'H'</span>))
-<span class="kw">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">32</span>, <span class="fl">128</span>, <span class="fl">127</span>, <span class="fl">3</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'C'</span>))
-<span class="kw">imgs</span><span class="op">$</span><span class="fu">masked_fill_</span>(<span class="kw">mask</span><span class="op">$</span><span class="fu">align_as</span>(<span class="kw">imgs</span>), <span class="fl">0</span>)
-
-<span class="co"># Example 2: Applying a per-channel-scale</span>
-<span class="kw">scale_channels</span> <span class="op">&lt;-</span> <span class="fu">function</span>(<span class="kw">input</span>, <span class="kw">scale</span>) {
-  <span class="kw">scale</span> <span class="op">&lt;-</span> <span class="kw">scale</span><span class="op">$</span><span class="fu">refine_names</span>(<span class="st">"C"</span>)
-  <span class="kw">input</span> <span class="op">*</span> <span class="kw">scale</span><span class="op">$</span><span class="fu">align_as</span>(<span class="kw">input</span>)
-}
-
-<span class="kw">num_channels</span> <span class="op">&lt;-</span> <span class="fl">3</span>
-<span class="kw">scale</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">num_channels</span>, names=<span class="st">'C'</span>)
-<span class="kw">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">32</span>, <span class="fl">128</span>, <span class="fl">128</span>, <span class="kw">num_channels</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'C'</span>))
-<span class="kw">more_imgs</span> <span class="op">=</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">32</span>, <span class="kw">num_channels</span>, <span class="fl">128</span>, <span class="fl">128</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>))
-<span class="kw">videos</span> <span class="op">=</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">3</span>, <span class="kw">num_channels</span>, <span class="fl">128</span>, <span class="fl">128</span>, <span class="fl">128</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'D'</span>))
-
-<span class="co"># scale_channels is agnostic to the dimension order of the input</span>
-<span class="fu">scale_channels</span>(<span class="kw">imgs</span>, <span class="kw">scale</span>)
-<span class="fu">scale_channels</span>(<span class="kw">more_imgs</span>, <span class="kw">scale</span>)
-<span class="fu">scale_channels</span>(<span class="kw">videos</span>, <span class="kw">scale</span>)
-</pre></div>
-</div>
-<div id="warning" class="section level3">
-<h3 class="hasAnchor">
-<a href="#warning" class="anchor"></a>Warning:</h3>
-<p>The named tensor API is experimental and subject to change.</p>
-</div>
-</div>
-<div id="align_to" class="section level2">
-<h2 class="hasAnchor">
-<a href="#align_to" class="anchor"></a>align_to</h2>
-<p>Permutes the dimensions of the <code>self</code> tensor to match the order specified in <code>names</code>, adding size-one dims for any new names.</p>
-<p>All of the dims of <code>self</code> must be named in order to use this method. The resulting tensor is a view on the original tensor.</p>
-<p>All dimension names of <code>self</code> must be present in <code>names</code>. <code>names</code> may contain additional names that are not in <code>self$names</code>; the output tensor has a size-one dimension for each of those new names.</p>
-<div id="arguments" class="section level3">
-<h3 class="hasAnchor">
-<a href="#arguments" class="anchor"></a>Arguments:</h3>
-<ul>
-<li>names (iterable of str): The desired dimension ordering of the output tensor. May contain up to one Ellipsis that is expanded to all unmentioned dim names of <code>self</code>.</li>
-</ul>
-</div>
-<div id="examples-1" class="section level3">
-<h3 class="hasAnchor">
-<a href="#examples-1" class="anchor"></a>Examples:</h3>
-<div id="warning-1" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-1" class="anchor"></a>Warning:</h4>
-<p>The named tensor API is experimental and subject to change.</p>
-</div>
-</div>
-</div>
-<div id="all" class="section level2">
-<h2 class="hasAnchor">
-<a href="#all" class="anchor"></a>all</h2>
-<p>all() -&gt; bool</p>
-<p>Returns TRUE if all elements in the tensor are TRUE, FALSE otherwise.</p>
-<div id="examples-2" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-2" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb3"><pre class="downlit">
-<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">1</span>, <span class="fl">2</span>)<span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span>())
-<span class="kw">a</span>
-<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>()
-</pre></div>
-<p>all(dim, keepdim=FALSE, out=NULL) -&gt; Tensor</p>
-<p>Returns TRUE if all elements in each row of the tensor in the given dimension <code>dim</code> are TRUE, FALSE otherwise.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size as <code>input</code> except in the dimension <code>dim</code> where it is of size 1. Otherwise, <code>dim</code> is squeezed (see <code>?torch_squeeze()),</code> resulting in the output tensor having 1 fewer dimension than <code>input</code>.</p>
-</div>
-<div id="arguments-1" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-1" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dim (int): the dimension to reduce</li>
-<li>keepdim (bool): whether the output tensor has <code>dim</code> retained or not</li>
-<li>out (Tensor, optional): the output tensor</li>
-</ul>
-</div>
-<div id="examples-3" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-3" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb4"><pre class="downlit">
-<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">4</span>, <span class="fl">2</span>)<span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span>())
-<span class="kw">a</span>
-<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>(dim=<span class="fl">2</span>)
-<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>(dim=<span class="fl">1</span>)
-</pre></div>
-</div>
-</div>
-<div id="allclose" class="section level2">
-<h2 class="hasAnchor">
-<a href="#allclose" class="anchor"></a>allclose</h2>
-<p>allclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_allclose.html">?torch_allclose</a></code></p>
-</div>
-<div id="angle" class="section level2">
-<h2 class="hasAnchor">
-<a href="#angle" class="anchor"></a>angle</h2>
-<p>angle() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_angle.html">?torch_angle</a></code></p>
-</div>
-<div id="any" class="section level2">
-<h2 class="hasAnchor">
-<a href="#any" class="anchor"></a>any</h2>
-<p>any() -&gt; bool</p>
-<p>Returns TRUE if any elements in the tensor are TRUE, FALSE otherwise.</p>
-<div id="examples-4" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-4" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb5"><pre class="downlit">
-<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">1</span>, <span class="fl">2</span>)<span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span>())
-<span class="kw">a</span>
-<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span>()
-</pre></div>
-<p>any(dim, keepdim=FALSE, out=NULL) -&gt; Tensor</p>
-<p>Returns TRUE if any elements in each row of the tensor in the given dimension <code>dim</code> are TRUE, FALSE otherwise.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size as <code>input</code> except in the dimension <code>dim</code> where it is of size 1. Otherwise, <code>dim</code> is squeezed (see <code>?torch_squeeze()),</code> resulting in the output tensor having 1 fewer dimension than <code>input</code>.</p>
-</div>
-<div id="arguments-2" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-2" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dim (int): the dimension to reduce</li>
-<li>keepdim (bool): whether the output tensor has <code>dim</code> retained or not</li>
-<li>out (Tensor, optional): the output tensor</li>
-</ul>
-</div>
-<div id="examples-5" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-5" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb6"><pre class="downlit">
-<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, <span class="fl">2</span>) <span class="op">&lt;</span> <span class="fl">0</span>
-<span class="kw">a</span>
-<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span>(<span class="fl">2</span>)
-<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span>(<span class="fl">1</span>)
-</pre></div>
-</div>
-</div>
-<div id="apply_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#apply_" class="anchor"></a>apply_</h2>
-<p>apply_(callable) -&gt; Tensor</p>
-<p>Applies the function <code>callable</code> to each element in the tensor, replacing each element with the value returned by <code>callable</code>.</p>
-<div id="note" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note" class="anchor"></a>Note:</h4>
-<p>This function only works with CPU tensors and should not be used in code sections that require high performance.</p>
-</div>
-</div>
-<div id="argmax" class="section level2">
-<h2 class="hasAnchor">
-<a href="#argmax" class="anchor"></a>argmax</h2>
-<p>argmax(dim=NULL, keepdim=FALSE) -&gt; LongTensor</p>
-<p>See <code><a href="../../reference/torch_argmax.html">?torch_argmax</a></code></p>
-</div>
-<div id="argmin" class="section level2">
-<h2 class="hasAnchor">
-<a href="#argmin" class="anchor"></a>argmin</h2>
-<p>argmin(dim=NULL, keepdim=FALSE) -&gt; LongTensor</p>
-<p>See <code><a href="../../reference/torch_argmin.html">?torch_argmin</a></code></p>
-</div>
-<div id="argsort" class="section level2">
-<h2 class="hasAnchor">
-<a href="#argsort" class="anchor"></a>argsort</h2>
-<p>argsort(dim=-1, descending=FALSE) -&gt; LongTensor</p>
-<p>See <code><a href="../../reference/torch_argsort.html">?torch_argsort</a></code></p>
-</div>
-<div id="as_strided" class="section level2">
-<h2 class="hasAnchor">
-<a href="#as_strided" class="anchor"></a>as_strided</h2>
-<p>as_strided(size, stride, storage_offset=0) -&gt; Tensor</p>
-<p>See [torch_as_strided()]</p>
-</div>
-<div id="as_subclass" class="section level2">
-<h2 class="hasAnchor">
-<a href="#as_subclass" class="anchor"></a>as_subclass</h2>
-<p>as_subclass(cls) -&gt; Tensor</p>
-<p>Makes a <code>cls</code> instance with the same data pointer as <code>self</code>. Changes in the output mirror changes in <code>self</code>, and the output stays attached to the autograd graph. <code>cls</code> must be a subclass of <code>Tensor</code>.</p>
-</div>
-<div id="asin" class="section level2">
-<h2 class="hasAnchor">
-<a href="#asin" class="anchor"></a>asin</h2>
-<p>asin() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_asin.html">?torch_asin</a></code></p>
-</div>
-<div id="asin_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#asin_" class="anchor"></a>asin_</h2>
-<p>asin_() -&gt; Tensor</p>
-<p>In-place version of <code>$asin</code></p>
-</div>
-<div id="asinh" class="section level2">
-<h2 class="hasAnchor">
-<a href="#asinh" class="anchor"></a>asinh</h2>
-<p>asinh() -&gt; Tensor</p>
-<p>See <code>?torch_asinh</code></p>
-</div>
-<div id="asinh_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#asinh_" class="anchor"></a>asinh_</h2>
-<p>asinh_() -&gt; Tensor</p>
-<p>In-place version of <code>$asinh</code></p>
-</div>
-<div id="atan" class="section level2">
-<h2 class="hasAnchor">
-<a href="#atan" class="anchor"></a>atan</h2>
-<p>atan() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_atan.html">?torch_atan</a></code></p>
-</div>
-<div id="atan2" class="section level2">
-<h2 class="hasAnchor">
-<a href="#atan2" class="anchor"></a>atan2</h2>
-<p>atan2(other) -&gt; Tensor</p>
-<p>See [torch_atan2()]</p>
-</div>
-<div id="atan2_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#atan2_" class="anchor"></a>atan2_</h2>
-<p>atan2_(other) -&gt; Tensor</p>
-<p>In-place version of <code>$atan2</code></p>
-</div>
-<div id="atan_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#atan_" class="anchor"></a>atan_</h2>
-<p>atan_() -&gt; Tensor</p>
-<p>In-place version of <code>$atan</code></p>
-</div>
-<div id="atanh" class="section level2">
-<h2 class="hasAnchor">
-<a href="#atanh" class="anchor"></a>atanh</h2>
-<p>atanh() -&gt; Tensor</p>
-<p>See <code>?torch_atanh</code></p>
-</div>
-<div id="atanh_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#atanh_" class="anchor"></a>atanh_</h2>
-<p>In-place version of <code>$atanh</code></p>
-</div>
-<div id="backward" class="section level2">
-<h2 class="hasAnchor">
-<a href="#backward" class="anchor"></a>backward</h2>
-<p>Computes the gradient of current tensor w.r.t. graph leaves.</p>
-<p>The graph is differentiated using the chain rule. If the tensor is non-scalar (i.e. its data has more than one element) and requires gradient, the function additionally requires specifying <code>gradient</code>. It should be a tensor of matching type and location, that contains the gradient of the differentiated function w.r.t. <code>self</code>.</p>
-<p>This function accumulates gradients in the leaves - you might need to zero <code>$grad</code> attributes or set them to <code>NULL</code> before calling it. See <code>Default gradient layouts&lt;default-grad-layouts&gt;</code> for details on the memory layout of accumulated gradients.</p>
-<div id="arguments-3" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-3" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>gradient (Tensor or NULL): Gradient w.r.t. the tensor. If it is a tensor, it will be automatically converted to a Tensor that does not require grad unless <code>create_graph</code> is TRUE. NULL values can be specified for scalar Tensors or ones that don’t require grad. If a NULL value would be acceptable then this argument is optional.</li>
-<li>retain_graph (bool, optional): If <code>FALSE</code>, the graph used to compute the grads will be freed. Note that in nearly all cases setting this option to TRUE is not needed and often can be worked around in a much more efficient way. Defaults to the value of <code>create_graph</code>.</li>
-<li>create_graph (bool, optional): If <code>TRUE</code>, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to <code>FALSE</code>.</li>
-</ul>
-</div>
-</div>
-<div id="baddbmm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#baddbmm" class="anchor"></a>baddbmm</h2>
-<p>baddbmm(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_baddbmm.html">?torch_baddbmm</a></code></p>
-</div>
-<div id="baddbmm_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#baddbmm_" class="anchor"></a>baddbmm_</h2>
-<p>baddbmm_(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
-<p>In-place version of <code>$baddbmm</code></p>
-</div>
-<div id="bernoulli" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bernoulli" class="anchor"></a>bernoulli</h2>
-<p>bernoulli(*, generator=NULL) -&gt; Tensor</p>
-<p>Returns a result tensor where each <span class="math inline">\(\texttt{result[i]}\)</span> is independently sampled from <span class="math inline">\(\text{Bernoulli}(\texttt{self[i]})\)</span>. <code>self</code> must have floating point <code>dtype</code>, and the result will have the same <code>dtype</code>.</p>
-<p>See <code><a href="../../reference/torch_bernoulli.html">?torch_bernoulli</a></code></p>
-</div>
-<div id="bernoulli_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bernoulli_" class="anchor"></a>bernoulli_</h2>
-<p>bernoulli_(p=0.5, *, generator=NULL) -&gt; Tensor</p>
-<p>Fills each location of <code>self</code> with an independent sample from <span class="math inline">\(\text{Bernoulli}(\texttt{p})\)</span>. <code>self</code> can have integral <code>dtype</code>.</p>
-<p>bernoulli_(p_tensor, *, generator=NULL) -&gt; Tensor</p>
-<p><code>p_tensor</code> should be a tensor containing probabilities to be used for drawing the binary random number.</p>
-<p>The <span class="math inline">\(\text{i}^{th}\)</span> element of <code>self</code> tensor will be set to a value sampled from <span class="math inline">\(\text{Bernoulli}(\texttt{p\_tensor[i]})\)</span>.</p>
-<p><code>self</code> can have integral <code>dtype</code>, but <code>p_tensor</code> must have floating point <code>dtype</code>.</p>
-<p>See also <code>$bernoulli</code> and <code><a href="../../reference/torch_bernoulli.html">?torch_bernoulli</a></code></p>
-</div>
-<div id="bfloat16" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bfloat16" class="anchor"></a>bfloat16</h2>
-<p>bfloat16(memory_format=torch_preserve_format) -&gt; Tensor <code>self$bfloat16()</code> is equivalent to <code>self$to(torch_bfloat16)</code>. See [to()].</p>
-<div id="arguments-4" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-4" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="bincount" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bincount" class="anchor"></a>bincount</h2>
-<p>bincount(weights=NULL, minlength=0) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_bincount.html">?torch_bincount</a></code></p>
-</div>
-<div id="bitwise_and" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_and" class="anchor"></a>bitwise_and</h2>
-<p>bitwise_and() -&gt; Tensor</p>
-<p>See [torch_bitwise_and()]</p>
-</div>
-<div id="bitwise_and_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_and_" class="anchor"></a>bitwise_and_</h2>
-<p>bitwise_and_() -&gt; Tensor</p>
-<p>In-place version of <code>$bitwise_and</code></p>
-</div>
-<div id="bitwise_not" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_not" class="anchor"></a>bitwise_not</h2>
-<p>bitwise_not() -&gt; Tensor</p>
-<p>See [torch_bitwise_not()]</p>
-</div>
-<div id="bitwise_not_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_not_" class="anchor"></a>bitwise_not_</h2>
-<p>bitwise_not_() -&gt; Tensor</p>
-<p>In-place version of <code>$bitwise_not</code></p>
-</div>
-<div id="bitwise_or" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_or" class="anchor"></a>bitwise_or</h2>
-<p>bitwise_or() -&gt; Tensor</p>
-<p>See [torch_bitwise_or()]</p>
-</div>
-<div id="bitwise_or_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_or_" class="anchor"></a>bitwise_or_</h2>
-<p>bitwise_or_() -&gt; Tensor</p>
-<p>In-place version of <code>$bitwise_or</code></p>
-</div>
-<div id="bitwise_xor" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_xor" class="anchor"></a>bitwise_xor</h2>
-<p>bitwise_xor() -&gt; Tensor</p>
-<p>See [torch_bitwise_xor()]</p>
-</div>
-<div id="bitwise_xor_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_xor_" class="anchor"></a>bitwise_xor_</h2>
-<p>bitwise_xor_() -&gt; Tensor</p>
-<p>In-place version of <code>$bitwise_xor</code></p>
-</div>
-<div id="bmm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bmm" class="anchor"></a>bmm</h2>
-<p>bmm(batch2) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_bmm.html">?torch_bmm</a></code></p>
-</div>
-<div id="bool" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bool" class="anchor"></a>bool</h2>
-<p>bool(memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p><code>self$bool()</code> is equivalent to <code>self$to(torch_bool)</code>. See [to()].</p>
-<div id="arguments-5" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-5" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="byte" class="section level2">
-<h2 class="hasAnchor">
-<a href="#byte" class="anchor"></a>byte</h2>
-<p>byte(memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p><code>self$byte()</code> is equivalent to <code>self$to(torch_uint8)</code>. See [to()].</p>
-<div id="arguments-6" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-6" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="cauchy_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cauchy_" class="anchor"></a>cauchy_</h2>
-<p>cauchy_(median=0, sigma=1, *, generator=NULL) -&gt; Tensor</p>
-<p>Fills the tensor with numbers drawn from the Cauchy distribution:</p>
-<p><span class="math display">\[
-f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2}
-\]</span></p>
-</div>
-<div id="ceil" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ceil" class="anchor"></a>ceil</h2>
-<p>ceil() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_ceil.html">?torch_ceil</a></code></p>
-</div>
-<div id="ceil_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ceil_" class="anchor"></a>ceil_</h2>
-<p>ceil_() -&gt; Tensor</p>
-<p>In-place version of <code>$ceil</code></p>
-</div>
-<div id="char" class="section level2">
-<h2 class="hasAnchor">
-<a href="#char" class="anchor"></a>char</h2>
-<p>char(memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p><code>self$char()</code> is equivalent to <code>self$to(torch_int8)</code>. See [to()].</p>
-<div id="arguments-7" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-7" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="cholesky" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cholesky" class="anchor"></a>cholesky</h2>
-<p>cholesky(upper=FALSE) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_cholesky.html">?torch_cholesky</a></code></p>
-</div>
-<div id="cholesky_inverse" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cholesky_inverse" class="anchor"></a>cholesky_inverse</h2>
-<p>cholesky_inverse(upper=FALSE) -&gt; Tensor</p>
-<p>See [torch_cholesky_inverse()]</p>
-</div>
-<div id="cholesky_solve" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cholesky_solve" class="anchor"></a>cholesky_solve</h2>
-<p>cholesky_solve(input2, upper=FALSE) -&gt; Tensor</p>
-<p>See [torch_cholesky_solve()]</p>
-</div>
-<div id="chunk" class="section level2">
-<h2 class="hasAnchor">
-<a href="#chunk" class="anchor"></a>chunk</h2>
-<p>chunk(chunks, dim=0) -&gt; List of Tensors</p>
-<p>See <code><a href="../../reference/torch_chunk.html">?torch_chunk</a></code></p>
-</div>
-<div id="clamp" class="section level2">
-<h2 class="hasAnchor">
-<a href="#clamp" class="anchor"></a>clamp</h2>
-<p>clamp(min, max) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_clamp.html">?torch_clamp</a></code></p>
-</div>
-<div id="clamp_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#clamp_" class="anchor"></a>clamp_</h2>
-<p>clamp_(min, max) -&gt; Tensor</p>
-<p>In-place version of <code>$clamp</code></p>
-</div>
-<div id="clone" class="section level2">
-<h2 class="hasAnchor">
-<a href="#clone" class="anchor"></a>clone</h2>
-<p>clone(memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p>Returns a copy of the <code>self</code> tensor. The copy has the same size and data type as <code>self</code>.</p>
-<div id="note-1" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-1" class="anchor"></a>Note:</h4>
-<p>Unlike <code>copy_()</code>, this function is recorded in the computation graph. Gradients propagating to the cloned tensor will propagate to the original tensor.</p>
-</div>
-<div id="arguments-8" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-8" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="conj" class="section level2">
-<h2 class="hasAnchor">
-<a href="#conj" class="anchor"></a>conj</h2>
-<p>conj() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_conj.html">?torch_conj</a></code></p>
-</div>
-<div id="contiguous" class="section level2">
-<h2 class="hasAnchor">
-<a href="#contiguous" class="anchor"></a>contiguous</h2>
-<p>contiguous(memory_format=torch_contiguous_format) -&gt; Tensor</p>
-<p>Returns a contiguous in memory tensor containing the same data as <code>self</code> tensor. If <code>self</code> tensor is already in the specified memory format, this function returns the <code>self</code> tensor.</p>
-<div id="arguments-9" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-9" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_contiguous_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="copy_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#copy_" class="anchor"></a>copy_</h2>
-<p>copy_(src, non_blocking=FALSE) -&gt; Tensor</p>
-<p>Copies the elements from <code>src</code> into <code>self</code> tensor and returns <code>self</code>.</p>
-<p>The <code>src</code> tensor must be :ref:<code>broadcastable &lt;broadcasting-semantics&gt;</code> with the <code>self</code> tensor. It may be of a different data type or reside on a different device.</p>
-<div id="arguments-10" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-10" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>src (Tensor): the source tensor to copy from</li>
-<li>non_blocking (bool): if <code>TRUE</code> and this copy is between CPU and GPU,</li>
-<li>the copy may occur asynchronously with respect to the host. For other</li>
-<li>cases, this argument has no effect.</li>
-</ul>
-</div>
-</div>
-<div id="cos" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cos" class="anchor"></a>cos</h2>
-<p>cos() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_cos.html">?torch_cos</a></code></p>
-</div>
-<div id="cos_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cos_" class="anchor"></a>cos_</h2>
-<p>cos_() -&gt; Tensor</p>
-<p>In-place version of <code>$cos</code></p>
-</div>
-<div id="cosh" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cosh" class="anchor"></a>cosh</h2>
-<p>cosh() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_cosh.html">?torch_cosh</a></code></p>
-</div>
-<div id="cosh_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cosh_" class="anchor"></a>cosh_</h2>
-<p>cosh_() -&gt; Tensor</p>
-<p>In-place version of <code>$cosh</code></p>
-</div>
-<div id="cpu" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cpu" class="anchor"></a>cpu</h2>
-<p>cpu(memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p>Returns a copy of this object in CPU memory.</p>
-<p>If this object is already in CPU memory and on the correct device, then no copy is performed and the original object is returned.</p>
-<div id="arguments-11" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-11" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="cross" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cross" class="anchor"></a>cross</h2>
-<p>cross(other, dim=-1) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_cross.html">?torch_cross</a></code></p>
-</div>
-<div id="cuda" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cuda" class="anchor"></a>cuda</h2>
-<p>cuda(device=NULL, non_blocking=FALSE, memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p>Returns a copy of this object in CUDA memory.</p>
-<p>If this object is already in CUDA memory and on the correct device, then no copy is performed and the original object is returned.</p>
-<div id="arguments-12" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-12" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>device (<code>torch_device</code>): The destination GPU device. Defaults to the current CUDA device.</li>
-<li>non_blocking (bool): If <code>TRUE</code> and the source is in pinned memory, the copy will be asynchronous with respect to the host. Otherwise, the argument has no effect. Default: <code>FALSE</code>.</li>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="cummax" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cummax" class="anchor"></a>cummax</h2>
-<p>cummax(dim) -&gt; (Tensor, Tensor)</p>
-<p>See <code><a href="../../reference/torch_cummax.html">?torch_cummax</a></code></p>
-</div>
-<div id="cummin" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cummin" class="anchor"></a>cummin</h2>
-<p>cummin(dim) -&gt; (Tensor, Tensor)</p>
-<p>See <code><a href="../../reference/torch_cummin.html">?torch_cummin</a></code></p>
-</div>
-<div id="cumprod" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cumprod" class="anchor"></a>cumprod</h2>
-<p>cumprod(dim, dtype=NULL) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_cumprod.html">?torch_cumprod</a></code></p>
-</div>
-<div id="cumsum" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cumsum" class="anchor"></a>cumsum</h2>
-<p>cumsum(dim, dtype=NULL) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_cumsum.html">?torch_cumsum</a></code></p>
-</div>
-<div id="data_ptr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#data_ptr" class="anchor"></a>data_ptr</h2>
-<p>data_ptr() -&gt; int</p>
-<p>Returns the address of the first element of <code>self</code> tensor.</p>
-</div>
-<div id="deg2rad" class="section level2">
-<h2 class="hasAnchor">
-<a href="#deg2rad" class="anchor"></a>deg2rad</h2>
-<p>deg2rad() -&gt; Tensor</p>
-<p>See [torch_deg2rad()]</p>
-</div>
-<div id="deg2rad_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#deg2rad_" class="anchor"></a>deg2rad_</h2>
-<p>deg2rad_() -&gt; Tensor</p>
-<p>In-place version of <code>$deg2rad</code></p>
-</div>
-<div id="dense_dim" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dense_dim" class="anchor"></a>dense_dim</h2>
-<p>dense_dim() -&gt; int</p>
-<p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns the number of dense dimensions. Otherwise, this throws an error.</p>
-<p>See also <code>$sparse_dim</code>.</p>
-</div>
-<div id="dequantize" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dequantize" class="anchor"></a>dequantize</h2>
-<p>dequantize() -&gt; Tensor</p>
-<p>Given a quantized Tensor, dequantize it and return the dequantized float Tensor.</p>
-</div>
-<div id="det" class="section level2">
-<h2 class="hasAnchor">
-<a href="#det" class="anchor"></a>det</h2>
-<p>det() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_det.html">?torch_det</a></code></p>
-</div>
-<div id="detach" class="section level2">
-<h2 class="hasAnchor">
-<a href="#detach" class="anchor"></a>detach</h2>
-<p>Returns a new Tensor, detached from the current graph.</p>
-<p>The result will never require gradient.</p>
-<div id="note-2" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-2" class="anchor"></a>Note:</h4>
-<p>Returned Tensor shares the same storage with the original one. In-place modifications on either of them will be seen, and may trigger errors in correctness checks. IMPORTANT NOTE: Previously, in-place size / stride / storage changes (such as <code>resize_</code> / <code>resize_as_</code> / <code>set_</code> / <code>transpose_</code>) to the returned tensor also update the original tensor. Now, these in-place changes will not update the original tensor anymore, and will instead trigger an error. For sparse tensors: In-place indices / values changes (such as <code>zero_</code> / <code>copy_</code> / <code>add_</code>) to the returned tensor will not update the original tensor anymore, and will instead trigger an error.</p>
-</div>
-</div>
-<div id="detach_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#detach_" class="anchor"></a>detach_</h2>
-<p>Detaches the Tensor from the graph that created it, making it a leaf. Views cannot be detached in-place.</p>
-</div>
-<div id="device" class="section level2">
-<h2 class="hasAnchor">
-<a href="#device" class="anchor"></a>device</h2>
-<p>Is the <code>torch_device</code> where this Tensor is.</p>
-</div>
-<div id="diag" class="section level2">
-<h2 class="hasAnchor">
-<a href="#diag" class="anchor"></a>diag</h2>
-<p>diag(diagonal=0) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_diag.html">?torch_diag</a></code></p>
-</div>
-<div id="diag_embed" class="section level2">
-<h2 class="hasAnchor">
-<a href="#diag_embed" class="anchor"></a>diag_embed</h2>
-<p>diag_embed(offset=0, dim1=-2, dim2=-1) -&gt; Tensor</p>
-<p>See [torch_diag_embed()]</p>
-</div>
-<div id="diagflat" class="section level2">
-<h2 class="hasAnchor">
-<a href="#diagflat" class="anchor"></a>diagflat</h2>
-<p>diagflat(offset=0) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_diagflat.html">?torch_diagflat</a></code></p>
-</div>
-<div id="diagonal" class="section level2">
-<h2 class="hasAnchor">
-<a href="#diagonal" class="anchor"></a>diagonal</h2>
-<p>diagonal(offset=0, dim1=0, dim2=1) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_diagonal.html">?torch_diagonal</a></code></p>
-</div>
-<div id="digamma" class="section level2">
-<h2 class="hasAnchor">
-<a href="#digamma" class="anchor"></a>digamma</h2>
-<p>digamma() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_digamma.html">?torch_digamma</a></code></p>
-</div>
-<div id="digamma_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#digamma_" class="anchor"></a>digamma_</h2>
-<p>digamma_() -&gt; Tensor</p>
-<p>In-place version of <code>$digamma</code></p>
-</div>
-<div id="dim" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dim" class="anchor"></a>dim</h2>
-<p>dim() -&gt; int</p>
-<p>Returns the number of dimensions of <code>self</code> tensor.</p>
-</div>
-<div id="dist" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dist" class="anchor"></a>dist</h2>
-<p>dist(other, p=2) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_dist.html">?torch_dist</a></code></p>
-</div>
-<div id="div" class="section level2">
-<h2 class="hasAnchor">
-<a href="#div" class="anchor"></a>div</h2>
-<p>div(value) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_div.html">?torch_div</a></code></p>
-</div>
-<div id="div_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#div_" class="anchor"></a>div_</h2>
-<p>div_(value) -&gt; Tensor</p>
-<p>In-place version of <code>$div</code></p>
-</div>
-<div id="dot" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dot" class="anchor"></a>dot</h2>
-<p>dot(tensor2) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_dot.html">?torch_dot</a></code></p>
-</div>
-<div id="double" class="section level2">
-<h2 class="hasAnchor">
-<a href="#double" class="anchor"></a>double</h2>
-<p>double(memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p><code>self$double()</code> is equivalent to <code>self$to(torch_float64)</code>. See [to()].</p>
-<div id="arguments-13" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-13" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="eig" class="section level2">
-<h2 class="hasAnchor">
-<a href="#eig" class="anchor"></a>eig</h2>
-<p>eig(eigenvectors=FALSE) -&gt; (Tensor, Tensor)</p>
-<p>See <code><a href="../../reference/torch_eig.html">?torch_eig</a></code></p>
-</div>
-<div id="element_size" class="section level2">
-<h2 class="hasAnchor">
-<a href="#element_size" class="anchor"></a>element_size</h2>
-<p>element_size() -&gt; int</p>
-<p>Returns the size in bytes of an individual element.</p>
-<div id="examples-6" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-6" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb7"><pre class="downlit">
-<span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>))<span class="op">$</span><span class="fu">element_size</span>()
-</pre></div>
-</div>
-</div>
-<div id="eq" class="section level2">
-<h2 class="hasAnchor">
-<a href="#eq" class="anchor"></a>eq</h2>
-<p>eq(other) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_eq.html">?torch_eq</a></code></p>
-</div>
-<div id="eq_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#eq_" class="anchor"></a>eq_</h2>
-<p>eq_(other) -&gt; Tensor</p>
-<p>In-place version of <code>$eq</code></p>
-</div>
-<div id="equal" class="section level2">
-<h2 class="hasAnchor">
-<a href="#equal" class="anchor"></a>equal</h2>
-<p>equal(other) -&gt; bool</p>
-<p>See <code><a href="../../reference/torch_equal.html">?torch_equal</a></code></p>
-</div>
-<div id="erf" class="section level2">
-<h2 class="hasAnchor">
-<a href="#erf" class="anchor"></a>erf</h2>
-<p>erf() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_erf.html">?torch_erf</a></code></p>
-</div>
-<div id="erf_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#erf_" class="anchor"></a>erf_</h2>
-<p>erf_() -&gt; Tensor</p>
-<p>In-place version of <code>$erf</code></p>
-</div>
-<div id="erfc" class="section level2">
-<h2 class="hasAnchor">
-<a href="#erfc" class="anchor"></a>erfc</h2>
-<p>erfc() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_erfc.html">?torch_erfc</a></code></p>
-</div>
-<div id="erfc_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#erfc_" class="anchor"></a>erfc_</h2>
-<p>erfc_() -&gt; Tensor</p>
-<p>In-place version of <code>$erfc</code></p>
-</div>
-<div id="erfinv" class="section level2">
-<h2 class="hasAnchor">
-<a href="#erfinv" class="anchor"></a>erfinv</h2>
-<p>erfinv() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_erfinv.html">?torch_erfinv</a></code></p>
-</div>
-<div id="erfinv_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#erfinv_" class="anchor"></a>erfinv_</h2>
-<p>erfinv_() -&gt; Tensor</p>
-<p>In-place version of <code>$erfinv</code></p>
-</div>
-<div id="exp" class="section level2">
-<h2 class="hasAnchor">
-<a href="#exp" class="anchor"></a>exp</h2>
-<p>exp() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_exp.html">?torch_exp</a></code></p>
-</div>
-<div id="exp_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#exp_" class="anchor"></a>exp_</h2>
-<p>exp_() -&gt; Tensor</p>
-<p>In-place version of <code>$exp</code></p>
-</div>
-<div id="expand" class="section level2">
-<h2 class="hasAnchor">
-<a href="#expand" class="anchor"></a>expand</h2>
-<p>expand(*sizes) -&gt; Tensor</p>
-<p>Returns a new view of the <code>self</code> tensor with singleton dimensions expanded to a larger size.</p>
-<p>Passing -1 as the size for a dimension means not changing the size of that dimension.</p>
-<p>Tensor can be also expanded to a larger number of dimensions, and the new ones will be appended at the front. For the new dimensions, the size cannot be set to -1.</p>
-<p>Expanding a tensor does not allocate new memory, but only creates a new view on the existing tensor where a dimension of size one is expanded to a larger size by setting the <code>stride</code> to 0. Any dimension of size 1 can be expanded to an arbitrary value without allocating new memory.</p>
-<div id="arguments-14" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-14" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>sizes (torch_Size or int…): the desired expanded size</li>
-</ul>
-</div>
-<div id="warning-2" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-2" class="anchor"></a>Warning:</h4>
-<p>More than one element of an expanded tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first.</p>
-</div>
-<div id="examples-7" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-7" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb8"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span>), ncol = <span class="fl">1</span>))
-<span class="kw">x</span><span class="op">$</span><span class="fu">size</span>()
-<span class="kw">x</span><span class="op">$</span><span class="fu">expand</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">3</span>, <span class="fl">4</span>))
-<span class="kw">x</span><span class="op">$</span><span class="fu">expand</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="op">-</span><span class="fl">1</span>, <span class="fl">4</span>))  <span class="co"># -1 means not changing the size of that dimension</span>
-</pre></div>
-</div>
-</div>
-<div id="expand_as" class="section level2">
-<h2 class="hasAnchor">
-<a href="#expand_as" class="anchor"></a>expand_as</h2>
-<p>expand_as(other) -&gt; Tensor</p>
-<p>Expand this tensor to the same size as <code>other</code>. <code>self$expand_as(other)</code> is equivalent to <code>self$expand(other.size())</code>.</p>
-<p>Please see <code>$expand</code> for more information about <code>expand</code>.</p>
-<div id="arguments-15" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-15" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>other (`$): The result tensor has the same size</li>
-<li>as <code>other</code>.</li>
-</ul>
-</div>
-</div>
-<div id="expm1" class="section level2">
-<h2 class="hasAnchor">
-<a href="#expm1" class="anchor"></a>expm1</h2>
-<p>expm1() -&gt; Tensor</p>
-<p>See [torch_expm1()]</p>
-</div>
-<div id="expm1_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#expm1_" class="anchor"></a>expm1_</h2>
-<p>expm1_() -&gt; Tensor</p>
-<p>In-place version of <code>$expm1</code></p>
-</div>
-<div id="exponential_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#exponential_" class="anchor"></a>exponential_</h2>
-<p>exponential_(lambd=1, *, generator=NULL) -&gt; Tensor</p>
-<p>Fills <code>self</code> tensor with elements drawn from the exponential distribution:</p>
-<p><span class="math display">\[
-f(x) = \lambda e^{-\lambda x}
-\]</span></p>
-</div>
-<div id="fft" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fft" class="anchor"></a>fft</h2>
-<p>fft(signal_ndim, normalized=FALSE) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_fft.html">?torch_fft</a></code></p>
-</div>
-<div id="fill_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fill_" class="anchor"></a>fill_</h2>
-<p>fill_(value) -&gt; Tensor</p>
-<p>Fills <code>self</code> tensor with the specified value.</p>
-</div>
-<div id="fill_diagonal_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fill_diagonal_" class="anchor"></a>fill_diagonal_</h2>
-<p>fill_diagonal_(fill_value, wrap=FALSE) -&gt; Tensor</p>
-<p>Fill the main diagonal of a tensor that has at least 2-dimensions. When dims&gt;2, all dimensions of input must be of equal length. This function modifies the input tensor in-place, and returns the input tensor.</p>
-<div id="arguments-16" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-16" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>fill_value (Scalar): the fill value</li>
-<li>wrap (bool): the diagonal ‘wrapped’ after N columns for tall matrices.</li>
-</ul>
-</div>
-<div id="examples-8" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-8" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb9"><pre class="downlit">
-<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">3</span>, <span class="fl">3</span>)
-<span class="kw">a</span><span class="op">$</span><span class="fu">fill_diagonal_</span>(<span class="fl">5</span>)
-<span class="kw">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">7</span>, <span class="fl">3</span>)
-<span class="kw">b</span><span class="op">$</span><span class="fu">fill_diagonal_</span>(<span class="fl">5</span>)
-<span class="kw">c</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">7</span>, <span class="fl">3</span>)
-<span class="kw">c</span><span class="op">$</span><span class="fu">fill_diagonal_</span>(<span class="fl">5</span>, wrap=<span class="fl">TRUE</span>)
-</pre></div>
-</div>
-</div>
-<div id="flatten" class="section level2">
-<h2 class="hasAnchor">
-<a href="#flatten" class="anchor"></a>flatten</h2>
-<p>flatten(input, start_dim=0, end_dim=-1) -&gt; Tensor</p>
-<p>see <code><a href="../../reference/torch_flatten.html">?torch_flatten</a></code></p>
-</div>
-<div id="flip" class="section level2">
-<h2 class="hasAnchor">
-<a href="#flip" class="anchor"></a>flip</h2>
-<p>flip(dims) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_flip.html">?torch_flip</a></code></p>
-</div>
-<div id="fliplr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fliplr" class="anchor"></a>fliplr</h2>
-<p>fliplr() -&gt; Tensor</p>
-<p>See <code>?torch_fliplr</code></p>
-</div>
-<div id="flipud" class="section level2">
-<h2 class="hasAnchor">
-<a href="#flipud" class="anchor"></a>flipud</h2>
-<p>flipud() -&gt; Tensor</p>
-<p>See <code>?torch_flipud</code></p>
-</div>
-<div id="float" class="section level2">
-<h2 class="hasAnchor">
-<a href="#float" class="anchor"></a>float</h2>
-<p>float(memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p><code>self$float()</code> is equivalent to <code>self$to(torch_float32)</code>. See [to()].</p>
-<div id="arguments-17" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-17" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="floor" class="section level2">
-<h2 class="hasAnchor">
-<a href="#floor" class="anchor"></a>floor</h2>
-<p>floor() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_floor.html">?torch_floor</a></code></p>
-</div>
-<div id="floor_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#floor_" class="anchor"></a>floor_</h2>
-<p>floor_() -&gt; Tensor</p>
-<p>In-place version of <code>$floor</code></p>
-</div>
-<div id="floor_divide" class="section level2">
-<h2 class="hasAnchor">
-<a href="#floor_divide" class="anchor"></a>floor_divide</h2>
-<p>floor_divide(value) -&gt; Tensor</p>
-<p>See [torch_floor_divide()]</p>
-</div>
-<div id="floor_divide_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#floor_divide_" class="anchor"></a>floor_divide_</h2>
-<p>floor_divide_(value) -&gt; Tensor</p>
-<p>In-place version of <code>$floor_divide</code></p>
-</div>
-<div id="fmod" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fmod" class="anchor"></a>fmod</h2>
-<p>fmod(divisor) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_fmod.html">?torch_fmod</a></code></p>
-</div>
-<div id="fmod_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fmod_" class="anchor"></a>fmod_</h2>
-<p>fmod_(divisor) -&gt; Tensor</p>
-<p>In-place version of <code>$fmod</code></p>
-</div>
-<div id="frac" class="section level2">
-<h2 class="hasAnchor">
-<a href="#frac" class="anchor"></a>frac</h2>
-<p>frac() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_frac.html">?torch_frac</a></code></p>
-</div>
-<div id="frac_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#frac_" class="anchor"></a>frac_</h2>
-<p>frac_() -&gt; Tensor</p>
-<p>In-place version of <code>$frac</code></p>
-</div>
-<div id="gather" class="section level2">
-<h2 class="hasAnchor">
-<a href="#gather" class="anchor"></a>gather</h2>
-<p>gather(dim, index) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_gather.html">?torch_gather</a></code></p>
-</div>
-<div id="ge" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ge" class="anchor"></a>ge</h2>
-<p>ge(other) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_ge.html">?torch_ge</a></code></p>
-</div>
-<div id="ge_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ge_" class="anchor"></a>ge_</h2>
-<p>ge_(other) -&gt; Tensor</p>
-<p>In-place version of <code>$ge</code></p>
-</div>
-<div id="geometric_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#geometric_" class="anchor"></a>geometric_</h2>
-<p>geometric_(p, *, generator=NULL) -&gt; Tensor</p>
-<p>Fills <code>self</code> tensor with elements drawn from the geometric distribution:</p>
-<p><span class="math display">\[
-f(X=k) = p^{k - 1} (1 - p)
-\]</span></p>
-</div>
-<div id="geqrf" class="section level2">
-<h2 class="hasAnchor">
-<a href="#geqrf" class="anchor"></a>geqrf</h2>
-<p>geqrf() -&gt; (Tensor, Tensor)</p>
-<p>See <code><a href="../../reference/torch_geqrf.html">?torch_geqrf</a></code></p>
-</div>
-<div id="ger" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ger" class="anchor"></a>ger</h2>
-<p>ger(vec2) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_ger.html">?torch_ger</a></code></p>
-</div>
-<div id="get_device" class="section level2">
-<h2 class="hasAnchor">
-<a href="#get_device" class="anchor"></a>get_device</h2>
-<p>get_device() -&gt; Device ordinal (Integer)</p>
-<p>For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides. For CPU tensors, an error is thrown.</p>
-<div id="examples-9" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-9" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb10"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>, device=<span class="st">'cuda:0'</span>)
-<span class="kw">x</span><span class="op">$</span><span class="fu">get_device</span>()
-<span class="kw">x</span><span class="op">$</span><span class="fu">cpu</span>()<span class="op">$</span><span class="fu">get_device</span>()  <span class="co"># RuntimeError: get_device is not implemented for type torch_FloatTensor</span>
-</pre></div>
-</div>
-</div>
-<div id="grad" class="section level2">
-<h2 class="hasAnchor">
-<a href="#grad" class="anchor"></a>grad</h2>
-<p>This attribute is <code>NULL</code> by default and becomes a Tensor the first time a call to <code>backward</code> computes gradients for <code>self</code>. The attribute will then contain the gradients computed and future calls to [backward()] will accumulate (add) gradients into it.</p>
-</div>
-<div id="gt" class="section level2">
-<h2 class="hasAnchor">
-<a href="#gt" class="anchor"></a>gt</h2>
-<p>gt(other) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_gt.html">?torch_gt</a></code></p>
-</div>
-<div id="gt_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#gt_" class="anchor"></a>gt_</h2>
-<p>gt_(other) -&gt; Tensor</p>
-<p>In-place version of <code>$gt</code></p>
-</div>
-<div id="half" class="section level2">
-<h2 class="hasAnchor">
-<a href="#half" class="anchor"></a>half</h2>
-<p>half(memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p><code>self$half()</code> is equivalent to <code>self$to(torch_float16)</code>. See [to()].</p>
-<div id="arguments-18" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-18" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="hardshrink" class="section level2">
-<h2 class="hasAnchor">
-<a href="#hardshrink" class="anchor"></a>hardshrink</h2>
-<p>hardshrink(lambd=0.5) -&gt; Tensor</p>
-<p>See [torch_nn.functional.hardshrink()]</p>
-</div>
-<div id="has_names" class="section level2">
-<h2 class="hasAnchor">
-<a href="#has_names" class="anchor"></a>has_names</h2>
-<p>Is <code>TRUE</code> if any of this tensor’s dimensions are named. Otherwise, is <code>FALSE</code>.</p>
-</div>
-<div id="histc" class="section level2">
-<h2 class="hasAnchor">
-<a href="#histc" class="anchor"></a>histc</h2>
-<p>histc(bins=100, min=0, max=0) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_histc.html">?torch_histc</a></code></p>
-</div>
-<div id="ifft" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ifft" class="anchor"></a>ifft</h2>
-<p>ifft(signal_ndim, normalized=FALSE) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_ifft.html">?torch_ifft</a></code></p>
-</div>
-<div id="imag" class="section level2">
-<h2 class="hasAnchor">
-<a href="#imag" class="anchor"></a>imag</h2>
-<p>Returns a new tensor containing imaginary values of the <code>self</code> tensor. The returned tensor and <code>self</code> share the same underlying storage.</p>
-<div id="warning-3" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-3" class="anchor"></a>Warning:</h4>
-<p>[imag()] is only supported for tensors with complex dtypes.</p>
-</div>
-<div id="examples-10" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-10" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb11"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, dtype=<span class="fu">torch_cfloat</span>())
-<span class="kw">x</span>
-<span class="kw">x</span><span class="op">$</span><span class="kw">imag</span>
-</pre></div>
-</div>
-</div>
-<div id="index_add" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_add" class="anchor"></a>index_add</h2>
-<p>index_add(tensor1, dim, index, tensor2) -&gt; Tensor</p>
-<p>Out-of-place version of <code>$index_add_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_add_</code>.</p>
-</div>
-<div id="index_add_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_add_" class="anchor"></a>index_add_</h2>
-<p>index_add_(dim, index, tensor) -&gt; Tensor</p>
-<p>Accumulate the elements of <code>tensor</code> into the <code>self</code> tensor by adding to the indices in the order given in <code>index</code>. For example, if <code>dim == 0</code> and <code>index[i] == j</code>, then the <code>i</code> th row of <code>tensor</code> is added to the <code>j</code> th row of <code>self</code>.</p>
-<p>The <code>dim</code> th dimension of <code>tensor</code> must have the same size as the length of <code>index</code> (which must be a vector), and all other dimensions must match <code>self</code>, or an error will be raised.</p>
-<div id="note-3" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-3" class="anchor"></a>Note:</h4>
-<p>In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting <code>torch_backends.cudnn.deterministic = TRUE</code>.</p>
-</div>
-<div id="arguments-19" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-19" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dim (int): dimension along which to index</li>
-<li>index (LongTensor): indices of <code>tensor</code> to select from</li>
-<li>tensor (Tensor): the tensor containing values to add</li>
-</ul>
-</div>
-<div id="examples-11" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-11" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb12"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
-<span class="kw">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol = <span class="fl">3</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())
-<span class="kw">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1L</span>, <span class="fl">4L</span>, <span class="fl">3L</span>))
-<span class="kw">x</span><span class="op">$</span><span class="fu">index_add_</span>(<span class="fl">1</span>, <span class="kw">index</span>, <span class="kw">t</span>)
-</pre></div>
-</div>
-</div>
-<div id="index_copy" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_copy" class="anchor"></a>index_copy</h2>
-<p>index_copy(tensor1, dim, index, tensor2) -&gt; Tensor</p>
-<p>Out-of-place version of <code>$index_copy_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_copy_</code>.</p>
-</div>
-<div id="index_copy_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_copy_" class="anchor"></a>index_copy_</h2>
-<p>index_copy_(dim, index, tensor) -&gt; Tensor</p>
-<p>Copies the elements of <code>tensor</code> into the <code>self</code> tensor by selecting the indices in the order given in <code>index</code>. For example, if <code>dim == 0</code> and <code>index[i] == j</code>, then the <code>i</code> th row of <code>tensor</code> is copied to the <code>j</code> th row of <code>self</code>.</p>
-<p>The <code>dim</code> th dimension of <code>tensor</code> must have the same size as the length of <code>index</code> (which must be a vector), and all other dimensions must match <code>self</code>, or an error will be raised.</p>
-<div id="arguments-20" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-20" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dim (int): dimension along which to index</li>
-<li>index (LongTensor): indices of <code>tensor</code> to select from</li>
-<li>tensor (Tensor): the tensor containing values to copy</li>
-</ul>
-</div>
-<div id="examples-12" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-12" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb13"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
-<span class="kw">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol = <span class="fl">3</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())
-<span class="kw">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="fl">5</span>, <span class="fl">3</span>))
-<span class="kw">x</span><span class="op">$</span><span class="fu">index_copy_</span>(<span class="fl">1</span>, <span class="kw">index</span>, <span class="kw">t</span>)
-</pre></div>
-</div>
-</div>
-<div id="index_fill" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_fill" class="anchor"></a>index_fill</h2>
-<p>index_fill(tensor1, dim, index, value) -&gt; Tensor</p>
-<p>Out-of-place version of <code>$index_fill_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_fill_</code>.</p>
-</div>
-<div id="index_fill_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_fill_" class="anchor"></a>index_fill_</h2>
-<p>index_fill_(dim, index, val) -&gt; Tensor</p>
-<p>Fills the elements of the <code>self</code> tensor with value <code>val</code> by selecting the indices in the order given in <code>index</code>.</p>
-<div id="arguments-21" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-21" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dim (int): dimension along which to index</li>
-<li>index (LongTensor): indices of <code>self</code> tensor to fill in</li>
-<li>val (float): the value to fill with</li>
-</ul>
-</div>
-<div id="examples-13" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-13" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb14"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol = <span class="fl">3</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())
-<span class="kw">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="fl">3</span>), dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_long</a></span>())
-<span class="kw">x</span><span class="op">$</span><span class="fu">index_fill_</span>(<span class="fl">1</span>, <span class="kw">index</span>, <span class="op">-</span><span class="fl">1</span>)
-</pre></div>
-</div>
-</div>
-<div id="index_put" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_put" class="anchor"></a>index_put</h2>
-<p>index_put(tensor1, indices, value, accumulate=FALSE) -&gt; Tensor</p>
-<p>Out-place version of <code>$index_put_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_put_</code>.</p>
-</div>
-<div id="index_put_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_put_" class="anchor"></a>index_put_</h2>
-<p>index_put_(indices, value, accumulate=FALSE) -&gt; Tensor</p>
-<p>Puts values from the tensor <code>value</code> into the tensor <code>self</code> using the indices specified in <code>indices</code> (which is a tuple of Tensors). The expression <code>tensor.index_put_(indices, value)</code> is equivalent to <code>tensor[indices] = value</code>. Returns <code>self</code>.</p>
-<p>If <code>accumulate</code> is <code>TRUE</code>, the elements in <code>value</code> are added to <code>self</code>. If accumulate is <code>FALSE</code>, the behavior is undefined if indices contain duplicate elements.</p>
-<div id="arguments-22" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-22" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>indices (tuple of LongTensor): tensors used to index into <code>self</code>.</li>
-<li>value (Tensor): tensor of same dtype as <code>self</code>.</li>
-<li>accumulate (bool): whether to accumulate into self</li>
-</ul>
-</div>
-</div>
-<div id="index_select" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_select" class="anchor"></a>index_select</h2>
-<p>index_select(dim, index) -&gt; Tensor</p>
-<p>See [torch_index_select()]</p>
-</div>
-<div id="indices" class="section level2">
-<h2 class="hasAnchor">
-<a href="#indices" class="anchor"></a>indices</h2>
-<p>indices() -&gt; Tensor</p>
-<p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns a view of the contained indices tensor. Otherwise, this throws an error.</p>
-<p>See also <code>Tensor.values</code>.</p>
-<div id="note-4" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-4" class="anchor"></a>Note:</h4>
-<p>This method can only be called on a coalesced sparse tensor. See <code>Tensor.coalesce</code> for details.</p>
-</div>
-</div>
-<div id="int" class="section level2">
-<h2 class="hasAnchor">
-<a href="#int" class="anchor"></a>int</h2>
-<p>int(memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p><code>self$int()</code> is equivalent to <code>self$to(torch_int32)</code>. See [to()].</p>
-<div id="arguments-23" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-23" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="int_repr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#int_repr" class="anchor"></a>int_repr</h2>
-<p>int_repr() -&gt; Tensor</p>
-<p>Given a quantized Tensor, <code>self$int_repr()</code> returns a CPU Tensor with uint8_t as data type that stores the underlying uint8_t values of the given Tensor.</p>
-</div>
-<div id="inverse" class="section level2">
-<h2 class="hasAnchor">
-<a href="#inverse" class="anchor"></a>inverse</h2>
-<p>inverse() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_inverse.html">?torch_inverse</a></code></p>
-</div>
-<div id="irfft" class="section level2">
-<h2 class="hasAnchor">
-<a href="#irfft" class="anchor"></a>irfft</h2>
-<p>irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_irfft.html">?torch_irfft</a></code></p>
-</div>
-<div id="is_complex" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_complex" class="anchor"></a>is_complex</h2>
-<p>is_complex() -&gt; bool</p>
-<p>Returns TRUE if the data type of <code>self</code> is a complex data type.</p>
-</div>
-<div id="is_contiguous" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_contiguous" class="anchor"></a>is_contiguous</h2>
-<p>is_contiguous(memory_format=torch_contiguous_format) -&gt; bool</p>
-<p>Returns TRUE if <code>self</code> tensor is contiguous in memory in the order specified by memory format.</p>
-<div id="arguments-24" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-24" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): Specifies memory allocation</li>
-<li>order. Default: <code>torch_contiguous_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="is_cuda" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_cuda" class="anchor"></a>is_cuda</h2>
-<p>Is <code>TRUE</code> if the Tensor is stored on the GPU, <code>FALSE</code> otherwise.</p>
-</div>
-<div id="is_floating_point" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_floating_point" class="anchor"></a>is_floating_point</h2>
-<p>is_floating_point() -&gt; bool</p>
-<p>Returns TRUE if the data type of <code>self</code> is a floating point data type.</p>
-</div>
-<div id="is_leaf" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_leaf" class="anchor"></a>is_leaf</h2>
-<p>All Tensors that have <code>requires_grad</code> which is <code>FALSE</code> will be leaf Tensors by convention.</p>
-<p>For Tensors that have <code>requires_grad</code> which is <code>TRUE</code>, they will be leaf Tensors if they were created by the user. This means that they are not the result of an operation and so <code>grad_fn</code> is NULL.</p>
-<p>Only leaf Tensors will have their <code>grad</code> populated during a call to [backward()]. To get <code>grad</code> populated for non-leaf Tensors, you can use [retain_grad()].</p>
-<div id="examples-14" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-14" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb15"><pre class="downlit">
-<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">10</span>, requires_grad=<span class="fl">TRUE</span>)
-<span class="kw">a</span><span class="op">$</span><span class="fu">is_leaf</span>()
-
-<span class="co"># b &lt;- torch_rand(10, requires_grad=TRUE)$cuda()</span>
-<span class="co"># b$is_leaf()</span>
-<span class="co"># FALSE</span>
-<span class="co"># b was created by the operation that cast a cpu Tensor into a cuda Tensor</span>
-
-<span class="kw">c</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">10</span>, requires_grad=<span class="fl">TRUE</span>) <span class="op">+</span> <span class="fl">2</span>
-<span class="kw">c</span><span class="op">$</span><span class="fu">is_leaf</span>()
-<span class="co"># c was created by the addition operation</span>
-
-<span class="co"># d &lt;- torch_rand(10)$cuda()</span>
-<span class="co"># d$is_leaf()</span>
-<span class="co"># TRUE</span>
-<span class="co"># d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)</span>
-
-<span class="co"># e &lt;- torch_rand(10)$cuda()$requires_grad_()</span>
-<span class="co"># e$is_leaf()</span>
-<span class="co"># TRUE</span>
-<span class="co"># e requires gradients and has no operations creating it</span>
-
-<span class="co"># f &lt;- torch_rand(10, requires_grad=TRUE, device="cuda")</span>
-<span class="co"># f$is_leaf</span>
-<span class="co"># TRUE</span>
-<span class="co"># f requires grad, has no operation creating it</span>
-</pre></div>
-</div>
-</div>
-<div id="is_meta" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_meta" class="anchor"></a>is_meta</h2>
-<p>Is <code>TRUE</code> if the Tensor is a meta tensor, <code>FALSE</code> otherwise. Meta tensors are like normal tensors, but they carry no data.</p>
-</div>
-<div id="is_pinned" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_pinned" class="anchor"></a>is_pinned</h2>
-<p>Returns true if this tensor resides in pinned memory.</p>
-</div>
-<div id="is_quantized" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_quantized" class="anchor"></a>is_quantized</h2>
-<p>Is <code>TRUE</code> if the Tensor is quantized, <code>FALSE</code> otherwise.</p>
-</div>
-<div id="is_set_to" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_set_to" class="anchor"></a>is_set_to</h2>
-<p>is_set_to(tensor) -&gt; bool</p>
-<p>Returns TRUE if this object refers to the same <code>THTensor</code> object from the Torch C API as the given tensor.</p>
-</div>
-<div id="is_shared" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_shared" class="anchor"></a>is_shared</h2>
-<p>Checks if tensor is in shared memory.</p>
-<p>This is always <code>TRUE</code> for CUDA tensors.</p>
-</div>
-<div id="is_signed" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_signed" class="anchor"></a>is_signed</h2>
-<p>is_signed() -&gt; bool</p>
-<p>Returns TRUE if the data type of <code>self</code> is a signed data type.</p>
-</div>
-<div id="isclose" class="section level2">
-<h2 class="hasAnchor">
-<a href="#isclose" class="anchor"></a>isclose</h2>
-<p>isclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -&gt; Tensor</p>
-<p>See <code>?torch_isclose</code></p>
-</div>
-<div id="isfinite" class="section level2">
-<h2 class="hasAnchor">
-<a href="#isfinite" class="anchor"></a>isfinite</h2>
-<p>isfinite() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_isfinite.html">?torch_isfinite</a></code></p>
-</div>
-<div id="isinf" class="section level2">
-<h2 class="hasAnchor">
-<a href="#isinf" class="anchor"></a>isinf</h2>
-<p>isinf() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_isinf.html">?torch_isinf</a></code></p>
-</div>
-<div id="isnan" class="section level2">
-<h2 class="hasAnchor">
-<a href="#isnan" class="anchor"></a>isnan</h2>
-<p>isnan() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_isnan.html">?torch_isnan</a></code></p>
-</div>
-<div id="istft" class="section level2">
-<h2 class="hasAnchor">
-<a href="#istft" class="anchor"></a>istft</h2>
-<p>See <code>?torch_istft</code> ## item</p>
-<p>item() -&gt; number</p>
-<p>Returns the value of this tensor as a standard Python number. This only works for tensors with one element. For other cases, see <code>$tolist</code>.</p>
-<p>This operation is not differentiable.</p>
-<div id="examples-15" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-15" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb16"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1.0</span>)
-<span class="kw">x</span><span class="op">$</span><span class="fu">item</span>()
-</pre></div>
-</div>
-</div>
-<div id="kthvalue" class="section level2">
-<h2 class="hasAnchor">
-<a href="#kthvalue" class="anchor"></a>kthvalue</h2>
-<p>kthvalue(k, dim=NULL, keepdim=FALSE) -&gt; (Tensor, LongTensor)</p>
-<p>See <code><a href="../../reference/torch_kthvalue.html">?torch_kthvalue</a></code></p>
-</div>
-<div id="le" class="section level2">
-<h2 class="hasAnchor">
-<a href="#le" class="anchor"></a>le</h2>
-<p>le(other) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_le.html">?torch_le</a></code></p>
-</div>
-<div id="le_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#le_" class="anchor"></a>le_</h2>
-<p>le_(other) -&gt; Tensor</p>
-<p>In-place version of <code>$le</code></p>
-</div>
-<div id="lerp" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lerp" class="anchor"></a>lerp</h2>
-<p>lerp(end, weight) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_lerp.html">?torch_lerp</a></code></p>
-</div>
-<div id="lerp_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lerp_" class="anchor"></a>lerp_</h2>
-<p>lerp_(end, weight) -&gt; Tensor</p>
-<p>In-place version of <code>$lerp</code></p>
-</div>
-<div id="lgamma" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lgamma" class="anchor"></a>lgamma</h2>
-<p>lgamma() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_lgamma.html">?torch_lgamma</a></code></p>
-</div>
-<div id="lgamma_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lgamma_" class="anchor"></a>lgamma_</h2>
-<p>lgamma_() -&gt; Tensor</p>
-<p>In-place version of <code>$lgamma</code></p>
-</div>
-<div id="log" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log" class="anchor"></a>log</h2>
-<p>log() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_log.html">?torch_log</a></code></p>
-</div>
-<div id="log10" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log10" class="anchor"></a>log10</h2>
-<p>log10() -&gt; Tensor</p>
-<p>See [torch_log10()]</p>
-</div>
-<div id="log10_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log10_" class="anchor"></a>log10_</h2>
-<p>log10_() -&gt; Tensor</p>
-<p>In-place version of <code>$log10</code></p>
-</div>
-<div id="log1p" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log1p" class="anchor"></a>log1p</h2>
-<p>log1p() -&gt; Tensor</p>
-<p>See [torch_log1p()]</p>
-</div>
-<div id="log1p_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log1p_" class="anchor"></a>log1p_</h2>
-<p>log1p_() -&gt; Tensor</p>
-<p>In-place version of <code>$log1p</code></p>
-</div>
-<div id="log2" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log2" class="anchor"></a>log2</h2>
-<p>log2() -&gt; Tensor</p>
-<p>See [torch_log2()]</p>
-</div>
-<div id="log2_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log2_" class="anchor"></a>log2_</h2>
-<p>log2_() -&gt; Tensor</p>
-<p>In-place version of <code>$log2</code></p>
-</div>
-<div id="log_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log_" class="anchor"></a>log_</h2>
-<p>log_() -&gt; Tensor</p>
-<p>In-place version of <code>$log</code></p>
-</div>
-<div id="log_normal_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log_normal_" class="anchor"></a>log_normal_</h2>
-<p>log_normal_(mean=1, std=2, *, generator=NULL)</p>
-<p>Fills <code>self</code> tensor with numbers samples from the log-normal distribution parameterized by the given mean <code>\mu</code> and standard deviation <code>\sigma</code>. Note that <code>mean</code> and <code>std</code> are the mean and standard deviation of the underlying normal distribution, and not of the returned distribution:</p>
-<p><span class="math display">\[
-f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
-\]</span></p>
-</div>
-<div id="logaddexp" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logaddexp" class="anchor"></a>logaddexp</h2>
-<p>logaddexp(other) -&gt; Tensor</p>
-<p>See <code>?torch_logaddexp</code></p>
-</div>
-<div id="logaddexp2" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logaddexp2" class="anchor"></a>logaddexp2</h2>
-<p>logaddexp2(other) -&gt; Tensor</p>
-<p>See [torch_logaddexp2()]</p>
-</div>
-<div id="logcumsumexp" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logcumsumexp" class="anchor"></a>logcumsumexp</h2>
-<p>logcumsumexp(dim) -&gt; Tensor</p>
-<p>See <code>?torch_logcumsumexp</code></p>
-</div>
-<div id="logdet" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logdet" class="anchor"></a>logdet</h2>
-<p>logdet() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_logdet.html">?torch_logdet</a></code></p>
-</div>
-<div id="logical_and" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_and" class="anchor"></a>logical_and</h2>
-<p>logical_and() -&gt; Tensor</p>
-<p>See [torch_logical_and()]</p>
-</div>
-<div id="logical_and_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_and_" class="anchor"></a>logical_and_</h2>
-<p>logical_and_() -&gt; Tensor</p>
-<p>In-place version of <code>$logical_and</code></p>
-</div>
-<div id="logical_not" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_not" class="anchor"></a>logical_not</h2>
-<p>logical_not() -&gt; Tensor</p>
-<p>See [torch_logical_not()]</p>
-</div>
-<div id="logical_not_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_not_" class="anchor"></a>logical_not_</h2>
-<p>logical_not_() -&gt; Tensor</p>
-<p>In-place version of <code>$logical_not</code></p>
-</div>
-<div id="logical_or" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_or" class="anchor"></a>logical_or</h2>
-<p>logical_or() -&gt; Tensor</p>
-<p>See [torch_logical_or()]</p>
-</div>
-<div id="logical_or_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_or_" class="anchor"></a>logical_or_</h2>
-<p>logical_or_() -&gt; Tensor</p>
-<p>In-place version of <code>$logical_or</code></p>
-</div>
-<div id="logical_xor" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_xor" class="anchor"></a>logical_xor</h2>
-<p>logical_xor() -&gt; Tensor</p>
-<p>See [torch_logical_xor()]</p>
-</div>
-<div id="logical_xor_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_xor_" class="anchor"></a>logical_xor_</h2>
-<p>logical_xor_() -&gt; Tensor</p>
-<p>In-place version of <code>$logical_xor</code></p>
-</div>
-<div id="logsumexp" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logsumexp" class="anchor"></a>logsumexp</h2>
-<p>logsumexp(dim, keepdim=FALSE) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_logsumexp.html">?torch_logsumexp</a></code></p>
-</div>
-<div id="long" class="section level2">
-<h2 class="hasAnchor">
-<a href="#long" class="anchor"></a>long</h2>
-<p>long(memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p><code>self$long()</code> is equivalent to <code>self$to(torch_int64)</code>. See [to()].</p>
-<div id="arguments-25" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-25" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="lstsq" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lstsq" class="anchor"></a>lstsq</h2>
-<p>lstsq(A) -&gt; (Tensor, Tensor)</p>
-<p>See <code><a href="../../reference/torch_lstsq.html">?torch_lstsq</a></code></p>
-</div>
-<div id="lt" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lt" class="anchor"></a>lt</h2>
-<p>lt(other) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_lt.html">?torch_lt</a></code></p>
-</div>
-<div id="lt_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lt_" class="anchor"></a>lt_</h2>
-<p>lt_(other) -&gt; Tensor</p>
-<p>In-place version of <code>$lt</code></p>
-</div>
-<div id="lu" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lu" class="anchor"></a>lu</h2>
-<p>See <code><a href="../../reference/torch_lu.html">?torch_lu</a></code> ## lu_solve</p>
-<p>lu_solve(LU_data, LU_pivots) -&gt; Tensor</p>
-<p>See [torch_lu_solve()]</p>
-</div>
-<div id="map_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#map_" class="anchor"></a>map_</h2>
-<p>map_(tensor, callable)</p>
-<p>Applies <code>callable</code> for each element in <code>self</code> tensor and the given <code>tensor</code> and stores the results in <code>self</code> tensor. <code>self</code> tensor and the given <code>tensor</code> must be broadcastable.</p>
-<p>The <code>callable</code> should have the signature:</p>
-<p><code>callable(a, b) -&gt; number</code></p>
-</div>
-<div id="masked_fill" class="section level2">
-<h2 class="hasAnchor">
-<a href="#masked_fill" class="anchor"></a>masked_fill</h2>
-<p>masked_fill(mask, value) -&gt; Tensor</p>
-<p>Out-of-place version of <code>$masked_fill_</code></p>
-</div>
-<div id="masked_fill_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#masked_fill_" class="anchor"></a>masked_fill_</h2>
-<p>masked_fill_(mask, value)</p>
-<p>Fills elements of <code>self</code> tensor with <code>value</code> where <code>mask</code> is TRUE. The shape of <code>mask</code> must be <code>broadcastable &lt;broadcasting-semantics&gt;</code> with the shape of the underlying tensor.</p>
-<div id="arguments-26" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-26" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>mask (BoolTensor): the boolean mask</li>
-<li>value (float): the value to fill in with</li>
-</ul>
-</div>
-</div>
-<div id="masked_scatter" class="section level2">
-<h2 class="hasAnchor">
-<a href="#masked_scatter" class="anchor"></a>masked_scatter</h2>
-<p>masked_scatter(mask, tensor) -&gt; Tensor</p>
-<p>Out-of-place version of <code>$masked_scatter_</code></p>
-</div>
-<div id="masked_scatter_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#masked_scatter_" class="anchor"></a>masked_scatter_</h2>
-<p>masked_scatter_(mask, source)</p>
-<p>Copies elements from <code>source</code> into <code>self</code> tensor at positions where the <code>mask</code> is TRUE. The shape of <code>mask</code> must be :ref:<code>broadcastable &lt;broadcasting-semantics&gt;</code> with the shape of the underlying tensor. The <code>source</code> should have at least as many elements as the number of ones in <code>mask</code></p>
-<div id="arguments-27" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-27" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>mask (BoolTensor): the boolean mask</li>
-<li>source (Tensor): the tensor to copy from</li>
-</ul>
-</div>
-<div id="note-5" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-5" class="anchor"></a>Note:</h4>
-<p>The <code>mask</code> operates on the <code>self</code> tensor, not on the given <code>source</code> tensor.</p>
-</div>
-</div>
-<div id="masked_select" class="section level2">
-<h2 class="hasAnchor">
-<a href="#masked_select" class="anchor"></a>masked_select</h2>
-<p>masked_select(mask) -&gt; Tensor</p>
-<p>See [torch_masked_select()]</p>
-</div>
-<div id="matmul" class="section level2">
-<h2 class="hasAnchor">
-<a href="#matmul" class="anchor"></a>matmul</h2>
-<p>matmul(tensor2) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_matmul.html">?torch_matmul</a></code></p>
-</div>
-<div id="matrix_power" class="section level2">
-<h2 class="hasAnchor">
-<a href="#matrix_power" class="anchor"></a>matrix_power</h2>
-<p>matrix_power(n) -&gt; Tensor</p>
-<p>See [torch_matrix_power()]</p>
-</div>
-<div id="max" class="section level2">
-<h2 class="hasAnchor">
-<a href="#max" class="anchor"></a>max</h2>
-<p>max(dim=NULL, keepdim=FALSE) -&gt; Tensor or (Tensor, Tensor)</p>
-<p>See <code><a href="../../reference/torch_max.html">?torch_max</a></code></p>
-</div>
-<div id="mean" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mean" class="anchor"></a>mean</h2>
-<p>mean(dim=NULL, keepdim=FALSE) -&gt; Tensor or (Tensor, Tensor)</p>
-<p>See <code><a href="../../reference/torch_mean.html">?torch_mean</a></code></p>
-</div>
-<div id="median" class="section level2">
-<h2 class="hasAnchor">
-<a href="#median" class="anchor"></a>median</h2>
-<p>median(dim=NULL, keepdim=FALSE) -&gt; (Tensor, LongTensor)</p>
-<p>See <code><a href="../../reference/torch_median.html">?torch_median</a></code></p>
-</div>
-<div id="min" class="section level2">
-<h2 class="hasAnchor">
-<a href="#min" class="anchor"></a>min</h2>
-<p>min(dim=NULL, keepdim=FALSE) -&gt; Tensor or (Tensor, Tensor)</p>
-<p>See <code><a href="../../reference/torch_min.html">?torch_min</a></code></p>
-</div>
-<div id="mm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mm" class="anchor"></a>mm</h2>
-<p>mm(mat2) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_mm.html">?torch_mm</a></code></p>
-</div>
-<div id="mode" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mode" class="anchor"></a>mode</h2>
-<p>mode(dim=NULL, keepdim=FALSE) -&gt; (Tensor, LongTensor)</p>
-<p>See <code><a href="../../reference/torch_mode.html">?torch_mode</a></code></p>
-</div>
-<div id="mul" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mul" class="anchor"></a>mul</h2>
-<p>mul(value) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_mul.html">?torch_mul</a></code></p>
-</div>
-<div id="mul_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mul_" class="anchor"></a>mul_</h2>
-<p>mul_(value)</p>
-<p>In-place version of <code>$mul</code></p>
-</div>
-<div id="multinomial" class="section level2">
-<h2 class="hasAnchor">
-<a href="#multinomial" class="anchor"></a>multinomial</h2>
-<p>multinomial(num_samples, replacement=FALSE, *, generator=NULL) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_multinomial.html">?torch_multinomial</a></code></p>
-</div>
-<div id="mv" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mv" class="anchor"></a>mv</h2>
-<p>mv(vec) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_mv.html">?torch_mv</a></code></p>
-</div>
-<div id="mvlgamma" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mvlgamma" class="anchor"></a>mvlgamma</h2>
-<p>mvlgamma(p) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_mvlgamma.html">?torch_mvlgamma</a></code></p>
-</div>
-<div id="mvlgamma_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mvlgamma_" class="anchor"></a>mvlgamma_</h2>
-<p>mvlgamma_(p) -&gt; Tensor</p>
-<p>In-place version of <code>$mvlgamma</code></p>
-</div>
-<div id="names" class="section level2">
-<h2 class="hasAnchor">
-<a href="#names" class="anchor"></a>names</h2>
-<p>Stores names for each of this tensor’s dimensions.</p>
-<p><code>names[idx]</code> corresponds to the name of tensor dimension <code>idx</code>. Names are either a string if the dimension is named or <code>NULL</code> if the dimension is unnamed.</p>
-<p>Dimension names may contain characters or underscore. Furthermore, a dimension name must be a valid Python variable name (i.e., does not start with underscore).</p>
-<p>Tensors may not have two named dimensions with the same name.</p>
-<div id="warning-4" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-4" class="anchor"></a>Warning:</h4>
-<p>The named tensor API is experimental and subject to change.</p>
-</div>
-</div>
-<div id="narrow" class="section level2">
-<h2 class="hasAnchor">
-<a href="#narrow" class="anchor"></a>narrow</h2>
-<p>narrow(dimension, start, length) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_narrow.html">?torch_narrow</a></code></p>
-<div id="examples-16" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-16" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb17"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol = <span class="fl">3</span>))
-<span class="kw">x</span><span class="op">$</span><span class="fu">narrow</span>(<span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">3</span>)
-<span class="kw">x</span><span class="op">$</span><span class="fu">narrow</span>(<span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span>)
-</pre></div>
-</div>
-</div>
-<div id="narrow_copy" class="section level2">
-<h2 class="hasAnchor">
-<a href="#narrow_copy" class="anchor"></a>narrow_copy</h2>
-<p>narrow_copy(dimension, start, length) -&gt; Tensor</p>
-<p>Same as <code>Tensor.narrow</code> except returning a copy rather than shared storage. This is primarily for sparse tensors, which do not have a shared-storage narrow method. Calling <code>narrow_copy` with</code>dimemsion &gt; self<span class="math inline">\(sparse_dim()`` will return a copy with the relevant dense dimension narrowed, and ``self\)</span>shape`` updated accordingly.</p>
-</div>
-<div id="ndim" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ndim" class="anchor"></a>ndim</h2>
-<p>Alias for <code>$dim()</code></p>
-</div>
-<div id="ndimension" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ndimension" class="anchor"></a>ndimension</h2>
-<p>ndimension() -&gt; int</p>
-<p>Alias for <code>$dim()</code></p>
-</div>
-<div id="ne" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ne" class="anchor"></a>ne</h2>
-<p>ne(other) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_ne.html">?torch_ne</a></code></p>
-</div>
-<div id="ne_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ne_" class="anchor"></a>ne_</h2>
-<p>ne_(other) -&gt; Tensor</p>
-<p>In-place version of <code>$ne</code></p>
-</div>
-<div id="neg" class="section level2">
-<h2 class="hasAnchor">
-<a href="#neg" class="anchor"></a>neg</h2>
-<p>neg() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_neg.html">?torch_neg</a></code></p>
-</div>
-<div id="neg_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#neg_" class="anchor"></a>neg_</h2>
-<p>neg_() -&gt; Tensor</p>
-<p>In-place version of <code>$neg</code></p>
-</div>
-<div id="nelement" class="section level2">
-<h2 class="hasAnchor">
-<a href="#nelement" class="anchor"></a>nelement</h2>
-<p>nelement() -&gt; int</p>
-<p>Alias for <code>$numel</code></p>
-</div>
-<div id="new_empty" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new_empty" class="anchor"></a>new_empty</h2>
-<p>new_empty(size, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
-<p>Returns a Tensor of size <code>size</code> filled with uninitialized data. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
-<div id="arguments-28" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-28" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
-<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
-<li>requires_grad (bool, optional): If autograd should record operations on the</li>
-<li>returned tensor. Default: <code>FALSE</code>.</li>
-</ul>
-</div>
-<div id="examples-17" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-17" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb18"><pre class="downlit">
-<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">5</span>)
-<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_empty</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">3</span>))
-</pre></div>
-</div>
-</div>
-<div id="new_full" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new_full" class="anchor"></a>new_full</h2>
-<p>new_full(size, fill_value, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
-<p>Returns a Tensor of size <code>size</code> filled with <code>fill_value</code>. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
-<div id="arguments-29" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-29" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>fill_value (scalar): the number to fill the output tensor with.</li>
-<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
-<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
-<li>requires_grad (bool, optional): If autograd should record operations on the</li>
-<li>returned tensor. Default: <code>FALSE</code>.</li>
-</ul>
-</div>
-<div id="examples-18" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-18" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb19"><pre class="downlit">
-<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span>())
-<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_full</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">3</span>, <span class="fl">4</span>), <span class="fl">3.141592</span>)
-</pre></div>
-</div>
-</div>
-<div id="new_ones" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new_ones" class="anchor"></a>new_ones</h2>
-<p>new_ones(size, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
-<p>Returns a Tensor of size <code>size</code> filled with <code>1</code>. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
-<div id="arguments-30" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-30" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>size (int…): a list, tuple, or <code>torch_Size</code> of integers defining the</li>
-<li>shape of the output tensor.</li>
-<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
-<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
-<li>requires_grad (bool, optional): If autograd should record operations on the</li>
-<li>returned tensor. Default: <code>FALSE</code>.</li>
-</ul>
-</div>
-<div id="examples-19" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-19" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb20"><pre class="downlit">
-<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_int32</a></span>())
-<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_ones</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">3</span>))
-</pre></div>
-</div>
-</div>
-<div id="new_tensor" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new_tensor" class="anchor"></a>new_tensor</h2>
-<p>new_tensor(data, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
-<p>Returns a new Tensor with <code>data</code> as the tensor data. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
-<div id="warning-5" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-5" class="anchor"></a>Warning:</h4>
-<p><code>new_tensor</code> always copies <code>data(). If you have a Tensor</code>data` and want to avoid a copy, use [$requires_grad_()] or [$detach()]. If you have a numpy array and want to avoid a copy, use [torch_from_numpy()].</p>
-<p>When data is a tensor <code>x</code>, [new_tensor()()] reads out ‘the data’ from whatever it is passed, and constructs a leaf variable. Therefore <code>tensor$new_tensor(x)</code> is equivalent to <code>x$clone()$detach()</code> and <code>tensor$new_tensor(x, requires_grad=TRUE)</code> is equivalent to <code>x$clone()$detach()$requires_grad_(TRUE)</code>. The equivalents using <code>clone()</code> and <code><a href="https://rdrr.io/r/base/detach.html">detach()</a></code> are recommended.</p>
-</div>
-<div id="arguments-31" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-31" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>data (array_like): The returned Tensor copies <code>data</code>.</li>
-<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
-<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
-<li>requires_grad (bool, optional): If autograd should record operations on the</li>
-<li>returned tensor. Default: <code>FALSE</code>.</li>
-</ul>
-</div>
-<div id="examples-20" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-20" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb21"><pre class="downlit">
-<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>), dtype=<span class="kw">torch_int8</span>)
-<span class="kw">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">4</span>, ncol = <span class="fl">2</span>)
-<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_tensor</span>(<span class="kw">data</span>)
-</pre></div>
-</div>
-</div>
-<div id="new_zeros" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new_zeros" class="anchor"></a>new_zeros</h2>
-<p>new_zeros(size, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
-<p>Returns a Tensor of size <code>size</code> filled with <code>0</code>. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
-<div id="arguments-32" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-32" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>size (int…): a list, tuple, or <code>torch_Size</code> of integers defining the</li>
-<li>shape of the output tensor.</li>
-<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
-<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
-<li>requires_grad (bool, optional): If autograd should record operations on the</li>
-<li>returned tensor. Default: <code>FALSE</code>.</li>
-</ul>
-</div>
-<div id="examples-21" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-21" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb22"><pre class="downlit">
-<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span>())
-<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_zeros</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">3</span>))
-</pre></div>
-</div>
-</div>
-<div id="nonzero" class="section level2">
-<h2 class="hasAnchor">
-<a href="#nonzero" class="anchor"></a>nonzero</h2>
-<p>nonzero() -&gt; LongTensor</p>
-<p>See <code><a href="../../reference/torch_nonzero.html">?torch_nonzero</a></code></p>
-</div>
-<div id="norm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#norm" class="anchor"></a>norm</h2>
-<p>See <code><a href="../../reference/torch_norm.html">?torch_norm</a></code> ## normal_</p>
-<p>normal_(mean=0, std=1, *, generator=NULL) -&gt; Tensor</p>
-<p>Fills <code>self</code> tensor with elements samples from the normal distribution parameterized by <code>mean</code> and <code>std</code>.</p>
-</div>
-<div id="numel" class="section level2">
-<h2 class="hasAnchor">
-<a href="#numel" class="anchor"></a>numel</h2>
-<p>numel() -&gt; int</p>
-<p>See <code>?torch_numel</code></p>
-</div>
-<div id="numpy" class="section level2">
-<h2 class="hasAnchor">
-<a href="#numpy" class="anchor"></a>numpy</h2>
-<p>numpy() -&gt; numpy.ndarray</p>
-<p>Returns <code>self</code> tensor as a NumPy :class:<code>ndarray</code>. This tensor and the returned <code>ndarray</code> share the same underlying storage. Changes to <code>self</code> tensor will be reflected in the :class:<code>ndarray</code> and vice versa.</p>
-</div>
-<div id="orgqr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#orgqr" class="anchor"></a>orgqr</h2>
-<p>orgqr(input2) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_orgqr.html">?torch_orgqr</a></code></p>
-</div>
-<div id="ormqr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ormqr" class="anchor"></a>ormqr</h2>
-<p>ormqr(input2, input3, left=TRUE, transpose=FALSE) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_ormqr.html">?torch_ormqr</a></code></p>
-</div>
-<div id="permute" class="section level2">
-<h2 class="hasAnchor">
-<a href="#permute" class="anchor"></a>permute</h2>
-<p>permute(*dims) -&gt; Tensor</p>
-<p>Returns a view of the original tensor with its dimensions permuted.</p>
-<div id="arguments-33" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-33" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dims (int…): The desired ordering of dimensions</li>
-</ul>
-</div>
-<div id="examples-22" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-22" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb23"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span>)
-<span class="kw">x</span><span class="op">$</span><span class="fu">size</span>()
-<span class="kw">x</span><span class="op">$</span><span class="fu">permute</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">2</span>))<span class="op">$</span><span class="fu">size</span>()
-</pre></div>
-</div>
-</div>
-<div id="pin_memory" class="section level2">
-<h2 class="hasAnchor">
-<a href="#pin_memory" class="anchor"></a>pin_memory</h2>
-<p>pin_memory() -&gt; Tensor</p>
-<p>Copies the tensor to pinned memory, if it’s not already pinned.</p>
-</div>
-<div id="pinverse" class="section level2">
-<h2 class="hasAnchor">
-<a href="#pinverse" class="anchor"></a>pinverse</h2>
-<p>pinverse() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_pinverse.html">?torch_pinverse</a></code></p>
-</div>
-<div id="polygamma" class="section level2">
-<h2 class="hasAnchor">
-<a href="#polygamma" class="anchor"></a>polygamma</h2>
-<p>polygamma(n) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_polygamma.html">?torch_polygamma</a></code></p>
-</div>
-<div id="polygamma_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#polygamma_" class="anchor"></a>polygamma_</h2>
-<p>polygamma_(n) -&gt; Tensor</p>
-<p>In-place version of <code>$polygamma</code></p>
-</div>
-<div id="pow" class="section level2">
-<h2 class="hasAnchor">
-<a href="#pow" class="anchor"></a>pow</h2>
-<p>pow(exponent) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_pow.html">?torch_pow</a></code></p>
-</div>
-<div id="pow_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#pow_" class="anchor"></a>pow_</h2>
-<p>pow_(exponent) -&gt; Tensor</p>
-<p>In-place version of <code>$pow</code></p>
-</div>
-<div id="prod" class="section level2">
-<h2 class="hasAnchor">
-<a href="#prod" class="anchor"></a>prod</h2>
-<p>prod(dim=NULL, keepdim=FALSE, dtype=NULL) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_prod.html">?torch_prod</a></code></p>
-</div>
-<div id="put_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#put_" class="anchor"></a>put_</h2>
-<p>put_(indices, tensor, accumulate=FALSE) -&gt; Tensor</p>
-<p>Copies the elements from <code>tensor</code> into the positions specified by indices. For the purpose of indexing, the <code>self</code> tensor is treated as if it were a 1-D tensor.</p>
-<p>If <code>accumulate</code> is <code>TRUE</code>, the elements in <code>tensor</code> are added to <code>self</code>. If accumulate is <code>FALSE</code>, the behavior is undefined if indices contain duplicate elements.</p>
-<div id="arguments-34" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-34" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>indices (LongTensor): the indices into self</li>
-<li>tensor (Tensor): the tensor containing values to copy from</li>
-<li>accumulate (bool): whether to accumulate into self</li>
-</ul>
-</div>
-<div id="examples-23" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-23" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb24"><pre class="downlit">
-<span class="kw">src</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">3</span><span class="op">:</span><span class="fl">8</span>, ncol = <span class="fl">3</span>))
-<span class="kw">src</span><span class="op">$</span><span class="fu">put_</span>(<span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">2</span>), <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">9</span><span class="op">:</span><span class="fl">10</span>))
-</pre></div>
-</div>
-</div>
-<div id="q_per_channel_axis" class="section level2">
-<h2 class="hasAnchor">
-<a href="#q_per_channel_axis" class="anchor"></a>q_per_channel_axis</h2>
-<p>q_per_channel_axis() -&gt; int</p>
-<p>Given a Tensor quantized by linear (affine) per-channel quantization, returns the index of dimension on which per-channel quantization is applied.</p>
-</div>
-<div id="q_per_channel_scales" class="section level2">
-<h2 class="hasAnchor">
-<a href="#q_per_channel_scales" class="anchor"></a>q_per_channel_scales</h2>
-<p>q_per_channel_scales() -&gt; Tensor</p>
-<p>Given a Tensor quantized by linear (affine) per-channel quantization, returns a Tensor of scales of the underlying quantizer. It has the number of elements that matches the corresponding dimensions (from q_per_channel_axis) of the tensor.</p>
-</div>
-<div id="q_per_channel_zero_points" class="section level2">
-<h2 class="hasAnchor">
-<a href="#q_per_channel_zero_points" class="anchor"></a>q_per_channel_zero_points</h2>
-<p>q_per_channel_zero_points() -&gt; Tensor</p>
-<p>Given a Tensor quantized by linear (affine) per-channel quantization, returns a tensor of zero_points of the underlying quantizer. It has the number of elements that matches the corresponding dimensions (from q_per_channel_axis) of the tensor.</p>
-</div>
-<div id="q_scale" class="section level2">
-<h2 class="hasAnchor">
-<a href="#q_scale" class="anchor"></a>q_scale</h2>
-<p>q_scale() -&gt; float</p>
-<p>Given a Tensor quantized by linear(affine) quantization, returns the scale of the underlying quantizer().</p>
-</div>
-<div id="q_zero_point" class="section level2">
-<h2 class="hasAnchor">
-<a href="#q_zero_point" class="anchor"></a>q_zero_point</h2>
-<p>q_zero_point() -&gt; int</p>
-<p>Given a Tensor quantized by linear(affine) quantization, returns the zero_point of the underlying quantizer().</p>
-</div>
-<div id="qr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#qr" class="anchor"></a>qr</h2>
-<p>qr(some=TRUE) -&gt; (Tensor, Tensor)</p>
-<p>See <code><a href="../../reference/torch_qr.html">?torch_qr</a></code></p>
-</div>
-<div id="qscheme" class="section level2">
-<h2 class="hasAnchor">
-<a href="#qscheme" class="anchor"></a>qscheme</h2>
-<p>qscheme() -&gt; torch_qscheme</p>
-<p>Returns the quantization scheme of a given QTensor.</p>
-</div>
-<div id="rad2deg" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rad2deg" class="anchor"></a>rad2deg</h2>
-<p>rad2deg() -&gt; Tensor</p>
-<p>See [torch_rad2deg()]</p>
-</div>
-<div id="rad2deg_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rad2deg_" class="anchor"></a>rad2deg_</h2>
-<p>rad2deg_() -&gt; Tensor</p>
-<p>In-place version of <code>$rad2deg</code></p>
-</div>
-<div id="random_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#random_" class="anchor"></a>random_</h2>
-<p>random_(from=0, to=NULL, *, generator=NULL) -&gt; Tensor</p>
-<p>Fills <code>self</code> tensor with numbers sampled from the discrete uniform distribution over <code>[from, to - 1]</code>. If not specified, the values are usually only bounded by <code>self</code> tensor’s data type. However, for floating point types, if unspecified, range will be <code>[0, 2^mantissa]</code> to ensure that every value is representable. For example, <code>torch_tensor(1, dtype=torch_double).random_()</code> will be uniform in <code>[0, 2^53]</code>.</p>
-</div>
-<div id="real" class="section level2">
-<h2 class="hasAnchor">
-<a href="#real" class="anchor"></a>real</h2>
-<p>Returns a new tensor containing real values of the <code>self</code> tensor. The returned tensor and <code>self</code> share the same underlying storage.</p>
-<div id="warning-6" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-6" class="anchor"></a>Warning:</h4>
-<p>[real()] is only supported for tensors with complex dtypes.</p>
-</div>
-<div id="examples-24" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-24" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb25"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, dtype=<span class="fu">torch_cfloat</span>())
-<span class="kw">x</span>
-<span class="kw">x</span><span class="op">$</span><span class="kw">real</span>
-</pre></div>
-</div>
-</div>
-<div id="reciprocal" class="section level2">
-<h2 class="hasAnchor">
-<a href="#reciprocal" class="anchor"></a>reciprocal</h2>
-<p>reciprocal() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_reciprocal.html">?torch_reciprocal</a></code></p>
-</div>
-<div id="reciprocal_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#reciprocal_" class="anchor"></a>reciprocal_</h2>
-<p>reciprocal_() -&gt; Tensor</p>
-<p>In-place version of <code>$reciprocal</code></p>
-</div>
-<div id="record_stream" class="section level2">
-<h2 class="hasAnchor">
-<a href="#record_stream" class="anchor"></a>record_stream</h2>
-<p>record_stream(stream)</p>
-<p>Ensures that the tensor memory is not reused for another tensor until all current work queued on <code>stream</code> are complete.</p>
-<div id="note-6" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-6" class="anchor"></a>Note:</h4>
-<p>The caching allocator is aware of only the stream where a tensor was allocated. Due to the awareness, it already correctly manages the life cycle of tensors on only one stream. But if a tensor is used on a stream different from the stream of origin, the allocator might reuse the memory unexpectedly. Calling this method lets the allocator know which streams have used the tensor.</p>
-</div>
-</div>
-<div id="refine_names" class="section level2">
-<h2 class="hasAnchor">
-<a href="#refine_names" class="anchor"></a>refine_names</h2>
-<p>Refines the dimension names of <code>self</code> according to <code>names</code>.</p>
-<p>Refining is a special case of renaming that “lifts” unnamed dimensions. A <code>NULL</code> dim can be refined to have any name; a named dim can only be refined to have the same name.</p>
-<p>Because named tensors can coexist with unnamed tensors, refining names gives a nice way to write named-tensor-aware code that works with both named and unnamed tensors.</p>
-<p><code>names</code> may contain up to one Ellipsis (<code>...</code>). The Ellipsis is expanded greedily; it is expanded in-place to fill <code>names</code> to the same length as <code>self$dim()</code> using names from the corresponding indices of <code>self$names</code>.</p>
-<div id="arguments-35" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-35" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>names (iterable of str): The desired names of the output tensor. May contain up to one Ellipsis.</li>
-</ul>
-</div>
-<div id="examples-25" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-25" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb26"><pre class="downlit">
-<span class="kw">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">32</span>, <span class="fl">3</span>, <span class="fl">128</span>, <span class="fl">128</span>)
-<span class="kw">named_imgs</span> <span class="op">&lt;-</span> <span class="kw">imgs</span><span class="op">$</span><span class="fu">refine_names</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>))
-<span class="kw">named_imgs</span><span class="op">$</span><span class="kw">names</span>
-</pre></div>
-</div>
-</div>
-<div id="register_hook" class="section level2">
-<h2 class="hasAnchor">
-<a href="#register_hook" class="anchor"></a>register_hook</h2>
-<p>Registers a backward hook.</p>
-<p>The hook will be called every time a gradient with respect to the Tensor is computed. The hook should have the following signature::</p>
-<p>hook(grad) -&gt; Tensor or NULL</p>
-<p>The hook should not modify its argument, but it can optionally return a new gradient which will be used in place of <code>grad</code>.</p>
-<p>This function returns a handle with a method <code>handle$remove()</code> that removes the hook from the module.</p>
-<div id="example" class="section level4">
-<h4 class="hasAnchor">
-<a href="#example" class="anchor"></a>Example</h4>
-<div class="sourceCode" id="cb27"><pre class="downlit">
-<span class="kw">v</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.</span>, <span class="fl">0.</span>, <span class="fl">0.</span>), requires_grad=<span class="fl">TRUE</span>)
-<span class="kw">h</span> <span class="op">&lt;-</span> <span class="kw">v</span><span class="op">$</span><span class="fu">register_hook</span>(<span class="fu">function</span>(<span class="kw">grad</span>) <span class="kw">grad</span> <span class="op">*</span> <span class="fl">2</span>)  <span class="co"># double the gradient</span>
-<span class="kw">v</span><span class="op">$</span><span class="fu">backward</span>(<span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1.</span>, <span class="fl">2.</span>, <span class="fl">3.</span>)))
-<span class="kw">v</span><span class="op">$</span><span class="kw">grad</span>
-<span class="kw">h</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/rm.html">remove</a></span>()
-</pre></div>
-</div>
-</div>
-<div id="remainder" class="section level2">
-<h2 class="hasAnchor">
-<a href="#remainder" class="anchor"></a>remainder</h2>
-<p>remainder(divisor) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_remainder.html">?torch_remainder</a></code></p>
-</div>
-<div id="remainder_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#remainder_" class="anchor"></a>remainder_</h2>
-<p>remainder_(divisor) -&gt; Tensor</p>
-<p>In-place version of <code>$remainder</code></p>
-</div>
-<div id="rename" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rename" class="anchor"></a>rename</h2>
-<p>Renames dimension names of <code>self</code>.</p>
-<p>There are two main usages:</p>
-<p><code>self$rename(**rename_map)</code> returns a view on tensor that has dims renamed as specified in the mapping <code>rename_map</code>.</p>
-<p><code>self$rename(*names)</code> returns a view on tensor, renaming all dimensions positionally using <code>names</code>. Use <code>self$rename(NULL)</code> to drop names on a tensor.</p>
-<p>One cannot specify both positional args <code>names</code> and keyword args <code>rename_map</code>.</p>
-<div id="examples-26" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-26" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb28"><pre class="downlit">
-<span class="kw">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">7</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>))
-<span class="kw">renamed_imgs</span> <span class="op">&lt;-</span> <span class="kw">imgs</span><span class="op">$</span><span class="fu">rename</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"Batch"</span>, <span class="st">"Channels"</span>, <span class="st">"Height"</span>, <span class="st">"Width"</span>))
-</pre></div>
-</div>
-</div>
-<div id="rename_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rename_" class="anchor"></a>rename_</h2>
-<p>In-place version of <code>$rename</code>.</p>
-</div>
-<div id="renorm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#renorm" class="anchor"></a>renorm</h2>
-<p>renorm(p, dim, maxnorm) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_renorm.html">?torch_renorm</a></code></p>
-</div>
-<div id="renorm_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#renorm_" class="anchor"></a>renorm_</h2>
-<p>renorm_(p, dim, maxnorm) -&gt; Tensor</p>
-<p>In-place version of <code>$renorm</code></p>
-</div>
-<div id="repeat" class="section level2">
-<h2 class="hasAnchor">
-<a href="#repeat" class="anchor"></a>repeat</h2>
-<p>repeat(*sizes) -&gt; Tensor</p>
-<p>Repeats this tensor along the specified dimensions.</p>
-<p>Unlike <code>$expand</code>, this function copies the tensor’s data.</p>
-<div id="arguments-36" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-36" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>sizes (torch_Size or int…): The number of times to repeat this tensor along each</li>
-<li>dimension</li>
-</ul>
-</div>
-<div id="examples-27" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-27" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb29"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>))
-<span class="kw">x</span><span class="op">$</span><span class="fu">`repeat`</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">4</span>, <span class="fl">2</span>))
-<span class="kw">x</span><span class="op">$</span><span class="fu">`repeat`</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">4</span>, <span class="fl">2</span>, <span class="fl">1</span>))<span class="op">$</span><span class="fu">size</span>()
-</pre></div>
-</div>
-</div>
-<div id="repeat_interleave" class="section level2">
-<h2 class="hasAnchor">
-<a href="#repeat_interleave" class="anchor"></a>repeat_interleave</h2>
-<p>repeat_interleave(repeats, dim=NULL) -&gt; Tensor</p>
-<p>See [torch_repeat_interleave()].</p>
-</div>
-<div id="requires_grad" class="section level2">
-<h2 class="hasAnchor">
-<a href="#requires_grad" class="anchor"></a>requires_grad</h2>
-<p>Is <code>TRUE</code> if gradients need to be computed for this Tensor, <code>FALSE</code> otherwise.</p>
-<div id="note-7" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-7" class="anchor"></a>Note:</h4>
-<p>The fact that gradients need to be computed for a Tensor do not mean that the <code>grad</code> attribute will be populated, see <code>is_leaf</code> for more details.</p>
-</div>
-</div>
-<div id="requires_grad_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#requires_grad_" class="anchor"></a>requires_grad_</h2>
-<p>requires_grad_(requires_grad=TRUE) -&gt; Tensor</p>
-<p>Change if autograd should record operations on this tensor: sets this tensor’s <code>requires_grad</code> attribute in-place. Returns this tensor.</p>
-<p>[requires_grad_()]’s main use case is to tell autograd to begin recording operations on a Tensor <code>tensor</code>. If <code>tensor</code> has <code>requires_grad=FALSE</code> (because it was obtained through a DataLoader, or required preprocessing or initialization), <code>tensor.requires_grad_()</code> makes it so that autograd will begin to record operations on <code>tensor</code>.</p>
-<div id="arguments-37" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-37" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>requires_grad (bool): If autograd should record operations on this tensor. Default: <code>TRUE</code>.</li>
-</ul>
-</div>
-<div id="examples-28" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-28" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb30"><pre class="downlit">
-<span class="co"># Let's say we want to preprocess some saved weights and use</span>
-<span class="co"># the result as new weights.</span>
-<span class="kw">saved_weights</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.1</span>, <span class="fl">0.2</span>, <span class="fl">0.3</span>, <span class="fl">0.25</span>)
-<span class="kw">loaded_weights</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="kw">saved_weights</span>)
-<span class="kw">weights</span> <span class="op">&lt;-</span> <span class="fu">preprocess</span>(<span class="kw">loaded_weights</span>)  <span class="co"># some function</span>
-<span class="kw">weights</span>
-
-<span class="co"># Now, start to record operations done to weights</span>
-<span class="kw">weights</span><span class="op">$</span><span class="fu">requires_grad_</span>()
-<span class="kw">out</span> <span class="op">&lt;-</span> <span class="kw">weights</span><span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
-<span class="kw">out</span><span class="op">$</span><span class="fu">backward</span>()
-<span class="kw">weights</span><span class="op">$</span><span class="kw">grad</span>
-</pre></div>
-</div>
-</div>
-<div id="reshape" class="section level2">
-<h2 class="hasAnchor">
-<a href="#reshape" class="anchor"></a>reshape</h2>
-<p>reshape(*shape) -&gt; Tensor</p>
-<p>Returns a tensor with the same data and number of elements as <code>self</code> but with the specified shape. This method returns a view if <code>shape</code> is compatible with the current shape. See <code>$view</code> on when it is possible to return a view.</p>
-<p>See <code><a href="../../reference/torch_reshape.html">?torch_reshape</a></code></p>
-<div id="arguments-38" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-38" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>shape (tuple of ints or int…): the desired shape</li>
-</ul>
-</div>
-</div>
-<div id="reshape_as" class="section level2">
-<h2 class="hasAnchor">
-<a href="#reshape_as" class="anchor"></a>reshape_as</h2>
-<p>reshape_as(other) -&gt; Tensor</p>
-<p>Returns this tensor as the same shape as <code>other</code>. <code>self$reshape_as(other)</code> is equivalent to <code>self$reshape(other.sizes())</code>. This method returns a view if <code>other.sizes()</code> is compatible with the current shape. See <code>$view</code> on when it is possible to return a view.</p>
-<p>Please see <code>reshape</code> for more information about <code>reshape</code>.</p>
-<div id="arguments-39" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-39" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>other (`$): The result tensor has the same shape</li>
-<li>as <code>other</code>.</li>
-</ul>
-</div>
-</div>
-<div id="resize_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#resize_" class="anchor"></a>resize_</h2>
-<p>resize_(*sizes, memory_format=torch_contiguous_format) -&gt; Tensor</p>
-<p>Resizes <code>self</code> tensor to the specified size. If the number of elements is larger than the current storage size, then the underlying storage is resized to fit the new number of elements. If the number of elements is smaller, the underlying storage is not changed. Existing elements are preserved but any new memory is uninitialized.</p>
-<div id="warning-7" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-7" class="anchor"></a>Warning:</h4>
-<p>This is a low-level method. The storage is reinterpreted as C-contiguous, ignoring the current strides (unless the target size equals the current size, in which case the tensor is left unchanged). For most purposes, you will instead want to use <code>$view()</code>, which checks for contiguity, or <code>$reshape()</code>, which copies data if needed. To change the size in-place with custom strides, see <code>$set_()</code>.</p>
-</div>
-<div id="arguments-40" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-40" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>sizes (torch_Size or int…): the desired size</li>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of Tensor. Default: <code>torch_contiguous_format</code>. Note that memory format of <code>self</code> is going to be unaffected if <code>self$size()</code> matches <code>sizes</code>.</li>
-</ul>
-</div>
-<div id="examples-29" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-29" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb31"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">6</span>, ncol = <span class="fl">2</span>))
-<span class="kw">x</span><span class="op">$</span><span class="fu">resize_</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">2</span>))
-</pre></div>
-</div>
-</div>
-<div id="resize_as_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#resize_as_" class="anchor"></a>resize_as_</h2>
-<p>resize_as_(tensor, memory_format=torch_contiguous_format) -&gt; Tensor</p>
-<p>Resizes the <code>self</code> tensor to be the same size as the specified <code>tensor</code>. This is equivalent to <code>self$resize_(tensor.size())</code>.</p>
-<div id="arguments-41" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-41" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of Tensor. Default: <code>torch_contiguous_format</code>. Note that memory format of <code>self</code> is going to be unaffected if <code>self$size()</code> matches <code>tensor.size()</code>.</li>
-</ul>
-</div>
-</div>
-<div id="retain_grad" class="section level2">
-<h2 class="hasAnchor">
-<a href="#retain_grad" class="anchor"></a>retain_grad</h2>
-<p>Enables <code>$grad</code> attribute for non-leaf Tensors.</p>
-</div>
-<div id="rfft" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rfft" class="anchor"></a>rfft</h2>
-<p>rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_rfft.html">?torch_rfft</a></code></p>
-</div>
-<div id="roll" class="section level2">
-<h2 class="hasAnchor">
-<a href="#roll" class="anchor"></a>roll</h2>
-<p>roll(shifts, dims) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_roll.html">?torch_roll</a></code></p>
-</div>
-<div id="rot90" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rot90" class="anchor"></a>rot90</h2>
-<p>rot90(k, dims) -&gt; Tensor</p>
-<p>See [torch_rot90()]</p>
-</div>
-<div id="round" class="section level2">
-<h2 class="hasAnchor">
-<a href="#round" class="anchor"></a>round</h2>
-<p>round() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_round.html">?torch_round</a></code></p>
-</div>
-<div id="round_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#round_" class="anchor"></a>round_</h2>
-<p>round_() -&gt; Tensor</p>
-<p>In-place version of <code>$round</code></p>
-</div>
-<div id="rsqrt" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rsqrt" class="anchor"></a>rsqrt</h2>
-<p>rsqrt() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_rsqrt.html">?torch_rsqrt</a></code></p>
-</div>
-<div id="rsqrt_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rsqrt_" class="anchor"></a>rsqrt_</h2>
-<p>rsqrt_() -&gt; Tensor</p>
-<p>In-place version of <code>$rsqrt</code></p>
-</div>
-<div id="scatter" class="section level2">
-<h2 class="hasAnchor">
-<a href="#scatter" class="anchor"></a>scatter</h2>
-<p>scatter(dim, index, src) -&gt; Tensor</p>
-<p>Out-of-place version of <code>$scatter_</code></p>
-</div>
-<div id="scatter_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#scatter_" class="anchor"></a>scatter_</h2>
-<p>scatter_(dim, index, src) -&gt; Tensor</p>
-<p>Writes all values from the tensor <code>src</code> into <code>self</code> at the indices specified in the <code>index</code> tensor. For each value in <code>src</code>, its output index is specified by its index in <code>src</code> for <code>dimension != dim</code> and by the corresponding value in <code>index</code> for <code>dimension = dim</code>.</p>
-<p>For a 3-D tensor, <code>self</code> is updated as:</p>
-<pre><code>self[index[i][j][k]][j][k] = src[i][j][k]  # if dim == 0
-self[i][index[i][j][k]][k] = src[i][j][k]  # if dim == 1
-self[i][j][index[i][j][k]] = src[i][j][k]  # if dim == 2</code></pre>
-<p>This is the reverse operation of the manner described in <code>$gather</code>.</p>
-<p><code>self</code>, <code>index</code> and <code>src</code> (if it is a Tensor) should have same number of dimensions. It is also required that <code>index.size(d) &lt;= src.size(d)</code> for all dimensions <code>d</code>, and that <code>index.size(d) &lt;= self$size(d)</code> for all dimensions <code>d != dim</code>.</p>
-<p>Moreover, as for <code>$gather</code>, the values of <code>index</code> must be between <code>0</code> and <code>self$size(dim) - 1</code> inclusive, and all values in a row along the specified dimension <code>dim</code> must be unique.</p>
-<div id="arguments-42" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-42" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dim (int): the axis along which to index</li>
-<li>index (LongTensor): the indices of elements to scatter,</li>
-<li>can be either empty or the same size of src. When empty, the operation returns identity</li>
-<li>src (Tensor): the source element(s) to scatter,</li>
-<li>incase <code>value</code> is not specified</li>
-<li>value (float): the source element(s) to scatter,</li>
-<li>incase <code>src</code> is not specified</li>
-</ul>
-</div>
-<div id="examples-30" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-30" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb33"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">2</span>, <span class="fl">5</span>)
-<span class="kw">x</span>
-<span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">3</span>, <span class="fl">5</span>)<span class="op">$</span><span class="fu">scatter_</span>(
-        <span class="fl">1</span>, 
-        <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/cbind.html">rbind</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">1</span>), <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>)), <span class="kw">x</span>)
-)
-
-<span class="kw">z</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">2</span>, <span class="fl">4</span>)<span class="op">$</span><span class="fu">scatter_</span>(
-        <span class="fl">2</span>, 
-        <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">3</span><span class="op">:</span><span class="fl">4</span>, ncol = <span class="fl">1</span>)), <span class="fl">1.23</span>
-)
-</pre></div>
-</div>
-</div>
-<div id="scatter_add" class="section level2">
-<h2 class="hasAnchor">
-<a href="#scatter_add" class="anchor"></a>scatter_add</h2>
-<p>scatter_add(dim, index, src) -&gt; Tensor</p>
-<p>Out-of-place version of <code>$scatter_add_</code></p>
-</div>
-<div id="scatter_add_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#scatter_add_" class="anchor"></a>scatter_add_</h2>
-<p>scatter_add_(dim, index, src) -&gt; Tensor</p>
-<p>Adds all values from the tensor <code>other</code> into <code>self</code> at the indices specified in the <code>index</code> tensor in a similar fashion as <code>~$scatter_</code>. For each value in <code>src</code>, it is added to an index in <code>self</code> which is specified by its index in <code>src</code> for <code>dimension != dim</code> and by the corresponding value in <code>index</code> for <code>dimension = dim</code>.</p>
-<p>For a 3-D tensor, <code>self</code> is updated as::</p>
-<pre><code>self[index[i][j][k]][j][k] += src[i][j][k]  # if dim == 0
-self[i][index[i][j][k]][k] += src[i][j][k]  # if dim == 1
-self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2</code></pre>
-<p><code>self</code>, <code>index</code> and <code>src</code> should have same number of dimensions. It is also required that <code>index.size(d) &lt;= src.size(d)</code> for all dimensions <code>d</code>, and that <code>index.size(d) &lt;= self$size(d)</code> for all dimensions <code>d != dim</code>.</p>
-<div id="note-8" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-8" class="anchor"></a>Note:</h4>
-<p>In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting <code>torch_backends.cudnn.deterministic = TRUE</code>.</p>
-</div>
-<div id="arguments-43" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-43" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dim (int): the axis along which to index</li>
-<li>index (LongTensor): the indices of elements to scatter and add,</li>
-<li>can be either empty or the same size of src. When empty, the operation returns identity.</li>
-<li>src (Tensor): the source elements to scatter and add</li>
-</ul>
-</div>
-<div id="examples-31" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-31" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb35"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">2</span>, <span class="fl">5</span>)
-<span class="kw">x</span>
-<span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">3</span>, <span class="fl">5</span>)<span class="op">$</span><span class="fu">scatter_add_</span>(<span class="fl">1</span>, <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/cbind.html">rbind</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">0</span>, <span class="fl">0</span>), <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">0</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>))), <span class="kw">x</span>)
-</pre></div>
-</div>
-</div>
-<div id="select" class="section level2">
-<h2 class="hasAnchor">
-<a href="#select" class="anchor"></a>select</h2>
-<p>select(dim, index) -&gt; Tensor</p>
-<p>Slices the <code>self</code> tensor along the selected dimension at the given index. This function returns a view of the original tensor with the given dimension removed.</p>
-<div id="arguments-44" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-44" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dim (int): the dimension to slice</li>
-<li>index (int): the index to select with</li>
-</ul>
-</div>
-<div id="note-9" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-9" class="anchor"></a>Note:</h4>
-<p><code>select</code> is equivalent to slicing. For example, <code>tensor$select(0, index)</code> is equivalent to <code>tensor[index]</code> and <code>tensor$select(2, index)</code> is equivalent to <code>tensor[:,:,index]</code>.</p>
-</div>
-</div>
-<div id="set_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#set_" class="anchor"></a>set_</h2>
-<p>set_(source=NULL, storage_offset=0, size=NULL, stride=NULL) -&gt; Tensor</p>
-<p>Sets the underlying storage, size, and strides. If <code>source</code> is a tensor, <code>self</code> tensor will share the same storage and have the same size and strides as <code>source</code>. Changes to elements in one tensor will be reflected in the other.</p>
-<div id="arguments-45" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-45" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>source (Tensor or Storage): the tensor or storage to use</li>
-<li>storage_offset (int, optional): the offset in the storage</li>
-<li>size (torch_Size, optional): the desired size. Defaults to the size of the source.</li>
-<li>stride (tuple, optional): the desired stride. Defaults to C-contiguous strides.</li>
-</ul>
-</div>
-</div>
-<div id="share_memory_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#share_memory_" class="anchor"></a>share_memory_</h2>
-<p>Moves the underlying storage to shared memory.</p>
-<p>This is a no-op if the underlying storage is already in shared memory and for CUDA tensors. Tensors in shared memory cannot be resized.</p>
-</div>
-<div id="short" class="section level2">
-<h2 class="hasAnchor">
-<a href="#short" class="anchor"></a>short</h2>
-<p>short(memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p><code>self$short()</code> is equivalent to <code>self$to(torch_int16)</code>. See [to()].</p>
-<div id="arguments-46" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-46" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
-<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-</div>
-</div>
-<div id="sigmoid" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sigmoid" class="anchor"></a>sigmoid</h2>
-<p>sigmoid() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_sigmoid.html">?torch_sigmoid</a></code></p>
-</div>
-<div id="sigmoid_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sigmoid_" class="anchor"></a>sigmoid_</h2>
-<p>sigmoid_() -&gt; Tensor</p>
-<p>In-place version of <code>$sigmoid</code></p>
-</div>
-<div id="sign" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sign" class="anchor"></a>sign</h2>
-<p>sign() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_sign.html">?torch_sign</a></code></p>
-</div>
-<div id="sign_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sign_" class="anchor"></a>sign_</h2>
-<p>sign_() -&gt; Tensor</p>
-<p>In-place version of <code>$sign</code></p>
-</div>
-<div id="sin" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sin" class="anchor"></a>sin</h2>
-<p>sin() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_sin.html">?torch_sin</a></code></p>
-</div>
-<div id="sin_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sin_" class="anchor"></a>sin_</h2>
-<p>sin_() -&gt; Tensor</p>
-<p>In-place version of <code>$sin</code></p>
-</div>
-<div id="sinh" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sinh" class="anchor"></a>sinh</h2>
-<p>sinh() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_sinh.html">?torch_sinh</a></code></p>
-</div>
-<div id="sinh_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sinh_" class="anchor"></a>sinh_</h2>
-<p>sinh_() -&gt; Tensor</p>
-<p>In-place version of <code>$sinh</code></p>
-</div>
-<div id="size" class="section level2">
-<h2 class="hasAnchor">
-<a href="#size" class="anchor"></a>size</h2>
-<p>size() -&gt; torch_Size</p>
-<p>Returns the size of the <code>self</code> tensor. The returned value is a subclass of <code>tuple</code>.</p>
-<div id="examples-32" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-32" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb36"><pre class="downlit">
-<span class="fu"><a href="../../reference/torch_empty.html">torch_empty</a></span>(<span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>)<span class="op">$</span><span class="fu">size</span>()
-</pre></div>
-</div>
-</div>
-<div id="slogdet" class="section level2">
-<h2 class="hasAnchor">
-<a href="#slogdet" class="anchor"></a>slogdet</h2>
-<p>slogdet() -&gt; (Tensor, Tensor)</p>
-<p>See <code><a href="../../reference/torch_slogdet.html">?torch_slogdet</a></code></p>
-</div>
-<div id="solve" class="section level2">
-<h2 class="hasAnchor">
-<a href="#solve" class="anchor"></a>solve</h2>
-<p>solve(A) -&gt; Tensor, Tensor</p>
-<p>See <code><a href="../../reference/torch_solve.html">?torch_solve</a></code></p>
-</div>
-<div id="sort" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sort" class="anchor"></a>sort</h2>
-<p>sort(dim=-1, descending=FALSE) -&gt; (Tensor, LongTensor)</p>
-<p>See <code><a href="../../reference/torch_sort.html">?torch_sort</a></code></p>
-</div>
-<div id="sparse_dim" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sparse_dim" class="anchor"></a>sparse_dim</h2>
-<p>sparse_dim() -&gt; int</p>
-<p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns the number of sparse dimensions. Otherwise, this throws an error.</p>
-<p>See also <code>Tensor.dense_dim</code>.</p>
-</div>
-<div id="sparse_mask" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sparse_mask" class="anchor"></a>sparse_mask</h2>
-<p>sparse_mask(input, mask) -&gt; Tensor</p>
-<p>Returns a new SparseTensor with values from Tensor <code>input</code> filtered by indices of <code>mask</code> and values are ignored. <code>input</code> and <code>mask</code> must have the same shape.</p>
-<div id="arguments-47" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-47" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>input (Tensor): an input Tensor</li>
-<li>mask (SparseTensor): a SparseTensor which we filter <code>input</code> based on its indices</li>
-</ul>
-</div>
-</div>
-<div id="split" class="section level2">
-<h2 class="hasAnchor">
-<a href="#split" class="anchor"></a>split</h2>
-<p>See <code><a href="../../reference/torch_split.html">?torch_split</a></code></p>
-</div>
-<div id="sqrt" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sqrt" class="anchor"></a>sqrt</h2>
-<p>sqrt() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_sqrt.html">?torch_sqrt</a></code></p>
-</div>
-<div id="sqrt_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sqrt_" class="anchor"></a>sqrt_</h2>
-<p>sqrt_() -&gt; Tensor</p>
-<p>In-place version of <code>$sqrt</code></p>
-</div>
-<div id="square" class="section level2">
-<h2 class="hasAnchor">
-<a href="#square" class="anchor"></a>square</h2>
-<p>square() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_square.html">?torch_square</a></code></p>
-</div>
-<div id="square_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#square_" class="anchor"></a>square_</h2>
-<p>square_() -&gt; Tensor</p>
-<p>In-place version of <code>$square</code></p>
-</div>
-<div id="squeeze" class="section level2">
-<h2 class="hasAnchor">
-<a href="#squeeze" class="anchor"></a>squeeze</h2>
-<p>squeeze(dim=NULL) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_squeeze.html">?torch_squeeze</a></code></p>
-</div>
-<div id="squeeze_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#squeeze_" class="anchor"></a>squeeze_</h2>
-<p>squeeze_(dim=NULL) -&gt; Tensor</p>
-<p>In-place version of <code>$squeeze</code></p>
-</div>
-<div id="std" class="section level2">
-<h2 class="hasAnchor">
-<a href="#std" class="anchor"></a>std</h2>
-<p>std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_std.html">?torch_std</a></code></p>
-</div>
-<div id="stft" class="section level2">
-<h2 class="hasAnchor">
-<a href="#stft" class="anchor"></a>stft</h2>
-<p>See <code><a href="../../reference/torch_stft.html">?torch_stft</a></code></p>
-</div>
-<div id="storage" class="section level2">
-<h2 class="hasAnchor">
-<a href="#storage" class="anchor"></a>storage</h2>
-<p>storage() -&gt; torch_Storage</p>
-<p>Returns the underlying storage.</p>
-</div>
-<div id="storage_offset" class="section level2">
-<h2 class="hasAnchor">
-<a href="#storage_offset" class="anchor"></a>storage_offset</h2>
-<p>storage_offset() -&gt; int</p>
-<p>Returns <code>self</code> tensor’s offset in the underlying storage in terms of number of storage elements (not bytes).</p>
-<div id="examples-33" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-33" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb37"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>))
-<span class="kw">x</span><span class="op">$</span><span class="fu">storage_offset</span>()
-<span class="kw">x</span>[<span class="fl">3</span><span class="op">:</span><span class="kw">N</span>]<span class="op">$</span><span class="fu">storage_offset</span>()
-</pre></div>
-</div>
-</div>
-<div id="storage_type" class="section level2">
-<h2 class="hasAnchor">
-<a href="#storage_type" class="anchor"></a>storage_type</h2>
-<p>storage_type() -&gt; type</p>
-<p>Returns the type of the underlying storage.</p>
-</div>
-<div id="stride" class="section level2">
-<h2 class="hasAnchor">
-<a href="#stride" class="anchor"></a>stride</h2>
-<p>stride(dim) -&gt; tuple or int</p>
-<p>Returns the stride of <code>self</code> tensor.</p>
-<p>Stride is the jump necessary to go from one element to the next one in the specified dimension <code>dim</code>. A tuple of all strides is returned when no argument is passed in. Otherwise, an integer value is returned as the stride in the particular dimension <code>dim</code>.</p>
-<div id="arguments-48" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-48" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dim (int, optional): the desired dimension in which stride is required</li>
-</ul>
-</div>
-<div id="examples-34" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-34" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb38"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, nrow = <span class="fl">2</span>))
-<span class="kw">x</span><span class="op">$</span><span class="fu">stride</span>()
-<span class="kw">x</span><span class="op">$</span><span class="fu">stride</span>(<span class="fl">1</span>)
-<span class="kw">x</span><span class="op">$</span><span class="fu">stride</span>(<span class="op">-</span><span class="fl">1</span>)
-</pre></div>
-</div>
-</div>
-<div id="sub" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sub" class="anchor"></a>sub</h2>
-<p>sub(other, *, alpha=1) -&gt; Tensor</p>
-<p>Subtracts a scalar or tensor from <code>self</code> tensor. If both <code>alpha</code> and <code>other</code> are specified, each element of <code>other</code> is scaled by <code>alpha</code> before being used.</p>
-<p>When <code>other</code> is a tensor, the shape of <code>other</code> must be <code>broadcastable &lt;broadcasting-semantics&gt;</code> with the shape of the underlying tensor.</p>
-</div>
-<div id="sub_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sub_" class="anchor"></a>sub_</h2>
-<p>sub_(other, *, alpha=1) -&gt; Tensor</p>
-<p>In-place version of <code>$sub</code></p>
-</div>
-<div id="sum" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sum" class="anchor"></a>sum</h2>
-<p>sum(dim=NULL, keepdim=FALSE, dtype=NULL) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_sum.html">?torch_sum</a></code></p>
-</div>
-<div id="sum_to_size" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sum_to_size" class="anchor"></a>sum_to_size</h2>
-<p>sum_to_size(*size) -&gt; Tensor</p>
-<p>Sum <code>this</code> tensor to <code>size</code>. <code>size</code> must be broadcastable to <code>this</code> tensor size.</p>
-<div id="arguments-49" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-49" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>size (int…): a sequence of integers defining the shape of the output tensor.</li>
-</ul>
-</div>
-</div>
-<div id="svd" class="section level2">
-<h2 class="hasAnchor">
-<a href="#svd" class="anchor"></a>svd</h2>
-<p>svd(some=TRUE, compute_uv=TRUE) -&gt; (Tensor, Tensor, Tensor)</p>
-<p>See <code><a href="../../reference/torch_svd.html">?torch_svd</a></code></p>
-</div>
-<div id="symeig" class="section level2">
-<h2 class="hasAnchor">
-<a href="#symeig" class="anchor"></a>symeig</h2>
-<p>symeig(eigenvectors=FALSE, upper=TRUE) -&gt; (Tensor, Tensor)</p>
-<p>See <code><a href="../../reference/torch_symeig.html">?torch_symeig</a></code></p>
-</div>
-<div id="t-1" class="section level2">
-<h2 class="hasAnchor">
-<a href="#t-1" class="anchor"></a>t</h2>
-<p>t() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_t.html">?torch_t</a></code></p>
-</div>
-<div id="t_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#t_" class="anchor"></a>t_</h2>
-<p>t_() -&gt; Tensor</p>
-<p>In-place version of <code>$t</code></p>
-</div>
-<div id="take" class="section level2">
-<h2 class="hasAnchor">
-<a href="#take" class="anchor"></a>take</h2>
-<p>take(indices) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_take.html">?torch_take</a></code></p>
-</div>
-<div id="tan" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tan" class="anchor"></a>tan</h2>
-<p>tan() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_tan.html">?torch_tan</a></code></p>
-</div>
-<div id="tan_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tan_" class="anchor"></a>tan_</h2>
-<p>tan_() -&gt; Tensor</p>
-<p>In-place version of <code>$tan</code></p>
-</div>
-<div id="tanh" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tanh" class="anchor"></a>tanh</h2>
-<p>tanh() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_tanh.html">?torch_tanh</a></code></p>
-</div>
-<div id="tanh_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tanh_" class="anchor"></a>tanh_</h2>
-<p>tanh_() -&gt; Tensor</p>
-<p>In-place version of <code>$tanh</code></p>
-</div>
-<div id="to" class="section level2">
-<h2 class="hasAnchor">
-<a href="#to" class="anchor"></a>to</h2>
-<p>to(*args, **kwargs) -&gt; Tensor</p>
-<p>Performs Tensor dtype and/or device conversion. A <code>torch_dtype</code> and :class:<code>torch_device</code> are inferred from the arguments of <code>self$to(*args, **kwargs)</code>.</p>
-<div id="note-10" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-10" class="anchor"></a>Note:</h4>
-<p>If the <code>self</code> Tensor already has the correct <code>torch_dtype</code> and :class:<code>torch_device</code>, then <code>self</code> is returned. Otherwise, the returned tensor is a copy of <code>self</code> with the desired <code>torch_dtype</code> and :class:<code>torch_device</code>.</p>
-<p>Here are the ways to call <code>to</code>:</p>
-<p>to(dtype, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p>Returns a Tensor with the specified <code>dtype</code></p>
-</div>
-<div id="arguments-50" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-50" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-<p>to(device=NULL, dtype=NULL, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -&gt; Tensor</p>
-<p>Returns a Tensor with the specified <code>device</code> and (optional) <code>dtype</code>. If <code>dtype</code> is <code>NULL</code> it is inferred to be <code>self$dtype</code>. When <code>non_blocking</code>, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor.</p>
-<p>When <code>copy</code> is set, a new Tensor is created even when the Tensor already matches the desired conversion.</p>
-</div>
-<div id="arguments-51" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-51" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of returned Tensor. Default: <code>torch_preserve_format</code>.</li>
-</ul>
-<p>function:: to(other, non_blocking=FALSE, copy=FALSE) -&gt; Tensor</p>
-<p>Returns a Tensor with same <code>torch_dtype</code> and :class:<code>torch_device</code> as the Tensor <code>other</code>. When <code>non_blocking</code>, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor.</p>
-<p>When <code>copy</code> is set, a new Tensor is created even when the Tensor already matches the desired conversion.</p>
-</div>
-<div id="examples-35" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-35" class="anchor"></a>Examples:</h4>
-<div class="sourceCode" id="cb39"><pre class="downlit">
-<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">2</span>)  <span class="co"># Initially dtype=float32, device=cpu</span>
-<span class="kw">tensor</span><span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span>())
-
-<span class="kw">other</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span>())
-<span class="kw">tensor</span><span class="op">$</span><span class="fu">to</span>(other = <span class="kw">other</span>, non_blocking=<span class="fl">TRUE</span>)
-</pre></div>
-</div>
-</div>
-<div id="to_mkldnn" class="section level2">
-<h2 class="hasAnchor">
-<a href="#to_mkldnn" class="anchor"></a>to_mkldnn</h2>
-<p>to_mkldnn() -&gt; Tensor Returns a copy of the tensor in <code>torch_mkldnn</code> layout.</p>
-</div>
-<div id="to_sparse" class="section level2">
-<h2 class="hasAnchor">
-<a href="#to_sparse" class="anchor"></a>to_sparse</h2>
-<p>to_sparse(sparseDims) -&gt; Tensor Returns a sparse copy of the tensor. PyTorch supports sparse tensors in <code>coordinate format &lt;sparse-docs&gt;</code>.</p>
-<div id="arguments-52" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-52" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>sparseDims (int, optional): the number of sparse dimensions to include in the new sparse tensor</li>
-</ul>
-</div>
-</div>
-<div id="tolist" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tolist" class="anchor"></a>tolist</h2>
-<p>tolist() -&gt; list or number</p>
-<p>Returns the tensor as a (nested) list. For scalars, a standard Python number is returned, just like with <code>$item</code>. Tensors are automatically moved to the CPU first if necessary.</p>
-<p>This operation is not differentiable.</p>
-</div>
-<div id="topk" class="section level2">
-<h2 class="hasAnchor">
-<a href="#topk" class="anchor"></a>topk</h2>
-<p>topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -&gt; (Tensor, LongTensor)</p>
-<p>See <code><a href="../../reference/torch_topk.html">?torch_topk</a></code></p>
-</div>
-<div id="trace" class="section level2">
-<h2 class="hasAnchor">
-<a href="#trace" class="anchor"></a>trace</h2>
-<p>trace() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_trace.html">?torch_trace</a></code></p>
-</div>
-<div id="transpose" class="section level2">
-<h2 class="hasAnchor">
-<a href="#transpose" class="anchor"></a>transpose</h2>
-<p>transpose(dim0, dim1) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_transpose.html">?torch_transpose</a></code></p>
-</div>
-<div id="transpose_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#transpose_" class="anchor"></a>transpose_</h2>
-<p>transpose_(dim0, dim1) -&gt; Tensor</p>
-<p>In-place version of <code>$transpose</code></p>
-</div>
-<div id="triangular_solve" class="section level2">
-<h2 class="hasAnchor">
-<a href="#triangular_solve" class="anchor"></a>triangular_solve</h2>
-<p>triangular_solve(A, upper=TRUE, transpose=FALSE, unitriangular=FALSE) -&gt; (Tensor, Tensor)</p>
-<p>See [torch_triangular_solve()]</p>
-</div>
-<div id="tril" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tril" class="anchor"></a>tril</h2>
-<p>tril(k=0) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_tril.html">?torch_tril</a></code></p>
-</div>
-<div id="tril_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tril_" class="anchor"></a>tril_</h2>
-<p>tril_(k=0) -&gt; Tensor</p>
-<p>In-place version of <code>$tril</code></p>
-</div>
-<div id="triu" class="section level2">
-<h2 class="hasAnchor">
-<a href="#triu" class="anchor"></a>triu</h2>
-<p>triu(k=0) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_triu.html">?torch_triu</a></code></p>
-</div>
-<div id="triu_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#triu_" class="anchor"></a>triu_</h2>
-<p>triu_(k=0) -&gt; Tensor</p>
-<p>In-place version of <code>$triu</code></p>
-</div>
-<div id="true_divide" class="section level2">
-<h2 class="hasAnchor">
-<a href="#true_divide" class="anchor"></a>true_divide</h2>
-<p>true_divide(value) -&gt; Tensor</p>
-<p>See [torch_true_divide()]</p>
-</div>
-<div id="true_divide_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#true_divide_" class="anchor"></a>true_divide_</h2>
-<p>true_divide_(value) -&gt; Tensor</p>
-<p>In-place version of <code>$true_divide_</code></p>
-</div>
-<div id="trunc" class="section level2">
-<h2 class="hasAnchor">
-<a href="#trunc" class="anchor"></a>trunc</h2>
-<p>trunc() -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_trunc.html">?torch_trunc</a></code></p>
-</div>
-<div id="trunc_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#trunc_" class="anchor"></a>trunc_</h2>
-<p>trunc_() -&gt; Tensor</p>
-<p>In-place version of <code>$trunc</code></p>
-</div>
-<div id="type" class="section level2">
-<h2 class="hasAnchor">
-<a href="#type" class="anchor"></a>type</h2>
-<p>type(dtype=NULL, non_blocking=FALSE, **kwargs) -&gt; str or Tensor Returns the type if <code>dtype</code> is not provided, else casts this object to the specified type.</p>
-<p>If this is already of the correct type, no copy is performed and the original object is returned.</p>
-<div id="arguments-53" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-53" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dtype (type or string): The desired type</li>
-<li>non_blocking (bool): If <code>TRUE</code>, and the source is in pinned memory</li>
-<li>and destination is on the GPU or vice versa, the copy is performed</li>
-<li>asynchronously with respect to the host. Otherwise, the argument</li>
-<li>has no effect. **kwargs: For compatibility, may contain the key <code>async</code> in place of</li>
-<li>the <code>non_blocking</code> argument. The <code>async</code> arg is deprecated.</li>
-</ul>
-</div>
-</div>
-<div id="type_as" class="section level2">
-<h2 class="hasAnchor">
-<a href="#type_as" class="anchor"></a>type_as</h2>
-<p>type_as(tensor) -&gt; Tensor</p>
-<p>Returns this tensor cast to the type of the given tensor.</p>
-<p>This is a no-op if the tensor is already of the correct type. This is equivalent to <code>self$type(tensor.type())</code></p>
-<div id="arguments-54" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-54" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>tensor (Tensor): the tensor which has the desired type</li>
-</ul>
-</div>
-</div>
-<div id="unbind" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unbind" class="anchor"></a>unbind</h2>
-<p>unbind(dim=0) -&gt; seq</p>
-<p>See <code><a href="../../reference/torch_unbind.html">?torch_unbind</a></code></p>
-</div>
-<div id="unflatten" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unflatten" class="anchor"></a>unflatten</h2>
-<p>Unflattens the named dimension <code>dim</code>, viewing it in the shape specified by <code>namedshape</code>.</p>
-<div id="arguments-55" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-55" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>namedshape: (iterable of <code>(name, size)</code> tuples).</li>
-</ul>
-</div>
-</div>
-<div id="unfold" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unfold" class="anchor"></a>unfold</h2>
-<p>unfold(dimension, size, step) -&gt; Tensor</p>
-<p>Returns a view of the original tensor which contains all slices of size <code>size</code> from <code>self</code> tensor in the dimension <code>dimension</code>.</p>
-<p>Step between two slices is given by <code>step</code>.</p>
-<p>If <code>sizedim</code> is the size of dimension <code>dimension</code> for <code>self</code>, the size of dimension <code>dimension</code> in the returned tensor will be <code>(sizedim - size) / step + 1</code>.</p>
-<p>An additional dimension of size <code>size</code> is appended in the returned tensor.</p>
-<div id="arguments-56" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-56" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>dimension (int): dimension in which unfolding happens</li>
-<li>size (int): the size of each slice that is unfolded</li>
-<li>step (int): the step between each slice</li>
-</ul>
-</div>
-</div>
-<div id="uniform_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#uniform_" class="anchor"></a>uniform_</h2>
-<p>uniform_(from=0, to=1) -&gt; Tensor</p>
-<p>Fills <code>self</code> tensor with numbers sampled from the continuous uniform distribution:</p>
-<p><span class="math display">\[
-P(x) = \dfrac{1}{\text{to} - \text{from}}
-\]</span></p>
-</div>
-<div id="unique" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unique" class="anchor"></a>unique</h2>
-<p>Returns the unique elements of the input tensor.</p>
-<p>See <code>?torch_unique</code></p>
-</div>
-<div id="unique_consecutive" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unique_consecutive" class="anchor"></a>unique_consecutive</h2>
-<p>Eliminates all but the first element from every consecutive group of equivalent elements.</p>
-<p>See [torch_unique_consecutive()]</p>
-</div>
-<div id="unsqueeze" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unsqueeze" class="anchor"></a>unsqueeze</h2>
-<p>unsqueeze(dim) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_unsqueeze.html">?torch_unsqueeze</a></code></p>
-</div>
-<div id="unsqueeze_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unsqueeze_" class="anchor"></a>unsqueeze_</h2>
-<p>unsqueeze_(dim) -&gt; Tensor</p>
-<p>In-place version of <code>$unsqueeze</code></p>
-</div>
-<div id="values" class="section level2">
-<h2 class="hasAnchor">
-<a href="#values" class="anchor"></a>values</h2>
-<p>values() -&gt; Tensor</p>
-<p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns a view of the contained values tensor. Otherwise, this throws an error.</p>
-<div id="note-11" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-11" class="anchor"></a>Note:</h4>
-<p>This method can only be called on a coalesced sparse tensor. See <code>Tensor$coalesce</code> for details.</p>
-</div>
-</div>
-<div id="var" class="section level2">
-<h2 class="hasAnchor">
-<a href="#var" class="anchor"></a>var</h2>
-<p>var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -&gt; Tensor</p>
-<p>See <code><a href="../../reference/torch_var.html">?torch_var</a></code></p>
-</div>
-<div id="view" class="section level2">
-<h2 class="hasAnchor">
-<a href="#view" class="anchor"></a>view</h2>
-<p>view(*shape) -&gt; Tensor</p>
-<p>Returns a new tensor with the same data as the <code>self</code> tensor but of a different <code>shape</code>.</p>
-<p>The returned tensor shares the same data and must have the same number of elements, but may have a different size. For a tensor to be viewed, the new view size must be compatible with its original size and stride, i.e., each new view dimension must either be a subspace of an original dimension, or only span across original dimensions <code>d, d+1, \dots, d+k</code> that satisfy the following contiguity-like condition that <code>\forall i = d, \dots, d+k-1</code>,</p>
-<p><span class="math display">\[
-\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]
-\]</span></p>
-<p>Otherwise, it will not be possible to view <code>self</code> tensor as <code>shape</code> without copying it (e.g., via <code>contiguous</code>). When it is unclear whether a <code>view</code> can be performed, it is advisable to use :meth:<code>reshape</code>, which returns a view if the shapes are compatible, and copies (equivalent to calling <code>contiguous</code>) otherwise.</p>
-<div id="arguments-57" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-57" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>shape (torch_Size or int…): the desired size</li>
-</ul>
-</div>
-</div>
-<div id="view_as" class="section level2">
-<h2 class="hasAnchor">
-<a href="#view_as" class="anchor"></a>view_as</h2>
-<p>view_as(other) -&gt; Tensor</p>
-<p>View this tensor as the same size as <code>other</code>. <code>self$view_as(other)</code> is equivalent to <code>self$view(other.size())</code>.</p>
-<p>Please see <code>$view</code> for more information about <code>view</code>.</p>
-<div id="arguments-58" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-58" class="anchor"></a>Arguments:</h4>
-<ul>
-<li>other (`$): The result tensor has the same size</li>
-<li>as <code>other</code>.</li>
-</ul>
-</div>
-</div>
-<div id="where" class="section level2">
-<h2 class="hasAnchor">
-<a href="#where" class="anchor"></a>where</h2>
-<p>where(condition, y) -&gt; Tensor</p>
-<p><code>self$where(condition, y)</code> is equivalent to <code><a href="../../reference/torch_where.html">torch_where(condition, self, y)</a></code>. See <code><a href="../../reference/torch_where.html">?torch_where</a></code></p>
-</div>
-<div id="zero_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#zero_" class="anchor"></a>zero_</h2>
-<p>zero_() -&gt; Tensor</p>
-<p>Fills <code>self</code> tensor with zeros.</p>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
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-
-      <footer><div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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diff --git a/static/docs/dev/articles/tensor/index_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/tensor/index_files/accessible-code-block-0.0.1/empty-anchor.js
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-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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-      <h1 data-toc-skip>Using autograd</h1>
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/using-autograd.Rmd"><code>vignettes/using-autograd.Rmd</code></a></small>
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-    
-    
-<div class="sourceCode" id="cb1"><pre class="downlit">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
-</pre></div>
-<p>So far, all we’ve been using from torch is <em>tensors</em>, but we’ve been performing all calculations ourselves – the computing the predictions, the loss, the gradients (and thus, the necessary updates to the weights), and the new weight values. In this chapter, we’ll make a significant change: Namely, we spare ourselves the cumbersome calculation of gradients, and have torch do it for us.</p>
-<p>Before we see that in action, let’s get some more background.</p>
-<div id="automatic-differentiation-with-autograd" class="section level2">
-<h2 class="hasAnchor">
-<a href="#automatic-differentiation-with-autograd" class="anchor"></a>Automatic differentiation with autograd</h2>
-<p>Torch uses a module called <em>autograd</em> to record operations performed on tensors, and store what has to be done to obtain the respective gradients. These actions are stored as functions, and those functions are applied in order when the gradient of the output (normally, the loss) with respect to those tensors is calculated: starting from the output node and <em>propagating</em> gradients <em>back</em> through the network. This is a form of <em>reverse mode automatic differentiation</em>.</p>
-<p>As users, we can see a bit of this implementation. As a prerequisite for this “recording” to happen, tensors have to be created with <code>requires_grad = TRUE</code>. E.g.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit">
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>,<span class="fl">2</span>, requires_grad = <span class="fl">TRUE</span>)
-</pre></div>
-<p>To be clear, this is a tensor <em>with respect to which</em> gradients have to be calculated – normally, a tensor representing a weight or a bias, not the input data <a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a>. If we now perform some operation on that tensor, assigning the result to <code>y</code></p>
-<div class="sourceCode" id="cb3"><pre class="downlit">
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
-</pre></div>
-<p>we find that <code>y</code> now has a non-empty <code>grad_fn</code> that tells torch how to compute the gradient of <code>y</code> with respect to <code>x</code>:</p>
-<div class="sourceCode" id="cb4"><pre class="downlit">
-<span class="kw">y</span><span class="op">$</span><span class="kw">grad_fn</span>
-<span class="co">#&gt; MeanBackward0</span>
-</pre></div>
-<p>Actual computation of gradients is triggered by calling <code>backward()</code> on the output tensor.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit">
-<span class="kw">y</span><span class="op">$</span><span class="fu">backward</span>()
-</pre></div>
-<p>That executed, <code>x</code> now has a non-empty field <code>grad</code> that stores the gradient of <code>y</code> with respect to <code>x</code>:</p>
-<div class="sourceCode" id="cb6"><pre class="downlit">
-<span class="kw">x</span><span class="op">$</span><span class="kw">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  0.2500  0.2500</span>
-<span class="co">#&gt;  0.2500  0.2500</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
-</pre></div>
-<p>With a longer chain of computations, we can peek at how torch builds up a graph of backward operations.</p>
-<p>Here is a slightly more complex example. We call <code>retain_grad()</code> on <code>y</code> and <code>z</code> just for demonstration purposes; by default, intermediate gradients – while of course they have to be computed – aren’t stored, in order to save memory.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit">
-<span class="kw">x1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>,<span class="fl">2</span>, requires_grad = <span class="fl">TRUE</span>)
-<span class="kw">x2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1.1</span>, requires_grad = <span class="fl">TRUE</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x1</span> <span class="op">*</span> (<span class="kw">x2</span> <span class="op">+</span> <span class="fl">2</span>)
-<span class="kw">y</span><span class="op">$</span><span class="fu">retain_grad</span>()
-<span class="kw">z</span> <span class="op">&lt;-</span> <span class="kw">y</span><span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>) <span class="op">*</span> <span class="fl">3</span>
-<span class="kw">z</span><span class="op">$</span><span class="fu">retain_grad</span>()
-<span class="kw">out</span> <span class="op">&lt;-</span> <span class="kw">z</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
-</pre></div>
-<p>Starting from <code>out$grad_fn</code>, we can follow the graph all back to the leaf nodes:</p>
-<div class="sourceCode" id="cb8"><pre class="downlit">
-<span class="co"># how to compute the gradient for mean, the last operation executed</span>
-<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span>
-<span class="co">#&gt; MeanBackward0</span>
-<span class="co"># how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3</span>
-<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>
-<span class="co">#&gt; [[1]]</span>
-<span class="co">#&gt; MulBackward1</span>
-<span class="co"># how to compute the gradient for pow in z = y.pow(2) * 3</span>
-<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>
-<span class="co">#&gt; [[1]]</span>
-<span class="co">#&gt; PowBackward0</span>
-<span class="co"># how to compute the gradient for the multiplication in y = x * (x + 2)</span>
-<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>
-<span class="co">#&gt; [[1]]</span>
-<span class="co">#&gt; MulBackward0</span>
-<span class="co"># how to compute the gradient for the two branches of y = x * (x + 2),</span>
-<span class="co"># where the left branch is a leaf node (AccumulateGrad for x1)</span>
-<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>
-<span class="co">#&gt; [[1]]</span>
-<span class="co">#&gt; torch::autograd::AccumulateGrad</span>
-<span class="co">#&gt; [[2]]</span>
-<span class="co">#&gt; AddBackward1</span>
-<span class="co"># here we arrive at the other leaf node (AccumulateGrad for x2)</span>
-<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">2</span>]]<span class="op">$</span><span class="kw">next_functions</span>
-<span class="co">#&gt; [[1]]</span>
-<span class="co">#&gt; torch::autograd::AccumulateGrad</span>
-</pre></div>
-<p>After calling <code>out$backward()</code>, all tensors in the graph will have their respective gradients created. Without our calls to <code>retain_grad</code> above, <code>z$grad</code> and <code>y$grad</code> would be empty:</p>
-<div class="sourceCode" id="cb9"><pre class="downlit">
-<span class="kw">out</span><span class="op">$</span><span class="fu">backward</span>()
-<span class="kw">z</span><span class="op">$</span><span class="kw">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  0.2500  0.2500</span>
-<span class="co">#&gt;  0.2500  0.2500</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
-<span class="kw">y</span><span class="op">$</span><span class="kw">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  4.6500  4.6500</span>
-<span class="co">#&gt;  4.6500  4.6500</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
-<span class="kw">x2</span><span class="op">$</span><span class="kw">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  18.6000</span>
-<span class="co">#&gt; [ CPUFloatType{1} ]</span>
-<span class="kw">x1</span><span class="op">$</span><span class="kw">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  14.4150  14.4150</span>
-<span class="co">#&gt;  14.4150  14.4150</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
-</pre></div>
-<p>Thus acquainted with autograd, we’re ready to modify our example.</p>
-</div>
-<div id="the-simple-network-now-using-autograd" class="section level2">
-<h2 class="hasAnchor">
-<a href="#the-simple-network-now-using-autograd" class="anchor"></a>The simple network, now using autograd</h2>
-<p>For a single new line calling <code>loss$backward()</code>, now a number of lines (that did manual backprop) are gone:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit">
-<span class="co">### generate training data -----------------------------------------------------</span>
-<span class="co"># input dimensionality (number of input features)</span>
-<span class="kw">d_in</span> <span class="op">&lt;-</span> <span class="fl">3</span>
-<span class="co"># output dimensionality (number of predicted features)</span>
-<span class="kw">d_out</span> <span class="op">&lt;-</span> <span class="fl">1</span>
-<span class="co"># number of observations in training set</span>
-<span class="kw">n</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="co"># create random data</span>
-<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">n</span>, <span class="kw">d_in</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span>[,<span class="fl">1</span>]<span class="op">*</span><span class="fl">0.2</span> <span class="op">-</span> <span class="kw">x</span>[<span class="kw">..</span>,<span class="fl">2</span>]<span class="op">*</span><span class="fl">1.3</span> <span class="op">-</span> <span class="kw">x</span>[<span class="kw">..</span>,<span class="fl">3</span>]<span class="op">*</span><span class="fl">0.5</span> <span class="op">+</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">n</span>)
-<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">y</span><span class="op">$</span><span class="fu">unsqueeze</span>(dim = <span class="fl">1</span>)
-<span class="co">### initialize weights ---------------------------------------------------------</span>
-<span class="co"># dimensionality of hidden layer</span>
-<span class="kw">d_hidden</span> <span class="op">&lt;-</span> <span class="fl">32</span>
-<span class="co"># weights connecting input to hidden layer</span>
-<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">d_in</span>, <span class="kw">d_hidden</span>, requires_grad = <span class="fl">TRUE</span>)
-<span class="co"># weights connecting hidden to output layer</span>
-<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">d_hidden</span>, <span class="kw">d_out</span>, requires_grad = <span class="fl">TRUE</span>)
-<span class="co"># hidden layer bias</span>
-<span class="kw">b1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">1</span>, <span class="kw">d_hidden</span>, requires_grad = <span class="fl">TRUE</span>)
-<span class="co"># output layer bias</span>
-<span class="kw">b2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">1</span>, <span class="kw">d_out</span>,requires_grad = <span class="fl">TRUE</span>)
-<span class="co">### network parameters ---------------------------------------------------------</span>
-<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
-<span class="co">### training loop --------------------------------------------------------------</span>
-<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">200</span>) {
-
-    <span class="co">### -------- Forward pass -------- </span>
-    <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w1</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/pkg/magrittr/man/aliases.html">add</a></span>(<span class="kw">b1</span>)<span class="op">$</span><span class="fu">clamp</span>(min = <span class="fl">0</span>)<span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/pkg/magrittr/man/aliases.html">add</a></span>(<span class="kw">b2</span>)
-    <span class="co">### -------- compute loss -------- </span>
-    <span class="kw">loss</span> <span class="op">&lt;-</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
-    <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">10</span> <span class="op">==</span> <span class="fl">0</span>) <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="kw">t</span>, <span class="fu"><a href="../reference/as_array.html">as_array</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
-    <span class="co">### -------- Backpropagation -------- </span>
-    <span class="co"># compute the gradient of loss with respect to all tensors with requires_grad = True.</span>
-    <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
- 
-    <span class="co">### -------- Update weights -------- </span>
-    
-    <span class="co"># Wrap in torch.no_grad() because this is a part we DON'T want to record for automatic gradient computation</span>
-    <span class="fu"><a href="../reference/with_no_grad.html">with_no_grad</a></span>({
-      
-      <span class="kw">w1</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span>)
-      <span class="kw">w2</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span>)
-      <span class="kw">b1</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">b1</span><span class="op">$</span><span class="kw">grad</span>)
-      <span class="kw">b2</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">b2</span><span class="op">$</span><span class="kw">grad</span>)
-      
-      <span class="co"># Zero the gradients after every pass, because they'd accumulate otherwise</span>
-      <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
-      <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
-      <span class="kw">b1</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
-      <span class="kw">b2</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
-    
-    })
-    
-}
-<span class="co">#&gt; 10 43.62013 </span>
-<span class="co">#&gt; 20 39.28248 </span>
-<span class="co">#&gt; 30 35.48614 </span>
-<span class="co">#&gt; 40 32.16391 </span>
-<span class="co">#&gt; 50 29.25816 </span>
-<span class="co">#&gt; 60 26.71713 </span>
-<span class="co">#&gt; 70 24.47915 </span>
-<span class="co">#&gt; 80 22.50302 </span>
-<span class="co">#&gt; 90 20.75658 </span>
-<span class="co">#&gt; 100 19.21028 </span>
-<span class="co">#&gt; 110 17.83859 </span>
-<span class="co">#&gt; 120 16.61867 </span>
-<span class="co">#&gt; 130 15.53453 </span>
-<span class="co">#&gt; 140 14.56742 </span>
-<span class="co">#&gt; 150 13.70137 </span>
-<span class="co">#&gt; 160 12.92414 </span>
-<span class="co">#&gt; 170 12.2292 </span>
-<span class="co">#&gt; 180 11.60689 </span>
-<span class="co">#&gt; 190 11.04558 </span>
-<span class="co">#&gt; 200 10.53848</span>
-</pre></div>
-<p>We still manually compute the forward pass, and we still manually update the weights. In the last two chapters of this section, we’ll see how these parts of the logic can be made more modular and reusable, as well.</p>
-</div>
-<div class="footnotes">
-<hr>
-<ol>
-<li id="fn1"><p>Unless we <em>want</em> to change the data, as in adversarial example generation<a href="#fnref1" class="footnote-back">↩︎</a></p></li>
-</ol>
-</div>
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deleted file mode 100644
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diff --git a/static/docs/dev/index.html b/static/docs/dev/index.html
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-<div class="page-header"><h1 class="hasAnchor">
-<a href="#torch-" class="anchor"></a>torch <a href="https://torch.mlverse.org"><img src="reference/figures/torch.png" align="right" height="139"></a>
-</h1></div>
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-<div id="installation" class="section level2">
-<h2 class="hasAnchor">
-<a href="#installation" class="anchor"></a>Installation</h2>
-<p>Run:</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a>remotes<span class="op">::</span><span class="kw">install_github</span>(<span class="st">"mlverse/torch"</span>)</span></code></pre></div>
-<p>At the first package load additional software will be installed.</p>
-</div>
-<div id="example" class="section level2">
-<h2 class="hasAnchor">
-<a href="#example" class="anchor"></a>Example</h2>
-<p>Currently this package is only a proof of concept and you can only create a Torch Tensor from an R object. And then convert back from a torch Tensor to an R object.</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="kw">library</span>(torch)</span>
-<span id="cb2-2"><a href="#cb2-2"></a>x &lt;-<span class="st"> </span><span class="kw">array</span>(<span class="kw">runif</span>(<span class="dv">8</span>), <span class="dt">dim =</span> <span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">2</span>))</span>
-<span id="cb2-3"><a href="#cb2-3"></a>y &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(x, <span class="dt">dtype =</span> <span class="kw">torch_float64</span>())</span>
-<span id="cb2-4"><a href="#cb2-4"></a>y</span>
-<span id="cb2-5"><a href="#cb2-5"></a><span class="co">#&gt; torch_tensor </span></span>
-<span id="cb2-6"><a href="#cb2-6"></a><span class="co">#&gt; (1,.,.) = </span></span>
-<span id="cb2-7"><a href="#cb2-7"></a><span class="co">#&gt;   0.5406  0.8648</span></span>
-<span id="cb2-8"><a href="#cb2-8"></a><span class="co">#&gt;   0.3097  0.9715</span></span>
-<span id="cb2-9"><a href="#cb2-9"></a><span class="co">#&gt; </span></span>
-<span id="cb2-10"><a href="#cb2-10"></a><span class="co">#&gt; (2,.,.) = </span></span>
-<span id="cb2-11"><a href="#cb2-11"></a><span class="co">#&gt;   0.1309  0.8992</span></span>
-<span id="cb2-12"><a href="#cb2-12"></a><span class="co">#&gt;   0.4849  0.1902</span></span>
-<span id="cb2-13"><a href="#cb2-13"></a><span class="co">#&gt; [ CPUDoubleType{2,2,2} ]</span></span>
-<span id="cb2-14"><a href="#cb2-14"></a><span class="kw">identical</span>(x, <span class="kw">as_array</span>(y))</span>
-<span id="cb2-15"><a href="#cb2-15"></a><span class="co">#&gt; [1] TRUE</span></span></code></pre></div>
-<div id="simple-autograd-example" class="section level3">
-<h3 class="hasAnchor">
-<a href="#simple-autograd-example" class="anchor"></a>Simple Autograd Example</h3>
-<p>In the following snippet we let torch, using the autograd feature, calculate the derivatives:</p>
-<div class="sourceCode" id="cb3"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a>x &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">1</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
-<span id="cb3-2"><a href="#cb3-2"></a>w &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">2</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
-<span id="cb3-3"><a href="#cb3-3"></a>b &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">3</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
-<span id="cb3-4"><a href="#cb3-4"></a>y &lt;-<span class="st"> </span>w <span class="op">*</span><span class="st"> </span>x <span class="op">+</span><span class="st"> </span>b</span>
-<span id="cb3-5"><a href="#cb3-5"></a>y<span class="op">$</span><span class="kw">backward</span>()</span>
-<span id="cb3-6"><a href="#cb3-6"></a>x<span class="op">$</span>grad</span>
-<span id="cb3-7"><a href="#cb3-7"></a><span class="co">#&gt; torch_tensor </span></span>
-<span id="cb3-8"><a href="#cb3-8"></a><span class="co">#&gt;  2</span></span>
-<span id="cb3-9"><a href="#cb3-9"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span>
-<span id="cb3-10"><a href="#cb3-10"></a>w<span class="op">$</span>grad</span>
-<span id="cb3-11"><a href="#cb3-11"></a><span class="co">#&gt; torch_tensor </span></span>
-<span id="cb3-12"><a href="#cb3-12"></a><span class="co">#&gt;  1</span></span>
-<span id="cb3-13"><a href="#cb3-13"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span>
-<span id="cb3-14"><a href="#cb3-14"></a>b<span class="op">$</span>grad</span>
-<span id="cb3-15"><a href="#cb3-15"></a><span class="co">#&gt; torch_tensor </span></span>
-<span id="cb3-16"><a href="#cb3-16"></a><span class="co">#&gt;  1</span></span>
-<span id="cb3-17"><a href="#cb3-17"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span></code></pre></div>
-</div>
-<div id="linear-regression" class="section level3">
-<h3 class="hasAnchor">
-<a href="#linear-regression" class="anchor"></a>Linear Regression</h3>
-<p>In the following example we are going to fit a linear regression from scratch using torch’s Autograd.</p>
-<p><strong>Note</strong> all methods that end with <code>_</code> (eg. <code>sub_</code>), will modify the tensors in place.</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>x &lt;-<span class="st"> </span><span class="kw">torch_randn</span>(<span class="dv">100</span>, <span class="dv">2</span>)</span>
-<span id="cb4-2"><a href="#cb4-2"></a>y &lt;-<span class="st"> </span><span class="fl">0.1</span> <span class="op">+</span><span class="st"> </span><span class="fl">0.5</span><span class="op">*</span>x[,<span class="dv">1</span>] <span class="op">-</span><span class="st"> </span><span class="fl">0.7</span><span class="op">*</span>x[,<span class="dv">2</span>]</span>
-<span id="cb4-3"><a href="#cb4-3"></a></span>
-<span id="cb4-4"><a href="#cb4-4"></a>w &lt;-<span class="st"> </span><span class="kw">torch_randn</span>(<span class="dv">2</span>, <span class="dv">1</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
-<span id="cb4-5"><a href="#cb4-5"></a>b &lt;-<span class="st"> </span><span class="kw">torch_zeros</span>(<span class="dv">1</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
-<span id="cb4-6"><a href="#cb4-6"></a></span>
-<span id="cb4-7"><a href="#cb4-7"></a>lr &lt;-<span class="st"> </span><span class="fl">0.5</span></span>
-<span id="cb4-8"><a href="#cb4-8"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="op">:</span><span class="dv">100</span>) {</span>
-<span id="cb4-9"><a href="#cb4-9"></a>  y_hat &lt;-<span class="st"> </span><span class="kw">torch_mm</span>(x, w) <span class="op">+</span><span class="st"> </span>b</span>
-<span id="cb4-10"><a href="#cb4-10"></a>  loss &lt;-<span class="st"> </span><span class="kw">torch_mean</span>((y <span class="op">-</span><span class="st"> </span>y_hat<span class="op">$</span><span class="kw">squeeze</span>(<span class="dv">1</span>))<span class="op">^</span><span class="dv">2</span>)</span>
-<span id="cb4-11"><a href="#cb4-11"></a>  </span>
-<span id="cb4-12"><a href="#cb4-12"></a>  loss<span class="op">$</span><span class="kw">backward</span>()</span>
-<span id="cb4-13"><a href="#cb4-13"></a>  </span>
-<span id="cb4-14"><a href="#cb4-14"></a>  <span class="kw">with_no_grad</span>({</span>
-<span id="cb4-15"><a href="#cb4-15"></a>    w<span class="op">$</span><span class="kw">sub_</span>(w<span class="op">$</span>grad<span class="op">*</span>lr)</span>
-<span id="cb4-16"><a href="#cb4-16"></a>    b<span class="op">$</span><span class="kw">sub_</span>(b<span class="op">$</span>grad<span class="op">*</span>lr)   </span>
-<span id="cb4-17"><a href="#cb4-17"></a>    </span>
-<span id="cb4-18"><a href="#cb4-18"></a>    w<span class="op">$</span>grad<span class="op">$</span><span class="kw">zero_</span>()</span>
-<span id="cb4-19"><a href="#cb4-19"></a>    b<span class="op">$</span>grad<span class="op">$</span><span class="kw">zero_</span>()</span>
-<span id="cb4-20"><a href="#cb4-20"></a>  })</span>
-<span id="cb4-21"><a href="#cb4-21"></a>}</span>
-<span id="cb4-22"><a href="#cb4-22"></a><span class="kw">print</span>(w)</span>
-<span id="cb4-23"><a href="#cb4-23"></a><span class="co">#&gt; torch_tensor </span></span>
-<span id="cb4-24"><a href="#cb4-24"></a><span class="co">#&gt; 1e-09 *</span></span>
-<span id="cb4-25"><a href="#cb4-25"></a><span class="co">#&gt;  5.2672</span></span>
-<span id="cb4-26"><a href="#cb4-26"></a><span class="co">#&gt;  -6.7969</span></span>
-<span id="cb4-27"><a href="#cb4-27"></a><span class="co">#&gt; [ CPUFloatType{2,1} ]</span></span>
-<span id="cb4-28"><a href="#cb4-28"></a><span class="kw">print</span>(b) </span>
-<span id="cb4-29"><a href="#cb4-29"></a><span class="co">#&gt; torch_tensor </span></span>
-<span id="cb4-30"><a href="#cb4-30"></a><span class="co">#&gt; 0.01 *</span></span>
-<span id="cb4-31"><a href="#cb4-31"></a><span class="co">#&gt; -9.6802</span></span>
-<span id="cb4-32"><a href="#cb4-32"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span></code></pre></div>
-</div>
-</div>
-<div id="contributing" class="section level2">
-<h2 class="hasAnchor">
-<a href="#contributing" class="anchor"></a>Contributing</h2>
-<p>No matter your current skills it’s possible to contribute to <code>torch</code> development. See the contributing guide for more information.</p>
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-    <h1>Class representing the context.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>AutogradContext.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Class representing the context.</p>
-<p>Class representing the context.</p>
-    </div>
-
-
-
-    <h2 class="hasAnchor" id="public-fields"><a class="anchor" href="#public-fields"></a>Public fields</h2>
-
-    <p><div class="r6-fields"></p><dl>
-<dt><code>ptr</code></dt><dd><p>(Dev related) pointer to the context c++ object.</p></dd>
-
-</dl><p></div></p>
-    <h2 class="hasAnchor" id="active-bindings"><a class="anchor" href="#active-bindings"></a>Active bindings</h2>
-
-    <p><div class="r6-active-bindings"></p><dl>
-<dt><code>needs_input_grad</code></dt><dd><p>boolean listing arguments of <code>forward</code> and whether they require_grad.</p></dd>
-
-<dt><code>saved_variables</code></dt><dd><p>list of objects that were saved for backward via <code>save_for_backward</code>.</p></dd>
-
-</dl><p></div></p>
-    <h2 class="hasAnchor" id="methods"><a class="anchor" href="#methods"></a>Methods</h2>
-
-    
-<h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Public methods</h3>
-
-<ul>
-<li><p><a href='#method-new'><code>AutogradContext$new()</code></a></p></li>
-<li><p><a href='#method-save_for_backward'><code>AutogradContext$save_for_backward()</code></a></p></li>
-<li><p><a href='#method-mark_non_differentiable'><code>AutogradContext$mark_non_differentiable()</code></a></p></li>
-<li><p><a href='#method-mark_dirty'><code>AutogradContext$mark_dirty()</code></a></p></li>
-<li><p><a href='#method-clone'><code>AutogradContext$clone()</code></a></p></li>
-</ul>
-<p><hr>
-<a id="method-new"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>new()</code></h3>
-<p>(Dev related) Initializes the context. Not user related.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>new</span>(
-  <span class='kw'>ptr</span>,
-  <span class='kw'>env</span>,
-  argument_names = <span class='kw'>NULL</span>,
-  argument_needs_grad = <span class='kw'>NULL</span>
-)</pre><p></div></p>
-
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>ptr</code></dt><dd><p>pointer to the c++ object</p></dd>
-
-<dt><code>env</code></dt><dd><p>environment that encloses both forward and backward</p></dd>
-
-<dt><code>argument_names</code></dt><dd><p>names of forward arguments</p></dd>
-
-<dt><code>argument_needs_grad</code></dt><dd><p>whether each argument in forward needs grad.</p></dd>
-
-</dl><p></div></p>
-<p><hr>
-<a id="method-save_for_backward"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>save_for_backward()</code></h3>
-<p>Saves given objects for a future call to backward().</p>
-<p>This should be called at most once, and only from inside the <code>forward()</code>
-method.</p>
-<p>Later, saved objects can be accessed through the <code>saved_variables</code> attribute.
-Before returning them to the user, a check is made to ensure they weren’t used
-in any in-place operation that modified their content.</p>
-<p>Arguments can also be any kind of R object.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>save_for_backward</span>(<span class='kw'>...</span>)</pre><p></div></p>
-
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>...</code></dt><dd><p>any kind of R object that will be saved for the backward pass.
-It's common to pass named arguments.</p></dd>
-
-</dl><p></div></p>
-<p><hr>
-<a id="method-mark_non_differentiable"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>mark_non_differentiable()</code></h3>
-<p>Marks outputs as non-differentiable.</p>
-<p>This should be called at most once, only from inside the <code>forward()</code> method,
-and all arguments should be outputs.</p>
-<p>This will mark outputs as not requiring gradients, increasing the efficiency
-of backward computation. You still need to accept a gradient for each output
-in <code>backward()</code>, but it’s always going to be a zero tensor with the same
-shape as the shape of a corresponding output.</p>
-<p>This is used e.g. for indices returned from a max Function.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>mark_non_differentiable</span>(<span class='kw'>...</span>)</pre><p></div></p>
-
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>...</code></dt><dd><p>non-differentiable outputs.</p></dd>
-
-</dl><p></div></p>
-<p><hr>
-<a id="method-mark_dirty"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>mark_dirty()</code></h3>
-<p>Marks given tensors as modified in an in-place operation.</p>
-<p>This should be called at most once, only from inside the <code>forward()</code> method,
-and all arguments should be inputs.</p>
-<p>Every tensor that’s been modified in-place in a call to <code>forward()</code> should
-be given to this function, to ensure correctness of our checks. It doesn’t
-matter whether the function is called before or after modification.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>mark_dirty</span>(<span class='kw'>...</span>)</pre><p></div></p>
-
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>...</code></dt><dd><p>tensors that are modified in-place.</p></dd>
-
-</dl><p></div></p>
-<p><hr>
-<a id="method-clone"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>clone()</code></h3>
-<p>The objects of this class are cloneable with this method.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>clone</span>(deep = <span class='fl'>FALSE</span>)</pre><p></div></p>
-
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>deep</code></dt><dd><p>Whether to make a deep clone.</p></dd>
-
-</dl><p></div></p>
-
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-
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-      <th>x</th>
-      <td><p>object to be converted into an array</p></td>
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-    <h1>Computes the sum of gradients of given tensors w.r.t. graph leaves.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>autograd_backward.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The graph is differentiated using the chain rule. If any of tensors are
-non-scalar (i.e. their data has more than one element) and require gradient,
-then the Jacobian-vector product would be computed, in this case the function
-additionally requires specifying <code>grad_tensors</code>. It should be a sequence of
-matching length, that contains the “vector” in the Jacobian-vector product,
-usually the gradient of the differentiated function w.r.t. corresponding
-tensors (None is an acceptable value for all tensors that don’t need gradient
-tensors).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>autograd_backward</span>(
-  <span class='kw'>tensors</span>,
-  grad_tensors = <span class='kw'>NULL</span>,
-  retain_graph = <span class='kw'>create_graph</span>,
-  create_graph = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(list of Tensor) – Tensors of which the derivative will
-be computed.</p></td>
-    </tr>
-    <tr>
-      <th>grad_tensors</th>
-      <td><p>(list of (Tensor or <code>NULL)) – The “vector” in the  Jacobian-vector product, usually gradients w.r.t. each element of  corresponding tensors. </code>NULL<code>values can be specified for scalar Tensors or  ones that don’t require grad. If a</code>NULL` value would be acceptable for all
-grad_tensors, then this argument is optional.</p></td>
-    </tr>
-    <tr>
-      <th>retain_graph</th>
-      <td><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute
-the grad will be freed. Note that in nearly all cases setting this option to
-<code>TRUE</code> is not needed and often can be worked around in a much more efficient
-way. Defaults to the value of <code>create_graph</code>.</p></td>
-    </tr>
-    <tr>
-      <th>create_graph</th>
-      <td><p>(bool, optional) – If <code>TRUE</code>, graph of the derivative will
-be constructed, allowing to compute higher order derivative products.
-Defaults to <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This function accumulates gradients in the leaves - you might need to zero
-them before calling it.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1</span>, requires_grad = <span class='fl'>TRUE</span>)
-<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fl'>2</span> <span class='op'>*</span> <span class='kw'>x</span>
-
-<span class='kw'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1</span>, requires_grad = <span class='fl'>TRUE</span>)
-<span class='kw'>b</span> <span class='op'>&lt;-</span> <span class='fl'>3</span> <span class='op'>*</span> <span class='kw'>a</span>
-
-<span class='fu'>autograd_backward</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>y</span>, <span class='kw'>b</span>))
-
-}
-</div></pre>
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diff --git a/static/docs/dev/reference/autograd_function.html b/static/docs/dev/reference/autograd_function.html
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-retained in the form of a DAG of functions, with edges denoting data
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-</div><!--/.navbar -->
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Records operation history and defines formulas for differentiating ops.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>autograd_function.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Every operation performed on Tensor's creates a new function object, that
-performs the computation, and records that it happened. The history is
-retained in the form of a DAG of functions, with edges denoting data
-dependencies (input &lt;- output). Then, when backward is called, the graph is
-processed in the topological ordering, by calling <code>backward()</code> methods of each
-Function object, and passing returned gradients on to next Function's.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>autograd_function</span>(<span class='kw'>forward</span>, <span class='kw'>backward</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>forward</th>
-      <td><p>Performs the operation. It must accept a context <code>ctx</code> as the first argument,
-followed by any number of arguments (tensors or other types). The context can be
-used to store tensors that can be then retrieved during the backward pass.
-See <a href='AutogradContext.html'>AutogradContext</a> for more information about context methods.</p></td>
-    </tr>
-    <tr>
-      <th>backward</th>
-      <td><p>Defines a formula for differentiating the operation. It must accept
-a context <code>ctx</code> as the first argument, followed by as many outputs did <code>forward()</code>
-return, and it should return a named list. Each argument is the gradient w.r.t
-the given output, and each element in the returned list should be the gradient
-w.r.t. the corresponding input. The context can be used to retrieve tensors saved
-during the forward pass. It also has an attribute <code>ctx$needs_input_grad</code> as a
-named list of booleans representing whether each input needs gradient.
-E.g., <code>backward()</code> will have <code>ctx$needs_input_grad$input = TRUE</code> if the <code>input</code>
-argument to <code>forward()</code> needs gradient computated w.r.t. the output.
-See <a href='AutogradContext.html'>AutogradContext</a> for more information about context methods.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>exp2</span> <span class='op'>&lt;-</span> <span class='fu'>autograd_function</span>(
-  forward = <span class='fu'>function</span>(<span class='kw'>ctx</span>, <span class='kw'>i</span>) {
-    <span class='kw'>result</span> <span class='op'>&lt;-</span> <span class='kw'>i</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/Log.html'>exp</a></span>()
-    <span class='kw'>ctx</span><span class='op'>$</span><span class='fu'>save_for_backward</span>(result = <span class='kw'>result</span>)
-    <span class='kw'>result</span>
-  },
-  backward = <span class='fu'>function</span>(<span class='kw'>ctx</span>, <span class='kw'>grad_output</span>) {
-    <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(i = <span class='kw'>grad_output</span> <span class='op'>*</span> <span class='kw'>ctx</span><span class='op'>$</span><span class='kw'>saved_variable</span><span class='op'>$</span><span class='kw'>result</span>)
-  }
-)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/dev/reference/autograd_grad.html b/static/docs/dev/reference/autograd_grad.html
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-    <div class="page-header">
-    <h1>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>autograd_grad.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>grad_outputs</code> should be a list of length matching output containing the “vector”
-in Jacobian-vector product, usually the pre-computed gradients w.r.t. each of
-the outputs. If an output doesn’t require_grad, then the gradient can be None).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>autograd_grad</span>(
-  <span class='kw'>outputs</span>,
-  <span class='kw'>inputs</span>,
-  grad_outputs = <span class='kw'>NULL</span>,
-  retain_graph = <span class='kw'>create_graph</span>,
-  create_graph = <span class='fl'>FALSE</span>,
-  allow_unused = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>outputs</th>
-      <td><p>(sequence of Tensor) – outputs of the differentiated function.</p></td>
-    </tr>
-    <tr>
-      <th>inputs</th>
-      <td><p>(sequence of Tensor) – Inputs w.r.t. which the gradient will be
-returned (and not accumulated into .grad).</p></td>
-    </tr>
-    <tr>
-      <th>grad_outputs</th>
-      <td><p>(sequence of Tensor) – The “vector” in the Jacobian-vector
-product. Usually gradients w.r.t. each output. None values can be specified for
-scalar Tensors or ones that don’t require grad. If a None value would be acceptable
-for all <code>grad_tensors</code>, then this argument is optional. Default: None.</p></td>
-    </tr>
-    <tr>
-      <th>retain_graph</th>
-      <td><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute the
-grad will be freed. Note that in nearly all cases setting this option to <code>TRUE</code> is
-not needed and often can be worked around in a much more efficient way.
-Defaults to the value of <code>create_graph</code>.</p></td>
-    </tr>
-    <tr>
-      <th>create_graph</th>
-      <td><p>(bool, optional) – If <code>TRUE, graph of the derivative will  be constructed, allowing to compute higher order derivative products.  Default: </code>FALSE`.</p></td>
-    </tr>
-    <tr>
-      <th>allow_unused</th>
-      <td><p>(bool, optional) – If <code>FALSE</code>, specifying inputs that were
-not used when computing outputs (and therefore their grad is always zero) is an
-error. Defaults to <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>If only_inputs is <code>TRUE</code>, the function will only return a list of gradients w.r.t
-the specified inputs. If it’s <code>FALSE</code>, then gradient w.r.t. all remaining leaves
-will still be computed, and will be accumulated into their <code>.grad</code> attribute.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>0.5</span>, requires_grad = <span class='fl'>TRUE</span>)
-<span class='kw'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>0.9</span>, requires_grad = <span class='fl'>TRUE</span>)
-<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Uniform.html'>runif</a></span>(<span class='fl'>100</span>))
-<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fl'>2</span> <span class='op'>*</span> <span class='kw'>x</span> <span class='op'>+</span> <span class='fl'>1</span>
-<span class='kw'>loss</span> <span class='op'>&lt;-</span> (<span class='kw'>y</span> <span class='op'>-</span> (<span class='kw'>w</span><span class='op'>*</span><span class='kw'>x</span> <span class='op'>+</span> <span class='kw'>b</span>))<span class='op'>^</span><span class='fl'>2</span>
-<span class='kw'>loss</span> <span class='op'>&lt;-</span> <span class='kw'>loss</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/mean.html'>mean</a></span>()
-
-<span class='kw'>o</span> <span class='op'>&lt;-</span> <span class='fu'>autograd_grad</span>(<span class='kw'>loss</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>w</span>, <span class='kw'>b</span>))
-<span class='kw'>o</span>
- 
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; -0.9935
-#&gt; [ CPUFloatType{1} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; -1.6206
-#&gt; [ CPUFloatType{1} ]
-#&gt; </div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/static/docs/dev/reference/autograd_set_grad_mode.html b/static/docs/dev/reference/autograd_set_grad_mode.html
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diff --git a/static/docs/dev/reference/cuda_device_count.html b/static/docs/dev/reference/cuda_device_count.html
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-    <h1>Data loader. Combines a dataset and a sampler, and provides
-single- or multi-process iterators over the dataset.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-dataloader.R'><code>R/utils-data-dataloader.R</code></a></small>
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-    <p>Data loader. Combines a dataset and a sampler, and provides
-single- or multi-process iterators over the dataset.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>dataloader</span>(
-  <span class='kw'>dataset</span>,
-  batch_size = <span class='fl'>1</span>,
-  shuffle = <span class='fl'>FALSE</span>,
-  sampler = <span class='kw'>NULL</span>,
-  batch_sampler = <span class='kw'>NULL</span>,
-  num_workers = <span class='fl'>0</span>,
-  collate_fn = <span class='kw'>NULL</span>,
-  pin_memory = <span class='fl'>FALSE</span>,
-  drop_last = <span class='fl'>FALSE</span>,
-  timeout = <span class='fl'>0</span>,
-  worker_init_fn = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dataset</th>
-      <td><p>(Dataset): dataset from which to load the data.</p></td>
-    </tr>
-    <tr>
-      <th>batch_size</th>
-      <td><p>(int, optional): how many samples per batch to load
-(default: <code>1</code>).</p></td>
-    </tr>
-    <tr>
-      <th>shuffle</th>
-      <td><p>(bool, optional): set to <code>TRUE</code> to have the data reshuffled
-at every epoch (default: <code>FALSE</code>).</p></td>
-    </tr>
-    <tr>
-      <th>sampler</th>
-      <td><p>(Sampler, optional): defines the strategy to draw samples from
-the dataset. If specified, <code>shuffle</code> must be False.</p></td>
-    </tr>
-    <tr>
-      <th>batch_sampler</th>
-      <td><p>(Sampler, optional): like sampler, but returns a batch of
-indices at a time. Mutually exclusive with <code>batch_size</code>,
-<code>shuffle</code>, <code>sampler</code>, and <code>drop_last</code>.</p></td>
-    </tr>
-    <tr>
-      <th>num_workers</th>
-      <td><p>(int, optional): how many subprocesses to use for data
-loading. 0 means that the data will be loaded in the main process.
-(default: <code>0</code>)</p></td>
-    </tr>
-    <tr>
-      <th>collate_fn</th>
-      <td><p>(callable, optional): merges a list of samples to form a mini-batch.</p></td>
-    </tr>
-    <tr>
-      <th>pin_memory</th>
-      <td><p>(bool, optional): If <code>TRUE</code>, the data loader will copy tensors
-into CUDA pinned memory before returning them.  If your data elements
-are a custom type, or your <code>collate_fn</code> returns a batch that is a custom type
-see the example below.</p></td>
-    </tr>
-    <tr>
-      <th>drop_last</th>
-      <td><p>(bool, optional): set to <code>TRUE</code> to drop the last incomplete batch,
-if the dataset size is not divisible by the batch size. If <code>FALSE</code> and
-the size of dataset is not divisible by the batch size, then the last batch
-will be smaller. (default: <code>FALSE</code>)</p></td>
-    </tr>
-    <tr>
-      <th>timeout</th>
-      <td><p>(numeric, optional): if positive, the timeout value for collecting a batch
-from workers. Should always be non-negative. (default: <code>0</code>)</p></td>
-    </tr>
-    <tr>
-      <th>worker_init_fn</th>
-      <td><p>(callable, optional): If not <code>NULL</code>, this will be called on each
-worker subprocess with the worker id (an int in <code>[0, num_workers - 1]</code>) as
-input, after seeding and before data loading. (default: <code>NULL</code>)</p></td>
-    </tr>
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-    <h1>Creates an iterator from a DataLoader</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-dataloader.R'><code>R/utils-data-dataloader.R</code></a></small>
-    <div class="hidden name"><code>dataloader_make_iter.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates an iterator from a DataLoader</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>dataloader_make_iter</span>(<span class='kw'>dataloader</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dataloader</th>
-      <td><p>a dataloader object.</p></td>
-    </tr>
-    </table>
-
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diff --git a/static/docs/dev/reference/dataloader_next.html b/static/docs/dev/reference/dataloader_next.html
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-it. All subclasses should overwrite get_item, supporting fetching a
-data sample for a given key. Subclasses could also optionally overwrite
-lenght, which is expected to return the size of the dataset by many
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-    <h1>An abstract class representing a <code>Dataset</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data.R'><code>R/utils-data.R</code></a></small>
-    <div class="hidden name"><code>dataset.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>All datasets that represent a map from keys to data samples should subclass
-it. All subclasses should overwrite <code>get_item</code>, supporting fetching a
-data sample for a given key. Subclasses could also optionally overwrite
-<code>lenght</code>, which is expected to return the size of the dataset by many
-<code>~torch.utils.data.Sampler</code> implementations and the default options
-of <code>~torch.utils.data.DataLoader</code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>dataset</span>(name = <span class='kw'>NULL</span>, inherit = <span class='kw'>Dataset</span>, <span class='kw'>...</span>, parent_env = <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span>())</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>name</th>
-      <td><p>a name for the dataset. It it's also used as the class
-for it.</p></td>
-    </tr>
-    <tr>
-      <th>inherit</th>
-      <td><p>you can optionally inherit from a dataset when creating a
-new dataset.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>public methods for the dataset class</p></td>
-    </tr>
-    <tr>
-      <th>parent_env</th>
-      <td><p>An environment to use as the parent of newly-created
-objects.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p><code>~torch.utils.data.DataLoader</code> by default constructs a index
-sampler that yields integral indices.  To make it work with a map-style
-dataset with non-integral indices/keys, a custom sampler must be provided.</p>
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-      <td><p>the generator to enumerate.</p></td>
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-    <tr>
-      <th>...</th>
-      <td><p>passed to specific methods.</p></td>
-    </tr>
-    </table>
-
-
-  </div>
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/static/docs/dev/reference/figures/torch.png b/static/docs/dev/reference/figures/torch.png
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-          <h2 id="section-tensor-creation-utilities" class="hasAnchor"><a href="#section-tensor-creation-utilities" class="anchor"></a>Tensor creation utilities</h2>
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-        </th>
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-      
-    </tbody><tbody>
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-      
-      <tr>
-        
-        <td>
-          <p><code><a href="torch_empty.html">torch_empty()</a></code> </p>
-        </td>
-        <td><p>Empty</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_arange.html">torch_arange()</a></code> </p>
-        </td>
-        <td><p>Arange</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_eye.html">torch_eye()</a></code> </p>
-        </td>
-        <td><p>Eye</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_full.html">torch_full()</a></code> </p>
-        </td>
-        <td><p>Full</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_linspace.html">torch_linspace()</a></code> </p>
-        </td>
-        <td><p>Linspace</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logspace.html">torch_logspace()</a></code> </p>
-        </td>
-        <td><p>Logspace</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ones.html">torch_ones()</a></code> </p>
-        </td>
-        <td><p>Ones</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rand.html">torch_rand()</a></code> </p>
-        </td>
-        <td><p>Rand</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randint.html">torch_randint()</a></code> </p>
-        </td>
-        <td><p>Randint</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randn.html">torch_randn()</a></code> </p>
-        </td>
-        <td><p>Randn</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randperm.html">torch_randperm()</a></code> </p>
-        </td>
-        <td><p>Randperm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_zeros.html">torch_zeros()</a></code> </p>
-        </td>
-        <td><p>Zeros</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_empty_like.html">torch_empty_like()</a></code> </p>
-        </td>
-        <td><p>Empty_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_full_like.html">torch_full_like()</a></code> </p>
-        </td>
-        <td><p>Full_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ones_like.html">torch_ones_like()</a></code> </p>
-        </td>
-        <td><p>Ones_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rand_like.html">torch_rand_like()</a></code> </p>
-        </td>
-        <td><p>Rand_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randint_like.html">torch_randint_like()</a></code> </p>
-        </td>
-        <td><p>Randint_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randn_like.html">torch_randn_like()</a></code> </p>
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-        <td><p>Randn_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_zeros_like.html">torch_zeros_like()</a></code> </p>
-        </td>
-        <td><p>Zeros_like</p></td>
-      </tr><tr>
-        
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-          <p><code><a href="as_array.html">as_array()</a></code> </p>
-        </td>
-        <td><p>Converts to array</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-tensor-attributes" class="hasAnchor"><a href="#section-tensor-attributes" class="anchor"></a>Tensor attributes</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
-      
-      
-    </tbody><tbody>
-      
-      
-      <tr>
-        
-        <td>
-          <p><code><a href="default_dtype.html">torch_set_default_dtype()</a></code> <code><a href="default_dtype.html">torch_get_default_dtype()</a></code> </p>
-        </td>
-        <td><p>Gets and sets the default floating point dtype.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_device.html">is_torch_device()</a></code> </p>
-        </td>
-        <td><p>Checks if object is a device</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_dtype.html">is_torch_dtype()</a></code> </p>
-        </td>
-        <td><p>Check if object is a torch data type</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_dtype.html">torch_float32()</a></code> <code><a href="torch_dtype.html">torch_float()</a></code> <code><a href="torch_dtype.html">torch_float64()</a></code> <code><a href="torch_dtype.html">torch_double()</a></code> <code><a href="torch_dtype.html">torch_float16()</a></code> <code><a href="torch_dtype.html">torch_half()</a></code> <code><a href="torch_dtype.html">torch_uint8()</a></code> <code><a href="torch_dtype.html">torch_int8()</a></code> <code><a href="torch_dtype.html">torch_int16()</a></code> <code><a href="torch_dtype.html">torch_short()</a></code> <code><a href="torch_dtype.html">torch_int32()</a></code> <code><a href="torch_dtype.html">torch_int()</a></code> <code><a href="torch_dtype.html">torch_int64()</a></code> <code><a href="torch_dtype.html">torch_long()</a></code> <code><a href="torch_dtype.html">torch_bool()</a></code> <code><a href="torch_dtype.html">torch_quint8()</a></code> <code><a href="torch_dtype.html">torch_qint8()</a></code> <code><a href="torch_dtype.html">torch_qint32()</a></code> </p>
-        </td>
-        <td><p>Torch data types</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_finfo.html">torch_finfo()</a></code> </p>
-        </td>
-        <td><p>Floating point type info</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_iinfo.html">torch_iinfo()</a></code> </p>
-        </td>
-        <td><p>Integer type info</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_qscheme.html">torch_per_channel_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_channel_symmetric()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_symmetric()</a></code> </p>
-        </td>
-        <td><p>Creates the corresponding Scheme object</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_reduction.html">torch_reduction_sum()</a></code> <code><a href="torch_reduction.html">torch_reduction_mean()</a></code> <code><a href="torch_reduction.html">torch_reduction_none()</a></code> </p>
-        </td>
-        <td><p>Creates the reduction objet</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_layout.html">is_torch_layout()</a></code> </p>
-        </td>
-        <td><p>Check if an object is a torch layout.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_memory_format.html">is_torch_memory_format()</a></code> </p>
-        </td>
-        <td><p>Check if an object is a memory format</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_qscheme.html">is_torch_qscheme()</a></code> </p>
-        </td>
-        <td><p>Checks if an object is a QScheme</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_undefined_tensor.html">is_undefined_tensor()</a></code> </p>
-        </td>
-        <td><p>Checks if a tensor is undefined</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-serialization" class="hasAnchor"><a href="#section-serialization" class="anchor"></a>Serialization</h2>
-          <p class="section-desc"></p>
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-      </tr>
-      
-      
-    </tbody><tbody>
-      
-      
-      <tr>
-        
-        <td>
-          <p><code><a href="load_state_dict.html">load_state_dict()</a></code> </p>
-        </td>
-        <td><p>Load a state dict file</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_load.html">torch_load()</a></code> </p>
-        </td>
-        <td><p>Loads a saved object</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_save.html">torch_save()</a></code> </p>
-        </td>
-        <td><p>Saves an object to a disk file.</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-mathematical-operations-on-tensors" class="hasAnchor"><a href="#section-mathematical-operations-on-tensors" class="anchor"></a>Mathematical operations on tensors</h2>
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-        </th>
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-      
-      
-    </tbody><tbody>
-      
-      
-      <tr>
-        
-        <td>
-          <p><code><a href="torch_abs.html">torch_abs()</a></code> </p>
-        </td>
-        <td><p>Abs</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_acos.html">torch_acos()</a></code> </p>
-        </td>
-        <td><p>Acos</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_adaptive_avg_pool1d.html">torch_adaptive_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_avg_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_add.html">torch_add()</a></code> </p>
-        </td>
-        <td><p>Add</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addbmm.html">torch_addbmm()</a></code> </p>
-        </td>
-        <td><p>Addbmm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addcdiv.html">torch_addcdiv()</a></code> </p>
-        </td>
-        <td><p>Addcdiv</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addcmul.html">torch_addcmul()</a></code> </p>
-        </td>
-        <td><p>Addcmul</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addmm.html">torch_addmm()</a></code> </p>
-        </td>
-        <td><p>Addmm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addmv.html">torch_addmv()</a></code> </p>
-        </td>
-        <td><p>Addmv</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addr.html">torch_addr()</a></code> </p>
-        </td>
-        <td><p>Addr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_allclose.html">torch_allclose()</a></code> </p>
-        </td>
-        <td><p>Allclose</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_angle.html">torch_angle()</a></code> </p>
-        </td>
-        <td><p>Angle</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_argmax.html">torch_argmax()</a></code> </p>
-        </td>
-        <td><p>Argmax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_argmin.html">torch_argmin()</a></code> </p>
-        </td>
-        <td><p>Argmin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_argsort.html">torch_argsort()</a></code> </p>
-        </td>
-        <td><p>Argsort</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_as_strided.html">torch_as_strided()</a></code> </p>
-        </td>
-        <td><p>As_strided</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_asin.html">torch_asin()</a></code> </p>
-        </td>
-        <td><p>Asin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_atan.html">torch_atan()</a></code> </p>
-        </td>
-        <td><p>Atan</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_atan2.html">torch_atan2()</a></code> </p>
-        </td>
-        <td><p>Atan2</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_avg_pool1d.html">torch_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Avg_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_baddbmm.html">torch_baddbmm()</a></code> </p>
-        </td>
-        <td><p>Baddbmm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bartlett_window.html">torch_bartlett_window()</a></code> </p>
-        </td>
-        <td><p>Bartlett_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bernoulli.html">torch_bernoulli()</a></code> </p>
-        </td>
-        <td><p>Bernoulli</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bincount.html">torch_bincount()</a></code> </p>
-        </td>
-        <td><p>Bincount</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bitwise_and.html">torch_bitwise_and()</a></code> </p>
-        </td>
-        <td><p>Bitwise_and</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bitwise_not.html">torch_bitwise_not()</a></code> </p>
-        </td>
-        <td><p>Bitwise_not</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bitwise_or.html">torch_bitwise_or()</a></code> </p>
-        </td>
-        <td><p>Bitwise_or</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bitwise_xor.html">torch_bitwise_xor()</a></code> </p>
-        </td>
-        <td><p>Bitwise_xor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_blackman_window.html">torch_blackman_window()</a></code> </p>
-        </td>
-        <td><p>Blackman_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bmm.html">torch_bmm()</a></code> </p>
-        </td>
-        <td><p>Bmm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_broadcast_tensors.html">torch_broadcast_tensors()</a></code> </p>
-        </td>
-        <td><p>Broadcast_tensors</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_can_cast.html">torch_can_cast()</a></code> </p>
-        </td>
-        <td><p>Can_cast</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cartesian_prod.html">torch_cartesian_prod()</a></code> </p>
-        </td>
-        <td><p>Cartesian_prod</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cat.html">torch_cat()</a></code> </p>
-        </td>
-        <td><p>Cat</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cdist.html">torch_cdist()</a></code> </p>
-        </td>
-        <td><p>Cdist</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ceil.html">torch_ceil()</a></code> </p>
-        </td>
-        <td><p>Ceil</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_celu.html">torch_celu()</a></code> </p>
-        </td>
-        <td><p>Celu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_celu_.html">torch_celu_()</a></code> </p>
-        </td>
-        <td><p>Celu_</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_chain_matmul.html">torch_chain_matmul()</a></code> </p>
-        </td>
-        <td><p>Chain_matmul</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cholesky.html">torch_cholesky()</a></code> </p>
-        </td>
-        <td><p>Cholesky</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cholesky_inverse.html">torch_cholesky_inverse()</a></code> </p>
-        </td>
-        <td><p>Cholesky_inverse</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cholesky_solve.html">torch_cholesky_solve()</a></code> </p>
-        </td>
-        <td><p>Cholesky_solve</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_chunk.html">torch_chunk()</a></code> </p>
-        </td>
-        <td><p>Chunk</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_clamp.html">torch_clamp()</a></code> </p>
-        </td>
-        <td><p>Clamp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_combinations.html">torch_combinations()</a></code> </p>
-        </td>
-        <td><p>Combinations</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conj.html">torch_conj()</a></code> </p>
-        </td>
-        <td><p>Conj</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv1d.html">torch_conv1d()</a></code> </p>
-        </td>
-        <td><p>Conv1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv2d.html">torch_conv2d()</a></code> </p>
-        </td>
-        <td><p>Conv2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv3d.html">torch_conv3d()</a></code> </p>
-        </td>
-        <td><p>Conv3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv_tbc.html">torch_conv_tbc()</a></code> </p>
-        </td>
-        <td><p>Conv_tbc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv_transpose1d.html">torch_conv_transpose1d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv_transpose2d.html">torch_conv_transpose2d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv_transpose3d.html">torch_conv_transpose3d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cos.html">torch_cos()</a></code> </p>
-        </td>
-        <td><p>Cos</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cosh.html">torch_cosh()</a></code> </p>
-        </td>
-        <td><p>Cosh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cosine_similarity.html">torch_cosine_similarity()</a></code> </p>
-        </td>
-        <td><p>Cosine_similarity</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cross.html">torch_cross()</a></code> </p>
-        </td>
-        <td><p>Cross</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cummax.html">torch_cummax()</a></code> </p>
-        </td>
-        <td><p>Cummax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cummin.html">torch_cummin()</a></code> </p>
-        </td>
-        <td><p>Cummin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cumprod.html">torch_cumprod()</a></code> </p>
-        </td>
-        <td><p>Cumprod</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cumsum.html">torch_cumsum()</a></code> </p>
-        </td>
-        <td><p>Cumsum</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_det.html">torch_det()</a></code> </p>
-        </td>
-        <td><p>Det</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_device.html">torch_device()</a></code> </p>
-        </td>
-        <td><p>Create a Device object</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diag.html">torch_diag()</a></code> </p>
-        </td>
-        <td><p>Diag</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diag_embed.html">torch_diag_embed()</a></code> </p>
-        </td>
-        <td><p>Diag_embed</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diagflat.html">torch_diagflat()</a></code> </p>
-        </td>
-        <td><p>Diagflat</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diagonal.html">torch_diagonal()</a></code> </p>
-        </td>
-        <td><p>Diagonal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_digamma.html">torch_digamma()</a></code> </p>
-        </td>
-        <td><p>Digamma</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_dist.html">torch_dist()</a></code> </p>
-        </td>
-        <td><p>Dist</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_div.html">torch_div()</a></code> </p>
-        </td>
-        <td><p>Div</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_dot.html">torch_dot()</a></code> </p>
-        </td>
-        <td><p>Dot</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_eig.html">torch_eig()</a></code> </p>
-        </td>
-        <td><p>Eig</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_einsum.html">torch_einsum()</a></code> </p>
-        </td>
-        <td><p>Einsum</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_empty_strided.html">torch_empty_strided()</a></code> </p>
-        </td>
-        <td><p>Empty_strided</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_eq.html">torch_eq()</a></code> </p>
-        </td>
-        <td><p>Eq</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_equal.html">torch_equal()</a></code> </p>
-        </td>
-        <td><p>Equal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_erf.html">torch_erf()</a></code> </p>
-        </td>
-        <td><p>Erf</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_erfc.html">torch_erfc()</a></code> </p>
-        </td>
-        <td><p>Erfc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_erfinv.html">torch_erfinv()</a></code> </p>
-        </td>
-        <td><p>Erfinv</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_exp.html">torch_exp()</a></code> </p>
-        </td>
-        <td><p>Exp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_expm1.html">torch_expm1()</a></code> </p>
-        </td>
-        <td><p>Expm1</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_fft.html">torch_fft()</a></code> </p>
-        </td>
-        <td><p>Fft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_flatten.html">torch_flatten()</a></code> </p>
-        </td>
-        <td><p>Flatten</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_flip.html">torch_flip()</a></code> </p>
-        </td>
-        <td><p>Flip</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_floor.html">torch_floor()</a></code> </p>
-        </td>
-        <td><p>Floor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_floor_divide.html">torch_floor_divide()</a></code> </p>
-        </td>
-        <td><p>Floor_divide</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_fmod.html">torch_fmod()</a></code> </p>
-        </td>
-        <td><p>Fmod</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_frac.html">torch_frac()</a></code> </p>
-        </td>
-        <td><p>Frac</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_gather.html">torch_gather()</a></code> </p>
-        </td>
-        <td><p>Gather</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ge.html">torch_ge()</a></code> </p>
-        </td>
-        <td><p>Ge</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_generator.html">torch_generator()</a></code> </p>
-        </td>
-        <td><p>Create a Generator object</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_geqrf.html">torch_geqrf()</a></code> </p>
-        </td>
-        <td><p>Geqrf</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ger.html">torch_ger()</a></code> </p>
-        </td>
-        <td><p>Ger</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_gt.html">torch_gt()</a></code> </p>
-        </td>
-        <td><p>Gt</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_hamming_window.html">torch_hamming_window()</a></code> </p>
-        </td>
-        <td><p>Hamming_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_hann_window.html">torch_hann_window()</a></code> </p>
-        </td>
-        <td><p>Hann_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_histc.html">torch_histc()</a></code> </p>
-        </td>
-        <td><p>Histc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ifft.html">torch_ifft()</a></code> </p>
-        </td>
-        <td><p>Ifft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_imag.html">torch_imag()</a></code> </p>
-        </td>
-        <td><p>Imag</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_index_select.html">torch_index_select()</a></code> </p>
-        </td>
-        <td><p>Index_select</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_inverse.html">torch_inverse()</a></code> </p>
-        </td>
-        <td><p>Inverse</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_irfft.html">torch_irfft()</a></code> </p>
-        </td>
-        <td><p>Irfft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_is_complex.html">torch_is_complex()</a></code> </p>
-        </td>
-        <td><p>Is_complex</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_is_floating_point.html">torch_is_floating_point()</a></code> </p>
-        </td>
-        <td><p>Is_floating_point</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_is_installed.html">torch_is_installed()</a></code> </p>
-        </td>
-        <td><p>Verifies if torch is installed</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isfinite.html">torch_isfinite()</a></code> </p>
-        </td>
-        <td><p>Isfinite</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isinf.html">torch_isinf()</a></code> </p>
-        </td>
-        <td><p>Isinf</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isnan.html">torch_isnan()</a></code> </p>
-        </td>
-        <td><p>Isnan</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_kthvalue.html">torch_kthvalue()</a></code> </p>
-        </td>
-        <td><p>Kthvalue</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_layout.html">torch_strided()</a></code> <code><a href="torch_layout.html">torch_sparse_coo()</a></code> </p>
-        </td>
-        <td><p>Creates the corresponding layout</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_le.html">torch_le()</a></code> </p>
-        </td>
-        <td><p>Le</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lerp.html">torch_lerp()</a></code> </p>
-        </td>
-        <td><p>Lerp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lgamma.html">torch_lgamma()</a></code> </p>
-        </td>
-        <td><p>Lgamma</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_log.html">torch_log()</a></code> </p>
-        </td>
-        <td><p>Log</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_log10.html">torch_log10()</a></code> </p>
-        </td>
-        <td><p>Log10</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_log1p.html">torch_log1p()</a></code> </p>
-        </td>
-        <td><p>Log1p</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_log2.html">torch_log2()</a></code> </p>
-        </td>
-        <td><p>Log2</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logdet.html">torch_logdet()</a></code> </p>
-        </td>
-        <td><p>Logdet</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logical_and.html">torch_logical_and()</a></code> </p>
-        </td>
-        <td><p>Logical_and</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logical_not.html">torch_logical_not</a></code> </p>
-        </td>
-        <td><p>Logical_not</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logical_or.html">torch_logical_or()</a></code> </p>
-        </td>
-        <td><p>Logical_or</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logical_xor.html">torch_logical_xor()</a></code> </p>
-        </td>
-        <td><p>Logical_xor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logsumexp.html">torch_logsumexp()</a></code> </p>
-        </td>
-        <td><p>Logsumexp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lstsq.html">torch_lstsq()</a></code> </p>
-        </td>
-        <td><p>Lstsq</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lt.html">torch_lt()</a></code> </p>
-        </td>
-        <td><p>Lt</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lu.html">torch_lu()</a></code> </p>
-        </td>
-        <td><p>LU</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lu_solve.html">torch_lu_solve()</a></code> </p>
-        </td>
-        <td><p>Lu_solve</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_manual_seed.html">torch_manual_seed()</a></code> </p>
-        </td>
-        <td><p>Sets the seed for generating random numbers.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_masked_select.html">torch_masked_select()</a></code> </p>
-        </td>
-        <td><p>Masked_select</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_matmul.html">torch_matmul()</a></code> </p>
-        </td>
-        <td><p>Matmul</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_matrix_power.html">torch_matrix_power()</a></code> </p>
-        </td>
-        <td><p>Matrix_power</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_matrix_rank.html">torch_matrix_rank()</a></code> </p>
-        </td>
-        <td><p>Matrix_rank</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_max.html">torch_max</a></code> </p>
-        </td>
-        <td><p>Max</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mean.html">torch_mean()</a></code> </p>
-        </td>
-        <td><p>Mean</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_median.html">torch_median()</a></code> </p>
-        </td>
-        <td><p>Median</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_memory_format.html">torch_contiguous_format()</a></code> <code><a href="torch_memory_format.html">torch_preserve_format()</a></code> <code><a href="torch_memory_format.html">torch_channels_last_format()</a></code> </p>
-        </td>
-        <td><p>Memory format</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_meshgrid.html">torch_meshgrid()</a></code> </p>
-        </td>
-        <td><p>Meshgrid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_min.html">torch_min</a></code> </p>
-        </td>
-        <td><p>Min</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mm.html">torch_mm()</a></code> </p>
-        </td>
-        <td><p>Mm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mode.html">torch_mode()</a></code> </p>
-        </td>
-        <td><p>Mode</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mul.html">torch_mul()</a></code> </p>
-        </td>
-        <td><p>Mul</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_multinomial.html">torch_multinomial()</a></code> </p>
-        </td>
-        <td><p>Multinomial</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mv.html">torch_mv()</a></code> </p>
-        </td>
-        <td><p>Mv</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mvlgamma.html">torch_mvlgamma()</a></code> </p>
-        </td>
-        <td><p>Mvlgamma</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_narrow.html">torch_narrow()</a></code> </p>
-        </td>
-        <td><p>Narrow</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ne.html">torch_ne()</a></code> </p>
-        </td>
-        <td><p>Ne</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_neg.html">torch_neg()</a></code> </p>
-        </td>
-        <td><p>Neg</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_nonzero.html">torch_nonzero()</a></code> </p>
-        </td>
-        <td><p>Nonzero</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_norm.html">torch_norm()</a></code> </p>
-        </td>
-        <td><p>Norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_normal.html">torch_normal()</a></code> </p>
-        </td>
-        <td><p>Normal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_orgqr.html">torch_orgqr()</a></code> </p>
-        </td>
-        <td><p>Orgqr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ormqr.html">torch_ormqr()</a></code> </p>
-        </td>
-        <td><p>Ormqr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_pdist.html">torch_pdist()</a></code> </p>
-        </td>
-        <td><p>Pdist</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_pinverse.html">torch_pinverse()</a></code> </p>
-        </td>
-        <td><p>Pinverse</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_pixel_shuffle.html">torch_pixel_shuffle()</a></code> </p>
-        </td>
-        <td><p>Pixel_shuffle</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_poisson.html">torch_poisson()</a></code> </p>
-        </td>
-        <td><p>Poisson</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_polygamma.html">torch_polygamma()</a></code> </p>
-        </td>
-        <td><p>Polygamma</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_pow.html">torch_pow()</a></code> </p>
-        </td>
-        <td><p>Pow</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_prod.html">torch_prod()</a></code> </p>
-        </td>
-        <td><p>Prod</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_promote_types.html">torch_promote_types()</a></code> </p>
-        </td>
-        <td><p>Promote_types</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_qr.html">torch_qr()</a></code> </p>
-        </td>
-        <td><p>Qr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_quantize_per_channel.html">torch_quantize_per_channel()</a></code> </p>
-        </td>
-        <td><p>Quantize_per_channel</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_quantize_per_tensor.html">torch_quantize_per_tensor()</a></code> </p>
-        </td>
-        <td><p>Quantize_per_tensor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_range.html">torch_range()</a></code> </p>
-        </td>
-        <td><p>Range</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_real.html">torch_real()</a></code> </p>
-        </td>
-        <td><p>Real</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_reciprocal.html">torch_reciprocal()</a></code> </p>
-        </td>
-        <td><p>Reciprocal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_relu.html">torch_relu()</a></code> </p>
-        </td>
-        <td><p>Relu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_relu_.html">torch_relu_()</a></code> </p>
-        </td>
-        <td><p>Relu_</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_remainder.html">torch_remainder()</a></code> </p>
-        </td>
-        <td><p>Remainder</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_renorm.html">torch_renorm()</a></code> </p>
-        </td>
-        <td><p>Renorm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_repeat_interleave.html">torch_repeat_interleave()</a></code> </p>
-        </td>
-        <td><p>Repeat_interleave</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_reshape.html">torch_reshape()</a></code> </p>
-        </td>
-        <td><p>Reshape</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_result_type.html">torch_result_type()</a></code> </p>
-        </td>
-        <td><p>Result_type</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rfft.html">torch_rfft()</a></code> </p>
-        </td>
-        <td><p>Rfft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_roll.html">torch_roll()</a></code> </p>
-        </td>
-        <td><p>Roll</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rot90.html">torch_rot90()</a></code> </p>
-        </td>
-        <td><p>Rot90</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_round.html">torch_round()</a></code> </p>
-        </td>
-        <td><p>Round</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rrelu_.html">torch_rrelu_()</a></code> </p>
-        </td>
-        <td><p>Rrelu_</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rsqrt.html">torch_rsqrt()</a></code> </p>
-        </td>
-        <td><p>Rsqrt</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_selu.html">torch_selu()</a></code> </p>
-        </td>
-        <td><p>Selu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_selu_.html">torch_selu_()</a></code> </p>
-        </td>
-        <td><p>Selu_</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sigmoid.html">torch_sigmoid()</a></code> </p>
-        </td>
-        <td><p>Sigmoid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sign.html">torch_sign()</a></code> </p>
-        </td>
-        <td><p>Sign</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sin.html">torch_sin()</a></code> </p>
-        </td>
-        <td><p>Sin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sinh.html">torch_sinh()</a></code> </p>
-        </td>
-        <td><p>Sinh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_slogdet.html">torch_slogdet()</a></code> </p>
-        </td>
-        <td><p>Slogdet</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_solve.html">torch_solve()</a></code> </p>
-        </td>
-        <td><p>Solve</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sort.html">torch_sort()</a></code> </p>
-        </td>
-        <td><p>Sort</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sparse_coo_tensor.html">torch_sparse_coo_tensor()</a></code> </p>
-        </td>
-        <td><p>Sparse_coo_tensor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_split.html">torch_split()</a></code> </p>
-        </td>
-        <td><p>Split</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sqrt.html">torch_sqrt()</a></code> </p>
-        </td>
-        <td><p>Sqrt</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_square.html">torch_square()</a></code> </p>
-        </td>
-        <td><p>Square</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_squeeze.html">torch_squeeze()</a></code> </p>
-        </td>
-        <td><p>Squeeze</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_stack.html">torch_stack()</a></code> </p>
-        </td>
-        <td><p>Stack</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_std.html">torch_std()</a></code> </p>
-        </td>
-        <td><p>Std</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_std_mean.html">torch_std_mean()</a></code> </p>
-        </td>
-        <td><p>Std_mean</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_stft.html">torch_stft()</a></code> </p>
-        </td>
-        <td><p>Stft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sum.html">torch_sum()</a></code> </p>
-        </td>
-        <td><p>Sum</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_svd.html">torch_svd()</a></code> </p>
-        </td>
-        <td><p>Svd</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_symeig.html">torch_symeig()</a></code> </p>
-        </td>
-        <td><p>Symeig</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_t.html">torch_t()</a></code> </p>
-        </td>
-        <td><p>T</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_take.html">torch_take()</a></code> </p>
-        </td>
-        <td><p>Take</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_tan.html">torch_tan()</a></code> </p>
-        </td>
-        <td><p>Tan</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_tanh.html">torch_tanh()</a></code> </p>
-        </td>
-        <td><p>Tanh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_tensor.html">torch_tensor()</a></code> </p>
-        </td>
-        <td><p>Converts R objects to a torch tensor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_tensordot.html">torch_tensordot()</a></code> </p>
-        </td>
-        <td><p>Tensordot</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_threshold_.html">torch_threshold_()</a></code> </p>
-        </td>
-        <td><p>Threshold_</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_topk.html">torch_topk()</a></code> </p>
-        </td>
-        <td><p>Topk</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_trace.html">torch_trace()</a></code> </p>
-        </td>
-        <td><p>Trace</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_transpose.html">torch_transpose()</a></code> </p>
-        </td>
-        <td><p>Transpose</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_trapz.html">torch_trapz()</a></code> </p>
-        </td>
-        <td><p>Trapz</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_triangular_solve.html">torch_triangular_solve()</a></code> </p>
-        </td>
-        <td><p>Triangular_solve</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_tril.html">torch_tril()</a></code> </p>
-        </td>
-        <td><p>Tril</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_tril_indices.html">torch_tril_indices()</a></code> </p>
-        </td>
-        <td><p>Tril_indices</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_triu.html">torch_triu()</a></code> </p>
-        </td>
-        <td><p>Triu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_triu_indices.html">torch_triu_indices()</a></code> </p>
-        </td>
-        <td><p>Triu_indices</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_true_divide.html">torch_true_divide()</a></code> </p>
-        </td>
-        <td><p>TRUE_divide</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_trunc.html">torch_trunc()</a></code> </p>
-        </td>
-        <td><p>Trunc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_unbind.html">torch_unbind()</a></code> </p>
-        </td>
-        <td><p>Unbind</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_unique_consecutive.html">torch_unique_consecutive()</a></code> </p>
-        </td>
-        <td><p>Unique_consecutive</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_unsqueeze.html">torch_unsqueeze()</a></code> </p>
-        </td>
-        <td><p>Unsqueeze</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_var.html">torch_var()</a></code> </p>
-        </td>
-        <td><p>Var</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_var_mean.html">torch_var_mean()</a></code> </p>
-        </td>
-        <td><p>Var_mean</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_where.html">torch_where()</a></code> </p>
-        </td>
-        <td><p>Where</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-neural-network-modules" class="hasAnchor"><a href="#section-neural-network-modules" class="anchor"></a>Neural network modules</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
-      
-      
-    </tbody><tbody>
-      
-      
-      <tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_avg_pool1d.html">nn_adaptive_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_avg_pool2d.html">nn_adaptive_avg_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_avg_pool3d.html">nn_adaptive_avg_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_log_softmax_with_loss.html">nn_adaptive_log_softmax_with_loss()</a></code> </p>
-        </td>
-        <td><p>AdaptiveLogSoftmaxWithLoss module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_max_pool1d.html">nn_adaptive_max_pool1d()</a></code> </p>
-        </td>
-        <td><p>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_max_pool2d.html">nn_adaptive_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_max_pool3d.html">nn_adaptive_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_avg_pool1d.html">nn_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Applies a 1D average pooling over an input signal composed of several
-input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_avg_pool2d.html">nn_avg_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D average pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_avg_pool3d.html">nn_avg_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D average pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_batch_norm1d.html">nn_batch_norm1d()</a></code> </p>
-        </td>
-        <td><p>BatchNorm1D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_batch_norm2d.html">nn_batch_norm2d()</a></code> </p>
-        </td>
-        <td><p>BatchNorm2D</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_bce_loss.html">nn_bce_loss()</a></code> </p>
-        </td>
-        <td><p>Binary cross entropy loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_bilinear.html">nn_bilinear()</a></code> </p>
-        </td>
-        <td><p>Bilinear module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_celu.html">nn_celu()</a></code> </p>
-        </td>
-        <td><p>CELU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv1d.html">nn_conv1d()</a></code> </p>
-        </td>
-        <td><p>Conv1D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv2d.html">nn_conv2d()</a></code> </p>
-        </td>
-        <td><p>Conv2D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv3d.html">nn_conv3d()</a></code> </p>
-        </td>
-        <td><p>Conv3D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv_transpose1d.html">nn_conv_transpose1d()</a></code> </p>
-        </td>
-        <td><p>ConvTranspose1D</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv_transpose2d.html">nn_conv_transpose2d()</a></code> </p>
-        </td>
-        <td><p>ConvTranpose2D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv_transpose3d.html">nn_conv_transpose3d()</a></code> </p>
-        </td>
-        <td><p>ConvTranpose3D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_cross_entropy_loss.html">nn_cross_entropy_loss()</a></code> </p>
-        </td>
-        <td><p>CrossEntropyLoss module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_dropout.html">nn_dropout()</a></code> </p>
-        </td>
-        <td><p>Dropout module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_dropout2d.html">nn_dropout2d()</a></code> </p>
-        </td>
-        <td><p>Dropout2D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_dropout3d.html">nn_dropout3d()</a></code> </p>
-        </td>
-        <td><p>Dropout3D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_elu.html">nn_elu()</a></code> </p>
-        </td>
-        <td><p>ELU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_embedding.html">nn_embedding()</a></code> </p>
-        </td>
-        <td><p>Embedding module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_fractional_max_pool2d.html">nn_fractional_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D fractional max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_fractional_max_pool3d.html">nn_fractional_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D fractional max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_gelu.html">nn_gelu()</a></code> </p>
-        </td>
-        <td><p>GELU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_glu.html">nn_glu()</a></code> </p>
-        </td>
-        <td><p>GLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hardshrink.html">nn_hardshrink()</a></code> </p>
-        </td>
-        <td><p>Hardshwink module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hardsigmoid.html">nn_hardsigmoid()</a></code> </p>
-        </td>
-        <td><p>Hardsigmoid module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hardswish.html">nn_hardswish()</a></code> </p>
-        </td>
-        <td><p>Hardswish module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hardtanh.html">nn_hardtanh()</a></code> </p>
-        </td>
-        <td><p>Hardtanh module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_identity.html">nn_identity()</a></code> </p>
-        </td>
-        <td><p>Identity module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_calculate_gain.html">nn_init_calculate_gain()</a></code> </p>
-        </td>
-        <td><p>Calculate gain</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_constant_.html">nn_init_constant_()</a></code> </p>
-        </td>
-        <td><p>Constant initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_dirac_.html">nn_init_dirac_()</a></code> </p>
-        </td>
-        <td><p>Dirac initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_eye_.html">nn_init_eye_()</a></code> </p>
-        </td>
-        <td><p>Eye initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_kaiming_normal_.html">nn_init_kaiming_normal_()</a></code> </p>
-        </td>
-        <td><p>Kaiming normal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_kaiming_uniform_.html">nn_init_kaiming_uniform_()</a></code> </p>
-        </td>
-        <td><p>Kaiming uniform initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_normal_.html">nn_init_normal_()</a></code> </p>
-        </td>
-        <td><p>Normal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_ones_.html">nn_init_ones_()</a></code> </p>
-        </td>
-        <td><p>Ones initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_orthogonal_.html">nn_init_orthogonal_()</a></code> </p>
-        </td>
-        <td><p>Orthogonal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_sparse_.html">nn_init_sparse_()</a></code> </p>
-        </td>
-        <td><p>Sparse initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_trunc_normal_.html">nn_init_trunc_normal_()</a></code> </p>
-        </td>
-        <td><p>Truncated normal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_uniform_.html">nn_init_uniform_()</a></code> </p>
-        </td>
-        <td><p>Uniform initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_xavier_normal_.html">nn_init_xavier_normal_()</a></code> </p>
-        </td>
-        <td><p>Xavier normal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_xavier_uniform_.html">nn_init_xavier_uniform_()</a></code> </p>
-        </td>
-        <td><p>Xavier uniform initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_zeros_.html">nn_init_zeros_()</a></code> </p>
-        </td>
-        <td><p>Zeros initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_leaky_relu.html">nn_leaky_relu()</a></code> </p>
-        </td>
-        <td><p>LeakyReLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_linear.html">nn_linear()</a></code> </p>
-        </td>
-        <td><p>Linear module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_log_sigmoid.html">nn_log_sigmoid()</a></code> </p>
-        </td>
-        <td><p>LogSigmoid module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_log_softmax.html">nn_log_softmax()</a></code> </p>
-        </td>
-        <td><p>LogSoftmax module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_lp_pool1d.html">nn_lp_pool1d()</a></code> </p>
-        </td>
-        <td><p>Applies a 1D power-average pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_lp_pool2d.html">nn_lp_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D power-average pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_pool1d.html">nn_max_pool1d()</a></code> </p>
-        </td>
-        <td><p>MaxPool1D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_pool2d.html">nn_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>MaxPool2D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_pool3d.html">nn_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D max pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code> </p>
-        </td>
-        <td><p>Computes a partial inverse of <code>MaxPool1d</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code> </p>
-        </td>
-        <td><p>Computes a partial inverse of <code>MaxPool2d</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code> </p>
-        </td>
-        <td><p>Computes a partial inverse of <code>MaxPool3d</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_module.html">nn_module()</a></code> </p>
-        </td>
-        <td><p>Base class for all neural network modules.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_module_list.html">nn_module_list()</a></code> </p>
-        </td>
-        <td><p>Holds submodules in a list.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_multihead_attention.html">nn_multihead_attention()</a></code> </p>
-        </td>
-        <td><p>MultiHead attention</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_prelu.html">nn_prelu()</a></code> </p>
-        </td>
-        <td><p>PReLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_relu.html">nn_relu()</a></code> </p>
-        </td>
-        <td><p>ReLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_relu6.html">nn_relu6()</a></code> </p>
-        </td>
-        <td><p>ReLu6 module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_rnn.html">nn_rnn()</a></code> </p>
-        </td>
-        <td><p>RNN module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_rrelu.html">nn_rrelu()</a></code> </p>
-        </td>
-        <td><p>RReLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_selu.html">nn_selu()</a></code> </p>
-        </td>
-        <td><p>SELU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_sequential.html">nn_sequential()</a></code> </p>
-        </td>
-        <td><p>A sequential container</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_sigmoid.html">nn_sigmoid()</a></code> </p>
-        </td>
-        <td><p>Sigmoid module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softmax.html">nn_softmax()</a></code> </p>
-        </td>
-        <td><p>Softmax module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softmax2d.html">nn_softmax2d()</a></code> </p>
-        </td>
-        <td><p>Softmax2d module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softmin.html">nn_softmin()</a></code> </p>
-        </td>
-        <td><p>Softmin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softplus.html">nn_softplus()</a></code> </p>
-        </td>
-        <td><p>Softplus module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softshrink.html">nn_softshrink()</a></code> </p>
-        </td>
-        <td><p>Softshrink module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softsign.html">nn_softsign()</a></code> </p>
-        </td>
-        <td><p>Softsign module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_tanh.html">nn_tanh()</a></code> </p>
-        </td>
-        <td><p>Tanh module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_tanhshrink.html">nn_tanhshrink()</a></code> </p>
-        </td>
-        <td><p>Tanhshrink module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_threshold.html">nn_threshold()</a></code> </p>
-        </td>
-        <td><p>Threshoold module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code> </p>
-        </td>
-        <td><p>Packs a Tensor containing padded sequences of variable length.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_rnn_pack_sequence.html">nn_utils_rnn_pack_sequence()</a></code> </p>
-        </td>
-        <td><p>Packs a list of variable length Tensors</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_rnn_pad_packed_sequence.html">nn_utils_rnn_pad_packed_sequence()</a></code> </p>
-        </td>
-        <td><p>Pads a packed batch of variable length sequences.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_rnn_pad_sequence.html">nn_utils_rnn_pad_sequence()</a></code> </p>
-        </td>
-        <td><p>Pad a list of variable length Tensors with <code>padding_value</code></p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-neural-networks-functional-module" class="hasAnchor"><a href="#section-neural-networks-functional-module" class="anchor"></a>Neural networks functional module</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
-      
-      
-    </tbody><tbody>
-      
-      
-      <tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_avg_pool1d.html">nnf_adaptive_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_avg_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_avg_pool2d.html">nnf_adaptive_avg_pool2d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_avg_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_avg_pool3d.html">nnf_adaptive_avg_pool3d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_avg_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_max_pool1d.html">nnf_adaptive_max_pool1d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_max_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_max_pool2d.html">nnf_adaptive_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_max_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_max_pool3d.html">nnf_adaptive_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_max_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_affine_grid.html">nnf_affine_grid()</a></code> </p>
-        </td>
-        <td><p>Affine_grid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_alpha_dropout.html">nnf_alpha_dropout()</a></code> </p>
-        </td>
-        <td><p>Alpha_dropout</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_avg_pool1d.html">nnf_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Avg_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_avg_pool2d.html">nnf_avg_pool2d()</a></code> </p>
-        </td>
-        <td><p>Avg_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_avg_pool3d.html">nnf_avg_pool3d()</a></code> </p>
-        </td>
-        <td><p>Avg_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_batch_norm.html">nnf_batch_norm()</a></code> </p>
-        </td>
-        <td><p>Batch_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_bilinear.html">nnf_bilinear()</a></code> </p>
-        </td>
-        <td><p>Bilinear</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_binary_cross_entropy.html">nnf_binary_cross_entropy()</a></code> </p>
-        </td>
-        <td><p>Binary_cross_entropy</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_binary_cross_entropy_with_logits.html">nnf_binary_cross_entropy_with_logits()</a></code> </p>
-        </td>
-        <td><p>Binary_cross_entropy_with_logits</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_celu.html">nnf_celu()</a></code> <code><a href="nnf_celu.html">nnf_celu_()</a></code> </p>
-        </td>
-        <td><p>Celu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv1d.html">nnf_conv1d()</a></code> </p>
-        </td>
-        <td><p>Conv1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv2d.html">nnf_conv2d()</a></code> </p>
-        </td>
-        <td><p>Conv2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv3d.html">nnf_conv3d()</a></code> </p>
-        </td>
-        <td><p>Conv3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv_tbc.html">nnf_conv_tbc()</a></code> </p>
-        </td>
-        <td><p>Conv_tbc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv_transpose1d.html">nnf_conv_transpose1d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv_transpose2d.html">nnf_conv_transpose2d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv_transpose3d.html">nnf_conv_transpose3d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_cosine_embedding_loss.html">nnf_cosine_embedding_loss()</a></code> </p>
-        </td>
-        <td><p>Cosine_embedding_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_cosine_similarity.html">nnf_cosine_similarity()</a></code> </p>
-        </td>
-        <td><p>Cosine_similarity</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_cross_entropy.html">nnf_cross_entropy()</a></code> </p>
-        </td>
-        <td><p>Cross_entropy</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_ctc_loss.html">nnf_ctc_loss()</a></code> </p>
-        </td>
-        <td><p>Ctc_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_dropout.html">nnf_dropout()</a></code> </p>
-        </td>
-        <td><p>Dropout</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_dropout2d.html">nnf_dropout2d()</a></code> </p>
-        </td>
-        <td><p>Dropout2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_dropout3d.html">nnf_dropout3d()</a></code> </p>
-        </td>
-        <td><p>Dropout3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_elu.html">nnf_elu()</a></code> <code><a href="nnf_elu.html">nnf_elu_()</a></code> </p>
-        </td>
-        <td><p>Elu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_embedding.html">nnf_embedding()</a></code> </p>
-        </td>
-        <td><p>Embedding</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_embedding_bag.html">nnf_embedding_bag()</a></code> </p>
-        </td>
-        <td><p>Embedding_bag</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_fold.html">nnf_fold()</a></code> </p>
-        </td>
-        <td><p>Fold</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_fractional_max_pool2d.html">nnf_fractional_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Fractional_max_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_fractional_max_pool3d.html">nnf_fractional_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Fractional_max_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_gelu.html">nnf_gelu()</a></code> </p>
-        </td>
-        <td><p>Gelu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_glu.html">nnf_glu()</a></code> </p>
-        </td>
-        <td><p>Glu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code> </p>
-        </td>
-        <td><p>Grid_sample</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_group_norm.html">nnf_group_norm()</a></code> </p>
-        </td>
-        <td><p>Group_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_gumbel_softmax.html">nnf_gumbel_softmax()</a></code> </p>
-        </td>
-        <td><p>Gumbel_softmax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hardshrink.html">nnf_hardshrink()</a></code> </p>
-        </td>
-        <td><p>Hardshrink</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hardsigmoid.html">nnf_hardsigmoid()</a></code> </p>
-        </td>
-        <td><p>Hardsigmoid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hardswish.html">nnf_hardswish()</a></code> </p>
-        </td>
-        <td><p>Hardswish</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hardtanh.html">nnf_hardtanh()</a></code> <code><a href="nnf_hardtanh.html">nnf_hardtanh_()</a></code> </p>
-        </td>
-        <td><p>Hardtanh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hinge_embedding_loss.html">nnf_hinge_embedding_loss()</a></code> </p>
-        </td>
-        <td><p>Hinge_embedding_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_instance_norm.html">nnf_instance_norm()</a></code> </p>
-        </td>
-        <td><p>Instance_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_interpolate.html">nnf_interpolate()</a></code> </p>
-        </td>
-        <td><p>Interpolate</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_kl_div.html">nnf_kl_div()</a></code> </p>
-        </td>
-        <td><p>Kl_div</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_l1_loss.html">nnf_l1_loss()</a></code> </p>
-        </td>
-        <td><p>L1_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_layer_norm.html">nnf_layer_norm()</a></code> </p>
-        </td>
-        <td><p>Layer_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_leaky_relu.html">nnf_leaky_relu()</a></code> </p>
-        </td>
-        <td><p>Leaky_relu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_linear.html">nnf_linear()</a></code> </p>
-        </td>
-        <td><p>Linear</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_local_response_norm.html">nnf_local_response_norm()</a></code> </p>
-        </td>
-        <td><p>Local_response_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_log_softmax.html">nnf_log_softmax()</a></code> </p>
-        </td>
-        <td><p>Log_softmax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_logsigmoid.html">nnf_logsigmoid()</a></code> </p>
-        </td>
-        <td><p>Logsigmoid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_lp_pool1d.html">nnf_lp_pool1d()</a></code> </p>
-        </td>
-        <td><p>Lp_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_lp_pool2d.html">nnf_lp_pool2d()</a></code> </p>
-        </td>
-        <td><p>Lp_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_margin_ranking_loss.html">nnf_margin_ranking_loss()</a></code> </p>
-        </td>
-        <td><p>Margin_ranking_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_pool1d.html">nnf_max_pool1d()</a></code> </p>
-        </td>
-        <td><p>Max_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_pool2d.html">nnf_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Max_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_pool3d.html">nnf_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Max_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_unpool1d.html">nnf_max_unpool1d()</a></code> </p>
-        </td>
-        <td><p>Max_unpool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_unpool2d.html">nnf_max_unpool2d()</a></code> </p>
-        </td>
-        <td><p>Max_unpool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_unpool3d.html">nnf_max_unpool3d()</a></code> </p>
-        </td>
-        <td><p>Max_unpool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_mse_loss.html">nnf_mse_loss()</a></code> </p>
-        </td>
-        <td><p>Mse_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_multi_head_attention_forward.html">nnf_multi_head_attention_forward()</a></code> </p>
-        </td>
-        <td><p>Multi head attention forward</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_multi_margin_loss.html">nnf_multi_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Multi_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_multilabel_margin_loss.html">nnf_multilabel_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Multilabel_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_multilabel_soft_margin_loss.html">nnf_multilabel_soft_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Multilabel_soft_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_nll_loss.html">nnf_nll_loss()</a></code> </p>
-        </td>
-        <td><p>Nll_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_normalize.html">nnf_normalize()</a></code> </p>
-        </td>
-        <td><p>Normalize</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_one_hot.html">nnf_one_hot()</a></code> </p>
-        </td>
-        <td><p>One_hot</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_pad.html">nnf_pad()</a></code> </p>
-        </td>
-        <td><p>Pad</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_pairwise_distance.html">nnf_pairwise_distance()</a></code> </p>
-        </td>
-        <td><p>Pairwise_distance</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_pdist.html">nnf_pdist()</a></code> </p>
-        </td>
-        <td><p>Pdist</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_pixel_shuffle.html">nnf_pixel_shuffle()</a></code> </p>
-        </td>
-        <td><p>Pixel_shuffle</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_poisson_nll_loss.html">nnf_poisson_nll_loss()</a></code> </p>
-        </td>
-        <td><p>Poisson_nll_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_prelu.html">nnf_prelu()</a></code> </p>
-        </td>
-        <td><p>Prelu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_relu.html">nnf_relu()</a></code> <code><a href="nnf_relu.html">nnf_relu_()</a></code> </p>
-        </td>
-        <td><p>Relu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_relu6.html">nnf_relu6()</a></code> </p>
-        </td>
-        <td><p>Relu6</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_rrelu.html">nnf_rrelu()</a></code> <code><a href="nnf_rrelu.html">nnf_rrelu_()</a></code> </p>
-        </td>
-        <td><p>Rrelu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_selu.html">nnf_selu()</a></code> <code><a href="nnf_selu.html">nnf_selu_()</a></code> </p>
-        </td>
-        <td><p>Selu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_sigmoid.html">nnf_sigmoid()</a></code> </p>
-        </td>
-        <td><p>Sigmoid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_smooth_l1_loss.html">nnf_smooth_l1_loss()</a></code> </p>
-        </td>
-        <td><p>Smooth_l1_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_soft_margin_loss.html">nnf_soft_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Soft_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softmax.html">nnf_softmax()</a></code> </p>
-        </td>
-        <td><p>Softmax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softmin.html">nnf_softmin()</a></code> </p>
-        </td>
-        <td><p>Softmin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softplus.html">nnf_softplus()</a></code> </p>
-        </td>
-        <td><p>Softplus</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softshrink.html">nnf_softshrink()</a></code> </p>
-        </td>
-        <td><p>Softshrink</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softsign.html">nnf_softsign()</a></code> </p>
-        </td>
-        <td><p>Softsign</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_tanhshrink.html">nnf_tanhshrink()</a></code> </p>
-        </td>
-        <td><p>Tanhshrink</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_threshold.html">nnf_threshold()</a></code> <code><a href="nnf_threshold.html">nnf_threshold_()</a></code> </p>
-        </td>
-        <td><p>Threshold</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_triplet_margin_loss.html">nnf_triplet_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Triplet_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_unfold.html">nnf_unfold()</a></code> </p>
-        </td>
-        <td><p>Unfold</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-optimizers" class="hasAnchor"><a href="#section-optimizers" class="anchor"></a>Optimizers</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
-      
-      
-    </tbody><tbody>
-      
-      
-      <tr>
-        
-        <td>
-          <p><code><a href="optim_adam.html">optim_adam()</a></code> </p>
-        </td>
-        <td><p>Implements Adam algorithm.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_required.html">optim_required()</a></code> </p>
-        </td>
-        <td><p>Dummy value indicating a required value.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_sgd.html">optim_sgd()</a></code> </p>
-        </td>
-        <td><p>SGD optimizer</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
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-          <p class="section-desc"></p>
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-          <p><code><a href="dataset.html">dataset()</a></code> </p>
-        </td>
-        <td><p>An abstract class representing a <code>Dataset</code>.</p></td>
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-single- or multi-process iterators over the dataset.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="dataloader_make_iter.html">dataloader_make_iter()</a></code> </p>
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-        </td>
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-        <td>
-          <p><code><a href="tensor_dataset.html">tensor_dataset()</a></code> </p>
-        </td>
-        <td><p>Dataset wrapping tensors.</p></td>
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-        
-        <td>
-          <p><code><a href="is_dataloader.html">is_dataloader()</a></code> </p>
-        </td>
-        <td><p>Checks if the object is a dataloader</p></td>
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-          <p><code><a href="autograd_function.html">autograd_function()</a></code> </p>
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-        <td><p>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</p></td>
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-        
-        <td>
-          <p><code><a href="autograd_set_grad_mode.html">autograd_set_grad_mode()</a></code> </p>
-        </td>
-        <td><p>Set grad mode</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="with_no_grad.html">with_no_grad()</a></code> </p>
-        </td>
-        <td><p>Temporarily modify gradient recording.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="with_enable_grad.html">with_enable_grad()</a></code> </p>
-        </td>
-        <td><p>Enable grad</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="AutogradContext.html">AutogradContext</a></code> </p>
-        </td>
-        <td><p>Class representing the context.</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-cuda-utilities" class="hasAnchor"><a href="#section-cuda-utilities" class="anchor"></a>Cuda utilities</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
-      
-      
-    </tbody><tbody>
-      
-      
-      <tr>
-        
-        <td>
-          <p><code><a href="cuda_current_device.html">cuda_current_device()</a></code> </p>
-        </td>
-        <td><p>Returns the index of a currently selected device.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="cuda_device_count.html">cuda_device_count()</a></code> </p>
-        </td>
-        <td><p>Returns the number of GPUs available.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="cuda_is_available.html">cuda_is_available()</a></code> </p>
-        </td>
-        <td><p>Returns a bool indicating if CUDA is currently available.</p></td>
-      </tr>
-    </tbody><tbody>
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-        <th colspan="2">
-          <h2 id="section-installation" class="hasAnchor"><a href="#section-installation" class="anchor"></a>Installation</h2>
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-          <p><code><a href="install_torch.html">install_torch()</a></code> </p>
-        </td>
-        <td><p>Install Torch</p></td>
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-    <h1>Install Torch</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/install.R'><code>R/install.R</code></a></small>
-    <div class="hidden name"><code>install_torch.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Installs Torch and its dependencies.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>install_torch</span>(
-  version = <span class='st'>"1.5.0"</span>,
-  type = <span class='fu'>install_type</span>(version = <span class='kw'>version</span>),
-  reinstall = <span class='fl'>FALSE</span>,
-  path = <span class='fu'>install_path</span>(),
-  <span class='kw'>...</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>version</th>
-      <td><p>The Torch version to install.</p></td>
-    </tr>
-    <tr>
-      <th>type</th>
-      <td><p>The installation type for Torch. Valid values are <code>"cpu"</code> or the 'CUDA' version.</p></td>
-    </tr>
-    <tr>
-      <th>reinstall</th>
-      <td><p>Re-install Torch even if its already installed?</p></td>
-    </tr>
-    <tr>
-      <th>path</th>
-      <td><p>Optional path to install or check for an already existing installation.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>other optional arguments (like <code>load</code> for manual installation.)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>When using <code>path</code> to install in a specific location, make sure the <code>TORCH_HOME</code> environment
-variable is set to this same path to reuse this installation. The <code>TORCH_INSTALL</code> environment
-variable can be set to <code>0</code> to prevent auto-installing torch and <code>TORCH_LOAD</code> set to <code>0</code>
-to avoid loading dependencies automatically. These environment variables are meant for advanced use
-cases and troubleshootinng only.</p>
-
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diff --git a/static/docs/dev/reference/is_dataloader.html b/static/docs/dev/reference/is_dataloader.html
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-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>a named list of tensors.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
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-    <h1>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_avg_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The output size is H, for any input size.
-The number of output features is equal to the number of input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool1d</span>(<span class='kw'>output_size</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size H</p></td>
-    </tr>
-    </table>
-
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-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># target output size of 5</span>
-<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_adaptive_avg_pool1d</span>(<span class='fl'>5</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
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diff --git a/static/docs/dev/reference/nn_adaptive_avg_pool2d.html b/static/docs/dev/reference/nn_adaptive_avg_pool2d.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_avg_pool2d.Rd</code></div>
-    </div>
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-    <p>The output is of size H x W, for any input size.
-The number of output features is equal to the number of input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool2d</span>(<span class='kw'>output_size</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form H x W.
-Can be a tuple (H, W) or a single H for a square image H x H.
-H and W can be either a <code>int</code>, or <code>NULL</code> which means the size will
-be the same as that of the input.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># target output size of 5x7</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>))
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-<span class='co'># target output size of 7x7 (square)</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool2d</span>(<span class='fl'>7</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-    <h1>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_avg_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The output is of size D x H x W, for any input size.
-The number of output features is equal to the number of input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool3d</span>(<span class='kw'>output_size</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the form D x H x W.
-Can be a tuple (D, H, W) or a single number D for a cube D x D x D.
-D, H and W can be either a <code>int</code>, or <code>None</code> which means the size will
-be the same as that of the input.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># target output size of 5x7x9</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>,<span class='fl'>9</span>))
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>, <span class='fl'>10</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-<span class='co'># target output size of 7x7x7 (cube)</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool3d</span>(<span class='fl'>7</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>, <span class='fl'>8</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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diff --git a/static/docs/dev/reference/nn_adaptive_log_softmax_with_loss.html b/static/docs/dev/reference/nn_adaptive_log_softmax_with_loss.html
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-    <h1>AdaptiveLogSoftmaxWithLoss module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn_adaptive.R'><code>R/nn_adaptive.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_log_softmax_with_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Efficient softmax approximation as described in
-<a href='https://arxiv.org/abs/1609.04309'>Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou</a></p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_log_softmax_with_loss</span>(
-  <span class='kw'>in_features</span>,
-  <span class='kw'>n_classes</span>,
-  <span class='kw'>cutoffs</span>,
-  div_value = <span class='fl'>4</span>,
-  head_bias = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_features</th>
-      <td><p>(int): Number of features in the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>n_classes</th>
-      <td><p>(int): Number of classes in the dataset</p></td>
-    </tr>
-    <tr>
-      <th>cutoffs</th>
-      <td><p>(Sequence): Cutoffs used to assign targets to their buckets</p></td>
-    </tr>
-    <tr>
-      <th>div_value</th>
-      <td><p>(float, optional): value used as an exponent to compute sizes
-of the clusters. Default: 4.0</p></td>
-    </tr>
-    <tr>
-      <th>head_bias</th>
-      <td><p>(bool, optional): If <code>True</code>, adds a bias term to the 'head' of the
-adaptive softmax. Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p><code>NamedTuple</code> with <code>output</code> and <code>loss</code> fields:</p><ul>
-<li><p><strong>output</strong> is a Tensor of size <code>N</code> containing computed target
-log probabilities for each example</p></li>
-<li><p><strong>loss</strong> is a Scalar representing the computed negative
-log likelihood loss</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Adaptive softmax is an approximate strategy for training models with large
-output spaces. It is most effective when the label distribution is highly
-imbalanced, for example in natural language modelling, where the word
-frequency distribution approximately follows the Zipf's law.</p>
-<p>Adaptive softmax partitions the labels into several clusters, according to
-their frequency. These clusters may contain different number of targets
-each.</p>
-<p>Additionally, clusters containing less frequent labels assign lower
-dimensional embeddings to those labels, which speeds up the computation.
-For each minibatch, only clusters for which at least one target is
-present are evaluated.</p>
-<p>The idea is that the clusters which are accessed frequently
-(like the first one, containing most frequent labels), should also be cheap
-to compute -- that is, contain a small number of assigned labels.
-We highly recommend taking a look at the original paper for more details.</p><ul>
-<li><p><code>cutoffs</code> should be an ordered Sequence of integers sorted
-in the increasing order.
-It controls number of clusters and the partitioning of targets into
-clusters. For example setting <code>cutoffs = c(10, 100, 1000)</code>
-means that first <code>10</code> targets will be assigned
-to the 'head' of the adaptive softmax, targets <code>11, 12, ..., 100</code> will be
-assigned to the first cluster, and targets <code>101, 102, ..., 1000</code> will be
-assigned to the second cluster, while targets
-<code>1001, 1002, ..., n_classes - 1</code> will be assigned
-to the last, third cluster.</p></li>
-<li><p><code>div_value</code> is used to compute the size of each additional cluster,
-which is given as
-\(\left\lfloor\frac{\mbox{in\_features}}{\mbox{div\_value}^{idx}}\right\rfloor\),
-where \(idx\) is the cluster index (with clusters
-for less frequent words having larger indices,
-and indices starting from \(1\)).</p></li>
-<li><p><code>head_bias</code> if set to True, adds a bias term to the 'head' of the
-adaptive softmax. See paper for details. Set to False in the official
-implementation.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This module returns a <code>NamedTuple</code> with <code>output</code>
-and <code>loss</code> fields. See further documentation for details.</p>
-<p>To compute log-probabilities for all classes, the <code>log_prob</code>
-method can be used.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Labels passed as inputs to this module should be sorted according to
-their frequency. This means that the most frequent label should be
-represented by the index <code>0</code>, and the least frequent
-label should be represented by the index <code>n_classes - 1</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>input: \((N, \mbox{in\_features})\)</p></li>
-<li><p>target: \((N)\) where each value satisfies \(0 &lt;= \mbox{target[i]} &lt;= \mbox{n\_classes}\)</p></li>
-<li><p>output1: \((N)\)</p></li>
-<li><p>output2: <code>Scalar</code></p></li>
-</ul>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_max_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The output size is H, for any input size.
-The number of output features is equal to the number of input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_max_pool1d</span>(<span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size H</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool1d.html'>nn_max_unpool1d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># target output size of 5</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool1d</span>(<span class='fl'>5</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-    <h1>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_max_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The output is of size H x W, for any input size.
-The number of output features is equal to the number of input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_max_pool2d</span>(<span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form H x W.
-Can be a tuple <code>(H, W)</code> or a single H for a square image H x H.
-H and W can be either a <code>int</code>, or <code>None</code> which means the size will
-be the same as that of the input.</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># target output size of 5x7</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>))
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-<span class='co'># target output size of 7x7 (square)</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool2d</span>(<span class='fl'>7</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-</li>
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-</div><!--/.navbar -->
-
-      
-
-      </header>
-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_max_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The output is of size D x H x W, for any input size.
-The number of output features is equal to the number of input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_max_pool3d</span>(<span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form D x H x W.
-Can be a tuple (D, H, W) or a single D for a cube D x D x D.
-D, H and W can be either a <code>int</code>, or <code>None</code> which means the size will
-be the same as that of the input.</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool3d.html'>nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># target output size of 5x7x9</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>,<span class='fl'>9</span>))
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>, <span class='fl'>10</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-<span class='co'># target output size of 7x7x7 (cube)</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool3d</span>(<span class='fl'>7</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>, <span class='fl'>8</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/dev/reference/nn_avg_pool1d.html b/static/docs/dev/reference/nn_avg_pool1d.html
deleted file mode 100644
index e03ed8e6d..000000000
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-    <div class="page-header">
-    <h1>Applies a 1D average pooling over an input signal composed of several
-input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_avg_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>In the simplest case, the output value of the layer with input size \((N, C, L)\),
-output \((N, C, L_{out})\) and <code>kernel_size</code> \(k\)
-can be precisely described as:</p>
-<p>$$
-  \mbox{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1}
-\mbox{input}(N_i, C_j, \mbox{stride} \times l + m)
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_avg_pool1d</span>(
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  ceil_mode = <span class='fl'>FALSE</span>,
-  count_include_pad = <span class='fl'>TRUE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
-for <code>padding</code> number of points.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can each be
-an <code>int</code> or a one-element tuple.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, L_{in})\)</p></li>
-<li><p>Output: \((N, C, L_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  L_{out} = \left\lfloor \frac{L_{in} +
-      2 \times \mbox{padding} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-  
-<span class='co'># pool with window of size=3, stride=2</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_avg_pool1d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='fu'>m</span>(<span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>8</span>))
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   0.0526  0.6293  0.1364
-#&gt; [ CPUFloatType{1,1,3} ]</div></pre>
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diff --git a/static/docs/dev/reference/nn_avg_pool2d.html b/static/docs/dev/reference/nn_avg_pool2d.html
deleted file mode 100644
index 572032062..000000000
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 2D average pooling over an input signal composed of several input
-planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_avg_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
-output \((N, C, H_{out}, W_{out})\) and <code>kernel_size</code> \((kH, kW)\)
-can be precisely described as:</p>
-<p>$$
-  out(N_i, C_j, h, w)  = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1}
-input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_avg_pool2d</span>(
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  ceil_mode = <span class='fl'>FALSE</span>,
-  count_include_pad = <span class='fl'>TRUE</span>,
-  divisor_override = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
-    </tr>
-    <tr>
-      <th>divisor_override</th>
-      <td><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
-for <code>padding</code> number of points.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
-and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] -
-      \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
-$$
-$$
-  W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \mbox{padding}[1] -
-      \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-  
-<span class='co'># pool of square window of size=3, stride=2</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_avg_pool2d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='co'># pool of non-square window</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_avg_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-output \((N, C, D_{out}, H_{out}, W_{out})\) and kernel_size \((kD, kH, kW)\)
-can be precisely described as:
-$$
-\begin{array}{ll}
-\mbox{out}(N_i, C_j, d, h, w) = &amp;amp; \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
-&amp;amp; \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW}
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 3D average pooling over an input signal composed of several input
-planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_avg_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
-output \((N, C, D_{out}, H_{out}, W_{out})\) and <code>kernel_size</code> \((kD, kH, kW)\)
-can be precisely described as:</p>
-<p>$$
-\begin{array}{ll}
-\mbox{out}(N_i, C_j, d, h, w) = &amp; \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
-&amp; \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW}
-\end{array}
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_avg_pool3d</span>(
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  ceil_mode = <span class='fl'>FALSE</span>,
-  count_include_pad = <span class='fl'>TRUE</span>,
-  divisor_override = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on all three sides</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
-    </tr>
-    <tr>
-      <th>divisor_override</th>
-      <td><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on all three sides
-for <code>padding</code> number of points.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
-<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
-the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] -
-      \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
-$$
-$$
-  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] -
-      \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
-$$
-$$
-  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] -
-      \mbox{kernel\_size}[2]}{\mbox{stride}[2]} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-  
-<span class='co'># pool of square window of size=3, stride=2</span>
-<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_avg_pool3d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='co'># pool of non-square window</span>
-<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_avg_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>,<span class='fl'>44</span>, <span class='fl'>31</span>)
-<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>BatchNorm1D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-batchnorm.R'><code>R/nn-batchnorm.R</code></a></small>
-    <div class="hidden name"><code>nn_batch_norm1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D
-inputs with optional additional channel dimension) as described in the paper
-<a href='https://arxiv.org/abs/1502.03167'>Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a></p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_batch_norm1d</span>(
-  <span class='kw'>num_features</span>,
-  eps = <span class='fl'>1e-05</span>,
-  momentum = <span class='fl'>0.1</span>,
-  affine = <span class='fl'>TRUE</span>,
-  track_running_stats = <span class='fl'>TRUE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_features</th>
-      <td><p>\(C\) from an expected input of size
-\((N, C, L)\) or \(L\) from input of size \((N, L)\)</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability.
-Default: 1e-5</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>the value used for the running_mean and running_var
-computation. Can be set to <code>NULL</code> for cumulative moving average
-(i.e. simple average). Default: 0.1</p></td>
-    </tr>
-    <tr>
-      <th>affine</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this module has
-learnable affine parameters. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>track_running_stats</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this
-module tracks the running mean and variance, and when set to <code>FALSE</code>,
-this module does not track such statistics and always uses batch
-statistics in both training and eval modes. Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
-$$</p>
-<p>The mean and standard-deviation are calculated per-dimension over
-the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors
-of size <code>C</code> (where <code>C</code> is the input size). By default, the elements of \(\gamma\)
-are set to 1 and the elements of \(\beta\) are set to 0.</p>
-<p>Also by default, during training this layer keeps running estimates of its
-computed mean and variance, which are then used for normalization during
-evaluation. The running estimates are kept with a default :attr:<code>momentum</code>
-of 0.1.
-If <code>track_running_stats</code> is set to <code>FALSE</code>, this layer then does not
-keep running estimates, and batch statistics are instead used during
-evaluation time as well.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>This <code>momentum</code> argument is different from one used in optimizer
-classes and the conventional notion of momentum. Mathematically, the
-update rule for running statistics here is
-\(\hat{x}_{\mbox{new}} = (1 - \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t\),
-where \(\hat{x}\) is the estimated statistic and \(x_t\) is the
-new observed value.</p>
-<p>Because the Batch Normalization is done over the <code>C</code> dimension, computing statistics
-on <code>(N, L)</code> slices, it's common terminology to call this Temporal Batch Normalization.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C)\) or \((N, C, L)\)</p></li>
-<li><p>Output: \((N, C)\) or \((N, C, L)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># With Learnable Parameters</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm1d</span>(<span class='fl'>100</span>)
-<span class='co'># Without Learnable Parameters</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm1d</span>(<span class='fl'>100</span>, affine = <span class='fl'>FALSE</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>100</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>) 
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    <div class="page-header">
-    <h1>BatchNorm2D</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-batchnorm.R'><code>R/nn-batchnorm.R</code></a></small>
-    <div class="hidden name"><code>nn_batch_norm2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs
-additional channel dimension) as described in the paper
-<a href='https://arxiv.org/abs/1502.03167'>Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_batch_norm2d</span>(
-  <span class='kw'>num_features</span>,
-  eps = <span class='fl'>1e-05</span>,
-  momentum = <span class='fl'>0.1</span>,
-  affine = <span class='fl'>TRUE</span>,
-  track_running_stats = <span class='fl'>TRUE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_features</th>
-      <td><p>\(C\) from an expected input of size
-\((N, C, H, W)\)</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability.
-Default: 1e-5</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>the value used for the running_mean and running_var
-computation. Can be set to <code>None</code> for cumulative moving average
-(i.e. simple average). Default: 0.1</p></td>
-    </tr>
-    <tr>
-      <th>affine</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this module has
-learnable affine parameters. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>track_running_stats</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this
-module tracks the running mean and variance, and when set to <code>FALSE</code>,
-this module does not track such statistics and uses batch statistics instead
-in both training and eval modes if the running mean and variance are <code>None</code>.
-Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
-$$</p>
-<p>The mean and standard-deviation are calculated per-dimension over
-the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors
-of size <code>C</code> (where <code>C</code> is the input size). By default, the elements of \(\gamma\) are set
-to 1 and the elements of \(\beta\) are set to 0. The standard-deviation is calculated
-via the biased estimator, equivalent to <code><a href='torch_var.html'>torch_var(input, unbiased=FALSE)</a></code>.
-Also by default, during training this layer keeps running estimates of its
-computed mean and variance, which are then used for normalization during
-evaluation. The running estimates are kept with a default <code>momentum</code>
-of 0.1.</p>
-<p>If <code>track_running_stats</code> is set to <code>FALSE</code>, this layer then does not
-keep running estimates, and batch statistics are instead used during
-evaluation time as well.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This <code>momentum</code> argument is different from one used in optimizer
-classes and the conventional notion of momentum. Mathematically, the
-update rule for running statistics here is
-\(\hat{x}_{\mbox{new}} = (1 - \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t\),
-where \(\hat{x}\) is the estimated statistic and \(x_t\) is the
-new observed value.
-Because the Batch Normalization is done over the <code>C</code> dimension, computing statistics
-on <code>(N, H, W)</code> slices, it's common terminology to call this Spatial Batch Normalization.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H, W)\)</p></li>
-<li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># With Learnable Parameters</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm2d</span>(<span class='fl'>100</span>)
-<span class='co'># Without Learnable Parameters</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm2d</span>(<span class='fl'>100</span>, affine=<span class='fl'>FALSE</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>100</span>, <span class='fl'>35</span>, <span class='fl'>45</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Binary cross entropy loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_bce_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that measures the Binary Cross Entropy
-between the target and the output:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_bce_loss</span>(weight = <span class='kw'>NULL</span>, reduction = <span class='st'>"mean"</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional): a manual rescaling weight given to the loss
-of each batch element. If given, has to be a Tensor of size <code>nbatch</code>.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
-<code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
-<code>'mean'</code>: the sum of the output will be divided by the number of
-elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
-and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The unreduced (i.e. with <code>reduction</code> set to <code>'none'</code>) loss can be described as:
-$$
-  \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
-l_n = - w_n \left[ y_n \cdot \log x_n + (1 - y_n) \cdot \log (1 - x_n) \right]
-$$
-where \(N\) is the batch size. If <code>reduction</code> is not <code>'none'</code>
-(default <code>'mean'</code>), then</p>
-<p>$$
-  \ell(x, y) = \left\{ \begin{array}{ll}
-\mbox{mean}(L), &amp; \mbox{if reduction} = \mbox{'mean';}\\
-\mbox{sum}(L),  &amp; \mbox{if reduction} = \mbox{'sum'.}
-\end{array}
-\right.
-$$</p>
-<p>This is used for measuring the error of a reconstruction in for example
-an auto-encoder. Note that the targets \(y\) should be numbers
-between 0 and 1.</p>
-<p>Notice that if \(x_n\) is either 0 or 1, one of the log terms would be
-mathematically undefined in the above loss equation. PyTorch chooses to set
-\(\log (0) = -\infty\), since \(\lim_{x\to 0} \log (x) = -\infty\).</p>
-<p>However, an infinite term in the loss equation is not desirable for several reasons.
-For one, if either \(y_n = 0\) or \((1 - y_n) = 0\), then we would be
-multiplying 0 with infinity. Secondly, if we have an infinite loss value, then
-we would also have an infinite term in our gradient, since
-\(\lim_{x\to 0} \frac{d}{dx} \log (x) = \infty\).</p>
-<p>This would make BCELoss's backward method nonlinear with respect to \(x_n\),
-and using it for things like linear regression would not be straight-forward.
-Our solution is that BCELoss clamps its log function outputs to be greater than
-or equal to -100. This way, we can always have a finite loss value and a linear
-backward method.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where \(*\) means, any number of additional
-dimensions</p></li>
-<li><p>Target: \((N, *)\), same shape as the input</p></li>
-<li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then \((N, *)\), same
-shape as input.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_sigmoid.html'>nn_sigmoid</a></span>()
-<span class='kw'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_bce_loss</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>3</span>, requires_grad=<span class='fl'>TRUE</span>)
-<span class='kw'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fl'>3</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span>(<span class='fu'>m</span>(<span class='kw'>input</span>), <span class='kw'>target</span>)
-<span class='kw'>output</span><span class='op'>$</span><span class='fu'>backward</span>()
-
-}
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
-    <div class="hidden name"><code>nn_bilinear.Rd</code></div>
-    </div>
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-    <p>Applies a bilinear transformation to the incoming data
-\(y = x_1^T A x_2 + b\)</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_bilinear</span>(<span class='kw'>in1_features</span>, <span class='kw'>in2_features</span>, <span class='kw'>out_features</span>, bias = <span class='fl'>TRUE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in1_features</th>
-      <td><p>size of each first input sample</p></td>
-    </tr>
-    <tr>
-      <th>in2_features</th>
-      <td><p>size of each second input sample</p></td>
-    </tr>
-    <tr>
-      <th>out_features</th>
-      <td><p>size of each output sample</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
-Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input1: \((N, *, H_{in1})\) \(H_{in1}=\mbox{in1\_features}\) and
-\(*\) means any number of additional dimensions. All but the last
-dimension of the inputs should be the same.</p></li>
-<li><p>Input2: \((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\).</p></li>
-<li><p>Output: \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)
-and all but the last dimension are the same shape as the input.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight: the learnable weights of the module of shape
-\((\mbox{out\_features}, \mbox{in1\_features}, \mbox{in2\_features})\).
-The values are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where
-\(k = \frac{1}{\mbox{in1\_features}}\)</p></li>
-<li><p>bias: the learnable bias of the module of shape \((\mbox{out\_features})\).
-If <code>bias</code> is <code>TRUE</code>, the values are initialized from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where
-\(k = \frac{1}{\mbox{in1\_features}}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_bilinear</span>(<span class='fl'>20</span>, <span class='fl'>30</span>, <span class='fl'>50</span>)
-<span class='kw'>input1</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>20</span>)
-<span class='kw'>input2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>30</span>)
-<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>m</span>(<span class='kw'>input1</span>, <span class='kw'>input2</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>()) 
-
-}
-</div><div class='output co'>#&gt; [1] 128  50</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_celu.Rd</code></div>
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-    <p>Applies the element-wise function:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_celu</span>(alpha = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>alpha</th>
-      <td><p>the \(\alpha\) value for the CELU formulation. Default: 1.0</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))
-$$</p>
-<p>More details can be found in the paper
-<a href='https://arxiv.org/abs/1704.07483'>Continuously Differentiable Exponential Linear Units</a>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_celu</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-planes.
-In the simplest case, the output value of the layer with input size
-\((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be
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-    <div class="page-header">
-    <h1>Conv1D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1D convolution over an input signal composed of several input
-planes.
-In the simplest case, the output value of the layer with input size
-\((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be
-precisely described as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_conv1d</span>(
-  <span class='kw'>in_channels</span>,
-  <span class='kw'>out_channels</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  dilation = <span class='fl'>1</span>,
-  groups = <span class='fl'>1</span>,
-  bias = <span class='fl'>TRUE</span>,
-  padding_mode = <span class='st'>"zeros"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): Zero-padding added to both sides of
-the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel
-elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input
-channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
-output. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
-<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) +
-  \sum_{k = 0}^{C_{in} - 1} \mbox{weight}(C_{\mbox{out}_j}, k)
-\star \mbox{input}(N_i, k)
-$$</p>
-<p>where \(\star\) is the valid
-<a href='https://en.wikipedia.org/wiki/Cross-correlation'>cross-correlation</a> operator,
-\(N\) is a batch size, \(C\) denotes a number of channels,
-\(L\) is a length of signal sequence.</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation, a single
-number or a one-element tuple.</p></li>
-<li><p><code>padding</code> controls the amount of implicit zero-paddings on both sides
-for <code>padding</code> number of points.</p></li>
-<li><p><code>dilation</code> controls the spacing between the kernel points; also
-known as the à trous algorithm. It is harder to describe, but this
-<a href='https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md'>link</a>
-has a nice visualization of what <code>dilation</code> does.</p></li>
-<li><p><code>groups</code> controls the connections between inputs and outputs.
-<code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
-<li><p>At groups=2, the operation becomes equivalent to having two conv
-layers side by side, each seeing half the input channels,
-and producing half the output channels, and both subsequently
-concatenated.</p></li>
-<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
-its own set of filters,
-of size \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
-</ul></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>Depending of the size of your kernel, several (of the last)
-columns of the input might be lost, because it is a valid
-<code>cross-correlation</code><em>, and not a full <code>cross-correlation</code></em>.
-It is up to the user to add proper padding.</p>
-<p>When <code>groups == in_channels</code> and <code>out_channels == K * in_channels</code>,
-where <code>K</code> is a positive integer, this operation is also termed in
-literature as depthwise convolution.
-In other words, for an input of size \((N, C_{in}, L_{in})\),
-a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
-\((C_{\mbox{in}}=C_{in}, C_{\mbox{out}}=C_{in} \times K, ..., \mbox{groups}=C_{in})\).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
-<li><p>Output: \((N, C_{out}, L_{out})\) where</p></li>
-</ul>
-
-<p>$$
-  L_{out} = \left\lfloor\frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation}
-    \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor
-$$</p>
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
-\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}, \mbox{kernel\_size})\).
-The values of these weights are sampled from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)</p></li>
-<li><p>bias (Tensor): the learnable bias of the module of shape
-(out_channels). If <code>bias</code> is <code>TRUE</code>, then the values of these weights are
-sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv1d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-    <h1>Conv2D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D convolution over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_conv2d</span>(
-  <span class='kw'>in_channels</span>,
-  <span class='kw'>out_channels</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  dilation = <span class='fl'>1</span>,
-  groups = <span class='fl'>1</span>,
-  bias = <span class='fl'>TRUE</span>,
-  padding_mode = <span class='st'>"zeros"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): Zero-padding added to both sides of
-the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input
-channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
-output. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
-<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>In the simplest case, the output value of the layer with input size
-\((N, C_{\mbox{in}}, H, W)\) and output \((N, C_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}})\)
-can be precisely described as:</p>
-<p>$$
-\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) +
-  \sum_{k = 0}^{C_{\mbox{in}} - 1} \mbox{weight}(C_{\mbox{out}_j}, k) \star \mbox{input}(N_i, k)
-$$</p>
-<p>where \(\star\) is the valid 2D cross-correlation operator,
-\(N\) is a batch size, \(C\) denotes a number of channels,
-\(H\) is a height of input planes in pixels, and \(W\) is
-width in pixels.</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation, a single
-number or a tuple.</p></li>
-<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
-sides for <code>padding</code> number of points for each dimension.</p></li>
-<li><p><code>dilation</code> controls the spacing between the kernel points; also
-known as the à trous algorithm. It is harder to describe, but this <code>link</code>_
-has a nice visualization of what <code>dilation</code> does.</p></li>
-<li><p><code>groups</code> controls the connections between inputs and outputs.
-<code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
-<li><p>At groups=2, the operation becomes equivalent to having two conv
-layers side by side, each seeing half the input channels,
-and producing half the output channels, and both subsequently
-concatenated.</p></li>
-<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
-its own set of filters, of size:
-\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
-</ul></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and
-width dimension</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
-and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>Depending of the size of your kernel, several (of the last)
-columns of the input might be lost, because it is a valid cross-correlation,
-and not a full cross-correlation.
-It is up to the user to add proper padding.</p>
-<p>When <code>groups == in_channels</code> and <code>out_channels == K * in_channels</code>,
-where <code>K</code> is a positive integer, this operation is also termed in
-literature as depthwise convolution.
-In other words, for an input of size :math:<code>(N, C_{in}, H_{in}, W_{in})</code>,
-a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
-\((in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})\).</p>
-<p>In some circumstances when using the CUDA backend with CuDNN, this operator
-may select a nondeterministic algorithm to increase performance. If this is
-undesirable, you can try to make the operation deterministic (potentially at
-a performance cost) by setting <code>backends_cudnn_deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C_{out}, H_{out}, W_{out})\) where
-$$
-  H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] - \mbox{dilation}[0]
-    \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
-$$
-$$
-  W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \mbox{padding}[1] - \mbox{dilation}[1]
-    \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
-$$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
-\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}\),
-\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\).
-The values of these weights are sampled from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
-<li><p>bias (Tensor): the learnable bias of the module of shape
-(out_channels). If <code>bias</code> is <code>TRUE</code>,
-then the values of these weights are
-sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># With square kernels and equal stride</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride = <span class='fl'>2</span>)
-<span class='co'># non-square kernels and unequal stride and with padding</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
-<span class='co'># non-square kernels and unequal stride and with padding and dilation</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>), dilation=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)  
-
-}
-</div></pre>
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-In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\)
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-    <h1>Conv3D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 3D convolution over an input signal composed of several input
-planes.
-In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\)
-and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_conv3d</span>(
-  <span class='kw'>in_channels</span>,
-  <span class='kw'>out_channels</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  dilation = <span class='fl'>1</span>,
-  groups = <span class='fl'>1</span>,
-  bias = <span class='fl'>TRUE</span>,
-  padding_mode = <span class='st'>"zeros"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  out(N_i, C_{out_j}) = bias(C_{out_j}) +
-  \sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i, k)
-$$</p>
-<p>where \(\star\) is the valid 3D <code>cross-correlation</code> operator</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
-<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
-sides for <code>padding</code> number of points for each dimension.</p></li>
-<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
-It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
-<li><p><code>groups</code> controls the connections between inputs and outputs.
-<code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p></li>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
-<li><p>At groups=2, the operation becomes equivalent to having two conv
-layers side by side, each seeing half the input channels,
-and producing half the output channels, and both subsequently
-concatenated.</p></li>
-<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
-its own set of filters, of size
-\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
-<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
-the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>Depending of the size of your kernel, several (of the last)
-columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
-and not a full <code>cross-correlation</code></em>.
-It is up to the user to add proper padding.</p>
-<p>When <code>groups == in_channels</code> and <code>out_channels == K * in_channels</code>,
-where <code>K</code> is a positive integer, this operation is also termed in
-literature as depthwise convolution.
-In other words, for an input of size \((N, C_{in}, D_{in}, H_{in}, W_{in})\),
-a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
-\((in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})\).</p>
-<p>In some circumstances when using the CUDA backend with CuDNN, this operator
-may select a nondeterministic algorithm to increase performance. If this is
-undesirable, you can try to make the operation deterministic (potentially at
-a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.
-Please see the notes on :doc:<code>/notes/randomness</code> for background.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where
-$$
-  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0]
-    \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
- $$
-$$
-  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1]
-    \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
- $$
-$$
-  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2]
-    \times (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor
- $$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
-\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}},\)
-\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\).
-The values of these weights are sampled from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
-<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels). If <code>bias</code> is <code>True</code>,
-then the values of these weights are
-sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># With square kernels and equal stride</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='co'># non-square kernels and unequal stride and with padding</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>, <span class='fl'>0</span>))
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>10</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-<div class="row">
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-    <div class="page-header">
-    <h1>ConvTranspose1D</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv_transpose1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1D transposed convolution operator over an input image
-composed of several input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_conv_transpose1d</span>(
-  <span class='kw'>in_channels</span>,
-  <span class='kw'>out_channels</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  output_padding = <span class='fl'>0</span>,
-  groups = <span class='fl'>1</span>,
-  bias = <span class='fl'>TRUE</span>,
-  dilation = <span class='fl'>1</span>,
-  padding_mode = <span class='st'>"zeros"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
-will be added to both sides of the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>(int or tuple, optional): Additional size added to one side
-of the output shape. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
-<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This module can be seen as the gradient of Conv1d with respect to its input.
-It is also known as a fractionally-strided convolution or
-a deconvolution (although it is not an actual deconvolution operation).</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
-<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
-sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
-below for details.</p></li>
-<li><p><code>output_padding</code> controls the additional size added to one side
-of the output shape. See note below for details.</p></li>
-<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the
-à trous algorithm. It is harder to describe, but this <a href='https://github.com/vdumoulin/conv_arithmetic'>link</a>
-has a nice visualization of what <code>dilation</code> does.</p></li>
-<li><p><code>groups</code> controls the connections between inputs and outputs.
-<code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
-<li><p>At groups=2, the operation becomes equivalent to having two conv
-layers side by side, each seeing half the input channels,
-and producing half the output channels, and both subsequently
-concatenated.</p></li>
-<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
-its own set of filters (of size
-\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
-</ul></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>Depending of the size of your kernel, several (of the last)
-columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
-and not a full <code>cross-correlation</code></em>.
-It is up to the user to add proper padding.</p>
-<p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
-amount of zero padding to both sizes of the input. This is set so that
-when a <code>~torch.nn.Conv1d</code> and a <code>~torch.nn.ConvTranspose1d</code>
-are initialized with same parameters, they are inverses of each other in
-regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
-<code>~torch.nn.Conv1d</code> maps multiple input shapes to the same output
-shape. <code>output_padding</code> is provided to resolve this ambiguity by
-effectively increasing the calculated output shape on one side. Note
-that <code>output_padding</code> is only used to find output shape, but does
-not actually add zero-padding to output.</p>
-<p>In some circumstances when using the CUDA backend with CuDNN, this operator
-may select a nondeterministic algorithm to increase performance. If this is
-undesirable, you can try to make the operation deterministic (potentially at
-a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
-<li><p>Output: \((N, C_{out}, L_{out})\) where
-$$
-  L_{out} = (L_{in} - 1) \times \mbox{stride} - 2 \times \mbox{padding} + \mbox{dilation}
-\times (\mbox{kernel\_size} - 1) + \mbox{output\_padding} + 1
-$$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
-\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
-\(\mbox{kernel\_size})\).
-The values of these weights are sampled from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)</p></li>
-<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels).
-If <code>bias</code> is <code>TRUE</code>, then the values of these weights are
-sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose1d</span>(<span class='fl'>32</span>, <span class='fl'>16</span>, <span class='fl'>2</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>10</span>, <span class='fl'>32</span>, <span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    <h1>ConvTranpose2D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv_transpose2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D transposed convolution operator over an input image
-composed of several input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_conv_transpose2d</span>(
-  <span class='kw'>in_channels</span>,
-  <span class='kw'>out_channels</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  output_padding = <span class='fl'>0</span>,
-  groups = <span class='fl'>1</span>,
-  bias = <span class='fl'>TRUE</span>,
-  dilation = <span class='fl'>1</span>,
-  padding_mode = <span class='st'>"zeros"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
-will be added to both sides of each dimension in the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>(int or tuple, optional): Additional size added to one side
-of each dimension in the output shape. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
-<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This module can be seen as the gradient of Conv2d with respect to its input.
-It is also known as a fractionally-strided convolution or
-a deconvolution (although it is not an actual deconvolution operation).</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
-<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
-sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
-below for details.</p></li>
-<li><p><code>output_padding</code> controls the additional size added to one side
-of the output shape. See note below for details.</p></li>
-<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
-It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
-<li><p><code>groups</code> controls the connections between inputs and outputs.
-<code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
-<li><p>At groups=2, the operation becomes equivalent to having two conv
-layers side by side, each seeing half the input channels,
-and producing half the output channels, and both subsequently
-concatenated.</p></li>
-<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
-its own set of filters (of size
-\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
-</ul></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
-can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimensions</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
-and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>Depending of the size of your kernel, several (of the last)
-columns of the input might be lost, because it is a valid <code>cross-correlation</code>_,
-and not a full <code>cross-correlation</code>. It is up to the user to add proper padding.</p>
-<p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
-amount of zero padding to both sizes of the input. This is set so that
-when a <a href='nn_conv2d.html'>nn_conv2d</a> and a nn_conv_transpose2d are initialized with same
-parameters, they are inverses of each other in
-regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
-<a href='nn_conv2d.html'>nn_conv2d</a> maps multiple input shapes to the same output
-shape. <code>output_padding</code> is provided to resolve this ambiguity by
-effectively increasing the calculated output shape on one side. Note
-that <code>output_padding</code> is only used to find output shape, but does
-not actually add zero-padding to output.</p>
-<p>In some circumstances when using the CUDA backend with CuDNN, this operator
-may select a nondeterministic algorithm to increase performance. If this is
-undesirable, you can try to make the operation deterministic (potentially at
-a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C_{out}, H_{out}, W_{out})\) where
-$$
-  H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0]
-\times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1
-$$
-$$
-  W_{out} = (W_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1]
-\times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1
-$$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
-\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
-\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\).
-The values of these weights are sampled from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
-<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels)
-If <code>bias</code> is <code>True</code>, then the values of these weights are
-sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># With square kernels and equal stride</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='co'># non-square kernels and unequal stride and with padding</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-<span class='co'># exact output size can be also specified as an argument</span>
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>16</span>, <span class='fl'>12</span>, <span class='fl'>12</span>)
-<span class='kw'>downsample</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>16</span>, <span class='fl'>16</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>, padding=<span class='fl'>1</span>)
-<span class='kw'>upsample</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span>(<span class='fl'>16</span>, <span class='fl'>16</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>, padding=<span class='fl'>1</span>)
-<span class='kw'>h</span> <span class='op'>&lt;-</span> <span class='fu'>downsample</span>(<span class='kw'>input</span>)
-<span class='kw'>h</span><span class='op'>$</span><span class='fu'>size</span>()
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>upsample</span>(<span class='kw'>h</span>, output_size=<span class='kw'>input</span><span class='op'>$</span><span class='fu'>size</span>())
-<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>()
-
-}
-</div><div class='output co'>#&gt; [1]  1 16 12 12</div></pre>
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-    <h1>ConvTranpose3D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv_transpose3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 3D transposed convolution operator over an input image composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_conv_transpose3d</span>(
-  <span class='kw'>in_channels</span>,
-  <span class='kw'>out_channels</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  output_padding = <span class='fl'>0</span>,
-  groups = <span class='fl'>1</span>,
-  bias = <span class='fl'>TRUE</span>,
-  dilation = <span class='fl'>1</span>,
-  padding_mode = <span class='st'>"zeros"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
-will be added to both sides of each dimension in the input. Default: 0
-output_padding (int or tuple, optional): Additional size added to one side
-of each dimension in the output shape. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>(int or tuple, optional): Additional size added to one side
-of each dimension in the output shape. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The transposed convolution operator multiplies each input value element-wise by a learnable kernel,
-and sums over the outputs from all input feature planes.</p>
-<p>This module can be seen as the gradient of Conv3d with respect to its input.
-It is also known as a fractionally-strided convolution or
-a deconvolution (although it is not an actual deconvolution operation).</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
-<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
-sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
-below for details.</p></li>
-<li><p><code>output_padding</code> controls the additional size added to one side
-of the output shape. See note below for details.</p></li>
-<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
-It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
-<li><p><code>groups</code> controls the connections between inputs and outputs.
-<code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
-<li><p>At groups=2, the operation becomes equivalent to having two conv
-layers side by side, each seeing half the input channels,
-and producing half the output channels, and both subsequently
-concatenated.</p></li>
-<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
-its own set of filters (of size
-\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
-</ul></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
-can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimensions</p></li>
-<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
-the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>Depending of the size of your kernel, several (of the last)
-columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
-and not a full <code>cross-correlation</code></em>.
-It is up to the user to add proper padding.</p>
-<p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
-amount of zero padding to both sizes of the input. This is set so that
-when a <code>~torch.nn.Conv3d</code> and a <code>~torch.nn.ConvTranspose3d</code>
-are initialized with same parameters, they are inverses of each other in
-regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
-<code>~torch.nn.Conv3d</code> maps multiple input shapes to the same output
-shape. <code>output_padding</code> is provided to resolve this ambiguity by
-effectively increasing the calculated output shape on one side. Note
-that <code>output_padding</code> is only used to find output shape, but does
-not actually add zero-padding to output.</p>
-<p>In some circumstances when using the CUDA backend with CuDNN, this operator
-may select a nondeterministic algorithm to increase performance. If this is
-undesirable, you can try to make the operation deterministic (potentially at
-a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where
-$$
-  D_{out} = (D_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0]
-\times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1
-$$
-$$
-  H_{out} = (H_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1]
-\times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1
-$$
-$$
-  W_{out} = (W_{in} - 1) \times \mbox{stride}[2] - 2 \times \mbox{padding}[2] + \mbox{dilation}[2]
-\times (\mbox{kernel\_size}[2] - 1) + \mbox{output\_padding}[2] + 1
-$$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
-\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
-\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\).
-The values of these weights are sampled from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
-<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels)
-If <code>bias</code> is <code>True</code>, then the values of these weights are
-sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='co'># With square kernels and equal stride</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='co'># non-square kernels and unequal stride and with padding</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span>, <span class='fl'>2</span>))
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>10</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-}
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>CrossEntropyLoss module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_cross_entropy_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This criterion combines <code><a href='nn_log_softmax.html'>nn_log_softmax()</a></code> and <code>nn_nll_loss()</code> in one single class.
-It is useful when training a classification problem with <code>C</code> classes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_cross_entropy_loss</span>(weight = <span class='kw'>NULL</span>, ignore_index = <span class='op'>-</span><span class='fl'>100</span>, reduction = <span class='st'>"mean"</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional): a manual rescaling weight given to each class.
-If given, has to be a Tensor of size <code>C</code></p></td>
-    </tr>
-    <tr>
-      <th>ignore_index</th>
-      <td><p>(int, optional): Specifies a target value that is ignored
-and does not contribute to the input gradient. When <code>size_average</code> is
-<code>TRUE</code>, the loss is averaged over non-ignored targets.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
-<code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
-<code>'mean'</code>: the sum of the output will be divided by the number of
-elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
-and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>If provided, the optional argument <code>weight</code> should be a 1D <code>Tensor</code>
-assigning weight to each of the classes.</p>
-<p>This is particularly useful when you have an unbalanced training set.
-The <code>input</code> is expected to contain raw, unnormalized scores for each class.
-<code>input</code> has to be a Tensor of size either \((minibatch, C)\) or
-\((minibatch, C, d_1, d_2, ..., d_K)\)
-with \(K \geq 1\) for the <code>K</code>-dimensional case (described later).</p>
-<p>This criterion expects a class index in the range \([0, C-1]\) as the
-<code>target</code> for each value of a 1D tensor of size <code>minibatch</code>; if <code>ignore_index</code>
-is specified, this criterion also accepts this class index (this index may not
-necessarily be in the class range).</p>
-<p>The loss can be described as:
-$$
-  \mbox{loss}(x, class) = -\log\left(\frac{\exp(x[class])}{\sum_j \exp(x[j])}\right)
-= -x[class] + \log\left(\sum_j \exp(x[j])\right)
-$$
-or in the case of the <code>weight</code> argument being specified:
-$$
-  \mbox{loss}(x, class) = weight[class] \left(-x[class] + \log\left(\sum_j \exp(x[j])\right)\right)
-$$</p>
-<p>The losses are averaged across observations for each minibatch.
-Can also be used for higher dimension inputs, such as 2D images, by providing
-an input of size \((minibatch, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\),
-where \(K\) is the number of dimensions, and a target of appropriate shape
-(see below).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C)\) where <code>C = number of classes</code>, or
-\((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\)
-in the case of <code>K</code>-dimensional loss.</p></li>
-<li><p>Target: \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\), or
-\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case of
-K-dimensional loss.</p></li>
-<li><p>Output: scalar.
-If <code>reduction</code> is <code>'none'</code>, then the same size as the target:
-\((N)\), or
-\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case
-of K-dimensional loss.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_cross_entropy_loss</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>, requires_grad=<span class='fl'>TRUE</span>)
-<span class='kw'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span>(low = <span class='fl'>1</span>, high = <span class='fl'>5</span>, size = <span class='fl'>3</span>, dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>)
-<span class='kw'>output</span><span class='op'>$</span><span class='fu'>backward</span>()
-
-}
-</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_dropout.html b/static/docs/dev/reference/nn_dropout.html
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-    <h1>Dropout module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
-    <div class="hidden name"><code>nn_dropout.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>During training, randomly zeroes some of the elements of the input
-tensor with probability <code>p</code> using samples from a Bernoulli
-distribution. Each channel will be zeroed out independently on every forward
-call.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_dropout</span>(p = <span class='fl'>0.5</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>p</th>
-      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This has proven to be an effective technique for regularization and
-preventing the co-adaptation of neurons as described in the paper
-<a href='https://arxiv.org/abs/1207.0580'>Improving neural networks by preventing co-adaptation of feature detectors</a>.</p>
-<p>Furthermore, the outputs are scaled by a factor of :math:<code>\frac{1}{1-p}</code> during
-training. This means that during evaluation the module simply computes an
-identity function.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((*)\). Input can be of any shape</p></li>
-<li><p>Output: \((*)\). Output is of the same shape as input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_dropout</span>(p = <span class='fl'>0.2</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Dropout2D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
-    <div class="hidden name"><code>nn_dropout2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randomly zero out entire channels (a channel is a 2D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 2D tensor \(\mbox{input}[i, j]\)).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_dropout2d</span>(p = <span class='fl'>0.5</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>p</th>
-      <td><p>(float, optional): probability of an element to be zero-ed.</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
-in-place</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Each channel will be zeroed out independently on every forward call with
-probability <code>p</code> using samples from a Bernoulli distribution.
-Usually the input comes from <a href='nn_conv2d.html'>nn_conv2d</a> modules.</p>
-<p>As described in the paper
-<a href='https://arxiv.org/abs/1411.4280'>Efficient Object Localization Using Convolutional Networks</a> ,
-if adjacent pixels within feature maps are strongly correlated
-(as is normally the case in early convolution layers) then i.i.d. dropout
-will not regularize the activations and will otherwise just result
-in an effective learning rate decrease.
-In this case, nn_dropout2d will help promote independence between
-feature maps and should be used instead.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H, W)\)</p></li>
-<li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_dropout2d</span>(p = <span class='fl'>0.2</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>32</span>, <span class='fl'>32</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
-    <div class="hidden name"><code>nn_dropout3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randomly zero out entire channels (a channel is a 3D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 3D tensor \(\mbox{input}[i, j]\)).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_dropout3d</span>(p = <span class='fl'>0.5</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>p</th>
-      <td><p>(float, optional): probability of an element to be zeroed.</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
-in-place</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Each channel will be zeroed out independently on every forward call with
-probability <code>p</code> using samples from a Bernoulli distribution.
-Usually the input comes from <a href='nn_conv2d.html'>nn_conv2d</a> modules.</p>
-<p>As described in the paper
-<a href='https://arxiv.org/abs/1411.4280'>Efficient Object Localization Using Convolutional Networks</a> ,
-if adjacent pixels within feature maps are strongly correlated
-(as is normally the case in early convolution layers) then i.i.d. dropout
-will not regularize the activations and will otherwise just result
-in an effective learning rate decrease.</p>
-<p>In this case, nn_dropout3d will help promote independence between
-feature maps and should be used instead.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, D, H, W)\)</p></li>
-<li><p>Output: \((N, C, D, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_dropout3d</span>(p = <span class='fl'>0.2</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>4</span>, <span class='fl'>32</span>, <span class='fl'>32</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
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-    <h1>ELU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_elu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the element-wise function:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_elu</span>(alpha = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>alpha</th>
-      <td><p>the \(\alpha\) value for the ELU formulation. Default: 1.0</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{ELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1))
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_elu</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span>  <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Embedding module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-sparse.R'><code>R/nn-sparse.R</code></a></small>
-    <div class="hidden name"><code>nn_embedding.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A simple lookup table that stores embeddings of a fixed dictionary and size.
-This module is often used to store word embeddings and retrieve them using indices.
-The input to the module is a list of indices, and the output is the corresponding
-word embeddings.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_embedding</span>(
-  <span class='kw'>num_embeddings</span>,
-  <span class='kw'>embedding_dim</span>,
-  padding_idx = <span class='kw'>NULL</span>,
-  max_norm = <span class='kw'>NULL</span>,
-  norm_type = <span class='fl'>2</span>,
-  scale_grad_by_freq = <span class='fl'>FALSE</span>,
-  sparse = <span class='fl'>FALSE</span>,
-  .weight = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_embeddings</th>
-      <td><p>(int): size of the dictionary of embeddings</p></td>
-    </tr>
-    <tr>
-      <th>embedding_dim</th>
-      <td><p>(int): the size of each embedding vector</p></td>
-    </tr>
-    <tr>
-      <th>padding_idx</th>
-      <td><p>(int, optional): If given, pads the output with the embedding vector at <code>padding_idx</code>
-(initialized to zeros) whenever it encounters the index.</p></td>
-    </tr>
-    <tr>
-      <th>max_norm</th>
-      <td><p>(float, optional): If given, each embedding vector with norm larger than <code>max_norm</code>
-is renormalized to have norm <code>max_norm</code>.</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>(float, optional): The p of the p-norm to compute for the <code>max_norm</code> option. Default <code>2</code>.</p></td>
-    </tr>
-    <tr>
-      <th>scale_grad_by_freq</th>
-      <td><p>(boolean, optional): If given, this will scale gradients by the inverse of frequency of
-the words in the mini-batch. Default <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>sparse</th>
-      <td><p>(bool, optional): If <code>True</code>, gradient w.r.t. <code>weight</code> matrix will be a sparse tensor.</p></td>
-    </tr>
-    <tr>
-      <th>.weight</th>
-      <td><p>(Tensor) embeddings weights (in case you want to set it manually)</p>
-<p>See Notes for more details regarding sparse gradients.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>Keep in mind that only a limited number of optimizers support
-sparse gradients: currently it's <code>optim.SGD</code> (<code>CUDA</code> and <code>CPU</code>),
-<code>optim.SparseAdam</code> (<code>CUDA</code> and <code>CPU</code>) and <code>optim.Adagrad</code> (<code>CPU</code>)</p>
-<p>With <code>padding_idx</code> set, the embedding vector at
-<code>padding_idx</code> is initialized to all zeros. However, note that this
-vector can be modified afterwards, e.g., using a customized
-initialization method, and thus changing the vector used to pad the
-output. The gradient for this vector from nn_embedding
-is always zero.</p>
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
-initialized from \(\mathcal{N}(0, 1)\)</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((*)\), LongTensor of arbitrary shape containing the indices to extract</p></li>
-<li><p>Output: \((*, H)\), where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is the input shape and \(H=\mbox{embedding\_dim}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># an Embedding module containing 10 tensors of size 3</span>
-<span class='kw'>embedding</span> <span class='op'>&lt;-</span> <span class='fu'>nn_embedding</span>(<span class='fl'>10</span>, <span class='fl'>3</span>)
-<span class='co'># a batch of 2 samples of 4 indices each</span>
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>4</span>,<span class='fl'>5</span>),<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>2</span>,<span class='fl'>9</span>)), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-<span class='fu'>embedding</span>(<span class='kw'>input</span>)
-<span class='co'># example with padding_idx</span>
-<span class='kw'>embedding</span> <span class='op'>&lt;-</span> <span class='fu'>nn_embedding</span>(<span class='fl'>10</span>, <span class='fl'>3</span>, padding_idx=<span class='fl'>1</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>3</span>,<span class='fl'>1</span>,<span class='fl'>6</span>), nrow = <span class='fl'>1</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-<span class='fu'>embedding</span>(<span class='kw'>input</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   0.0000  0.0000  0.0000
-#&gt;  -1.2943 -1.0279  0.6483
-#&gt;   0.0000  0.0000  0.0000
-#&gt;   0.4053  0.7866 -0.3922
-#&gt; [ CPUFloatType{1,4,3} ]</div></pre>
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diff --git a/static/docs/dev/reference/nn_fractional_max_pool2d.html b/static/docs/dev/reference/nn_fractional_max_pool2d.html
deleted file mode 100644
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-    <h1>Applies a 2D fractional max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_fractional_max_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fractional MaxPooling is described in detail in the paper
-<a href='https://arxiv.org/abs/1412.6071'>Fractional MaxPooling</a> by Ben Graham</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_fractional_max_pool2d</span>(
-  <span class='kw'>kernel_size</span>,
-  output_size = <span class='kw'>NULL</span>,
-  output_ratio = <span class='kw'>NULL</span>,
-  return_indices = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over.
-Can be a single number k (for a square kernel of k x k) or a tuple <code>(kh, kw)</code></p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form <code>oH x oW</code>.
-Can be a tuple <code>(oH, oW)</code> or a single number oH for a square image <code>oH x oH</code></p></td>
-    </tr>
-    <tr>
-      <th>output_ratio</th>
-      <td><p>If one wants to have an output size as a ratio of the input size, this option can be given.
-This has to be a number or tuple in the range (0, 1)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The max-pooling operation is applied in \(kH \times kW\) regions by a stochastic
-step size determined by the target output size.
-The number of output features is equal to the number of input planes.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># pool of square window of size=3, and target output size 13x12</span>
-<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool2d</span>(<span class='fl'>3</span>, output_size=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>13</span>, <span class='fl'>12</span>))
-<span class='co'># pool of square window and target output size being half of input image size</span>
-<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool2d</span>(<span class='fl'>3</span>, output_ratio=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.5</span>, <span class='fl'>0.5</span>))
-<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
-<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Applies a 3D fractional max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_fractional_max_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fractional MaxPooling is described in detail in the paper
-<a href='https://arxiv.org/abs/1412.6071'>Fractional MaxPooling</a> by Ben Graham</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_fractional_max_pool3d</span>(
-  <span class='kw'>kernel_size</span>,
-  output_size = <span class='kw'>NULL</span>,
-  output_ratio = <span class='kw'>NULL</span>,
-  return_indices = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over.
-Can be a single number k (for a square kernel of k x k x k) or a tuple <code>(kt x kh x kw)</code></p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form <code>oT x oH x oW</code>.
-Can be a tuple <code>(oT, oH, oW)</code> or a single number oH for a square image <code>oH x oH x oH</code></p></td>
-    </tr>
-    <tr>
-      <th>output_ratio</th>
-      <td><p>If one wants to have an output size as a ratio of the input size, this option can be given.
-This has to be a number or tuple in the range (0, 1)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool3d.html'>nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The max-pooling operation is applied in \(kTxkHxkW\) regions by a stochastic
-step size determined by the target output size.
-The number of output features is equal to the number of input planes.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># pool of cubic window of size=3, and target output size 13x12x11</span>
-<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool3d</span>(<span class='fl'>3</span>, output_size=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>13</span>, <span class='fl'>12</span>, <span class='fl'>11</span>))
-<span class='co'># pool of cubic window and target output size being half of input size</span>
-<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool3d</span>(<span class='fl'>3</span>, output_ratio=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.5</span>, <span class='fl'>0.5</span>, <span class='fl'>0.5</span>))
-<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>, <span class='fl'>16</span>)
-<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_gelu.html b/static/docs/dev/reference/nn_gelu.html
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-$$\mbox{GELU}(x) = x * \Phi(x)$$</p>
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-
-    <p>where \(\Phi(x)\) is the Cumulative Distribution Function for Gaussian Distribution.</p>
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-
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-
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-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
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-    <h1>GLU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_glu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the gated linear unit function
-\({GLU}(a, b)= a \otimes \sigma(b)\) where \(a\) is the first half
-of the input matrices and \(b\) is the second half.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_glu</span>(dim = <span class='op'>-</span><span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>(int): the dimension on which to split the input. Default: -1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((\ast_1, N, \ast_2)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((\ast_1, M, \ast_2)\) where \(M=N/2\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_glu</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-    <h1>Hardshwink module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_hardshrink.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the hard shrinkage function element-wise:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_hardshrink</span>(lambd = <span class='fl'>0.5</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>lambd</th>
-      <td><p>the \(\lambda\) value for the Hardshrink formulation. Default: 0.5</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{HardShrink}(x) =
-  \left\{ \begin{array}{ll}
-x, &amp; \mbox{ if } x &gt; \lambda \\
-x, &amp; \mbox{ if } x &lt; -\lambda \\
-0, &amp; \mbox{ otherwise }
-\end{array}
-\right.
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
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-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardshrink</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
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-</div></pre>
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-    <h1>Hardsigmoid module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_hardsigmoid.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the element-wise function:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_hardsigmoid</span>()</pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-\mbox{Hardsigmoid}(x) = \left\{ \begin{array}{ll}
-  0 &amp; \mbox{if~} x \le -3, \\
-  1 &amp; \mbox{if~} x \ge +3, \\
-  x / 6 + 1 / 2 &amp; \mbox{otherwise}
-\end{array}
-\right.
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardsigmoid</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
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-</div></pre>
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-    <h1>Hardswish module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_hardswish.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the hardswish function, element-wise, as described in the paper:
-<a href='https://arxiv.org/abs/1905.02244'>Searching for MobileNetV3</a></p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_hardswish</span>()</pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$ \mbox{Hardswish}(x) = \left\{
-  \begin{array}{ll}
-  0 &amp; \mbox{if } x \le -3, \\
-  x &amp; \mbox{if } x \ge +3, \\
-  x \cdot (x + 3)/6 &amp; \mbox{otherwise}
-  \end{array}
-  \right. $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardswish</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-}
-
-}
-</div></pre>
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-    <h1>Hardtanh module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_hardtanh.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the HardTanh function element-wise
-HardTanh is defined as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_hardtanh</span>(min_val = <span class='op'>-</span><span class='fl'>1</span>, max_val = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>min_val</th>
-      <td><p>minimum value of the linear region range. Default: -1</p></td>
-    </tr>
-    <tr>
-      <th>max_val</th>
-      <td><p>maximum value of the linear region range. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-\mbox{HardTanh}(x) = \left\{ \begin{array}{ll}
-  1 &amp; \mbox{ if } x &gt; 1 \\
-  -1 &amp; \mbox{ if } x &lt; -1 \\
-  x &amp; \mbox{ otherwise } \\
-\end{array}
-\right.
-$$</p>
-<p>The range of the linear region :math:<code>[-1, 1]</code> can be adjusted using
-<code>min_val</code> and <code>max_val</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardtanh</span>(<span class='op'>-</span><span class='fl'>2</span>, <span class='fl'>2</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-      <th>nonlinearity</th>
-      <td><p>the non-linear function</p></td>
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-      <td><p>optional parameter for the non-linear function</p></td>
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-<span class='fu'>nn_init_constant_</span>(<span class='kw'>w</span>, <span class='fl'>0.3</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.3000  0.3000  0.3000  0.3000  0.3000
-#&gt;  0.3000  0.3000  0.3000  0.3000  0.3000
-#&gt;  0.3000  0.3000  0.3000  0.3000  0.3000
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-    <h1>Dirac initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_dirac_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the 3, 4, 5-dimensional input <code>Tensor</code> with the Dirac
-delta function. Preserves the identity of the inputs in <code>Convolutional</code>
-layers, where as many input channels are preserved as possible. In case
-of groups&gt;1, each group of channels preserves identity.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_dirac_</span>(<span class='kw'>tensor</span>, groups = <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>a 3, 4, 5-dimensional <code>torch.Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(optional) number of groups in the conv layer (default: 1)</p></td>
-    </tr>
-    </table>
-
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-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
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-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>16</span>, <span class='fl'>5</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_dirac_</span>(<span class='kw'>w</span>)
-}
-
-}
-</div></pre>
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diff --git a/static/docs/dev/reference/nn_init_eye_.html b/static/docs/dev/reference/nn_init_eye_.html
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-    <div class="hidden name"><code>nn_init_eye_.Rd</code></div>
-    </div>
-
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-    <p>Fills the 2-dimensional input <code>Tensor</code> with the identity matrix.
-Preserves the identity of the inputs in <code>Linear</code> layers, where as
-many inputs are preserved as possible.</p>
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-
-    <pre class="usage"><span class='fu'>nn_init_eye_</span>(<span class='kw'>tensor</span>)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>a 2-dimensional torch tensor.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_eye_</span>(<span class='kw'>w</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  0  0  0  0
-#&gt;  0  1  0  0  0
-#&gt;  0  0  1  0  0
-#&gt; [ CPUFloatType{3,5} ]</div></pre>
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-    <h1>Kaiming normal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_kaiming_normal_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the input <code>Tensor</code> with values according to the method
-described in <code>Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification</code> - He, K. et al. (2015), using a
-normal distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_kaiming_normal_</span>(
-  <span class='kw'>tensor</span>,
-  a = <span class='fl'>0</span>,
-  mode = <span class='st'>"fan_in"</span>,
-  nonlinearity = <span class='st'>"leaky_relu"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>torch.Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td><p>the negative slope of the rectifier used after this layer (only used
-with <code>'leaky_relu'</code>)</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
-the magnitude of the variance of the weights in the forward pass. Choosing
-'fan_out' preserves the magnitudes in the backwards pass.</p></td>
-    </tr>
-    <tr>
-      <th>nonlinearity</th>
-      <td><p>the non-linear function. recommended to use only with 'relu'
-or 'leaky_relu' (default).</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_kaiming_normal_</span>(<span class='kw'>w</span>, mode = <span class='st'>"fan_in"</span>, nonlinearity = <span class='st'>"leaky_relu"</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.5594  0.2408  0.3946  0.5860 -0.4834
-#&gt; -0.0442  0.7170 -0.3028  0.4015 -0.8906
-#&gt; -0.5157 -0.1763  0.9366  0.4640 -0.5356
-#&gt; [ CPUFloatType{3,5} ]</div></pre>
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-    <div class="page-header">
-    <h1>Kaiming uniform initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_kaiming_uniform_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the input <code>Tensor</code> with values according to the method
-described in <code>Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification</code> - He, K. et al. (2015), using a
-uniform distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_kaiming_uniform_</span>(
-  <span class='kw'>tensor</span>,
-  a = <span class='fl'>0</span>,
-  mode = <span class='st'>"fan_in"</span>,
-  nonlinearity = <span class='st'>"leaky_relu"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>torch.Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td><p>the negative slope of the rectifier used after this layer (only used
-with <code>'leaky_relu'</code>)</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
-the magnitude of the variance of the weights in the forward pass. Choosing
-'fan_out' preserves the magnitudes in the backwards pass.</p></td>
-    </tr>
-    <tr>
-      <th>nonlinearity</th>
-      <td><p>the non-linear function. recommended to use only with 'relu'
-or 'leaky_relu' (default).</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_kaiming_uniform_</span>(<span class='kw'>w</span>, mode = <span class='st'>"fan_in"</span>, nonlinearity = <span class='st'>"leaky_relu"</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.7460  0.2070 -0.1066 -0.4344 -0.4666
-#&gt; -0.5351 -0.4524  0.0950 -1.0077 -0.2169
-#&gt; -0.9525  0.8753  0.0070 -0.4553 -0.3445
-#&gt; [ CPUFloatType{3,5} ]</div></pre>
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diff --git a/static/docs/dev/reference/nn_init_normal_.html b/static/docs/dev/reference/nn_init_normal_.html
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-    <h1>Normal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_normal_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the input Tensor with values drawn from the normal distribution</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_normal_</span>(<span class='kw'>tensor</span>, mean = <span class='fl'>0</span>, std = <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional Tensor</p></td>
-    </tr>
-    <tr>
-      <th>mean</th>
-      <td><p>the mean of the normal distribution</p></td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td><p>the standard deviation of the normal distribution</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_normal_</span>(<span class='kw'>w</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.0569 -1.0900  1.2740 -1.7728  0.0593
-#&gt; -1.7131 -0.1353  0.8191  0.1481 -0.9940
-#&gt; -0.7544 -1.0298  0.4237  1.4650  0.0575
-#&gt; [ CPUFloatType{3,5} ]</div></pre>
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diff --git a/static/docs/dev/reference/nn_init_ones_.html b/static/docs/dev/reference/nn_init_ones_.html
deleted file mode 100644
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-    <div class="hidden name"><code>nn_init_ones_.Rd</code></div>
-    </div>
-
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-    <p>Fills the input Tensor with the scalar value <code>1</code></p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_ones_</span>(<span class='kw'>tensor</span>)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_ones_</span>(<span class='kw'>w</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  1  1  1  1
-#&gt;  1  1  1  1  1
-#&gt;  1  1  1  1  1
-#&gt; [ CPUFloatType{3,5} ]</div></pre>
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-<meta property="og:description" content="Fills the input Tensor with a (semi) orthogonal matrix, as
-described in Exact solutions to the nonlinear dynamics of learning in deep linear neural networks - Saxe, A. et al. (2013). The input tensor must have
-at least 2 dimensions, and for tensors with more than 2 dimensions the
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-    <h1>Orthogonal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_orthogonal_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the input <code>Tensor</code> with a (semi) orthogonal matrix, as
-described in <code>Exact solutions to the nonlinear dynamics of learning in deep linear neural networks</code> - Saxe, A. et al. (2013). The input tensor must have
-at least 2 dimensions, and for tensors with more than 2 dimensions the
-trailing dimensions are flattened.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_orthogonal_</span>(<span class='kw'>tensor</span>, gain = <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>gain</th>
-      <td><p>optional scaling factor</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>)
-<span class='fu'>nn_init_orthogonal_</span>(<span class='kw'>w</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6794 -0.2821 -0.3651 -0.1874  0.5388
-#&gt;  0.0227 -0.5370  0.4321 -0.6955 -0.2017
-#&gt;  0.0083  0.0587  0.7419  0.2345  0.6254
-#&gt; [ CPUFloatType{3,5} ]</div></pre>
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diff --git a/static/docs/dev/reference/nn_init_sparse_.html b/static/docs/dev/reference/nn_init_sparse_.html
deleted file mode 100644
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-    <div class="hidden name"><code>nn_init_sparse_.Rd</code></div>
-    </div>
-
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-    <p>Fills the 2D input <code>Tensor</code> as a sparse matrix, where the
-non-zero elements will be drawn from the normal distribution
-as described in <code>Deep learning via Hessian-free optimization</code> - Martens, J. (2010).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_sparse_</span>(<span class='kw'>tensor</span>, <span class='kw'>sparsity</span>, std = <span class='fl'>0.01</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>sparsity</th>
-      <td><p>The fraction of elements in each column to be set to zero</p></td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td><p>the standard deviation of the normal distribution used to generate
-the non-zero values</p></td>
-    </tr>
-    </table>
-
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-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
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-<span class='fu'>nn_init_sparse_</span>(<span class='kw'>w</span>, sparsity = <span class='fl'>0.1</span>)
-}
-}
-</div></pre>
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-    <h1>Truncated normal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_trunc_normal_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the input Tensor with values drawn from a truncated
-normal distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_trunc_normal_</span>(<span class='kw'>tensor</span>, mean = <span class='fl'>0</span>, std = <span class='fl'>1</span>, a = <span class='op'>-</span><span class='fl'>2</span>, b = <span class='op'>-</span><span class='fl'>2</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional Tensor</p></td>
-    </tr>
-    <tr>
-      <th>mean</th>
-      <td><p>the mean of the normal distribution</p></td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td><p>the standard deviation of the normal distribution</p></td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td><p>the minimum cutoff value</p></td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td><p>the maximum cutoff value</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_trunc_normal_</span>(<span class='kw'>w</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -2 -2 -2 -2 -2
-#&gt; -2 -2 -2 -2 -2
-#&gt; -2 -2 -2 -2 -2
-#&gt; [ CPUFloatType{3,5} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_init_uniform_.html b/static/docs/dev/reference/nn_init_uniform_.html
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-    <p>Fills the input Tensor with values drawn from the uniform distribution</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_uniform_</span>(<span class='kw'>tensor</span>, a = <span class='fl'>0</span>, b = <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional Tensor</p></td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td><p>the lower bound of the uniform distribution</p></td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td><p>the upper bound of the uniform distribution</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_uniform_</span>(<span class='kw'>w</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.8556  0.9331  0.3515  0.8071  0.4948
-#&gt;  0.6075  0.9042  0.7181  0.7329  0.7563
-#&gt;  0.2584  0.5293  0.9757  0.3030  0.3341
-#&gt; [ CPUFloatType{3,5} ]</div></pre>
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-    <h1>Xavier normal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_xavier_normal_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the input <code>Tensor</code> with values according to the method
-described in <code>Understanding the difficulty of training deep feedforward neural networks</code> - Glorot, X. &amp; Bengio, Y. (2010), using a normal
-distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_xavier_normal_</span>(<span class='kw'>tensor</span>, gain = <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>gain</th>
-      <td><p>an optional scaling factor</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_xavier_normal_</span>(<span class='kw'>w</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.2535 -0.2197  0.5425 -3.0052 -4.2446
-#&gt; -0.3570 -1.6970 -2.0154 -0.5348  2.7582
-#&gt;  0.8714 -0.8924  0.7675  3.2553 -1.4333
-#&gt; [ CPUFloatType{3,5} ]</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_xavier_uniform_.Rd</code></div>
-    </div>
-
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-    <p>Fills the input <code>Tensor</code> with values according to the method
-described in <code>Understanding the difficulty of training deep feedforward neural networks</code> - Glorot, X. &amp; Bengio, Y. (2010), using a uniform
-distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_xavier_uniform_</span>(<span class='kw'>tensor</span>, gain = <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>gain</th>
-      <td><p>an optional scaling factor</p></td>
-    </tr>
-    </table>
-
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-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_xavier_uniform_</span>(<span class='kw'>w</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.3397  1.1040 -3.0453 -1.7935  0.9545
-#&gt; -0.0194 -2.4483  2.9345  2.2750 -2.4048
-#&gt; -0.4406 -2.2409  0.4155 -0.1573  1.9776
-#&gt; [ CPUFloatType{3,5} ]</div></pre>
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diff --git a/static/docs/dev/reference/nn_init_zeros_.html b/static/docs/dev/reference/nn_init_zeros_.html
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-    <h1>Zeros initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_zeros_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the input Tensor with the scalar value <code>0</code></p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_zeros_</span>(<span class='kw'>tensor</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional tensor</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_zeros_</span>(<span class='kw'>w</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  0  0  0  0
-#&gt;  0  0  0  0  0
-#&gt;  0  0  0  0  0
-#&gt; [ CPUFloatType{3,5} ]</div></pre>
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diff --git a/static/docs/dev/reference/nn_leaky_relu.html b/static/docs/dev/reference/nn_leaky_relu.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_leaky_relu.Rd</code></div>
-    </div>
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-    <p>Applies the element-wise function:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_leaky_relu</span>(negative_slope = <span class='fl'>0.01</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>negative_slope</th>
-      <td><p>Controls the angle of the negative slope. Default: 1e-2</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{LeakyReLU}(x) = \max(0, x) + \mbox{negative\_slope} * \min(0, x)
-$$
-or</p>
-<p>$$
-  \mbox{LeakyRELU}(x) =
-  \left\{ \begin{array}{ll}
-x, &amp; \mbox{ if } x \geq 0 \\
-\mbox{negative\_slope} \times x, &amp; \mbox{ otherwise }
-\end{array}
-\right.
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_leaky_relu</span>(<span class='fl'>0.1</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    <h1>Linear module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
-    <div class="hidden name"><code>nn_linear.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a linear transformation to the incoming data: <code>y = xA^T + b</code></p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_linear</span>(<span class='kw'>in_features</span>, <span class='kw'>out_features</span>, bias = <span class='fl'>TRUE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_features</th>
-      <td><p>size of each input sample</p></td>
-    </tr>
-    <tr>
-      <th>out_features</th>
-      <td><p>size of each output sample</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
-Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: <code>(N, *, H_in)</code> where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means any number of
-additional dimensions and <code>H_in = in_features</code>.</p></li>
-<li><p>Output: <code>(N, *, H_out)</code> where all but the last dimension
-are the same shape as the input and :math:<code>H_out = out_features</code>.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight: the learnable weights of the module of shape
-<code>(out_features, in_features)</code>. The values are
-initialized from \(U(-\sqrt{k}, \sqrt{k})\)s, where
-\(k = \frac{1}{\mbox{in\_features}}\)</p></li>
-<li><p>bias: the learnable bias of the module of shape \((\mbox{out\_features})\).
-If <code>bias</code> is <code>TRUE</code>, the values are initialized from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{1}{\mbox{in\_features}}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_linear</span>(<span class='fl'>20</span>, <span class='fl'>30</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>20</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>())
-
-}
-</div><div class='output co'>#&gt; [1] 128  30</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-<meta property="og:description" content="Applies the element-wise function:
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-  \mbox{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
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-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_log_sigmoid</span>()
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-    <h1>LogSoftmax module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_log_softmax.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the \(\log(\mbox{Softmax}(x))\) function to an n-dimensional
-input Tensor. The LogSoftmax formulation can be simplified as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_log_softmax</span>(<span class='kw'>dim</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>(int): A dimension along which LogSoftmax will be computed.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>a Tensor of the same dimension and shape as the input with
-values in the range [-inf, 0)</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{LogSoftmax}(x_{i}) = \log\left(\frac{\exp(x_i) }{ \sum_j \exp(x_j)} \right)
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((*)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((*)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_log_softmax</span>(<span class='fl'>1</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
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-</div></pre>
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diff --git a/static/docs/dev/reference/nn_lp_pool1d.html b/static/docs/dev/reference/nn_lp_pool1d.html
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-    <div class="page-header">
-    <h1>Applies a 1D power-average pooling over an input signal composed of several input
-planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_lp_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>On each window, the function computed is:</p>
-<p>$$
-  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_lp_pool1d</span>(<span class='kw'>norm_type</span>, <span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, ceil_mode = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>norm_type</th>
-      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>a single int, the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>a single int, the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    
-<ul>
-<li><p>At p = \(\infty\), one gets Max Pooling</p></li>
-<li><p>At p = 1, one gets Sum Pooling (which is proportional to Average Pooling)</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>If the sum to the power of <code>p</code> is zero, the gradient of this function is
-not defined. This implementation will set the gradient to zero in this case.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, L_{in})\)</p></li>
-<li><p>Output: \((N, C, L_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  L_{out} = \left\lfloor\frac{L_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># power-2 pool of window of length 3, with stride 2.</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lp_pool1d</span>(<span class='fl'>2</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_lp_pool2d.html b/static/docs/dev/reference/nn_lp_pool2d.html
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 2D power-average pooling over an input signal composed of several input
-planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_lp_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>On each window, the function computed is:</p>
-<p>$$
-  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_lp_pool2d</span>(<span class='kw'>norm_type</span>, <span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, ceil_mode = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>norm_type</th>
-      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    
-<ul>
-<li><p>At p = \(\infty\), one gets Max Pooling</p></li>
-<li><p>At p = 1, one gets Sum Pooling (which is proportional to average pooling)</p></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
-and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>If the sum to the power of <code>p</code> is zero, the gradient of this function is
-not defined. This implementation will set the gradient to zero in this case.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  H_{out} = \left\lfloor\frac{H_{in} - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
-$$
-$$
-  W_{out} = \left\lfloor\frac{W_{in} - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-  
-<span class='co'># power-2 pool of square window of size=3, stride=2</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lp_pool2d</span>(<span class='fl'>2</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='co'># pool of non-square window of power 1.2</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lp_pool2d</span>(<span class='fl'>1.2</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>MaxPool1D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1D max pooling over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_max_pool1d</span>(
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  dilation = <span class='fl'>1</span>,
-  return_indices = <span class='fl'>FALSE</span>,
-  ceil_mode = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements in the window</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
-Useful for  <code><a href='nn_max_unpool1d.html'>nn_max_unpool1d()</a></code> later.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>In the simplest case, the output value of the layer with input size \((N, C, L)\)
-and output \((N, C, L_{out})\) can be precisely described as:</p>
-<p>$$
-  out(N_i, C_j, k) = \max_{m=0, \ldots, \mbox{kernel\_size} - 1}
-input(N_i, C_j, stride \times k + m)
-$$</p>
-<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
-for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
-It is harder to describe, but this <a href='https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md'>link</a>
-has a nice visualization of what <code>dilation</code> does.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, L_{in})\)</p></li>
-<li><p>Output: \((N, C, L_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  L_{out} = \left\lfloor \frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation}
-    \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># pool of size=3, stride=2</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool1d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D max pooling over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_max_pool2d</span>(
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  dilation = <span class='fl'>1</span>,
-  return_indices = <span class='fl'>FALSE</span>,
-  ceil_mode = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements in the window</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
-Useful for <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code> later.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
-output \((N, C, H_{out}, W_{out})\) and <code>kernel_size</code> \((kH, kW)\)
-can be precisely described as:</p>
-<p>$$
-\begin{array}{ll}
-out(N_i, C_j, h, w) ={} &amp; \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
-&amp; \mbox{input}(N_i, C_j, \mbox{stride[0]} \times h + m,
-               \mbox{stride[1]} \times w + n)
-\end{array}
-$$</p>
-<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
-for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
-It is harder to describe, but this <code>link</code> has a nice visualization of what <code>dilation</code> does.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
-and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  H_{out} = \left\lfloor\frac{H_{in} + 2 * \mbox{padding[0]} - \mbox{dilation[0]}
-    \times (\mbox{kernel\_size[0]} - 1) - 1}{\mbox{stride[0]}} + 1\right\rfloor
-$$</p>
-<p>$$
-  W_{out} = \left\lfloor\frac{W_{in} + 2 * \mbox{padding[1]} - \mbox{dilation[1]}
-    \times (\mbox{kernel\_size[1]} - 1) - 1}{\mbox{stride[1]}} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># pool of square window of size=3, stride=2</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool2d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='co'># pool of non-square window</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_max_pool3d.html b/static/docs/dev/reference/nn_max_pool3d.html
deleted file mode 100644
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-planes. — nn_max_pool3d • torch</title>
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-planes. — nn_max_pool3d" />
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-output \((N, C, D_{out}, H_{out}, W_{out})\) and kernel_size \((kD, kH, kW)\)
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-    <div class="page-header">
-    <h1>Applies a 3D max pooling over an input signal composed of several input
-planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
-output \((N, C, D_{out}, H_{out}, W_{out})\) and <code>kernel_size</code> \((kD, kH, kW)\)
-can be precisely described as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_max_pool3d</span>(
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  dilation = <span class='fl'>1</span>,
-  return_indices = <span class='fl'>FALSE</span>,
-  ceil_mode = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on all three sides</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements in the window</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
-Useful for <code>torch_nn.MaxUnpool3d</code> later</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-\begin{array}{ll}
-\mbox{out}(N_i, C_j, d, h, w) = &amp; \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
- &amp; \mbox{input}(N_i, C_j, \mbox{stride[0]} \times d + k, \mbox{stride[1]} \times h + m, \mbox{stride[2]} \times w + n)
-\end{array}
-$$</p>
-<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
-for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
-It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.
-The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
-<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
-the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where
-$$
-  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0] \times
-    (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
-$$</p></li>
-</ul>
-
-<p>$$
-  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1] \times
-    (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
-$$</p>
-<p>$$
-  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2] \times
-    (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># pool of square window of size=3, stride=2</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool3d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='co'># pool of non-square window</span>
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>,<span class='fl'>44</span>, <span class='fl'>31</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/dev/reference/nn_max_unpool1d.html b/static/docs/dev/reference/nn_max_unpool1d.html
deleted file mode 100644
index 62890c5db..000000000
--- a/static/docs/dev/reference/nn_max_unpool1d.html
+++ /dev/null
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-    <h1>Computes a partial inverse of <code>MaxPool1d</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_unpool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>MaxPool1d</code> is not fully invertible, since the non-maximal values are lost.
-<code>MaxUnpool1d</code> takes in as input the output of <code>MaxPool1d</code>
-including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_max_unpool1d</span>(<span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, padding = <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple): Stride of the max pooling window.
-It is set to <code>kernel_size</code> by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple): Padding that was added to the input</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p><code>MaxPool1d</code> can map several input sizes to the same output
-sizes. Hence, the inversion process can get ambiguous.
-To accommodate this, you can provide the needed output size
-as an additional argument <code>output_size</code> in the forward call.
-See the Inputs and Example below.</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
-    
-
-<ul>
-<li><p><code>input</code>: the input Tensor to invert</p></li>
-<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool1d.html'>nn_max_pool1d()</a></code></p></li>
-<li><p><code>output_size</code> (optional): the targeted output size</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H_{in})\)</p></li>
-<li><p>Output: \((N, C, H_{out})\), where
-$$
-  H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{kernel\_size}[0]
-$$
-or as given by <code>output_size</code> in the call operator</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>pool</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_max_pool1d.html'>nn_max_pool1d</a></span>(<span class='fl'>2</span>, stride=<span class='fl'>2</span>, return_indices=<span class='fl'>TRUE</span>)
-<span class='kw'>unpool</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_unpool1d</span>(<span class='fl'>2</span>, stride=<span class='fl'>2</span>)
-
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/array.html'>array</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>8</span><span class='op'>/</span><span class='fl'>1</span>, dim = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>8</span>)))
-<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span>(<span class='kw'>input</span>)
-<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
-
-<span class='co'># Example showcasing the use of output_size</span>
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/array.html'>array</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>8</span><span class='op'>/</span><span class='fl'>1</span>, dim = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>8</span>)))
-<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span>(<span class='kw'>input</span>)
-<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]], output_size=<span class='kw'>input</span><span class='op'>$</span><span class='fu'>size</span>())
-<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,1,.,.) = 
-#&gt;   0
-#&gt;   2
-#&gt;   0
-#&gt;   4
-#&gt;   0
-#&gt;   6
-#&gt;   0
-#&gt;   8
-#&gt; [ CPUFloatType{1,1,8,1} ]</div></pre>
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-    <h1>Computes a partial inverse of <code>MaxPool2d</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_unpool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>MaxPool2d</code> is not fully invertible, since the non-maximal values are lost.
-<code>MaxUnpool2d</code> takes in as input the output of <code>MaxPool2d</code>
-including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_max_unpool2d</span>(<span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, padding = <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple): Stride of the max pooling window.
-It is set to <code>kernel_size</code> by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple): Padding that was added to the input</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p><code>MaxPool2d</code> can map several input sizes to the same output
-sizes. Hence, the inversion process can get ambiguous.
-To accommodate this, you can provide the needed output size
-as an additional argument <code>output_size</code> in the forward call.
-See the Inputs and Example below.</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
-    
-
-<ul>
-<li><p><code>input</code>: the input Tensor to invert</p></li>
-<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool2d.html'>nn_max_pool2d()</a></code></p></li>
-<li><p><code>output_size</code> (optional): the targeted output size</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, H_{out}, W_{out})\), where
-$$
-  H_{out} = (H_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
-$$
-$$
-  W_{out} = (W_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
-$$
-or as given by <code>output_size</code> in the call operator</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>pool</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_max_pool2d.html'>nn_max_pool2d</a></span>(<span class='fl'>2</span>, stride=<span class='fl'>2</span>, return_indices=<span class='fl'>TRUE</span>)
-<span class='kw'>unpool</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_unpool2d</span>(<span class='fl'>2</span>, stride=<span class='fl'>2</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>)
-<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span>(<span class='kw'>input</span>)
-<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
-
-<span class='co'># specify a different output size than input size</span>
-<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]], output_size=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>5</span>, <span class='fl'>5</span>))
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,1,.,.) = 
-#&gt;   0.0000  0.0000  0.0000  1.1624  0.0000
-#&gt;   0.6646  0.0000  0.0000  0.0000  1.0838
-#&gt;  -0.5098  0.0000  0.0000  0.0000  0.0000
-#&gt;   0.0000  0.0000  0.0000  0.0000  0.0000
-#&gt;   0.0000  0.0000  0.0000  0.0000  0.0000
-#&gt; [ CPUFloatType{1,1,5,5} ]</div></pre>
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-MaxUnpool3d takes in as input the output of MaxPool3d
-including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero." />
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-    <h1>Computes a partial inverse of <code>MaxPool3d</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_unpool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>MaxPool3d</code> is not fully invertible, since the non-maximal values are lost.
-<code>MaxUnpool3d</code> takes in as input the output of <code>MaxPool3d</code>
-including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_max_unpool3d</span>(<span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, padding = <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple): Stride of the max pooling window.
-It is set to <code>kernel_size</code> by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple): Padding that was added to the input</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p><code>MaxPool3d</code> can map several input sizes to the same output
-sizes. Hence, the inversion process can get ambiguous.
-To accommodate this, you can provide the needed output size
-as an additional argument <code>output_size</code> in the forward call.
-See the Inputs section below.</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
-    
-
-<ul>
-<li><p><code>input</code>: the input Tensor to invert</p></li>
-<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool3d.html'>nn_max_pool3d()</a></code></p></li>
-<li><p><code>output_size</code> (optional): the targeted output size</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  D_{out} = (D_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
-$$
-$$
-  H_{out} = (H_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
-$$
-$$
-  W_{out} = (W_{in} - 1) \times \mbox{stride[2]} - 2 \times \mbox{padding[2]} + \mbox{kernel\_size[2]}
-$$</p>
-<p>or as given by <code>output_size</code> in the call operator</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-  
-<span class='co'># pool of square window of size=3, stride=2</span>
-<span class='kw'>pool</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_max_pool3d.html'>nn_max_pool3d</a></span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>, return_indices=<span class='fl'>TRUE</span>)
-<span class='kw'>unpool</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_unpool3d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
-<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span>(<span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>51</span>, <span class='fl'>33</span>, <span class='fl'>15</span>))
-<span class='kw'>unpooled_output</span> <span class='op'>&lt;-</span> <span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
-<span class='kw'>unpooled_output</span><span class='op'>$</span><span class='fu'>size</span>()
-
-}
-</div><div class='output co'>#&gt; [1] 20 16 51 33 15</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
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-      <div class="copyright">
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diff --git a/static/docs/dev/reference/nn_module.html b/static/docs/dev/reference/nn_module.html
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-    <h1>Base class for all neural network modules.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
-    <div class="hidden name"><code>nn_module.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Your models should also subclass this class.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_module</span>(
-  classname = <span class='kw'>NULL</span>,
-  inherit = <span class='kw'>nn_Module</span>,
-  <span class='kw'>...</span>,
-  parent_env = <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span>()
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>classname</th>
-      <td><p>an optional name for the module</p></td>
-    </tr>
-    <tr>
-      <th>inherit</th>
-      <td><p>an optional module to inherit from</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>methods implementation</p></td>
-    </tr>
-    <tr>
-      <th>parent_env</th>
-      <td><p>passed to <code><a href='https://rdrr.io/pkg/R6/man/R6Class.html'>R6::R6Class()</a></code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Modules can also contain other Modules, allowing to nest them in a tree
-structure. You can assign the submodules as regular attributes.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>model</span> <span class='op'>&lt;-</span> <span class='fu'>nn_module</span>(
- initialize = <span class='fu'>function</span>() {
-   <span class='kw'>self</span><span class='op'>$</span><span class='kw'>conv1</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>1</span>, <span class='fl'>20</span>, <span class='fl'>5</span>)
-   <span class='kw'>self</span><span class='op'>$</span><span class='kw'>conv2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>20</span>, <span class='fl'>20</span>, <span class='fl'>5</span>)
- },
- forward = <span class='fu'>function</span>(<span class='kw'>input</span>) {
-   <span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='kw'>self</span><span class='op'>$</span><span class='fu'>conv1</span>(<span class='kw'>input</span>)
-   <span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nnf_relu.html'>nnf_relu</a></span>(<span class='kw'>input</span>)
-   <span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='kw'>self</span><span class='op'>$</span><span class='fu'>conv2</span>(<span class='kw'>input</span>)
-   <span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nnf_relu.html'>nnf_relu</a></span>(<span class='kw'>input</span>)
-   <span class='kw'>input</span>
- }
-)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_module_list.html b/static/docs/dev/reference/nn_module_list.html
deleted file mode 100644
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-</div><!--/.navbar -->
-
-      
-
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Holds submodules in a list.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
-    <div class="hidden name"><code>nn_module_list.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>nn_module_list can be indexed like a regular R list, but
-modules it contains are properly registered, and will be visible by all
-<code>nn_module</code> methods.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_module_list</span>(modules = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>())</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>modules</th>
-      <td><p>a list of modules to add</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>my_module</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_module.html'>nn_module</a></span>(
- initialize = <span class='fu'>function</span>() {
-   <span class='kw'>self</span><span class='op'>$</span><span class='kw'>linears</span> <span class='op'>&lt;-</span> <span class='fu'>nn_module_list</span>(<span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>10</span>, <span class='fu'>function</span>(<span class='kw'>x</span>) <span class='fu'><a href='nn_linear.html'>nn_linear</a></span>(<span class='fl'>10</span>, <span class='fl'>10</span>)))
- },
- forward = <span class='fu'>function</span>(<span class='kw'>x</span>) {
-  <span class='co'>for</span> (<span class='kw'>i</span> <span class='co'>in</span> <span class='fl'>1</span><span class='op'>:</span><span class='fu'><a href='https://rdrr.io/r/base/length.html'>length</a></span>(<span class='kw'>self</span><span class='op'>$</span><span class='kw'>linears</span>))
-    <span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='kw'>self</span><span class='op'>$</span><span class='kw'>linears</span>[[<span class='kw'>i</span>]](<span class='kw'>x</span>)
-  <span class='kw'>x</span>
- }
-)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/static/docs/dev/reference/nn_multihead_attention.html b/static/docs/dev/reference/nn_multihead_attention.html
deleted file mode 100644
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--- a/static/docs/dev/reference/nn_multihead_attention.html
+++ /dev/null
@@ -1,330 +0,0 @@
-<!-- Generated by pkgdown: do not edit by hand -->
-<!DOCTYPE html>
-<html lang="en">
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-<meta http-equiv="X-UA-Compatible" content="IE=edge">
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-    <h1>MultiHead attention</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_multihead_attention.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Allows the model to jointly attend to information
-from different representation subspaces.
-See reference: Attention Is All You Need</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_multihead_attention</span>(
-  <span class='kw'>embed_dim</span>,
-  <span class='kw'>num_heads</span>,
-  dropout = <span class='fl'>0</span>,
-  bias = <span class='fl'>TRUE</span>,
-  add_bias_kv = <span class='fl'>FALSE</span>,
-  add_zero_attn = <span class='fl'>FALSE</span>,
-  kdim = <span class='kw'>NULL</span>,
-  vdim = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>embed_dim</th>
-      <td><p>total dimension of the model.</p></td>
-    </tr>
-    <tr>
-      <th>num_heads</th>
-      <td><p>parallel attention heads.</p></td>
-    </tr>
-    <tr>
-      <th>dropout</th>
-      <td><p>a Dropout layer on attn_output_weights. Default: 0.0.</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>add bias as module parameter. Default: True.</p></td>
-    </tr>
-    <tr>
-      <th>add_bias_kv</th>
-      <td><p>add bias to the key and value sequences at dim=0.</p></td>
-    </tr>
-    <tr>
-      <th>add_zero_attn</th>
-      <td><p>add a new batch of zeros to the key and
-value sequences at dim=1.</p></td>
-    </tr>
-    <tr>
-      <th>kdim</th>
-      <td><p>total number of features in key. Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>vdim</th>
-      <td><p>total number of features in value. Default: <code>NULL</code>.
-Note: if kdim and vdim are <code>NULL</code>, they will be set to embed_dim such that
-query, key, and value have the same number of features.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{MultiHead}(Q, K, V) = \mbox{Concat}(head_1,\dots,head_h)W^O
-\mbox{where} head_i = \mbox{Attention}(QW_i^Q, KW_i^K, VW_i^V)
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-
-<p>Inputs:</p><ul>
-<li><p>query: \((L, N, E)\) where L is the target sequence length, N is the batch size, E is
-the embedding dimension.</p></li>
-<li><p>key: \((S, N, E)\), where S is the source sequence length, N is the batch size, E is
-the embedding dimension.</p></li>
-<li><p>value: \((S, N, E)\) where S is the source sequence length, N is the batch size, E is
-the embedding dimension.</p></li>
-<li><p>key_padding_mask: \((N, S)\) where N is the batch size, S is the source sequence length.
-If a ByteTensor is provided, the non-zero positions will be ignored while the position
-with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
-value of <code>True</code> will be ignored while the position with the value of <code>False</code> will be unchanged.</p></li>
-<li><p>attn_mask: 2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
-3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
-S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
-positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
-while the zero positions will be unchanged. If a BoolTensor is provided, positions with <code>True</code>
-is not allowed to attend while <code>False</code> values will be unchanged. If a FloatTensor
-is provided, it will be added to the attention weight.</p></li>
-</ul>
-
-<p>Outputs:</p><ul>
-<li><p>attn_output: \((L, N, E)\) where L is the target sequence length, N is the batch size,
-E is the embedding dimension.</p></li>
-<li><p>attn_output_weights: \((N, L, S)\) where N is the batch size,
-L is the target sequence length, S is the source sequence length.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>multihead_attn</span> <span class='op'>=</span> <span class='fu'>nn_multihead_attention</span>(<span class='kw'>embed_dim</span>, <span class='kw'>num_heads</span>)
-<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>multihead_attn</span>(<span class='kw'>query</span>, <span class='kw'>key</span>, <span class='kw'>value</span>)
-<span class='kw'>attn_output</span> <span class='op'>&lt;-</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
-<span class='kw'>attn_output_weights</span> <span class='op'>&lt;-</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
-}
-
-}
-</div></pre>
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diff --git a/static/docs/dev/reference/nn_prelu.html b/static/docs/dev/reference/nn_prelu.html
deleted file mode 100644
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--- a/static/docs/dev/reference/nn_prelu.html
+++ /dev/null
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-<meta property="og:description" content="Applies the element-wise function:
-$$
-  \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
-$$
-or
-$$
-  \mbox{PReLU}(x) =
-  \left\{ \begin{array}{ll}
-x, &amp;amp; \mbox{ if } x \geq 0 \\
-ax, &amp;amp; \mbox{ otherwise }
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-</div><!--/.navbar -->
-
-      
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-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>PReLU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_prelu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the element-wise function:
-$$
-  \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
-$$
-or
-$$
-  \mbox{PReLU}(x) =
-  \left\{ \begin{array}{ll}
-x, &amp; \mbox{ if } x \geq 0 \\
-ax, &amp; \mbox{ otherwise }
-\end{array}
-\right.
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_prelu</span>(num_parameters = <span class='fl'>1</span>, init = <span class='fl'>0.25</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_parameters</th>
-      <td><p>(int): number of \(a\) to learn.
-Although it takes an int as input, there is only two values are legitimate:
-1, or the number of channels at input. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>init</th>
-      <td><p>(float): the initial value of \(a\). Default: 0.25</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Here \(a\) is a learnable parameter. When called without arguments, <code>nn.prelu()</code> uses a single
-parameter \(a\) across all input channels. If called with <code>nn_prelu(nChannels)</code>,
-a separate \(a\) is used for each input channel.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>weight decay should not be used when learning \(a\) for good performance.</p>
-<p>Channel dim is the 2nd dim of input. When input has dims &lt; 2, then there is
-no channel dim and the number of channels = 1.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of shape (<code>num_parameters</code>).</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_prelu</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
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diff --git a/static/docs/dev/reference/nn_relu.html b/static/docs/dev/reference/nn_relu.html
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-    <h1>ReLU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_relu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the rectified linear unit function element-wise
-$$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_relu</span>(inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_relu</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000
-#&gt;  1.7223
-#&gt; [ CPUFloatType{2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_relu6.html b/static/docs/dev/reference/nn_relu6.html
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-    <h1>ReLu6 module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_relu6.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the element-wise function:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_relu6</span>(inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{ReLU6}(x) = \min(\max(0,x), 6)
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_relu6</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>RNN module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-rnn.R'><code>R/nn-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_rnn.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a multi-layer Elman RNN with \(\tanh\) or \(\mbox{ReLU}\) non-linearity
-to an input sequence.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_rnn</span>(
-  <span class='kw'>input_size</span>,
-  <span class='kw'>hidden_size</span>,
-  num_layers = <span class='fl'>1</span>,
-  nonlinearity = <span class='kw'>NULL</span>,
-  bias = <span class='fl'>TRUE</span>,
-  batch_first = <span class='fl'>FALSE</span>,
-  dropout = <span class='fl'>0</span>,
-  bidirectional = <span class='fl'>FALSE</span>,
-  <span class='kw'>...</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input_size</th>
-      <td><p>The number of expected features in the input <code>x</code></p></td>
-    </tr>
-    <tr>
-      <th>hidden_size</th>
-      <td><p>The number of features in the hidden state <code>h</code></p></td>
-    </tr>
-    <tr>
-      <th>num_layers</th>
-      <td><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
-would mean stacking two RNNs together to form a <code>stacked RNN</code>,
-with the second RNN taking in outputs of the first RNN and
-computing the final results. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>nonlinearity</th>
-      <td><p>The non-linearity to use. Can be either <code>'tanh'</code> or
-<code>'relu'</code>. Default: <code>'tanh'</code></p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and
-<code>b_hh</code>. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>If <code>TRUE</code>, then the input and output tensors are provided
-as <code>(batch, seq, feature)</code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>dropout</th>
-      <td><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
-RNN layer except the last layer, with dropout probability equal to
-<code>dropout</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>bidirectional</th>
-      <td><p>If <code>TRUE</code>, becomes a bidirectional RNN. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>other arguments that can be passed to the super class.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>For each element in the input sequence, each layer computes the following
-function:</p>
-<p>$$
-h_t = \tanh(W_{ih} x_t + b_{ih} + W_{hh} h_{(t-1)} + b_{hh})
-$$</p>
-<p>where \(h_t\) is the hidden state at time <code>t</code>, \(x_t\) is
-the input at time <code>t</code>, and \(h_{(t-1)}\) is the hidden state of the
-previous layer at time <code>t-1</code> or the initial hidden state at time <code>0</code>.
-If <code>nonlinearity</code> is <code>'relu'</code>, then \(\mbox{ReLU}\) is used instead of
-\(\tanh\).</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
-    
-
-<ul>
-<li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
-of the input sequence. The input can also be a packed variable length
-sequence.</p></li>
-<li><p><strong>h_0</strong> of shape <code>(num_layers * num_directions, batch, hidden_size)</code>: tensor
-containing the initial hidden state for each element in the batch.
-Defaults to zero if not provided. If the RNN is bidirectional,
-num_directions should be 2, else it should be 1.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="outputs"><a class="anchor" href="#outputs"></a>Outputs</h2>
-
-    
-
-<ul>
-<li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
-containing the output features (<code>h_t</code>) from the last layer of the RNN,
-for each <code>t</code>.  If a :class:<code>nn_packed_sequence</code> has
-been given as the input, the output will also be a packed sequence.
-For the unpacked case, the directions can be separated
-using <code>output$view(seq_len, batch, num_directions, hidden_size)</code>,
-with forward and backward being direction <code>0</code> and <code>1</code> respectively.
-Similarly, the directions can be separated in the packed case.</p></li>
-<li><p><strong>h_n</strong> of shape <code>(num_layers * num_directions, batch, hidden_size)</code>: tensor
-containing the hidden state for <code>t = seq_len</code>.
-Like <em>output</em>, the layers can be separated using
-<code>h_n$view(num_layers, num_directions, batch, hidden_size)</code>.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input1: \((L, N, H_{in})\) tensor containing input features where
-\(H_{in}=\mbox{input\_size}\) and <code>L</code> represents a sequence length.</p></li>
-<li><p>Input2: \((S, N, H_{out})\) tensor
-containing the initial hidden state for each element in the batch.
-\(H_{out}=\mbox{hidden\_size}\)
-Defaults to zero if not provided. where \(S=\mbox{num\_layers} * \mbox{num\_directions}\)
-If the RNN is bidirectional, num_directions should be 2, else it should be 1.</p></li>
-<li><p>Output1: \((L, N, H_{all})\) where \(H_{all}=\mbox{num\_directions} * \mbox{hidden\_size}\)</p></li>
-<li><p>Output2: \((S, N, H_{out})\) tensor containing the next hidden state
-for each element in the batch</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p><code>weight_ih_l[k]</code>: the learnable input-hidden weights of the k-th layer,
-of shape <code>(hidden_size, input_size)</code> for <code>k = 0</code>. Otherwise, the shape is
-<code>(hidden_size, num_directions * hidden_size)</code></p></li>
-<li><p><code>weight_hh_l[k]</code>: the learnable hidden-hidden weights of the k-th layer,
-of shape <code>(hidden_size, hidden_size)</code></p></li>
-<li><p><code>bias_ih_l[k]</code>: the learnable input-hidden bias of the k-th layer,
-of shape <code>(hidden_size)</code></p></li>
-<li><p><code>bias_hh_l[k]</code>: the learnable hidden-hidden bias of the k-th layer,
-of shape <code>(hidden_size)</code></p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\)
-where \(k = \frac{1}{\mbox{hidden\_size}}\)</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>rnn</span> <span class='op'>&lt;-</span> <span class='fu'>nn_rnn</span>(<span class='fl'>10</span>, <span class='fl'>20</span>, <span class='fl'>2</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>5</span>, <span class='fl'>3</span>, <span class='fl'>10</span>)
-<span class='kw'>h0</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>20</span>)
-<span class='fu'>rnn</span>(<span class='kw'>input</span>, <span class='kw'>h0</span>)
-
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  Columns 1 to 9 -0.8602  0.7216  0.3266  0.8357 -0.8318 -0.7262 -0.5799  0.3267 -0.8229
-#&gt;   0.2916  0.7007 -0.1747  0.2275  0.5811  0.3989  0.8118 -0.4147  0.9549
-#&gt;  -0.2039 -0.5070 -0.5266  0.2145  0.0190  0.2925  0.4282  0.0952  0.9035
-#&gt; 
-#&gt; Columns 10 to 18  0.1591 -0.0737 -0.5804 -0.7511  0.4861 -0.3389 -0.7901 -0.0480  0.8364
-#&gt;   0.5735  0.2642 -0.3489 -0.3905 -0.4259 -0.7028 -0.6733  0.3174  0.4279
-#&gt;  -0.3629  0.5666 -0.2726 -0.0245 -0.0063 -0.5204 -0.4250  0.4900  0.8270
-#&gt; 
-#&gt; Columns 19 to 20 -0.5864 -0.4069
-#&gt;   0.2587  0.4719
-#&gt;   0.4167  0.0462
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  Columns 1 to 9  0.0137 -0.1287  0.5749  0.5469 -0.9044 -0.0804 -0.6204 -0.0413  0.0935
-#&gt;  -0.6848  0.4890 -0.0942 -0.1103 -0.0217 -0.4984  0.1846  0.3287  0.3348
-#&gt;   0.1436  0.0356  0.1660 -0.3226 -0.1395  0.0974 -0.0301  0.3542 -0.1401
-#&gt; 
-#&gt; Columns 10 to 18  0.9305  0.4168 -0.5855 -0.4370  0.3152 -0.4037 -0.5856  0.5313  0.5544
-#&gt;   0.1797 -0.4514 -0.3527 -0.0240 -0.1382 -0.2236 -0.2003 -0.3207  0.4496
-#&gt;   0.6126 -0.2820  0.1942 -0.1835 -0.0809 -0.2318 -0.5392  0.4116  0.4101
-#&gt; 
-#&gt; Columns 19 to 20  0.4298 -0.1336
-#&gt;  -0.3980  0.1120
-#&gt;   0.1441  0.5668
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  Columns 1 to 9  0.3053  0.2568  0.1533 -0.0598 -0.6290 -0.3303 -0.6717  0.0997 -0.4493
-#&gt;  -0.2654  0.2218  0.1562  0.6225 -0.2445 -0.3430 -0.1357  0.3488 -0.1540
-#&gt;  -0.3478 -0.2184  0.3229  0.5487 -0.1260  0.0456 -0.2855  0.0861 -0.3995
-#&gt; 
-#&gt; Columns 10 to 18  0.8370  0.1429 -0.3049 -0.2669  0.1932  0.4366 -0.6523  0.4230  0.4680
-#&gt;   0.3967  0.0752 -0.2987 -0.4755  0.0148 -0.2945 -0.0849 -0.5490  0.5403
-#&gt;   0.3668 -0.1499 -0.2851 -0.2811  0.2466  0.0035 -0.3844  0.1584  0.4427
-#&gt; 
-#&gt; Columns 19 to 20  0.4681  0.1083
-#&gt;  -0.3819  0.2308
-#&gt;   0.0151  0.1606
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  Columns 1 to 9  0.3388 -0.2725  0.1452 -0.0834 -0.3342 -0.2455 -0.5233 -0.0978 -0.1853
-#&gt;   0.0111  0.2142  0.3291  0.4073 -0.7684  0.0134 -0.2729  0.0240 -0.1758
-#&gt;   0.3657  0.3161  0.5496 -0.0741 -0.2450 -0.0835 -0.5178 -0.0820 -0.4004
-#&gt; 
-#&gt; Columns 10 to 18  0.6758  0.4195 -0.4921 -0.2915  0.1368  0.4970 -0.2517  0.6257  0.3349
-#&gt;   0.5671 -0.1759 -0.0776 -0.3747  0.2027 -0.2634 -0.4303  0.1031  0.6133
-#&gt;   0.8021 -0.0940  0.2150 -0.3331 -0.0110  0.4765 -0.2428  0.2160  0.3590
-#&gt; 
-#&gt; Columns 19 to 20 -0.0779  0.0241
-#&gt;  -0.1087 -0.2026
-#&gt;   0.0155  0.2027
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  Columns 1 to 9 -0.3744  0.0714  0.0463  0.3634 -0.6211 -0.2500 -0.2722  0.0371 -0.3010
-#&gt;  -0.5140  0.1369  0.3463  0.3656 -0.5993 -0.0989 -0.3265 -0.0147 -0.0154
-#&gt;  -0.2268  0.0464 -0.0493  0.3440 -0.5042 -0.1807 -0.4559  0.0087 -0.3569
-#&gt; 
-#&gt; Columns 10 to 18  0.3764 -0.4206 -0.3947 -0.2861 -0.1350  0.2085  0.1166  0.5903  0.5067
-#&gt;   0.5849 -0.1580 -0.6916 -0.4892 -0.0685 -0.1258 -0.2522  0.4786  0.2862
-#&gt;   0.3959 -0.0003 -0.5916 -0.3833  0.0314  0.1823  0.0415  0.3136 -0.0097
-#&gt; 
-#&gt; Columns 19 to 20 -0.2183  0.0309
-#&gt;   0.2303  0.1540
-#&gt;  -0.3777 -0.1460
-#&gt; [ CPUFloatType{5,3,20} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  Columns 1 to 9  0.4455  0.0625  0.1735 -0.2736 -0.2345  0.2214 -0.6394  0.4117  0.2452
-#&gt;  -0.3142 -0.4928 -0.3782 -0.6126  0.6180  0.2001  0.4070  0.4264  0.4202
-#&gt;   0.1653 -0.3302  0.4631  0.4052 -0.5041  0.6584 -0.0185  0.1668  0.0802
-#&gt; 
-#&gt; Columns 10 to 18 -0.7638  0.1827 -0.6471  0.3327  0.7053 -0.0893  0.7612  0.2357 -0.5982
-#&gt;  -0.5755  0.5408 -0.4381  0.1185  0.6081 -0.4424  0.7388 -0.2726 -0.4224
-#&gt;  -0.2852  0.2175 -0.3230  0.6165  0.4056 -0.0945 -0.1662 -0.0628 -0.4486
-#&gt; 
-#&gt; Columns 19 to 20 -0.0306  0.2791
-#&gt;   0.6370 -0.3998
-#&gt;   0.1249  0.2034
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  Columns 1 to 9 -0.3744  0.0714  0.0463  0.3634 -0.6211 -0.2500 -0.2722  0.0371 -0.3010
-#&gt;  -0.5140  0.1369  0.3463  0.3656 -0.5993 -0.0989 -0.3265 -0.0147 -0.0154
-#&gt;  -0.2268  0.0464 -0.0493  0.3440 -0.5042 -0.1807 -0.4559  0.0087 -0.3569
-#&gt; 
-#&gt; Columns 10 to 18  0.3764 -0.4206 -0.3947 -0.2861 -0.1350  0.2085  0.1166  0.5903  0.5067
-#&gt;   0.5849 -0.1580 -0.6916 -0.4892 -0.0685 -0.1258 -0.2522  0.4786  0.2862
-#&gt;   0.3959 -0.0003 -0.5916 -0.3833  0.0314  0.1823  0.0415  0.3136 -0.0097
-#&gt; 
-#&gt; Columns 19 to 20 -0.2183  0.0309
-#&gt;   0.2303  0.1540
-#&gt;  -0.3777 -0.1460
-#&gt; [ CPUFloatType{2,3,20} ]
-#&gt; </div></pre>
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-    <h1>RReLU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_rrelu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the randomized leaky rectified liner unit function, element-wise,
-as described in the paper:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_rrelu</span>(lower = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>8</span>, upper = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>3</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>lower</th>
-      <td><p>lower bound of the uniform distribution. Default: \(\frac{1}{8}\)</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>upper bound of the uniform distribution. Default: \(\frac{1}{3}\)</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p><code>Empirical Evaluation of Rectified Activations in Convolutional Network</code>.</p>
-<p>The function is defined as:</p>
-<p>$$
-\mbox{RReLU}(x) =
-\left\{ \begin{array}{ll}
-x &amp; \mbox{if } x \geq 0 \\
-ax &amp; \mbox{ otherwise }
-\end{array}
-\right.
-$$</p>
-<p>where \(a\) is randomly sampled from uniform distribution
-\(\mathcal{U}(\mbox{lower}, \mbox{upper})\).
-See: https://arxiv.org/pdf/1505.00853.pdf</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_rrelu</span>(<span class='fl'>0.1</span>, <span class='fl'>0.3</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.8606
-#&gt;  0.8451
-#&gt; [ CPUFloatType{2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_selu.html b/static/docs/dev/reference/nn_selu.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_selu.Rd</code></div>
-    </div>
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-    <p>Applied element-wise, as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_selu</span>(inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>inplace</th>
-      <td><p>(bool, optional): can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{SELU}(x) = \mbox{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))
-$$</p>
-<p>with \(\alpha = 1.6732632423543772848170429916717\) and
-\(\mbox{scale} = 1.0507009873554804934193349852946\).</p>
-<p>More details can be found in the paper
-<a href='https://arxiv.org/abs/1706.02515'>Self-Normalizing Neural Networks</a>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_selu</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-    <h1>A sequential container</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
-    <div class="hidden name"><code>nn_sequential.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A sequential container.
-Modules will be added to it in the order they are passed in the constructor.
-See examples.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_sequential</span>(<span class='kw'>...</span>, name = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>sequence of modules to be added</p></td>
-    </tr>
-    <tr>
-      <th>name</th>
-      <td><p>optional name for the generated module.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>model</span> <span class='op'>&lt;-</span> <span class='fu'>nn_sequential</span>(
-  <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>1</span>, <span class='fl'>20</span>, <span class='fl'>5</span>),
-  <span class='fu'><a href='nn_relu.html'>nn_relu</a></span>(),
-  <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>20</span>, <span class='fl'>64</span>, <span class='fl'>5</span>),
-  <span class='fu'><a href='nn_relu.html'>nn_relu</a></span>()
-)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>32</span>, <span class='fl'>1</span>, <span class='fl'>28</span>, <span class='fl'>28</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>model</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_sigmoid.Rd</code></div>
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-    </div>
-
-    <pre class="usage"><span class='fu'>nn_sigmoid</span>()</pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)}
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_sigmoid</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
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-</div></pre>
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-Softmax is defined as:" />
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-    <h1>Softmax module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softmax.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the Softmax function to an n-dimensional input Tensor
-rescaling them so that the elements of the n-dimensional output Tensor
-lie in the range <code>[0,1]</code> and sum to 1.
-Softmax is defined as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_softmax</span>(<span class='kw'>dim</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>(int): A dimension along which Softmax will be computed (so every slice
-along dim will sum to 1).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>:
-a Tensor of the same dimension and shape as the input with
-values in the range <code>[0, 1]</code></p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}
-$$</p>
-<p>When the input Tensor is a sparse tensor then the unspecifed
-values are treated as <code>-Inf</code>.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This module doesn't work directly with NLLLoss,
-which expects the Log to be computed between the Softmax and itself.
-Use <code>LogSoftmax</code> instead (it's faster and has better numerical properties).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((*)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((*)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softmax</span>(<span class='fl'>1</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Softmax2d module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softmax2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies SoftMax over features to each spatial location.
-When given an image of <code>Channels x Height x Width</code>, it will
-apply <code>Softmax</code> to each location \((Channels, h_i, w_j)\)</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_softmax2d</span>()</pre>
-
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>a Tensor of the same dimension and shape as the input with
-values in the range <code>[0, 1]</code></p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H, W)\)</p></li>
-<li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softmax2d</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>12</span>, <span class='fl'>13</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-    <h1>Softmin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softmin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the Softmin function to an n-dimensional input Tensor
-rescaling them so that the elements of the n-dimensional output Tensor
-lie in the range <code>[0, 1]</code> and sum to 1.
-Softmin is defined as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_softmin</span>(<span class='kw'>dim</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>(int): A dimension along which Softmin will be computed (so every slice
-along dim will sum to 1).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>a Tensor of the same dimension and shape as the input, with
-values in the range <code>[0, 1]</code>.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)}
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((*)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((*)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softmin</span>(dim = <span class='fl'>1</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_softplus.html b/static/docs/dev/reference/nn_softplus.html
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-    <h1>Softplus module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softplus.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the element-wise function:
-$$
-  \mbox{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_softplus</span>(beta = <span class='fl'>1</span>, threshold = <span class='fl'>20</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>beta</th>
-      <td><p>the \(\beta\) value for the Softplus formulation. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>threshold</th>
-      <td><p>values above this revert to a linear function. Default: 20</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>SoftPlus is a smooth approximation to the ReLU function and can be used
-to constrain the output of a machine to always be positive.
-For numerical stability the implementation reverts to the linear function
-when \(input \times \beta &gt; threshold\).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softplus</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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diff --git a/static/docs/dev/reference/nn_softshrink.html b/static/docs/dev/reference/nn_softshrink.html
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-    <h1>Softshrink module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softshrink.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the soft shrinkage function elementwise:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_softshrink</span>(lambd = <span class='fl'>0.5</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>lambd</th>
-      <td><p>the \(\lambda\) (must be no less than zero) value for the Softshrink formulation. Default: 0.5</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{SoftShrinkage}(x) =
-  \left\{ \begin{array}{ll}
-x - \lambda, &amp; \mbox{ if } x &gt; \lambda \\
-x + \lambda, &amp; \mbox{ if } x &lt; -\lambda \\
-0, &amp; \mbox{ otherwise }
-\end{array}
-\right.
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softshrink</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softsign.Rd</code></div>
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-$$
-  \mbox{SoftSign}(x) = \frac{x}{ 1 + |x|}
-$$</p>
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-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
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-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softsign</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
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-    <h1>Tanh module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_tanh.Rd</code></div>
-    </div>
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-    <p>Applies the element-wise function:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_tanh</span>()</pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)} {\exp(x) + \exp(-x)}
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_tanh</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
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diff --git a/static/docs/dev/reference/nn_tanhshrink.html b/static/docs/dev/reference/nn_tanhshrink.html
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-
-    <p>$$
-  \mbox{Tanhshrink}(x) = x - \tanh(x)
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
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-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_tanhshrink</span>()
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
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-    <h1>Threshoold module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_threshold.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Thresholds each element of the input Tensor.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_threshold</span>(<span class='kw'>threshold</span>, <span class='kw'>value</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>threshold</th>
-      <td><p>The value to threshold at</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>The value to replace with</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Threshold is defined as:
-$$
-  y = 
-  \left\{ \begin{array}{ll}
-  x, &amp;\mbox{ if } x &gt; \mbox{threshold} \\
-  \mbox{value}, &amp;\mbox{ otherwise }
-  \end{array}
-  \right.
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_threshold</span>(<span class='fl'>0.1</span>, <span class='fl'>20</span>)
-<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
-
-}
-</div></pre>
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-    <h1>Packs a Tensor containing padded sequences of variable length.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_rnn_pack_padded_sequence.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>input</code> can be of size <code>T x B x *</code> where <code>T</code> is the length of the
-longest sequence (equal to <code>lengths[1]</code>), <code>B</code> is the batch size, and
-<code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is any number of dimensions (including 0). If <code>batch_first</code> is
-<code>TRUE</code>, <code>B x T x *</code> <code>input</code> is expected.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_utils_rnn_pack_padded_sequence</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>lengths</span>,
-  batch_first = <span class='fl'>FALSE</span>,
-  enforce_sorted = <span class='fl'>TRUE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor): padded batch of variable length sequences.</p></td>
-    </tr>
-    <tr>
-      <th>lengths</th>
-      <td><p>(Tensor): list of sequences lengths of each batch element.</p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>(bool, optional): if <code>TRUE</code>, the input is expected in <code>B x T x *</code>
-format.</p></td>
-    </tr>
-    <tr>
-      <th>enforce_sorted</th>
-      <td><p>(bool, optional): if <code>TRUE</code>, the input is expected to
-contain sequences sorted by length in a decreasing order. If
-<code>FALSE</code>, the input will get sorted unconditionally. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>a <code>PackedSequence</code> object</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>For unsorted sequences, use <code>enforce_sorted = FALSE</code>. If <code>enforce_sorted</code> is
-<code>TRUE</code>, the sequences should be sorted by length in a decreasing order, i.e.
-<code>input[,1]</code> should be the longest sequence, and <code>input[,B]</code> the shortest
-one. <code>enforce_sorted = TRUE</code> is only necessary for ONNX export.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function accepts any input that has at least two dimensions. You
-can apply it to pack the labels, and use the output of the RNN with
-them to compute the loss directly. A Tensor can be retrieved from
-a <code>PackedSequence</code> object by accessing its <code>.data</code> attribute.</p>
-
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-    <h1>Packs a list of variable length Tensors</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_rnn_pack_sequence.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>sequences</code> should be a list of Tensors of size <code>L x *</code>, where <code>L</code> is
-the length of a sequence and <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is any number of trailing dimensions,
-including zero.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_utils_rnn_pack_sequence</span>(<span class='kw'>sequences</span>, enforce_sorted = <span class='fl'>TRUE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>sequences</th>
-      <td><p><code>(list[Tensor])</code>: A list of sequences of decreasing length.</p></td>
-    </tr>
-    <tr>
-      <th>enforce_sorted</th>
-      <td><p>(bool, optional): if <code>TRUE</code>, checks that the input
-contains sequences sorted by length in a decreasing order. If
-<code>FALSE</code>, this condition is not checked. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>a <code>PackedSequence</code> object</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>For unsorted sequences, use <code>enforce_sorted = FALSE</code>. If <code>enforce_sorted</code>
-is <code>TRUE</code>, the sequences should be sorted in the order of decreasing length.
-<code>enforce_sorted = TRUE</code> is only necessary for ONNX export.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-<span class='kw'>z</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>6</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-
-<span class='kw'>p</span> <span class='op'>&lt;-</span> <span class='fu'>nn_utils_rnn_pack_sequence</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='kw'>z</span>))
-
-}
-</div></pre>
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-    <h1>Pads a packed batch of variable length sequences.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_rnn_pad_packed_sequence.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>It is an inverse operation to <code><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence()</a></code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_utils_rnn_pad_packed_sequence</span>(
-  <span class='kw'>sequence</span>,
-  batch_first = <span class='fl'>FALSE</span>,
-  padding_value = <span class='fl'>0</span>,
-  total_length = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>sequence</th>
-      <td><p>(PackedSequence): batch to pad</p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>(bool, optional): if <code>True</code>, the output will be in ``B x T x *`
-format.</p></td>
-    </tr>
-    <tr>
-      <th>padding_value</th>
-      <td><p>(float, optional): values for padded elements.</p></td>
-    </tr>
-    <tr>
-      <th>total_length</th>
-      <td><p>(int, optional): if not <code>NULL</code>, the output will be padded to
-have length <code>total_length</code>. This method will throw <code>ValueError</code>
-if <code>total_length</code> is less than the max sequence length in
-<code>sequence</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>Tuple of Tensor containing the padded sequence, and a Tensor
-containing the list of lengths of each sequence in the batch.
-Batch elements will be re-ordered as they were ordered originally when
-the batch was passed to <code><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence()</a></code> or
-<code><a href='nn_utils_rnn_pack_sequence.html'>nn_utils_rnn_pack_sequence()</a></code>.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The returned Tensor's data will be of size <code>T x B x *</code>, where <code>T</code> is the length
-of the longest sequence and <code>B</code> is the batch size. If <code>batch_first</code> is <code>TRUE</code>,
-the data will be transposed into <code>B x T x *</code> format.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p><code>total_length</code> is useful to implement the
-<code>pack sequence -&gt; recurrent network -&gt; unpack sequence</code> pattern in a
-<code>nn_module</code> wrapped in <code>~torch.nn.DataParallel</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>seq</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>0</span>,<span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>6</span>)))        
-<span class='kw'>lens</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>1</span>,<span class='fl'>3</span>)
-<span class='kw'>packed</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence</a></span>(<span class='kw'>seq</span>, <span class='kw'>lens</span>, batch_first = <span class='fl'>TRUE</span>,
-                                            enforce_sorted = <span class='fl'>FALSE</span>)
-<span class='kw'>packed</span>
-<span class='fu'>nn_utils_rnn_pad_packed_sequence</span>(<span class='kw'>packed</span>, batch_first=<span class='fl'>TRUE</span>)
-
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  1  2  0
-#&gt;  3  0  0
-#&gt;  4  5  6
-#&gt; [ CPUFloatType{3,3} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  2
-#&gt;  1
-#&gt;  3
-#&gt; [ CPULongType{3} ]
-#&gt; </div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-      <div class="copyright">
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diff --git a/static/docs/dev/reference/nn_utils_rnn_pad_sequence.html b/static/docs/dev/reference/nn_utils_rnn_pad_sequence.html
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-    <div class="page-header">
-    <h1>Pad a list of variable length Tensors with <code>padding_value</code></h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_rnn_pad_sequence.Rd</code></div>
-    </div>
-
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-    <p><code>pad_sequence</code> stacks a list of Tensors along a new dimension,
-and pads them to equal length. For example, if the input is list of
-sequences with size <code>L x *</code> and if batch_first is False, and <code>T x B x *</code>
-otherwise.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_utils_rnn_pad_sequence</span>(<span class='kw'>sequences</span>, batch_first = <span class='fl'>FALSE</span>, padding_value = <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>sequences</th>
-      <td><p><code>(list[Tensor])</code>: list of variable length sequences.</p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>(bool, optional): output will be in <code>B x T x *</code> if <code>TRUE</code>,
-or in <code>T x B x *</code> otherwise</p></td>
-    </tr>
-    <tr>
-      <th>padding_value</th>
-      <td><p>(float, optional): value for padded elements. Default: 0.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>Tensor of size <code>T x B x *</code> if <code>batch_first</code> is <code>FALSE</code>.
-Tensor of size <code>B x T x *</code> otherwise</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p><code>B</code> is batch size. It is equal to the number of elements in <code>sequences</code>.
-<code>T</code> is length of the longest sequence.
-<code>L</code> is length of the sequence.
-<code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is any number of trailing dimensions, including none.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function returns a Tensor of size <code>T x B x *</code> or <code>B x T x *</code>
-where <code>T</code> is the length of the longest sequence. This function assumes
-trailing dimensions and type of all the Tensors in sequences are same.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fl'>25</span>, <span class='fl'>300</span>)
-<span class='kw'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fl'>22</span>, <span class='fl'>300</span>)
-<span class='kw'>c</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fl'>15</span>, <span class='fl'>300</span>)
-<span class='fu'>nn_utils_rnn_pad_sequence</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>a</span>, <span class='kw'>b</span>, <span class='kw'>c</span>))<span class='op'>$</span><span class='fu'>size</span>()
-
-}
-</div><div class='output co'>#&gt; [1]  25   3 300</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_adaptive_avg_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1D adaptive average pooling over an input signal composed of
-several input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_adaptive_avg_pool1d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size (single integer)</p></td>
-    </tr>
-    </table>
-
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-several input planes.</p>
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-      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
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-several input planes.</p>
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-      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
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-      <th>output_size</th>
-      <td><p>the target output size (single integer)</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_adaptive_max_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D adaptive max pooling over an input signal composed of
-several input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_adaptive_max_pool2d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size (single integer or double-integer tuple)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return pooling indices. Default: <code>FALSE</code></p></td>
-    </tr>
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-    <h1>Adaptive_max_pool3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_adaptive_max_pool3d.Rd</code></div>
-    </div>
-
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-    <p>Applies a 3D adaptive max pooling over an input signal composed of
-several input planes.</p>
-    </div>
-
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-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
-    </tr>
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-      <th>output_size</th>
-      <td><p>the target output size (single integer or triple-integer tuple)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return pooling indices. Default:<code>FALSE</code></p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-vision.R'><code>R/nnf-vision.R</code></a></small>
-    <div class="hidden name"><code>nnf_affine_grid.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Generates a 2D or 3D flow field (sampling grid), given a batch of
-affine matrices <code>theta</code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_affine_grid</span>(<span class='kw'>theta</span>, <span class='kw'>size</span>, align_corners = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>theta</th>
-      <td><p>(Tensor) input batch of affine matrices with shape
-(\(N \times 2 \times 3\)) for 2D or  (\(N \times 3 \times 4\)) for 3D</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(torch.Size) the target output image size. (\(N \times C \times H \times W\)
-for 2D or \(N \times C \times D \times H \times W\) for 3D)
-Example: torch.Size((32, 3, 24, 24))</p></td>
-    </tr>
-    <tr>
-      <th>align_corners</th>
-      <td><p>(bool, optional) if <code>True</code>, consider <code>-1</code> and <code>1</code>
-to refer to the centers of the corner pixels rather than the image corners.
-Refer to <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code> for a more complete description. A grid generated by
-<code>nnf_affine_grid()</code> should be passed to <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code>  with the same setting for
-this option. Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>This function is often used in conjunction with <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code>
-to build <code>Spatial Transformer Networks</code>_ .</p>
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-
-    <pre class="usage"><span class='fu'>nnf_alpha_dropout</span>(<span class='kw'>input</span>, p = <span class='fl'>0.5</span>, training = <span class='fl'>FALSE</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
-Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
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-    <h1>Avg_pool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_avg_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1D average pooling over an input signal composed of several
-input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_avg_pool1d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  ceil_mode = <span class='fl'>FALSE</span>,
-  count_include_pad = <span class='fl'>TRUE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window. Can be a single number or a
-tuple <code>(kW,)</code>.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Can be a single number or a tuple
-<code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
-output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when True, will include the zero-padding in the
-averaging calculation. Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Avg_pool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_avg_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies 2D average-pooling operation in \(kH * kW\) regions by step size
-\(sH * sW\) steps. The number of output features is equal to the number of
-input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_avg_pool2d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  ceil_mode = <span class='fl'>FALSE</span>,
-  count_include_pad = <span class='fl'>TRUE</span>,
-  divisor_override = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>size of the pooling region. Can be a single number or a
-tuple <code>(kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>stride of the pooling operation. Can be a single number or a
-tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
-to compute the output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when True, will include the zero-padding in the
-averaging calculation. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>divisor_override</th>
-      <td><p>if specified, it will be used as divisor, otherwise
-size of the pooling region will be used. Default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Avg_pool3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_avg_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies 3D average-pooling operation in \(kT * kH * kW\) regions by step
-size \(sT * sH * sW\) steps. The number of output features is equal to
-\(\lfloor \frac{ \mbox{input planes} }{sT} \rfloor\).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_avg_pool3d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  ceil_mode = <span class='fl'>FALSE</span>,
-  count_include_pad = <span class='fl'>TRUE</span>,
-  divisor_override = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>size of the pooling region. Can be a single number or a
-tuple <code>(kT, kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>stride of the pooling operation. Can be a single number or a
-tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
-to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when True, will include the zero-padding in the
-averaging calculation</p></td>
-    </tr>
-    <tr>
-      <th>divisor_override</th>
-      <td><p>NA if specified, it will be used as divisor, otherwise
-size of the pooling region will be used. Default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_batch_norm.html b/static/docs/dev/reference/nnf_batch_norm.html
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-    <div class="page-header">
-    <h1>Batch_norm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-batchnorm.R'><code>R/nnf-batchnorm.R</code></a></small>
-    <div class="hidden name"><code>nnf_batch_norm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies Batch Normalization for each channel across a batch of data.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_batch_norm</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>running_mean</span>,
-  <span class='kw'>running_var</span>,
-  weight = <span class='kw'>NULL</span>,
-  bias = <span class='kw'>NULL</span>,
-  training = <span class='fl'>FALSE</span>,
-  momentum = <span class='fl'>0.1</span>,
-  eps = <span class='fl'>1e-05</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor</p></td>
-    </tr>
-    <tr>
-      <th>running_mean</th>
-      <td><p>the running_mean tensor</p></td>
-    </tr>
-    <tr>
-      <th>running_var</th>
-      <td><p>the running_var tensor</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>the weight tensor</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>the bias tensor</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>bool wether it's training. Default: FALSE</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>the value used for the <code>running_mean</code> and <code>running_var</code> computation.
-Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
-    </tr>
-    </table>
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-linear.R'><code>R/nnf-linear.R</code></a></small>
-    <div class="hidden name"><code>nnf_bilinear.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a bilinear transformation to the incoming data:
-\(y = x_1 A x_2 + b\)</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_bilinear</span>(<span class='kw'>input1</span>, <span class='kw'>input2</span>, <span class='kw'>weight</span>, bias = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input1</th>
-      <td><p>\((N, *, H_{in1})\) where \(H_{in1}=\mbox{in1\_features}\)
-and \(*\) means any number of additional dimensions.
-All but the last dimension of the inputs should be the same.</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>\((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>\((\mbox{out\_features}, \mbox{in1\_features},
-\mbox{in2\_features})\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>\((\mbox{out\_features})\)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>output \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)
-and all but the last dimension are the same shape as the input.</p>
-
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-    <h1>Binary_cross_entropy</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_binary_cross_entropy.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Function that measures the Binary Cross Entropy
-between the target and the output.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_binary_cross_entropy</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>target</span>,
-  weight = <span class='kw'>NULL</span>,
-  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>)
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(tensor) weight for each value.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Binary_cross_entropy_with_logits</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_binary_cross_entropy_with_logits.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Function that measures Binary Cross Entropy between target and output
-logits.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_binary_cross_entropy_with_logits</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>target</span>,
-  weight = <span class='kw'>NULL</span>,
-  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>),
-  pos_weight = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>Tensor of arbitrary shape</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>Tensor of the same shape as input</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional) a manual rescaling weight if provided it's
-repeated to match input tensor shape.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    <tr>
-      <th>pos_weight</th>
-      <td><p>(Tensor, optional) a weight of positive examples.
-Must be a vector with length equal to the number of classes.</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_celu.html b/static/docs/dev/reference/nnf_celu.html
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-
-    <div class="ref-description">
-    <p>Applies element-wise, \(CELU(x) = max(0,x) + min(0, \alpha * (exp(x \alpha) - 1))\).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_celu</span>(<span class='kw'>input</span>, alpha = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)
-
-<span class='fu'>nnf_celu_</span>(<span class='kw'>input</span>, alpha = <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Conv1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1D convolution over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv1d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  bias = <span class='kw'>NULL</span>,
-  stride = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  dilation = <span class='fl'>1</span>,
-  groups = <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels, in_channels/groups , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or
-a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by
-the number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_conv2d.html b/static/docs/dev/reference/nnf_conv2d.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv2d.Rd</code></div>
-    </div>
-
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-    <p>Applies a 2D convolution over an input image composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv2d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  bias = <span class='kw'>NULL</span>,
-  stride = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  dilation = <span class='fl'>1</span>,
-  groups = <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a
-tuple <code>(sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a tuple <code>(dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by the
-number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 3D convolution over an input image composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv3d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  bias = <span class='kw'>NULL</span>,
-  stride = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  dilation = <span class='fl'>1</span>,
-  groups = <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a
-tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by
-the number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_conv_tbc.html b/static/docs/dev/reference/nnf_conv_tbc.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
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-    <p>Applies a 1-dimensional sequence convolution over an input sequence.
-Input and output dimensions are (Time, Batch, Channels) - hence TBC.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv_tbc</span>(<span class='kw'>input</span>, <span class='kw'>weight</span>, <span class='kw'>bias</span>, pad = <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{sequence length} \times 
-batch \times \mbox{in\_channels})\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} 
-\times \mbox{out\_channels}\))</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>bias of shape (\(\mbox{out\_channels}\))</p></td>
-    </tr>
-    <tr>
-      <th>pad</th>
-      <td><p>number of timesteps to pad. Default: 0</p></td>
-    </tr>
-    </table>
-
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-    <h1>Conv_transpose1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv_transpose1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1D transposed convolution operator over an input signal
-composed of several input planes, sometimes also called "deconvolution".</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv_transpose1d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  bias = <span class='kw'>NULL</span>,
-  stride = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  output_padding = <span class='fl'>0</span>,
-  groups = <span class='fl'>1</span>,
-  dilation = <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels, in_channels/groups , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or
-a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>padding applied to the output</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by
-the number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
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-    <h1>Conv_transpose2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv_transpose2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution".</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv_transpose2d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  bias = <span class='kw'>NULL</span>,
-  stride = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  output_padding = <span class='fl'>0</span>,
-  groups = <span class='fl'>1</span>,
-  dilation = <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a
-tuple <code>(sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>padding applied to the output</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by the
-number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a tuple <code>(dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Conv_transpose3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv_transpose3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 3D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution"</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv_transpose3d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  bias = <span class='kw'>NULL</span>,
-  stride = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  output_padding = <span class='fl'>0</span>,
-  groups = <span class='fl'>1</span>,
-  dilation = <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a
-tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>padding applied to the output</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by
-the number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Cosine_embedding_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_cosine_embedding_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that measures the loss given input tensors x_1, x_2 and a
-Tensor label y with values 1 or -1. This is used for measuring whether two inputs
-are similar or dissimilar, using the cosine distance, and is typically used
-for learning nonlinear embeddings or semi-supervised learning.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_cosine_embedding_loss</span>(
-  <span class='kw'>input1</span>,
-  <span class='kw'>input2</span>,
-  <span class='kw'>target</span>,
-  margin = <span class='fl'>0</span>,
-  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>)
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input1</th>
-      <td><p>the input x_1 tensor</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>the input x_2 tensor</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>the target tensor</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Should be a number from -1 to 1 , 0 to 0.5 is suggested. If margin
-is missing, the default value is 0.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
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-
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-    <h1>Cosine_similarity</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-distance.R'><code>R/nnf-distance.R</code></a></small>
-    <div class="hidden name"><code>nnf_cosine_similarity.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Returns cosine similarity between x1 and x2, computed along dim.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_cosine_similarity</span>(<span class='kw'>x1</span>, <span class='kw'>x2</span>, dim = <span class='fl'>1</span>, eps = <span class='fl'>1e-08</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x1</th>
-      <td><p>(Tensor) First input.</p></td>
-    </tr>
-    <tr>
-      <th>x2</th>
-      <td><p>(Tensor) Second input (of size matching x1).</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) Dimension of vectors. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid division by zero.
-Default: 1e-8</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-    \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
-$$</p>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>Cross_entropy</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_cross_entropy.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This criterion combines <code>log_softmax</code> and <code>nll_loss</code> in a single
-function.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_cross_entropy</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>target</span>,
-  weight = <span class='kw'>NULL</span>,
-  ignore_index = <span class='op'>-</span><span class='fl'>100</span>,
-  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>)
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) \((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\)
-in case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\)
-in the case of K-dimensional loss.</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>(Tensor) \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
-or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional) a manual rescaling weight given to each class. If
-given, has to be a Tensor of size <code>C</code></p></td>
-    </tr>
-    <tr>
-      <th>ignore_index</th>
-      <td><p>(int, optional) Specifies a target value that is ignored
-and does not contribute to the input gradient.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Ctc_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_ctc_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The Connectionist Temporal Classification loss.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_ctc_loss</span>(
-  <span class='kw'>log_probs</span>,
-  <span class='kw'>targets</span>,
-  <span class='kw'>input_lengths</span>,
-  <span class='kw'>target_lengths</span>,
-  blank = <span class='fl'>0</span>,
-  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>),
-  zero_infinity = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>log_probs</th>
-      <td><p>\((T, N, C)\) where C = number of characters in alphabet including blank,
-T = input length, and N = batch size. The logarithmized probabilities of
-the outputs (e.g. obtained with <a href='nnf_log_softmax.html'>nnf_log_softmax</a>).</p></td>
-    </tr>
-    <tr>
-      <th>targets</th>
-      <td><p>\((N, S)\) or <code>(sum(target_lengths))</code>. Targets cannot be blank.
-In the second form, the targets are assumed to be concatenated.</p></td>
-    </tr>
-    <tr>
-      <th>input_lengths</th>
-      <td><p>\((N)\). Lengths of the inputs (must each be \(\leq T\))</p></td>
-    </tr>
-    <tr>
-      <th>target_lengths</th>
-      <td><p>\((N)\). Lengths of the targets</p></td>
-    </tr>
-    <tr>
-      <th>blank</th>
-      <td><p>(int, optional) Blank label. Default \(0\).</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    <tr>
-      <th>zero_infinity</th>
-      <td><p>(bool, optional) Whether to zero infinite losses and the
-associated gradients. Default: <code>FALSE</code> Infinite losses mainly occur when the
-inputs are too short to be aligned to the targets.</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-distribution.</p>
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-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
-Default: <code>FALSE</code></p></td>
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-    <h1>Dropout2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
-    <div class="hidden name"><code>nnf_dropout2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randomly zero out entire channels (a channel is a 2D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 2D tensor \(input[i, j]\)) of the input tensor).
-Each channel will be zeroed out independently on every forward call with
-probability <code>p</code> using samples from a Bernoulli distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_dropout2d</span>(<span class='kw'>input</span>, p = <span class='fl'>0.5</span>, training = <span class='fl'>TRUE</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>probability of a channel to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
-Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
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diff --git a/static/docs/dev/reference/nnf_dropout3d.html b/static/docs/dev/reference/nnf_dropout3d.html
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-
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-<div class="row">
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-    <div class="page-header">
-    <h1>Dropout3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
-    <div class="hidden name"><code>nnf_dropout3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randomly zero out entire channels (a channel is a 3D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 3D tensor \(input[i, j]\)) of the input tensor).
-Each channel will be zeroed out independently on every forward call with
-probability <code>p</code> using samples from a Bernoulli distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_dropout3d</span>(<span class='kw'>input</span>, p = <span class='fl'>0.5</span>, training = <span class='fl'>TRUE</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>probability of a channel to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
-Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
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-    <div class="hidden name"><code>nnf_elu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies element-wise,
-$$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_elu</span>(<span class='kw'>input</span>, alpha = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)
-
-<span class='fu'>nnf_elu_</span>(<span class='kw'>input</span>, alpha = <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>the alpha value for the ELU formulation. Default: 1.0</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>)
-<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fu'>nnf_elu</span>(<span class='kw'>x</span>, alpha = <span class='fl'>1</span>)
-<span class='fu'>nnf_elu_</span>(<span class='kw'>x</span>, alpha = <span class='fl'>1</span>)
-<span class='fu'><a href='torch_equal.html'>torch_equal</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>)
-
-}
-</div><div class='output co'>#&gt; [1] TRUE</div></pre>
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diff --git a/static/docs/dev/reference/nnf_embedding.html b/static/docs/dev/reference/nnf_embedding.html
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-    <div class="page-header">
-    <h1>Embedding</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
-    <div class="hidden name"><code>nnf_embedding.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A simple lookup table that looks up embeddings in a fixed dictionary and size.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_embedding</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  padding_idx = <span class='kw'>NULL</span>,
-  max_norm = <span class='kw'>NULL</span>,
-  norm_type = <span class='fl'>2</span>,
-  scale_grad_by_freq = <span class='fl'>FALSE</span>,
-  sparse = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(LongTensor) Tensor containing indices into the embedding matrix</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor) The embedding matrix with number of rows equal to the
-maximum possible index + 1, and number of columns equal to the embedding size</p></td>
-    </tr>
-    <tr>
-      <th>padding_idx</th>
-      <td><p>(int, optional) If given, pads the output with the embedding
-vector at <code>padding_idx</code> (initialized to zeros) whenever it encounters the index.</p></td>
-    </tr>
-    <tr>
-      <th>max_norm</th>
-      <td><p>(float, optional) If given, each embedding vector with norm larger
-than <code>max_norm</code> is renormalized to have norm <code>max_norm</code>. Note: this will modify
-<code>weight</code> in-place.</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>(float, optional) The p of the p-norm to compute for the <code>max_norm</code>
-option. Default <code>2</code>.</p></td>
-    </tr>
-    <tr>
-      <th>scale_grad_by_freq</th>
-      <td><p>(boolean, optional) If given, this will scale gradients
-by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>sparse</th>
-      <td><p>(bool, optional) If <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
-sparse tensor. See Notes under <code>nn_embedding</code> for more details regarding
-sparse gradients.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This module is often used to retrieve word embeddings using indices.
-The input to the module is a list of indices, and the embedding matrix,
-and the output is the corresponding word embeddings.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-</div>
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-    <div class="page-header">
-    <h1>Embedding_bag</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
-    <div class="hidden name"><code>nnf_embedding_bag.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Computes sums, means or maxes of <code>bags</code> of embeddings, without instantiating the
-intermediate embeddings.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_embedding_bag</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  offsets = <span class='kw'>NULL</span>,
-  max_norm = <span class='kw'>NULL</span>,
-  norm_type = <span class='fl'>2</span>,
-  scale_grad_by_freq = <span class='fl'>FALSE</span>,
-  mode = <span class='st'>"mean"</span>,
-  sparse = <span class='fl'>FALSE</span>,
-  per_sample_weights = <span class='kw'>NULL</span>,
-  include_last_offset = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(LongTensor) Tensor containing bags of indices into the embedding matrix</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor) The embedding matrix with number of rows equal to the
-maximum possible index + 1, and number of columns equal to the embedding size</p></td>
-    </tr>
-    <tr>
-      <th>offsets</th>
-      <td><p>(LongTensor, optional) Only used when <code>input</code> is 1D. <code>offsets</code>
-determines the starting index position of each bag (sequence) in <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>max_norm</th>
-      <td><p>(float, optional) If given, each embedding vector with norm
-larger than <code>max_norm</code> is renormalized to have norm <code>max_norm</code>.
-Note: this will modify <code>weight</code> in-place.</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>(float, optional) The <code>p</code> in the <code>p</code>-norm to compute for the
-<code>max_norm</code> option. Default <code>2</code>.</p></td>
-    </tr>
-    <tr>
-      <th>scale_grad_by_freq</th>
-      <td><p>(boolean, optional) if given, this will scale gradients
-by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.                                            Note: this option is not supported when <code>mode="max"</code>.</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>(string, optional) <code>"sum"</code>, <code>"mean"</code> or <code>"max"</code>. Specifies
-the way to reduce the bag. Default: 'mean'</p></td>
-    </tr>
-    <tr>
-      <th>sparse</th>
-      <td><p>(bool, optional) if <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
-sparse tensor. See Notes under <code>nn_embedding</code> for more details regarding
-sparse gradients. Note: this option is not supported when <code>mode="max"</code>.</p></td>
-    </tr>
-    <tr>
-      <th>per_sample_weights</th>
-      <td><p>(Tensor, optional) a tensor of float / double weights,
-or NULL to indicate all weights should be taken to be 1. If specified,
-<code>per_sample_weights</code> must have exactly the same shape as input and is treated
-as having the same <code>offsets</code>, if those are not <code>NULL</code>.</p></td>
-    </tr>
-    <tr>
-      <th>include_last_offset</th>
-      <td><p>(bool, optional) if <code>TRUE</code>, the size of offsets is
-equal to the number of bags + 1.</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-
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-    <p>Combines an array of sliding local blocks into a large containing
-tensor.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_fold</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>output_size</span>,
-  <span class='kw'>kernel_size</span>,
-  dilation = <span class='fl'>1</span>,
-  padding = <span class='fl'>0</span>,
-  stride = <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the shape of the spatial dimensions of the output (i.e.,
-<code>output$sizes()[-c(1,2)]</code>)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the sliding blocks</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements within the
-neighborhood. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides of input.
-Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the sliding blocks in the input spatial dimensions.
-Default: 1</p></td>
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-
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-
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-    <h1>Fractional_max_pool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_fractional_max_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies 2D fractional max pooling over an input signal composed of several input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_fractional_max_pool2d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>kernel_size</span>,
-  output_size = <span class='kw'>NULL</span>,
-  output_ratio = <span class='kw'>NULL</span>,
-  return_indices = <span class='fl'>FALSE</span>,
-  random_samples = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over. Can be a
-single number \(k\) (for a square kernel of \(k * k\)) or
-a tuple <code>(kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form \(oH * oW\).
-Can be a tuple <code>(oH, oW)</code> or a single number \(oH\) for a square image \(oH * oH\)</p></td>
-    </tr>
-    <tr>
-      <th>output_ratio</th>
-      <td><p>If one wants to have an output size as a ratio of the input size,
-this option can be given. This has to be a number or tuple in the range (0, 1)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>True</code>, will return the indices along with the outputs.</p></td>
-    </tr>
-    <tr>
-      <th>random_samples</th>
-      <td><p>optional random samples.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Fractional MaxPooling is described in detail in the paper <code>Fractional MaxPooling</code>_ by Ben Graham</p>
-<p>The max-pooling operation is applied in \(kH * kW\) regions by a stochastic
-step size determined by the target output size.
-The number of output features is equal to the number of input planes.</p>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
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-
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-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_fractional_max_pool3d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>kernel_size</span>,
-  output_size = <span class='kw'>NULL</span>,
-  output_ratio = <span class='kw'>NULL</span>,
-  return_indices = <span class='fl'>FALSE</span>,
-  random_samples = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over. Can be a single number \(k\)
-(for a square kernel of \(k * k * k\)) or a tuple <code>(kT, kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the form \(oT * oH * oW\).
-Can be a tuple <code>(oT, oH, oW)</code> or a single number \(oH\) for a cubic output
-\(oH * oH * oH\)</p></td>
-    </tr>
-    <tr>
-      <th>output_ratio</th>
-      <td><p>If one wants to have an output size as a ratio of the
-input size, this option can be given. This has to be a number or tuple in the
-range (0, 1)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>True</code>, will return the indices along with the outputs.</p></td>
-    </tr>
-    <tr>
-      <th>random_samples</th>
-      <td><p>undocumented argument.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Fractional MaxPooling is described in detail in the paper <code>Fractional MaxPooling</code>_ by Ben Graham</p>
-<p>The max-pooling operation is applied in \(kT * kH * kW\) regions by a stochastic
-step size determined by the target output size.
-The number of output features is equal to the number of input planes.</p>
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_gelu.Rd</code></div>
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-
-    <div class="ref-description">
-    <p>Gelu</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_gelu</span>(<span class='kw'>input</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="gelu-input-gt-tensor-"><a class="anchor" href="#gelu-input-gt-tensor-"></a>gelu(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies element-wise the function
-\(GELU(x) = x * \Phi(x)\)</p>
-<p>where \(\Phi(x)\) is the Cumulative Distribution Function for
-Gaussian Distribution.</p>
-<p>See <a href='https://arxiv.org/abs/1606.08415'>Gaussian Error Linear Units (GELUs)</a>.</p>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_glu.Rd</code></div>
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-    <p>The gated linear unit. Computes:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_glu</span>(<span class='kw'>input</span>, dim = <span class='op'>-</span><span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input tensor</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension on which to split the input. Default: -1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$GLU(a, b) = a \otimes \sigma(b)$$</p>
-<p>where <code>input</code> is split in half along <code>dim</code> to form <code>a</code> and <code>b</code>, \(\sigma\)
-is the sigmoid function and \(\otimes\) is the element-wise product
-between matrices.</p>
-<p>See <a href='https://arxiv.org/abs/1612.08083'>Language Modeling with Gated Convolutional Networks</a>.</p>
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-    <h1>Grid_sample</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-vision.R'><code>R/nnf-vision.R</code></a></small>
-    <div class="hidden name"><code>nnf_grid_sample.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Given an <code>input</code> and a flow-field <code>grid</code>, computes the
-<code>output</code> using <code>input</code> values and pixel locations from <code>grid</code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_grid_sample</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>grid</span>,
-  mode = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"bilinear"</span>, <span class='st'>"nearest"</span>),
-  padding_mode = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"zeros"</span>, <span class='st'>"border"</span>, <span class='st'>"reflection"</span>),
-  align_corners = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input of shape \((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) (4-D case)                    or \((N, C, D_{\mbox{in}}, H_{\mbox{in}}, W_{\mbox{in}})\) (5-D case)</p></td>
-    </tr>
-    <tr>
-      <th>grid</th>
-      <td><p>(Tensor) flow-field of shape \((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\) (4-D case)                   or \((N, D_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}}, 3)\) (5-D case)</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>(str) interpolation mode to calculate output values <code>'bilinear'</code> | <code>'nearest'</code>.
-Default: <code>'bilinear'</code></p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(str) padding mode for outside grid values <code>'zeros'</code> | <code>'border'</code>
-| <code>'reflection'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    <tr>
-      <th>align_corners</th>
-      <td><p>(bool, optional) Geometrically, we consider the pixels of the
-input  as squares rather than points. If set to <code>True</code>, the extrema (<code>-1</code> and
-<code>1</code>) are considered as referring to the center points of the input's corner pixels.
-If set to <code>False</code>, they are instead considered as referring to the corner
-points of the input's corner pixels, making the sampling more resolution
-agnostic. This option parallels the <code>align_corners</code> option in  <code><a href='nnf_interpolate.html'>nnf_interpolate()</a></code>, and
-so whichever option is used here should also be used there to resize the input
-image before grid sampling. Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Currently, only spatial (4-D) and volumetric (5-D) <code>input</code> are
-supported.</p>
-<p>In the spatial (4-D) case, for <code>input</code> with shape
-\((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) and <code>grid</code> with shape
-\((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\), the output will have shape
-\((N, C, H_{\mbox{out}}, W_{\mbox{out}})\).</p>
-<p>For each output location <code>output[n, :, h, w]</code>, the size-2 vector
-<code>grid[n, h, w]</code> specifies <code>input</code> pixel locations <code>x</code> and <code>y</code>,
-which are used to interpolate the output value <code>output[n, :, h, w]</code>.
-In the case of 5D inputs, <code>grid[n, d, h, w]</code> specifies the
-<code>x</code>, <code>y</code>, <code>z</code> pixel locations for interpolating
-<code>output[n, :, d, h, w]</code>. <code>mode</code> argument specifies <code>nearest</code> or
-<code>bilinear</code> interpolation method to sample the input pixels.</p>
-<p><code>grid</code> specifies the sampling pixel locations normalized by the
-<code>input</code> spatial dimensions. Therefore, it should have most values in
-the range of <code>[-1, 1]</code>. For example, values <code>x = -1, y = -1</code> is the
-left-top pixel of <code>input</code>, and values  <code>x = 1, y = 1</code> is the
-right-bottom pixel of <code>input</code>.</p>
-<p>If <code>grid</code> has values outside the range of <code>[-1, 1]</code>, the corresponding
-outputs are handled as defined by <code>padding_mode</code>. Options are</p><ul>
-<li><p><code>padding_mode="zeros"</code>: use <code>0</code> for out-of-bound grid locations,</p></li>
-<li><p><code>padding_mode="border"</code>: use border values for out-of-bound grid locations,</p></li>
-<li><p><code>padding_mode="reflection"</code>: use values at locations reflected by
-the border for out-of-bound grid locations. For location far away
-from the border, it will keep being reflected until becoming in bound,
-e.g., (normalized) pixel location <code>x = -3.5</code> reflects by border <code>-1</code>
-and becomes <code>x' = 1.5</code>, then reflects by border <code>1</code> and becomes
-<code>x'' = -0.5</code>.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>This function is often used in conjunction with <code><a href='nnf_affine_grid.html'>nnf_affine_grid()</a></code>
-to build <code>Spatial Transformer Networks</code>_ .</p>
-
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diff --git a/static/docs/dev/reference/nnf_group_norm.html b/static/docs/dev/reference/nnf_group_norm.html
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-    <h1>Gumbel_softmax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_gumbel_softmax.Rd</code></div>
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-    <p>Samples from the Gumbel-Softmax distribution and
-optionally discretizes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_gumbel_softmax</span>(<span class='kw'>logits</span>, tau = <span class='fl'>1</span>, hard = <span class='fl'>FALSE</span>, dim = <span class='op'>-</span><span class='fl'>1</span>)</pre>
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-    <table class="ref-arguments">
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-    <tr>
-      <th>logits</th>
-      <td><p><code>[..., num_features]</code> unnormalized log probabilities</p></td>
-    </tr>
-    <tr>
-      <th>tau</th>
-      <td><p>non-negative scalar temperature</p></td>
-    </tr>
-    <tr>
-      <th>hard</th>
-      <td><p>if <code>True</code>, the returned samples will be discretized as one-hot vectors,        but will be differentiated as if it is the soft sample in autograd</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) A dimension along which softmax will be computed. Default: -1.</p></td>
-    </tr>
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-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>NA If set to <code>True</code>, will do this operation in-place. Default: <code>False</code></p></td>
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-    <h1>Hardswish</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_hardswish.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the hardswish function, element-wise, as described in the paper:
-Searching for MobileNetV3.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_hardswish</span>(<span class='kw'>input</span>, inplace = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$ \mbox{Hardswish}(x) = \left\{
-  \begin{array}{ll}
-  0 &amp; \mbox{if } x \le -3, \\
-  x &amp; \mbox{if } x \ge +3, \\
-  x \cdot (x + 3)/6 &amp; \mbox{otherwise}
-  \end{array}
-  \right. $$</p>
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-    <h1>Hardtanh</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_hardtanh.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the HardTanh function element-wise.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_hardtanh</span>(<span class='kw'>input</span>, min_val = <span class='op'>-</span><span class='fl'>1</span>, max_val = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)
-
-<span class='fu'>nnf_hardtanh_</span>(<span class='kw'>input</span>, min_val = <span class='op'>-</span><span class='fl'>1</span>, max_val = <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>min_val</th>
-      <td><p>minimum value of the linear region range. Default: -1</p></td>
-    </tr>
-    <tr>
-      <th>max_val</th>
-      <td><p>maximum value of the linear region range. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
-
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-    <h1>Hinge_embedding_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_hinge_embedding_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Measures the loss given an input tensor xx and a labels tensor yy (containing 1 or -1).
-This is usually used for measuring whether two inputs are similar or dissimilar, e.g.
-using the L1 pairwise distance as xx , and is typically used for learning nonlinear
-embeddings or semi-supervised learning.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_hinge_embedding_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, margin = <span class='fl'>1</span>, reduction = <span class='st'>"mean"</span>)</pre>
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-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
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-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Has a default value of 1.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-instancenorm.R'><code>R/nnf-instancenorm.R</code></a></small>
-    <div class="hidden name"><code>nnf_instance_norm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies Instance Normalization for each channel in each data sample in a
-batch.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_instance_norm</span>(
-  <span class='kw'>input</span>,
-  running_mean = <span class='kw'>NULL</span>,
-  running_var = <span class='kw'>NULL</span>,
-  weight = <span class='kw'>NULL</span>,
-  bias = <span class='kw'>NULL</span>,
-  use_input_stats = <span class='fl'>TRUE</span>,
-  momentum = <span class='fl'>0.1</span>,
-  eps = <span class='fl'>1e-05</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>running_mean</th>
-      <td><p>the running_mean tensor</p></td>
-    </tr>
-    <tr>
-      <th>running_var</th>
-      <td><p>the running var tensor</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>the weight tensor</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>the bias tensor</p></td>
-    </tr>
-    <tr>
-      <th>use_input_stats</th>
-      <td><p>whether to use input stats</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>a double for the momentum</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>an eps double for numerical stability</p></td>
-    </tr>
-    </table>
-
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-    <div class="page-header">
-    <h1>Interpolate</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-upsampling.R'><code>R/nnf-upsampling.R</code></a></small>
-    <div class="hidden name"><code>nnf_interpolate.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Down/up samples the input to either the given <code>size</code> or the given
-<code>scale_factor</code></p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_interpolate</span>(
-  <span class='kw'>input</span>,
-  size = <span class='kw'>NULL</span>,
-  scale_factor = <span class='kw'>NULL</span>,
-  mode = <span class='st'>"nearest"</span>,
-  align_corners = <span class='fl'>FALSE</span>,
-  recompute_scale_factor = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(int or <code>Tuple[int]</code> or <code>Tuple[int, int]</code> or <code>Tuple[int, int, int]</code>)
-output spatial size.</p></td>
-    </tr>
-    <tr>
-      <th>scale_factor</th>
-      <td><p>(float or <code>Tuple[float]</code>) multiplier for spatial size.
-Has to match input size if it is a tuple.</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>(str) algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear'
-| 'bicubic' | 'trilinear' | 'area' Default: 'nearest'</p></td>
-    </tr>
-    <tr>
-      <th>align_corners</th>
-      <td><p>(bool, optional) Geometrically, we consider the pixels
-of the input and output as squares rather than points. If set to TRUE,
-the input and output tensors are aligned by the center points of their corner
-pixels, preserving the values at the corner pixels. If set to False, the
-input and output tensors are aligned by the corner points of their corner pixels,
-and the interpolation uses edge value padding for out-of-boundary values,
-making this operation <em>independent</em> of input size when <code>scale_factor</code> is kept
-the same. This only has an effect when <code>mode</code>  is <code>'linear'</code>, <code>'bilinear'</code>,
-<code>'bicubic'</code> or <code>'trilinear'</code>.  Default: <code>False</code></p></td>
-    </tr>
-    <tr>
-      <th>recompute_scale_factor</th>
-      <td><p>(bool, optional) recompute the scale_factor
-for use in the interpolation calculation.  When <code>scale_factor</code> is passed
-as a parameter, it is used to compute the <code>output_size</code>.  If <code>recompute_scale_factor</code>
-is ```True`` or not specified, a new <code>scale_factor</code> will be computed based on
-the output and input sizes for use in the interpolation computation (i.e. the
-computation will be identical to if the computed `output_size` were passed-in
-explicitly).  Otherwise, the passed-in `scale_factor` will be used in the
-interpolation computation.  Note that when `scale_factor` is floating-point,
-the recomputed scale_factor may differ from the one passed in due to rounding
-and precision issues.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The algorithm used for interpolation is determined by <code>mode</code>.</p>
-<p>Currently temporal, spatial and volumetric sampling are supported, i.e.
-expected inputs are 3-D, 4-D or 5-D in shape.</p>
-<p>The input dimensions are interpreted in the form:
-<code>mini-batch x channels x [optional depth] x [optional height] x width</code>.</p>
-<p>The modes available for resizing are: <code>nearest</code>, <code>linear</code> (3D-only),
-<code>bilinear</code>, <code>bicubic</code> (4D-only), <code>trilinear</code> (5D-only), <code>area</code></p>
-
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-    <h1>Kl_div</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_kl_div.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The Kullback-Leibler divergence Loss.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_kl_div</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
-
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-  <span class='kw'>input</span>,
-  <span class='kw'>normalized_shape</span>,
-  weight = <span class='kw'>NULL</span>,
-  bias = <span class='kw'>NULL</span>,
-  eps = <span class='fl'>1e-05</span>
-)</pre>
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-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>normalized_shape</th>
-      <td><p>input shape from an expected input of size. If a single
-integer is used, it is treated as a singleton list, and this module will normalize
-over the last dimension which is expected to be of that specific size.</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>the weight tensor</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>the bias tensor</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
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-\(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\)</p>
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-      <td><p>Controls the angle of the negative slope. Default: 1e-2</p></td>
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-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
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-      <th>input</th>
-      <td><p>\((N, *, in\_features)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means any number of
-additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>\((out\_features, in\_features)\) the weights tensor.</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional tensor \((out\_features)\)</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
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-    <p>Applies local response normalization over an input signal composed of
-several input planes, where channels occupy the second dimension.
-Applies normalization across channels.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_local_response_norm</span>(<span class='kw'>input</span>, <span class='kw'>size</span>, alpha = <span class='fl'>1e-04</span>, beta = <span class='fl'>0.75</span>, k = <span class='fl'>1</span>)</pre>
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>amount of neighbouring channels used for normalization</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>multiplicative factor. Default: 0.0001</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>exponent. Default: 0.75</p></td>
-    </tr>
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-    <pre class="usage"><span class='fu'>nnf_log_softmax</span>(<span class='kw'>input</span>, dim = <span class='kw'>NULL</span>, dtype = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
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-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) A dimension along which log_softmax will be computed.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.
-If specified, the input tensor is casted to <code>dtype</code> before the operation
-is performed. This is useful for preventing data type overflows.
-Default: <code>NULL</code>.</p></td>
-    </tr>
-    </table>
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-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>While mathematically equivalent to log(softmax(x)), doing these two
-operations separately is slower, and numerically unstable. This function
-uses an alternative formulation to compute the output and gradient correctly.</p>
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-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    </table>
-
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-several input planes. If the sum of all inputs to the power of <code>p</code> is
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-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>a single int, the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>a single int, the stride of the window. Default value is kernel_size</p></td>
-    </tr>
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-      <th>ceil_mode</th>
-      <td><p>when True, will use ceil instead of floor to compute the output shape</p></td>
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-    <h1>Lp_pool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_lp_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D power-average pooling over an input signal composed of
-several input planes. If the sum of all inputs to the power of <code>p</code> is
-zero, the gradient is set to zero as well.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_lp_pool2d</span>(<span class='kw'>input</span>, <span class='kw'>norm_type</span>, <span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, ceil_mode = <span class='fl'>FALSE</span>)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>a single int, the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>a single int, the stride of the window. Default value is kernel_size</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use ceil instead of floor to compute the output shape</p></td>
-    </tr>
-    </table>
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diff --git a/static/docs/dev/reference/nnf_margin_ranking_loss.html b/static/docs/dev/reference/nnf_margin_ranking_loss.html
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-mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_margin_ranking_loss</span>(<span class='kw'>input1</span>, <span class='kw'>input2</span>, <span class='kw'>target</span>, margin = <span class='fl'>0</span>, reduction = <span class='st'>"mean"</span>)</pre>
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input1</th>
-      <td><p>the first tensor</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>the second input tensor</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>the target tensor</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Has a default value of 00 .</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-    <h1>Max_pool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1D max pooling over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_max_pool1d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  dilation = <span class='fl'>1</span>,
-  ceil_mode = <span class='fl'>FALSE</span>,
-  return_indices = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window. Can be a single number or a
-tuple <code>(kW,)</code>.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Can be a single number or a tuple
-<code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>controls the spacing between the kernel points; also known as
-the à trous algorithm.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
-output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return the indices where the max occurs.</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_max_pool2d.html b/static/docs/dev/reference/nnf_max_pool2d.html
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index 11cad302e..000000000
--- a/static/docs/dev/reference/nnf_max_pool2d.html
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-    <h1>Max_pool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D max pooling over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_max_pool2d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>kernel_size</span>,
-  padding = <span class='fl'>0</span>,
-  dilation = <span class='fl'>1</span>,
-  ceil_mode = <span class='fl'>FALSE</span>,
-  return_indices = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>size of the pooling region. Can be a single number or a
-tuple <code>(kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>stride of the pooling operation. Can be a single number or a
-tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>controls the spacing between the kernel points; also known as
-the à trous algorithm.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
-to compute the output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return the indices where the max occurs.</p></td>
-    </tr>
-    </table>
-
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-    <h1>Max_pool3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 3D max pooling over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_max_pool3d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  dilation = <span class='fl'>1</span>,
-  ceil_mode = <span class='fl'>FALSE</span>,
-  return_indices = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>size of the pooling region. Can be a single number or a
-tuple <code>(kT, kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>stride of the pooling operation. Can be a single number or a
-tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>controls the spacing between the kernel points; also known as
-the à trous algorithm.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
-to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return the indices where the max occurs.</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/dev/reference/nnf_max_unpool1d.html b/static/docs/dev/reference/nnf_max_unpool1d.html
deleted file mode 100644
index 3c9d92b4d..000000000
--- a/static/docs/dev/reference/nnf_max_unpool1d.html
+++ /dev/null
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-    <h1>Max_unpool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
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-    <p>Computes a partial inverse of <code>MaxPool1d</code>.</p>
-    </div>
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-    <pre class="usage"><span class='fu'>nnf_max_unpool1d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>indices</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  output_size = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input Tensor to invert</p></td>
-    </tr>
-    <tr>
-      <th>indices</th>
-      <td><p>the indices given out by max pool</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>Padding that was added to the input</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the targeted output size</p></td>
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diff --git a/static/docs/dev/reference/nnf_max_unpool2d.html b/static/docs/dev/reference/nnf_max_unpool2d.html
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-    <h1>Max_unpool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_unpool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Computes a partial inverse of <code>MaxPool2d</code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_max_unpool2d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>indices</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  output_size = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input Tensor to invert</p></td>
-    </tr>
-    <tr>
-      <th>indices</th>
-      <td><p>the indices given out by max pool</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>Padding that was added to the input</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the targeted output size</p></td>
-    </tr>
-    </table>
-
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diff --git a/static/docs/dev/reference/nnf_max_unpool3d.html b/static/docs/dev/reference/nnf_max_unpool3d.html
deleted file mode 100644
index 88546322b..000000000
--- a/static/docs/dev/reference/nnf_max_unpool3d.html
+++ /dev/null
@@ -1,264 +0,0 @@
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-  <span class='kw'>input</span>,
-  <span class='kw'>indices</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='kw'>NULL</span>,
-  padding = <span class='fl'>0</span>,
-  output_size = <span class='kw'>NULL</span>
-)</pre>
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-      <td><p>the indices given out by max pool</p></td>
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-      <td><p>Size of the max pooling window.</p></td>
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-      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
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-      <td><p>Padding that was added to the input</p></td>
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-    <h1>Mse_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_mse_loss.Rd</code></div>
-    </div>
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-    <p>Measures the element-wise mean squared error.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_mse_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
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-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
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-    <h1>Multi head attention forward</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_multi_head_attention_forward.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Allows the model to jointly attend to information from different representation
-subspaces. See reference: Attention Is All You Need</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_multi_head_attention_forward</span>(
-  <span class='kw'>query</span>,
-  <span class='kw'>key</span>,
-  <span class='kw'>value</span>,
-  <span class='kw'>embed_dim_to_check</span>,
-  <span class='kw'>num_heads</span>,
-  <span class='kw'>in_proj_weight</span>,
-  <span class='kw'>in_proj_bias</span>,
-  <span class='kw'>bias_k</span>,
-  <span class='kw'>bias_v</span>,
-  <span class='kw'>add_zero_attn</span>,
-  <span class='kw'>dropout_p</span>,
-  <span class='kw'>out_proj_weight</span>,
-  <span class='kw'>out_proj_bias</span>,
-  training = <span class='fl'>TRUE</span>,
-  key_padding_mask = <span class='kw'>NULL</span>,
-  need_weights = <span class='fl'>TRUE</span>,
-  attn_mask = <span class='kw'>NULL</span>,
-  use_separate_proj_weight = <span class='fl'>FALSE</span>,
-  q_proj_weight = <span class='kw'>NULL</span>,
-  k_proj_weight = <span class='kw'>NULL</span>,
-  v_proj_weight = <span class='kw'>NULL</span>,
-  static_k = <span class='kw'>NULL</span>,
-  static_v = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>query</th>
-      <td><p>\((L, N, E)\) where L is the target sequence length, N is the batch size, E is
-the embedding dimension.</p></td>
-    </tr>
-    <tr>
-      <th>key</th>
-      <td><p>\((S, N, E)\), where S is the source sequence length, N is the batch size, E is
-the embedding dimension.</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>\((S, N, E)\) where S is the source sequence length, N is the batch size, E is
-the embedding dimension.</p></td>
-    </tr>
-    <tr>
-      <th>embed_dim_to_check</th>
-      <td><p>total dimension of the model.</p></td>
-    </tr>
-    <tr>
-      <th>num_heads</th>
-      <td><p>parallel attention heads.</p></td>
-    </tr>
-    <tr>
-      <th>in_proj_weight</th>
-      <td><p>input projection weight and bias.</p></td>
-    </tr>
-    <tr>
-      <th>in_proj_bias</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>bias_k</th>
-      <td><p>bias of the key and value sequences to be added at dim=0.</p></td>
-    </tr>
-    <tr>
-      <th>bias_v</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>add_zero_attn</th>
-      <td><p>add a new batch of zeros to the key and
-value sequences at dim=1.</p></td>
-    </tr>
-    <tr>
-      <th>dropout_p</th>
-      <td><p>probability of an element to be zeroed.</p></td>
-    </tr>
-    <tr>
-      <th>out_proj_weight</th>
-      <td><p>the output projection weight and bias.</p></td>
-    </tr>
-    <tr>
-      <th>out_proj_bias</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>key_padding_mask</th>
-      <td><p>\((N, S)\) where N is the batch size, S is the source sequence length.
-If a ByteTensor is provided, the non-zero positions will be ignored while the position
-with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
-value of <code>True</code> will be ignored while the position with the value of <code>False</code> will be unchanged.</p></td>
-    </tr>
-    <tr>
-      <th>need_weights</th>
-      <td><p>output attn_output_weights.</p></td>
-    </tr>
-    <tr>
-      <th>attn_mask</th>
-      <td><p>2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
-3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
-S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
-positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
-while the zero positions will be unchanged. If a BoolTensor is provided, positions with <code>True</code>
-is not allowed to attend while <code>False</code> values will be unchanged. If a FloatTensor
-is provided, it will be added to the attention weight.</p></td>
-    </tr>
-    <tr>
-      <th>use_separate_proj_weight</th>
-      <td><p>the function accept the proj. weights for
-query, key, and value in different forms. If false, in_proj_weight will be used,
-which is a combination of q_proj_weight, k_proj_weight, v_proj_weight.</p></td>
-    </tr>
-    <tr>
-      <th>q_proj_weight</th>
-      <td><p>input projection weight and bias.</p></td>
-    </tr>
-    <tr>
-      <th>k_proj_weight</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>v_proj_weight</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>static_k</th>
-      <td><p>static key and value used for attention operators.</p></td>
-    </tr>
-    <tr>
-      <th>static_v</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    </table>
-
-
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-<meta property="og:description" content="Creates a criterion that optimizes a multi-class classification hinge loss
-(margin-based loss) between input x (a 2D mini-batch Tensor) and output y
-(which is a 1D tensor of target class indices, 0 &amp;lt;= y &amp;lt;= x$size(2) - 1 )." />
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-    <div class="page-header">
-    <h1>Multi_margin_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_multi_margin_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that optimizes a multi-class classification hinge loss
-(margin-based loss) between input x (a 2D mini-batch Tensor) and output y
-(which is a 1D tensor of target class indices, <code>0 &lt;= y &lt;= x$size(2) - 1</code> ).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_multi_margin_loss</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>target</span>,
-  p = <span class='fl'>1</span>,
-  margin = <span class='fl'>1</span>,
-  weight = <span class='kw'>NULL</span>,
-  reduction = <span class='st'>"mean"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
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-    <h1>Multilabel_margin_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_multilabel_margin_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that optimizes a multi-class multi-classification hinge loss
-(margin-based loss) between input x (a 2D mini-batch Tensor) and output y (which
-is a 2D Tensor of target class indices).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_multilabel_margin_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
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-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
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-
-    <div class="ref-description">
-    <p>Creates a criterion that optimizes a multi-label one-versus-all loss based on
-max-entropy, between input x and target y of size (N, C).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_multilabel_soft_margin_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, <span class='kw'>weight</span>, reduction = <span class='st'>"mean"</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
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-      <td><p>weight tensor to apply on the loss.</p></td>
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-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_nll_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The negative log likelihood loss.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_nll_loss</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>target</span>,
-  weight = <span class='kw'>NULL</span>,
-  ignore_index = <span class='op'>-</span><span class='fl'>100</span>,
-  reduction = <span class='st'>"mean"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>\((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\) in
-case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\) in
-the case of K-dimensional loss.</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>\((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
-or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional) a manual rescaling weight given to each class.
-If given, has to be a Tensor of size <code>C</code></p></td>
-    </tr>
-    <tr>
-      <th>ignore_index</th>
-      <td><p>(int, optional) Specifies a target value that is ignored and
-does not contribute to the input gradient.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-    <h1>Normalize</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
-    <div class="hidden name"><code>nnf_normalize.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Performs \(L_p\) normalization of inputs over specified dimension.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_normalize</span>(<span class='kw'>input</span>, p = <span class='fl'>2</span>, dim = <span class='fl'>1</span>, eps = <span class='fl'>1e-12</span>, out = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of any shape</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(float) the exponent value in the norm formulation. Default: 2</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float) small value to avoid division by zero. Default: 1e-12</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor. If <code>out</code> is used, this                            operation won't be differentiable.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
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-\(n_{dim}\) -element vector \(v\) along dimension <code>dim</code> is transformed as</p>
-<p>$$
-        v = \frac{v}{\max(\Vert v \Vert_p, \epsilon)}.
-$$</p>
-<p>With the default arguments it uses the Euclidean norm over vectors along
-dimension \(1\) for normalization.</p>
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-    <h1>One_hot</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
-    <div class="hidden name"><code>nnf_one_hot.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Takes LongTensor with index values of shape <code>(*)</code> and returns a tensor
-of shape <code>(*, num_classes)</code> that have zeros everywhere except where the
-index of last dimension matches the corresponding value of the input tensor,
-in which case it will be 1.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_one_hot</span>(<span class='kw'>tensor</span>, num_classes = <span class='op'>-</span><span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>(LongTensor) class values of any shape.</p></td>
-    </tr>
-    <tr>
-      <th>num_classes</th>
-      <td><p>(int) Total number of classes. If set to -1, the number
-of classes will be inferred as one greater than the largest class value in
-the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>One-hot on Wikipedia: https://en.wikipedia.org/wiki/One-hot</p>
-
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-    <h1>Pad</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-padding.R'><code>R/nnf-padding.R</code></a></small>
-    <div class="hidden name"><code>nnf_pad.Rd</code></div>
-    </div>
-
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-    <p>Pads tensor.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_pad</span>(<span class='kw'>input</span>, <span class='kw'>pad</span>, mode = <span class='st'>"constant"</span>, value = <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) N-dimensional tensor</p></td>
-    </tr>
-    <tr>
-      <th>pad</th>
-      <td><p>(tuple) m-elements tuple, where \(\frac{m}{2} \leq\) input dimensions
-and \(m\) is even.</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>fill value for 'constant' padding. Default: 0.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="padding-size"><a class="anchor" href="#padding-size"></a>Padding size</h2>
-
-    
-
-
-<p>The padding size by which to pad some dimensions of <code>input</code>
-are described starting from the last dimension and moving forward.
-\(\left\lfloor\frac{\mbox{len(pad)}}{2}\right\rfloor\) dimensions
-of <code>input</code> will be padded.
-For example, to pad only the last dimension of the input tensor, then
-<code>pad</code> has the form
-\((\mbox{padding\_left}, \mbox{padding\_right})\);
-to pad the last 2 dimensions of the input tensor, then use
-\((\mbox{padding\_left}, \mbox{padding\_right},\)
-\(\mbox{padding\_top}, \mbox{padding\_bottom})\);
-to pad the last 3 dimensions, use
-\((\mbox{padding\_left}, \mbox{padding\_right},\)
-\(\mbox{padding\_top}, \mbox{padding\_bottom}\)
-\(\mbox{padding\_front}, \mbox{padding\_back})\).</p>
-    <h2 class="hasAnchor" id="padding-mode"><a class="anchor" href="#padding-mode"></a>Padding mode</h2>
-
-    
-
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-distance.R'><code>R/nnf-distance.R</code></a></small>
-    <div class="hidden name"><code>nnf_pairwise_distance.Rd</code></div>
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-
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-    <p>Computes the batchwise pairwise distance between vectors using the p-norm.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_pairwise_distance</span>(<span class='kw'>x1</span>, <span class='kw'>x2</span>, p = <span class='fl'>2</span>, eps = <span class='fl'>1e-06</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x1</th>
-      <td><p>(Tensor) First input.</p></td>
-    </tr>
-    <tr>
-      <th>x2</th>
-      <td><p>(Tensor) Second input (of size matching x1).</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>the norm degree. Default: 2</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid division by zero.
-Default: 1e-8</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>Determines whether or not to keep the vector dimension. Default: False</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-distance.R'><code>R/nnf-distance.R</code></a></small>
-    <div class="hidden name"><code>nnf_pdist.Rd</code></div>
-    </div>
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-    <p>Computes the p-norm distance between every pair of row vectors in the input.
-This is identical to the upper triangular portion, excluding the diagonal, of
-<code>torch_norm(input[:, None] - input, dim=2, p=p)</code>. This function will be faster
-if the rows are contiguous.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_pdist</span>(<span class='kw'>input</span>, p = <span class='fl'>2</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \(N \times M\).</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>p value for the p-norm distance to calculate between each vector pair
-\(\in [0, \infty]\).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>If input has shape \(N \times M\) then the output will have shape
-\(\frac{1}{2} N (N - 1)\).</p>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pixelshuffle.R'><code>R/nnf-pixelshuffle.R</code></a></small>
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-tensor of shape \((*, C, H \times r, W \times r)\).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_pixel_shuffle</span>(<span class='kw'>input</span>, <span class='kw'>upscale_factor</span>)</pre>
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-      <th>input</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
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-      <th>upscale_factor</th>
-      <td><p>(int) factor to increase spatial resolution by</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_poisson_nll_loss.Rd</code></div>
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-
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-    <p>Poisson negative log likelihood loss.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_poisson_nll_loss</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>target</span>,
-  log_input = <span class='fl'>TRUE</span>,
-  full = <span class='fl'>FALSE</span>,
-  eps = <span class='fl'>1e-08</span>,
-  reduction = <span class='st'>"mean"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>log_input</th>
-      <td><p>if <code>TRUE</code> the loss is computed as \(\exp(\mbox{input}) - \mbox{target} * \mbox{input}\),
-if <code>FALSE</code> then loss is \(\mbox{input} - \mbox{target} * \log(\mbox{input}+\mbox{eps})\).
-Default: <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>full</th>
-      <td><p>whether to compute full loss, i. e. to add the Stirling approximation
-term. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid evaluation of \(\log(0)\) when
-<code>log_input</code>=<code>FALSE</code>. Default: 1e-8</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-dimensions</p></td>
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-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
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-
-    <pre class="usage"><span class='fu'>nnf_rrelu</span>(<span class='kw'>input</span>, lower = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>8</span>, upper = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>3</span>, training = <span class='fl'>FALSE</span>, inplace = <span class='fl'>FALSE</span>)
-
-<span class='fu'>nnf_rrelu_</span>(<span class='kw'>input</span>, lower = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>8</span>, upper = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>3</span>, training = <span class='fl'>FALSE</span>)</pre>
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-    <table class="ref-arguments">
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-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>lower</th>
-      <td><p>lower bound of the uniform distribution. Default: 1/8</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>upper bound of the uniform distribution. Default: 1/3</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>bool wether it's a training pass. DEfault: FALSE</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
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-with \(\alpha=1.6732632423543772848170429916717\) and
-\(scale=1.0507009873554804934193349852946\).</p>
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diff --git a/static/docs/dev/reference/nnf_sigmoid.html b/static/docs/dev/reference/nnf_sigmoid.html
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-    <table class="ref-arguments">
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-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_softmax.Rd</code></div>
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-
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-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) A dimension along which softmax will be computed.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed. This is useful for preventing data type overflows.
-Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Softmax is defined as:</p>
-<p>$$Softmax(x_{i}) = exp(x_i)/\sum_j exp(x_j)$$</p>
-<p>It is applied to all slices along dim, and will re-scale them so that the elements
-lie in the range <code>[0, 1]</code> and sum to 1.</p>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_softmin.Rd</code></div>
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-    <p>Applies a softmin function.</p>
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-
-    <pre class="usage"><span class='fu'>nnf_softmin</span>(<span class='kw'>input</span>, <span class='kw'>dim</span>, dtype = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) A dimension along which softmin will be computed
-(so every slice        along dim will sum to 1).</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed.
-This is useful for preventing data type overflows. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Note that</p>
-<p>$$Softmin(x) = Softmax(-x)$$.</p>
-<p>See <a href='nnf_softmax.html'>nnf_softmax</a> definition for mathematical formula.</p>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
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-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>the beta value for the Softplus formulation. Default: 1</p></td>
-    </tr>
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-      <th>threshold</th>
-      <td><p>values above this revert to a linear function. Default: 20</p></td>
-    </tr>
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>lambd</th>
-      <td><p>the lambda (must be no less than zero) value for the Softshrink
-formulation. Default: 0.5</p></td>
-    </tr>
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-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
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-
-<span class='fu'>nnf_threshold_</span>(<span class='kw'>input</span>, <span class='kw'>threshold</span>, <span class='kw'>value</span>)</pre>
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-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>threshold</th>
-      <td><p>The value to threshold at</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>The value to replace with</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
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-x2 , x3 and a margin with a value greater than 0 . This is used for measuring
-a relative similarity between samples. A triplet is composed by a, p and n (i.e.,
-anchor, positive examples and negative examples respectively). The shapes of all
-input tensors should be (N, D)." />
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-    <h1>Triplet_margin_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_triplet_margin_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that measures the triplet loss given an input tensors x1 ,
-x2 , x3 and a margin with a value greater than 0 . This is used for measuring
-a relative similarity between samples. A triplet is composed by a, p and n (i.e.,
-anchor, positive examples and negative examples respectively). The shapes of all
-input tensors should be (N, D).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_triplet_margin_loss</span>(
-  <span class='kw'>anchor</span>,
-  <span class='kw'>positive</span>,
-  <span class='kw'>negative</span>,
-  margin = <span class='fl'>1</span>,
-  p = <span class='fl'>2</span>,
-  eps = <span class='fl'>1e-06</span>,
-  swap = <span class='fl'>FALSE</span>,
-  reduction = <span class='st'>"mean"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>anchor</th>
-      <td><p>the anchor input tensor</p></td>
-    </tr>
-    <tr>
-      <th>positive</th>
-      <td><p>the positive input tensor</p></td>
-    </tr>
-    <tr>
-      <th>negative</th>
-      <td><p>the negative input tensor</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Default: 1.</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>The norm degree for pairwise distance. Default: 2.</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid division by zero.</p></td>
-    </tr>
-    <tr>
-      <th>swap</th>
-      <td><p>The distance swap is described in detail in the paper Learning shallow
-convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
-Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
-
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diff --git a/static/docs/dev/reference/nnf_unfold.html b/static/docs/dev/reference/nnf_unfold.html
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-    <h1>Unfold</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-fold.R'><code>R/nnf-fold.R</code></a></small>
-    <div class="hidden name"><code>nnf_unfold.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Extracts sliding local blocks from an batched input tensor.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_unfold</span>(<span class='kw'>input</span>, <span class='kw'>kernel_size</span>, dilation = <span class='fl'>1</span>, padding = <span class='fl'>0</span>, stride = <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the sliding blocks</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements within the
-neighborhood. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides of input.
-Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the sliding blocks in the input spatial dimensions.
-Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-
-<p>Currently, only 4-D input tensors (batched image-like tensors) are
-supported.</p>
-
-
-<p>More than one element of the unfolded tensor may refer to a single
-memory location. As a result, in-place operations (especially ones that
-are vectorized) may result in incorrect behavior. If you need to write
-to the tensor, please clone it first.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-    <h1>Implements Adam algorithm.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-adam.R'><code>R/optim-adam.R</code></a></small>
-    <div class="hidden name"><code>optim_adam.Rd</code></div>
-    </div>
-
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-    <p>It has been proposed in <a href='https://arxiv.org/abs/1412.6980'>Adam: A Method for Stochastic Optimization</a>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>optim_adam</span>(
-  <span class='kw'>params</span>,
-  lr = <span class='fl'>0.001</span>,
-  betas = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.9</span>, <span class='fl'>0.999</span>),
-  eps = <span class='fl'>1e-08</span>,
-  weight_decay = <span class='fl'>0</span>,
-  amsgrad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>params</th>
-      <td><p>(iterable): iterable of parameters to optimize or dicts defining
-parameter groups</p></td>
-    </tr>
-    <tr>
-      <th>lr</th>
-      <td><p>(float, optional): learning rate (default: 1e-3)</p></td>
-    </tr>
-    <tr>
-      <th>betas</th>
-      <td><p>(<code>Tuple[float, float]</code>, optional): coefficients used for computing
-running averages of gradient and its square (default: (0.9, 0.999))</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional): term added to the denominator to improve
-numerical stability (default: 1e-8)</p></td>
-    </tr>
-    <tr>
-      <th>weight_decay</th>
-      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>amsgrad</th>
-      <td><p>(boolean, optional): whether to use the AMSGrad variant of this
-algorithm from the paper <a href='https://openreview.net/forum?id=ryQu7f-RZ'>On the Convergence of Adam and Beyond</a>
-(default: FALSE)</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>optimizer</span> <span class='op'>&lt;-</span> <span class='fu'>optim_adam</span>(<span class='kw'>model</span><span class='op'>$</span><span class='fu'>parameters</span>(), lr=<span class='fl'>0.1</span>)
-<span class='kw'>optimizer</span><span class='op'>$</span><span class='fu'>zero_grad</span>()
-<span class='fu'>loss_fn</span>(<span class='fu'>model</span>(<span class='kw'>input</span>), <span class='kw'>target</span>)<span class='op'>$</span><span class='fu'>backward</span>()
-<span class='kw'>optimizer</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/step.html'>step</a></span>()
-}
-
-}
-</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim.R'><code>R/optim.R</code></a></small>
-    <div class="hidden name"><code>optim_required.Rd</code></div>
-    </div>
-
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-    <p>export</p>
-    </div>
-
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diff --git a/static/docs/dev/reference/optim_sgd.html b/static/docs/dev/reference/optim_sgd.html
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-    <h1>SGD optimizer</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-sgd.R'><code>R/optim-sgd.R</code></a></small>
-    <div class="hidden name"><code>optim_sgd.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Implements stochastic gradient descent (optionally with momentum).
-Nesterov momentum is based on the formula from
-On the importance of initialization and momentum in deep learning.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>optim_sgd</span>(
-  <span class='kw'>params</span>,
-  lr = <span class='fu'><a href='optim_required.html'>optim_required</a></span>(),
-  momentum = <span class='fl'>0</span>,
-  dampening = <span class='fl'>0</span>,
-  weight_decay = <span class='fl'>0</span>,
-  nesterov = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>params</th>
-      <td><p>(iterable): iterable of parameters to optimize or dicts defining
-parameter groups</p></td>
-    </tr>
-    <tr>
-      <th>lr</th>
-      <td><p>(float): learning rate</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>(float, optional): momentum factor (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>dampening</th>
-      <td><p>(float, optional): dampening for momentum (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>weight_decay</th>
-      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>nesterov</th>
-      <td><p>(bool, optional): enables Nesterov momentum (default: FALSE)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>The implementation of SGD with Momentum-Nesterov subtly differs from
-Sutskever et. al. and implementations in some other frameworks.</p>
-<p>Considering the specific case of Momentum, the update can be written as
-$$
-  \begin{array}{ll}
-v_{t+1} &amp; = \mu * v_{t} + g_{t+1}, \\
-p_{t+1} &amp; = p_{t} - \mbox{lr} * v_{t+1},
-\end{array}
-$$</p>
-<p>where \(p\), \(g\), \(v\) and \(\mu\) denote the
-parameters, gradient, velocity, and momentum respectively.</p>
-<p>This is in contrast to Sutskever et. al. and
-other frameworks which employ an update of the form</p>
-<p>$$
-  \begin{array}{ll}
-v_{t+1} &amp; = \mu * v_{t} + \mbox{lr} * g_{t+1}, \\
-p_{t+1} &amp; = p_{t} - v_{t+1}.
-\end{array}
-$$
-The Nesterov version is analogously modified.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>optimizer</span> <span class='op'>&lt;-</span> <span class='fu'>optim_sgd</span>(<span class='kw'>model</span><span class='op'>$</span><span class='fu'>parameters</span>(), lr=<span class='fl'>0.1</span>, momentum=<span class='fl'>0.9</span>)
-<span class='kw'>optimizer</span><span class='op'>$</span><span class='fu'>zero_grad</span>()
-<span class='fu'>loss_fn</span>(<span class='fu'>model</span>(<span class='kw'>input</span>), <span class='kw'>target</span>)<span class='op'>$</span><span class='fu'>backward</span>()
-<span class='kw'>optimizer</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/step.html'>step</a></span>()
-}
-
-}
-</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-pipe.R'><code>R/utils-pipe.R</code></a></small>
-    <div class="hidden name"><code>pipe.Rd</code></div>
-    </div>
-
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-    <p>See <code>magrittr::%&gt;%</code> for details.</p>
-    </div>
-
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-    <h1>Abs</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_abs.Rd</code></div>
-    </div>
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-    <div class="ref-description">
-    <p>Abs</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_abs</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="abs-input-gt-tensor-"><a class="anchor" href="#abs-input-gt-tensor-"></a>abs(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise absolute value of the given <code>input</code> tensor.</p>
-<p>$$
-    \mbox{out}_{i} = |\mbox{input}_{i}|
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_abs</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='fl'>3</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt; [ CPUFloatType{3} ]</div></pre>
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="acos-input-gt-tensor-"><a class="anchor" href="#acos-input-gt-tensor-"></a>acos(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the arccosine  of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \cos^{-1}(\mbox{input}_{i})
-$$</p>
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-<span class='kw'>a</span>
-<span class='fu'>torch_acos</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2.3624
-#&gt;     nan
-#&gt;     nan
-#&gt;     nan
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <h1>Adaptive_avg_pool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_adaptive_avg_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Adaptive_avg_pool1d</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_adaptive_avg_pool1d</span>(<span class='kw'>self</span>, <span class='kw'>output_size</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size (single integer)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="adaptive-avg-pool-d-input-output-size-gt-tensor-"><a class="anchor" href="#adaptive-avg-pool-d-input-output-size-gt-tensor-"></a>adaptive_avg_pool1d(input, output_size) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 1D adaptive average pooling over an input signal composed of
-several input planes.</p>
-<p>See <code><a href='nn_adaptive_avg_pool1d.html'>nn_adaptive_avg_pool1d()</a></code> for details and output shape.</p>
-
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diff --git a/static/docs/dev/reference/torch_add.html b/static/docs/dev/reference/torch_add.html
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-    <h1>Add</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_add.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Add</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_add</span>(<span class='kw'>self</span>, <span class='kw'>other</span>, alpha = <span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor/Number) the second input tensor/number.</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number) the scalar multiplier for <code>other</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="add-input-other-out-null-"><a class="anchor" href="#add-input-other-out-null-"></a>add(input, other, out=NULL) </h2>
-
-    
-
-
-<p>Adds the scalar <code>other</code> to each element of the input <code>input</code>
-and returns a new resulting tensor.</p>
-<p>$$
-    \mbox{out} = \mbox{input} + \mbox{other}
-$$
-If <code>input</code> is of type FloatTensor or DoubleTensor, <code>other</code> must be
-a real number, otherwise it should be an integer.</p>
-    <h2 class="hasAnchor" id="add-input-other-alpha-out-null-"><a class="anchor" href="#add-input-other-alpha-out-null-"></a>add(input, other, *, alpha=1, out=NULL) </h2>
-
-    
-
-
-<p>Each element of the tensor <code>other</code> is multiplied by the scalar
-<code>alpha</code> and added to each element of the tensor <code>input</code>.
-The resulting tensor is returned.</p>
-<p>The shapes of <code>input</code> and <code>other</code> must be
-broadcastable .</p>
-<p>$$
-    \mbox{out} = \mbox{input} + \mbox{alpha} \times \mbox{other}
-$$
-If <code>other</code> is of type FloatTensor or DoubleTensor, <code>alpha</code> must be
-a real number, otherwise it should be an integer.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_add</span>(<span class='kw'>a</span>, <span class='fl'>20</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>1</span>))
-<span class='kw'>b</span>
-<span class='fu'>torch_add</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.5056 -0.0263 -1.7229  1.2536
-#&gt; -1.5042 -0.0249 -1.7215  1.2550
-#&gt; -2.3481 -0.8689 -2.5654  0.4110
-#&gt; -0.8068  0.6725 -1.0240  1.9524
-#&gt; [ CPUFloatType{4,4} ]</div></pre>
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-    <h1>Addbmm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_addbmm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Addbmm</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_addbmm</span>(<span class='kw'>self</span>, <span class='kw'>batch1</span>, <span class='kw'>batch2</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) matrix to be added</p></td>
-    </tr>
-    <tr>
-      <th>batch1</th>
-      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>batch2</th>
-      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for <code>batch1 @ batch2</code> (\(\alpha\))</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"></a>addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a batch matrix-matrix product of matrices stored
-in <code>batch1</code> and <code>batch2</code>,
-with a reduced add step (all matrix multiplications get accumulated
-along the first dimension).
-<code>input</code> is added to the final result.</p>
-<p><code>batch1</code> and <code>batch2</code> must be 3-D tensors each containing the
-same number of matrices.</p>
-<p>If <code>batch1</code> is a \((b \times n \times m)\) tensor, <code>batch2</code> is a
-\((b \times m \times p)\) tensor, <code>input</code> must be
-broadcastable  with a \((n \times p)\) tensor
-and <code>out</code> will be a \((n \times p)\) tensor.</p>
-<p>$$
-    out = \beta\ \mbox{input} + \alpha\ (\sum_{i=0}^{b-1} \mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
-$$
-For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and <code>alpha</code>
-must be real numbers, otherwise they should be integers.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>))
-<span class='kw'>batch1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='kw'>batch2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='fu'>torch_addbmm</span>(<span class='kw'>M</span>, <span class='kw'>batch1</span>, <span class='kw'>batch2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;   3.9924  -5.2266  13.4727  -5.3496   2.0658
-#&gt;   8.6956  14.2839   7.5615  -5.8298  -7.3296
-#&gt;  -3.0861  -1.4753  -0.9565  -1.6031   3.0930
-#&gt; [ CPUFloatType{3,5} ]</div></pre>
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-    <h1>Addcdiv</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_addcdiv.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Addcdiv</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_addcdiv</span>(<span class='kw'>self</span>, <span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>, value = <span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to be added</p></td>
-    </tr>
-    <tr>
-      <th>tensor1</th>
-      <td><p>(Tensor) the numerator tensor</p></td>
-    </tr>
-    <tr>
-      <th>tensor2</th>
-      <td><p>(Tensor) the denominator tensor</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>(Number, optional) multiplier for \(\mbox{tensor1} / \mbox{tensor2}\)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addcdiv-input-tensor-tensor-value-out-null-gt-tensor-"><a class="anchor" href="#addcdiv-input-tensor-tensor-value-out-null-gt-tensor-"></a>addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs the element-wise division of <code>tensor1</code> by <code>tensor2</code>,
-multiply the result by the scalar <code>value</code> and add it to <code>input</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Integer division with addcdiv is deprecated, and in a future release
-addcdiv will perform a true division of <code>tensor1</code> and <code>tensor2</code>.
-The current addcdiv behavior can be replicated using <code><a href='torch_floor_divide.html'>torch_floor_divide()</a></code>
-for integral inputs
-(<code>input</code> + <code>value</code> * <code>tensor1</code> // <code>tensor2</code>)
-and <code><a href='torch_div.html'>torch_div()</a></code> for float inputs
-(<code>input</code> + <code>value</code> * <code>tensor1</code> / <code>tensor2</code>).
-The new addcdiv behavior can be implemented with <code><a href='torch_true_divide.html'>torch_true_divide()</a></code>
-(<code>input</code> + <code>value</code> * torch.true_divide(<code>tensor1</code>,
-<code>tensor2</code>).</p>
-<p>$$
-    \mbox{out}_i = \mbox{input}_i + \mbox{value} \times \frac{\mbox{tensor1}_i}{\mbox{tensor2}_i}
-$$</p>
-<p>The shapes of <code>input</code>, <code>tensor1</code>, and <code>tensor2</code> must be
-broadcastable .</p>
-<p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code> must be
-a real number, otherwise an integer.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>t1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))
-<span class='kw'>t2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_addcdiv</span>(<span class='kw'>t</span>, <span class='kw'>t1</span>, <span class='kw'>t2</span>, <span class='fl'>0.1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6795 -1.4350  0.7786
-#&gt; -0.7099 -1.3980  0.7970
-#&gt; -0.7182 -1.3879  0.8020
-#&gt; [ CPUFloatType{3,3} ]</div></pre>
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-    </li>
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Addcmul</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_addcmul.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Addcmul</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_addcmul</span>(<span class='kw'>self</span>, <span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>, value = <span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to be added</p></td>
-    </tr>
-    <tr>
-      <th>tensor1</th>
-      <td><p>(Tensor) the tensor to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>tensor2</th>
-      <td><p>(Tensor) the tensor to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>(Number, optional) multiplier for \(tensor1 .* tensor2\)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addcmul-input-tensor-tensor-value-out-null-gt-tensor-"><a class="anchor" href="#addcmul-input-tensor-tensor-value-out-null-gt-tensor-"></a>addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs the element-wise multiplication of <code>tensor1</code>
-by <code>tensor2</code>, multiply the result by the scalar <code>value</code>
-and add it to <code>input</code>.</p>
-<p>$$
-    \mbox{out}_i = \mbox{input}_i + \mbox{value} \times \mbox{tensor1}_i \times \mbox{tensor2}_i
-$$
-The shapes of <code>tensor</code>, <code>tensor1</code>, and <code>tensor2</code> must be
-broadcastable .</p>
-<p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code> must be
-a real number, otherwise an integer.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>t1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))
-<span class='kw'>t2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_addcmul</span>(<span class='kw'>t</span>, <span class='kw'>t1</span>, <span class='kw'>t2</span>, <span class='fl'>0.1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.1395 -0.4021  0.5521
-#&gt;  0.5035 -0.6059  0.7264
-#&gt;  0.6091 -0.6650  0.7770
-#&gt; [ CPUFloatType{3,3} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_addmm.html b/static/docs/dev/reference/torch_addmm.html
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-    <div class="page-header">
-    <h1>Addmm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_addmm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Addmm</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_addmm</span>(<span class='kw'>self</span>, <span class='kw'>mat1</span>, <span class='kw'>mat2</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) matrix to be added</p></td>
-    </tr>
-    <tr>
-      <th>mat1</th>
-      <td><p>(Tensor) the first matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>mat2</th>
-      <td><p>(Tensor) the second matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for \(mat1 @ mat2\) (\(\alpha\))</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-"></a>addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a matrix multiplication of the matrices <code>mat1</code> and <code>mat2</code>.
-The matrix <code>input</code> is added to the final result.</p>
-<p>If <code>mat1</code> is a \((n \times m)\) tensor, <code>mat2</code> is a
-\((m \times p)\) tensor, then <code>input</code> must be
-broadcastable  with a \((n \times p)\) tensor
-and <code>out</code> will be a \((n \times p)\) tensor.</p>
-<p><code>alpha</code> and <code>beta</code> are scaling factors on matrix-vector product between
-<code>mat1</code> and <code>mat2</code> and the added matrix <code>input</code> respectively.</p>
-<p>$$
-    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{mat1}_i \mathbin{@} \mbox{mat2}_i)
-$$
-For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
-<code>alpha</code> must be real numbers, otherwise they should be integers.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='kw'>mat1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='kw'>mat2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_addmm</span>(<span class='kw'>M</span>, <span class='kw'>mat1</span>, <span class='kw'>mat2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.0326  0.1547  2.7714
-#&gt; -2.8031  1.0522  1.4061
-#&gt; [ CPUFloatType{2,3} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_addmv.html b/static/docs/dev/reference/torch_addmv.html
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-    <h1>Addmv</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_addmv.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Addmv</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_addmv</span>(<span class='kw'>self</span>, <span class='kw'>mat</span>, <span class='kw'>vec</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) vector to be added</p></td>
-    </tr>
-    <tr>
-      <th>mat</th>
-      <td><p>(Tensor) matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>vec</th>
-      <td><p>(Tensor) vector to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for \(mat @ vec\) (\(\alpha\))</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-"></a>addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a matrix-vector product of the matrix <code>mat</code> and
-the vector <code>vec</code>.
-The vector <code>input</code> is added to the final result.</p>
-<p>If <code>mat</code> is a \((n \times m)\) tensor, <code>vec</code> is a 1-D tensor of
-size <code>m</code>, then <code>input</code> must be
-broadcastable  with a 1-D tensor of size <code>n</code> and
-<code>out</code> will be 1-D tensor of size <code>n</code>.</p>
-<p><code>alpha</code> and <code>beta</code> are scaling factors on matrix-vector product between
-<code>mat</code> and <code>vec</code> and the added tensor <code>input</code> respectively.</p>
-<p>$$
-    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{mat} \mathbin{@} \mbox{vec})
-$$
-For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
-<code>alpha</code> must be real numbers, otherwise they should be integers</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>))
-<span class='kw'>mat</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='kw'>vec</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='fu'>torch_addmv</span>(<span class='kw'>M</span>, <span class='kw'>mat</span>, <span class='kw'>vec</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.2283
-#&gt;  1.6100
-#&gt; [ CPUFloatType{2} ]</div></pre>
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-    <h1>Addr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_addr.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Addr</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_addr</span>(<span class='kw'>self</span>, <span class='kw'>vec1</span>, <span class='kw'>vec2</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) matrix to be added</p></td>
-    </tr>
-    <tr>
-      <th>vec1</th>
-      <td><p>(Tensor) the first vector of the outer product</p></td>
-    </tr>
-    <tr>
-      <th>vec2</th>
-      <td><p>(Tensor) the second vector of the outer product</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for \(\mbox{vec1} \otimes \mbox{vec2}\) (\(\alpha\))</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addr-input-vec-vec-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addr-input-vec-vec-beta-alpha-out-null-gt-tensor-"></a>addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs the outer-product of vectors <code>vec1</code> and <code>vec2</code>
-and adds it to the matrix <code>input</code>.</p>
-<p>Optional values <code>beta</code> and <code>alpha</code> are scaling factors on the
-outer product between <code>vec1</code> and <code>vec2</code> and the added matrix
-<code>input</code> respectively.</p>
-<p>$$
-    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{vec1} \otimes \mbox{vec2})
-$$
-If <code>vec1</code> is a vector of size <code>n</code> and <code>vec2</code> is a vector
-of size <code>m</code>, then <code>input</code> must be
-broadcastable  with a matrix of size
-\((n \times m)\) and <code>out</code> will be a matrix of size
-\((n \times m)\).</p>
-<p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
-<code>alpha</code> must be real numbers, otherwise they should be integers</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>vec1</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>4.</span>)
-<span class='kw'>vec2</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>3.</span>)
-<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='fu'>torch_addr</span>(<span class='kw'>M</span>, <span class='kw'>vec1</span>, <span class='kw'>vec2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  2
-#&gt;  2  4
-#&gt;  3  6
-#&gt; [ CPUFloatType{3,2} ]</div></pre>
-  </div>
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diff --git a/static/docs/dev/reference/torch_allclose.html b/static/docs/dev/reference/torch_allclose.html
deleted file mode 100644
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-    <div class="page-header">
-    <h1>Allclose</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_allclose.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Allclose</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_allclose</span>(<span class='kw'>self</span>, <span class='kw'>other</span>, rtol = <span class='fl'>1e-05</span>, atol = <span class='fl'>0</span>, equal_nan = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) first tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) second tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>rtol</th>
-      <td><p>(float, optional) relative tolerance. Default: 1e-05</p></td>
-    </tr>
-    <tr>
-      <th>atol</th>
-      <td><p>(float, optional) absolute tolerance. Default: 1e-08</p></td>
-    </tr>
-    <tr>
-      <th>equal_nan</th>
-      <td><p>(bool, optional) if <code>TRUE</code>, then two <code>NaN</code> s will be compared as equal. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-"><a class="anchor" href="#allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-"></a>allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -&gt; bool </h2>
-
-    
-
-
-<p>This function checks if all <code>input</code> and <code>other</code> satisfy the condition:</p>
-<p>$$
-    \vert \mbox{input} - \mbox{other} \vert \leq \mbox{atol} + \mbox{rtol} \times \vert \mbox{other} \vert
-$$
-elementwise, for all elements of <code>input</code> and <code>other</code>. The behaviour of this function is analogous to
-<code>numpy.allclose &lt;https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html&gt;</code>_</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.</span>, <span class='fl'>1e-07</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.1</span>, <span class='fl'>1e-08</span>)))
-<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.</span>, <span class='fl'>1e-08</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.1</span>, <span class='fl'>1e-09</span>)))
-<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)))
-<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)), equal_nan=<span class='fl'>TRUE</span>)
-}
-</div><div class='output co'>#&gt; [1] TRUE</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_angle.html b/static/docs/dev/reference/torch_angle.html
deleted file mode 100644
index 5eae3de6f..000000000
--- a/static/docs/dev/reference/torch_angle.html
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@@ -1,254 +0,0 @@
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Angle</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_angle.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Angle</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_angle</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="angle-input-gt-tensor-"><a class="anchor" href="#angle-input-gt-tensor-"></a>angle(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise angle (in radians) of the given <code>input</code> tensor.</p>
-<p>$$
-    \mbox{out}_{i} = angle(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_angle</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span>)))<span class='op'>*</span><span class='fl'>180</span><span class='op'>/</span><span class='fl'>3.14159</span>
-}
-
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    <div class="page-header">
-    <h1>Arange</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_arange.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Arange</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_arange</span>(
-  <span class='kw'>start</span>,
-  <span class='kw'>end</span>,
-  step = <span class='fl'>1</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(Number) the starting value for the set of points. Default: <code>0</code>.</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(Number) the ending value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>step</th>
-      <td><p>(Number) the gap between each pair of adjacent points. Default: <code>1</code>.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a 1-D tensor of size \(\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil\)
-with values from the interval <code>[start, end)</code> taken with common difference
-<code>step</code> beginning from <code>start</code>.</p>
-<p>Note that non-integer <code>step</code> is subject to floating point rounding errors when
-comparing against <code>end</code>; to avoid inconsistency, we advise adding a small epsilon to <code>end</code>
-in such cases.</p>
-<p>$$
-    \mbox{out}_{{i+1}} = \mbox{out}_{i} + \mbox{step}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_arange</span>(start = <span class='fl'>0</span>, end = <span class='fl'>5</span>)
-<span class='fu'>torch_arange</span>(<span class='fl'>1</span>, <span class='fl'>4</span>)
-<span class='fu'>torch_arange</span>(<span class='fl'>1</span>, <span class='fl'>2.5</span>, <span class='fl'>0.5</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.0000
-#&gt;  1.5000
-#&gt;  2.0000
-#&gt; [ CPUFloatType{3} ]</div></pre>
-  </div>
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diff --git a/static/docs/dev/reference/torch_argmax.html b/static/docs/dev/reference/torch_argmax.html
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-    <h1>Argmax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_argmax.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Argmax</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_argmax</span>(<span class='kw'>self</span>, dim = <span class='kw'>NULL</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce. If <code>NULL</code>, the argmax of the flattened input is returned.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=NULL</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="argmax-input-gt-longtensor-"><a class="anchor" href="#argmax-input-gt-longtensor-"></a>argmax(input) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns the indices of the maximum value of all elements in the <code>input</code> tensor.</p>
-<p>This is the second value returned by <code>torch_max</code>. See its
-documentation for the exact semantics of this method.</p>
-    <h2 class="hasAnchor" id="argmax-input-dim-keepdim-false-gt-longtensor-"><a class="anchor" href="#argmax-input-dim-keepdim-false-gt-longtensor-"></a>argmax(input, dim, keepdim=False) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns the indices of the maximum values of a tensor across a dimension.</p>
-<p>This is the second value returned by <code>torch_max</code>. See its
-documentation for the exact semantics of this method.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_argmax</span>(<span class='kw'>a</span>)
-}
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_argmax</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  1
-#&gt;  0
-#&gt;  2
-#&gt; [ CPULongType{4} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Argmin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_argmin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Argmin</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_argmin</span>(<span class='kw'>self</span>, dim = <span class='kw'>NULL</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce. If <code>NULL</code>, the argmin of the flattened input is returned.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=NULL</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="argmin-input-gt-longtensor-"><a class="anchor" href="#argmin-input-gt-longtensor-"></a>argmin(input) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns the indices of the minimum value of all elements in the <code>input</code> tensor.</p>
-<p>This is the second value returned by <code>torch_min</code>. See its
-documentation for the exact semantics of this method.</p>
-    <h2 class="hasAnchor" id="argmin-input-dim-keepdim-false-out-null-gt-longtensor-"><a class="anchor" href="#argmin-input-dim-keepdim-false-out-null-gt-longtensor-"></a>argmin(input, dim, keepdim=False, out=NULL) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns the indices of the minimum values of a tensor across a dimension.</p>
-<p>This is the second value returned by <code>torch_min</code>. See its
-documentation for the exact semantics of this method.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_argmin</span>(<span class='kw'>a</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_argmin</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  1
-#&gt;  3
-#&gt;  0
-#&gt; [ CPULongType{4} ]</div></pre>
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-    <div class="page-header">
-    <h1>Argsort</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_argsort.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Argsort</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_argsort</span>(<span class='kw'>self</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, descending = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to sort along</p></td>
-    </tr>
-    <tr>
-      <th>descending</th>
-      <td><p>(bool, optional) controls the sorting order (ascending or descending)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="argsort-input-dim-descending-false-gt-longtensor-"><a class="anchor" href="#argsort-input-dim-descending-false-gt-longtensor-"></a>argsort(input, dim=-1, descending=False) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns the indices that sort a tensor along a given dimension in ascending
-order by value.</p>
-<p>This is the second value returned by <code>torch_sort</code>.  See its documentation
-for the exact semantics of this method.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_argsort</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2  3  1  1
-#&gt;  1  1  2  3
-#&gt;  3  2  3  0
-#&gt;  0  0  0  2
-#&gt; [ CPULongType{4,4} ]</div></pre>
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-    <h1>As_strided</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_as_strided.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>As_strided</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_as_strided</span>(<span class='kw'>self</span>, <span class='kw'>size</span>, <span class='kw'>stride</span>, storage_offset = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(tuple or ints) the shape of the output tensor</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(tuple or ints) the stride of the output tensor</p></td>
-    </tr>
-    <tr>
-      <th>storage_offset</th>
-      <td><p>(int, optional) the offset in the underlying storage of the output tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="as-strided-input-size-stride-storage-offset-gt-tensor-"><a class="anchor" href="#as-strided-input-size-stride-storage-offset-gt-tensor-"></a>as_strided(input, size, stride, storage_offset=0) -&gt; Tensor </h2>
-
-    
-
-
-<p>Create a view of an existing <code>torch_Tensor</code> <code>input</code> with specified
-<code>size</code>, <code>stride</code> and <code>storage_offset</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>More than one element of a created tensor may refer to a single memory
-location. As a result, in-place operations (especially ones that are
-vectorized) may result in incorrect behavior. If you need to write to
-the tensors, please clone them first.</p><pre>Many PyTorch functions, which return a view of a tensor, are internally
-implemented with this function. Those functions, like
-`torch_Tensor.expand`, are easier to read and are therefore more
-advisable to use.
-</pre>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='kw'>x</span>
-<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'>torch_as_strided</span>(<span class='kw'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>), <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='kw'>t</span>
-<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'>torch_as_strided</span>(<span class='kw'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>), <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>), <span class='fl'>1</span>)
-<span class='kw'>t</span>
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.4686  0.4060
-#&gt; -0.7271  0.7971
-#&gt; [ CPUFloatType{2,2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Asin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_asin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Asin</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_asin</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="asin-input-out-null-gt-tensor-"><a class="anchor" href="#asin-input-out-null-gt-tensor-"></a>asin(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the arcsine  of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \sin^{-1}(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_asin</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.2463
-#&gt;  0.4046
-#&gt;     nan
-#&gt;     nan
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <div class="page-header">
-    <h1>Atan</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_atan.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Atan</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_atan</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="atan-input-out-null-gt-tensor-"><a class="anchor" href="#atan-input-out-null-gt-tensor-"></a>atan(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the arctangent  of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \tan^{-1}(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_atan</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.8554
-#&gt;  0.0723
-#&gt;  0.2426
-#&gt;  0.4531
-#&gt; [ CPUFloatType{4} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_atan2.html b/static/docs/dev/reference/torch_atan2.html
deleted file mode 100644
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-    <div class="page-header">
-    <h1>Atan2</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_atan2.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Atan2</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_atan2</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="atan-input-other-out-null-gt-tensor-"><a class="anchor" href="#atan-input-other-out-null-gt-tensor-"></a>atan2(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Element-wise arctangent of \(\mbox{input}_{i} / \mbox{other}_{i}\)
-with consideration of the quadrant. Returns a new tensor with the signed angles
-in radians between vector \((\mbox{other}_{i}, \mbox{input}_{i})\)
-and vector \((1, 0)\). (Note that \(\mbox{other}_{i}\), the second
-parameter, is the x-coordinate, while \(\mbox{input}_{i}\), the first
-parameter, is the y-coordinate.)</p>
-<p>The shapes of <code>input</code> and <code>other</code> must be
-broadcastable .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_atan2</span>(<span class='kw'>a</span>, <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.2870
-#&gt;  1.5029
-#&gt; -1.8669
-#&gt;  1.6678
-#&gt; [ CPUFloatType{4} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_avg_pool1d.html b/static/docs/dev/reference/torch_avg_pool1d.html
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-    </li>
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-    <div class="page-header">
-    <h1>Avg_pool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_avg_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Avg_pool1d</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_avg_pool1d</span>(
-  <span class='kw'>self</span>,
-  <span class='kw'>kernel_size</span>,
-  stride = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
-  padding = <span class='fl'>0L</span>,
-  ceil_mode = <span class='fl'>FALSE</span>,
-  count_include_pad = <span class='fl'>TRUE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window. Can be a single number or a tuple <code>(kW,)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Can be a single number or a tuple <code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when <code>TRUE</code>, will include the zero-padding in the averaging calculation. Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-"><a class="anchor" href="#avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-"></a>avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 1D average pooling over an input signal composed of several
-input planes.</p>
-<p>See <code><a href='nn_avg_pool1d.html'>nn_avg_pool1d()</a></code> for details and output shape.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Baddbmm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_baddbmm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Baddbmm</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_baddbmm</span>(<span class='kw'>self</span>, <span class='kw'>batch1</span>, <span class='kw'>batch2</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to be added</p></td>
-    </tr>
-    <tr>
-      <th>batch1</th>
-      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>batch2</th>
-      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for \(\mbox{batch1} \mathbin{@} \mbox{batch2}\) (\(\alpha\))</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"></a>baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a batch matrix-matrix product of matrices in <code>batch1</code>
-and <code>batch2</code>.
-<code>input</code> is added to the final result.</p>
-<p><code>batch1</code> and <code>batch2</code> must be 3-D tensors each containing the same
-number of matrices.</p>
-<p>If <code>batch1</code> is a \((b \times n \times m)\) tensor, <code>batch2</code> is a
-\((b \times m \times p)\) tensor, then <code>input</code> must be
-broadcastable  with a
-\((b \times n \times p)\) tensor and <code>out</code> will be a
-\((b \times n \times p)\) tensor. Both <code>alpha</code> and <code>beta</code> mean the
-same as the scaling factors used in <code>torch_addbmm</code>.</p>
-<p>$$
-    \mbox{out}_i = \beta\ \mbox{input}_i + \alpha\ (\mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
-$$
-For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
-<code>alpha</code> must be real numbers, otherwise they should be integers.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>5</span>))
-<span class='kw'>batch1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='kw'>batch2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='fu'>torch_baddbmm</span>(<span class='kw'>M</span>, <span class='kw'>batch1</span>, <span class='kw'>batch2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -0.2584 -2.6929  0.6160 -1.6423  0.8025
-#&gt;   0.2455 -1.1414 -0.5247  1.0682 -0.8817
-#&gt;   0.2355  3.1211 -1.3214 -1.5004  0.6250
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   1.8552 -5.0803  0.8169 -4.2376 -1.8853
-#&gt;   1.3858  2.3906  3.9870 -3.1393  0.7165
-#&gt;  -0.4664  3.9877 -0.1988 -0.1155  0.0829
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -1.1199  0.9852  0.3080 -0.4241  3.0025
-#&gt;  -1.2278 -1.0366 -2.1857 -1.8891  2.2009
-#&gt;  -0.1157 -1.1892 -2.1727 -1.2401 -2.8900
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  -2.3506  2.4605  4.3548  1.5006 -2.0862
-#&gt;  -0.0346 -2.9013 -2.2608 -0.7393  1.4398
-#&gt;   1.8647  1.2969 -2.6661  1.7913  1.2749
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;   3.4445  0.8321  1.5949  3.1712  0.9686
-#&gt;  -1.5061  0.8003  0.1955 -1.5428 -0.3699
-#&gt;   1.6651 -0.7486  3.2466  0.8145 -0.3720
-#&gt; 
-#&gt; (6,.,.) = 
-#&gt;  -1.6226  2.1973  0.6045 -3.4120 -3.9838
-#&gt;   0.8162  0.6681 -1.4512  1.6129  2.7812
-#&gt;   1.5525  0.8972 -1.1557 -0.6764 -0.5946
-#&gt; 
-#&gt; (7,.,.) = 
-#&gt;  -0.9051  0.9925  1.1191 -1.9036 -2.1757
-#&gt;  -0.7349 -3.0937 -1.1059  0.8470 -0.9424
-#&gt;  -0.3014 -2.0780 -0.0127 -0.8122 -2.6149
-#&gt; 
-#&gt; (8,.,.) = 
-#&gt;  -0.3522  3.9736  2.7883 -0.9258 -3.1464
-#&gt;  -1.2867  2.4220 -0.4939  3.0901  1.2294
-#&gt;   0.6984  0.3625  1.4785 -0.5818  0.6997
-#&gt; 
-#&gt; (9,.,.) = 
-#&gt;  -0.5180  0.7039 -1.8772 -0.8713 -1.9113
-#&gt;   1.9969  0.4036 -1.5668 -1.0861  0.2708
-#&gt;  -0.4019  1.5036  2.4329 -0.5662  0.3743
-#&gt; 
-#&gt; (10,.,.) = 
-#&gt;  -1.1879 -3.4441 -0.7698  2.1815  0.0826
-#&gt;  -0.4070 -0.3045 -2.2918 -2.8612 -1.8219
-#&gt;   2.3615 -1.6956 -1.6156 -0.7896 -1.6263
-#&gt; [ CPUFloatType{10,3,5} ]</div></pre>
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-    <h1>Bartlett_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_bartlett_window.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Bartlett_window</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_bartlett_window</span>(
-  <span class='kw'>window_length</span>,
-  periodic = <span class='fl'>TRUE</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) the size of returned window</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
-</pre>
-
-    <h2 class="hasAnchor" id="bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Bartlett window function.</p>
-<p>$$
-    w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \left\{ \begin{array}{ll}
-        \frac{2n}{N - 1} &amp; \mbox{if } 0 \leq n \leq \frac{N - 1}{2} \\
-        2 - \frac{2n}{N - 1} &amp; \mbox{if } \frac{N - 1}{2} &lt; n &lt; N \\
-    \end{array}
-    \right. ,
-$$
-where \(N\) is the full window size.</p>
-<p>The input <code>window_length</code> is a positive integer controlling the
-returned window size. <code>periodic</code> flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-<code>torch_bartlett_window(L, periodic=TRUE)</code> equal to
-<code>torch_bartlett_window(L + 1, periodic=False)[:-1])</code>.</p>
-
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-    <h1>Bernoulli</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bernoulli.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Bernoulli</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_bernoulli</span>(<span class='kw'>self</span>, <span class='kw'>p</span>, generator = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of probability values for the Bernoulli
-distribution</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(Number) a probability value. If <code>p</code> is passed than it's used instead of
-the values in <code>self</code> tensor.</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bernoulli-input-generator-null-out-null-gt-tensor-"><a class="anchor" href="#bernoulli-input-generator-null-out-null-gt-tensor-"></a>bernoulli(input, *, generator=NULL, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Draws binary random numbers (0 or 1) from a Bernoulli distribution.</p>
-<p>The <code>input</code> tensor should be a tensor containing probabilities
-to be used for drawing the binary random number.
-Hence, all values in <code>input</code> have to be in the range:
-\(0 \leq \mbox{input}_i \leq 1\).</p>
-<p>The \(\mbox{i}^{th}\) element of the output tensor will draw a
-value \(1\) according to the \(\mbox{i}^{th}\) probability value given
-in <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} \sim \mathrm{Bernoulli}(p = \mbox{input}_{i})
-$$
-The returned <code>out</code> tensor only has values 0 or 1 and is of the same
-shape as <code>input</code>.</p>
-<p><code>out</code> can have integral <code>dtype</code>, but <code>input</code> must have floating
-point <code>dtype</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))<span class='op'>$</span><span class='fu'>uniform_</span>(<span class='fl'>0</span>, <span class='fl'>1</span>)  <span class='co'># generate a uniform random matrix with range c(0, 1)</span>
-<span class='kw'>a</span>
-<span class='fu'>torch_bernoulli</span>(<span class='kw'>a</span>)
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>)) <span class='co'># probability of drawing "1" is 1</span>
-<span class='fu'>torch_bernoulli</span>(<span class='kw'>a</span>)
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>)) <span class='co'># probability of drawing "1" is 0</span>
-<span class='fu'>torch_bernoulli</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  0  0
-#&gt;  0  0  0
-#&gt;  0  0  0
-#&gt; [ CPUFloatType{3,3} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_bincount.html b/static/docs/dev/reference/torch_bincount.html
deleted file mode 100644
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+++ /dev/null
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-    <div class="page-header">
-    <h1>Bincount</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bincount.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Bincount</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_bincount</span>(<span class='kw'>self</span>, weights = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(), minlength = <span class='fl'>0L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) 1-d int tensor</p></td>
-    </tr>
-    <tr>
-      <th>weights</th>
-      <td><p>(Tensor) optional, weight for each value in the input tensor.        Should be of same size as input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>minlength</th>
-      <td><p>(int) optional, minimum number of bins. Should be non-negative.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bincount-input-weights-null-minlength-gt-tensor-"><a class="anchor" href="#bincount-input-weights-null-minlength-gt-tensor-"></a>bincount(input, weights=NULL, minlength=0) -&gt; Tensor </h2>
-
-    
-
-
-<p>Count the frequency of each value in an array of non-negative ints.</p>
-<p>The number of bins (size 1) is one larger than the largest value in
-<code>input</code> unless <code>input</code> is empty, in which case the result is a
-tensor of size 0. If <code>minlength</code> is specified, the number of bins is at least
-<code>minlength</code> and if <code>input</code> is empty, then the result is tensor of size
-<code>minlength</code> filled with zeros. If <code>n</code> is the value at position <code>i</code>,
-<code>out[n] += weights[i]</code> if <code>weights</code> is specified else
-<code>out[n] += 1</code>.</p>
-<p>.. include:: cuda_deterministic.rst</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span>(<span class='fl'>0</span>, <span class='fl'>8</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>5</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
-<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_linspace.html'>torch_linspace</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, steps=<span class='fl'>5</span>)
-<span class='kw'>input</span>
-<span class='kw'>weights</span>
-<span class='fu'>torch_bincount</span>(<span class='kw'>input</span>, <span class='kw'>weights</span>)
-<span class='kw'>input</span><span class='op'>$</span><span class='fu'>bincount</span>(<span class='kw'>weights</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000
-#&gt;  0.0000
-#&gt;  0.2500
-#&gt;  0.5000
-#&gt;  0.0000
-#&gt;  1.0000
-#&gt;  0.7500
-#&gt; [ CPUFloatType{7} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_bitwise_and.html b/static/docs/dev/reference/torch_bitwise_and.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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-    <pre class="usage"><span class='fu'>torch_bitwise_and</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>NA the first input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>NA the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bitwise-and-input-other-out-null-gt-tensor-"><a class="anchor" href="#bitwise-and-input-other-out-null-gt-tensor-"></a>bitwise_and(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the bitwise AND of <code>input</code> and <code>other</code>. The input tensor must be of
-integral or Boolean types. For bool tensors, it computes the logical AND.</p>
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bitwise_not.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Bitwise_not</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_bitwise_not</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bitwise-not-input-out-null-gt-tensor-"><a class="anchor" href="#bitwise-not-input-out-null-gt-tensor-"></a>bitwise_not(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the bitwise NOT of the given input tensor. The input tensor must be of
-integral or Boolean types. For bool tensors, it computes the logical NOT.</p>
-
-  </div>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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-    </div>
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-    <pre class="usage"><span class='fu'>torch_bitwise_or</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>NA the first input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>NA the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bitwise-or-input-other-out-null-gt-tensor-"><a class="anchor" href="#bitwise-or-input-other-out-null-gt-tensor-"></a>bitwise_or(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
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-<p>Computes the bitwise OR of <code>input</code> and <code>other</code>. The input tensor must be of
-integral or Boolean types. For bool tensors, it computes the logical OR.</p>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bitwise_xor.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Bitwise_xor</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_bitwise_xor</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>NA the first input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>NA the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bitwise-xor-input-other-out-null-gt-tensor-"><a class="anchor" href="#bitwise-xor-input-other-out-null-gt-tensor-"></a>bitwise_xor(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the bitwise XOR of <code>input</code> and <code>other</code>. The input tensor must be of
-integral or Boolean types. For bool tensors, it computes the logical XOR.</p>
-
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-    <div class="page-header">
-    <h1>Blackman_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_blackman_window.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Blackman_window</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_blackman_window</span>(
-  <span class='kw'>window_length</span>,
-  periodic = <span class='fl'>TRUE</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) the size of returned window</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
-</pre>
-
-    <h2 class="hasAnchor" id="blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Blackman window function.</p>
-<p>$$
-    w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right)
-$$
-where \(N\) is the full window size.</p>
-<p>The input <code>window_length</code> is a positive integer controlling the
-returned window size. <code>periodic</code> flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-<code>torch_blackman_window(L, periodic=TRUE)</code> equal to
-<code>torch_blackman_window(L + 1, periodic=False)[:-1])</code>.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
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-    <div class="page-header">
-    <h1>Bmm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bmm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Bmm</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_bmm</span>(<span class='kw'>self</span>, <span class='kw'>mat2</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>mat2</th>
-      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function does not broadcast .
-For broadcasting matrix products, see <code><a href='torch_matmul.html'>torch_matmul</a></code>.</p>
-    <h2 class="hasAnchor" id="bmm-input-mat-out-null-gt-tensor-"><a class="anchor" href="#bmm-input-mat-out-null-gt-tensor-"></a>bmm(input, mat2, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a batch matrix-matrix product of matrices stored in <code>input</code>
-and <code>mat2</code>.</p>
-<p><code>input</code> and <code>mat2</code> must be 3-D tensors each containing
-the same number of matrices.</p>
-<p>If <code>input</code> is a \((b \times n \times m)\) tensor, <code>mat2</code> is a
-\((b \times m \times p)\) tensor, <code>out</code> will be a
-\((b \times n \times p)\) tensor.</p>
-<p>$$
-    \mbox{out}_i = \mbox{input}_i \mathbin{@} \mbox{mat2}_i
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='kw'>mat2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='kw'>res</span> <span class='op'>=</span> <span class='fu'>torch_bmm</span>(<span class='kw'>input</span>, <span class='kw'>mat2</span>)
-<span class='kw'>res</span>
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -0.6715 -0.0222 -0.3052 -0.0132  0.3527
-#&gt;   0.0642  1.4605 -0.2209 -1.2005  0.5709
-#&gt;  -1.0371 -0.8810 -1.1245 -0.0821 -2.1417
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   4.7513 -4.5550 -2.1236  5.3799  3.4089
-#&gt;   0.5636 -3.6664 -1.1034  1.4131  0.0695
-#&gt;  -1.2193  2.9154  1.1902 -5.7150  2.1859
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -2.5769  4.3691  5.2896  2.8948  1.6145
-#&gt;  -0.4768  1.2495  2.7310  0.7684  0.0762
-#&gt;   3.6303 -3.9340 -5.5709 -3.1720 -1.6945
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;   3.6557  1.9226 -2.5345  2.2762  2.9229
-#&gt;  -1.5318 -2.9690 -0.5611  0.6546  2.4618
-#&gt;   2.3266 -2.9180 -0.3267  1.6594  2.0594
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;   3.0726 -0.5080  1.5528 -0.8115  1.3036
-#&gt;  -2.4255 -0.0913 -1.9783  0.8748 -1.8288
-#&gt;   1.9180  1.2736  0.5709  0.4949 -0.0467
-#&gt; 
-#&gt; (6,.,.) = 
-#&gt;   0.6454  2.1866 -1.0247 -2.6391 -0.7656
-#&gt;  -1.3438 -2.0773 -0.1758  1.0906  0.6958
-#&gt;  -0.7500 -0.8464  0.6230  1.0011  1.2585
-#&gt; 
-#&gt; (7,.,.) = 
-#&gt;   2.7764 -2.0832  0.6146 -0.0110  1.1089
-#&gt;  -3.2453 -1.8295 -3.1091  0.0794 -0.3670
-#&gt;  -0.4551  0.8043 -2.0120 -0.3138 -0.0596
-#&gt; 
-#&gt; (8,.,.) = 
-#&gt;  -3.5918  1.9420 -1.9538 -1.1828  0.0897
-#&gt;   3.2484 -4.0688 -1.4353  1.3496 -3.0432
-#&gt;  -4.9755  7.0424  0.8682 -0.7064  4.2925
-#&gt; 
-#&gt; (9,.,.) = 
-#&gt;   0.3845 -3.2398 -2.1125 -0.3747 -1.6631
-#&gt;   0.8932 -3.2300 -1.0624 -0.8982 -0.7203
-#&gt;  -0.6003  0.1961 -0.2679  2.1828 -1.3433
-#&gt; 
-#&gt; (10,.,.) = 
-#&gt;   0.1576 -0.0329  4.1183 -1.2813 -0.6126
-#&gt;   5.1602  1.4532  6.5321  2.7236 -1.1874
-#&gt;   0.3057 -0.3436 -4.9753 -1.7966  1.4027
-#&gt; [ CPUFloatType{10,3,5} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_broadcast_tensors.html b/static/docs/dev/reference/torch_broadcast_tensors.html
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-    <h1>Broadcast_tensors</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_broadcast_tensors.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Broadcast_tensors</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_broadcast_tensors</span>(<span class='kw'>tensors</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>a list containing any number of tensors of the same type</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="broadcast-tensors-tensors-gt-list-of-tensors-"><a class="anchor" href="#broadcast-tensors-tensors-gt-list-of-tensors-"></a>broadcast_tensors(tensors) -&gt; List of Tensors </h2>
-
-    
-
-
-<p>Broadcasts the given tensors according to broadcasting-semantics.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>3</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>2</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
-<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_broadcast_tensors</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>))
-<span class='kw'>out</span>[[<span class='fl'>1</span>]]
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  1  2
-#&gt;  0  1  2
-#&gt; [ CPUFloatType{2,3} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_can_cast.html b/static/docs/dev/reference/torch_can_cast.html
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-    <h1>Can_cast</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_can_cast.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Can_cast</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_can_cast</span>(<span class='kw'>from</span>, <span class='kw'>to</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>from</th>
-      <td><p>(dtype) The original <code>torch_dtype</code>.</p></td>
-    </tr>
-    <tr>
-      <th>to</th>
-      <td><p>(dtype) The target <code>torch_dtype</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="can-cast-from-to-gt-bool-"><a class="anchor" href="#can-cast-from-to-gt-bool-"></a>can_cast(from, to) -&gt; bool </h2>
-
-    
-
-
-<p>Determines if a type conversion is allowed under PyTorch casting rules
-described in the type promotion documentation .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_can_cast</span>(<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_float</a></span>())
-<span class='fu'>torch_can_cast</span>(<span class='fu'><a href='torch_dtype.html'>torch_float</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
-}
-</div><div class='output co'>#&gt; [1] FALSE</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-  </div>
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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diff --git a/static/docs/dev/reference/torch_cartesian_prod.html b/static/docs/dev/reference/torch_cartesian_prod.html
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-    <h1>Cartesian_prod</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cartesian_prod.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Do cartesian product of the given sequence of tensors.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cartesian_prod</span>(<span class='kw'>tensors</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>a list containing any number of 1 dimensional tensors.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>)
-<span class='kw'>tensor_a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='kw'>a</span>)
-<span class='kw'>tensor_b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='kw'>b</span>)
-<span class='fu'>torch_cartesian_prod</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>tensor_a</span>, <span class='kw'>tensor_b</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  4
-#&gt;  1  5
-#&gt;  2  4
-#&gt;  2  5
-#&gt;  3  4
-#&gt;  3  5
-#&gt; [ CPUFloatType{6,2} ]</div></pre>
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-    <h1>Cat</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cat.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cat</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cat</span>(<span class='kw'>tensors</span>, dim = <span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(sequence of Tensors) any python sequence of tensors of the same type.        Non-empty tensors provided must have the same shape, except in the        cat dimension.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension over which the tensors are concatenated</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cat-tensors-dim-out-null-gt-tensor-"><a class="anchor" href="#cat-tensors-dim-out-null-gt-tensor-"></a>cat(tensors, dim=0, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Concatenates the given sequence of <code>seq</code> tensors in the given dimension.
-All tensors must either have the same shape (except in the concatenating
-dimension) or be empty.</p>
-<p><code>torch_cat</code> can be seen as an inverse operation for <code><a href='torch_split.html'>torch_split()</a></code>
-and <code><a href='torch_chunk.html'>torch_chunk</a></code>.</p>
-<p><code>torch_cat</code> can be best understood via examples.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='kw'>x</span>
-<span class='fu'>torch_cat</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>x</span>, <span class='kw'>x</span>), <span class='fl'>1</span>)
-<span class='fu'>torch_cat</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>x</span>, <span class='kw'>x</span>), <span class='fl'>2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3085  0.8908 -0.7002 -0.3085  0.8908 -0.7002 -0.3085  0.8908 -0.7002
-#&gt; -0.4217  0.5408 -0.2483 -0.4217  0.5408 -0.2483 -0.4217  0.5408 -0.2483
-#&gt; [ CPUFloatType{2,9} ]</div></pre>
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-    <h1>Cdist</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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-
-    <pre class="usage"><span class='fu'>torch_cdist</span>(<span class='kw'>x1</span>, <span class='kw'>x2</span>, p = <span class='fl'>2L</span>, compute_mode = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x1</th>
-      <td><p>(Tensor) input tensor of shape \(B \times P \times M\).</p></td>
-    </tr>
-    <tr>
-      <th>x2</th>
-      <td><p>(Tensor) input tensor of shape \(B \times R \times M\).</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></td>
-    </tr>
-    <tr>
-      <th>compute_mode</th>
-      <td><p>NA 'use_mm_for_euclid_dist_if_necessary' - will use matrix multiplication approach to calculate        euclidean distance (p = 2) if P &gt; 25 or R &gt; 25        'use_mm_for_euclid_dist' - will always use matrix multiplication approach to calculate        euclidean distance (p = 2)        'donot_use_mm_for_euclid_dist' - will never use matrix multiplication approach to calculate        euclidean distance (p = 2)        Default: use_mm_for_euclid_dist_if_necessary.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Computes batched the p-norm distance between each pair of the two collections of row vectors.</p>
-
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-    <h1>Ceil</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_ceil.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ceil</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_ceil</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ceil-input-out-null-gt-tensor-"><a class="anchor" href="#ceil-input-out-null-gt-tensor-"></a>ceil(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the ceil of the elements of <code>input</code>,
-the smallest integer greater than or equal to each element.</p>
-<p>$$
-    \mbox{out}_{i} = \left\lceil \mbox{input}_{i} \right\rceil = \left\lfloor \mbox{input}_{i} \right\rfloor + 1
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_ceil</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt; -1
-#&gt;  1
-#&gt;  1
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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-    <pre class="usage"><span class='fu'>torch_celu</span>(<span class='kw'>self</span>, alpha = <span class='fl'>1</span>)</pre>
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
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-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="celu-input-alpha-gt-tensor-"><a class="anchor" href="#celu-input-alpha-gt-tensor-"></a>celu(input, alpha=1.) -&gt; Tensor </h2>
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-    <h1>Celu_</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_celu_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Celu_</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_celu_</span>(<span class='kw'>self</span>, alpha = <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="celu-input-alpha-gt-tensor-"><a class="anchor" href="#celu-input-alpha-gt-tensor-"></a>celu_(input, alpha=1.) -&gt; Tensor </h2>
-
-    
-
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-<p>In-place version of <code><a href='torch_celu.html'>torch_celu()</a></code>.</p>
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-    <h1>Chain_matmul</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_chain_matmul.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Chain_matmul</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_chain_matmul</span>(<span class='kw'>matrices</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>matrices</th>
-      <td><p>(Tensors...) a sequence of 2 or more 2-D tensors whose product is to be determined.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Returns the matrix product of the \(N\) 2-D tensors. This product is efficiently computed
-using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
-of arithmetic operations (<code>[CLRS]</code>_). Note that since this is a function to compute the product, \(N\)
-needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
-If \(N\) is 1, then this is a no-op - the original matrix is returned as is.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='kw'>c</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>6</span>))
-<span class='kw'>d</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>6</span>, <span class='fl'>7</span>))
-<span class='fu'>torch_chain_matmul</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>a</span>, <span class='kw'>b</span>, <span class='kw'>c</span>, <span class='kw'>d</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;   1.1241  -4.0513   0.7230   3.8937   2.2374  -0.4167   4.1885
-#&gt; -16.9092   1.9523  -4.8296   0.5629  -8.4329  15.0521 -15.9307
-#&gt;  -9.0977  -2.1344  -5.5280   2.5053  13.2387  -2.7841 -13.3227
-#&gt; [ CPUFloatType{3,7} ]</div></pre>
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-    <h1>Cholesky</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cholesky.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cholesky</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cholesky</span>(<span class='kw'>self</span>, upper = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor \(A\) of size \((*, n, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more
-batch dimensions consisting of symmetric positive-definite matrices.</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(bool, optional) flag that indicates whether to return a
-upper or lower triangular matrix. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cholesky-input-upper-false-out-null-gt-tensor-"><a class="anchor" href="#cholesky-input-upper-false-out-null-gt-tensor-"></a>cholesky(input, upper=False, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the Cholesky decomposition of a symmetric positive-definite
-matrix \(A\) or for batches of symmetric positive-definite matrices.</p>
-<p>If <code>upper</code> is <code>TRUE</code>, the returned matrix <code>U</code> is upper-triangular, and
-the decomposition has the form:</p>
-<p>$$
-  A = U^TU
-$$
-If <code>upper</code> is <code>FALSE</code>, the returned matrix <code>L</code> is lower-triangular, and
-the decomposition has the form:</p>
-<p>$$
-    A = LL^T
-$$
-If <code>upper</code> is <code>TRUE</code>, and \(A\) is a batch of symmetric positive-definite
-matrices, then the returned tensor will be composed of upper-triangular Cholesky factors
-of each of the individual matrices. Similarly, when <code>upper</code> is <code>FALSE</code>, the returned
-tensor will be composed of lower-triangular Cholesky factors of each of the individual
-matrices.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>a</span>, <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) <span class='co'># make symmetric positive-definite</span>
-<span class='kw'>l</span> <span class='op'>=</span> <span class='fu'>torch_cholesky</span>(<span class='kw'>a</span>)
-<span class='kw'>a</span>
-<span class='kw'>l</span>
-<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>l</span>, <span class='kw'>l</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>())
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>a</span>, <span class='kw'>a</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>2</span>)) <span class='op'>+</span> <span class='fl'>1e-03</span> <span class='co'># make symmetric positive-definite</span>
-<span class='kw'>l</span> <span class='op'>=</span> <span class='fu'>torch_cholesky</span>(<span class='kw'>a</span>)
-<span class='kw'>z</span> <span class='op'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>l</span>, <span class='kw'>l</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>2</span>))
-<span class='fu'><a href='torch_max.html'>torch_max</a></span>(<span class='fu'><a href='torch_abs.html'>torch_abs</a></span>(<span class='kw'>z</span> <span class='op'>-</span> <span class='kw'>a</span>)) <span class='co'># Max non-zero</span>
-}
-}
-</div></pre>
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-    <h1>Cholesky_inverse</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cholesky_inverse.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cholesky_inverse</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cholesky_inverse</span>(<span class='kw'>self</span>, upper = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input 2-D tensor \(u\), a upper or lower triangular           Cholesky factor</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(bool, optional) whether to return a lower (default) or upper triangular matrix</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cholesky-inverse-input-upper-false-out-null-gt-tensor-"><a class="anchor" href="#cholesky-inverse-input-upper-false-out-null-gt-tensor-"></a>cholesky_inverse(input, upper=False, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the inverse of a symmetric positive-definite matrix \(A\) using its
-Cholesky factor \(u\): returns matrix <code>inv</code>. The inverse is computed using
-LAPACK routines <code>dpotri</code> and <code>spotri</code> (and the corresponding MAGMA routines).</p>
-<p>If <code>upper</code> is <code>FALSE</code>, \(u\) is lower triangular
-such that the returned tensor is</p>
-<p>$$
-    inv = (uu^{{T}})^{{-1}}
-$$
-If <code>upper</code> is <code>TRUE</code> or not provided, \(u\) is upper
-triangular such that the returned tensor is</p>
-<p>$$
-    inv = (u^T u)^{{-1}}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>a</span>, <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) <span class='op'>+</span> <span class='fl'>1e-05</span> <span class='op'>*</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>3</span>) <span class='co'># make symmetric positive definite</span>
-<span class='kw'>u</span> <span class='op'>=</span> <span class='fu'><a href='torch_cholesky.html'>torch_cholesky</a></span>(<span class='kw'>a</span>)
-<span class='kw'>a</span>
-<span class='fu'>torch_cholesky_inverse</span>(<span class='kw'>u</span>)
-<span class='kw'>a</span><span class='op'>$</span><span class='fu'>inverse</span>()
-}
-}
-</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_cholesky_solve.html b/static/docs/dev/reference/torch_cholesky_solve.html
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-    <div class="page-header">
-    <h1>Cholesky_solve</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cholesky_solve.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cholesky_solve</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cholesky_solve</span>(<span class='kw'>self</span>, <span class='kw'>input2</span>, upper = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) input matrix \(b\) of size \((*, m, k)\),                where \(*\) is zero or more batch dimensions</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>(Tensor) input matrix \(u\) of size \((*, m, m)\),                where \(*\) is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cholesky-solve-input-input-upper-false-out-null-gt-tensor-"><a class="anchor" href="#cholesky-solve-input-input-upper-false-out-null-gt-tensor-"></a>cholesky_solve(input, input2, upper=False, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Solves a linear system of equations with a positive semidefinite
-matrix to be inverted given its Cholesky factor matrix \(u\).</p>
-<p>If <code>upper</code> is <code>FALSE</code>, \(u\) is and lower triangular and <code>c</code> is
-returned such that:</p>
-<p>$$
-    c = (u u^T)^{{-1}} b
-$$
-If <code>upper</code> is <code>TRUE</code> or not provided, \(u\) is upper triangular
-and <code>c</code> is returned such that:</p>
-<p>$$
-    c = (u^T u)^{{-1}} b
-$$
-<code>torch_cholesky_solve(b, u)</code> can take in 2D inputs <code>b, u</code> or inputs that are
-batches of 2D matrices. If the inputs are batches, then returns
-batched outputs <code>c</code></p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>a</span>, <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) <span class='co'># make symmetric positive definite</span>
-<span class='kw'>u</span> <span class='op'>=</span> <span class='fu'><a href='torch_cholesky.html'>torch_cholesky</a></span>(<span class='kw'>a</span>)
-<span class='kw'>a</span>
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='kw'>b</span>
-<span class='fu'>torch_cholesky_solve</span>(<span class='kw'>b</span>, <span class='kw'>u</span>)
-<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'>inverse</span>(), <span class='kw'>b</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -46.3842  47.5502
-#&gt; -23.6674  24.4634
-#&gt; -68.0892  70.6750
-#&gt; [ CPUFloatType{3,2} ]</div></pre>
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-    <h1>Chunk</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_chunk.Rd</code></div>
-    </div>
-
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-    <p>Chunk</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_chunk</span>(<span class='kw'>self</span>, <span class='kw'>chunks</span>, dim = <span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to split</p></td>
-    </tr>
-    <tr>
-      <th>chunks</th>
-      <td><p>(int) number of chunks to return</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension along which to split the tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="chunk-input-chunks-dim-gt-list-of-tensors-"><a class="anchor" href="#chunk-input-chunks-dim-gt-list-of-tensors-"></a>chunk(input, chunks, dim=0) -&gt; List of Tensors </h2>
-
-    
-
-
-<p>Splits a tensor into a specific number of chunks. Each chunk is a view of
-the input tensor.</p>
-<p>Last chunk will be smaller if the tensor size along the given dimension
-<code>dim</code> is not divisible by <code>chunks</code>.</p>
-
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-
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Clamp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_clamp.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Clamp</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_clamp</span>(<span class='kw'>self</span>, min = <span class='kw'>NULL</span>, max = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>min</th>
-      <td><p>(Number) lower-bound of the range to be clamped to</p></td>
-    </tr>
-    <tr>
-      <th>max</th>
-      <td><p>(Number) upper-bound of the range to be clamped to</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="clamp-input-min-max-out-null-gt-tensor-"><a class="anchor" href="#clamp-input-min-max-out-null-gt-tensor-"></a>clamp(input, min, max, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Clamp all elements in <code>input</code> into the range <code><a href='https://rdrr.io/r/base/Extract.html'>[</a></code> <code>min</code>, <code>max</code> <code>]</code> and return
-a resulting tensor:</p>
-<p>$$
-    y_i = \left\{ \begin{array}{ll}
-        \mbox{min} &amp; \mbox{if } x_i &lt; \mbox{min} \\
-        x_i &amp; \mbox{if } \mbox{min} \leq x_i \leq \mbox{max} \\
-        \mbox{max} &amp; \mbox{if } x_i &gt; \mbox{max}
-    \end{array}
-    \right.
-$$
-If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, args <code>min</code>
-and <code>max</code> must be real numbers, otherwise they should be integers.</p>
-    <h2 class="hasAnchor" id="clamp-input-min-out-null-gt-tensor-"><a class="anchor" href="#clamp-input-min-out-null-gt-tensor-"></a>clamp(input, *, min, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Clamps all elements in <code>input</code> to be larger or equal <code>min</code>.</p>
-<p>If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code>
-should be a real number, otherwise it should be an integer.</p>
-    <h2 class="hasAnchor" id="clamp-input-max-out-null-gt-tensor-"><a class="anchor" href="#clamp-input-max-out-null-gt-tensor-"></a>clamp(input, *, max, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Clamps all elements in <code>input</code> to be smaller or equal <code>max</code>.</p>
-<p>If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code>
-should be a real number, otherwise it should be an integer.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_clamp</span>(<span class='kw'>a</span>, min=<span class='op'>-</span><span class='fl'>0.5</span>, max=<span class='fl'>0.5</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_clamp</span>(<span class='kw'>a</span>, min=<span class='fl'>0.5</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_clamp</span>(<span class='kw'>a</span>, max=<span class='fl'>0.5</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.1404
-#&gt; -1.2990
-#&gt; -0.8958
-#&gt;  0.3838
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <h1>Combinations</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_combinations.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Combinations</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_combinations</span>(<span class='kw'>self</span>, r = <span class='fl'>2L</span>, with_replacement = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) 1D vector.</p></td>
-    </tr>
-    <tr>
-      <th>r</th>
-      <td><p>(int, optional) number of elements to combine</p></td>
-    </tr>
-    <tr>
-      <th>with_replacement</th>
-      <td><p>(boolean, optional) whether to allow duplication in combination</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="combinations-input-r-with-replacement-false-gt-seq-"><a class="anchor" href="#combinations-input-r-with-replacement-false-gt-seq-"></a>combinations(input, r=2, with_replacement=False) -&gt; seq </h2>
-
-    
-
-
-<p>Compute combinations of length \(r\) of the given tensor. The behavior is similar to
-python's <code>itertools.combinations</code> when <code>with_replacement</code> is set to <code>False</code>, and
-<code>itertools.combinations_with_replacement</code> when <code>with_replacement</code> is set to <code>TRUE</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)
-<span class='kw'>tensor_a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='kw'>a</span>)
-<span class='fu'>torch_combinations</span>(<span class='kw'>tensor_a</span>)
-<span class='fu'>torch_combinations</span>(<span class='kw'>tensor_a</span>, r=<span class='fl'>3</span>)
-<span class='fu'>torch_combinations</span>(<span class='kw'>tensor_a</span>, with_replacement=<span class='fl'>TRUE</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  1
-#&gt;  1  2
-#&gt;  1  3
-#&gt;  2  2
-#&gt;  2  3
-#&gt;  3  3
-#&gt; [ CPUFloatType{6,2} ]</div></pre>
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-    <div class="page-header">
-    <h1>Conj</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conj.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conj</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_conj</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conj-input-gt-tensor-"><a class="anchor" href="#conj-input-gt-tensor-"></a>conj(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise conjugate of the given <code>input</code> tensor.</p>
-<p>$$
-    \mbox{out}_{i} = conj(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_conj</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span>)))
-}
-}
-</div></pre>
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diff --git a/static/docs/dev/reference/torch_conv1d.html b/static/docs/dev/reference/torch_conv1d.html
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-    <h1>Conv1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv1d</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_conv1d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
-  stride = <span class='fl'>1L</span>,
-  padding = <span class='fl'>0L</span>,
-  dilation = <span class='fl'>1L</span>,
-  groups = <span class='fl'>1L</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or      a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a      single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or      a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a>conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 1D convolution over an input signal composed of several input
-planes.</p>
-<p>See <code><a href='nn_conv1d.html'>nn_conv1d()</a></code> for details and output shape.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>filters</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>33</span>, <span class='fl'>16</span>, <span class='fl'>3</span>))
-<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>))
-<span class='fu'><a href='nnf_conv1d.html'>nnf_conv1d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>filters</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  Columns 1 to 8  -4.5211  -9.4968   5.3277   7.7845  -2.6823  -5.4277   8.5245  -8.0147
-#&gt;    6.2418   2.7598   3.8328   6.1864  -1.9854  -7.5853  -5.9902  -3.7955
-#&gt;    6.8105   7.5747  -5.2595   7.0060   7.7744 -10.2868   0.4837   8.6171
-#&gt;    3.1948  -0.1489   3.2062   2.2065   9.2954  -1.3400   4.8504   5.2831
-#&gt;   -0.4339  -6.5858   4.4029   2.5576   6.4505   7.0042   7.3167  -1.4442
-#&gt;   14.8472  -9.3290   8.6522  -0.0753   0.1931 -17.0327  -7.4684   9.8034
-#&gt;   -3.6632  -0.1979  -0.7408  12.2581  -4.5087  -0.2325   4.2078 -12.1068
-#&gt;   -3.3205  -4.7422  -1.3699  -2.9343  -4.7168 -10.9685   4.7316   0.5853
-#&gt;   -6.3931  -9.7081  12.5025   9.5175  -7.6512   2.3744  11.2049  -5.2641
-#&gt;    2.5546  -1.9421  -4.5333   5.9539  -0.3733   3.5822  -3.6771   8.7318
-#&gt;    3.0220   3.6437  -2.2578   0.2790 -11.8502  -4.6849  -7.8466   6.8457
-#&gt;   -4.2072   1.6039  -3.1554   0.9960 -12.8763   1.0389  -3.5548   4.3494
-#&gt;    4.6420   0.3115   7.1600   6.9480   1.5268  -1.3167  -6.7689   7.7381
-#&gt;    1.7342  -9.3460   9.6720  -6.6306   1.8167   4.3456   4.4033   5.5310
-#&gt;   19.1535  -9.5004  -8.2563   0.7321  -8.3529   1.6314  -1.2181  -3.0188
-#&gt;    9.9743 -10.2711  -9.5126   8.0078  -2.4244   8.8406   7.6682  -0.1571
-#&gt;    0.6751  -9.0659  -7.1891   0.3308  -6.6314  -3.7696   1.0387   1.0181
-#&gt;    3.8847  -4.8734   0.4057  -0.0418  -2.0884 -10.1454  -4.3254   5.8255
-#&gt;   -7.6175  18.6441   3.1694   5.2782  13.6528  -1.5698  -3.0908  -4.7899
-#&gt;  -11.4639   1.9722  -0.4552   8.7959   3.3793  -6.6962   5.4218  -0.8863
-#&gt;    5.6714   9.2373   8.1382   4.6097  -5.3474  -4.4175 -11.7299  -4.4757
-#&gt;   -1.7376  -9.7243   7.9851   0.0662  -2.6785  -1.8786  -8.4554  -9.9153
-#&gt;    0.5143  -0.1940   3.6082   8.2976   5.2119  -5.9049   5.7097  -8.6147
-#&gt;    7.1443  -2.0987   0.8306  -2.5664  -1.9702   8.4253   9.9662   5.2327
-#&gt;  -10.6585  -7.6718  10.8194 -13.9552  -5.0140  -3.9913  16.0730   6.4535
-#&gt;   -3.7443   7.8117   3.4180 -10.5484  -2.2099   5.1321   0.7110  -0.0485
-#&gt;  -16.3346  -0.0447   4.4218   2.3424 -12.4458   5.0640   2.4950   2.4423
-#&gt;   -2.6998  -1.2516  -1.3204   4.8870   2.1952  -2.9384  -3.2211   6.4875
-#&gt;    4.0238  -0.0943   2.6340  11.2777  -9.4544  -6.3829   5.3211  -9.7761
-#&gt;    2.7976  -9.5347  -9.9403   0.2818   1.4312   5.6771  -0.1496   9.9674
-#&gt;   -5.2529   7.1297  -0.5335 -12.0659  -7.3763   6.5253   1.3087  -5.8897
-#&gt;   -3.0223  -5.4902  -0.6541 -12.6590   2.0787  -7.6472  -7.7483   8.0279
-#&gt;   -6.9976   0.2545  -8.1147   2.5681 -14.6329  -1.4679   9.4138   2.9373
-#&gt; 
-#&gt; Columns 9 to 16   6.0357  -1.2389  -0.8589 -10.1724  -3.2807  -3.7986 -12.5706  10.1129
-#&gt;    3.7368  -9.6587   4.6291   2.2136  12.8553 -15.3321   4.8319   3.2613
-#&gt;    6.7717   3.3350   3.6341   5.8900   5.5921  -2.5951  -7.4825  -5.1595
-#&gt;    3.2116  -7.0500   3.2679 -10.3518   0.1666   1.0066   5.1893  -1.2037
-#&gt;   10.1390   2.3590  11.4192  -1.3574   1.1045   2.7505  -3.8278  -0.2962
-#&gt;   -5.3000 -10.3082   1.4219   1.2487   1.7875  12.7334 -20.5018   2.5324
-#&gt;   -2.1367   4.5341 -10.1502  -5.4904   9.9707 -14.5805  16.3018  -8.4627
-#&gt;   -3.5692  -4.0995   4.3460  10.7171  14.5495   4.8934   5.0829   0.6142
-#&gt;    4.8772   7.5163   0.6921  -9.2970   4.3186  -0.0035  -5.0408   4.6974
-#&gt;  -11.2589  -2.5627   5.7199  -2.3366  -4.4996   9.7410  -5.3053   0.0082
-#&gt;    9.8923  -6.2676  -1.0004  -3.3505  -5.1920  -6.7011   3.1914  -5.5311
-#&gt;   -1.3606  11.5684  -3.3478  10.2244  -1.9592  -5.5666   2.9802  -4.7672
-#&gt;   -2.6014  -1.0908  -2.8087   2.3925  -3.8892  12.6607  -7.3351  -1.6092
-#&gt;  -11.2849   7.6765  -4.4905  -9.9599  -8.7923   9.7989  -7.5131   3.2353
-#&gt;    1.4298  -6.6854  -1.4447   0.7692  -8.3756   9.0919   0.5140   8.6922
-#&gt;   -3.3853   5.8442   2.0796   6.4215  -7.5768  -2.5490   4.3519   0.3078
-#&gt;    2.9561  -5.1087   5.7565   3.6909  -2.3562   5.5153  14.2157 -11.2702
-#&gt;   -2.9476  -4.3692  -3.4441   0.6712  -4.3232  -6.9520   6.3228   6.3020
-#&gt;    5.2194  -5.7996 -16.0701 -14.6944  -7.0026  -8.6357   1.4874  -5.5908
-#&gt;  -12.9218   6.6610   4.1050  -1.3081   3.6222  12.1323  -1.1134   4.9711
-#&gt;   -4.1198  -4.4185   3.6837  -6.8814 -12.2977  12.2319   7.4809   1.1126
-#&gt;    1.6515   0.0308   2.3900   0.3951   3.7474  -5.2954  12.3762   4.8733
-#&gt;    5.5765   1.1680   9.5386 -10.5268  -2.7231   2.5916 -18.7927   3.5616
-#&gt;   -7.7255  16.2926   5.7876  -6.4659  -5.5329   4.6502  -5.1558  -3.8303
-#&gt;    1.3816   2.7878  -0.5637 -10.2594   1.0385   0.8937 -15.0442   1.1873
-#&gt;   -0.3693   2.1782   1.5209   7.2224  -6.0204  -9.0611  -1.9551  10.8911
-#&gt;   -4.4368   9.4501  -5.1747   1.8251  -7.1026  -4.8051   7.4352   2.0855
-#&gt;    5.3637   8.5797   8.1376   1.8916   6.6414  -7.0961   5.7800  -4.3981
-#&gt;   -5.6333  -6.6532 -11.7548  -0.6948   1.7993  -1.6410   4.2484  -1.9371
-#&gt;  -12.5822  -1.1374  -8.8246   7.3248  -2.8081   4.5473  -6.5221 -10.9417
-#&gt;   -3.5139  -3.5843  -6.9748 -14.0897   2.3503   1.1693   8.9756  -7.1494
-#&gt;   -2.4042  -0.0490   5.9454   7.7797   3.0696   8.6558  -1.2573  -6.5729
-#&gt;    1.3952   3.7930  -0.4028   0.5113 -11.7310   2.4550  -1.9955 -10.1252
-#&gt; 
-#&gt; Columns 17 to 24  -2.4301   5.8381   1.6913  -5.3767 -12.2052  -0.7851  -4.3784  -3.0262
-#&gt;   -9.9705  13.2941 -10.8448   4.3698  -4.1939  -4.0892 -13.3334   0.6781
-#&gt;   -4.1279   3.1144   8.6012  -7.9024   4.6066   2.5016   4.8152  -1.5555
-#&gt;   -1.3710  -2.5206   6.0768  -2.1991   0.5382   1.7524  -2.3244  -0.0939
-#&gt;    0.5871   1.4366   0.1000   2.3483  -4.4679  -2.0864   7.6705  -4.3178
-#&gt;   10.3989   8.1667  13.0359 -22.3141  10.9689  -1.6156 -12.8153  -4.7914
-#&gt;    3.3604   3.9997  -1.8255  11.1889   0.7957   1.5762   9.9511  -3.3324
-#&gt;    3.8327  -0.3332  -7.2695  11.3693  -4.0560 -11.2653   0.3253   4.0057
-#&gt;    9.8992  -3.3123   2.2788   4.3650  -6.8658   6.3413   3.6581 -13.0785
-#&gt;    7.6490  11.1919   7.0903  -9.3290   1.0512   7.5291  -9.0361   3.2329
-#&gt;   -9.0821  13.1732   5.1849 -12.8490  -6.9486  13.6064 -14.5919  -8.8459
-#&gt;    2.5474   1.0467  -2.1881   5.0587 -11.1727  -3.2417   0.3285   3.5172
-#&gt;   15.7768  -5.4507  12.3704  -3.4392   3.9959  11.6062   7.1424   3.8500
-#&gt;    5.8194  -0.1278   1.5749  -1.7362  -5.6897  -0.7849   1.8253   0.9438
-#&gt;   -6.8736   0.4906   0.1499  -5.6963  -2.7704   5.7561  -2.8277   4.8168
-#&gt;    3.7665   9.7627  -0.2434   3.5563  -1.4267   5.4137  -2.4613  -4.3601
-#&gt;    3.6014  -0.3617   3.1708   8.2724   8.7588  -4.4275  -0.5803  -7.0555
-#&gt;    8.5559  10.6463  -3.2903   8.6056  -3.1097  -6.0373   0.9652   5.1143
-#&gt;  -15.2979  -0.5665  -1.2035   4.8840  -6.2518 -10.4447  -2.4389  -9.7871
-#&gt;    8.1693 -10.2391   6.4956  10.3267   4.0144  -1.1864  -6.5685   7.7377
-#&gt;   -3.0977  -5.7513  10.5489  12.4314  -7.3573   0.9570  -3.7780  17.5590
-#&gt;   -0.9430   9.3588  -3.1912   4.0080   6.5122   7.3634   9.3293  -3.3412
-#&gt;   -7.3061   5.7940   6.9542   0.7779  -4.2586   4.3830  -4.6577  -2.6417
-#&gt;   -4.3079   6.3732  -1.6854  -1.6811   1.2555  -4.1108  -2.6977  15.4820
-#&gt;   13.9438   5.0109  -5.8744  -9.0641   2.1540  -1.1852   0.1385  -7.3769
-#&gt;    4.3161  -4.9924  -6.3829   2.9226  -7.4368   1.6097   0.5578  10.0448
-#&gt;    1.3784  -4.2481 -15.5011  11.2261  -4.6052  -1.9206  -0.3791   9.8532
-#&gt;   -0.3952  -0.1837   3.8665  12.0350  -7.4608   2.5226   7.3759  -0.4076
-#&gt;    8.1567   8.0173   1.6161  -6.7069   4.9042  17.3261  16.0679  -5.2786
-#&gt;   -3.0949   3.8394  -6.4788  -0.0435  -1.3106   0.0546  -3.9360  10.0898
-#&gt;   -2.5548  -3.0874  -7.4182   5.1927  -6.6272 -10.6406  -4.1815  -2.0588
-#&gt;    8.2959  -2.0633  -0.4990  -0.4141   3.9547  -9.6191   0.6471   8.3015
-#&gt;    3.8530  -7.5597   7.5888   7.6879 -22.1031  -0.6661   8.7314  -7.0918
-#&gt; 
-#&gt; Columns 25 to 32  -8.9597   6.0680  12.0474  -1.5144   4.8011  11.6970   9.5581  -4.7033
-#&gt;   -0.9846  -4.5095  -9.1632  -2.7643   4.2898  -0.8413  -3.3402 -10.4773
-#&gt;    4.4531  -0.1074  -0.5189  -6.2329   2.1034  -0.1222  -3.5688  -5.5690
-#&gt;    1.2441  -1.7356   1.2555   6.6123   7.2551  -1.0991   6.8598   4.2183
-#&gt;  -15.0930   5.7144  13.9315   6.7557   7.2663  -6.4929  16.3147   8.1080
-#&gt;   12.0608  -1.9912   1.4748  -5.5571  -0.5816   6.2731   0.3363   5.9882
-#&gt;   -0.3085   1.2542  -1.8071   1.0375   6.8450   0.8793   6.0417 -12.0746
-#&gt;    2.1653  -5.5155   1.4760  -8.1621   1.5925  -9.1643   0.2677  -9.8533
-#&gt;   -2.7902   9.0649   1.2366  -2.4539  -4.7349   2.4139  -0.4359 -13.1859
-#&gt;    3.9345  10.2751   5.4685   3.4521   7.0935   9.9029   7.1332  10.7832
-#&gt;    2.4211   8.2302  -8.0482  -7.1705   5.7297  14.8089 -12.0157  -6.9272
-#&gt;   -2.1862  -0.7423   0.9452  -0.6647  -1.3066   3.0874  -1.5927   3.5555
-#&gt;    2.1976  -2.9381   0.7982  -0.8058 -12.4293   0.9838   1.9014   0.2605
-#&gt;   -7.2673  -3.1758  21.4223  13.9584  -3.0848   7.7941  22.2072   9.3540
-#&gt;    0.3523  -1.4075  -2.4227  -1.4964  -4.1407   4.8199  -7.9482   2.8954
-#&gt;   -6.7061   2.8209   8.0312   2.9056  -2.0679   9.9597  -2.0321  -0.4648
-#&gt;   11.7855   3.0399  -5.9188   5.9431  -2.4957   1.7008   5.2292   4.0304
-#&gt;   -1.2205 -10.3066   3.9026   0.5512  -2.8366   4.0274   7.7387   4.9550
-#&gt;  -12.1012   7.4116   3.2816  -5.6092  10.2293   5.6664   7.9021   8.3295
-#&gt;    5.3941  -8.2513   2.9193   6.0754  -9.9599  -7.5390  10.3567  -5.9119
-#&gt;    3.8294   1.0644  -4.5566  -4.9086 -10.0490   2.4272  -6.4179  -8.1086
-#&gt;    5.8446   7.7385  -7.6980  -5.6945  10.4785   0.1084  -7.4372  -5.7872
-#&gt;  -15.2084   0.5880   2.1813  -3.8739  -1.9831  12.9578   8.7138   3.0895
-#&gt;    9.7404  -1.2047   5.2846   1.0594  -1.1844   4.5035  -1.6784   2.4622
-#&gt;   -4.9402   0.2057   7.9746   6.1000  -3.5703  -2.9845  17.0432  -4.6442
-#&gt;   -2.0733  -2.8729  -0.9227  -3.6590   0.0119   1.5163   0.0028   1.8085
-#&gt;   -5.2190  -5.4564   1.7421   7.4822  -9.8889   5.5244  -9.8100   1.4475
-#&gt;    0.6100  -0.9258  -4.7239   3.1743   4.6037   2.5007   2.2037  -4.9151
-#&gt;   -4.5279   5.3911  -1.1330  -7.3188   5.3942   3.2617   1.4152 -11.8065
-#&gt;   -3.8630  -8.4645   0.9354   9.6748   1.1226  -5.2700  -5.1108  11.8329
-#&gt;  -10.5534  -3.3181  -2.1636   1.7472   3.2188  -6.0182   1.5907  -2.9187
-#&gt;   -0.1346 -15.9447   3.3701   5.5777  -1.8152  -5.1460   7.6463   7.3089
-#&gt;   -7.6271   3.4176   9.4009   4.4539  -3.9150   3.0842   9.6176  -8.4943
-#&gt; 
-#&gt; Columns 33 to 40   6.3309  17.0364  -5.0353  -4.1187  11.1126   3.6483   5.2820 -12.7031
-#&gt;    4.4397  -9.7818  -7.4067 -17.7348   0.2392   1.9611  11.4284   3.4128
-#&gt;    0.5552 -10.5311  -0.5385  -1.4755   1.5633 -11.8407   0.0613  -2.3387
-#&gt;    6.8783   2.8905  11.5990  -0.3098  -2.4943   3.4798  -0.3233  -1.0312
-#&gt;   -0.0015   3.8581  -0.8959  -4.9016   1.5886  -3.5914  -3.5286   4.0839
-#&gt;  -13.7462  -6.1749  -2.7959   1.0185  -7.1467   4.4057  -2.2846  -4.8795
-#&gt;    5.0485   6.0030   0.5213   0.4541  -6.0159  -7.8799   2.2222  -2.9301
-#&gt;   -8.8284  -5.4096  -0.2393 -14.3306   7.9825   9.1810   3.9968  -3.0232
-#&gt;    1.8099  10.4815  -6.4478  -2.8732   6.7348  -0.0101   4.1975   4.8500
-#&gt;    0.0110   0.4630   6.0029   5.8660  -5.8726   7.7870  -7.1615   8.1021
-#&gt;    6.0814   1.3995  -6.6715   9.0410  -7.4192  -5.7202  -2.2104  -2.5900
-#&gt;   -4.8563  -6.6659  -4.1082   8.5162  -5.6568  -2.9055  -3.8392   4.1652
-#&gt;   -9.0904  11.4835  -0.5248   1.8237  -0.5413  17.2256   3.2534   2.4245
-#&gt;   -0.4059   5.5198  20.8168  -2.8167   3.1686   2.0505  -2.8608   8.5664
-#&gt;  -11.7813   0.2462   2.6641  -6.4141   1.0159  -5.6504   4.6345   0.9756
-#&gt;   -7.9726   4.5682   0.4573  -3.0118   3.6109  -7.6169  -3.1374  -6.5100
-#&gt;   -3.2450   3.2664  -4.9615  11.5923  -5.3097  10.7864   5.0760  -2.9818
-#&gt;    1.5539  -0.2976   8.0115   1.2077  12.4956  10.8670   1.1633  -4.3756
-#&gt;   11.1443   6.0393  -2.6407   3.8202   3.0209  -6.7043  -3.7604   1.6433
-#&gt;    7.0163   3.9540   0.4079 -10.6267   2.8459   2.0656  -2.4379  19.0352
-#&gt;   -2.3645  -7.3365 -10.7093   9.5852   9.5133  15.6849   1.1164   3.5391
-#&gt;    3.7900   1.8654  -8.4134   6.3283  -2.5204  -0.9447   6.6671  -6.3301
-#&gt;   -0.8446   2.8525   9.1996 -14.8579   6.8667  -7.2367  -3.2354   2.6091
-#&gt;    1.5406  -7.0332   6.7711   2.8278  -1.2009   2.8466  -7.4621  10.8416
-#&gt;   -1.2757   9.5299   4.3467  -1.3923   8.3051   4.9167  -3.8118   3.5806
-#&gt;    1.0694   1.2813  -1.9239   1.8090   7.3812   0.6462 -18.2351 -11.5421
-#&gt;   -0.2318  -3.0378   6.2491  -8.6637   5.4559  -7.7911  -2.4369  -1.2175
-#&gt;    7.7519  -6.3247  -0.0324   7.7782  -4.3372  -5.8822  -5.9436   4.1412
-#&gt;  -11.8686  16.8197  -2.4831  -0.3928  -0.3550   0.8281   1.7210 -18.0475
-#&gt;   -6.8567  -1.7695   8.0885  -5.2155  -1.9626  -2.9767  -1.9256   2.8060
-#&gt;    8.0202  -3.7397   1.7781  -5.6620  -3.0994   3.0631  -5.2539   0.2889
-#&gt;   -3.1854  -7.0925   5.1891   7.0453  -3.6352  12.7287  -3.0601  16.6590
-#&gt;   -9.6543   0.0120   6.4093  -6.4205   0.3516  -0.7535  -1.9284  -1.3115
-#&gt; 
-#&gt; Columns 41 to 48   3.4182   4.4183  -5.2130   1.8115  -2.4776   9.1178 -10.8803  -1.3451
-#&gt;    8.2588   7.1177  -4.4785   5.9317   1.0937   7.6488   6.7447  10.8620
-#&gt;    0.7473  -0.0974  -2.5552  -6.9186  -6.0898  -4.8563  -1.3569  -8.0035
-#&gt;    6.4887   2.3776   3.2777  -6.7803  -7.7598   0.6271   7.9305  -1.8552
-#&gt;    8.0324  -5.1641  -8.0045   8.1091   4.7690  -3.2195  -4.8217  -0.2701
-#&gt;    6.3601  -9.7357  -6.2739 -13.0223   5.6398   1.4987  -3.4715  -5.8827
-#&gt;    5.7907  13.7410  -3.7555   2.7048  -8.5813   0.0651   3.8896  -4.8699
-#&gt;   -3.1150  -6.1272  -8.7806  13.0594   9.1360   4.1213  -0.5146 -10.6455
-#&gt;   11.9715   5.7424 -12.4139  -2.0597   7.4455   3.0353   1.0908  -7.9611
-#&gt;   11.7003   8.3866   4.6030   9.8225  -2.3902   8.7004   6.1896   9.8718
-#&gt;   11.3908   3.2093  -0.3263   6.5570   4.2394   3.3151  -0.7654  -0.8577
-#&gt;   -7.1899  -0.5171 -10.2602  13.0129   5.2471  -8.6816  -4.1241  -2.5502
-#&gt;   10.0128  -5.7370  -7.0369 -14.2115  -3.2665   6.0096  -3.0272   2.3381
-#&gt;  -12.0559 -12.2685  -0.7760  -6.3868   7.5062 -13.2926   0.5958  -0.0808
-#&gt;    6.6806  -3.3655 -14.8449  -1.4938   1.2611   0.3283  -2.0808   4.3862
-#&gt;   12.7465  10.2326  -8.4345  13.4454  10.7211   8.4600  -0.7885   6.4131
-#&gt;   18.4789  -3.9546  -6.8123  -0.3013   2.3120   9.2182   0.7449  11.6885
-#&gt;   -3.9107 -13.9707 -19.0525  -5.2666   1.1739  -2.8660  -5.5457  -3.4450
-#&gt;    4.4940   0.8204   4.6326   7.6794  -4.2279  -6.9069  -4.0324   7.0190
-#&gt;   -1.2073  -5.7289   3.3953   2.5379   6.3413   7.3179  -6.7071   5.4787
-#&gt;    9.8489  12.4979  -8.1411  -5.3482   1.0144  -1.6472   1.7211   6.3687
-#&gt;    5.9410  -2.7530  -0.3611   7.4208   4.0254   4.8058  10.5008  -3.0000
-#&gt;    0.1707   6.0003   0.0052   6.0979  -6.8061   8.9315  -2.5703   2.7611
-#&gt;   12.8179   5.0785   0.8893  -0.2505   7.8847 -12.7643 -11.0092  -5.0285
-#&gt;   -5.7432 -14.0672  -4.0416  -3.8227   2.1280  -1.7831 -19.2372  -4.9089
-#&gt;  -13.3371  -1.2760  -1.2423   4.8594  -5.7237  -5.8040  -7.0171  -6.0620
-#&gt;  -22.6395   1.4445 -20.3492  -1.2436  -7.1753  -2.6548  -2.5140  -2.9050
-#&gt;   -1.1791 -12.0300  -5.5032  -1.1453   0.1367 -10.0761   3.1004  -5.0046
-#&gt;  -11.2678  -0.6072  -8.3359  -8.6296 -12.8451  17.4804  -7.2580  -5.2038
-#&gt;   -3.4071  14.2809   4.7588   8.9549  -4.5704   7.2120   1.9946   1.1422
-#&gt;    0.1673   7.9842  14.4208  -1.6541  12.3878  -6.3362   6.9060 -10.2869
-#&gt;   -0.7586 -16.2660   0.6054   2.9862  11.8419  -1.3047  -0.9713   1.5911
-#&gt;   -3.4611   1.7307  -7.4610  -0.2025  -1.6172   3.4507  -1.4488 -10.8695
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  Columns 1 to 8  -5.1401   2.9891   3.6854  -2.0045  -7.0004  -1.9791  -1.2630   1.2771
-#&gt;  -10.2495   1.2853 -11.1399  -5.6623  -2.6578 -12.5207  -1.0489  -0.7527
-#&gt;  -14.6691  -5.3106  -0.6014  -1.1323   8.8475   9.6805   4.3286  -5.6615
-#&gt;    6.2327   3.8982  -5.4331  -2.0544  -6.8983  -1.9068   0.7070   2.9490
-#&gt;  -11.1247   0.9452   3.5510   2.1306   1.8040  -7.0209  -5.7147  -0.9105
-#&gt;   -9.7903  -0.9747   0.0376  -3.3293  -7.8518  16.6061  -6.4422  -7.3911
-#&gt;   15.0601  -1.0823  -8.0279  -9.8401  14.4032  -9.2007  -8.0591   0.5929
-#&gt;   -5.7909 -10.7714  -7.4481  -0.8013   5.2140   0.0737  -8.4705 -11.3678
-#&gt;    0.9693  -7.7606 -11.4238   2.1937   6.3889  -2.1902 -11.8824  -0.6918
-#&gt;   -3.2925  12.7191   1.9418  -1.1069  -7.2375   8.9899   2.9341   9.1909
-#&gt;   -0.5819  -5.2088  11.9081   3.9668  -4.2356   4.5495   6.1691   1.1785
-#&gt;    4.7884 -12.5136  11.1392   5.2280   3.8334  -6.0489  -7.1419   2.8084
-#&gt;   -4.0696   9.0168  -0.6686   0.1935   0.5556  12.1796   6.6020   3.2729
-#&gt;   -1.7762   7.5589   4.8489   7.8705  -7.6317  -0.1487   1.4549   9.2803
-#&gt;   -7.2859   6.8746  -1.6356  -2.2162  -0.2173  -8.0906  -0.5528  -8.4503
-#&gt;    0.2101  -1.4912  -3.6644  -7.0901   3.4420  -4.5725  -3.8631  -3.1659
-#&gt;   15.2942   7.2586 -17.7463 -12.3811  16.2385 -13.7671   0.8749   7.0659
-#&gt;    5.2415   3.7180   4.5902  -1.5188  -2.1454   4.7362   5.2021  -5.2732
-#&gt;   -0.8768  -3.3167  -2.5345 -15.6709   7.7426  -1.4924  11.1874   7.2779
-#&gt;    2.0993   1.4607   3.3814   4.2640   5.2703  -6.6622 -13.0638  -2.3759
-#&gt;   22.5019  -8.1139 -14.3416   6.5399 -10.6754   2.3931  11.9964  11.7181
-#&gt;    4.9253   1.1006   7.8105   3.4480   1.9546   2.9792  -3.9934  -2.3985
-#&gt;   -4.4107  -2.9882   2.5620   1.5775  -0.7672   0.5119  -0.3191   1.9106
-#&gt;   14.3325   7.4474  -3.9746  -0.1538  -1.2816  -3.3035   3.7315  14.5845
-#&gt;   -0.6938  14.9001   5.5102   1.8017   5.6054   3.8851  -3.0517   1.8312
-#&gt;   13.1884   5.2263   9.1952  21.3386  -3.4863  -0.5790   7.7154   2.4801
-#&gt;    7.7607  -7.5605   6.3781   6.5496  -1.6575  -0.9981  -4.5876   5.7385
-#&gt;    2.5876 -13.7355  -2.2563   9.9073   7.9363   3.2230  -4.0402  -3.4254
-#&gt;    3.8922  12.9799   5.0126 -16.9307  10.5720   9.1794   2.4405  -3.8747
-#&gt;   -2.2463  -6.7408   1.7032  -4.0927  -9.6229   8.4270   0.2554  -4.6074
-#&gt;    9.0040   0.8309 -13.1169  -5.0419  -7.4778 -12.5897  -2.6151  -2.9553
-#&gt;   -1.7402  -8.1894  -0.1711   9.5450  -1.2410  -6.6030  -2.1840   1.4335
-#&gt;  -13.7242  -8.5122  -1.2834   4.0773   5.8318 -12.5343  -0.4867  -3.7614
-#&gt; 
-#&gt; Columns 9 to 16   9.8571  -5.4970  -4.7961  -1.1280   0.1739  11.3412  14.5539  -3.0924
-#&gt;   -6.9112  10.5632   6.8550   3.6621   1.5868   4.3023   4.1590   3.9751
-#&gt;    1.7595   2.2515   0.7001   2.6531  -5.5236  -9.9334 -10.5692   2.0439
-#&gt;   -1.3575  -1.0243  -2.1813  -0.5363   3.2841   8.4488   1.8828  -5.4478
-#&gt;    5.2268   0.5351  -8.8676  -8.1249  11.8586  10.5842   3.1557  -6.0980
-#&gt;    0.1332  -1.4302   8.6076   1.4880   1.1752 -12.0895  -7.8671  -5.7512
-#&gt;   -4.7422   1.1416 -10.7403   7.7678 -22.9666  -1.5633  -4.5176   1.7204
-#&gt;   -2.8337  18.5891   2.0773   9.4748  -6.1321  -4.7474  -6.6705   2.5098
-#&gt;   -0.5635  -5.0364  -0.3154  -3.0790  -0.6538   1.7954   5.8419  -1.6884
-#&gt;   -1.6573  -5.0337  -6.8207   1.8921   8.1757   0.7998   2.3158  -5.7294
-#&gt;   -2.1578   2.2613  -3.5485   2.3373   1.3449  -6.8268  -2.6639  -0.6141
-#&gt;   -0.4174   8.5788   2.3053   0.5438 -11.3671  -0.7244   5.3468   3.1103
-#&gt;   -2.7455  -4.7882  10.7898  -5.2450   4.8261  -2.5860  -4.0618   0.5309
-#&gt;   -2.2616   3.9130 -10.8658  -9.8858  10.6163   5.8770   1.8931  -9.0417
-#&gt;   -4.0858  -6.9691  11.1223 -15.3887  10.6941   0.5416  -5.1616  -6.1336
-#&gt;    6.3874  -3.8447  -8.1305   2.3320   0.4516  -3.8186  -7.0528   2.8678
-#&gt;  -11.8405 -13.1693   4.0353   1.2665   8.5606  -8.1881 -16.8899   6.8920
-#&gt;  -12.1745   8.2331  -1.7491  -5.6638  -4.7077   3.1822  -3.6249  -7.6037
-#&gt;    7.0239   5.3627   3.1592   7.6618   3.2653  15.4284  11.3256  -4.1272
-#&gt;   -2.1601  -0.7444  -6.5572   3.1464  -4.9748  -9.5653   6.4273   9.9759
-#&gt;   -7.1593   0.5837   6.4136  11.6499   5.6336 -18.0972   4.9821  -6.7565
-#&gt;    6.1681  -0.8783  -9.0454   4.7079  -5.3446  -3.3585 -15.1350  -6.0367
-#&gt;    9.8582  -2.5755   6.9850  -8.6046  -4.1694   3.8017  16.3042  -5.0872
-#&gt;   -0.3317  -2.4142   0.3969  12.5264  -8.8336 -13.4539   3.1462  14.2006
-#&gt;   -0.8866  -4.0586  -9.5497  -0.1005   8.6526  -3.6987   2.2418   6.8930
-#&gt;   -2.2903   8.4748  -9.4138  -2.3453  -2.6753  -3.7506   4.7780  -6.1718
-#&gt;   -8.5915  -0.7176   9.7090 -10.5553  -9.5967   0.5687  10.0290  -4.5417
-#&gt;   -1.8588  10.0673  -1.7840   2.6291 -21.9098  -1.6862   5.8108   7.0017
-#&gt;    7.3925   6.3586  -1.5571  -5.5032  -0.1496  -1.0453 -27.3416 -20.0032
-#&gt;    6.2774   2.8662  -0.6706   4.4540   2.5717   4.4916   2.8127   5.6678
-#&gt;  -12.0906   3.3666  -9.6580   3.0007  -2.7190   1.1171   9.3504   6.3476
-#&gt;  -11.5325   8.9243   2.9290   2.1237   0.2138  -9.3603  -1.8829  10.7308
-#&gt;   -3.6002   3.6075   7.0636 -16.9638  -5.2289   6.5603   3.7788   4.4138
-#&gt; 
-#&gt; Columns 17 to 24 -17.6896 -10.9216   6.1166   9.3809   3.4033  -1.7750 -17.5410  -7.0076
-#&gt;   -3.3511  -5.0163  -4.9899   6.6577   6.4240   8.1514  -3.7802   9.6353
-#&gt;    8.1529   7.8445   5.2203   4.5544  -8.6214  -8.6967   4.6334  -8.8516
-#&gt;   -6.3675  -5.5294  -0.7452   0.2887  -0.0210  -3.5340 -11.7942   0.0769
-#&gt;    3.2059   5.8265 -10.6364   1.6278  -1.4653   0.1535 -12.2484   2.3563
-#&gt;   -8.6023 -12.7386   3.5851   4.3380   4.1190   5.5795   3.2848  -9.5142
-#&gt;  -22.9352 -17.3708   1.3332   1.2703   4.0690  -5.6978  -0.9998  -5.5937
-#&gt;    4.8173  13.6861   9.5823  12.1606   1.1511  -2.6682  21.1044   3.8797
-#&gt;  -11.1495  -2.2240   7.4969   8.8557  -1.2520   2.1998 -13.4179   3.2909
-#&gt;  -14.2831 -10.8502 -19.7032   4.0125   1.2352  -7.9701   0.3782  -7.6624
-#&gt;    6.7731   4.0463   1.6472 -13.3156  -2.5884  -4.7533   2.5037   2.9595
-#&gt;   -7.7285   8.1643   2.6828 -12.0793   7.5963  -0.5689   2.4415  -0.1157
-#&gt;   -9.2450  -4.1374   5.7683   3.9325   0.9523  -4.3374  -7.0388   5.7721
-#&gt;  -10.2972  -2.6818  -4.2265   0.2421   2.0533 -11.4911 -10.7471  -3.3639
-#&gt;   -3.6158   9.9967  -7.8270   5.7009   5.3671   5.6912  -8.7022  22.0794
-#&gt;   -0.8002  -5.0739 -13.3168  -1.0714   2.7998  -4.3167  -0.3845   3.0065
-#&gt;    4.3178  -9.9193   2.7227   4.7676   8.7756  -1.2959  -3.8048  15.4358
-#&gt;  -14.2307  -5.3521   3.8728   1.7946   7.8943  -2.9211   1.3611   7.3936
-#&gt;   -7.7010  -3.2103  -5.8694  -4.6234  -2.1603  -9.5469  -2.4468  -9.2347
-#&gt;   -8.2030  -7.5076  -4.5605  -7.1145  14.2618  -5.6253   5.3856 -12.1237
-#&gt;  -18.9178 -14.9456  13.2634   8.5863   5.4541  -4.1651  -4.4314   0.0651
-#&gt;    4.5710  -5.7293  -6.6324  -0.9889  -5.2860  10.7173  -3.8948   0.6377
-#&gt;  -12.6227  -5.5911 -19.6339   0.5543   2.8215   3.9667  -2.7955 -10.6250
-#&gt;   -5.3722  -4.5845   0.8822  -4.5569   5.4575 -11.4895  -5.4398 -13.3669
-#&gt;    7.7363  -3.3747  -0.7506   3.8520  -4.7260  -7.4649   3.8072   0.5870
-#&gt;   -6.5552  -0.3465   4.0443  -0.7468  -6.7656  -8.8605  12.5171  -4.0613
-#&gt;   -9.1364  14.0149   6.5995   5.7865  -4.2879  -1.4943   5.4850   6.0146
-#&gt;    2.8183   8.9986  11.6363 -15.4410  -0.8250   4.4861  -0.8706   0.2726
-#&gt;   -9.2320   0.4260   4.3830   4.7455  -6.4861  -5.4021   8.0438  -6.3451
-#&gt;    8.2466   9.3315 -10.9963  -1.2862  -2.3195  -0.0738   9.5236  -2.3628
-#&gt;   -4.9558 -10.8690   1.6072   0.9882   6.6080  -2.3783   2.8413   6.8132
-#&gt;   -6.6503  -1.6043   2.9194  -0.2525   7.0461 -12.9666  17.4623   0.4977
-#&gt;  -14.3724  15.3996   5.5160   4.5464  -1.9399   0.3317  -6.0057   5.5294
-#&gt; 
-#&gt; Columns 25 to 32   6.6036   8.1161  -1.7021   2.3542   5.4502  15.3679   2.3495  -1.1373
-#&gt;  -10.4590  -5.7762  -1.7290   5.5274  -3.4514  -9.0553 -13.4629  -3.3394
-#&gt;   -3.3505  -6.5129   2.8572  -5.7496  -5.7892  -2.1036 -10.6981  -1.0462
-#&gt;   -5.3248   8.4740   3.7026   8.6871   1.9993   2.6370   6.6531  11.9987
-#&gt;   -4.6731   6.4509   4.2315  -3.8841   3.4378   7.8674   4.6799   0.1933
-#&gt;    1.1077   4.8246  -8.1914   4.4516 -10.4259  -2.4289 -14.7650  -6.6720
-#&gt;   -7.9066  -8.0686  -6.0699 -14.7133   7.2229   0.8254  13.3259   1.1986
-#&gt;   -3.3064  -0.4407  -3.9130 -10.2883  -4.5379 -12.3062  -2.8917 -11.3214
-#&gt;   -8.6617   9.1532  -0.8713  -2.5023  -1.7706  -4.5747   6.5376   0.8857
-#&gt;   -9.2647  15.1937   2.2581   5.8134   4.5687   1.4851  10.7703  11.0167
-#&gt;   -6.7107  -2.7804  -5.9362   3.1535  -3.1476  -3.6306  -6.8308  -3.3945
-#&gt;    1.3362  -7.6712   0.6695  -5.1662  -1.0520 -10.0864  -2.5184 -11.7377
-#&gt;    2.7128   8.5904  -1.2155  10.4349   2.4146   2.6046   3.7720   3.4512
-#&gt;   -2.7816   4.9606  17.2960  -0.6522   1.8488   8.0943  16.1911   9.1931
-#&gt;    0.9872  -6.9494   7.2456   5.1044 -13.1207   5.2223  -6.1384   2.1601
-#&gt;   -8.5292  -5.7445   4.7904  -2.1745  -1.9215  -8.3205   3.1108   5.4473
-#&gt;    6.7802   4.5973   5.1196   8.1843  -3.1226  -2.4572  15.8010   4.4050
-#&gt;    7.6354   6.2707  13.0527   4.1820  -5.4294  -9.7027   3.2210   3.8803
-#&gt;   -0.7812  -3.7505  -0.9112   4.9820   0.5696  -1.0266   3.7235  -5.4364
-#&gt;   10.5026  -7.9487   2.8596   4.3223   1.5365   1.7406   9.5535   1.7360
-#&gt;   -3.8686  -0.4613  -1.6795   3.2893  -5.7041  -7.2631  -6.2078  -5.8585
-#&gt;    3.9595   3.9136  -5.6314  -7.5519  -2.7341   1.7137   2.3271   6.7937
-#&gt;   -4.3176   0.8101  -6.8722   8.1672 -12.3054  -0.3632  -2.1652   3.6764
-#&gt;   -2.1259  -6.6305   5.5957   4.1752   2.8399  -4.5020  -4.1701  -4.1770
-#&gt;    5.0907  10.0696  -2.3253  -4.2847   6.3644  -1.1183   8.1597   0.5469
-#&gt;   -5.2295   8.0413  -2.3816  -2.4416   3.4511  -4.8031   2.4804  -6.6455
-#&gt;   -5.3982   5.4411   2.2564   2.8912  -3.3218  -7.7255  14.2990   2.9612
-#&gt;    4.3862  -3.1984   2.7287   3.2133   2.5256  -2.7044  -0.3442   0.8886
-#&gt;    3.3121   1.7107 -10.6645 -12.6965   0.9991   4.8000   7.5047  -4.7880
-#&gt;   -1.8414  -1.6665  -4.8782  -3.1876   4.7051  -0.9972   2.7037   8.4290
-#&gt;   -9.5753  -7.3074   0.9161  -8.9668   6.5210   4.2724  -6.1515  -7.1107
-#&gt;    1.1067  -2.8359  10.3105   3.2704  -6.3521  -5.6610   0.1581  -1.1411
-#&gt;   -9.9139 -11.1382   5.5292  -7.1405  -1.1180   3.4732  -0.0515 -10.2581
-#&gt; 
-#&gt; Columns 33 to 40   7.1652   1.2287 -13.5560  -4.8686  -5.3708   1.9578  15.5168  -2.9651
-#&gt;    1.2481  -7.9181   0.0644  -2.0892   2.6178   4.4872   4.8869  -5.1832
-#&gt;    0.2735  -2.0000  11.5172   7.3277  -3.1502  -2.9008  -8.6955  -2.6462
-#&gt;   -0.4419   4.0096  -0.4510 -10.1711  -3.2182  -1.0538   3.1831   4.0236
-#&gt;    3.5192   2.1975  -8.2017  -4.8915   4.1506  -1.0803   6.4634  10.1137
-#&gt;  -18.3514   5.7033   1.6655   4.5916   3.9825   4.7471   1.4548   5.2980
-#&gt;   -1.7045   3.8765   3.8355  -6.0887  -9.9006   1.8306   3.2691  -3.0254
-#&gt;  -11.4083  13.8799   6.5979   0.9711   5.3733   3.5109   3.9793  -1.4302
-#&gt;  -10.8453  11.8382   6.3904   0.5694  -0.7862  -1.1519   8.5066  -6.4562
-#&gt;   -2.8916   2.0212   2.9703   1.5858   5.3421   7.2600  -1.5795   9.5646
-#&gt;    4.0802  -6.1128   1.8854   5.0854   0.6063   8.3303  -1.9129 -14.2563
-#&gt;   -5.1472  -0.0662   0.9003  -0.8377  -2.3747   6.5395  -1.4825   3.9956
-#&gt;    5.6830   2.1316 -12.0016  -2.0996  -3.2013   4.8972  -7.4217   4.6644
-#&gt;    3.9509   7.2460  -6.1521 -14.3227   9.2101  -2.4229  -3.4947   8.6166
-#&gt;   -2.6759  -8.1423   4.2009   1.0540  -5.2791   0.8042  -5.5540  -0.1667
-#&gt;    2.4905   1.0666   8.9896   7.3039  10.4994   6.2647   0.2095  -3.6636
-#&gt;   -7.2757  -5.5625   7.1330   0.8365  -2.3153  -3.1432   0.2337  -0.7092
-#&gt;   -2.0980  -5.1387  -1.7914   7.4071   9.0824  -4.4115  -3.0730  -2.9847
-#&gt;    6.3396 -18.2766  -4.7137   6.9787  -4.3450  -2.0121   5.6916  -2.2878
-#&gt;    1.9989   8.1358  -8.9209   0.3194   3.9223   1.0879  -3.1660   8.3189
-#&gt;   -7.2509   7.1466   6.9537  -3.5602   4.7089   1.4702   0.5506 -15.6706
-#&gt;    4.9224   3.4705   9.2749   1.7143  -1.9687  -2.6109   4.7653  -1.7043
-#&gt;   -9.2648   2.4274  -4.6019   2.8622   0.2581 -11.0945  10.6314   7.1175
-#&gt;    1.8639   7.8980   1.6288  -5.2198   4.3706   6.0685  -0.1800  -0.7179
-#&gt;    3.2921   5.3475 -14.2696  -8.3453  10.4627  -7.7334  -3.4741   1.9138
-#&gt;   -1.0758   7.9298  -3.9682   5.0119   8.2256  -2.2145   3.0054  -5.3200
-#&gt;  -12.9566   7.9280   0.0164  -8.5893   1.5244  -5.3425  -1.8154   2.4401
-#&gt;    0.8506  -3.8422   3.2239   0.1755 -10.1490  -2.7065  -0.2887  -3.7045
-#&gt;    1.0652   6.4283   4.8548  -3.2470 -13.0942   6.9307   5.0737   0.3171
-#&gt;    3.8739   6.0818   0.7001 -10.6699  -1.0735   2.0271  -7.3934   8.3590
-#&gt;    1.0804   0.8694   3.7074  -3.6680   3.8043   7.6345   9.1481  -9.8256
-#&gt;   -4.9177   6.4454   1.2122  -2.4715   0.1887  -0.1556  -6.9056   7.7501
-#&gt;   -4.1760   1.5984  10.4468  -4.6939  -5.1929   9.5267   1.3337   5.0863
-#&gt; 
-#&gt; Columns 41 to 48   0.0405  -9.5878  10.0952  15.4148  -0.5976   0.7963  -5.5943  10.7921
-#&gt;   16.3629  -3.5819   5.5243   0.2670  -5.6984   4.3231  -3.2319  -1.2505
-#&gt;    1.4282  10.5400  -1.4845  -9.9821  -7.8239  -3.7967   6.7649 -12.8533
-#&gt;    8.0498  -5.9473  -6.5123   0.2838   2.9873   4.1648   1.5601  -3.4950
-#&gt;   -0.8596   0.5478   3.4920  -0.5004  10.1191  -6.8280 -11.4663  -1.9811
-#&gt;    5.8049  15.3434  -2.2790  -7.3346  -0.8419  -4.7339  -0.1636 -12.6907
-#&gt;    6.0463  -7.2811  19.3027  -4.2404 -12.9967   0.0865   0.1548   2.5554
-#&gt;    8.4615   7.6812  -4.2476  -7.4295  -7.4097   4.8185   2.9464  -8.6620
-#&gt;   -1.7452  -4.0130  10.0046   2.3216 -11.4648   5.2419   0.6076  -1.3365
-#&gt;  -14.0807   5.1180  -9.3910   5.8205  10.6765  -9.9342  -1.0504  -5.0189
-#&gt;   13.2376  -0.2804   8.6606 -14.3538  -5.9908   7.9020  -8.7896   0.6314
-#&gt;   12.3987   2.2480   7.2934  -6.0149 -12.9985   5.4448  -9.3546  -0.1917
-#&gt;   -7.0654  -0.8432  -6.1276  -8.4352  -1.0537  -2.0589   4.5447  -2.9847
-#&gt;   -7.4080   4.3264  -9.4735   7.0444   4.2558   2.9492 -10.9013   0.7372
-#&gt;   11.6950  -9.5635   9.1511 -16.2792  -7.7242  15.8298 -12.3277   4.7026
-#&gt;   -0.2540 -11.2306  11.8295  -5.3432  -0.9107   1.0168 -14.5350   1.3697
-#&gt;   -0.7330  -0.4783  -1.7710  -4.8379  -0.5939   5.4838   5.7920   1.7615
-#&gt;   -2.5330  -3.6687   4.5876   6.1261  -0.0873   2.3374  -5.2648   2.0655
-#&gt;    2.0273   6.8663   7.2672  11.6367  -1.5750  -1.0413   9.2695   2.5478
-#&gt;    0.7622  -5.2750  -7.2366  15.5378  -5.5701   2.1129 -12.8660   3.3016
-#&gt;   -1.7983   7.3632  -3.1614   7.2167 -16.2810  -2.4361   0.3686  10.4941
-#&gt;    0.6089  -7.5083   4.7364   0.0799   8.1950  -3.1154   3.4227  -2.4040
-#&gt;   17.6533  -5.4287   6.7579  11.1121  -4.3991   7.6764  -8.0220  -1.4114
-#&gt;    9.4218   4.5275 -10.9237   3.4291 -17.6475  -2.9225  -4.7114   2.2295
-#&gt;  -12.1978  10.3869  -7.1949   5.3491   6.6061 -10.3745  -7.6148   0.9015
-#&gt;   -6.6805   4.8011  -0.3284  -0.0606  -2.2321   0.9008 -18.7628   4.1777
-#&gt;   -2.3978  -3.6844  -2.6985  -0.3578  -3.2746   2.9670   7.2708  -7.6612
-#&gt;   17.4282  -4.3948  -3.8382  -3.7594  -7.7857   7.0744   2.4519   3.2632
-#&gt;   -5.4875  -0.4926  13.2292  -5.2483  -6.3564   5.8925  -0.2431   2.8434
-#&gt;   -2.9212  -7.4122  -6.6158   3.0582  12.4344   8.9955  -1.1278   1.5566
-#&gt;   -7.6723  -4.1985   8.7090   7.2673   1.2156  -1.4923  -1.0183   2.4944
-#&gt;    1.4382  14.2864 -12.1800  -9.8127   6.4559   4.6857  -4.7795  -6.5996
-#&gt;   -1.3952  -5.6770   1.7847  -6.1678  -3.0106  10.9054  -1.0151  -9.1303
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  Columns 1 to 8   9.1638   3.6882  -1.4900   1.9021   5.2824   1.2595  -2.3194  -6.2817
-#&gt;   14.4916   3.3362   1.7776   2.0018  -3.8694  -2.6993   7.4487   0.4453
-#&gt;    4.8686   3.1936  -3.9411  -7.1315   9.6472 -16.0619  13.5716   8.7610
-#&gt;    3.6759  -3.5180  -0.7284   0.0284   6.6126  -0.1099   0.3720   2.6339
-#&gt;   17.3720   2.1112  -0.6589  -0.7924  -7.1850   7.0953   0.3570   0.2443
-#&gt;    0.2817   4.0050  -3.7902   0.5616   1.1883  -0.7157   4.0918   0.9116
-#&gt;   -5.7054  10.1260  12.8775   8.4681   6.4144  10.6390   0.8582   4.9601
-#&gt;    8.0631  11.4496 -14.9217  -9.1295 -11.4756  -8.2810  12.9009   0.2227
-#&gt;   10.0878  12.0147   4.4469 -13.3732 -13.4876  10.7037  -7.2164  10.5185
-#&gt;  -11.8816   3.5237   0.8997  19.7903   2.5893  -6.8781  10.6479 -18.5587
-#&gt;  -11.5629  -9.2503   3.0108   7.0590  10.8706  -2.7321 -13.8865  -1.9753
-#&gt;   -6.2120  -9.5173  -6.2259  -4.9643  -4.6812  15.0502  -7.6963   0.1510
-#&gt;   -0.3463   1.8706   2.0464   2.4487  -3.4562  -4.5221  -6.0647  -2.1696
-#&gt;   -7.9619  -6.1449   3.6920  -3.0701  17.2400  12.2838  -3.0448  -3.9877
-#&gt;    3.8974  -8.3683   8.5289   1.8103  -0.6915  -2.0672  -3.0284  11.8991
-#&gt;    4.1077   5.2857  12.4167   4.6372  -0.5276   2.7953   2.3234  -4.8525
-#&gt;    2.0778   5.3054   6.7339  -4.4931  -2.8300  -2.7427 -10.5154   5.1110
-#&gt;   -0.4390   0.9020   2.8509   3.1979   1.3508  -6.2378  -9.7670  -3.7932
-#&gt;   10.0147   0.6865  -3.9110   7.8018   6.7673  -1.0454   6.3263   0.5413
-#&gt;    4.8330   3.8636   2.5759  -8.9623   3.9439  -6.2807   4.9803   3.1210
-#&gt;   20.7881   4.8485  -8.6650  -9.7318   6.7770   4.9582  11.9298   1.1469
-#&gt;   -3.5349  -0.3239   5.3343   9.0849   1.3093  10.7023  -3.4873  -2.8219
-#&gt;    1.5887   0.2202   2.4243   9.0131   1.8756   2.8847   4.9547  -2.3830
-#&gt;    2.0008  -1.3657 -10.2702  -5.0324  -0.8556   2.2124  14.0296  -9.4469
-#&gt;   -8.7122  -2.7868   0.6741  -7.5294  -2.2701  -0.7498  -5.6424  -6.6196
-#&gt;  -14.7183  -3.5053  -4.5020   1.6182  -7.2199   4.0114  -3.5095 -11.6948
-#&gt;  -16.4266   6.0878  -7.1184  -6.9250  -1.0367   5.3469  -4.4815  10.5077
-#&gt;    3.6201  -5.8641  -2.6717  -3.7096  -4.1037   1.3777  -1.6717   4.3828
-#&gt;   -9.2749   4.1664  15.2274  13.8957  -1.5371  11.2868   2.0850  -3.5902
-#&gt;   -7.1083  -9.1817   2.6311   9.4226  -1.2113   2.5381   6.7706  -6.3328
-#&gt;    3.7839   5.2132   4.3150   2.4450  -2.5459  -0.5037   7.8490   3.3446
-#&gt;  -11.4313   3.4480  -7.0541  -6.0500   7.8839  -5.7576   7.9950   4.3476
-#&gt;    6.4377   0.1415  -2.6402  -6.5055   2.8678   5.4720   6.6924  11.5225
-#&gt; 
-#&gt; Columns 9 to 16   1.6338   2.5904  -1.3795   2.5861   5.3855  -3.6711  -4.0181   7.0750
-#&gt;  -10.5976   2.6834   6.7337   0.5742  -1.8189  -9.0122  -1.6219   1.4393
-#&gt;   -3.6160   5.0279  -0.8405  -1.1030   0.8432  -2.2966   6.3687  -6.1715
-#&gt;    8.2379  10.0968  13.6375  -4.3996  -2.4214  -1.4974  -1.9525   0.7837
-#&gt;    3.9122  -4.2968  -3.2637   6.0831  10.3034   4.7541  -2.7630   2.5158
-#&gt;   -2.5482   4.3048  15.1010  -1.7226   6.4048  -2.7931  -5.1406  -2.1162
-#&gt;   15.2691  -1.8263   2.6939   8.8252   1.1932   0.6629 -10.7840 -11.3197
-#&gt;   -3.3560  -5.4537  -3.3969   3.7776  -0.0694   1.0253  -0.5168  -4.6132
-#&gt;   20.9841 -10.1630   9.4555   4.4206   2.1666  -7.6388 -12.5039  -1.6079
-#&gt;   -0.9275   0.6090  -5.5709   6.3542 -11.2367  -0.2272  -0.7234  -1.2992
-#&gt;   -8.2740  -8.0906   1.8479   5.3004  -6.5082  -5.9983   6.4064   1.3401
-#&gt;   -0.3353  -4.9388  -0.5536  10.9004   1.8917  -1.0431  -3.3708   5.8208
-#&gt;    6.0412   3.6215   3.1179  -2.5438  -2.7352  -6.0695  -8.7271   6.1917
-#&gt;   -4.4348   0.4532   0.6532  -1.7140   3.0609  -0.2905   3.6017  11.6244
-#&gt;    7.7500  -5.0567   0.1274  -0.3418  -2.1921  -5.8314   7.6361  -2.8354
-#&gt;    5.1753  -8.4772 -17.5655   9.5376   0.2942  -3.7497  -6.3106 -11.0164
-#&gt;   11.8703  -4.5021  -2.4982   0.2357  -1.6528   4.6353  -1.5200  -3.8943
-#&gt;   -1.8370   8.3091  -1.0949  -1.8411   3.6213  -1.6838   7.3284  11.1192
-#&gt;    0.9923  -6.5147  -8.6741  13.4892  -5.2216  -1.4116  13.9634   3.6243
-#&gt;    0.7987   1.4784  -0.2801   3.2003   4.5473  -9.7629 -10.7815  -0.9872
-#&gt;    0.5355  13.7313  -5.8606   2.0004  -1.9300  -8.1917  -9.1222   3.0077
-#&gt;    2.4176  -6.4259  -0.5226  -2.4502   1.6403   5.7646   4.3107  -0.3507
-#&gt;    3.1944 -14.8918   6.8699   5.7918   4.8419  -0.0710   4.8238   4.9116
-#&gt;   10.4076   8.9117  -1.2833  10.8395   0.3169  -7.5445  -5.8118  -1.1427
-#&gt;   -1.8550  -4.0886  -2.0849   0.0480   7.6252   6.9561  -7.3016   9.7992
-#&gt;   -0.3027   2.5162  -0.6379  -4.7078  -6.2117   7.5104  -0.4397  11.9003
-#&gt;   -0.5846  -0.3860   4.8536  -0.3456  -3.3124  -0.1860   7.4323  12.0997
-#&gt;    2.1446  -9.9060   2.6589   6.6331   5.9130   4.5521   1.3591   2.8319
-#&gt;    4.5539  -1.6960   2.6203   1.2476  -0.7105  17.3567   4.1383  -4.4724
-#&gt;   -7.3011   2.4913 -10.8191  -5.2914  -1.5731   9.8724  -2.9685  -7.7653
-#&gt;    4.9472   4.9418  -6.9768  -5.7289  -3.4887  -1.9116  -3.0334 -13.1024
-#&gt;   -8.2603  -4.6515  -2.0829  -7.3269  -1.0011  -0.2815  -3.3144   6.4622
-#&gt;    4.1325  -9.4771  -4.7516   5.0727   2.7396  -2.5412  -0.3594   2.2085
-#&gt; 
-#&gt; Columns 17 to 24  -1.9096   8.8365  -3.7854   8.2781  -5.6603  10.1425  -0.3567   4.2259
-#&gt;   -4.2879  -4.7475  -4.6487  -8.4129  -3.8061   7.6757  -1.2777  -5.9397
-#&gt;    1.4533   5.0441  -6.1458 -13.4315  -9.4030  -4.4679   0.2848   6.1462
-#&gt;   -2.6478  -2.0549  -4.7424  -4.8533   2.5207   0.9564  -2.4556 -10.7582
-#&gt;    0.7277  14.3967   7.1568   3.0980  -1.7226  -7.0385  -7.1300  -6.1912
-#&gt;    0.9612  -5.4163  -3.8963  -5.7145  -9.3681  -3.1415   9.4374  -0.5752
-#&gt;   -2.0262 -13.7083 -11.9087 -14.6716  -4.0001   7.4422   1.7686  -6.4241
-#&gt;   -2.1961   9.3324  10.3146  -6.6045  -5.2707   6.2233  -2.3908  -6.1348
-#&gt;    0.4135  -1.8079 -11.0733  -5.1140  -2.7110  -9.7772 -10.0638  -5.6880
-#&gt;    4.3132  -5.4741  11.7573   0.0477   9.3906   7.6040  -3.7346   1.8079
-#&gt;    0.7705 -11.0970  -3.3342  -1.0715  -1.3398  18.5944  10.4294  -4.0884
-#&gt;    3.8526  -4.7988   5.1229  -1.3176  -7.4406   4.5414   4.0471   2.4845
-#&gt;    2.6426  -3.8908   0.0568  -2.0527   0.5920   5.9071  -9.6883   5.0108
-#&gt;   -0.4555  -0.0195   7.1886  10.4334   5.7257  -0.1726   6.2006  10.3319
-#&gt;  -10.0016  -0.3585  -1.0483   7.1960  -3.6162   7.9824  -3.2704   3.0012
-#&gt;    2.9868  -1.6780  -2.7331  -5.2944  -5.5285   2.9330  -9.0217  -5.1850
-#&gt;    1.0708  -3.4549   4.3333   8.4961   1.7040  -6.7628  -7.9385 -21.8955
-#&gt;   -1.0820  -0.0958   9.9405   1.3104 -10.3388  -3.4642   1.7120   2.3893
-#&gt;   -4.4309   1.5273   4.7975  -6.1996  -2.8116   7.4166   9.8426  11.5083
-#&gt;   -1.6813   4.2031   1.1643   5.1260   4.4465   4.4902 -18.5925  12.1389
-#&gt;   -3.9087 -11.4436  -1.3064  -0.2479 -11.4403  -1.3369   5.6264   1.9846
-#&gt;    6.2025   2.6212  -1.2286  -0.3254   3.8101   3.3465   8.3057  -9.0287
-#&gt;    0.6409   7.3732  -1.6294 -11.0359   5.4635   3.5923   0.7846   8.1205
-#&gt;    2.0369  -4.5326   7.1004   4.5283  -5.2854   0.7526  -9.5406  -5.4061
-#&gt;    1.8293   2.8323  -5.8847   0.1115   8.2994  -3.0108  -3.4122  -1.0538
-#&gt;    1.2936   1.4286   2.4235   5.6016   6.6240   3.5033   9.5232   9.0168
-#&gt;    2.4278 -18.7473   5.9898  -2.5130  13.0707  -6.0240  -2.2090   2.6924
-#&gt;    8.5765   2.9447   6.3192  -6.0189  -8.7248   5.2498  -2.6647  -6.2861
-#&gt;   -4.6449   1.5774 -15.6237  -9.4836   1.3785  12.6079   9.8665  -1.7048
-#&gt;    1.4699   3.2630  -1.6208  -7.5943   4.2511   6.0164 -10.1087  -5.7031
-#&gt;  -10.5737  -9.8401   3.7970   0.8300   1.8599  -0.7041   4.2164  -2.1421
-#&gt;    8.6079  -2.9979  15.0159   5.7356   3.7048  -1.6607   2.0224   3.7717
-#&gt;   -9.6679  -3.1480   1.6026   0.2444  -1.7441   0.4920  -8.4345   9.4231
-#&gt; 
-#&gt; Columns 25 to 32   3.2746  -5.3544   1.5765   7.5040  -4.5201  -0.5418   6.2918   7.5506
-#&gt;    8.0350  -5.0183   1.9608  -0.7341   7.2593  -4.7468  -6.8602   9.3783
-#&gt;  -16.1267  -0.5098  -6.5662   4.9825  -1.7978  -1.1050   2.6394   4.6272
-#&gt;   -9.9211  -2.1541   4.8102  -0.0452   4.6456   2.8887  -7.0923  -8.1923
-#&gt;   -4.4179  -2.7234  12.2220   5.5959  -4.5837  -3.5218  -1.1515  -3.0429
-#&gt;    4.3317   2.8163   1.0977  -2.7213  -5.0059   7.0347  15.0545  -6.6500
-#&gt;   17.7210   3.2446  -9.6511  -1.3928  -2.0326  -1.5506   8.7502   2.2437
-#&gt;    6.3377  -1.9100 -10.6635  13.3942   4.0902 -11.1767  10.2042   1.9022
-#&gt;    9.6561  -9.8284  -5.9367   7.9524  -4.3415   0.2088   3.2906  -3.4456
-#&gt;  -11.8223   5.9699  11.4183  -9.5866  -4.9338  -1.0064  -1.9083  -7.0137
-#&gt;   -2.5451  -8.3295   7.3965  -0.7297 -10.3471   0.7232  12.8614  -3.1322
-#&gt;    7.8272  -8.7667  -2.9602   0.0829  -0.2064  -0.9705   9.4479  -4.1733
-#&gt;    2.8266  -6.3005  -6.5065   7.7835   0.6127  -6.6281  -1.5653   3.1334
-#&gt;   -3.4354  -0.0973   2.3852   8.9264   2.2043  -6.5506  -2.5536   7.7894
-#&gt;    1.0857  -1.2386   4.2825   6.4698  -0.6658   3.6187   4.8883   0.7578
-#&gt;   -0.8499   0.2777   1.0856   4.4989  -9.2753  -1.4724   7.6150  -2.9526
-#&gt;   -2.8339  12.6430   1.0018  -8.3258  -7.3588   4.5547  -8.0005   5.0431
-#&gt;    9.8237  -4.1843  -1.8351   5.0574  -2.7442  -9.7080   5.0830  15.7520
-#&gt;  -10.4930  -5.9900   6.8784   3.5352  -1.5194  -2.0144  -2.8355  12.8470
-#&gt;    2.3724   4.6273  -0.8265   7.3959  -0.9122  -2.4860  -5.7102   0.4236
-#&gt;   18.2937  -7.5459  -6.8879  -6.7061   9.4459  -5.6910   2.6115   8.6910
-#&gt;    7.6993  -2.9758  10.4464  -4.0682  -7.9124  -1.8266   3.0931  -6.0889
-#&gt;   -2.0825  -7.9164   6.7615   3.5457  -8.3937   4.3983   3.8133  -4.0383
-#&gt;   -3.8639   2.7301  -9.5979  -7.7191   8.3549   4.0140 -11.3592  -9.6572
-#&gt;    0.7260   3.4571   1.0465   1.2831  -9.6611   1.8875   1.9767   4.9788
-#&gt;    6.4373  -7.5893   1.5515   1.4521   3.0363  -0.2058   8.6404  -3.8844
-#&gt;   11.2941  -2.6352  -5.9661   7.8476  11.0102  -3.4241  -3.9193   8.9509
-#&gt;    1.6454 -12.4079  -1.1798   1.7690  -2.8280   0.6160   4.9564  -5.9775
-#&gt;    1.0796   5.9774  -5.2497   2.3239 -11.7842  -1.8620  19.0429   4.3127
-#&gt;   -3.6569   3.6842   2.0205  -2.4212   2.7987  -5.1057  -3.7479 -15.4273
-#&gt;   17.2857   6.0287  -6.2474  -4.7911  13.0454  -2.6981   0.0203   4.3614
-#&gt;    2.2502  -5.5985  -5.4976   3.4159  10.3124  -3.8081  -5.4465   5.3485
-#&gt;    8.4785  -7.3481 -10.9371  14.8331  -0.3773  -7.2166   9.9784   8.7579
-#&gt; 
-#&gt; Columns 33 to 40   2.7270  -7.9337   3.0442  -3.2324   5.0968  -8.8133  -1.5646   0.4553
-#&gt;   -0.6123   5.8192  -3.3059  -7.4606  11.7601   5.9135  -3.7672   3.5025
-#&gt;   -5.9533   1.6598   1.2663   1.3688  -2.5138  -3.5297 -12.8067  -4.5856
-#&gt;    4.6837   5.5700   2.3480   1.5268 -13.7004  -3.0379  -7.1278  -3.7806
-#&gt;    2.3456  -4.1646   2.4204   2.0263 -12.8087 -10.0932  -1.8687  -1.0477
-#&gt;   12.7492   1.1593   2.2423  -6.7539   1.2866  -2.1353  -3.8833   7.8830
-#&gt;    2.9246  -5.5101  -2.7993   6.9452 -14.1776   5.9957  -2.6510 -14.4305
-#&gt;  -10.2064 -11.9850   2.7062  -4.1056   9.9222  11.4376  13.5092   5.6613
-#&gt;    6.5666 -11.6369  -2.6579   2.5734  -9.0492  -7.3555   5.7350   1.8483
-#&gt;    7.4013  15.1306   5.5468  -8.4331   6.1373  -5.2782  -1.2845   9.0169
-#&gt;  -17.3856  12.7137  10.9987   0.6501   1.7954  -2.8360   2.8169  -4.7141
-#&gt;   -0.0865  -3.4360   0.1270  12.4347   1.2475  -1.1502   3.3937  -2.2314
-#&gt;    6.8137   0.0782   0.7062  -3.9208  -0.9139   4.2389   2.0474   6.6697
-#&gt;    8.8109  -8.2822   3.3965   4.8730  -6.0713  -8.4035  -5.4321  -8.8131
-#&gt;   -4.9365   6.2728  -1.3693  -9.0276  -0.2487  -7.6638   4.3875   8.9971
-#&gt;   -7.0940   8.4277   5.2412  -5.5605   0.1342  -0.5596   8.3581   3.9793
-#&gt;   -2.1248   2.7771  -0.3900  -4.2181 -15.6403   2.3492  -0.8064  10.9438
-#&gt;    3.4889  -6.3853   1.9135  -3.5356   9.1790   1.3777  -6.8929   2.3073
-#&gt;  -11.9688  10.7468   5.1027   3.2463   8.3063 -10.3530  -7.8666 -17.9094
-#&gt;    3.0060 -10.8223  -0.0481 -10.8708   2.0900   9.8168   6.1275   3.3863
-#&gt;    5.7040   2.2652  -4.3219   9.9070  15.3590   7.8989   1.1080  13.5319
-#&gt;    7.9169  -1.2335  -3.9784   7.6832  -1.1059  -0.7177  13.7950   6.2807
-#&gt;    4.4005   5.4885  11.6842 -11.3028   9.0294 -19.0239  -3.0357   5.5033
-#&gt;    1.6626   9.4081   1.1769  -5.7453   8.8326 -11.9755  -2.4104  -0.2612
-#&gt;    5.8816  -9.3429   9.7465  -2.4170 -10.4708   2.9159   2.9384  -6.7119
-#&gt;   -0.5342  -4.0436   4.0985   1.3764  13.2097  -1.8393  -0.4871  -1.4581
-#&gt;    3.5573 -11.1040  -6.1510  -5.9582   5.3674  -1.0641  -5.1077  -3.6593
-#&gt;   -3.4162  -1.1720  -2.0505   6.6118  -2.4773  -5.7614  -2.0102  -2.2830
-#&gt;   -2.8083  -0.1999  -1.0913   5.0746  -4.6572  -0.3549   5.7718  -4.5167
-#&gt;    6.2798   8.1515  -2.0182  -4.3132  -0.3090  -0.3500   6.3381  -5.6897
-#&gt;   -4.0200  -1.0147  -8.2818   3.5267  -0.0907   4.9804   4.8122 -11.5208
-#&gt;   13.5642  -1.4253   2.1087   1.8842   2.9682   6.5714   6.0068  -5.3748
-#&gt;   -9.9573 -11.5934   0.8787   4.4379  -8.8546   0.1908  -4.0970   0.5520
-#&gt; 
-#&gt; Columns 41 to 48 -13.6990   6.0994  -4.0083  -4.7074   6.8078  -0.9892  -9.9459   7.3632
-#&gt;   10.0281   4.6947   4.3700   1.3390   1.3851 -18.7811  -8.3067   3.9895
-#&gt;    4.4250  10.4432  -8.3346  -0.1559 -11.2942   2.5633   0.4035  -1.5904
-#&gt;  -13.1346  -4.1501  11.3052   0.4464  -2.3904   5.3489  11.0314   5.1866
-#&gt;  -12.6952  -0.0865   5.9078  -1.5039   6.8966  10.7753  -0.3963  -4.1022
-#&gt;    1.4878  18.0642  -1.9133   9.2827  -5.0665   6.7192  -7.0001   0.8716
-#&gt;    6.8158   2.9055  18.1284  -6.8542   5.0309 -10.3151   0.7765  -0.7594
-#&gt;    2.3084  12.6763  -5.2665   5.0701 -19.1033   5.6522  -6.9090  -9.1928
-#&gt;   -4.8443  -1.7718   7.0805  -8.1864  -3.8195   9.5498   4.3077  -1.1990
-#&gt;   -6.8742   7.3798   1.0192  11.4079   1.1065   3.1814   1.7155  -6.1052
-#&gt;    9.7984   0.3396  -3.2393 -12.1877   0.9552 -12.5333  18.9759   2.4872
-#&gt;    7.4531  -7.7480   4.7834  -3.2901   8.5760 -16.3754   2.2548   4.4882
-#&gt;    2.0563   2.9639   0.7186  -4.8170   0.6058   2.9648   5.3442   5.8889
-#&gt;  -12.1073   3.0810   6.4878   5.9060  -6.5616  17.4975  11.1186  -0.4636
-#&gt;   -1.1596  -8.2558   9.1484   8.9128  -1.1632  -8.3799  15.6732   2.9884
-#&gt;    7.2792  -3.1738  12.0571   5.1902  -0.1626 -10.4349  12.2274 -12.8027
-#&gt;    8.0527 -13.3527  12.1685   3.6479   6.7252  -7.2775   7.5232  -3.5610
-#&gt;    2.6051   0.7191   5.9742  17.9007   7.6856   4.3161   8.7152   7.3950
-#&gt;   -5.2621  -1.0898  -0.9831   2.3303   4.0158  -1.4421   1.2123  10.3861
-#&gt;   -8.5903  -1.8761  -8.5211   2.5497   6.7354  13.9106 -12.8571 -12.5142
-#&gt;    0.0941  -5.3999  24.1123  -7.1255   2.8773 -16.7836  -3.3038  20.7143
-#&gt;   12.1291   1.4191   8.2710   4.5049   7.4567   0.4674  -1.4945  -9.5993
-#&gt;   -8.8135   5.4663  -1.4522  17.7497 -11.4320  10.0672   1.7712   0.9021
-#&gt;  -13.5751  -9.3506 -11.1927   4.4933  -0.3094  -6.1581   4.8745   5.6488
-#&gt;   -3.0318   4.2075 -11.5131  -4.9852  -2.5882  25.7359  -0.8112   3.4950
-#&gt;   -4.2945  -4.8818  -2.1800   0.1707   3.1357   1.0756  -0.0102  14.2639
-#&gt;    1.0251  -2.3155  -2.0633   0.8194 -18.8048   8.2694  10.7665  -1.5381
-#&gt;    9.3727  -7.1302   1.3266  -1.6873   5.9775  -8.2901   2.2250   1.3075
-#&gt;   15.1908  21.7887  12.5970  -3.1801  -6.2805   5.3284   2.0791   5.9754
-#&gt;   -2.0165   4.5341  -1.4496  -0.3634  -7.9436  -1.5858   2.9892  -6.2853
-#&gt;  -11.8131  -2.8974  12.0290  -4.4577   6.5901  -5.0178  -1.2114   0.1450
-#&gt;   -3.0505   5.5202   5.1887  17.7819  -9.3771   7.8198  -0.9549  -6.4348
-#&gt;   -2.2150  -9.7721   7.7687  -6.3133 -11.4031  -0.5476  11.9820   6.5145
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  Columns 1 to 6 -1.0112e+01  3.3471e+00 -1.3616e+00 -2.1247e+00  5.7172e+00 -3.3766e+00
-#&gt;  -5.5103e+00 -2.0571e+00 -1.0424e+01  6.2259e+00  3.3973e+00 -2.3115e+00
-#&gt;   4.8724e-01  4.0242e-01 -4.5937e+00 -2.2638e+00  2.5783e+00 -1.0165e+01
-#&gt;  -1.3277e+00 -2.2101e+00 -1.1448e+01 -4.6314e+00  8.0288e+00 -1.3634e+00
-#&gt;  -1.1003e+01  9.8206e-01  3.7082e+00  8.7723e-01 -9.2096e-01 -1.4884e+01
-#&gt;   1.0733e+01 -6.1340e+00 -8.2997e-01 -7.0681e+00  1.7225e+01 -2.3796e-01
-#&gt;   1.7044e+00 -1.2443e+01 -3.0234e+00  7.0558e+00  1.5335e+00 -7.3160e+00
-#&gt;   9.7687e+00 -1.0346e+01 -4.6944e+00  1.2418e+01  9.1774e+00 -4.9059e+00
-#&gt;  -5.3925e+00 -7.7073e+00 -5.2122e+00 -7.5964e+00  3.4759e+00 -4.8854e+00
-#&gt;  -2.5245e+00  1.3693e+00 -7.9335e+00 -3.0052e+00 -4.7442e+00 -5.4619e+00
-#&gt;   9.1789e+00  2.3954e+00  1.8388e+00  6.6659e+00 -3.7532e+00 -2.2209e-01
-#&gt;   6.4793e+00 -6.3747e+00  1.0809e+01  1.5801e+00 -1.6157e+00  3.7508e+00
-#&gt;   5.4332e+00 -9.8376e-01  4.4191e+00  1.1465e+00  5.4529e+00  1.0006e+01
-#&gt;  -8.9912e+00  1.3781e+01  1.6810e+01  9.3054e+00  3.3205e+00 -6.4306e-01
-#&gt;  -5.9036e+00  1.6212e+00  8.6597e+00  4.2362e-01  1.8497e+01 -9.2830e-01
-#&gt;  -1.8661e+00 -1.2202e+01 -3.1351e-01  2.9656e-01 -3.0618e+00 -1.2222e+01
-#&gt;   8.8221e+00 -2.3012e-01 -2.9986e+00  6.8013e-01 -1.0559e+00 -1.3142e-01
-#&gt;   6.2336e+00  1.8242e+00  1.5744e+01  2.1720e+01  1.0548e+01 -1.2644e+00
-#&gt;  -8.6821e+00  1.2136e+01 -2.5783e+00  2.3486e+00 -6.9567e+00 -1.6242e+00
-#&gt;  -2.0051e+01 -1.4335e+01  1.5804e+00  6.2208e+00 -3.6396e+00  1.1610e+01
-#&gt;  -1.8263e+00  1.7206e+00 -1.1096e+00  8.4240e+00  7.7596e+00  3.7382e+01
-#&gt;  -3.9534e+00 -1.0579e+01 -8.3731e-01 -3.6626e+00 -3.1335e+00 -4.9331e+00
-#&gt;  -1.4798e+01 -6.9726e-02 -4.4334e-01 -1.3876e+01  4.0623e-01 -3.7889e+00
-#&gt;  -1.2029e+01 -1.4548e+00 -5.1477e+00 -4.0436e+00  1.6159e+00  1.2940e+00
-#&gt;   1.6687e-01  1.2436e+01  1.0004e+01  1.5325e+01 -8.9823e+00 -1.1777e+00
-#&gt;   3.4169e+00 -2.7338e+00 -2.0387e+00  1.1031e+01 -1.3249e+00  8.2960e+00
-#&gt;  -3.0174e+00 -7.5245e+00  1.0318e+01  4.9593e+00 -6.4577e-01  8.6410e+00
-#&gt;   1.3122e+00 -3.2492e+00  7.8946e+00  4.6445e+00 -8.6816e+00 -5.6111e-01
-#&gt;   8.2926e+00  3.4976e-02 -2.1509e+00  2.2035e+00 -4.5308e+00 -4.7269e+00
-#&gt;   8.4734e+00 -1.4172e+01 -7.6505e+00 -3.7782e+00 -1.0233e+01 -1.3623e+01
-#&gt;  -2.5275e+00  6.8621e+00 -7.1320e+00  5.0068e+00  7.3348e+00  9.6411e+00
-#&gt;   3.1039e+00  1.3843e-01  9.0797e+00  1.4394e+01  5.1962e-02  8.2065e+00
-#&gt;  -6.5152e+00 -1.1077e+00  9.5506e+00 -2.7328e+00  4.7962e+00  4.6798e+00
-#&gt; 
-#&gt; Columns 7 to 12  9.0915e-01 -4.8507e+00  3.5735e+00 -1.1061e+01  4.2308e+00  9.4729e+00
-#&gt;   1.2588e+01 -5.7802e+00  8.4831e-01 -1.2209e+01 -7.3142e-01 -1.0195e+01
-#&gt;  -2.9747e+00  2.9470e+00 -5.2203e+00  6.0193e+00  3.4138e+00  1.2754e+00
-#&gt;  -1.2441e+01 -1.2913e+01 -1.0962e+00  1.0757e+00 -5.9521e+00  6.9617e+00
-#&gt;  -3.9733e+00 -3.6765e+00  3.5905e+00  3.9549e+00  3.6163e+00  1.8031e+01
-#&gt;   5.1766e+00  3.5441e+00 -1.1045e+01  5.6578e+00 -5.2652e-01 -1.1689e+01
-#&gt;   1.0444e+01 -2.3066e+00 -1.5921e+00 -1.2035e+01 -1.6862e+01  8.0318e+00
-#&gt;   1.4448e+01 -8.5392e+00 -6.5439e+00  8.8330e+00  2.4662e+00  1.7070e+01
-#&gt;   8.4425e+00 -2.7230e+00 -8.3967e+00 -4.2847e-01  3.1762e+00  1.2518e+01
-#&gt;  -1.1085e+01  1.2033e+01  3.8647e+00  1.0912e+01  2.7584e+00 -9.5705e+00
-#&gt;  -7.0238e+00 -5.0663e+00  3.9037e+00  2.7000e+00  3.1464e+00 -2.0549e+01
-#&gt;   1.8263e-04 -5.8711e+00 -5.9530e-01  6.3656e+00 -1.2664e+00 -4.5975e+00
-#&gt;   8.6123e+00  4.5974e+00 -3.3963e+00  5.3083e-01  2.0801e+00  4.9645e+00
-#&gt;  -1.2969e+01 -1.2069e+01  3.6391e-01  3.2708e+00  3.0565e+00  9.5147e-01
-#&gt;   4.0444e+00 -1.0903e-01  2.7879e+00 -1.0245e+01  7.9732e-01 -8.9985e+00
-#&gt;  -4.3686e+00 -2.1010e+00  5.4852e+00  2.4809e+00  4.9809e+00 -2.7096e+00
-#&gt;   4.2026e-02 -3.3784e+00 -3.5722e+00  1.9948e+00  4.6537e+00  4.9913e+00
-#&gt;   7.1223e+00 -8.8386e+00  1.3878e-01 -2.4567e+00 -5.1301e-01  9.3124e-02
-#&gt;  -1.1334e+01 -1.1561e+01  1.4488e+00 -2.8247e+00 -5.2599e-01  3.7736e+00
-#&gt;   8.6531e+00 -1.8703e+00  6.3663e+00  4.5445e+00 -9.4502e+00  2.1929e+01
-#&gt;  -3.8242e+00  3.8667e+00 -9.8981e+00 -4.5285e+00  1.0047e+01 -1.0197e+01
-#&gt;   1.1412e+01  1.0785e+01  4.1062e+00  4.9059e+00  4.3113e+00 -1.0775e+01
-#&gt;  -2.1055e+00 -2.2854e+00  8.8080e+00 -4.8985e-01 -1.2271e+00 -1.9677e+00
-#&gt;  -1.2080e+01 -5.4187e+00  2.6792e+00  1.2422e+01  8.7993e+00 -8.0201e+00
-#&gt;   2.7771e+00 -2.6999e+00  1.5546e+00 -1.9201e+00  2.2215e+00 -3.8125e+00
-#&gt;   4.6389e+00 -1.1439e+00  4.1924e+00  7.3352e-01  4.8079e+00 -7.8209e+00
-#&gt;   2.5970e+00 -1.3085e+00 -1.0007e+01 -4.1583e+00 -5.5402e+00 -2.2660e+00
-#&gt;   3.9755e-01 -1.2809e+01  3.4965e+00  7.0042e+00  8.7248e-01 -5.0461e-02
-#&gt;   1.1679e+01  4.0716e+00 -1.3175e+00 -1.3380e+01  1.0924e-01  2.2306e+00
-#&gt;  -1.0361e+01  4.2332e+00  4.0548e+00 -3.5524e+00  2.1230e-01 -9.7114e+00
-#&gt;  -6.1087e+00 -3.6003e+00 -2.5415e+00 -1.3684e+01 -6.3948e+00  8.4737e+00
-#&gt;   4.9227e-01 -2.2997e+00 -4.8309e+00  7.4153e+00  6.8542e+00 -2.7628e+00
-#&gt;  -4.8763e+00 -1.5902e+01 -6.6848e+00 -3.5800e+00  2.5431e+00  7.4237e+00
-#&gt; 
-#&gt; Columns 13 to 18  8.7762e-02  7.1714e+00  1.1026e+00 -9.9179e-01  7.3657e+00 -1.6047e-01
-#&gt;  -3.7119e+00  8.5651e+00 -2.5977e+00  1.0632e+01  4.0098e+00  1.5631e+01
-#&gt;  -1.7670e+01 -2.4800e+00  5.1912e+00 -4.0729e+00 -5.0762e-01  8.5344e-01
-#&gt;   1.0137e+01  8.3111e+00  3.4280e+00  2.1319e+00  1.5073e+00  6.0546e+00
-#&gt;  -9.0924e-01  4.1294e+00  5.1359e+00  9.8821e+00  8.5953e+00 -4.6543e+00
-#&gt;   8.1961e-01 -3.2497e+00 -5.0936e+00 -8.0253e+00  8.4008e+00 -2.7479e+00
-#&gt;  -1.8067e+01  9.3005e+00 -1.3667e+01 -3.9912e+00 -3.4964e+00  8.4975e+00
-#&gt;  -9.3409e+00  4.1040e+00 -1.2275e+01  8.9566e+00  1.0862e+01  6.3246e+00
-#&gt;   2.1361e+00  1.0027e+01 -7.5637e+00  2.9521e+00  8.4092e+00  8.9326e+00
-#&gt;   7.2681e+00  4.6691e+00 -2.7870e+00 -1.1207e+01 -4.2184e+00 -1.8876e+00
-#&gt;  -1.4366e+01  5.4543e+00 -9.4583e+00 -4.3067e-01 -1.8231e+01 -9.0281e+00
-#&gt;  -1.1537e+01 -8.7874e+00 -5.4080e+00  6.8629e+00 -3.8882e+00 -5.0561e+00
-#&gt;   2.3820e+00 -2.0133e+00  2.6330e-01 -8.5427e+00  9.2810e+00  3.3737e+00
-#&gt;   1.1930e+01 -7.5857e+00  2.1302e+01  1.9400e+00 -5.1519e+00 -1.6799e+01
-#&gt;   3.6742e+00 -6.9128e+00  6.9173e+00  5.9880e+00  1.4536e+00  3.0263e+00
-#&gt;  -3.5203e+00  2.5933e+00 -6.5773e+00 -5.7644e-01 -1.1184e+01 -1.0615e+01
-#&gt;   1.4126e+01 -2.2447e-01 -1.2739e+01 -7.8148e+00  3.3369e+00  3.3853e+00
-#&gt;   6.0322e+00 -1.4096e+00 -7.2744e+00  1.6488e+00 -7.9439e+00 -1.3093e+01
-#&gt;   8.9444e-01  4.2094e+00  5.2502e+00  2.3117e+00 -9.9741e+00  2.9547e+00
-#&gt;   7.1002e+00 -7.1193e+00  2.6779e-01  5.4576e+00  8.9095e+00  1.2031e+01
-#&gt;   8.9023e+00 -1.0615e+01 -4.2561e+00  8.6329e-01  6.6213e+00  4.9633e+00
-#&gt;   8.5443e+00  5.6447e+00 -8.6013e+00  9.6252e-01 -1.0022e+01 -2.3035e+00
-#&gt;   3.1216e+00  7.6132e+00 -3.0575e+00  6.1654e+00 -1.2059e+01  1.1994e+00
-#&gt;   4.4961e+00 -8.7430e+00 -9.3553e-02  3.4536e+00  2.9707e-02  2.4857e+00
-#&gt;  -1.9219e+00 -3.3159e+00 -1.5960e+00 -7.4165e+00 -1.4722e+00 -1.7041e+01
-#&gt;  -4.7987e+00 -1.6518e+00 -7.9667e+00  3.1436e+00 -2.9826e+00 -1.4640e+01
-#&gt;   4.8461e+00  9.7134e-01  4.5367e+00  7.2616e+00 -1.7757e+00  2.0883e+00
-#&gt;  -5.4625e+00 -2.4813e+00 -6.1206e+00  1.0285e+01 -7.0900e+00  3.4351e+00
-#&gt;  -7.3717e+00  1.4271e+01 -5.1981e+00 -1.6692e+01 -8.7618e+00 -1.0691e+01
-#&gt;   8.3203e+00 -5.2049e+00  1.3069e+00 -1.2900e+00 -1.5379e+00  2.0779e+00
-#&gt;  -1.0234e+01  6.7857e+00 -1.5660e+00  4.7258e+00  1.2781e+00  6.4700e+00
-#&gt;  -2.6069e+00 -1.2369e+01  7.3610e+00  1.1886e+01 -3.7007e-01 -7.5270e+00
-#&gt;  -1.5037e+01 -1.0232e+00  6.6786e+00  8.4618e+00  5.5318e+00  4.3620e-01
-#&gt; 
-#&gt; Columns 19 to 24  5.7083e-01 -7.5863e+00  9.1426e-01 -1.7237e+00 -5.8659e+00  2.4597e+00
-#&gt;   5.4493e+00  1.2295e+00  8.7414e-01 -4.7571e+00 -6.2499e+00 -6.9546e+00
-#&gt;  -7.0165e+00  5.2276e+00  6.2705e+00  5.6708e+00  6.5905e-01 -2.8967e+00
-#&gt;   7.4033e+00  8.0693e+00  6.7459e+00 -6.6437e-01  2.4957e+00  2.3157e+00
-#&gt;  -5.6917e+00 -6.0679e+00  4.0942e+00  1.5724e+00 -8.4093e+00 -2.0876e+00
-#&gt;  -1.5460e-01 -1.0720e+01  6.4008e+00  1.0295e+00 -3.5683e+00 -1.5835e+01
-#&gt;   5.6507e+00  1.3671e+01 -5.7780e-01  1.1597e+00  2.0982e+01  6.7265e+00
-#&gt;  -4.0544e+00 -1.1178e+01 -1.0030e+01  2.2962e+00 -8.4013e+00 -6.5914e+00
-#&gt;   8.8869e+00 -6.3657e+00 -9.9479e-01  1.7387e+00  2.9704e+00 -1.5186e+01
-#&gt;   6.8844e-01  1.4262e+00 -5.8471e+00  1.0560e+00 -1.4675e+01 -1.4091e+00
-#&gt;  -1.7246e+00  4.5756e+00  3.7786e+00 -6.4119e+00 -1.1570e+01 -2.4411e-01
-#&gt;  -1.0844e+01 -2.9134e-01  5.1817e-01  7.1209e-01 -5.2000e-01 -2.9817e+00
-#&gt;   1.3719e+01 -2.3466e+00  5.6873e-01  5.8377e+00  1.8936e+00 -6.6294e+00
-#&gt;  -1.0969e+01 -7.2444e+00  1.1928e+00  1.4683e+00  2.5794e+00  1.3438e+00
-#&gt;   4.5953e+00 -3.2601e+00  1.4678e+00 -4.9820e+00  2.7704e+00 -7.5684e+00
-#&gt;  -6.6908e+00  3.3866e+00 -4.3879e+00 -9.9487e+00 -3.1816e+00 -3.6760e+00
-#&gt;   1.0754e+01  4.2612e+00 -5.5945e+00 -3.0038e+00  4.5483e+00  8.6919e+00
-#&gt;  -1.8592e+01 -1.5070e+01 -7.2117e+00  5.6861e+00  4.6978e+00  1.1954e+00
-#&gt;  -3.9209e-03  1.1021e+00  1.3217e-01  8.2271e+00 -9.2945e+00  6.8354e+00
-#&gt;   1.9651e+00  1.5967e+00 -1.1512e+01 -3.8996e+00 -1.0661e+01  1.0231e+00
-#&gt;   3.7949e+00 -1.1066e+01 -4.5282e+00  3.8800e+00 -3.1530e+00  3.8697e+00
-#&gt;  -3.6794e+00  8.3582e-02  5.2362e+00 -1.4446e+01  7.5672e-01  1.5240e+00
-#&gt;   4.6490e+00 -3.3643e-01  1.0386e+00  1.7666e+00 -6.5442e+00 -1.2930e+01
-#&gt;   2.0355e+00  7.6981e+00 -1.3823e+00 -9.1467e+00 -2.5934e+00  9.7391e+00
-#&gt;  -2.6448e+00 -7.6750e+00 -1.0842e-01 -5.0217e+00  7.9808e-01  1.1039e+01
-#&gt;  -1.5401e+01 -1.2851e+01 -2.1397e+00 -2.4203e+00 -9.3384e+00 -5.2499e+00
-#&gt;   3.1033e+00 -3.0021e+00 -1.6597e+01  1.1402e+01  1.3298e+01 -8.4431e+00
-#&gt;  -3.1857e+00  1.4222e+00  9.8030e+00  6.3074e+00  7.0264e+00  6.4952e+00
-#&gt;  -2.9397e-01  2.5037e+00  7.8284e+00  7.4687e+00  5.8207e+00  1.0803e+01
-#&gt;   2.8672e+00  1.0449e+01  3.3084e+00 -2.4426e+00  4.4038e+00  6.0503e+00
-#&gt;   5.0582e-01 -6.4413e+00 -1.3190e+01 -6.6810e+00  5.3601e-01  4.7405e+00
-#&gt;  -7.7069e+00 -7.1590e+00 -9.6216e+00 -2.5701e+00 -2.7843e+00 -2.9234e+00
-#&gt;  -5.1101e+00 -1.2322e+01 -9.6732e+00  8.6825e+00  6.3627e+00 -1.4029e+01
-#&gt; 
-#&gt; Columns 25 to 30  1.8748e+01 -8.0027e+00 -3.6156e+00 -9.0885e+00  8.2632e+00  5.0230e+00
-#&gt;   8.5035e-01 -8.2449e+00 -4.3842e-01  1.4861e+01  1.2952e+01 -1.0816e+00
-#&gt;  -2.8503e+00  8.6596e-01 -9.6940e+00 -2.3666e+00  7.1528e+00  5.1803e+00
-#&gt;   5.9745e+00  5.3883e+00  1.4714e+00 -5.6116e+00  1.6522e+00  1.2471e+01
-#&gt;   9.2610e+00 -9.0645e+00  9.5014e+00  6.7266e+00 -1.9906e+00  8.0631e+00
-#&gt;   1.9009e+01  9.9139e+00 -1.7175e-02 -6.9024e+00  1.7315e+01  1.7158e+01
-#&gt;   6.0398e+00  4.0051e+00 -6.2787e+00  1.2950e+00 -7.9533e-01 -1.5215e+01
-#&gt;   2.1904e+01  2.7433e+00 -1.4907e+01  3.6577e+00  1.0522e+01  7.4133e+00
-#&gt;   8.7887e+00  1.1447e+01  3.1187e-01 -6.8633e+00  7.9583e+00  1.2619e+00
-#&gt;  -5.4513e+00 -7.5983e-01 -1.0464e+00 -4.0561e+00 -9.2308e-02  1.5563e+00
-#&gt;  -5.1769e+00 -5.6100e+00 -1.1947e+01  1.6235e+00  6.6239e-01 -7.2575e+00
-#&gt;   1.2717e+00  1.4775e+00 -1.7732e+00  6.4762e+00  6.3587e+00 -1.6032e+01
-#&gt;   7.5191e+00 -1.0988e-01  3.8793e+00 -1.2765e+01  7.7084e-01  5.4164e+00
-#&gt;   1.1044e+01  1.1892e+01  4.5108e+00 -5.1543e+00 -3.8679e+00 -2.3338e+00
-#&gt;   1.6349e+01  8.2474e+00  8.9649e+00  5.6091e+00  2.3721e+00 -6.8284e+00
-#&gt;   3.2010e-02 -3.3612e-01  1.9278e+00  5.7180e+00  4.8876e-01 -1.2503e+01
-#&gt;   3.4077e+00  2.8166e+01  1.2754e+00 -1.2347e+00 -4.9305e+00 -8.2640e-02
-#&gt;   2.4614e+01  1.8154e+01  6.1271e+00  8.8538e+00  2.7340e+00 -5.1353e+00
-#&gt;  -1.2496e+01 -3.3215e+00 -1.1190e+01 -1.4850e+01 -8.0524e+00  1.2065e+01
-#&gt;   2.4147e+00  1.3702e-01  3.9529e+00 -2.7569e+00  1.1834e+01 -2.1574e+00
-#&gt;   6.4455e+00  1.1134e+01  4.0323e+00 -1.0277e+01 -6.4360e+00  2.6449e+00
-#&gt;   7.5787e+00 -5.1670e+00  1.1731e+00  6.8484e+00 -7.5549e+00 -1.1463e+01
-#&gt;  -2.8867e+00  6.3397e+00  2.0691e+00 -1.0346e+01  2.7565e+00  9.1311e-01
-#&gt;  -5.9203e+00  4.1673e+00 -4.2566e+00 -4.7346e+00  5.7207e+00 -1.3692e+01
-#&gt;   3.2966e+00 -2.1380e+00  4.9819e+00 -5.0196e-01  2.7676e+00  1.5741e+00
-#&gt;   4.4740e-02 -8.9238e+00  4.2311e+00  8.0103e+00  3.6590e+00 -1.1892e+01
-#&gt;  -2.3997e+00  2.1324e+01  3.3356e+00  8.5935e+00 -2.5332e+00 -9.8269e+00
-#&gt;   2.9663e+00 -1.5625e+00 -3.7146e+00  1.4064e+00  4.7036e+00 -5.2658e+00
-#&gt;   1.4097e+01  1.3359e+00 -1.2004e+01 -6.4156e+00 -1.0058e+00 -5.6385e+00
-#&gt;  -4.3876e+00 -1.8724e+01 -7.3324e+00 -3.9628e+00  3.3561e+00 -3.6125e+00
-#&gt;  -6.4157e+00 -1.4969e+00  3.1962e+00 -6.3158e-02 -4.5283e+00  5.7183e+00
-#&gt;  -1.9412e+00  1.3723e+01  3.2587e-01  5.7423e+00  4.8182e-01 -6.4758e+00
-#&gt;   1.0819e+01  1.1767e+01 -1.3721e+00 -6.3697e+00 -4.2399e+00  1.9277e+00
-#&gt; 
-#&gt; Columns 31 to 36 -6.4975e+00  1.2407e+01  9.1186e+00 -3.4259e-01  6.8174e+00 -3.6096e+00
-#&gt;  -4.1288e+00 -1.3211e+00 -1.3101e+00 -1.7194e+00 -5.2005e+00  4.7925e+00
-#&gt;  -2.5269e+00 -4.2248e+00 -7.0187e+00 -9.4899e+00 -8.0102e+00 -8.2779e+00
-#&gt;   6.5134e-01  5.9743e+00  1.0857e+01  1.5613e+01  1.0043e+01  4.4142e+00
-#&gt;   7.2989e+00  2.8189e+00  7.4914e+00  6.0752e+00  9.1167e+00 -2.1324e+00
-#&gt;  -8.1983e+00 -1.8667e-01 -1.2484e+01 -9.3700e+00 -1.2500e+01 -1.1728e+01
-#&gt;   3.4454e+00  1.3299e+01  1.0501e+01 -6.7992e+00 -1.6106e+01  7.0709e+00
-#&gt;  -4.2006e+00 -1.9695e+01 -9.9737e+00 -1.1730e+01  4.3745e+00 -1.4141e+01
-#&gt;   3.7954e+00  6.5373e+00  5.8362e+00 -1.0007e+01  3.4654e+00 -1.9365e+00
-#&gt;  -1.2385e+00  7.0874e+00  8.8854e+00  1.0915e+01  1.4639e+01 -7.0953e+00
-#&gt;   2.0880e-01  1.0870e+01 -2.8183e+00 -2.6951e+00 -7.5149e+00 -1.0790e+00
-#&gt;   4.0409e+00 -2.0800e+00 -3.7934e+00 -5.0785e+00 -8.7745e+00  9.4630e+00
-#&gt;  -5.0258e+00  4.2557e+00  2.5527e+00  2.3203e+00 -1.5513e+00 -2.6171e+00
-#&gt;   1.7872e+01  1.0807e+00  1.7350e+01  7.7203e+00  1.3236e+01  5.5008e+00
-#&gt;   5.9088e+00  4.8168e+00 -5.4489e+00 -6.1204e+00  3.9468e+00  9.8808e+00
-#&gt;  -7.8373e-01  3.4962e+00  5.7633e+00 -2.9164e+00  1.7681e+00 -3.6453e+00
-#&gt;  -1.1629e+00  5.6584e+00  4.4623e+00  6.4923e+00 -1.1691e+00  1.3406e+01
-#&gt;   6.8949e+00  2.0668e+00  9.0667e+00  7.7525e-01 -1.7731e+00  1.7329e-02
-#&gt;   6.4812e+00  6.0815e+00  4.5252e+00  6.3626e+00  1.0790e+00 -4.6700e+00
-#&gt;  -9.8235e+00 -7.0160e+00  5.6095e+00 -1.7757e+00  1.4606e+00  7.3764e+00
-#&gt;  -1.1905e+01  2.8678e+00 -2.7650e+00  2.3743e+00  4.2044e+00  1.0439e+00
-#&gt;  -4.5212e+00 -1.0256e+00  6.2475e+00  6.5621e+00 -1.1511e+00 -2.2257e+00
-#&gt;  -3.0211e+00  1.1068e+00  1.2440e+00 -6.3198e-01  5.1176e+00 -8.9878e+00
-#&gt;  -3.3556e+00 -1.5754e+00  2.3598e+00  4.0587e-02  7.3244e+00  3.3158e+00
-#&gt;   6.7194e+00  3.9954e+00  1.1162e+01  6.2291e-01 -2.9964e+00 -5.9726e+00
-#&gt;   2.5455e+00 -2.9296e+00 -4.7931e+00 -7.1494e+00  7.6879e+00 -2.2782e+00
-#&gt;   1.3540e+01 -1.9973e+00 -4.3522e+00 -6.6225e+00  2.7325e+00  7.4021e+00
-#&gt;  -3.0375e+00 -4.1049e+00  1.3049e+00  2.7012e+00 -3.0317e+00  3.6220e+00
-#&gt;  -4.8837e+00  1.5460e+00  3.0058e-01 -6.0486e-01 -1.9994e+01 -6.8006e+00
-#&gt;   8.6551e-01 -6.3941e+00  4.1351e+00  1.1401e+01  4.3845e+00 -7.0502e+00
-#&gt;   7.8472e+00  5.5323e+00  2.5964e+00 -4.8687e+00  5.5820e+00  9.1536e+00
-#&gt;   1.2262e+01  1.1421e+00 -1.9864e+00  4.5118e+00  7.8386e+00  5.3671e+00
-#&gt;   1.2306e+01 -4.9957e+00 -2.5748e+00 -1.2788e+01  2.3839e+00  1.2678e+01
-#&gt; 
-#&gt; Columns 37 to 42 -4.5231e-01 -8.9962e+00  1.3237e+01 -6.0060e+00  2.2332e+00 -3.9510e+00
-#&gt;   2.8541e+00 -9.7703e+00 -3.0858e+00 -7.0231e+00  3.0564e+00  4.0302e+00
-#&gt;   1.0953e+01  6.1881e+00  1.6921e+01  3.4299e+00  5.1109e+00  4.3093e+00
-#&gt;   8.5351e+00 -8.0919e+00  1.4947e+00  6.4474e+00 -3.9937e+00 -2.6966e+00
-#&gt;  -1.2084e+01  1.0634e+01  3.6811e+00  1.2988e-01  2.1417e+00  5.5734e+00
-#&gt;   2.2142e+01 -9.7619e+00  2.5312e+00 -3.4604e+00  6.0561e+00 -6.7766e+00
-#&gt;  -1.3589e+00  3.5218e+00  6.0655e+00 -1.3651e+01  1.4354e+00  2.8873e+00
-#&gt;  -6.4443e+00 -6.1112e+00 -1.4642e+00  4.7183e-02  1.7320e+01  3.1355e+00
-#&gt;  -4.8275e+00  1.6873e+00 -7.4479e+00  8.5165e+00  1.1356e+01  5.5532e+00
-#&gt;   2.7963e-01  2.5671e+00 -9.9574e+00 -7.1222e+00 -1.7681e+00 -8.2093e+00
-#&gt;   1.2879e+01  1.0192e-02 -1.0748e+01 -1.0385e+00 -6.7796e+00 -1.0253e+01
-#&gt;  -6.5085e+00  6.1781e+00 -4.0648e+00 -5.2046e-01 -7.1260e-01 -6.2581e+00
-#&gt;   7.9099e+00  7.5757e+00  2.3792e+00  1.1680e+00  8.1230e+00 -3.3057e+00
-#&gt;  -3.7047e+00  4.4541e+00  2.5766e-01  2.8481e+00 -5.4595e+00 -3.7416e+00
-#&gt;  -1.0940e+00 -8.6400e+00 -1.0045e+00 -5.8022e+00 -5.8387e+00  1.1878e+00
-#&gt;  -1.4651e+01 -6.8074e-01 -1.6425e+00 -3.2517e+00  4.9738e+00  3.9378e-01
-#&gt;  -9.3736e+00 -7.9568e+00 -1.8479e+01  6.7208e-01  4.5614e-01  1.0215e+01
-#&gt;  -6.0625e+00 -5.7748e+00 -2.0492e+00 -7.6435e+00 -2.5873e-01 -2.3904e+00
-#&gt;   3.3443e+00  1.5687e+01 -1.5764e+00  3.7520e+00 -1.6601e+01  8.2128e-01
-#&gt;   3.4153e+00 -7.8393e+00  2.7386e+00 -1.6475e+00  9.7404e-01  5.7568e+00
-#&gt;  -9.5615e+00  5.0437e-01  2.9145e+00  6.8014e+00  8.0951e+00 -4.9523e-01
-#&gt;  -1.1554e+00 -3.9778e+00 -5.3838e+00 -5.8841e+00 -3.9546e+00 -6.0382e+00
-#&gt;   1.1098e+01  6.5841e-01 -1.1881e+00  3.4483e+00 -3.7131e+00 -5.2113e+00
-#&gt;  -7.4178e+00  2.6692e+00  1.1213e+01  1.2063e+01 -3.4636e+00 -1.2369e+01
-#&gt;  -3.2425e+00  1.0743e+01 -3.2939e+00  2.8866e+00  3.7852e+00 -2.3506e+00
-#&gt;  -4.9844e+00  1.1310e+01 -1.4618e+00  7.5072e+00 -7.2638e-01 -7.1381e+00
-#&gt;  -2.2038e+00  7.1951e+00 -5.2094e-01  2.5474e+00  5.8806e+00 -3.7292e+00
-#&gt;   1.5294e+01  5.2273e+00  2.4986e-01  1.7957e-01 -1.1722e+00 -5.7048e-01
-#&gt;   6.4488e+00  3.8147e+00  5.0933e-01 -6.9241e+00  4.0917e+00 -2.0530e+00
-#&gt;  -1.0840e+01 -2.9233e+00  2.9194e+00 -5.4995e+00  9.2367e-01 -4.4357e+00
-#&gt;  -1.6839e+01 -4.0339e+00 -6.7075e+00 -1.0009e+01 -7.9727e+00  6.3396e+00
-#&gt;  -8.7753e+00 -1.7340e+00 -4.0701e+00 -7.5262e+00 -1.8332e+00 -6.7080e-01
-#&gt;  -8.2750e+00  1.7046e+00 -3.4343e+00 -1.5057e+00  8.4479e+00 -3.4135e+00
-#&gt; 
-#&gt; Columns 43 to 48 -1.6882e-01  4.2442e+00  1.8592e+00 -6.3162e+00  8.0707e-01  6.5728e+00
-#&gt;   7.8900e+00  2.9386e+00 -7.1527e+00 -2.3796e+00  2.7690e+00 -2.1294e+00
-#&gt;  -7.1835e+00 -1.5122e+01 -4.5862e+00 -9.7829e+00  1.5674e+00 -5.1918e+00
-#&gt;   3.5845e-01 -2.6493e-01  4.9734e+00 -4.2491e-01 -7.3972e-01 -4.4768e+00
-#&gt;   4.5313e+00 -1.0579e+00 -5.6861e-01  5.4579e-01  3.3700e+00  7.5607e+00
-#&gt;  -3.2541e+00 -5.8288e+00  1.5792e+00 -6.7790e+00  2.0238e+01 -1.3540e+00
-#&gt;  -2.8112e+00 -7.0336e-01 -3.2585e+00 -6.7185e+00 -1.4150e+01  1.5353e+00
-#&gt;   4.7229e-02 -4.9533e+00  1.6295e+00  9.4965e+00  3.4505e+00  1.0620e+01
-#&gt;  -2.5112e+00 -7.9322e+00 -2.4560e+00 -2.0374e+00  2.4646e+00  1.2418e+01
-#&gt;   7.1985e+00  1.2420e+01 -1.6777e+00  2.4219e+00  7.0211e-01 -5.4978e+00
-#&gt;  -6.2526e+00  1.3043e+00  4.0347e+00  4.7717e+00  6.4203e+00  7.0244e+00
-#&gt;  -5.5146e+00 -4.6845e+00 -2.5413e+00  9.6778e+00 -3.6403e+00  9.6343e+00
-#&gt;   1.2891e+00 -1.9301e-01 -2.4135e+00 -4.9414e+00 -1.9879e+00 -1.2417e+00
-#&gt;  -3.7285e+00  2.5621e+00  5.9432e+00  7.2565e+00  3.0150e+00  6.5979e+00
-#&gt;   6.0645e+00  2.0264e+00  9.6085e+00 -9.4627e+00  1.2005e+01  4.1076e+00
-#&gt;   1.9194e+00  3.2341e+00  2.7068e+00 -3.0397e+00  1.8949e+00  6.8634e-01
-#&gt;  -7.4308e-01  1.6424e-01  8.3229e-01 -4.0531e+00 -2.9164e+00 -5.1861e+00
-#&gt;   4.5021e+00  4.9174e+00  3.0379e+00  6.2597e+00  2.6631e+00  8.8211e+00
-#&gt;   3.2229e-01 -3.0466e+00 -5.3610e+00 -6.4293e-01 -1.3308e+00 -1.3689e-01
-#&gt;  -1.6440e+00  2.6513e+00  1.2962e+01  5.3444e+00 -8.9417e+00 -2.3827e+00
-#&gt;  -2.0776e+00 -8.1380e+00 -1.5237e+01 -1.7953e+00 -1.1482e+00  2.9542e+00
-#&gt;   6.2996e+00 -4.6910e+00  1.9017e+00  1.6551e+00  2.4013e-01 -4.8128e+00
-#&gt;  -4.0055e-01 -2.5299e+00  1.8765e+00 -9.0638e+00  8.3679e+00  6.1752e+00
-#&gt;  -5.8667e+00  2.2100e+00 -1.0782e+01  1.5145e+00  2.9402e+00 -1.1317e+01
-#&gt;  -8.2153e+00  2.8141e+00  5.1554e-01  7.1420e+00  4.9116e+00  4.9417e+00
-#&gt;   5.0275e-01  1.4310e+01 -3.1030e+00  1.5989e+01  6.6009e+00  8.5879e+00
-#&gt;  -2.6755e+00 -6.6576e-01 -3.2903e+00  5.6116e+00 -6.3954e+00  7.4072e+00
-#&gt;  -5.1928e+00 -8.2541e+00 -7.0330e+00  1.4764e+00 -9.6986e+00 -4.4201e+00
-#&gt;  -3.4929e+00 -5.2845e+00 -4.4462e+00 -1.0620e+01 -2.3369e+00  1.0337e+01
-#&gt;   4.4335e+00  1.1753e+00  2.3456e-01 -1.0198e-01 -1.2194e+01 -1.7427e+01
-#&gt;   8.6392e+00  1.5712e+01  2.5301e+00  6.6372e+00  4.9079e+00  1.2361e+01
-#&gt;   1.8356e+00  4.0627e+00 -2.4972e+00  1.3432e+01  4.9386e+00  1.0441e+00
-#&gt;  -8.0380e+00 -1.2403e+01  5.0904e+00 -5.0010e+00 -4.8448e+00  2.1295e+01
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  Columns 1 to 8   9.1613   8.2991   7.6040   8.1782   6.9389  -5.0259   9.2590  -8.1261
-#&gt;   -4.8191  -7.1544   1.7203  -6.3751  -3.7681  -3.4671  13.0190   3.2728
-#&gt;  -13.5150   1.2322  -5.7343   9.8446  -3.1652  -9.6226  -6.8487  -1.8621
-#&gt;   -9.5074  13.5577  -0.7798   3.9261  12.8969   2.3291  -0.4428   1.0697
-#&gt;   12.9629   0.9506  -7.1108  -2.9726   0.2362  -1.3054   1.7417  -7.6233
-#&gt;   -0.3377   5.4891  -6.3686   4.7420   6.0707  -7.7690  -2.0833   3.1617
-#&gt;   -6.8064  -2.3800  -6.1936  14.0016   7.7724   3.9431  12.3943  10.1548
-#&gt;    0.0479  -7.3995  -0.7551  -7.2081  -4.7976 -14.5088  -3.9666  -7.0305
-#&gt;   -1.8141  -2.1556  -1.4320  10.7429   2.7160   0.7747   9.5463  -1.0485
-#&gt;    6.3679  -4.3240  -6.7363   4.4685  -4.1918   3.9300   5.5266  -1.7249
-#&gt;  -17.5873  -7.5833   3.7725   3.3439   5.1755   4.7891 -10.3130   9.4372
-#&gt;    6.2029  -2.7531   5.4131  -1.8756  -1.6971  -1.7727   0.3697   6.7504
-#&gt;   -0.6765   0.2527 -13.4446  -4.2196  -9.1203   2.0369   0.8856  -3.5593
-#&gt;    6.2482  -1.8817  10.0089   0.6771  15.8629   0.2645   7.3470 -18.2978
-#&gt;   -0.0504   8.8709  -8.6164   3.3178  15.9061  -4.7193  -0.6836   2.0761
-#&gt;   -1.0521 -10.3832 -16.5788   7.9256   2.2016  -1.2637  -1.4237   5.6009
-#&gt;    8.6336  -1.7091  -1.2794   5.3309  15.6803  12.9497 -23.6703   5.9908
-#&gt;    9.9920  -4.8561  12.2662  -0.1210   4.2440   6.3717   0.4311  -8.8306
-#&gt;   -0.4806   5.7560  16.4261  -9.7476   8.6227  11.7342 -13.0886  -2.8786
-#&gt;   -5.9637  -3.9484 -12.4812   6.2214  -1.5387  -4.0629  10.6590   3.9558
-#&gt;   -3.9738  -1.4120  -8.8628  -0.2112   5.9420   5.4613  -4.8047  -9.9938
-#&gt;    1.9375  -4.7948   2.7477  -3.4319  -4.4319   1.7355   1.0735  -4.7807
-#&gt;   -7.0011  11.3609 -10.9404   3.1856  -0.7285  -1.4351  13.0011   1.1440
-#&gt;    0.4161  -4.3094   8.4987   2.7340   9.9241  -7.4585   7.0775   7.5226
-#&gt;    8.7424  -5.4995   2.2738   6.8182  -6.7486  -1.6147  11.4680  -8.4409
-#&gt;    6.0288  -9.2562  -1.9750  -3.7899  -7.6220   1.2814  15.1953  -4.7243
-#&gt;   -5.5384   1.6893  15.2562   6.3841 -16.5381   9.8554  13.8114  -8.3554
-#&gt;   -4.9636   3.6860   6.2781  -1.4235   1.9235 -10.2765  -8.6942   2.7797
-#&gt;    1.8420   2.3823  -1.3318  -5.8958   1.4559   4.4760  -3.7185  -2.8814
-#&gt;    5.1750   5.3071   0.0042   4.2632  -5.0950 -17.6095   8.6054   5.5446
-#&gt;    2.9617   9.2168   0.5810  -0.0925  21.0798   7.8306   4.8449   8.4560
-#&gt;    5.7312  -4.1153   0.2683  11.6803   2.7581  -9.6470   3.0381 -13.8417
-#&gt;    2.9447  11.6478   0.8605   8.6830  12.8186   3.8165  -2.6950  -3.9646
-#&gt; 
-#&gt; Columns 9 to 16  -9.7557 -15.8197   0.1953   5.1358   0.6444  12.0133  -5.7794  -2.8522
-#&gt;    0.8935  -2.1286  15.4620   5.0600  -7.1966   8.4958   2.5522  -5.1468
-#&gt;    3.7308   2.0613   2.5432   3.6060 -10.4972  -8.1204  13.5918   5.0739
-#&gt;    4.3285  -0.2906   2.9047   1.8724   8.3966   5.5033   0.9179 -14.8534
-#&gt;    1.1537  -0.1559  -2.6827   4.2720  -1.0665  -1.8948 -12.1378  -9.5899
-#&gt;   -0.2700   2.9188   3.1222   2.6060  -4.2723  -6.5321  12.5702  13.2824
-#&gt;    2.0853  -3.6612  -2.5049  -1.0016   8.8343   5.3921   5.2973   4.3964
-#&gt;   12.3019   6.7703   4.5614   0.7636  -9.1130  -3.2390   7.0821   0.0113
-#&gt;    1.8852   1.5856   2.7650  -4.8714   8.4180   1.8328  -1.4965   3.2500
-#&gt;   -3.7423 -13.0790   0.4281  -1.9262  10.1946  -2.5612  -3.5839 -18.3707
-#&gt;    6.9609  -0.0321   4.4236  -1.9210   0.0309  -2.4037   8.8617  16.4988
-#&gt;   -0.6325   8.3897   1.7947  -9.6964   2.2538   3.7631   4.7193  11.0192
-#&gt;   -7.9534  -5.6953   7.3429   9.9494   2.3537  -6.7344  -3.2452  -4.0446
-#&gt;  -10.7466   2.4288  -6.8095  -2.7737   6.6723 -11.2328  -7.3931  -0.3115
-#&gt;   -8.6084   1.0055   4.5362   2.6958   9.3759   0.6540  -0.2950   5.2184
-#&gt;    0.0726   1.5488  -2.3741  -3.2175  15.4786   0.5697  -8.9183   2.2715
-#&gt;   -1.2338   5.8865  -1.0645   1.5532   7.7297  -7.3256  -2.2767  -1.6490
-#&gt;   -2.3800   6.9041  11.7777   4.9980  -9.1074 -13.4418  -7.8662   4.5290
-#&gt;   -7.1316  -4.9611   0.9536   5.6800 -11.9898  -4.5499  -0.8437  -6.9456
-#&gt;   -1.7682  -6.1748   0.3240   7.7115  -3.2916   5.2473   7.6050  -2.2805
-#&gt;   -9.2416 -14.3205   6.9299  -0.1089   0.3967   3.6394  -7.0087   2.0513
-#&gt;    5.6716  -5.8453  -6.2268 -16.6210  -3.3991 -12.4511  -3.5585   2.8879
-#&gt;   -2.2385  -6.5272  -7.2118  -7.5772  -7.6967   2.8656   1.5288 -12.9450
-#&gt;  -11.1249  -5.4503   0.2108  -5.4112  15.6044  10.7757   3.1778  -2.4537
-#&gt;    2.3132   4.7735  -4.5690   3.1046   3.3629  -2.7383   0.4665  -0.6499
-#&gt;    0.1357   2.3919  -5.0523  -3.9378   7.7435   5.2019  -9.1053   1.2589
-#&gt;   -1.4870   8.6008  -1.3855   3.7906  -1.6828  -8.1171  -0.0431   1.1907
-#&gt;    4.1218   0.8365  12.1401  -6.4988 -14.5849  -5.6845   6.4183   1.2475
-#&gt;   -3.3586   2.4948  -3.3798 -14.9763 -10.6797  -6.9628  -6.0756   7.0615
-#&gt;    3.4328   2.8540  -0.2785  -7.8560  13.2286   4.2070  -8.4352  -3.3656
-#&gt;   -0.7541  -6.0453   1.8967   1.7063   7.6670  24.9929   3.3783   3.4112
-#&gt;   -9.4389   1.7108  -4.7285   4.9526   5.8806 -16.8272  12.0732  -1.1680
-#&gt;   -1.1594  -5.7637   2.8316  -1.1274  -1.5102   4.4594  10.2340   0.8676
-#&gt; 
-#&gt; Columns 17 to 24  -1.5238   9.8384   5.7449  -9.3480  -0.9581   1.4748  -0.3042  -6.0419
-#&gt;   -0.4602  -4.0454  -7.2522   8.0435   6.7443  -3.5454   5.2688   2.1479
-#&gt;   -4.6719  -3.4955  -3.7819   9.3097   1.2157   1.0916   1.4243   7.0631
-#&gt;    2.1693   6.2269   0.8640  -2.4609  -0.7738  -5.9537   2.1862   9.2605
-#&gt;   12.3664  13.8617   0.7888   0.2502   4.2929   7.9399  -6.8761 -13.2101
-#&gt;  -10.1717 -12.1070  -0.4040  -2.9965  -0.9616  -7.6830   3.8850  10.0197
-#&gt;   -3.7198   8.6903 -12.9272 -13.6259 -17.1470  -2.2901   3.4551   0.7693
-#&gt;   -8.6065  -2.9522  -3.0438  17.2210  17.0824   5.4312 -11.4513  -7.2385
-#&gt;   -0.1049  14.7501  -1.9674  -5.6212   8.2875   6.0612  -6.1579   0.4230
-#&gt;    7.2717   2.7806   1.8725  -2.2096  -8.3142  -4.0870   0.4447  -1.2250
-#&gt;  -14.0482 -17.7344  -2.1734   5.1975  -1.6179  -6.0194   8.4805  15.2150
-#&gt;  -17.2225  -4.8391  -2.2177   0.5366  -4.5957   2.7548  -0.6901   4.7354
-#&gt;   -1.3594   0.9737  -5.2658  -4.1495  -0.1534  -0.9130  -0.4675   2.4166
-#&gt;   -0.3955   1.9565   2.4128 -11.0391  -0.7344  -4.0053  -5.9491  -1.2282
-#&gt;   -9.4412   5.5376  -4.5938  -9.6103  -0.9913   6.7613   6.1261   3.5000
-#&gt;   -8.5197   7.1351   4.2777   2.6707   0.9774   4.4367  -8.9137  -8.4111
-#&gt;    6.0335  -4.3090  -0.7581  -3.0171   0.3206  -1.4864  -0.0219  -1.0055
-#&gt;   -1.3931  -9.1187  -5.6927  -0.3123  -7.0901  -7.3740  -3.2368 -10.0304
-#&gt;    1.8411  -1.9478  -4.0545  -3.4431  -2.8676  -1.3437  -1.2653   4.4758
-#&gt;   10.0088   5.7941   1.8113   5.1199  -3.8707   0.7269  -5.8266 -13.0870
-#&gt;    8.7984 -15.9625  -5.1043 -10.6068  -7.0843  -7.6777  -7.1200   4.1012
-#&gt;   -4.5807   5.8128  -5.3525  -2.5920  -8.0924  -7.8807  -0.8773  -4.7682
-#&gt;   -5.3858  11.5077  -0.2821  -8.4322  -0.3286  -9.3410  -1.5083   7.1097
-#&gt;  -15.8773   0.5099  17.9380  -4.1765  -3.3163  -2.1109   4.6666  12.9972
-#&gt;    4.9382  -0.9111   2.0237  -3.4405   9.3426  -4.0226  -2.9897  -4.4058
-#&gt;    3.0078  -4.0675   3.7038   1.4374   1.5139  -7.5909  -1.6865  -1.8383
-#&gt;    0.5178   9.5646  -5.9170   1.6104   4.8849  -2.6388  -0.9133  -3.1416
-#&gt;  -13.2061  -2.7154   2.9976   0.2292  -1.2781  -1.1891  -1.9121   4.3753
-#&gt;   -3.1847  -9.3658 -11.9043  -7.1020  -8.4749  -6.3427  -1.3188   2.2862
-#&gt;   -5.9106   6.3599   5.5383   4.5068   3.7876   7.7633  -0.7427  -0.4227
-#&gt;    7.2144  -0.2517   1.2259  -7.6309  -3.5911  14.5189   5.0216  -4.6949
-#&gt;  -10.7247  -4.0605  -7.8864  -4.8296   8.8215   2.0081   4.9473  -6.8053
-#&gt;   -3.3283   7.5987 -11.3508   2.2370   0.4815  13.9879   1.1191   2.4317
-#&gt; 
-#&gt; Columns 25 to 32  -1.4988   1.3005  -3.4758   1.2166   2.8099  -1.7178   4.3926  -4.7752
-#&gt;    0.6620  -2.4485  -4.5177  -1.7295  -3.0856  -4.9551   6.5406  -5.8741
-#&gt;    5.4229  -6.7348   2.2635 -13.0168   2.6152 -16.0166  -0.2999   1.9319
-#&gt;   -1.4025  -6.5230  -1.7559  -0.3908   5.6007  -1.6290   1.2919   6.9027
-#&gt;    3.4165  -2.5170   4.2234   1.0054  -2.4291  -2.2806  -6.8195  -9.9199
-#&gt;   -4.1637  -8.0903 -10.0430   2.7667   9.2077 -18.1935  -4.1328  -3.2950
-#&gt;    2.2508  15.0718  15.5181  -8.1560  -8.8148   8.3708   8.9758  -1.1343
-#&gt;    8.5523  -8.1665   0.4724  -8.5061   2.9606 -12.7662 -18.6537  -8.8854
-#&gt;   -1.8003   4.0893   9.8603 -24.4713  -3.8472   6.4799  -6.4142  -7.1352
-#&gt;   -5.4896 -16.1776   2.2414  21.6264  -2.3485   3.6891 -11.3026  10.8274
-#&gt;   -3.7620   0.6027  -8.6184   6.6649   1.6445   7.0190  -5.0294   2.7711
-#&gt;    9.4180   1.9067  -5.6146  -0.7032 -12.0352  10.4970  -5.4958  -0.3922
-#&gt;    1.9311  -5.9141  -3.5011  14.1359   1.4336  10.9292  -4.7577  -1.2406
-#&gt;   -2.9904  -9.6813   4.1102   3.9724   7.1969  15.4264  -7.2213  -3.3539
-#&gt;   -8.2366   0.9488   5.2832   3.6863   3.4010   0.2955  12.5067  -5.2035
-#&gt;  -13.4851  -1.5518   5.6163   0.7274  -9.1298  -0.6782  -7.3200  -1.8362
-#&gt;  -10.0903   8.6196   6.3490   7.8733  -1.0734   5.0691  -2.2430  -3.6292
-#&gt;   -9.2695  -4.0218   3.2642  13.3833  11.9698   2.3435  -3.7587 -11.1128
-#&gt;   10.9907  -8.8113 -12.2929   1.0378   2.9066  -0.2390   1.0853   1.8105
-#&gt;    3.9568   1.1202   5.9543  -1.1115  -3.1149   1.5121  -1.7361  -7.7855
-#&gt;   12.5080   7.3327 -23.6326   7.8572 -15.1723  10.8267  -5.1933   0.2567
-#&gt;  -13.4704   7.1686   8.0664  -7.1942  -0.3070  -1.4759  -2.7404  -3.8138
-#&gt;    1.3207 -14.8324   6.2604  -8.5331  10.1267 -10.8230   2.7589  -0.3691
-#&gt;   10.5016  -9.3716  -8.2702   0.7867 -15.7208  -1.2938  -3.8011  18.5457
-#&gt;    0.5318  -3.5556  11.9022   5.2302   7.8690   9.7246 -12.5113   3.5147
-#&gt;   -3.2133  -3.8903   0.7806  12.3065   1.0983   5.7461 -14.3548  20.6426
-#&gt;    6.5660   3.6150   0.2243 -13.1763   6.0506  10.8582  13.0374  -4.4218
-#&gt;    4.9922   4.2883   1.0145  -1.2875 -13.3480   6.6736   2.5604  -0.5461
-#&gt;   -8.5355  -1.1896  11.5512  -1.0664  -3.1389  -3.0508   1.7444  -5.0944
-#&gt;   -0.5240  -6.6854   1.6383   9.5264  -3.0418   3.2932   2.5215  11.2992
-#&gt;    4.3792   0.6125  -3.8458   4.9822  -7.9229  11.6484   3.2482   4.2693
-#&gt;    2.1182  -5.4400  -5.6371   5.1793  -5.1707   5.8520 -10.7276   4.6066
-#&gt;   10.1970  -6.0940   2.1198 -16.5437  -2.8577  10.1596   1.4070  -5.0530
-#&gt; 
-#&gt; Columns 33 to 40   2.4434  -4.7660   6.6659  -6.8883   4.3161   4.5981  -2.2450   0.5027
-#&gt;    4.4401  -2.3135  -0.5045   0.0192   3.7010  -4.3685   2.8893   0.5040
-#&gt;    8.0033   6.5201  -0.7619  -0.8393   6.5808  -3.3534  -5.9059  -1.0411
-#&gt;   -0.7204   7.4676   3.2693   4.2787  -1.9218 -10.2587  -1.2621  -0.1321
-#&gt;   -2.5893 -10.4789  -1.2925   4.1682   5.2278 -12.8619  -4.6707  -5.7190
-#&gt;    8.5751  -0.1591   4.4581  11.9677   2.1684   5.5904 -14.0343   0.0018
-#&gt;   -8.9794  17.5853  -4.6156  -2.4800  -7.7840  -2.4933   1.8193  -1.8583
-#&gt;    1.7917   3.6316   6.0857  -5.1365   1.0635  -2.7962  -7.6203   3.5136
-#&gt;  -11.8847  19.2277   8.1501  -1.2405 -14.4933 -10.9815  -5.3027   0.0386
-#&gt;    1.6279 -16.4170  -2.0957  -2.0642   5.5658 -16.8222  -1.9182   0.1303
-#&gt;    1.6552  11.3303  -0.5155 -10.3416   5.4719   2.7090  -2.0093  -4.3790
-#&gt;    1.0294  14.7630  -2.1219  -4.1670   2.1193   5.6321  -4.1579  -0.8295
-#&gt;   -0.0568  -4.7691   8.5268  -2.2873   4.4059  -0.8074  -4.4107   5.1871
-#&gt;   -5.2649  -4.5989  -5.5825  -0.8751  -9.8517   2.3665   5.3991   1.2193
-#&gt;    0.5932   3.7669  -8.4986  -7.0292   6.5756  -1.1056  -7.6131   7.6851
-#&gt;   -4.5939  -2.3951   3.1026   0.6606  -8.1711 -15.2650 -10.1749   5.0405
-#&gt;   -6.8807   1.7919   7.3042   8.1553 -13.0794  -0.5813  -5.0708  -2.5351
-#&gt;   -4.9684 -10.6504  -0.5721   7.1142  -3.5750   6.8489  -6.5095   2.8569
-#&gt;   -3.0485  -3.3076  11.1464 -17.6827  21.0728   7.9631   6.5734  -4.6264
-#&gt;   -8.1356   5.7837   7.1010  -5.9716  -5.8083 -12.5508   1.9317   5.3365
-#&gt;   -3.9094  -2.5593  15.1274  -3.6534  -5.2195   8.2666   4.0784  -8.2882
-#&gt;  -12.0009  -2.1286  -8.1245   2.6126  -6.4047   4.9795   7.3524   2.3985
-#&gt;  -12.4428  -0.3967   0.4717   0.3295  14.2997  -0.7366  -0.6217   8.5197
-#&gt;    5.0303   6.1294  13.3281  -9.2592  -5.6271   1.5544  -8.4792  -9.1722
-#&gt;   -0.9523  -4.9790   3.4717   9.5711  -8.5808  -8.1858   2.3200  -3.2797
-#&gt;    4.5269 -10.4837  -5.3591   1.2692   7.3759   3.1948   6.5075  -0.1577
-#&gt;    1.4586   5.1032  -4.0572  -0.2207 -13.1058  14.9663  12.8850   8.8634
-#&gt;  -12.4930  13.8326   0.4180  -3.5766   8.4553   2.8925  -4.5446   2.8116
-#&gt;  -10.9751   1.2261  -0.9567   9.2892   9.1996   7.0981  13.8614   9.0606
-#&gt;    7.7918  -7.2087  -6.5799   7.0959   2.0646  -9.7638  -4.1928   9.2289
-#&gt;    1.6533   1.2870  -0.0182 -13.0842   0.6178  -0.5552  -4.2201 -19.3942
-#&gt;   -2.2661  -0.5142  -7.1386  -8.3120  -1.2078   1.6434  -6.7425   3.2869
-#&gt;   -3.8877  15.0025  -5.2346 -10.5686   4.4135  -2.4165  -7.5133  -2.8991
-#&gt; 
-#&gt; Columns 41 to 48   2.9983  -0.8264  -6.2157   0.3263  -2.4402  -1.5918  -0.0851   9.4905
-#&gt;    2.4019  -1.2246  10.9702   5.9760 -10.3602  10.0766  -5.6424  -3.9464
-#&gt;   -2.2259  -8.0696  -5.3889  -2.5355  -1.5785   1.4922   3.9313   3.6487
-#&gt;    4.3752   1.3446   6.6149   1.1079  -5.5993  13.3874  -2.4808  -3.6026
-#&gt;   12.0425  -1.8281 -10.2379   0.7260  18.8044   8.0943  -8.6910  -4.8258
-#&gt;   -2.1528   9.3368  -9.1097   6.7517  -6.0211  -9.9604  10.7281 -11.4681
-#&gt;    5.9794  -2.6296   3.4011  -4.1497  -9.6533  -6.6914   8.9476   0.3263
-#&gt;    6.3954  -7.2028   3.1968  -1.9858   6.4910  -0.9719  11.1239 -10.3912
-#&gt;    2.4235  -0.4624  -4.6499  -0.1831   1.7744   0.8738   6.9849  -3.6237
-#&gt;    6.4585  -1.7884   6.1685  -0.3586   4.6228  -2.1953   0.2383   0.8428
-#&gt;   -6.3138  -4.5906  -5.1866   7.4798   1.9488 -10.7605   9.8877  -2.4989
-#&gt;   -1.1204   3.3282   3.2806  -2.9751   3.5554  -2.0607   0.9874 -10.1379
-#&gt;    5.3563  10.4930  -3.1608   0.6433  -5.4996  -0.2462   4.6982   2.2973
-#&gt;   -1.5738  10.6577 -15.3409  -1.8469   5.4888   5.6670 -20.8759   1.5554
-#&gt;   -4.6547  18.5460 -15.6752   1.4308  -0.7663   9.7970 -11.1023  -1.1944
-#&gt;    2.0632   4.2472 -12.6380   1.7288   7.0870  -1.5645   0.0075   1.8948
-#&gt;    1.3558  13.1133 -12.0548   1.1284  -1.4743  -0.3827  -0.8787   1.3329
-#&gt;    4.1632   8.7800  -2.6362  -0.7297  -5.4515  -7.7162 -12.6394  -1.8968
-#&gt;    3.4425 -15.1725   2.8592  -3.8578 -12.3784   1.0967   2.3752   7.9780
-#&gt;   -0.2100  -3.4869  12.8837   2.0803   7.0197  13.6932  -8.9668 -11.0464
-#&gt;    2.4023   3.0655   1.3237 -11.0964 -11.5857  -7.3049   3.0948  -9.8260
-#&gt;    2.9141   1.8035  -3.3103   9.8115  -1.6180 -16.4526   3.2590  -7.2065
-#&gt;   -1.4414  -6.3942   6.4439  -6.5856  -8.3956   2.8836  -7.7110  -8.9953
-#&gt;   -3.7701   7.0923   5.6232   5.8855  -8.2455   5.1591  -4.1244  -3.9120
-#&gt;    2.2153  -5.9867  -8.4623   6.4775   4.4429  -3.8179  -3.1636  10.9581
-#&gt;    5.8014  -3.4685   6.6051  -2.6017   1.7290  -0.3225  -0.5797   2.0211
-#&gt;  -10.1719  -2.9828  14.3163 -13.3452   4.9357   2.6551   1.4080  -6.4666
-#&gt;    3.9187  -6.6580   0.6554   5.0834 -11.1975   7.3970   7.9602  -9.5025
-#&gt;   -5.6350   8.2572   1.1683  -9.3537  -6.0271 -26.4947   2.1266  15.0842
-#&gt;   -5.9367  -2.6309   4.8026  -2.4477   6.4585   6.3888   2.3314   8.4861
-#&gt;    8.7245  -1.6941   4.7363  -4.6386  -4.9573  -2.1141   6.3323  -0.1167
-#&gt;    7.0192   2.4109  -5.4206  -3.7984  -2.7332   0.6929  -0.2306 -11.9946
-#&gt;   -7.0402  -3.2097  -9.6620 -10.3457   7.1866  11.2731  -1.7026  -5.9052
-#&gt; 
-#&gt; (6,.,.) = 
-#&gt;  Columns 1 to 8  -4.6011  17.1559 -13.8131  -1.1403  -3.3061   2.9790   5.0861  -0.0740
-#&gt;   -0.0987   6.0179 -11.0058   9.2233  12.6534  13.2463  -4.1438   6.1404
-#&gt;    9.2484  11.8153   3.9228   2.0692  -0.2756   3.4121   4.8050  -2.6949
-#&gt;    6.7681  14.4377   2.5213   4.5518  -4.3439  -1.0407   4.3140   4.2854
-#&gt;   -7.9353   3.1622   3.2608  11.3273 -12.9874  -7.0283  -2.4618  -5.2543
-#&gt;   -5.6295  12.5631   1.9220 -11.5318   9.2327   6.1514  17.9259   3.7353
-#&gt;   15.3620  -8.1857   1.9281   9.3062   2.9538  13.7701   1.8961   6.9746
-#&gt;  -10.0459  -7.8570  -5.2662  11.8922   3.8556   4.4334   7.8467   1.1345
-#&gt;    8.7132   4.2797   2.5443   3.7586  -9.1776  -7.1358   9.6965  -0.9349
-#&gt;    9.4710   6.7939  12.4996 -12.0980  -2.7193  -0.0153  -8.3034  -7.7884
-#&gt;    9.9563  -1.1397  -7.5126   1.1220   5.6382   8.9841  -3.2489   7.8641
-#&gt;   -3.0505 -11.6102  -0.5692   3.6677  12.4903   1.5491  -0.6410   2.5430
-#&gt;    1.4716   6.2180  13.9920 -11.2472  -2.4067   1.5459   1.7280   0.3331
-#&gt;  -13.8961   8.4343   1.9281  -4.4630  -0.4555 -14.5913   0.2220   1.9767
-#&gt;   -6.3404  -6.5548 -20.3785   3.4072   1.9562  -2.3075   0.2949  -8.2409
-#&gt;    6.4621   1.6498   4.1052  13.7976  -6.1357  -1.6628  -0.0928  -0.8523
-#&gt;   16.0422 -11.0051  -2.8650 -10.8535  -1.9547   1.2751  -8.4437  -0.8108
-#&gt;   -9.0713  -5.4811  -9.0048  -5.2372   0.0570  -2.0069   1.2579  -0.3389
-#&gt;   13.2875   9.8499   4.6777   8.3273  -4.6013  18.1616  -1.0520   1.8478
-#&gt;    4.4173  -2.4177  -3.5739  -4.5656  -7.8999   6.2647  -0.8017  -0.3069
-#&gt;    7.3961  13.0126  -5.6249  -1.0545  15.0055   2.0817  -6.1694  20.3214
-#&gt;   -1.8191   3.3675 -18.9430  -2.1732 -10.4446 -12.4837   5.5292   0.0033
-#&gt;    6.5710   7.4824   2.4777   6.5701 -11.5220  -5.6845   7.2702  -5.4807
-#&gt;   11.4849  14.7423   0.6012   5.0839  16.7938  -2.6747  -5.9542   0.1309
-#&gt;  -10.4083  -3.5977   5.1322  -6.1124  -8.3116  -0.9367   1.5478   0.2618
-#&gt;  -14.1997  -6.7784   6.4569  -8.9822  -0.7656   1.6445 -11.4367  -3.7498
-#&gt;    6.3417 -21.6533   3.0243  -8.4193   4.1133  -8.8494   0.7591  -9.0049
-#&gt;    5.8842  -4.3889  -3.9358   4.5806   4.8882  -0.4653  -9.9391   1.3930
-#&gt;    5.6211  -0.7580   3.7426  -5.0792   0.7349   7.0626   1.8306   8.6426
-#&gt;   -9.7197   0.6834   6.8113   6.5197   0.8103  -7.8580   6.6851  -0.3513
-#&gt;   -3.4910 -19.6474  -9.8920   6.7040   6.4430   7.4744  -8.8198   6.2562
-#&gt;  -10.9179 -14.8518  -0.2086 -15.4034   4.1543  -2.5253  -5.0211  -3.2436
-#&gt;   -4.0963  -7.4371  -2.4754   4.8320   5.3858  -2.9223  -0.8330  -5.0697
-#&gt; 
-#&gt; Columns 9 to 16  -6.6719   2.4542  -7.3215   6.8666   9.4501  -0.3132  -4.9254   2.9776
-#&gt;   14.0825   0.9049  -8.4674   7.3480  -9.3850   0.3362   9.4196  10.0958
-#&gt;   -3.1757   3.2848  11.0092  -3.4234  -0.6380  -1.7324   1.3560  -6.3853
-#&gt;   -6.7140  -0.4641  -5.9579 -17.4093  -5.4696  -4.2666  -0.2221  -5.8614
-#&gt;   -0.6885  -0.9120  -3.4951   9.0176   3.1868  -4.5776 -10.6878  -0.7265
-#&gt;    0.1045  13.7871  -3.3197 -12.0361  10.4781  -6.8878  18.3086  -9.8102
-#&gt;   -1.7629   6.3448  -9.3468 -12.3837   3.8153   0.6211 -17.1105   2.0460
-#&gt;    2.2746  -4.7960  -6.4859   2.5512   4.6648   6.0021   2.3145   1.1503
-#&gt;   -2.4893  -1.5687 -13.7171  -3.9880  -0.9222  -2.6776  -9.4604  -0.7735
-#&gt;    4.5402  -1.0827  -4.1280  -9.9716  -6.0161  -0.6032   2.3039  -2.9215
-#&gt;   -1.8651   7.1416  17.6665  -2.0959 -15.2655  -6.0369  -3.3971  17.8093
-#&gt;    1.3265  -2.2562   5.9889  10.7039  -2.7202   6.5968  -9.9362  10.5241
-#&gt;   -0.3206  -6.7276  -4.7460  -3.3448   1.4371  -9.9478   4.7984  -5.4996
-#&gt;    0.3063 -11.2494   7.6942  -4.2396   6.4453   1.4218  -6.3413  -2.2858
-#&gt;    8.9872  -1.0883  -0.0106   9.8179  -6.3827   5.5861   2.2552   2.0424
-#&gt;    1.5191   3.3570   1.0389  -1.1569  -9.9436  -4.1287 -11.9049   3.0430
-#&gt;    0.6560 -11.2046 -13.1328  -7.2688  -1.4367  -1.6023  -9.6737   3.7171
-#&gt;    1.8570  -7.4486  -0.7643   6.2601  -6.8568   0.2000   3.7664  -6.0978
-#&gt;   11.7091  12.2174   5.2854  10.1029 -13.5426   2.6050  -0.5178   5.7563
-#&gt;   -1.0447 -10.4205  -8.5034  -4.0953   4.0973   0.9380  -1.0551   2.3241
-#&gt;    6.4425  -0.5270   2.9474   4.2525  10.5571  -4.0465   2.4688   6.8205
-#&gt;   -7.0847   3.1878   2.4467   3.7633  -0.0050   4.6347   1.2514   4.7464
-#&gt;    1.0661  20.6947  -5.9104  14.8457  -5.8671  -3.7575   9.1179   0.5123
-#&gt;    3.6299  -1.7858 -10.2735  -1.3320  -4.4259  -2.5579  -1.9221   5.8192
-#&gt;   -3.5785   4.9147   1.0922  -2.6558   3.7411  -8.7387  -6.3300   5.1792
-#&gt;   -3.2358   1.6540   7.2837  17.0975  -2.5003   7.5551  -0.6301  -2.4820
-#&gt;    1.9792  -1.3244  -4.6021   3.4931   6.3981   2.3832   6.7822  -4.2359
-#&gt;   -6.7393  -6.6463   4.2809   5.2641  -2.9314  -2.4447  -5.8251   2.1017
-#&gt;    5.9703   6.3618   1.5003 -10.8189   1.4581  14.8551 -12.2845   4.3682
-#&gt;    1.3386  -0.0221  -0.5382  -7.7487  -9.4829   5.8419  -2.9132  -4.5791
-#&gt;    0.3972   7.7624  -4.8611  -4.6877   8.8754   0.9636   0.5786  -3.5882
-#&gt;   -0.8565  -1.7343   5.3994   7.1850   5.6352   9.1722   3.8340  -0.2539
-#&gt;    3.4546  -1.3482   5.1703   6.9300   3.8917   2.0822 -12.0254   5.4756
-#&gt; 
-#&gt; Columns 17 to 24   9.1410  -4.6715 -11.8277  -0.5674   7.2144   5.9532   5.1630  -3.0127
-#&gt;   -7.7256   0.5462  -2.0261   4.7077   5.6244  -1.4374   6.8913   4.2715
-#&gt;    6.7361   9.0528  -6.6738  -8.5932  -8.5103  -5.1810  -2.2084   3.2512
-#&gt;   -6.6492  -3.5772   5.4383  15.2066  14.3116  -1.0722   8.6204  -0.1620
-#&gt;    8.7017  -7.8557  -2.3007   0.3131   5.4473   3.4416   3.1413 -11.1208
-#&gt;   13.6431  -4.8537  -2.4312  -3.0439   2.9064  -1.7232  -0.8271  -0.6256
-#&gt;   -0.3683   3.0724  -0.0457  -1.2115   4.5559   0.1659  17.9356   3.7203
-#&gt;    6.4079   0.8354   3.3950   0.4321   1.4492 -10.8048  -3.2980   4.6580
-#&gt;   10.0848  -0.3486   3.3684 -12.4599  12.3331  -5.8252   9.6216  -1.6610
-#&gt;    3.4838   1.2053   3.3206  13.5729  -5.0510   2.1259  -4.8707   1.4423
-#&gt;   -9.7060   2.5276  -2.9237   1.2384  -8.3499 -11.2698  -5.2936  -0.6241
-#&gt;    3.3614  -1.0650  -8.1626  -5.0707   3.2281   0.6324  -0.9354  -3.5268
-#&gt;    4.7277 -10.9997   5.0609  15.3501  21.2539   1.0990   2.5604  -4.1809
-#&gt;   -2.4034 -10.3769   4.2809   8.1751   7.5557  -6.2885  -2.2706   1.3895
-#&gt;   -7.0907  -6.4549  -1.9959   5.2416  12.6819  -2.4913  -0.5606  -0.7743
-#&gt;   -2.2130   4.8851   1.6466   2.2099 -10.5474  -5.9116  -3.8344   1.5176
-#&gt;  -12.7625   3.6632  12.0666   5.5806   1.8108  14.3611 -13.9742   2.4020
-#&gt;  -11.1207  -0.6156   2.0324   1.6196   1.0054  -2.5092  -6.4802   3.2643
-#&gt;   -0.4212   1.7372   1.4412   9.9730  -5.8658   7.8543 -14.5563   6.8621
-#&gt;    4.0270  -2.2623  -0.3121  -0.3280   8.9480   1.7867   7.4439  -1.9184
-#&gt;   -6.2863  10.1384  -4.9743   9.9337  18.2362   4.4891   0.7699  -7.5557
-#&gt;    1.0764  12.0407  -2.4925  -7.3359  -8.9175   3.3125   0.2415  -2.9558
-#&gt;    5.7820  -6.8363   0.9470  -6.2567   6.7562  -4.7062  -0.0338   6.5371
-#&gt;    4.8835  -1.0235  -6.3162   5.9854   5.2803   8.4883 -14.7119   2.7781
-#&gt;    0.5622 -19.3155   0.9244  -0.2856  -1.5179  -4.8493  -2.3202   2.4978
-#&gt;   -1.4257  -7.1916  -3.6128  -9.1141  -2.6992  -9.2053   2.5335  -4.4162
-#&gt;   -6.8747  -6.3916   3.2923  -8.8304   4.5187  -4.1352   4.0686   4.3218
-#&gt;   -1.7492   7.0544  -2.2555  -6.9414   7.5464  -2.4689   4.0410   0.2256
-#&gt;   -0.0671  -3.2189   7.0610   7.7604 -10.1122  -6.3793   6.3874   9.6638
-#&gt;    0.1141   3.1621   0.3727   7.2362  -9.9334  -5.6206   0.6885   2.5693
-#&gt;   -4.3021  -2.6160  -1.9825  -1.1219   0.9572 -11.8500   0.4138  -1.5788
-#&gt;    1.6017  -3.1071   0.4784   2.9770   5.9101  -7.1176 -10.8066   8.8917
-#&gt;    2.7985  -2.3562  -5.5993  -2.5174   8.1146 -16.7748  -5.6155  -3.1062
-#&gt; 
-#&gt; Columns 25 to 32   0.8446   2.4044  -1.9319  -4.4223  -9.6491  -4.7749  -0.4503  -3.0547
-#&gt;    3.9894   0.8862  -0.3507  -6.7889  -4.7644   9.2572   2.8051   6.2755
-#&gt;   -1.5143   0.8520   4.8140  -2.9089  -6.2062  -6.0682   1.2473  -0.8472
-#&gt;    2.9874  -8.0279  -6.8222   2.6873   0.4657  -5.6350   1.1802   4.4861
-#&gt;    0.1795  13.7681  -3.4721  -5.0327   8.1523   9.2060   1.2814  -9.3025
-#&gt;   16.8968  -7.5151  -1.6525 -13.0141  -8.0597  -7.5346  10.6342  -5.4350
-#&gt;   -0.9741 -15.7876  15.8898   6.3198  -1.8913 -28.0491  -6.0149   8.7465
-#&gt;  -12.2322  -4.3875   3.7241  -5.4544  11.5575  10.6161  -2.0998  14.8236
-#&gt;    0.7991 -14.7625  -3.1572   0.7101   8.1642 -10.5151   9.7528  12.0755
-#&gt;    6.5326   7.2579  -9.5126  -1.5740  -4.6196  14.0520  -1.4773   5.5620
-#&gt;    4.9015  -3.9368   0.5989   5.2388  -9.3340  -4.5601  -3.0986  13.6968
-#&gt;    1.0719  -4.3384   9.8443 -10.2845  -5.3760  -2.2379  -5.2300  -3.3567
-#&gt;   14.5270  -2.8854 -12.0783  -4.4583   3.4906   3.9708   7.5022  -2.4572
-#&gt;   -3.2821  -2.5201   5.2848   2.4140   9.2039   2.8812  -7.2255  -6.2720
-#&gt;    1.3654  -5.4071   2.8315  -5.9915   5.9244  -0.3972  -1.5473  -2.7216
-#&gt;    0.7895  -5.6126   1.8673  -3.3374  10.1247   2.7775  -6.4114  13.6867
-#&gt;    6.6922   1.2275 -11.0742  12.5937   2.1475  -2.6909  -8.2551   0.4380
-#&gt;    4.1752   0.9775   0.7422 -10.2445  11.6329  16.6579  -7.4806 -12.3208
-#&gt;   -5.2675   8.4776  -8.6773   3.3536  -9.2119  -4.1957 -16.2995   6.3357
-#&gt;   -1.6758 -11.5879   2.7113   6.9362  10.5063   6.4480  13.5510  -2.2547
-#&gt;    6.6658  -2.2592 -12.1844   2.8839  -6.7935   1.1912  -3.6765  -4.1006
-#&gt;    1.2998   1.2949  -1.3425   1.7091  17.8363  -2.1229  -0.3490  -3.1975
-#&gt;   12.2334  -1.1756   3.5598 -13.3114   4.7234 -11.8661 -10.3281   2.6856
-#&gt;    2.4621 -10.6354  -3.2076  -6.2773 -10.3069   8.0860  -3.8629   7.2660
-#&gt;    5.5662  -0.2481  -5.5049  -0.4396   8.1751  -2.7787  -5.8247  -4.6528
-#&gt;   -1.8195  -3.1912   7.6911  -8.8373   4.8918   8.5692   1.7511   2.6763
-#&gt;  -13.1182  -4.6410  10.6748  -6.7432   2.2837   2.0653  -0.4308   3.3745
-#&gt;    4.9065  -2.4769   4.2135  -1.7919  -1.5576  -4.0881  -4.7283  -0.5514
-#&gt;    1.1211  -6.0653  -4.7508   2.3265   8.1275 -20.9640 -12.8425  10.6862
-#&gt;   -0.9781   2.4650  10.8866 -11.2128  -2.2247   1.6020  -5.5485   4.7362
-#&gt;   -3.5926 -13.4881   4.8628  16.8825  -4.0933  -5.0824  -2.7033   7.9502
-#&gt;   -5.8508  -7.4697  14.5913   0.3663   0.9808   1.2902  -6.0414 -11.4014
-#&gt;   -5.0823  -2.7415   2.3744  -4.4659  -1.7825 -15.8793  -2.0529   6.5192
-#&gt; 
-#&gt; Columns 33 to 40   2.6158  -3.0753   2.6472   6.7657   1.5088  -4.2734  -1.3428  -1.9151
-#&gt;   -3.7284   8.6976   1.6726  -3.8546  -6.3398  -7.1568   1.8022  -7.3787
-#&gt;    2.6724  -1.9342  -2.6601   6.6184   4.1796  -0.1458   0.8823   3.2014
-#&gt;    1.1584  -0.5886  -8.2231   4.0671   2.5080  -7.5798  -7.5053  -1.0746
-#&gt;    2.4198  10.5440  -7.5917 -10.0083  -7.4606  -6.5574   9.2363   9.1526
-#&gt;    2.5340  -7.3747   6.5168   4.4041   5.6709   6.8223 -15.6684  -0.9025
-#&gt;   -4.8695   7.5415  -1.2845  12.5515   0.4313  -1.1265   5.5949  -7.6371
-#&gt;    8.6884   7.1947  -9.2425   0.5988   4.1851  -2.3986  -8.9563   5.5626
-#&gt;   -5.7558   6.3694   0.7807   3.1686  -0.6903   4.9412   2.4110  -3.4620
-#&gt;    0.2265 -14.0489   8.2244   3.0518  -4.9083   1.2285   0.3939  -7.4803
-#&gt;   -1.0594  -4.1211  -2.0576   1.7056   3.2868   6.7020   1.8807 -10.1864
-#&gt;   -6.9906   8.7637   2.8246  -0.0698  -5.3659   0.4439   1.5262  -2.9926
-#&gt;    3.9792   1.3786   1.1054  -8.9793  -0.8686  -0.7475   4.2566  -0.9324
-#&gt;   -4.8919   6.6063  -8.9286 -18.2325  -6.6745   6.3516   0.6881   1.6877
-#&gt;   -8.5160   9.2397   7.5456  -6.8326  -8.1944  -4.7174   4.1938   0.0690
-#&gt;    7.0149   0.4601  -1.5239   3.0693  -5.2151   0.0441   9.7223   2.7027
-#&gt;   -4.9491  -8.3356   5.1315  -9.3584  10.6821   2.6752   5.4818   2.1986
-#&gt;   -2.2771  -0.8397  -8.2478  -4.2335  -2.4324   0.6923  -2.5936   1.6252
-#&gt;    6.6926  -8.1672  -1.2691  -4.8296  10.3421  -5.5036   8.1658  -9.8338
-#&gt;    5.7534   2.2730  -7.1606  12.1566   1.1832 -13.1255   2.7100   3.0889
-#&gt;   -1.3969  -1.7612   2.3318  -2.3131  -3.0137   4.9530  -4.4405   2.2613
-#&gt;   -9.1820   9.8698  -2.2202  -2.8527  -1.4732   4.1364  -5.9269   2.2270
-#&gt;   -2.2568  -6.1337  -2.1487   7.5791  -2.4767   0.8034   7.8051  -0.7946
-#&gt;  -13.0195  -0.5097  21.0940   0.5272  -5.7185  -3.0578   5.5039  -1.0589
-#&gt;    2.3830  -2.2118  -8.7776  -7.8740   1.6069   7.0312   4.8595   3.9153
-#&gt;   -3.4007   0.2239  -2.6585   3.6651  -6.8753  -4.1735  -9.5685  -7.9010
-#&gt;   -6.1817  -3.2244   5.8800  -7.0170 -12.8760   5.1406  -4.9709  -1.4044
-#&gt;   -8.1504   5.4770  -6.7944   2.9177  -1.7789  -7.0646   5.0146   2.3032
-#&gt;   -0.1587  -4.6075   6.5898  -1.1252  -3.2739   7.0829  -1.8971  -8.2446
-#&gt;    4.9494  -1.3200   0.7108  11.3834  -0.5919   1.9659  -5.9221   7.8298
-#&gt;    2.4449  11.2262  -0.0684   2.3745  -1.7291  -3.5869   1.2010 -12.7901
-#&gt;   -7.1705  12.3806   0.5425 -16.1337  -4.1741   0.9869  -2.3636   5.3122
-#&gt;   -1.6145  11.3669  -5.5819   3.9176   1.2761   2.2113   7.9025  -2.2650
-#&gt; 
-#&gt; Columns 41 to 48   0.2125  -5.1004  -6.3856  -8.8158  -0.2449   7.8677 -13.5295  -5.2617
-#&gt;   12.2686  -7.1538   0.1933  -1.7471  -7.3491   2.3267   1.7600  11.9186
-#&gt;   -0.4285   5.8589   4.4461   7.7819  -0.6682   1.9765   0.9930  -0.5353
-#&gt;   -2.7692  -4.4001  -5.8345  -0.0684   4.0746   4.1359 -10.5215   5.9349
-#&gt;    2.8326 -13.1694 -10.9287   8.3692  10.3587  11.4379  -9.5713  -3.4393
-#&gt;   -9.1738  15.6793 -16.0524  -7.4145   5.7631   1.1164  13.1113   1.8928
-#&gt;    2.3915  -4.3884   0.1700  -4.2228 -12.3916   0.5587  -7.0417  -0.5775
-#&gt;   12.3394  -6.9279  -0.5580   8.5290   8.1458  16.7137   7.6943  -1.5574
-#&gt;   -1.1335  -5.8132   4.6990  -1.3075   2.9808  25.5090  -3.2641  -5.3452
-#&gt;   -8.3921  -0.5183   1.1554  -5.5029   6.5615  -6.0135   2.6089  -4.2357
-#&gt;   -0.4111  -0.3032  18.5133  -4.0653   1.2591 -10.7067 -13.0404   9.8669
-#&gt;    7.9978  -3.0882  -2.3177   0.4324  -1.3937   5.9923   2.5791  -9.4750
-#&gt;   -8.5793  -0.3205 -18.8970   5.0210   6.8065   2.1221  10.3198   2.5984
-#&gt;   -4.5230  -6.6179  -7.4316  11.0973  -1.1367   5.5786  -9.8507 -15.7608
-#&gt;    7.4877  -7.2748   0.2127   1.4627  -0.2828   5.3574  -1.7254   0.7135
-#&gt;    2.6833 -18.5120  12.6707   2.0215   9.3793  13.8531  -0.4838 -10.4726
-#&gt;   -0.3525  -0.8188   0.4052   0.5138  -1.5883  -0.8770   6.0451  -2.6040
-#&gt;    2.8872 -11.1480  -8.1583   3.4286   2.0695   8.8677  11.0427  -2.6508
-#&gt;   -0.6280  -8.0605   4.8617   4.1984  -1.9286 -16.7375 -12.1875   5.3507
-#&gt;   -8.5250  -3.2192  -4.1009   9.4527  -4.5549   7.6869  14.1630  -8.2732
-#&gt;   -1.2216   0.4383   3.2146  -2.7947   5.9063  11.0631   3.8883  -1.6990
-#&gt;   -4.4259  -2.3486  17.6357  -4.7616   4.1182  -4.0969  -9.7592   3.5164
-#&gt;   -2.6727 -15.9790   8.3620 -12.7896   4.3340   1.1878  -5.9710  -1.1624
-#&gt;    7.6952   1.8875  -4.1077  -0.4681   4.9294   6.0547  -4.1902  -8.9070
-#&gt;   -2.5159  -3.5792  -2.8914   3.7157   1.6082  -0.6608  -0.7963  -1.2621
-#&gt;    6.8719  -9.9961   2.6646  -3.4960   7.7787  11.3205  -0.6853  -2.0225
-#&gt;    0.5968  -0.1122   7.1179  -6.9875   2.9136  -4.1256   5.3715 -11.4436
-#&gt;    2.5336  -6.5106   4.6012   4.6963  -4.7689   1.7571   0.0413   0.1907
-#&gt;   -1.8477   2.0561   0.5724  -3.1906  -6.1864  -8.7263  -1.8621   5.8985
-#&gt;   10.0692  -3.8631   3.8337  -6.1160  -7.4528 -15.4211  -0.4797  -7.2067
-#&gt;   10.2156   5.6486  -3.8942   2.2989  -7.4075   0.3157  -2.0070  11.6593
-#&gt;    3.0562   2.3002  -4.6943   8.2808   9.0532  -1.9655  10.2006  -3.9533
-#&gt;    6.5407  -2.5491   1.3901   8.4121  -7.6074   8.3332  -1.7874 -10.9301
-#&gt; 
-#&gt; (7,.,.) = 
-#&gt;  Columns 1 to 6 -4.7539e+00 -5.4413e+00 -5.1079e+00  5.7424e+00 -3.2858e+00  2.8523e+00
-#&gt;  -6.7830e+00  5.1682e+00  1.1901e+01  2.6195e+00 -1.5841e+01 -1.2344e+00
-#&gt;  -8.9084e+00 -2.0126e+00 -2.3906e+00 -7.2197e-01  1.8293e+00  2.2422e+00
-#&gt;  -2.2283e+00  1.2108e+01  5.8294e+00  2.0895e+00  9.5415e+00  2.8183e+00
-#&gt;   5.4032e+00  1.2340e+00 -1.0125e+01  8.2682e-01 -2.8566e+00 -1.2956e+00
-#&gt;  -8.9067e+00  5.9378e+00 -1.2394e+01  9.7711e+00 -4.2268e+00 -5.6503e+00
-#&gt;  -2.1833e+01  1.0082e+01  4.8581e+00  3.4860e+00  2.6590e+00  9.8115e+00
-#&gt;  -2.2773e+00  9.6036e+00 -4.5954e-01 -1.7047e+01 -3.6290e+00  9.4021e+00
-#&gt;  -1.5954e+00 -5.7688e-01  9.4539e+00  7.8412e+00 -1.0081e+00  1.7600e+00
-#&gt;   5.8402e+00 -1.2048e+00  2.2213e+00  5.4119e+00  4.3289e+00 -1.0109e+00
-#&gt;  -9.1306e+00 -1.0265e+00  5.7474e+00 -1.2769e+01 -1.9749e+00  3.0200e+00
-#&gt;  -9.5275e+00  4.9209e-01  6.4155e+00 -1.1126e+01 -3.2687e+00 -7.0478e-01
-#&gt;   5.6497e+00  4.7677e+00  3.2332e+00  1.0894e+01  1.0828e+01 -4.6356e-01
-#&gt;   1.4086e+01  1.0060e+00 -1.9602e+00 -1.3395e+01 -2.5796e+00 -1.6707e+00
-#&gt;   1.1046e-01  3.6704e+00  2.5898e+00  2.7610e+00 -6.3168e+00 -1.1920e+01
-#&gt;  -9.3498e-01 -5.5432e-01 -2.5432e+00  3.2789e+00  5.6506e+00  3.4255e+00
-#&gt;   9.6526e+00  6.2269e+00  3.0615e+00  3.0221e+00  4.2950e+00  4.1346e+00
-#&gt;   3.6330e+00  8.4372e-02  1.9772e+00  9.3576e-01 -8.4566e+00  2.7650e+00
-#&gt;   7.9453e-01 -7.8722e+00  1.2383e+01 -5.5762e+00 -5.1102e+00 -7.2610e+00
-#&gt;   4.5315e+00  2.9623e+00 -3.9322e-01  6.4155e+00  7.1330e+00 -6.9086e+00
-#&gt;  -3.7325e+00  7.7851e+00  1.1725e+01  2.3080e+00 -6.5245e+00  9.0756e+00
-#&gt;  -3.4757e+00  6.2335e+00 -3.9576e+00  6.0785e+00 -1.0743e+01  5.4288e+00
-#&gt;   2.5921e+00 -9.7639e-01 -1.1226e+01  9.2475e+00  1.6820e-01 -1.3328e+01
-#&gt;  -4.9560e-02 -6.7230e+00  1.2880e+01  2.4603e+00  2.6749e+00  2.8815e+00
-#&gt;   6.8667e+00 -6.0821e+00 -1.7291e+01 -2.8446e+00  7.3183e+00  2.7881e+00
-#&gt;  -3.1957e+00 -1.2676e+01 -3.6320e+00 -8.2129e+00 -5.0482e+00 -3.1158e-01
-#&gt;  -5.5740e+00  3.2234e+00  3.9514e+00 -7.8050e+00 -5.2679e+00 -9.3996e+00
-#&gt;  -5.5803e+00 -1.3676e+00  1.1302e+01  1.1772e+00 -2.2943e+00  8.6424e+00
-#&gt;  -1.5530e+01  8.4487e+00 -1.2640e+00 -8.5742e+00  9.6575e+00 -1.0051e+00
-#&gt;   9.0720e-02  5.9025e+00 -9.3707e+00 -6.8515e+00  1.6141e+01  6.6600e+00
-#&gt;  -3.8016e-01  3.1985e-01  6.2127e+00 -9.4619e+00 -1.5340e+01  4.5520e+00
-#&gt;   4.4106e+00  9.3050e+00  3.6057e+00 -8.6731e+00 -9.8965e+00 -9.9702e-01
-#&gt;  -7.2330e+00  2.2393e+00 -3.9111e+00 -1.1024e+01  1.9067e-01 -4.9675e+00
-#&gt; 
-#&gt; Columns 7 to 12  7.1885e+00  1.4839e+00 -5.6653e+00  3.4803e+00  8.7571e+00 -2.3640e+00
-#&gt;  -8.1678e+00 -4.7150e+00 -1.6376e+00  3.9226e+00 -1.1381e+01  6.1535e+00
-#&gt;   4.8645e+00 -1.6419e+01  2.4629e+00  2.1655e+00  3.9177e+00  9.3438e+00
-#&gt;  -1.0545e+01  1.2575e+00  2.9298e+00  5.8718e-01 -8.6144e+00 -5.2109e+00
-#&gt;   8.4732e+00  1.7923e+00 -5.0859e+00 -5.0231e+00  1.3080e+00 -6.3420e+00
-#&gt;  -8.3233e+00  9.3958e+00  8.5539e+00 -8.5061e+00  2.1961e+00  8.2712e+00
-#&gt;   8.8868e-01 -1.0496e+01  4.2031e+00  4.9101e+00  2.6637e+00  1.0277e+01
-#&gt;  -3.4287e+00 -4.3696e+00 -1.0436e+00 -1.5413e+01 -1.7801e+00  1.2636e+01
-#&gt;   1.0143e-01 -4.3399e+00 -6.1799e+00 -4.3494e+00 -5.6792e+00  5.2218e+00
-#&gt;  -3.5415e+00  1.9713e+01  4.3422e+00 -8.2040e+00 -1.0832e+01 -5.4304e+00
-#&gt;  -1.0349e+01  6.1643e-01  1.4575e+01  1.8970e+01  2.5316e+00 -1.0199e+01
-#&gt;  -4.2850e+00 -1.2453e+00  1.1344e+00  5.2100e+00  1.1571e+01  2.7518e+00
-#&gt;  -7.0114e+00 -1.8016e+00  4.4194e+00 -1.2386e+01 -3.5900e+00 -6.3103e+00
-#&gt;   8.3670e+00  1.8407e+01 -7.4904e+00  1.0170e+01  7.9329e+00 -8.0079e+00
-#&gt;   6.0158e+00  9.1701e+00  7.4872e-01  2.0611e+00 -7.5288e-01 -2.9542e+00
-#&gt;   1.6501e+00  5.1554e-01 -1.1614e+00 -6.6588e+00  1.4745e-02 -7.1027e+00
-#&gt;   4.7209e+00  3.3345e+00  7.9440e+00 -1.6757e+01 -6.1704e+00 -1.1924e+01
-#&gt;  -3.0643e+00  1.2380e+01  7.0281e+00 -8.5020e+00 -4.4068e+00  4.4527e+00
-#&gt;  -1.0004e+00 -4.4409e+00  1.3646e+01  6.9884e+00  1.4947e+01 -2.6068e+00
-#&gt;   2.2670e+00  2.5166e+00 -7.6694e+00 -9.8289e+00  7.3805e-01 -9.1742e+00
-#&gt;  -1.1621e+00  2.4311e+00 -1.0818e+01  2.8172e+00  1.0460e+01  1.2008e+01
-#&gt;   1.0508e+00 -2.8342e+00  1.5692e+00  7.2430e+00  9.0263e-01  4.5200e+00
-#&gt;  -1.0811e+01  8.7829e+00  1.9100e+00  1.5394e+01 -2.8776e+00  1.1744e+01
-#&gt;   2.8977e+00  1.0320e+01 -1.1240e+01  2.8720e-02  1.0370e+01 -5.7076e+00
-#&gt;   2.9303e+00  7.8268e+00 -1.3908e+00  3.1532e+00  1.8492e+00 -9.7415e+00
-#&gt;  -8.7779e+00  1.6457e+01 -2.9540e+00 -4.8756e+00  1.3998e+01  5.0867e+00
-#&gt;  -6.0820e+00 -6.3480e-01  1.8978e+00  1.7259e+01 -2.1651e+01  2.5541e+01
-#&gt;  -1.2380e+01 -5.8338e+00 -4.8561e+00  8.1746e+00  5.6594e+00 -4.2823e+00
-#&gt;   3.0256e+00 -1.4104e+00  7.1363e+00 -4.7794e+00  1.5764e+01  1.2340e+01
-#&gt;  -5.7086e+00  3.9810e+00 -1.6972e+00 -2.5669e+00 -4.6632e+00 -1.2236e+00
-#&gt;   7.3610e+00  9.1834e+00  9.5446e-01 -2.0375e+00  6.9810e-01  9.6967e+00
-#&gt;  -5.0042e-02  1.2364e+01 -5.0638e+00  5.7442e-01  6.9132e+00  2.6120e+00
-#&gt;   5.2512e+00  4.4501e+00  8.3533e+00  3.0131e+00  5.0744e+00  1.1640e+01
-#&gt; 
-#&gt; Columns 13 to 18  6.4217e-01 -8.2729e+00  5.8952e+00  1.5816e+00 -2.7197e+00 -8.5711e-01
-#&gt;  -5.7058e+00 -3.6849e+00 -1.8430e+01 -8.4779e+00 -6.3064e+00 -2.1269e+00
-#&gt;   3.1376e+00  9.6012e+00 -1.9084e+00  2.4968e+00  2.2953e+00  3.0745e+00
-#&gt;  -8.8981e+00 -4.1556e+00 -1.6813e+00 -3.9189e+00  2.9930e+00 -3.7223e+00
-#&gt;  -1.3562e+01 -4.1456e+00  1.3425e-01  7.7092e-02 -6.9110e-01 -2.9953e+00
-#&gt;   1.0018e+00  5.7615e+00 -7.1374e+00 -2.5596e+00 -9.9927e+00  3.1027e+00
-#&gt;   2.0344e+00  8.4738e+00 -5.2202e+00  2.2161e+01 -1.1830e+01 -6.0402e+00
-#&gt;  -1.0051e+01 -2.9788e+00 -1.8476e+01  1.9074e+00 -5.6018e+00  7.9315e+00
-#&gt;  -8.9211e+00  3.8496e+00  1.1668e+01  7.8425e+00 -7.3754e+00  1.3376e+00
-#&gt;  -3.0324e+00 -3.6875e+00  8.9725e+00 -2.0518e-01  5.6736e+00 -6.9675e+00
-#&gt;   4.6973e+00  5.3883e+00 -7.1293e+00 -1.1233e+01 -5.7495e-01 -4.5240e+00
-#&gt;  -2.5101e+00  2.7008e+00 -6.4778e-01 -1.4951e+00 -3.5185e+00 -4.1225e+00
-#&gt;   1.2273e+00  2.5521e+00  1.1687e+01 -7.1245e+00 -1.1701e+00  8.5959e+00
-#&gt;  -5.4090e+00 -1.7780e+00 -1.5010e+00 -6.2108e+00 -1.3957e+00 -4.0800e+00
-#&gt;   1.7233e+00  1.1662e+01 -8.2722e+00 -6.0465e+00  3.9486e+00 -1.1707e+01
-#&gt;  -5.4021e+00  4.8696e+00 -6.4250e+00  1.5577e+01  1.2539e+00 -2.8722e+00
-#&gt;  -2.9845e-01 -1.2836e+00  8.4602e-01  7.4733e+00  8.3738e+00 -7.7960e-01
-#&gt;   6.6093e-01  4.8584e+00 -1.2108e+01  1.4025e+00 -6.3669e+00  4.9571e+00
-#&gt;   1.0637e+01 -5.3751e+00  4.6582e-01 -1.6639e+01  8.3868e+00 -1.4484e+01
-#&gt;   9.1160e+00 -1.3406e+00  2.6342e+00 -5.8375e-01  7.6810e+00  5.1810e-01
-#&gt;   9.7270e+00 -4.9832e+00  2.1767e+01 -4.3469e-02 -5.9855e+00  2.5649e+00
-#&gt;   5.9265e+00  1.3072e+01 -3.1605e+00  6.1756e+00 -1.8455e+01  4.9888e+00
-#&gt;  -1.8837e+00  8.8244e+00 -2.8677e+00  2.8433e+00 -4.2382e-01 -1.2451e+01
-#&gt;   1.3189e+00 -2.1531e+00  2.0073e+01  4.5793e+00  9.2091e+00 -6.5673e+00
-#&gt;  -2.2074e+00 -2.4625e+00 -1.9409e+00  4.1527e+00 -6.7864e-01  1.0673e+01
-#&gt;   2.9539e+00 -3.8146e+00  1.8435e+00  1.6268e+00 -5.6074e+00  2.2445e+00
-#&gt;  -1.2559e+01  5.6896e+00 -9.1792e+00  6.8188e+00 -9.8085e+00  4.0340e+00
-#&gt;   2.2429e+00 -2.3235e+00  4.1542e-01  1.7298e+00 -3.4710e+00  5.4292e+00
-#&gt;   7.3370e+00  6.4150e+00 -5.8965e+00  5.8676e+00 -5.5686e+00 -1.2922e+01
-#&gt;  -1.0226e+01 -2.5040e+00 -7.2621e+00  7.5980e+00  3.6877e+00  5.9463e+00
-#&gt;   3.5129e+00 -1.4033e+01 -2.7303e+00 -2.2790e+00 -2.0664e+00 -4.9359e+00
-#&gt;   9.9530e+00  2.1002e+00 -7.2573e+00 -9.6047e+00 -1.0723e+01  4.9687e+00
-#&gt;  -4.6553e+00  3.7289e+00  1.4248e+00  2.8286e+00  2.9919e+00  2.3431e+00
-#&gt; 
-#&gt; Columns 19 to 24  4.4848e-01  4.4693e+00 -6.3724e+00  4.9259e+00 -3.5856e+00 -2.6267e+00
-#&gt;   2.9036e+00 -2.5478e+00 -1.3419e+00  2.0028e+01  5.0062e+00  9.5935e+00
-#&gt;   7.4184e+00 -1.3881e+00 -3.7800e+00 -1.3710e+01  6.8624e+00  9.4015e+00
-#&gt;  -9.6968e+00  7.1971e+00 -6.3063e+00  6.6175e-01 -2.4158e+00  5.3438e+00
-#&gt;  -5.7711e+00 -5.9183e+00  8.4398e-02  7.5502e+00 -4.0251e+00  3.0765e-02
-#&gt;  -7.9337e+00  1.1721e+01 -6.5230e+00  2.7910e+00  1.0360e+00  6.2584e+00
-#&gt;  -2.5334e-01  6.7635e+00 -1.1703e+01 -1.7203e+01  1.1183e+01 -5.9886e+00
-#&gt;   7.5426e+00  6.5731e-01  7.6812e+00 -7.7991e+00  8.6480e-02  6.0639e+00
-#&gt;   1.6340e-01  9.2168e+00 -7.0082e+00 -1.1437e+01 -1.5904e+00  1.7731e+00
-#&gt;  -2.9604e+00  2.2554e+00 -2.1149e+00  1.3128e+01 -1.1453e+01  1.1947e+00
-#&gt;   4.8544e+00  2.9421e+00 -5.0153e+00 -1.2096e+01  8.6836e+00 -5.9044e-04
-#&gt;   2.3762e+00 -6.0172e+00  4.5227e-01 -1.1809e+01  3.8551e+00 -3.7681e+00
-#&gt;  -7.0848e+00  4.0765e+00 -3.3096e+00  5.4421e+00 -3.1864e-01  4.0312e+00
-#&gt;  -1.3626e+01 -2.3332e-01  1.5759e+00 -7.1369e-01 -8.5911e+00  4.6943e+00
-#&gt;   3.1454e+00  1.6401e+00  9.2734e-01  3.9320e+00  2.0222e+00 -3.4654e+00
-#&gt;   4.7070e+00  7.3319e+00  5.2018e+00 -1.3303e+01  2.6234e+00 -1.4202e+01
-#&gt;   9.0199e-01  1.5914e+01  6.2503e+00 -2.8658e+00 -2.2443e-01 -2.1187e+01
-#&gt;  -1.1828e+01 -5.9511e+00  3.4826e+00 -1.5351e+00 -1.3216e+01 -1.0937e+01
-#&gt;  -4.1074e+00 -7.9675e+00  4.8711e-01 -6.9737e-01  1.6260e+00  8.6457e+00
-#&gt;  -4.7047e+00  9.7343e-01  1.4849e+00  1.8518e+00  2.8288e+00 -4.0186e+00
-#&gt;  -3.5703e+00  1.0034e+01  2.7862e+00  8.5154e-01 -1.1205e+01 -6.7563e+00
-#&gt;  -4.0535e+00  1.1688e+00  8.6364e-01 -4.3251e+00  5.6891e+00 -4.3892e+00
-#&gt;  -6.0090e+00 -1.6502e+00 -1.7136e+00  9.0528e+00 -1.3701e+00  1.2534e+01
-#&gt;   8.0822e+00  4.3403e+00 -1.1260e+01 -6.3962e+00 -1.7518e+01  3.3022e-01
-#&gt;  -8.6023e+00 -1.4518e+00 -5.4160e+00 -2.6692e+00 -4.0958e+00 -3.2359e+00
-#&gt;  -3.2993e+00 -8.2374e+00 -3.6237e+00  9.0098e+00 -1.1073e+01 -2.8098e+00
-#&gt;   4.3212e+00 -1.3441e+01  5.2033e+00  2.1516e+00  1.2355e+00 -9.7284e-01
-#&gt;  -7.1675e+00  2.2520e+00 -7.2210e+00 -8.5190e+00 -4.7134e-02  8.3228e+00
-#&gt;  -2.0781e-02  5.5276e+00  8.7644e+00 -1.8433e+01  9.2556e+00 -4.1435e-01
-#&gt;   9.8560e+00 -6.9458e+00  7.7471e+00 -1.9743e-01  3.4151e+00  5.3738e+00
-#&gt;   1.4205e+00  4.9860e+00 -2.3740e+00  2.5141e+00 -8.7893e+00 -4.3035e+00
-#&gt;  -9.3106e-01 -3.1540e+00 -1.1902e+00  3.9371e+00 -2.5225e+00  5.2944e+00
-#&gt;  -8.1773e-01  1.5696e+00  5.8713e+00 -1.0795e+01  9.3062e-01 -9.4059e+00
-#&gt; 
-#&gt; Columns 25 to 30 -1.1500e+00  1.3874e+00  1.5228e+00  9.5166e+00  2.5277e-01 -9.3443e-01
-#&gt;   1.2968e+00 -9.3009e+00  6.5709e+00 -4.9279e+00 -1.1357e+01 -1.6489e+00
-#&gt;   1.4674e+00 -1.3013e+00 -5.6328e+00 -1.5707e+00  9.4950e+00  5.2640e-01
-#&gt;  -5.8011e+00 -7.9256e-01 -5.9693e+00 -2.2962e+00  8.5209e-01  3.8725e+00
-#&gt;   5.6699e+00  2.4977e+00 -5.1925e+00  6.0237e+00  8.2626e+00  9.6763e+00
-#&gt;  -2.9173e+00  2.8341e+00 -1.2038e+01  1.4634e+00 -1.0139e+01  2.7329e-04
-#&gt;  -5.6692e+00  5.7683e-01 -2.3568e-01 -1.1943e+01  8.4431e-01 -7.5014e+00
-#&gt;  -8.1396e+00  3.9429e+00 -4.9840e+00 -1.1285e+01  1.2505e+01  4.0157e+00
-#&gt;  -9.3753e+00  5.3496e+00 -7.6139e+00 -1.2471e+01  3.5634e+00  1.6547e+01
-#&gt;   4.9120e+00 -1.2762e+00  6.2383e+00 -1.8401e+00  1.3484e+00 -1.7395e+00
-#&gt;   2.1673e+00 -6.1427e+00  1.0571e+01  1.1749e+00 -1.9581e+00 -1.1389e+01
-#&gt;   4.7283e+00  1.5248e-02  7.5819e+00 -2.7482e+00  1.2268e+00 -2.5948e+00
-#&gt;  -2.5091e-01  2.1257e+00 -7.7324e+00 -1.8332e+00 -1.0998e+01  7.6363e+00
-#&gt;  -8.6394e+00 -1.1446e+00 -2.7243e+00  3.4401e+00  1.3226e+01  1.6670e+01
-#&gt;   4.0031e+00 -7.5468e+00 -8.8178e+00 -3.0361e+00 -1.1447e+01  1.2848e+01
-#&gt;   5.1134e+00  2.6222e+00  7.7532e+00 -2.7503e+00  1.7884e+00  8.7907e+00
-#&gt;   2.9411e+00 -2.3387e+00 -1.0094e+00 -8.3216e-01 -1.4511e+00  6.0140e+00
-#&gt;  -4.1030e+00 -1.5923e+00  3.8562e+00 -5.6707e+00 -3.7063e-01  8.1246e+00
-#&gt;   1.8108e+01 -1.7204e+00  1.6166e+00 -1.6047e-01  1.2411e+01 -8.2865e+00
-#&gt;  -7.0544e+00  5.3615e-01 -4.6967e+00 -4.8349e+00  4.9891e+00  4.2742e-01
-#&gt;   1.1610e+01 -4.1747e+00 -8.7390e+00 -1.4839e+01 -6.4961e+00  5.4956e+00
-#&gt;  -7.5511e+00 -4.0169e+00  5.6852e+00  3.3636e+00 -3.7525e-01 -6.1710e+00
-#&gt;   6.8455e+00  3.3660e+00 -2.8912e-01  1.8043e+00  3.4320e+00 -1.2618e+00
-#&gt;   1.3317e+01 -1.4499e+00  3.6563e+00 -6.0832e+00 -3.2239e+00  1.1305e+01
-#&gt;  -1.1126e+01  6.9176e+00  1.0767e+00  7.3398e+00  2.4294e+00  5.5499e-01
-#&gt;   2.3884e+00  6.7238e+00  1.4183e+00 -5.4177e+00 -3.7543e+00  2.3000e+00
-#&gt;  -1.0991e+01  2.8692e+00  2.2609e+00 -1.7290e+01  8.6789e+00  9.1537e+00
-#&gt;   2.9401e+00 -5.5397e+00  5.1425e+00  7.5573e+00  9.3476e+00 -6.1292e+00
-#&gt;  -1.3484e+01  2.5654e-01  1.7346e+00  6.5049e+00  1.8949e+00 -1.4447e+01
-#&gt;  -6.5891e+00 -1.2870e+00  8.3358e+00  3.6151e+00 -3.1838e-02 -6.9046e+00
-#&gt;   1.6962e-01 -5.4295e+00 -2.4793e+00 -1.1786e+01 -3.2697e+00  4.6963e+00
-#&gt;  -1.6070e+00 -2.5307e+00 -3.2523e+00 -6.3779e+00  8.7298e+00  1.0215e+01
-#&gt;  -4.6647e+00 -3.8529e+00 -8.2969e+00 -5.0701e+00  9.5676e+00  1.3626e+01
-#&gt; 
-#&gt; Columns 31 to 36 -2.2060e+00 -5.5344e+00 -6.3464e+00  2.7369e+00  1.4191e+00 -1.1964e+01
-#&gt;   1.5156e+01 -2.0124e+00  1.1892e+01 -3.4633e-01  1.9918e+01  1.1318e+01
-#&gt;  -8.1686e+00  3.0305e+00  8.5996e+00  1.9406e+00  3.6290e-01 -3.0435e+00
-#&gt;   6.9524e+00  6.2075e+00 -7.7053e-01 -2.4295e+00  6.9275e-03 -6.1182e+00
-#&gt;  -6.6054e-02  3.5161e+00  3.2674e+00  3.3507e-01 -6.7924e-01  7.8778e+00
-#&gt;  -2.3222e+00  1.3738e+01 -5.9227e+00  3.5072e+00  2.8251e-02  3.7623e+00
-#&gt;   9.5737e-01 -5.4463e+00 -1.4650e+00  3.3058e+00 -2.5602e+00  1.9640e+00
-#&gt;  -5.0583e+00  4.0970e+00  1.3948e+01  1.4711e+00  1.4923e+00  7.1669e+00
-#&gt;  -1.9251e+00  1.5857e+00 -2.9922e+00  1.6526e+01  5.4539e+00  2.0191e+00
-#&gt;   1.0348e+01 -8.9513e+00 -5.3406e+00 -1.3873e+01 -8.8218e-01  4.2771e+00
-#&gt;  -9.2822e-01 -3.8322e+00  4.4457e+00 -3.0565e+00 -7.9995e+00 -9.4467e+00
-#&gt;  -2.0836e-02 -4.0346e+00 -2.0850e+00  3.4090e+00 -5.1700e+00  1.8383e+00
-#&gt;   1.3852e+00  7.2298e+00 -6.6947e+00  6.8874e+00  2.3790e+00  1.1032e+00
-#&gt;  -1.2859e+01 -8.0581e+00 -6.4520e+00  5.4716e+00 -5.7399e+00 -1.6577e+01
-#&gt;   9.8543e-01 -5.3918e+00  1.4509e+00  3.1979e+00 -2.0104e+00  2.2318e+00
-#&gt;   1.1149e+01 -4.8040e+00 -2.6745e+00  3.8266e+00  3.4322e+00  8.1728e+00
-#&gt;   1.5185e+01 -2.5387e+00 -6.7673e+00 -7.2051e+00  1.2942e-01  8.1339e+00
-#&gt;   2.8459e+00 -6.8872e+00 -1.0618e+00  8.3649e-01  5.1515e+00 -1.0842e+00
-#&gt;  -6.8484e+00 -1.4852e+01  1.2157e+01 -4.0615e+00 -1.8284e+01 -6.4152e+00
-#&gt;   6.0675e+00  9.9034e+00 -8.0769e+00 -1.4556e+00  1.0838e+01  4.7218e+00
-#&gt;   7.1679e+00 -1.6753e+01 -6.9527e+00  8.2589e+00  1.6721e+01 -1.6522e+01
-#&gt;  -9.7630e+00 -2.1942e+00 -3.5215e+00 -6.9583e+00  2.9094e+00  2.5652e+00
-#&gt;   5.5285e+00 -4.1641e+00  5.6695e+00 -7.9047e-01 -1.5686e+00  2.6590e+00
-#&gt;   1.2457e+01 -2.0151e+01 -1.6862e+01  2.5823e+00  5.7753e-01 -6.8121e+00
-#&gt;  -5.1423e+00  4.7242e+00 -3.5800e+00  3.1541e+00 -5.5512e+00 -6.7078e-01
-#&gt;   3.8677e+00 -1.6917e+00 -6.9193e-01  9.1666e-01 -1.0568e+00  5.9121e-01
-#&gt;   3.9157e+00 -1.2616e+01  8.5170e+00  8.5553e+00 -2.4270e+00 -5.7165e+00
-#&gt;  -3.1449e+00  4.4540e+00  2.9780e+00 -6.2952e+00 -1.0517e+00 -5.1592e-01
-#&gt;  -1.3898e+01 -3.1409e-01  2.0906e+00 -1.4861e+00 -7.9936e+00  1.2545e+00
-#&gt;  -2.2651e+00  2.6939e+00 -1.1613e-01 -1.2790e+01 -5.9707e+00  8.4696e+00
-#&gt;   4.7070e+00 -9.8628e+00 -2.3817e+00  1.0628e+01  3.0988e+00 -2.5199e+00
-#&gt;  -2.0017e+00 -1.1406e+01  1.1926e+00  5.6329e+00  8.4147e-01  2.3371e+00
-#&gt;  -6.2982e+00 -8.9997e+00 -4.1064e+00  2.5614e+00 -4.1151e+00 -1.0163e+01
-#&gt; 
-#&gt; Columns 37 to 42 -4.2081e+00 -2.0369e+00  6.3289e+00  4.7795e+00 -3.5318e+00  5.1012e+00
-#&gt;  -2.0082e+00 -4.9980e+00 -5.9752e+00 -1.7102e-01  5.4852e+00  2.9964e+00
-#&gt;  -1.8324e-01 -6.7910e+00 -6.1012e+00  2.1815e+00 -1.3528e+00  6.7556e+00
-#&gt;   3.9192e+00  5.7327e+00  1.0073e+01  5.0234e+00 -3.5486e+00  9.1210e-01
-#&gt;   1.7822e+00 -7.4778e+00  1.8562e+00 -2.7767e+00 -3.9856e+00  1.3563e+00
-#&gt;   9.8246e+00 -6.4452e+00  4.9000e+00  1.2819e+01 -2.9897e+00  1.1187e+01
-#&gt;  -4.2267e+00  1.1860e+01 -4.6046e+00 -4.2045e+00 -7.5284e+00  6.8747e+00
-#&gt;   2.0491e+00 -3.8604e+00 -8.1168e+00 -1.0940e+01 -8.5145e+00 -5.2389e+00
-#&gt;  -7.7080e+00  4.2201e+00  1.2684e+00 -6.2902e+00 -1.5707e+01 -2.5072e+00
-#&gt;   4.0226e+00  6.5053e+00  1.0942e+01 -7.3371e+00  3.8208e+00 -1.3210e+01
-#&gt;   1.1751e+00  1.6616e+01  9.0641e+00  1.1510e+00  1.4379e+00 -6.9141e+00
-#&gt;  -6.7640e+00  5.0162e+00  2.2537e+00  6.9975e+00  7.7748e+00 -2.7385e+00
-#&gt;  -7.1380e+00 -1.3420e+01  4.9848e+00  7.7631e+00  8.9783e+00  3.4990e+00
-#&gt;  -5.7746e+00  2.8495e+00  1.5928e+01  2.5062e+00  4.6991e-01  9.7321e-02
-#&gt;   7.2607e-01 -1.6783e+00  3.3901e+00  4.2941e+00 -8.0335e+00 -1.4022e-01
-#&gt;  -6.0546e+00  9.9502e+00  8.1512e+00 -7.4439e+00 -9.0048e+00 -1.1860e+01
-#&gt;  -9.8764e+00  1.0981e+01  3.0035e+00  8.0460e+00  2.4881e+00 -2.6455e+00
-#&gt;  -4.7601e+00  7.1399e+00  1.5831e+01  1.9427e+01  8.0240e+00 -5.9206e-01
-#&gt;  -4.6025e+00  1.4546e+01  5.7997e+00  5.5612e+00  1.0490e+01  2.7551e-02
-#&gt;  -9.0220e+00 -1.0578e+01 -6.2618e+00 -1.2487e+01 -4.4233e+00  6.3704e-01
-#&gt;  -1.3621e+01 -1.1352e+01  3.2002e+00 -1.1477e+00  1.5860e+01 -9.7488e+00
-#&gt;   2.2784e+00  3.9175e+00 -4.1808e+00  1.4110e+00 -1.0120e+01 -2.5788e+00
-#&gt;  -9.1375e+00  7.7145e+00  7.0947e+00  1.3033e+01  5.9013e+00  3.7214e+00
-#&gt;  -8.1568e+00 -2.1544e+00 -3.7443e+00 -5.1572e+00  5.7300e+00 -4.8034e+00
-#&gt;  -9.8366e+00  7.0619e+00  1.2789e+01 -2.2159e+00  2.9897e+00  2.1895e+00
-#&gt;  -6.9763e+00  6.3981e+00  1.6305e+00 -3.2823e-01  9.7608e+00 -8.0508e+00
-#&gt;  -9.6687e+00  1.2095e+01 -6.8029e-01 -3.3248e+00  9.8117e+00 -1.1996e+00
-#&gt;  -4.4008e+00  5.6494e-01  5.7199e+00  1.4156e+01  1.1082e+01  2.5047e+00
-#&gt;   3.3603e+00  1.3962e+01  1.2866e+00  5.1314e+00 -5.8989e+00  9.2444e+00
-#&gt;   1.0710e+01  1.5410e+00 -4.5894e-01 -9.9939e+00 -3.4178e+00 -7.2154e+00
-#&gt;   1.1765e+01  4.5199e-01 -1.0404e+00 -1.4434e+01 -9.8020e+00 -1.3184e+01
-#&gt;  -8.7925e+00 -3.1863e+00  3.0785e+00  2.5502e+00  5.4133e+00 -3.9451e+00
-#&gt;  -8.4798e+00  4.4024e-01  6.9758e+00  5.5895e+00 -1.0347e+01 -9.5507e+00
-#&gt; 
-#&gt; Columns 43 to 48  5.1626e-01 -2.7479e-01 -8.3173e+00  9.8194e+00  7.6279e+00 -5.9505e+00
-#&gt;  -2.5000e+00  1.6838e+01 -5.1766e+00  9.9239e+00  1.1735e+01  2.3976e+00
-#&gt;   1.0156e+00  1.7259e+00 -5.8446e+00 -1.0279e+01 -8.5805e+00  1.5301e+00
-#&gt;  -4.0688e+00 -8.3111e+00 -8.6006e+00  4.3628e+00  7.3769e-01  7.6408e+00
-#&gt;  -3.7066e+00 -1.4923e-01  1.1617e+01  5.6185e+00  1.6021e+00 -2.7455e+00
-#&gt;  -2.7256e+00  3.1180e+00 -3.2540e+00 -1.2448e+01  6.2868e+00  5.9846e+00
-#&gt;  -1.5505e+00 -6.7706e+00 -7.9348e+00 -8.8921e+00 -6.0152e-01 -3.4996e+00
-#&gt;   3.2389e+00  1.2338e+00  5.2481e+00 -1.1140e+01 -1.6283e+00 -9.2459e+00
-#&gt;  -1.3188e+00 -1.0878e+01  8.8089e-01 -3.3523e+00 -3.6253e+00 -1.4771e+01
-#&gt;   9.1206e+00  3.1400e+00  1.0897e+01  1.3103e+01  8.3766e+00  1.4171e+01
-#&gt;   9.0992e+00  1.0891e+00 -1.2041e-01  3.6707e+00 -4.0958e+00  9.0787e+00
-#&gt;   4.6689e+00 -5.0886e+00  3.7800e-02 -3.9023e-01  1.6733e+00 -7.8891e+00
-#&gt;  -3.6988e+00  3.9844e+00 -3.8608e+00 -4.3038e+00  9.6176e+00 -1.4931e+00
-#&gt;  -6.3437e+00  2.6941e+00  2.5419e+00  7.7102e+00 -1.0403e+00 -1.3522e+00
-#&gt;  -1.0599e+01 -2.2414e+00 -3.0626e+00  2.2778e+00 -1.5873e+00 -2.1073e+00
-#&gt;   9.6136e+00  8.3774e+00  8.2091e+00  9.5933e+00 -2.6863e+00 -4.5552e+00
-#&gt;  -7.0635e+00  5.3787e-01 -2.3794e+00  9.3626e-02 -2.5076e+00  3.3860e+00
-#&gt;  -2.4422e+00  1.0805e+01 -3.8123e+00 -3.1182e-01  3.3791e+00 -1.7556e+01
-#&gt;   3.7553e+00 -5.6472e-01 -2.2464e+00  9.9940e+00 -2.3699e+00  1.5208e+01
-#&gt;  -4.2271e+00  1.5778e+00 -2.7242e+00  1.1653e+00  8.0265e+00 -5.7576e+00
-#&gt;   7.3829e+00  6.8495e-01 -1.5826e+01  6.5264e+00 -8.1072e+00 -1.2060e+01
-#&gt;   1.9205e+00  4.3595e-01  5.5910e+00  7.3589e-01 -5.6670e+00 -7.8853e+00
-#&gt;   1.3904e+01 -4.5670e+00  6.0862e+00  4.7485e+00  3.0475e+00  9.5297e+00
-#&gt;   2.4129e+00 -9.5154e+00 -1.6439e+01  7.1795e+00 -4.6202e+00  5.5906e+00
-#&gt;  -1.7815e-01  4.5322e+00  3.4740e+00 -7.7822e+00 -1.6879e+00 -5.2648e+00
-#&gt;   1.6149e+01  4.3107e+00 -6.7068e-01 -1.6106e-01  1.0856e+00 -9.6606e+00
-#&gt;   2.9834e+00 -1.5717e+01  6.5438e-01 -8.6329e+00 -2.8295e+00 -5.9671e+00
-#&gt;  -4.4819e+00 -3.8925e+00 -1.0053e+01 -3.9285e-01  3.8505e+00 -2.8390e+00
-#&gt;   4.9744e+00 -4.6638e+00 -4.5231e-01 -1.3836e+01 -3.9313e+00 -1.0857e+01
-#&gt;   4.0774e+00 -1.6197e+00  3.8858e+00  3.5687e+00  7.0244e+00  7.1655e+00
-#&gt;  -8.3267e+00 -4.6400e+00 -2.3328e-01  9.7209e+00 -2.1513e+00  1.6261e+00
-#&gt;  -6.3907e-01  3.5003e+00  7.4059e+00  3.1514e+00  1.5017e-01  5.9836e-01
-#&gt;  -1.1430e+01 -1.3017e+01 -4.0840e+00 -3.2955e+00  4.5561e+00 -8.4987e+00
-#&gt; 
-#&gt; (8,.,.) = 
-#&gt;  Columns 1 to 8  10.6267  -1.5330  11.9548  -0.9866   5.0780  -3.6522  -1.3350   4.9022
-#&gt;   12.7229 -13.5522  -2.1358   5.0673   3.2253   0.0527   2.8279  -2.1166
-#&gt;   -5.8637   1.8494 -15.2688  -0.0243 -14.0303   5.4494 -11.2061  11.8298
-#&gt;    6.6794   1.3257   4.9576   3.2109   3.8503  -4.6277  -3.8890   2.2640
-#&gt;    2.8465  -0.6248   8.8022   7.4374   6.1560   2.1932  -0.2645  -8.2327
-#&gt;    6.3078  -4.1849 -17.6453   6.1986   2.6101  -8.9272  -6.9723  13.2983
-#&gt;    2.9268   0.5782  -0.6684 -17.9788  -4.1762   1.9302  -2.3356  -2.1135
-#&gt;   -1.4787 -12.3836   4.7917   2.6118   5.8052  11.5548  -3.8155   1.8737
-#&gt;    1.3110  -3.3231  -1.9698  -4.2910  10.4212  -5.4499  -3.5149   1.4003
-#&gt;   17.8225  -2.6599   2.0824   3.9929   4.5901  -8.9987  -2.6961   2.1369
-#&gt;   -6.2416   4.8682  -8.5449  -4.2649  14.1296 -10.5918   1.5645   5.7272
-#&gt;   -3.8649  -2.1820   5.5270  -3.9252   6.8287  -1.1447   6.1648  -8.9593
-#&gt;    7.7191   1.9837  -8.5589  -6.4866   3.2634  -9.3859  -0.4159   5.2604
-#&gt;    8.2401   2.6718  13.3165   7.4686  -0.7432   0.3645  -3.8826   3.8987
-#&gt;   -2.1717   4.8656 -16.3214  -1.3052   5.3614   2.8415  -2.8339  -0.3766
-#&gt;   -8.6081  10.5584  -4.5715  -6.4498   1.6126  -3.9898  -9.5237  -4.3991
-#&gt;   -3.1244  11.4909 -17.3938   2.2175   3.0631  -4.0858  -7.6803  -6.6535
-#&gt;    3.9864 -10.0013   1.3461   6.1839  -2.6287   0.9060   1.2726  -2.3636
-#&gt;    3.9412  13.3179   2.0010   2.3001  -8.6135  12.6366   5.3501  -7.1454
-#&gt;    4.1819  -8.8175   5.8926   7.1121  -0.2062   7.3752  -1.0206   4.6575
-#&gt;    2.6663  -5.8432  -0.4756  -6.5844  -0.1549 -13.6821  -5.3146  -3.7353
-#&gt;   -5.8596   1.5997  -1.7886   3.7246   4.7217  -4.9460  -0.2440  -4.1931
-#&gt;    8.7457   3.0808  -7.1172   0.9700   6.3935   0.9901  -5.9033  -3.4605
-#&gt;    1.1871  -0.0050  10.5000   8.1388  -4.7634  -8.5128  -1.3253   6.0633
-#&gt;    1.9490   0.2024   5.4524   3.0546  -3.1017  -6.3211   4.7174   7.2727
-#&gt;    8.7575 -17.8949  14.5233  -3.9486   4.9596  -2.7799   6.4560  -9.2513
-#&gt;   -1.1880  -5.9611  -4.5665  -1.1613   2.3978   1.4468   4.7764  -2.5183
-#&gt;   -0.4410  -6.7219   0.4433  -7.9999   6.5604   2.7506  -1.3535   0.1816
-#&gt;   -7.9985   1.4949  -0.6031 -12.2934   2.2977  -4.8696  -8.6827  -7.7342
-#&gt;    0.7353  -0.6659  11.1382  -4.7090  -3.9811  -1.6999   3.3566   2.4592
-#&gt;   -0.7875  -2.1156  12.6495   1.6336  -3.3868   3.4999   5.4789  -5.0451
-#&gt;    8.1472  -3.7786  -0.4446   7.9780   0.3618  -0.5694   0.4496   1.4119
-#&gt;   -6.0382   1.4533 -12.1207  -1.2204   8.6365   8.2427  -6.9326  -0.2467
-#&gt; 
-#&gt; Columns 9 to 16  -5.1756   8.5118  -7.7063   6.1010   1.1873  -4.4190  -2.6629  -6.5092
-#&gt;   -7.5741   0.5485 -12.0347   7.3604  -1.0385  -4.4141  -1.2392   2.8957
-#&gt;    1.5225   1.2368   3.1530  -1.6509  -1.7315  -0.2901   2.7771   6.9281
-#&gt;   -1.8515   8.9959   2.6615   0.2895  -1.3595   0.4391   4.9168  -1.6851
-#&gt;   -4.8045   0.0044  -2.3835  -1.0299  -2.0842 -11.3656 -12.4329   0.8495
-#&gt;  -15.0460  -3.1696   3.3482  -7.6116   1.6925   5.4108  12.1960   8.9745
-#&gt;   14.1222   7.3576  -0.6318  11.9637   6.3952  13.5117   1.4936 -10.7382
-#&gt;   -5.7711   2.1574   0.2650  -6.2664   0.7005  -6.7930  -3.1726   2.0089
-#&gt;    9.0434   2.2008   2.7697  10.2680   1.9477   1.4196 -11.3791  -2.1725
-#&gt;  -17.3245  -3.2154  10.1837 -11.9332  11.6250  -2.2788  13.5062 -11.2383
-#&gt;   11.6537 -15.7148   0.2889   6.7810  -4.8466   1.5972  11.2904   9.1018
-#&gt;    7.3197 -13.2294  -3.2393   1.5257  -1.6928   3.3585   3.2404   1.3894
-#&gt;   -0.7356  -0.4572   0.3681  -3.1916   7.9295  -4.4005  -6.6949   1.7652
-#&gt;    0.2690   2.8153   0.4969  -5.5821  -3.6145  -5.6619  -0.5993   2.6966
-#&gt;    4.2981  -5.3981   0.2628   9.2664  -0.9776   2.8817  -9.8257   2.1622
-#&gt;    2.7407 -10.6368   7.1661   7.2878  -1.0547   8.2052   7.6517   6.9416
-#&gt;   10.5092  -6.8727   5.6315   8.5364   0.2192   0.7203   6.9316  -2.0347
-#&gt;   -9.1334  -5.1024  -1.6681   0.4628   1.0234   4.2223  10.2495  11.4277
-#&gt;    3.8139   8.8629  -5.5882  -7.6577   5.8327  -5.3676   5.9636  -0.5249
-#&gt;   -5.6754   5.7935  -6.5054   3.0985   1.3383  -8.7394  -3.3719 -13.3778
-#&gt;    8.0579   9.8169   8.3260   2.3255  -2.4509  -4.8961   7.3090  -4.3036
-#&gt;    2.3203  -8.4415   7.2544   7.2275   5.2468   0.5502  -0.6445   1.8270
-#&gt;  -17.7283   1.4572  -9.7638   3.2731   2.3236  12.7668  -3.5255  -4.2992
-#&gt;    1.6096   9.7601   6.7646  -5.9098  -1.7771   3.8205   5.8618 -17.5326
-#&gt;   -4.9746   1.4479  -1.4392  -2.6541  -3.2390  -6.0846   4.8333   3.6995
-#&gt;  -15.5283  -3.7563   4.2214  -1.4567   0.2057   1.0697   8.5204  -2.0645
-#&gt;    0.1472   5.0025   3.4604   0.7696  -7.0289  13.6685 -10.8780   0.1098
-#&gt;    6.1972  -4.1656  -9.4480   3.1426   3.5773  -0.1649  -0.1649  -6.5168
-#&gt;   12.7938   7.9682  -4.8083   8.2376  13.2378   6.6967   8.3872   4.7972
-#&gt;    2.7529   1.8640   5.6918  -5.8528  -0.0744   3.5740   5.8349  -0.3225
-#&gt;    7.4432   8.7275   1.4205   0.7736  -2.4029   3.8096  -6.2556  -3.8529
-#&gt;   -9.4366  -9.9036   6.3553  -8.5642  -3.8872  -7.5120   0.5023   2.7392
-#&gt;   16.7308   3.0208   2.7770   2.5883   0.8318  -1.7339 -11.6733   3.2302
-#&gt; 
-#&gt; Columns 17 to 24   1.6343   4.2638  -7.4498   0.9667   0.7651  -8.2876   4.6111   5.6415
-#&gt;   -1.5266   6.7617   1.4840  13.5817  -3.2421  -3.1790   1.5877  -3.0139
-#&gt;   -5.0877   5.1384  13.1743   5.4214   9.8689   4.3819  -7.3179   4.4986
-#&gt;   -2.5581  -1.8407  -7.4731   2.8519  -0.6313  -4.2203  -0.2014   2.3781
-#&gt;   11.5584   2.3689   1.1813   7.2791   1.6198   4.3579  -0.0039   1.4425
-#&gt;  -10.7670  13.2095   4.5210  -6.1871   1.1220 -12.0605   3.2072   4.0065
-#&gt;  -15.2329  -1.7268 -13.5491  -4.8743   4.4851   7.4284 -11.1554   8.9644
-#&gt;   -5.3267   6.9608  11.3609  -0.7774  -4.2155   2.1795  -9.5520  -3.9699
-#&gt;   -4.8638  -0.3728   2.8820   6.7274   9.9707  -2.3426  -7.2450  13.7580
-#&gt;   -2.5758   4.4859  -3.6932  -5.1393   4.0688  -4.0409  12.0636   6.5917
-#&gt;  -13.6997  14.3376   6.5398  -0.1635   3.4950  -1.8487  -0.4477  10.4422
-#&gt;    0.4612  10.3622  -0.2173  -4.9527  -5.5034   5.1928  -3.6832   4.2130
-#&gt;   -6.1803 -10.3125   4.9167  -0.4824   6.0276  -0.1407  -3.4065   1.6624
-#&gt;   11.8508  -7.8972   3.9787  -0.9284  -2.9017  -3.1254  -0.3485  -1.7566
-#&gt;   -5.7377   4.5098  -5.7286  -9.5293  -3.1278   0.9848 -11.5592   1.2733
-#&gt;  -10.5537   7.3076   4.7719  -3.4494  10.2625  15.0984  -6.0440   4.3702
-#&gt;  -13.7478  -3.8463   0.5187 -10.2282  -7.4419 -11.5029   5.2895   8.4388
-#&gt;   -5.5946   2.1433   2.0359  -9.8323  -9.3426   2.1133   5.5029  -2.7737
-#&gt;    1.1743   6.1273   9.7381  10.9814   6.3794   8.5378  -3.0726  -4.6218
-#&gt;   -0.2067  -3.9660  -0.7433   3.6825  -9.8990  -5.1984 -10.6630  -6.1165
-#&gt;   -2.0169   5.9197  -0.0218   7.8375  -0.9747  -6.4920  10.2333  -7.8108
-#&gt;   -4.8927   5.7826  -7.5954   0.7657  -0.1505  -9.8101   4.4262  10.4891
-#&gt;    0.9509   7.9597  -4.0268   9.2844   6.5545   3.5953  -4.8329   9.3631
-#&gt;    4.0582   8.3132  -3.2870  -1.2566  -5.9489   4.3939   5.2248  -3.9360
-#&gt;   10.3279  -9.0691   9.3816  -1.1101   1.0798  -2.4345  10.2492  -4.9520
-#&gt;    3.0299   5.3533  -0.9295  -9.3098  -0.3698   8.4023   4.6579  -5.2489
-#&gt;    5.7968  -7.6533   0.6171   0.4126  -4.6290  -5.2426  -4.4618  -4.4819
-#&gt;   -3.6382   9.8743  -1.6539   2.3893   0.9648  -0.0965  -4.6040   3.2194
-#&gt;   -6.2614  -6.0749  -4.4325  -6.0923  11.4216   0.1747  -6.6910   9.3361
-#&gt;    3.7301  -6.1403  -9.2752  -3.0329   2.0033   7.8517   4.3895  -1.7015
-#&gt;    2.9891  -5.1293  -9.8981  -0.4019  -9.5432  12.2091   0.3967  -1.8913
-#&gt;    9.3571   1.7504   2.3830  -0.7915  -3.9371  -3.7798   2.4423   0.8011
-#&gt;    2.7259   2.5156   5.2357  -4.1804   1.6821   3.8290 -11.3112   9.3292
-#&gt; 
-#&gt; Columns 25 to 32   1.9191   1.6059   2.0831   1.5791  -7.9761   3.3916  12.0069   2.5687
-#&gt;    2.0538  -4.9680  -5.3897  -1.2076  -7.3517   0.9597  -5.1130  -3.3678
-#&gt;   -2.4811 -13.0746  -8.5736 -10.0878  -6.8513  -8.4645  -5.0854  -4.4974
-#&gt;    7.6438   9.2085  -0.5732   2.4714   2.6798   5.7891  13.9626   2.6188
-#&gt;   -0.4362  13.9346  -6.3062  -7.9900  -2.3453  -2.0473   7.6922  12.4112
-#&gt;   -3.2268 -15.8566   5.0329  -4.8989   2.7829  -7.5685   3.9654 -11.4871
-#&gt;    4.8841   3.1943  -0.7978   5.1878  -3.6804  -0.6158   2.2200  -1.0574
-#&gt;   -5.9430   2.1924   0.8140  -3.7598  -2.5603 -10.9249  -2.1902  -9.9801
-#&gt;   -8.9867   3.6373   3.8498   3.2419  -7.4691  -6.7314  17.6403  -0.3917
-#&gt;    4.9525   1.1729  -1.5161   3.3861   2.5296   5.5365   9.0596   7.4538
-#&gt;    4.1101 -14.5829 -12.0565   8.7124  -6.7615   6.1871  -3.8751  -5.4149
-#&gt;   -3.5618   1.3074  -7.9959   0.5547  -0.4202  -4.7575  -9.0741  -2.2564
-#&gt;   -5.4367  -3.7891   1.2769   5.8198  -1.1758   1.3900   5.2062  -2.3372
-#&gt;   13.3377   6.7872   2.1996  -2.6313   6.7888   4.9082   7.8534  17.5427
-#&gt;   -3.7223  -3.0922  -2.8451  12.6232   2.3590   5.4467  -4.3444  -4.4481
-#&gt;    3.5194   1.3105  -3.8297   6.5131 -18.2092  -4.1873   2.4873  10.2067
-#&gt;   16.2110  -4.3301  -2.8808  17.0213  12.6396  15.3565  -2.6812   2.6058
-#&gt;    7.5522  10.8054  17.0908   7.1060   4.2718   7.2969   0.2761  -2.5380
-#&gt;   14.2152  13.1697  -8.4743 -14.9023  -7.0246  14.5311   1.2982   4.4643
-#&gt;   -1.0551   8.3995   2.9078  -0.4551  -0.3019   5.5504  10.2923  -9.2580
-#&gt;   -1.3005  -4.0224  -4.3104   1.0219   6.7774  -2.1394  -2.9722  -5.8480
-#&gt;    3.4426   0.3149   9.0122   4.1117   7.1760   2.2815   1.1026   5.7632
-#&gt;    7.7602   2.3719   2.9944  -5.6826 -13.4485  -2.7297  18.3691  -7.1761
-#&gt;   -0.7188 -13.4509  -3.3675   4.3348   1.0514  -2.1075  -3.5551  10.4512
-#&gt;   -1.7439  -5.3167   6.2256   0.5784  -6.7355   1.1678   4.6659   3.6961
-#&gt;  -11.4386   4.4568   3.9124  -1.6352  -9.6814  -1.0747  -0.7769   1.0876
-#&gt;   -4.7964  -4.7782  10.5777  -6.5258   1.8538   5.0302   6.9203  -8.8850
-#&gt;    3.7554   3.6392  -7.3377  -0.0674   2.5838  -0.9933  -2.9957  -0.8057
-#&gt;   10.7751  -0.3876  -1.6086   6.9390  -0.2490   5.2473  -6.6446  -3.0113
-#&gt;   -0.3604   4.1186   2.8453   2.5144  -3.0283  -3.0051  -3.1627  -1.7939
-#&gt;   -4.9378   6.9038   7.9510   5.1719   6.6559 -10.7457 -10.1074   7.4739
-#&gt;    3.0438  -2.1014  12.6202  -4.3386   6.7635  -2.5796 -13.0794  -4.3453
-#&gt;  -10.2563  -1.9642  -4.2257   5.6998   2.4064 -10.1478  -5.9262   1.7054
-#&gt; 
-#&gt; Columns 33 to 40   1.8660  -6.7666   8.4215 -11.6535   5.7805   0.6168  -2.3382   3.4805
-#&gt;    9.2755   1.4452  16.5265   4.1485   8.5045  -8.6109   4.9164   8.1869
-#&gt;   -1.4584  -1.9340   7.4060   5.0065   6.7532  -1.9081  -2.3070  -0.4638
-#&gt;    8.2844   9.8254  -3.1096  -9.6821   0.5096   8.3655  12.8550  14.0672
-#&gt;    5.1692  -3.3787  -0.3267   3.2325  10.9893   9.3332  -5.1964  -4.0618
-#&gt;    5.3389  10.6517   8.2986   1.4368   7.9578  -0.3080  -2.1591   8.9587
-#&gt;   -9.2415  -8.5843  -5.1324   7.5611  -3.5871  11.1791  10.4420  -4.1750
-#&gt;    4.3108  12.5806  12.4036   7.7773  -0.6770  -0.7961  -4.2253  -0.4318
-#&gt;  -12.1204   4.7955   0.3289   5.5722   3.7828   8.6958   2.7070  -1.5169
-#&gt;    1.6654 -10.4359 -15.8625  -9.4729  -0.3271  13.6491  -9.0197  11.8174
-#&gt;   -3.5324   0.6502   6.0860   3.5250  -3.3720  -4.5717  -1.5268  -0.3676
-#&gt;    7.0121   3.5755   3.2031  13.9571 -10.8879   2.3335   1.7500  -4.5789
-#&gt;   -7.5055  -4.3935  -2.6674   2.7562   0.7717   6.6377  -7.2557  11.1306
-#&gt;    4.9399  -6.2989  -1.3229  -0.4454   2.9554   0.0609   0.6741   0.2936
-#&gt;    3.1095 -13.1997   3.2600  -7.0410  11.3602  -0.0764  -0.5404 -10.8516
-#&gt;   -3.0533  -7.8063  -3.2996  -1.6086   6.4248   7.5665   0.9304  -9.2192
-#&gt;   -9.9882  -8.0349  -9.8510  -0.0297  -0.7235   7.8582  11.2715  -2.5772
-#&gt;    6.0317   2.7272   7.7132   3.1757   2.2203   0.9918   5.5393   4.5305
-#&gt;    5.0344   9.1500  -0.5582   2.9277   4.6935  -2.5138   3.4636   7.0040
-#&gt;    0.2511  -5.7453  -1.6805   2.9419  -3.5829   0.6032  -6.2644   5.6935
-#&gt;   -7.1688 -11.1007  -4.7595   3.1063  -1.4702   5.7107   4.1910  -1.4435
-#&gt;  -15.4089  -3.8468  -0.7967  -3.8971  -1.5161  -9.7356   4.4387  -5.9763
-#&gt;    7.9153   5.0431   2.8860 -10.6633  11.7263  -2.6603   0.4066  -0.4802
-#&gt;    2.9004  -6.7488  -7.1080  -3.1000   7.3893   3.0466   2.5811   1.4015
-#&gt;   -9.9420  -0.4985   4.5590   3.0504   6.8359  -6.3257  -2.2027  -0.1064
-#&gt;    9.2098   4.9761  -6.9910  -7.1942  -9.2376   1.9598  -3.2546  -4.1828
-#&gt;    7.7904  11.0375  -2.6587   5.1664  -8.7896  -0.2322   2.2164  -6.8209
-#&gt;    1.6107   5.4279   5.4778  13.0415  -3.8095  -1.1889  -1.5809   2.8254
-#&gt;    2.9088  -4.3920   4.4066   1.6600   0.6908  -6.4360   3.4150   2.2299
-#&gt;    6.2390  -4.1222  -6.3350  -8.8106  -0.9612  -0.5811   0.8807   0.9325
-#&gt;   -1.1845  -0.3939 -12.5561  -2.3443  -3.8756   6.6916   5.2031  -5.8417
-#&gt;    3.3795  -1.0320  -5.4294  10.4887  -0.7915  -5.1567   2.1296  -0.5058
-#&gt;   -2.3876  -5.1298   4.5437   2.0384  -2.0306   9.3875  -4.6834 -18.7991
-#&gt; 
-#&gt; Columns 41 to 48   1.1061  -2.7836  -3.9741  15.9884   1.8558  -9.9070 -15.8664   5.5127
-#&gt;   -0.1253   1.0378   2.9000   5.4694  -6.7453  -5.4469  -1.1210   6.5677
-#&gt;   -8.0221  -5.5194  -0.1925   0.2666   2.9236  16.5875   3.6986   6.5417
-#&gt;    5.5787  -0.4180   2.0315  -1.5825   0.2508  -1.7259  -3.8366  -7.4899
-#&gt;    6.9343   2.1054   2.9522   2.0798 -11.5388  -4.7710   0.6935   2.0647
-#&gt;   -1.2315  -8.8902  -0.7929  -3.5411   3.5532   9.6591  -5.3329   0.2587
-#&gt;  -10.5497  -0.8680 -15.3896  -1.7142  16.9510  -4.0778   3.3404  -9.3676
-#&gt;   -2.5179   3.5679  -2.2822   2.8728  -5.8095  -3.2634  -0.3505   4.1987
-#&gt;    5.9489  -4.2743  -7.8725   7.3308  14.0033  -7.3314  -3.1856   2.1309
-#&gt;   -1.8528   0.0196   0.6426 -15.4453   1.3544  -0.1745  -7.3596  -2.8323
-#&gt;   -2.8055  -6.3885   1.3577   2.9851   2.3138   1.6949   3.7327 -12.9947
-#&gt;   -3.2401   1.2260  -3.7801   1.3267   4.1860  -2.2448  11.2956  -0.8245
-#&gt;    3.0580  -0.2092   5.1924   3.8649   4.0115  -1.0658  -1.2471  -3.5113
-#&gt;   -0.7948   2.4682   7.7439  -9.5981  -5.0289  -7.4061  -0.7104  -4.7848
-#&gt;    7.3624  -3.8945   2.0387   6.1376  -8.8513   9.4125  -6.2628  -8.8044
-#&gt;    2.2187   6.0597  -5.9484  -8.1658  -8.1474   3.7909  11.5093  -4.8349
-#&gt;    4.5342   2.4122  -6.8902 -11.7207  -1.4304   7.0546   9.5912 -17.5289
-#&gt;    4.2533   5.5604  -2.1809  -6.4083  -7.1128  -1.8501  -0.6687  -3.2393
-#&gt;    3.0739   5.1393  -4.5549  10.8957  -7.8774   1.7580   0.2905  -5.0701
-#&gt;    2.3203   2.9756  -0.6623   3.4217   2.6857 -13.5338  -5.4143   4.4185
-#&gt;    9.5811  -6.5317   3.8659  -5.3676  14.6278   0.5866   9.3689   5.1275
-#&gt;   -0.7603  -7.4677  -6.5878  -3.5122  10.4062   0.0781   1.0814   3.7621
-#&gt;   11.4878  -5.6496 -12.1664  10.1729   0.3861  16.2271  -1.8383  -4.4393
-#&gt;   -7.2893   2.6801   0.4218  -1.3583   6.0766   1.6256   7.9568   1.6025
-#&gt;   -2.9454   3.0408  12.7993  -3.1762  -3.9627  -8.2896   1.0986   2.9714
-#&gt;   -3.1814   3.5824   6.6958  -5.8658  -2.9324   1.1185   0.5423   1.5612
-#&gt;   -1.6639   1.6365  -7.8388   5.7822   6.8370  -4.5236   2.6823  -3.0868
-#&gt;    0.9813  -2.0828  -5.5996  -0.9058  14.3890   2.3568   4.8710  -1.4019
-#&gt;  -10.3766  -7.6264  -6.8824   4.6173   5.4813   4.4033  -1.1608  12.0404
-#&gt;   -4.0191  -0.5465   4.1120  -3.3333  -7.1505  -3.2690  -5.5474   3.6195
-#&gt;   -6.5237   0.3980   7.3254   5.5003  -2.8165 -17.1050  -5.7455 -11.6782
-#&gt;   -1.8527  -0.1356  13.9957  -9.6109  -1.7720   5.5877   2.5734  -5.6933
-#&gt;   -1.7664  -4.3733  -6.2156   8.8904   9.4249   0.0872 -12.2081  -8.2407
-#&gt; 
-#&gt; (9,.,.) = 
-#&gt;  Columns 1 to 8   1.2727   0.7947   6.3502  10.1174  -2.0993  12.4052  -0.8450   6.7725
-#&gt;   -0.2480  -1.5488  -9.1696   0.9180  -3.8422  10.4795 -13.6680  -3.9646
-#&gt;  -16.1687   4.5180  -7.4923  -2.3028   0.6485  -3.4693   4.5111  -3.9788
-#&gt;    0.4997  12.9055  -1.5361   5.4710   6.5385  14.3694   4.4529   2.8910
-#&gt;    2.0515 -12.1308   6.5957  11.1291   1.8139   9.4861   4.8082   5.1097
-#&gt;   -9.0149   2.3534   2.8045   0.3850   6.9330 -12.6929  -7.9406  -6.4994
-#&gt;    7.9588   1.0687 -13.5362  -0.6685  -4.4685  -0.1666   1.8338  -4.2802
-#&gt;   -8.5766  -4.9083   1.0407   2.7210  -8.3287 -13.0549   1.1261 -10.1616
-#&gt;   -6.2961  -8.1152   2.4334   3.8341  -0.8027   4.4339  14.0288   0.4751
-#&gt;   10.2367  -3.1896   2.7195   2.0366   9.8000  -0.1385   7.8917  -0.6765
-#&gt;   -3.9258  -0.6769  -0.6077 -16.0999  -1.5842   6.2211 -17.9857   0.7445
-#&gt;    0.7506  -3.4107   1.4630  -3.0619  -0.8172  -2.8962  -3.4711  -4.3129
-#&gt;   -7.7745   2.0225   8.2249   4.1008   8.2804   1.7686   5.7056 -11.3001
-#&gt;   -9.8095  10.0310  -3.9628  10.3113  12.6886  -0.1600   9.7580  -5.5670
-#&gt;    5.0643   3.1665   0.1356   2.9833  -4.3723   3.3063 -12.2398   1.7883
-#&gt;    6.5146  -5.4947  -6.1984  -0.0290  -9.1675   3.5201   3.5843   1.8570
-#&gt;    3.1795   0.9674  12.9662   4.7784   5.1080   0.6296  -3.4359   6.1574
-#&gt;   -1.9084   3.3942   1.6040   9.1999   6.5966  -4.1668 -10.6066  -5.8937
-#&gt;    6.7784  -1.2142   5.2745  -2.6285  -0.2018  16.0741   1.2867   2.5509
-#&gt;   -9.2000   8.8929  -1.4202  -1.5829   5.0250   3.7207   8.9846  -8.2380
-#&gt;   -5.2525   1.1821   4.5749   1.5192  -8.8395   4.1355   1.9965   4.4740
-#&gt;    5.7290  -3.9939   4.0842  -4.8736  -2.7224  -1.0739  -3.5409  -0.4937
-#&gt;    8.9140  -0.0628   3.8525  -0.3625  -3.6122  15.4490  -3.2768   5.4705
-#&gt;   -5.0312   6.3902  -2.5557   1.5833  -1.8559   3.6910  25.9317  11.8591
-#&gt;   -7.5585   0.8486   2.8198   5.1466   1.8474  -1.5436   3.1012  -0.7887
-#&gt;    3.2555  -5.8554   3.0249   4.7538 -12.5738  -8.3026  -4.8830   1.1565
-#&gt;    5.4692  -2.8696  -0.2479  -0.9303   4.0537  -8.7299  -7.2921  -6.7582
-#&gt;   -9.0325  10.7970  -0.1677  -6.8605   2.1043   5.9452   6.1499  -6.5583
-#&gt;   14.7171  -4.0022  -1.9396  -3.0839  -4.4153  -6.3962 -14.1813   5.7413
-#&gt;   11.9121   1.7008  -6.6890  -4.6928  -2.9126  -2.2466   3.2721   1.2574
-#&gt;    5.9104   0.7366 -13.2928  -3.1790  -6.9385 -10.7531   5.2875  10.5105
-#&gt;   -5.8379   6.6916   1.1129   0.5975  15.3188  -2.5986   6.2656 -12.3082
-#&gt;   -2.0066   2.2237  -4.9708  -1.6855  -8.0716  -8.7898   0.6300   1.7496
-#&gt; 
-#&gt; Columns 9 to 16   1.6733   2.3436   1.4441   1.9994   5.2381  -8.3048   5.4268  -2.2515
-#&gt;   -6.7337   4.0991   4.3872   2.1892  18.3630   8.2522   5.1960  -7.9713
-#&gt;   -0.1746  -6.6540  -5.2438   2.5079  -6.0234   5.4855   3.5857   6.4173
-#&gt;    6.4629   2.5912   0.6784   0.2426   5.9792  -6.6569  -4.0979   2.0376
-#&gt;   -3.4663   2.3085  -1.8146   9.6773  -5.9598 -20.3387   1.7090   9.1780
-#&gt;    3.1476  -5.6848   0.4116  11.3939   3.6959   1.2802   4.8044  -8.9299
-#&gt;    2.4967  -3.4852   4.5795 -13.3539  -2.2939  12.4433  -6.3381   2.4502
-#&gt;    5.4875  -7.2355   2.6503  13.5026   1.4544  -9.4959  -9.6052  -8.9315
-#&gt;   -1.3698   0.7453  -7.9493  -5.2047   9.6339   5.4836  -8.2805  -6.8471
-#&gt;   17.3736 -16.6013   5.8894  -6.4616  -2.2949  -5.0347   4.4342   0.3525
-#&gt;    9.2694  -1.4579  -6.6776  -0.2303   2.2658   6.7840   9.9323   3.3962
-#&gt;   -0.4572   1.5268   4.1483  -5.9837   9.5798   2.8204  -6.3039   6.4951
-#&gt;    5.1113  -9.3366  -1.1826  -3.0704   0.9322  -0.7962  -9.0024  -0.5572
-#&gt;    3.5269   8.2785   5.7232   4.1618   1.2213 -19.8468  -9.4017   7.6111
-#&gt;   -5.6832   5.1610  -3.8415   6.8764  11.2551  -5.2121   1.3418 -14.4996
-#&gt;    5.6403  -3.1612  -5.5939  -1.9641  -6.1300  -0.8564   6.3386  -1.5628
-#&gt;    5.1455   0.4552  -7.2914  -3.7189 -12.9895   0.4665  -8.7907 -13.2591
-#&gt;    7.4056   2.2636  -4.5990   3.5393   1.8371 -11.3837  -8.6425 -10.6841
-#&gt;    4.0143  -5.6757   3.9829  14.1796  -2.4098   8.7514   9.6256   2.4514
-#&gt;    3.8758  -4.0818   3.4339  -2.5418  -2.1145   0.1659 -10.6054  -0.1982
-#&gt;   -3.7340  -1.6896  -0.1352  -2.7195  18.3941   3.6468  -9.5564 -15.0131
-#&gt;   -5.5654   4.7692  -1.7110  -4.6424  -1.6933  -4.9327  -2.3912  -7.8144
-#&gt;   -2.2390   1.5692  -3.2417   2.6582   5.6139  -2.0186   9.3802  -9.4414
-#&gt;   -0.2952 -15.3988   3.4580  -5.3291   4.4596   4.2255  -2.9917  -0.7035
-#&gt;   -1.4325  -1.7484   1.7505   5.6896 -10.0743  -5.7925   6.7003   8.0741
-#&gt;    8.6304  -4.0565   6.8468  -2.8796   3.9542  -2.1927   2.6352   0.1214
-#&gt;    2.2337  12.0604  -5.4389  -0.0184  16.2537   3.7085 -13.0257  -0.7820
-#&gt;   -1.3414  -5.0043   8.0420 -14.7617  -3.0255   9.5927 -14.4219   0.3458
-#&gt;    0.7607  -5.8913   6.3093   2.1686  -5.8200  -4.3621   2.5128  -8.9090
-#&gt;   -1.9476  -2.9938   6.4978  -4.7559   0.1936  -7.3867  12.0910   8.1099
-#&gt;    0.3731  -1.7079   7.2613   7.8208   2.1109   7.3929  -0.3925   7.5514
-#&gt;    0.8392  -2.5591  12.3787   3.8017   0.1658  -1.5592 -16.6999   0.0258
-#&gt;    5.8279   4.3304  -2.6491  -3.7199  14.1196  -1.4198  -9.0144   8.6540
-#&gt; 
-#&gt; Columns 17 to 24   6.2367  -1.9945   2.0168   2.3044   0.7235   6.5730   2.3409   2.2699
-#&gt;    1.5861  -4.6067   6.2817   0.5383   0.1626 -12.0255  -1.1399   3.2955
-#&gt;   -2.8590   6.3236   3.1594   3.6227  -0.1728  -1.4638 -14.0678  -1.4998
-#&gt;   -0.0078   7.8049   7.5358   9.1935 -10.3158  -9.5879   2.3351  -3.9972
-#&gt;   12.3666  14.7108   0.3099  -6.5577  -8.8668   4.5466  -0.2885  -3.4030
-#&gt;   -3.0455  -6.4280   5.0816   0.3441   3.7722   5.1249  -2.5096  -1.3170
-#&gt;   -6.5394  -2.2778  -6.2217  -2.1181  18.8499 -13.4703  -9.6274  -4.1407
-#&gt;   -1.5421  14.3783   6.0251  -6.2724  13.7341  -3.4373 -16.0382   5.8961
-#&gt;    6.3313   7.8930  -1.6167   5.6517   2.3097 -10.6372 -10.5451  -1.5701
-#&gt;   -6.4343   6.3610 -10.5084   0.9049  -7.4887   9.5349   9.5575   0.5532
-#&gt;    6.3555 -12.5931  -0.4728  11.4631  10.9192  -3.1959  -1.4895   8.6773
-#&gt;    7.5717  -1.5319   0.1935   1.9712   9.2971 -10.3342   4.9145   5.9349
-#&gt;  -16.6264  -0.3318 -13.8808   4.2977  -4.0893  -8.3443  -7.3324  -8.6380
-#&gt;    6.8643   0.7000   0.1165  -3.8228  -1.7641   7.1493  12.8470   3.7912
-#&gt;   -0.0545  -4.4218  -2.0256  -2.8204  -0.9797   4.6155  10.0975  -4.2269
-#&gt;    2.3496   6.0976  -4.3312  -2.6425  13.0352   1.1327   0.5229  -1.6521
-#&gt;   -3.1339   9.1006 -10.1140  -3.8163   5.2570   3.9164  -1.1975  -4.3352
-#&gt;  -10.4536  -4.9992  11.7831  -5.9920   5.8077   8.8607   7.4058  -1.5225
-#&gt;    0.9515   6.2278   4.1100   2.5869   2.8496   6.2294  -0.7931  14.9432
-#&gt;   -4.6739  -0.1348  -9.7274   8.0626  -8.8258 -12.5441   4.6012   1.1684
-#&gt;   -2.9986  -7.1411 -22.5996   4.9067  15.4323   2.3979  10.8443   1.3498
-#&gt;   10.4310  -9.1357   1.5798  -6.0143  11.0481   5.4341   6.7135   7.0742
-#&gt;    8.5918  -3.0880   1.0786   8.7185  -9.4562  -1.1207  17.2399   3.9853
-#&gt;   16.2035   1.0193  -8.4315   1.6141   4.5086  -6.2439  -5.9229   0.2681
-#&gt;   -1.9260   2.9239  -6.4507   1.9535   3.5817   3.0195  -5.3757   3.7140
-#&gt;    1.3954   3.5727  -4.7595  -1.5473  -0.3001   5.6653  11.3151   9.0850
-#&gt;    1.2765   2.2759   8.7375  -4.7034  -5.4694 -11.4814  14.1636   0.8931
-#&gt;    9.3590  -0.3417  -0.7877   7.3982   9.7302 -22.6239  -6.6121   5.2700
-#&gt;   -8.7272   5.6148  -3.6666  -8.1927  18.2836  14.9145 -10.1294  -1.5587
-#&gt;   -0.0085   3.6464   8.5908  -2.9603   1.2685  -4.0842   0.1188 -10.2912
-#&gt;    0.0837  -1.1325  -0.9579  -4.6574  11.9999  11.6048   0.6013   2.3378
-#&gt;   -1.2049   3.6238  -0.2869   2.1376  12.5567   1.5172   5.4576  16.2751
-#&gt;    0.3741  11.7013  -8.3689   4.7339   7.2277  -1.6880   8.4890   2.3979
-#&gt; 
-#&gt; Columns 25 to 32   7.3890   1.1722  11.1404  -0.6275  -2.2581   6.7168  -5.5449  10.2584
-#&gt;    0.0631  -1.1588   2.6691   6.0662  -0.6681   6.2215 -11.3312   7.0348
-#&gt;    5.3507  -9.6257  -7.6733  -1.5075  -9.1357   7.2253  -2.2284  -3.6004
-#&gt;   -8.3390  -7.0528   7.6089   6.3794  -9.1577   4.1465   4.2300  -7.7868
-#&gt;    3.3363   6.5585  11.2087  -3.2675  -6.6934  -6.3075   2.5486  -3.1695
-#&gt;   13.2028  -8.5417   6.5156  -1.7958 -17.7095  18.0146 -12.7109  -6.8604
-#&gt;   -3.9953   5.6413 -13.9766 -12.8336   2.4302  -0.2792  -1.8480   2.9308
-#&gt;   -3.5229  -9.2404  -2.3083   2.5634  -9.9002   5.0341 -14.5620  -5.8757
-#&gt;    7.9510  -6.1437 -10.3587  -3.4317   0.4371  -7.0732   3.8047  -2.8958
-#&gt;   -3.9814  13.1879  13.3376  -4.0805  -7.8574  -2.8657  -3.1964  -3.9550
-#&gt;   -0.7134   4.3965  -8.4849  -6.4583  -2.5447   9.3164  -1.6600  -9.0793
-#&gt;   -0.4133   2.2780  -5.7760  -3.7102   1.2768  -9.4371   3.7084   2.1963
-#&gt;    3.5259  -8.6858   2.5827  -2.8093   6.0646   0.6056   2.2535  -2.6148
-#&gt;    4.1773   4.5655  11.0781   0.0591  -2.7009  -7.7991   8.4896  -2.9255
-#&gt;    5.5055  -4.2515   2.7884  -6.9243   4.5165   7.4063  -8.4759   0.7660
-#&gt;   -1.6071  12.0682   3.2349 -14.9882  -4.9375  -5.2951  -6.7601  -1.7672
-#&gt;  -11.0715   5.1147  -4.4899 -13.9252   2.7665  -1.0465   3.2217 -16.7316
-#&gt;    0.5132   4.4839   6.5234  -3.3046 -11.8241   2.5909  -7.5244   3.2524
-#&gt;   -4.0356   6.3940   8.6878   2.2701  10.4550   0.4905   5.2078   3.1168
-#&gt;   -3.9246  -0.8343   4.7819  -3.6101   7.2047  -4.2204 -15.9327  12.5156
-#&gt;   -1.8245  -2.0451  -6.5619  -0.1391  -6.2414  -0.7159  -3.0588   2.5831
-#&gt;    4.3972   8.3593  -4.7495  -4.5452  -8.3299   0.5224  -3.7321  -0.8761
-#&gt;    7.5761   8.7954  10.5896   7.1028  -4.4634   7.5632 -10.2875   8.4693
-#&gt;   -9.0174  -2.8030  -6.9526 -17.8910   0.9091  -5.2551  -4.0771   4.3309
-#&gt;   -0.8523   2.7138   3.7531  -8.5644   6.8431  -5.0554   2.8318  -2.7851
-#&gt;    2.4739   8.2340  11.5290   0.7304   3.8502  -8.3300   5.0353   4.2013
-#&gt;    2.7862   3.3270 -11.1609  15.9038   2.3667  -2.6115   5.7576   7.5323
-#&gt;   -3.4897  -1.1633 -11.1861  -0.5541   6.3716  -5.6403  -1.3534   7.7061
-#&gt;    4.5318   7.0960   0.4284 -16.3448  -4.7002   6.6004   1.5548   1.5919
-#&gt;  -11.0051  -3.2867  10.3913   5.3419   2.2438   1.1349  -5.6632   8.3345
-#&gt;    2.8781   2.7398  -5.4397   5.2176  10.0229  -1.7316  -0.6965   0.7915
-#&gt;    2.4742   0.8875  -1.6433   2.7281   5.9895  -9.3800  -5.4005   0.6868
-#&gt;    4.4059  -5.2528  -7.0642   5.4632   0.9754  -6.9504  -2.0992  -5.2770
-#&gt; 
-#&gt; Columns 33 to 40   7.4466 -16.1270   7.7623  -2.3204  -0.0335   5.7939   3.6170  -7.7525
-#&gt;    4.2852  -4.6216  -0.3457  -5.8391   6.7673   4.6181  -3.7234   7.6108
-#&gt;    5.2584  -0.3940  -0.6896   8.8440 -14.0776   5.0972  -9.8381   2.4050
-#&gt;    1.2894   0.2099   0.7095   0.7678  -6.8311  11.3139   9.9237  -5.3738
-#&gt;   -4.4999  -3.7956  -5.5482   0.4723   4.6266  -0.6697  15.3350  -4.4003
-#&gt;    5.5884  -6.8622  11.8448  -5.0110  -0.9112  15.7745  -5.2344  -9.0143
-#&gt;    0.8843   4.4619   7.6231  -8.6055  -4.3278   3.3613  -6.5353   5.1224
-#&gt;   18.5652  -2.6757  -7.2716   6.8249   8.3647  -2.2832  -3.7893   5.0383
-#&gt;    0.8702   1.7575   7.7316   2.0010   5.8754   7.3197   4.3097 -16.6690
-#&gt;   -9.4173   3.8186  -1.2098  -1.7060  10.9183   2.4799   1.1724  -6.1172
-#&gt;    2.7595  -6.9976   6.0959  -4.3505 -16.9760   1.8520  -2.1792   2.6241
-#&gt;  -11.2405   3.5718  -3.6426  -9.5767  -0.0805 -11.9251  -6.7674   7.4688
-#&gt;    3.0797   3.2441  -6.0500   0.9516  13.0595   4.3575   5.8780 -10.4970
-#&gt;   -9.1789   8.8035 -13.6098  -1.9235   5.6181  -6.9958   7.5010   0.1316
-#&gt;    5.1129  -2.7349   7.3268  -2.2627  -4.4940  -0.0892  -0.9897   3.1911
-#&gt;    5.0424  -0.5169   8.5215   1.6657  -0.1798   5.8811  -0.8069  -6.0700
-#&gt;    4.1074  11.9456   4.5618  -7.3110   4.6538  12.8371  -3.6143 -13.6678
-#&gt;    7.0659  -6.8284  -8.8943   1.3400  14.4458  -4.6365  -9.0426   4.2903
-#&gt;   -4.5028 -11.7163  -8.2133   7.8198  -5.3822  -5.9274   5.7452   3.9589
-#&gt;    5.4042   7.4754  -3.0849   7.6898  11.3614   1.0060  -3.7437  -4.9908
-#&gt;   -5.4003   7.9007   2.6796  -0.2328   7.6192  -3.0853 -11.5710  -0.4463
-#&gt;   -4.9393  -2.2258  13.9636  -6.0169  -4.5435  -3.9273  -6.5915   2.6723
-#&gt;   -0.2920 -21.2883   6.1001   2.2776  -4.3563   0.7039   8.4649  -0.4097
-#&gt;   -9.8968   7.7958   2.7428  -9.0735   4.8088  -2.3101 -12.8601  -3.2447
-#&gt;   -1.0660  -0.8830  -7.5097  -9.9017   8.5181  -0.8397   6.9231 -11.8567
-#&gt;   -3.2171  -4.9295  -3.9119   0.5919  12.2833 -18.3573  -3.2209  -2.5432
-#&gt;   -4.4099   6.9763 -10.7214  -7.3058  13.0559  -9.1926 -11.0336   6.9720
-#&gt;   -4.1410  -6.3178  -0.2991  -2.1099  -7.1164 -10.7386  -5.5606   6.7652
-#&gt;   -0.5633 -14.0766  16.4578   2.6047 -14.1952  -0.6802   4.8974  -1.8753
-#&gt;   -2.5182   2.8519   0.1485 -10.9692  -0.7770   4.2582   6.9629   3.6324
-#&gt;   -1.2033  10.8350  -0.1348   1.1973  -2.3085  -1.9432   2.0285   5.3372
-#&gt;   -7.3475  17.4146  -5.8687 -18.5738  10.6342  -5.9207 -10.4362  13.5840
-#&gt;    6.4045   2.2557 -11.9177   6.8501  -0.2501 -11.5088  -1.7576   5.8040
-#&gt; 
-#&gt; Columns 41 to 48  -1.1528  -0.3059 -16.4855   6.3151  12.4840  -8.8370  10.2698   0.4192
-#&gt;   -1.0645  -5.1154   0.9039   0.5932 -15.5728 -19.0617  -2.9011   0.5381
-#&gt;   -5.9651   2.7723   7.2793  -2.0492   2.2371   0.0455  -3.4007  -1.9928
-#&gt;   -9.0765  -5.0615  -1.0609  -5.9322   6.5024   2.8618   2.0332  -1.2715
-#&gt;   -5.7609   3.9622  -7.2723   7.5854   6.7168   1.4700   2.4001   0.7216
-#&gt;   -2.2888   6.2430   0.4112  -9.4229  -0.2957 -17.4502  -1.1558   4.7818
-#&gt;    2.5637 -11.2712 -16.8971  -3.8829  -6.7493   3.0228 -18.6445 -18.2898
-#&gt;   -1.3241  13.5192   0.5421  -5.0751 -11.4945   6.8363   7.4407  15.1910
-#&gt;    5.4025  -4.4140  -7.6300   8.2625   3.7924   6.3986   0.2250   3.4656
-#&gt;  -13.5206  -3.1000  -1.7910  -4.1973   1.6695   4.6003  -6.2950   5.1187
-#&gt;    7.4625 -16.6311   0.9130   3.5331 -12.5655   0.9049  -1.1733  -6.3070
-#&gt;    4.4416  -2.4409  -2.5807   2.5126 -16.8289   4.1233  -9.5715  -8.4342
-#&gt;    2.8251  -0.3969   5.5610   7.9408   4.9915  -3.6653   2.5352   8.1580
-#&gt;   -2.0478  -5.3894   7.7031   0.5548   3.7467   9.4199  11.4782   6.0224
-#&gt;    7.4393   2.1407  -0.3872   8.7566  -8.0891  -4.7715  -6.0568   4.3396
-#&gt;   -3.2912  -6.3157  -7.2984   1.1823  -5.4720  12.9587  -3.8822   2.2596
-#&gt;    8.5865   0.0979   1.1206   3.6751  -8.3467  -2.2304   6.1171   4.4236
-#&gt;    3.2218   8.7014   9.6906   1.5297   0.4616   4.7557  10.5951  11.3981
-#&gt;    1.1881  -0.8344   0.1860   1.6291  -1.1841  -2.3651   0.9128  -5.5114
-#&gt;    3.0337  -3.4850   2.5466  -0.9734   6.0508   0.4377   6.6559   1.2407
-#&gt;    7.8733   1.6111   7.2698  -0.2805  -7.8808 -10.5510  17.0050   5.4621
-#&gt;    2.3686   3.7411  -8.7561  -0.3537  -6.7154   2.3282  -7.9159  -0.5190
-#&gt;    5.8606   1.1017  -3.9584 -13.7049  -0.4072  -4.4535 -17.2705  -2.2617
-#&gt;   -3.5316  -8.2218   0.9173   6.2296  -5.5192   4.8388   8.2720  -8.9849
-#&gt;    8.8247  -3.4977   6.7595   3.5766   7.6676   6.4403  10.1844   4.7541
-#&gt;    6.8293  -2.8047   5.5055  -2.2400   4.1690   6.5581   2.5577   4.1593
-#&gt;    1.0939  -1.1086  11.9396  -3.1603  -5.0743   7.3548  -8.5145  10.2618
-#&gt;   12.5169  -4.8524   5.4853  -0.0607  -6.4473   6.1951  -3.1305 -11.7052
-#&gt;    1.6918   4.1365 -17.9707  -0.0724  -1.8701  -5.7530  -7.8280   1.7331
-#&gt;  -14.3737  -0.6248  -8.6392  -7.6581  -5.1361   8.9324 -11.4385   2.9201
-#&gt;    1.3806 -10.5504  -4.8660   0.8397  -4.6240  -0.4345   7.9949  -5.6029
-#&gt;    6.9728  -8.3446  17.9969   5.0847 -14.4933   0.8242  -0.5790   3.2053
-#&gt;   -1.9145  -1.8569  -9.2596  -0.3423  -8.6451   8.5267  -5.8793  11.0983
-#&gt; 
-#&gt; (10,.,.) = 
-#&gt;  Columns 1 to 8 -10.1444  -0.1197   6.1807  -1.0053  -2.0849   0.0699 -12.0112  -3.2874
-#&gt;   -3.1653  -6.9392   0.2056  -8.2556   3.8173   0.8364  -2.2848  -1.6908
-#&gt;    2.4892  -2.1405   3.2682   3.7020   2.7202  -2.2279   7.3648  -6.8454
-#&gt;    2.9837  -6.3264  -4.7223  -2.4405   0.2809  -5.4343  -8.1034  -1.4769
-#&gt;   -4.7097   0.5069   8.5231  -0.6921   3.4181   1.1326   2.4659   7.2362
-#&gt;    8.5167  -3.9543  -8.7454  13.2610   1.4841  12.0319  10.7573   0.9984
-#&gt;   -7.7347  -1.3899  -3.6051  14.9848  -1.1423   4.1534  -3.4723  -6.3956
-#&gt;    3.6568   2.2901   1.0027  15.0810  -2.5122   2.1880   9.9740   5.2039
-#&gt;   -6.1722  -5.2133  -2.6996  15.7291  -4.7288  -1.0254  -9.1485  -8.5797
-#&gt;    2.6281  -4.8917   0.0488 -13.6920   7.3183   0.0322  -1.2933  13.4125
-#&gt;    5.5547   7.6762  -3.0312   3.2153   4.8022  -2.2270   2.6986   3.0999
-#&gt;    2.2517   7.3051  -2.1973  10.1782   1.2391   7.4305   2.2057   6.8127
-#&gt;   -2.1870  -8.1362  -3.8371  -0.7230  -0.6442  -1.5647  -8.7335  -4.8724
-#&gt;   -2.9060  -1.1704   3.6592  14.3591  -4.1817  -0.4568   1.2303   8.9840
-#&gt;    2.3303  -1.3262  -2.8247   4.9564  -1.1007  -3.1644  -4.7322   1.7455
-#&gt;  -10.0532   3.3348   0.3513   3.0392  -0.1441   2.2660  -0.0103   6.5989
-#&gt;   -4.8320   7.6628  -9.4277   0.9490   9.5274  -3.0630   4.7274   2.5657
-#&gt;   -2.0232   1.4861  -1.3839   9.0808   5.3553  13.0703   6.7612  10.0549
-#&gt;   -0.2220   0.7726  -4.4601 -19.9362   8.9378 -12.2011  -2.3527   7.6363
-#&gt;   -2.2890  -8.4458   6.3903   0.1126  -3.3015   4.6090 -12.9531  -8.0556
-#&gt;  -11.7049  -2.1021  -6.4660 -16.2126  12.2413   3.8341  -7.1119  -1.9723
-#&gt;    1.8800  -1.0128  -3.4049   8.2863  -2.2683  16.9409  14.8881   1.1372
-#&gt;   10.2134  -9.9597  -4.5950  -2.6111   7.8690   6.3251   0.1397   9.5329
-#&gt;   -0.2442  10.1605 -11.8760  -3.2294   4.5349   2.5919  -7.0485  -2.7287
-#&gt;   -3.3376   1.8925  11.3034   9.4839  -4.2519  -2.2193   1.1300   3.0766
-#&gt;    2.1370   8.3614   3.9244  -1.2833  -8.2233   3.9677   2.0777   8.9599
-#&gt;   10.8593  -0.9007   6.7030  -0.9812   0.8233  -2.4821  -2.5597   5.8398
-#&gt;    9.5524  -0.5780  -5.6405  11.5832  -0.1386  12.8104   2.3735  -0.5978
-#&gt;   -1.2722  -1.1479  -7.5273  13.1904   4.4276   1.4398  20.0420  14.1377
-#&gt;    7.6020  -4.5962  -3.1030  -2.4022  -9.8691  -6.9569  -2.7503   4.0840
-#&gt;   -9.7593  -1.1617  -5.6452   0.8097   4.3845 -12.7132 -10.8348  -8.8871
-#&gt;    4.8035  -6.9074  -2.6160  16.8739  -5.2030   2.0943   6.6433   7.4874
-#&gt;   -1.3797  -1.6136   5.2215  11.4710  -1.5928  -2.4512  -5.0652  -6.6179
-#&gt; 
-#&gt; Columns 9 to 16   5.5971   3.3503   3.0399   1.5089   6.5852   8.2251  -3.5754  -3.0470
-#&gt;   -2.1679   0.6357  -9.9250   4.5543  -8.4732   4.1226  -1.1196  -7.3521
-#&gt;    3.0059  -8.1070  -5.0422  -0.5537  -2.2180   3.6298  17.4162  11.8070
-#&gt;    2.4282   6.6659   2.3878   7.0510   3.2980  11.7437  -2.7162 -10.3219
-#&gt;    7.0477   3.6082   1.0901   1.6695  19.0706  -5.2758  -9.4188  -0.5452
-#&gt;   -0.0727  -3.7624  -0.2612  -1.5356  -6.0739  -1.2194   2.4480   2.4479
-#&gt;  -14.1243  -8.2015   4.8352  -6.1864  -8.6873   7.1435  -3.8778  -8.7173
-#&gt;    5.6458  -0.2373  -1.4555  -7.3198  -1.9871  -3.8647  14.2421  -0.7649
-#&gt;   -6.9534   8.4321   6.9555  -6.9628   2.3235  12.4117  -3.1565 -11.3416
-#&gt;   -0.0680   4.7544  -3.5090  10.3493   0.6760 -11.0194  -3.3329 -12.5326
-#&gt;   -6.4896  -0.1720  -0.8692   2.3702 -10.5436 -12.6579  -6.5503  -7.4314
-#&gt;    0.9592   4.2373   5.5375  -9.3086   2.3612  -0.0491 -12.7195   6.7641
-#&gt;   -6.2570   7.3455   1.0243   8.0582   3.7594   4.8832   8.1351  -3.5624
-#&gt;    9.3877  14.8057  -7.0687  13.5598   4.5594  -8.4408 -13.0884  -2.8163
-#&gt;   -1.9477   3.0723   0.3970  -3.7000   0.6065  -7.4526   1.6409   4.2809
-#&gt;   -3.8656  -4.5890   6.0006   0.3630  -4.0398 -13.4596  -0.2644 -11.4151
-#&gt;  -11.6858   5.8926   5.6883  -2.6628   7.9590 -15.4193   1.3021  -8.8959
-#&gt;   10.2620  10.0805   0.8881   2.1135  -8.4767 -10.3706  -5.8807  -6.7612
-#&gt;   15.5988   1.5759   4.6415  -6.6841   1.8256   2.1773   7.7047  -2.4036
-#&gt;   -1.7554  10.1871   0.1717   8.8373   0.5827  11.9677  -4.9050  -5.4659
-#&gt;   -3.4155  12.2013  -3.5790   4.1829  -6.5768  10.4924  -5.8793   7.1799
-#&gt;    8.7648  -5.7493   2.9277  -1.9567   0.2121  -4.2309 -10.9851 -14.6578
-#&gt;   -1.3215   4.1420  -2.0567  -0.8449  -4.5076  15.3526   7.2459 -17.8256
-#&gt;    1.7266   7.2688   4.5867  10.4419  -3.9505  -1.5175   7.9106  14.7962
-#&gt;   -2.8269   5.9840  -6.8438  13.2157   0.2065 -11.6715   1.7323  -9.2430
-#&gt;   -0.8036   5.4124  -5.9386   8.1885  -7.0184  -0.2899   0.7574  -0.2613
-#&gt;   -9.4918  10.7308   1.9126  -5.9229   0.7225  13.9246  -8.0345  -3.9507
-#&gt;   -0.3737  10.7904   0.3930   1.8292  -3.0731  17.2355   4.4696 -12.3296
-#&gt;   -6.3498 -14.2862   3.5689  -7.3537   2.2323   0.0415  -2.4524 -17.9123
-#&gt;   -3.7963 -10.9490  -5.4024  -4.4347   1.9053   4.3605   1.4738   0.6189
-#&gt;   -2.2874  -0.9531  -2.4671  -0.7776 -12.8957  -6.0696  -8.2405  12.4174
-#&gt;    5.4513  16.8570  -6.9805   1.5265  -2.7185 -10.3574   2.0088   1.7247
-#&gt;    0.7555   6.7158   1.6428  -6.0460   5.2859   2.0256   0.5786   1.1257
-#&gt; 
-#&gt; Columns 17 to 24   2.0546   4.5130  -3.5583   5.2486  -2.8548 -12.6624  -6.9093 -10.5471
-#&gt;   -0.9874  -8.1809   6.5242  -0.4941  -6.5382  15.4531   0.5094  -6.7447
-#&gt;   -8.8779   6.4355   5.3725   9.9155  -4.5599   3.4196   3.6503   2.3468
-#&gt;   -5.1098   0.4484  -3.2751  -3.1076   0.7331  -4.2806   3.3959   5.6992
-#&gt;   -8.7335   0.2951  -0.2281  -0.8039   2.9881  -4.2292  -7.4910   7.1774
-#&gt;   -0.5602   0.7600  -4.9385  17.8288  -6.8998  -3.4093  12.8449 -14.4115
-#&gt;    0.2940   7.4019   1.0543   2.8513   7.0540  11.7508  -3.0525  -6.7163
-#&gt;   -7.8914  19.1951   2.0116   1.4168   6.8541  -1.7511   4.9517   1.9864
-#&gt;    8.4330  15.2160   1.4476   6.5956   8.0267  -4.6903   8.5887  -3.2045
-#&gt;  -10.2050 -15.4986 -10.3315  -4.3585  -5.7238  -7.2673   0.3522  -9.4704
-#&gt;    0.2761  -5.9805  -2.0919  13.4959  -5.7172  -0.9133   5.5280 -10.4246
-#&gt;   10.7527  -3.2177   6.7307   1.6578   1.8964   8.5094  -9.9920   3.7888
-#&gt;   -3.5996  -5.1115 -12.6203   0.4422  -6.3140  -2.8705   6.3011 -10.2108
-#&gt;   -7.4819 -18.5726   6.4890 -18.9001  11.4158 -13.4495  -6.7460  -9.2744
-#&gt;    3.3155  -4.0529  -2.0516  12.3725  -6.6102   8.1598   1.6291  -5.0605
-#&gt;  -10.6442   2.0086  -4.5135   1.4218  -1.4568  -1.1463  -2.8058  -1.1260
-#&gt;    0.9578   3.6068   1.8238  -4.7084   7.8040   3.8151   1.4850   3.9342
-#&gt;   -9.7519 -16.1863   1.8806  -5.4929   4.1605   0.6467  -7.4427  -5.3136
-#&gt;   -8.4564  -2.3386 -11.0684  -2.1825  -7.2447  -3.4031  -8.9010   0.2455
-#&gt;    5.2485   5.8136   1.7191  -5.2376  -0.0547   5.6880   1.2841  -8.1107
-#&gt;    9.7796  -9.9287  -6.9444  -9.4663   8.0682   0.3722  -3.7671   1.9767
-#&gt;   -9.2235  -3.6328   1.1223   0.9097  -5.5333   5.6833   1.6964   1.0425
-#&gt;   -4.3343   3.1634 -16.2895  12.2644  -4.3793  -4.7752   5.0388 -15.4954
-#&gt;   18.8700   6.9469   9.8524  -3.1646   4.8083  -5.6064   3.5272  -4.6715
-#&gt;   -2.1762   1.8967   8.2813  -8.7359   8.5017  -8.9679   4.8829 -10.0292
-#&gt;    5.7125  -9.5609  -5.6244  -6.3954   7.8815 -10.4683  -3.5783  -2.0970
-#&gt;   11.0892   2.6825   4.9245  -5.6623   5.2303   8.5283  -0.0718  -4.3520
-#&gt;    3.4371  -3.0183   3.4137   0.8304  -1.4545   1.0420   1.8797  -3.0861
-#&gt;   -7.8636   5.6335  -0.1999  10.1900  -3.0338  -0.9648  -2.7204  -0.0394
-#&gt;   -6.5611  -5.0444   5.6717  -3.5566  -5.8613   4.8603   0.8347   9.9316
-#&gt;    7.0669   5.4207  -1.2769  -4.6852  14.6142  -1.0059  -4.3319  -0.1178
-#&gt;   -4.2236 -11.3471   2.3117  -5.8895   1.1529   1.3514   1.0220 -16.6680
-#&gt;    8.2076  10.2725  -0.7926   8.6331  13.6297   4.1326 -12.5739  -1.2670
-#&gt; 
-#&gt; Columns 25 to 32   9.0555   3.9767  -2.1810   0.4481   3.6355  -2.6770   4.4128   1.6824
-#&gt;   12.4359   3.8405   1.7793   9.6326   9.7602   5.3470  17.5119   1.6207
-#&gt;   -0.5088  -5.9857   4.8201  -1.8090   0.0253  -1.8950  -9.5534   0.6362
-#&gt;    4.3448 -10.0364  -2.8532   2.8793   1.1018 -10.9793  -1.3335  -6.8807
-#&gt;   -2.7940  -4.1484  -5.0693  -1.4739  -1.1530 -11.0881 -11.8643 -10.0651
-#&gt;    9.0626   7.6274   9.9464  10.4134  10.2154  -5.3797   0.3904  12.5294
-#&gt;  -15.2923   2.6005   2.0667  -7.9929  -2.1251   6.1416  14.2507   3.9267
-#&gt;   20.2569  12.9307  10.2105  -0.8746   1.9489   1.6081   3.1222  -7.4608
-#&gt;   -0.8630  10.1065   1.9037  -2.5357   3.5577  -9.8352  -1.1748  -1.3857
-#&gt;   -3.9054 -13.4426  -0.6768   2.7202   1.7984  -3.2778  -0.9666  13.4866
-#&gt;   -4.3562  -3.4356  -0.8184   5.5668   3.5301  -2.6200   2.1550   4.6616
-#&gt;   -0.8789   8.1751   0.5661   5.2339  -5.5995   0.8778   8.8602  -0.3175
-#&gt;    2.8875   3.1513  -6.6853  -0.1071   9.6118   4.1933  -1.2963  -3.5391
-#&gt;   -8.2166  -7.3978  -1.0758  -4.2469 -11.2469  -6.2172 -14.8048  -8.0152
-#&gt;   -2.9892   6.0409   3.2805   1.2410   3.2025   4.0625   0.7113  -1.9655
-#&gt;  -12.4479   0.3363   5.6861   0.1953   4.9344   4.8320  -6.3443  -4.1509
-#&gt;  -11.6489   8.6715   2.5906  13.6689  -5.2892  -2.8286  -2.7305   4.6068
-#&gt;    8.5245   6.1462   1.1552   7.5443   2.7243  -7.0538  -9.5521  -6.8945
-#&gt;    1.4022  -4.7211  -4.7684   7.6947  -0.2346  -2.1076   0.8359  -2.4188
-#&gt;    3.6303  -3.0204   6.1398  -1.6303  -5.6167   4.5231  -6.4745   6.7870
-#&gt;    9.6137  -5.2266   0.4564  11.5572   4.7374   4.8876  15.8521   8.6110
-#&gt;   -2.7291  -5.2290   3.4725  -4.5103  -1.4037  -6.4851   5.4105   5.6147
-#&gt;    5.3347  -5.7938   2.9378   1.3535   9.0743   5.5539  -7.8256  11.1756
-#&gt;    0.2743  -3.4336   8.9149   1.1778 -13.7422  -2.8204   4.7420   2.4837
-#&gt;   -5.9624  -0.2840  -2.3787  -1.6480  -7.2812   3.0294 -12.7217  -6.2091
-#&gt;    9.3566  -5.3545 -12.4742  -3.5459   1.5503   4.8993  -4.2130 -10.1991
-#&gt;    2.1690   7.6918  -5.1142  -0.4864  -4.8431  -0.8477 -12.8715   2.6212
-#&gt;    8.5247  -0.6487   2.9307  -0.6054  -1.8770 -14.3489  -1.6105  -2.6003
-#&gt;   -8.4713   6.2716  -1.3494   5.1054  -2.5884   3.4053   5.5117  -3.7052
-#&gt;   -2.6630 -10.3161  -2.9556  -0.7866  -4.1446   9.5719   7.1801  -5.3201
-#&gt;   -2.7644  -2.3416   3.4993  -8.0865  -2.9271   2.9227  20.2140   5.2788
-#&gt;    8.2781  -3.1644   4.5415   6.5872  -2.1386   8.6819  -4.8881  10.7308
-#&gt;   -6.0502   6.9568   1.5363  -4.0881  -0.4895   0.0576  -4.3354  -4.7347
-#&gt; 
-#&gt; Columns 33 to 40   0.7578   3.1417  16.5717  -6.6833   9.1098  -8.2940  -2.6904  -7.0581
-#&gt;  -10.6832  -6.8104   5.4104  -9.1549 -10.6346  -4.1629   0.8964  10.9814
-#&gt;   -6.2788 -13.7318   1.3338   5.3147   5.5536  12.9749   2.5235  12.8341
-#&gt;   -0.1089  -2.8497  -0.5344  10.4130  -8.4964   6.0989  -3.7887  -2.8574
-#&gt;   13.4943  18.7751  -4.7567   3.7820  10.4945  11.0919  -3.7461  -7.2143
-#&gt;   -6.7856 -17.2794   9.9050  -3.4710   4.2878 -16.3939   0.6752  -5.0135
-#&gt;   -5.4563  -6.8422   0.1214  -0.3491  -8.4881   0.5261  -8.9195  16.5997
-#&gt;  -16.8810  -0.6915   7.7340 -14.7307  -7.1816  -0.2595   3.2477  12.4542
-#&gt;   -9.4649  -1.8413   4.0424   2.8748 -10.6575   6.2521   4.9620  -2.1477
-#&gt;    2.6169  -1.1594   3.0891   4.3297   0.1142  -9.6263  -9.6510 -12.5282
-#&gt;   -1.4988 -13.4900  -0.3911   2.3856   3.1218  -0.7858  -5.4760   2.3447
-#&gt;    2.2051   1.5264  -6.6379  -1.7279   4.0117   8.0338   6.4586   4.7617
-#&gt;   -8.8728 -10.1634  -9.1656   5.8057  -9.5091  -7.4276   3.5549  -4.4995
-#&gt;    6.8864   7.9416  -3.7849   8.8226   0.3242  12.0163  -4.3704 -26.0601
-#&gt;   14.2091  -9.5589  -1.4731  -2.9862   0.2723  -2.5337   1.3785  -2.9376
-#&gt;   12.7019  -4.7535  -4.2859  -7.0992  -1.8343  -2.0608  -4.8201  -3.1361
-#&gt;   12.5973  -4.0727  -7.8676   5.6273  -2.0102  -4.4779  -4.6051   0.5560
-#&gt;   -7.5227  -3.0607   1.0835 -17.3162  -4.5552  -3.1035  -2.6551  -4.6564
-#&gt;    0.8747  -0.0583  -6.6304   6.8277   9.8828   7.2040  -8.8589  -0.1032
-#&gt;    4.2869  11.6822   9.1535  -9.8231 -16.2034   0.2689   1.0362  -1.8294
-#&gt;  -17.7511  -6.4315 -16.2652 -14.5400  -9.1459  -8.1781   6.2554   5.1453
-#&gt;    4.4051   3.6693   2.3898  -3.2076   9.9637  -9.2541  -7.4140  -8.1407
-#&gt;    8.6925  -9.6214  15.5877   1.7642  15.8649   8.1337   5.8204 -11.5316
-#&gt;    4.7143  -2.7363  -8.7010   5.4189   6.9730   8.9110  15.6204  -2.3280
-#&gt;   -1.2083   2.3353  -0.1196   5.1215  -1.5125   5.7496  -1.2933  -4.5341
-#&gt;   -1.6540   7.7396   0.7685  -4.5954  -5.1257   3.0261   0.6978   1.7724
-#&gt;  -19.3702   1.4281  21.2253 -10.5462   0.3617   8.2309   9.6356  -1.5614
-#&gt;   -3.8966   0.3030  -6.3600  11.8487   4.8418   8.0074  10.3173   8.4069
-#&gt;   -8.3437 -11.1290   7.0622  -4.0098   6.2378  -6.3287 -12.8703   0.3349
-#&gt;    2.8801   2.3927   7.6229  -1.4162   4.3486  -5.6719   3.1610  -0.5743
-#&gt;   -5.0980  13.3765  -9.9368   6.8186 -15.5441  -9.9898  -6.3102   7.0537
-#&gt;    2.0890  -6.6566  -4.7321   7.7873   0.9842   2.0113   4.7259  -4.1844
-#&gt;   -1.8009  -1.0738   5.4435  11.8413  -5.2262  10.2379   6.8946   6.5677
-#&gt; 
-#&gt; Columns 41 to 48 -15.1034   4.2612  -3.0378  -1.5681  -6.0128   0.0381  -3.4975  11.6571
-#&gt;   -9.2897   2.1662  -6.7061  -0.9977   7.1920   1.1223  -0.8631   8.7066
-#&gt;    8.8398   8.0260   7.9312  -8.2322  11.1833 -11.4206  -4.3504  -3.1813
-#&gt;   -8.8027  -6.2503  -3.3837  -4.6914  -3.5584  -7.9464 -10.3860   9.8983
-#&gt;  -20.1082   3.8590   3.6515   5.1925  -2.3029  -0.6698   0.6267   1.4074
-#&gt;   12.5819  -8.4862  10.7777  -0.1754   7.3898  -9.9413  -1.0357  10.1966
-#&gt;   -3.8575   5.3984   1.3863  -2.6811   9.2741   3.3141   5.8792  -1.2668
-#&gt;   -5.6999  -9.1780  -2.2034   3.2855   4.4731  -7.2104   2.0820 -12.7165
-#&gt;    1.7375  -1.3915  -6.1227  -3.1401  -6.8664   1.7445  -4.4614   0.0165
-#&gt;  -10.5523   8.8380  -4.0959  13.3185  -1.5588  -7.3645  12.0501  -1.9209
-#&gt;   12.4562   2.1660   0.5182  -3.8735  -1.7395   4.1616  -7.7855  10.3945
-#&gt;   -0.1160  -6.4934   2.2916  -1.7268  -3.6592   5.8576  -0.6836  -3.2697
-#&gt;    2.2219   2.8975   8.8390  10.6105   2.3376  -0.2479  -6.2151   2.9496
-#&gt;   -8.7401  -6.3975  11.7335   1.0064   2.5566   1.5934   1.2270  -2.4329
-#&gt;    2.3822  -7.0842   8.1927  -6.1830   6.8344   4.5934  -7.2927  11.9882
-#&gt;   -2.4304  -0.5193  -1.8556   0.8503   6.1451  -3.6321   6.1960  -6.3619
-#&gt;    7.3570   2.6804  -0.3453   6.7231  -4.8938   5.5750  -5.8652   3.2427
-#&gt;   -3.7548  -5.1043   7.7943  12.5542   3.3805  -6.0210  14.5897   4.9062
-#&gt;  -16.3750  -1.5935  -4.7430  10.7819 -14.4564   6.0710  -5.1389   4.5142
-#&gt;   -5.6084  -4.6240  -0.3802   0.3596  -0.5490  -6.2475  10.8499  -3.2836
-#&gt;    0.2351   1.6983   4.3092   5.4396   7.3431   3.0413  -8.1423  -6.6941
-#&gt;    1.0739  10.6362  -3.1644  -5.3578   1.7076  -2.6591   8.1859   4.8885
-#&gt;   -4.8582  -2.5123   7.4802   9.6237   6.5262  -6.1949   2.5341  11.0141
-#&gt;   -4.5966  -5.5363  -7.1235  -2.6343  -4.5953  -1.2482  -5.3086  -6.0242
-#&gt;    0.9331   6.3451  -2.0679   2.8115   1.8415  -1.6032   7.6612  -5.9402
-#&gt;   -5.5541  -0.4009  -3.6417  10.5372  -1.7765   1.3043   4.4225 -11.7097
-#&gt;    6.0071 -15.5743  -2.1037   1.1968  -0.3587  -9.2418   7.0970 -11.8214
-#&gt;    0.1896   0.8383   7.5479  -0.2790  -6.7927  -2.3654  -3.7691   2.0807
-#&gt;    0.3517   8.9742   8.1081  -1.9491  -7.6595   9.3279   0.9517   5.8000
-#&gt;   -2.1998  -3.3504  -5.5685  -5.5382   0.6019  -9.8444   7.0675  -1.6601
-#&gt;   -7.8045  -7.3213 -10.4706 -11.2519  -1.0217  13.0651   3.7952  -3.3913
-#&gt;    1.7352  -8.5863   6.2992  -1.9649  11.4420  -1.2370   2.1108  -9.9580
-#&gt;   -8.3519  -3.1415   3.5700  -9.3012  -0.8553  -6.1725  -8.7139   2.7983
-#&gt; 
-#&gt; (11,.,.) = 
-#&gt;  Columns 1 to 8  -0.4065   0.3733  -4.9418  -2.5444  -0.4269  -2.3134   4.8941  -0.8093
-#&gt;   -3.9490   2.8360  -7.8948 -10.1886  -6.6243   8.0728  -6.8247  -8.5681
-#&gt;   15.7751   6.9586  12.3404   1.7637   2.4500   1.5491   5.7051  -0.3620
-#&gt;   10.8021   0.2294  -0.3678   1.1018   0.3001  -1.3607  -7.3719 -11.1524
-#&gt;   -1.5832   5.2977   5.1451   8.6519  12.8501 -11.9625 -13.5828 -12.8568
-#&gt;   14.7526 -25.2582  11.2414 -10.8526   3.6804 -12.2809   4.2074   3.7940
-#&gt;    4.7425   6.5113   0.9051   9.8353 -19.5225 -11.0475  11.3969  -2.9696
-#&gt;   -0.2588  -1.6735   3.2664  -2.4508   2.0230  10.2835  13.0567  -2.2903
-#&gt;    5.8186   5.5486  -2.6917   9.1814 -14.2814 -13.4509  23.9479   1.5268
-#&gt;   -1.5159  -6.5262   0.6499  -1.3358  -1.5957  -8.3566 -16.4827   5.8943
-#&gt;   13.7363  -5.3735 -10.1303  12.2127 -21.5547  -2.2451 -10.1460   3.7306
-#&gt;   -4.1351   2.3896  -8.8859   8.1949  -3.7437  -0.2092  -0.3543  -2.1541
-#&gt;    6.8591  -4.2608   0.6945   2.4353   4.4051 -10.6185   7.9163   4.6866
-#&gt;   -9.4047   4.7700  -6.2253  10.2729   2.2728 -16.9734  -9.3953  -5.3911
-#&gt;   -5.2235  -7.6770  -6.1902   7.4507   2.6723  -0.9827  -0.1777   9.7876
-#&gt;    1.6364   4.9116   3.7863  18.4638 -10.2864 -13.9360 -14.4613   6.0181
-#&gt;    4.3663   2.6462  -7.8092  14.5016   2.6161 -13.2271  -8.7625   9.3916
-#&gt;  -12.9208  -9.0466  -5.7335  -1.5262   2.0520  -1.4658 -10.2035  -0.6461
-#&gt;   -2.9945   7.7872  -4.9715  -9.7311  -9.4418   3.9034 -11.5247  -7.1616
-#&gt;   -7.0632   3.4746  -2.5540  -1.8362   1.6946   1.3066  10.2940   1.3677
-#&gt;   -7.4355   7.1904 -12.2559  -4.5289   8.6964   2.4529  -4.0359   9.4217
-#&gt;    0.9931  -9.9861  -1.9184   0.8332  -1.9971  -0.5197   0.2696   2.5889
-#&gt;    0.1138  -8.5071   7.1351  -2.9754  -5.4180  -2.1957  -8.5821  -0.1802
-#&gt;    2.2487  13.3909  -3.1474  14.5452  -1.3535  -9.8315  -3.2029  15.9731
-#&gt;   -4.9562   0.0241   4.3295   9.6773  -2.5262 -16.1202   2.4468   1.2283
-#&gt;  -17.6846  -0.7314  -5.7830   3.6544   4.2291  18.3080   4.7494  -8.1206
-#&gt;   -4.4530  -2.2752   4.9867  -8.3215   1.8340  13.9693   3.1138  -0.0328
-#&gt;   10.3178   9.8976  -1.2669  10.0317  -5.8547   2.7303  -0.2024  -8.8060
-#&gt;    0.1836  -0.9229  -0.6900   6.6011  -1.2826  -9.8224   0.0422   2.5354
-#&gt;    0.5093   1.8134   5.6027  -2.4341   5.3465   5.4162 -12.9173  -1.8831
-#&gt;  -14.8555  -3.1140  -7.3100   4.0106  -6.3849  -1.4725  11.3388   5.9771
-#&gt;   -9.9643 -11.2454   1.9553   3.2548   0.9408   3.4225   5.9520   1.9936
-#&gt;   -1.5928   5.2065  -2.0930  11.4440   0.2092  -5.8274   7.5043   2.0086
-#&gt; 
-#&gt; Columns 9 to 16  -6.6169  -8.1902   5.0266   6.4120  -6.6086  -1.0369  -6.2245  -6.1113
-#&gt;   -3.7437  -1.5854  -0.7841  -8.4521  -3.2718   3.1174   6.8958  -9.3045
-#&gt;   -6.3384  -0.1090   0.8269   8.2913  -3.6110  -5.6872   3.3065   7.9924
-#&gt;   -0.6727  -2.8524   6.7465  -3.6963   5.9552   8.5082  -6.7335 -10.7753
-#&gt;    1.2784   0.2024  -3.4024   1.8413  -4.4857   8.5434  -7.1870 -16.6286
-#&gt;    1.5627   1.5892  -6.1431  -5.5961   9.1524   2.3702  -3.2485  10.8133
-#&gt;    6.6103  -3.5428  14.4612   5.3538   8.9282  -8.1752  -3.8113  -3.2387
-#&gt;   -3.6234 -13.0198  -4.5940  -0.4798  -2.6011   5.1536  -4.4763  -1.5089
-#&gt;   -0.2607  -5.3447   9.8566   1.7696   2.4419   0.1251 -13.8497   0.5855
-#&gt;    3.1480   9.0100   5.9859  -4.3696   3.2455  10.8881  -4.4636  -6.0595
-#&gt;   11.4104  -4.4447  13.9657 -10.6378   1.1552  -6.7267  -7.9371   9.4279
-#&gt;    2.0993   0.0677   2.8770   0.0905   0.4610 -11.8818   5.8134   5.8336
-#&gt;    5.8539   2.1568  -1.6642  -3.3244   2.2336  13.0165   1.8834  -1.4875
-#&gt;    0.9044   0.7234 -10.9195   6.9512  -0.7992   0.2557  -2.9085 -18.5231
-#&gt;    4.4072   8.1618 -10.2635  -2.8191   0.0990  -1.8735   3.5201  -9.6331
-#&gt;    6.8141  -2.0672   6.4769   4.7583  10.0280   1.5180  -1.7741 -10.7480
-#&gt;   15.7787   3.8636 -10.1122   2.0041  17.6030   9.6008 -14.1090 -12.1805
-#&gt;    9.0562  -2.3520 -12.8690  -1.0220  11.1011   4.4242   3.6283  -4.7839
-#&gt;  -18.4780   1.8691   0.9700  -8.3391 -11.2408   1.3406  -1.0684  -3.9904
-#&gt;    7.9038  -3.2055  -0.1340   0.5850   4.5324   4.2040 -13.8655  -1.9722
-#&gt;    8.3416 -12.8515  11.6022  -4.9377  -2.0272  11.0121 -10.0131   6.4627
-#&gt;    3.6874   1.2326   3.0702   2.2967   4.9829  -1.8628   5.6073   5.5645
-#&gt;   -9.9668   3.3399  11.8095 -13.4320   4.1859  13.1744  -4.0726   4.1033
-#&gt;   -0.6063   3.6117   8.5402  -1.7508  -7.5643 -14.1135   4.4667   1.8291
-#&gt;    3.7841   0.7526  -4.3858   2.5023  -2.7591   7.8405  -3.7657  -2.2848
-#&gt;    3.6828   0.2956   5.1395  -2.2088   1.9904  -1.2970   5.6008   6.2989
-#&gt;   -9.1232   4.5321   3.8092  -0.6690  -5.0277   0.8247  -1.2407   0.4033
-#&gt;    1.3944 -10.8417  10.9063  -2.3554   8.9226  -9.4533   0.4431  12.5413
-#&gt;    0.3267   5.9452  -4.8291   5.0796   9.0114   8.5521   3.0670   7.3523
-#&gt;   -9.5158   4.8945  -3.6059   1.7755   2.0108  -1.8582  10.6360   0.7403
-#&gt;    3.8182 -14.0825  10.2512  -6.7911  -4.1445  -4.4493  -6.6355  -0.4116
-#&gt;    3.0210  -6.3195  -9.4838   3.4367   2.6870  -3.3832   6.2773  -0.6656
-#&gt;   -1.4142  -2.9029   0.1764   7.9304 -11.1266  -3.8590  -6.5021  -9.9080
-#&gt; 
-#&gt; Columns 17 to 24  -4.0930  -4.5181  -5.2655   9.8320  -7.8854  -5.6521   3.1092   0.9531
-#&gt;    2.9228   1.4038   7.1894   9.1849  -0.5671  -2.6497   7.0618   1.7120
-#&gt;    9.4688   8.8127   6.1029   5.8817  -3.1110  -3.9875   4.5102   0.4198
-#&gt;   -6.6718  -7.4166  -7.0232   1.5379   4.3677  -2.2961   1.4356   1.3788
-#&gt;   -3.8511  -5.2643  -1.3943   2.9241  -9.0401  -7.7337  -8.1440   1.7110
-#&gt;   -8.6257  19.2307   7.5962   2.2189   6.4803   0.4694  -4.1282   6.1623
-#&gt;   -7.8104   3.2873   8.6170  -1.8585  10.2682   2.3056   6.6186  -2.8600
-#&gt;    4.8768  10.6870   5.3352   1.1687  -1.3124 -15.2437  -7.6791  13.5669
-#&gt;   -3.2459   2.2527   3.0650  -1.2121  -1.4278  -6.1449  -5.4305   8.4404
-#&gt;  -17.9093   4.2365   3.1774  -4.0650   5.6158   8.9471 -13.6689   9.2945
-#&gt;   11.2748  -1.4307   4.4491  -9.0105   2.2047  17.7377  12.0109   1.0471
-#&gt;   -0.0922   7.1994   6.8592 -11.3057   5.3053   3.6700  -1.1002  -5.3242
-#&gt;   -9.1647  10.3208  -1.1451   0.0316  -5.5381  -1.1451  -1.7143  -4.0003
-#&gt;   -4.9152   4.6267 -10.7798  -7.0208   7.4075   0.0483  -3.2193   4.8316
-#&gt;    1.1094   1.4675  -5.1187   2.2898   1.8867  -0.4757  -3.7803 -18.9511
-#&gt;   -2.3814   3.7498   8.4790   1.6264   1.5180  -0.6003  -2.4415  -1.3903
-#&gt;    2.9963  -0.9212   7.9276  -6.8732   4.3828  -1.3126  -3.2746  -4.2495
-#&gt;    5.8221   4.6522   0.4106   0.1016   0.5523  -0.9171   0.2275  -0.4206
-#&gt;    1.9444  -6.0020   5.8769   4.0688   5.4620  12.1884  10.6892   6.5657
-#&gt;  -11.2197   0.5472 -12.8046  -2.2224  -5.3079  -9.8584   1.3448  -9.3448
-#&gt;    3.9777   5.9299  21.6860  -2.7647   1.1157   5.0698  11.8783   6.3351
-#&gt;    8.1342  -5.8285   2.6438  -0.8756  -3.3222  -5.3593   9.2648   5.0946
-#&gt;   -4.1237   6.6831 -10.6160   7.5885   1.2249  -5.2781  -3.4212   6.2127
-#&gt;   -9.3534  10.0838   3.4102  -9.8190   6.5630   6.0582   0.3089  -7.8308
-#&gt;   -3.6003   3.9356  -6.4059  -5.0070  -0.1384   0.9920  -1.9624   5.6952
-#&gt;   -9.2499   6.2038  -7.7192  -9.3855  -5.5047  -5.6180  -5.6179   1.6925
-#&gt;    3.0892   3.7061  -5.6627 -10.3394  -3.7551  -4.0087  -8.9208  14.4189
-#&gt;    3.6990   4.1025  -1.3897  -7.2029  -1.7174  -2.7562   5.1747  -6.9339
-#&gt;    9.6255   3.7694   9.0254   1.5594   3.9277   1.5942   4.7406   8.7704
-#&gt;   -8.6338  -2.7928   1.3076   4.2683  11.5232  -0.4970  -3.7647   2.6174
-#&gt;  -12.6868 -17.6324   4.9777  -3.7318   3.4197  13.7387  -3.3937 -14.2841
-#&gt;  -12.7480  12.0771   0.4876  -5.0986   8.6861  -5.5675 -12.8265  -7.0948
-#&gt;   12.1111   5.0909   0.7583  -3.2049  -0.5450   4.7975   1.0892  -8.8651
-#&gt; 
-#&gt; Columns 25 to 32   4.3621   1.0809  -1.8063   2.5755   5.9216  -4.6670 -10.7289   0.9042
-#&gt;   10.4264  10.9042  -0.8361   1.2001  -6.2200  -2.0162   1.1667   2.6611
-#&gt;   -3.2774  -4.7125  -6.7408   6.8743  -1.0300   1.1015   7.0837   4.7770
-#&gt;   -3.1314  -1.4692  -0.4053   8.9566   2.7922   3.4076  -6.7965  -8.1493
-#&gt;   12.9294   7.8245   1.7290  -0.3596   4.1082   9.8764   5.4868  -1.6125
-#&gt;   -5.0490   2.6147  -1.5522   0.0913   3.2961 -10.7022   2.0310   5.2007
-#&gt;    1.7255  -6.8151   4.1413  12.4308 -10.5830   8.2514 -11.0222   4.6276
-#&gt;    0.9884   2.3468  12.8020  -1.1404 -10.9831   4.4680  19.4036   5.6015
-#&gt;   -4.5399  -7.1975  -1.1284   3.7308  10.7009  -0.8993  -8.8410   4.6523
-#&gt;    9.7872  -3.2337  -0.2542  -7.6435   2.3001  -1.5135  -9.1422  -4.9405
-#&gt;   -0.1627  -9.7338   4.4670   8.2372   0.6663  -7.3087   2.0563   4.5202
-#&gt;    7.3018   2.2982   7.9366   6.1666  -2.0508   1.1488   1.8407   0.2958
-#&gt;   -2.4210   2.2146 -10.8689  -9.7331  -3.0587  -4.8348  -6.8465   3.7742
-#&gt;    7.0975   0.8873  -2.1891  -1.5733  -1.7389   1.1650   3.6073  -8.7377
-#&gt;    5.5773   2.2390  -1.9577   5.4439  -6.7480  -7.7650   3.4005   3.6194
-#&gt;   19.1973  -2.6560   2.8744  -4.8762  -6.0355  12.2780  -2.6248  -5.4073
-#&gt;   -5.3426  -4.8019  -3.7196  13.0715  -0.5828  -2.3047  -7.3227  -2.8937
-#&gt;    6.4692  -1.3625 -10.9203  -5.4036  -4.2280  -9.1396   2.3956   4.9862
-#&gt;    6.5161   5.9092  -4.0407   5.6034  -0.0456  -6.9798  -6.8836   1.9076
-#&gt;   -4.3301   6.1824  -0.1439   3.9148   0.5359  14.0085  -4.7570  -0.6855
-#&gt;   -2.0723 -10.0257  -7.0078  -4.6273  -3.9611  -8.1663 -10.3759  -3.0529
-#&gt;   -4.6059  -8.5951  -0.0103  -0.1802   8.6361  -1.6766   0.6896   3.0082
-#&gt;   -1.8160   5.5170  -2.2983 -11.3040  12.6976   8.5074 -15.5927   1.9285
-#&gt;   -0.0404   3.8675  13.4591   9.6320   1.8987   5.3641  -9.0839 -13.7920
-#&gt;   -6.7456  -0.2328   1.5946  -6.3957  -6.3968   8.9422  -1.0751  -1.6686
-#&gt;    3.0861  -8.3388  -3.9782 -12.1815  -3.9396  10.5619   2.0218   5.6840
-#&gt;  -16.0454  -5.0204   5.4227 -10.2790  11.2709  -1.3906  -4.6231   7.3845
-#&gt;   -1.1110   1.2919   2.8037   7.2382  -0.3280   4.6790   3.4888  -2.8207
-#&gt;   -5.7768 -14.0873  -5.5952   5.7304  -9.2732  -2.0473   3.3029   8.7167
-#&gt;    4.3807   3.1936  13.8141  -6.0330  -8.2118   5.2851   4.4089 -10.6238
-#&gt;    2.8497   4.1798  12.8005   8.0077 -11.5207  -6.4445   3.1037   0.0139
-#&gt;    0.3044   7.2686   3.4626  -0.0363 -12.7245   0.0271   7.1017   0.8473
-#&gt;    5.0428   2.3927   4.6792   8.9717  -1.8414  -0.0304   2.4369   2.3182
-#&gt; 
-#&gt; Columns 33 to 40  -1.5760   5.3386 -14.6031  -7.6944  -0.6574   7.2257   8.2271  -1.2603
-#&gt;   -5.1075  -3.8519  -5.7879 -10.1168   4.6383  -5.8722  -6.1994  -1.0544
-#&gt;   -1.3317  11.3491   5.6024   5.6618  11.0041  -1.4766   2.8885  -1.8403
-#&gt;    0.7144  -1.4254 -11.7728 -10.7799   0.7433  -4.0738   1.4542  -5.9838
-#&gt;    0.5950   0.5638  -1.9447  -6.2701  -2.1194   6.9246  -9.1679  -2.7590
-#&gt;   -3.6279   2.6818  -0.1781  13.9888  -6.1857  -0.1197   9.5732 -11.6506
-#&gt;   -0.8700  -0.3521  -2.9208  -1.6221  10.9982 -11.3240   6.7899  -2.3066
-#&gt;   -6.2275   6.5157   4.7989  -7.7423   4.0506  -9.2199  -4.3806   5.7905
-#&gt;    6.5192   0.6419   9.0008   1.2202  -1.3320 -12.0039   1.7076   0.6866
-#&gt;    3.4017  -7.6453 -19.3289   2.5680  -1.3780   2.6319 -12.2961  -1.4990
-#&gt;   -1.4087   3.2940  -3.8308   1.8348   7.1875  -4.2201  -6.1472  -0.6545
-#&gt;   -1.3726   2.7583   7.3644  -1.9175   0.6390   0.8916  -0.7886   4.3412
-#&gt;   -5.1503  -2.9040  -2.5467   8.7303  -4.3547   5.8903  -1.3960  -8.8703
-#&gt;   10.0849  -4.2154   0.2431 -15.8185   0.0902   8.3824   5.3978  -8.2067
-#&gt;   -2.6607   5.0911   3.4083  -1.4822   9.8208  -0.2088  -4.0525  -4.7655
-#&gt;    4.1654   6.5209  -3.6171  -1.9760   7.1264  -0.8163  -7.3557  -7.9313
-#&gt;   -2.3817 -17.7056  11.1392   1.0621  16.9536   3.1658 -19.2871  -2.6297
-#&gt;   -0.9798   1.5288  11.6850   1.3760   7.5177   7.2926   0.7936  -8.7946
-#&gt;    4.0981  -4.7909  -0.9465 -22.7382   4.7601   9.9332 -13.9269   1.7551
-#&gt;   -6.9321   9.8260  -6.5586 -11.9991  -1.5948  -4.2403   8.2431  -4.9654
-#&gt;   -1.4323   7.4762  -3.4168   7.6308   2.9534   3.8559  -5.6866   8.4558
-#&gt;    3.1341  -6.9858  -2.9924   1.8577  -3.3021  -9.0978   3.1185   1.4986
-#&gt;    5.5532   7.0947  -7.5357  -3.1795   6.1115  -9.0255   2.5356  -0.7883
-#&gt;    8.5618  -4.5646   1.5088  -6.7674   8.0030   1.2573   0.3153   8.5697
-#&gt;   11.1967  -3.6168   9.1657   3.7304   1.2324   5.1916   1.8337  -6.7253
-#&gt;   -0.1202   1.4395   4.4111   1.3272  -0.5739  -0.8915   7.0042   5.7914
-#&gt;    5.3096  -4.5818   5.9639   9.4122  -5.0426   0.6176   1.7884   6.2525
-#&gt;   -6.1136   8.3116   6.4252   0.7775  12.2901  -8.8587   0.5197   1.6230
-#&gt;   -0.0787  -7.5895  -5.7253   4.7818   4.8031  -5.3445   8.7482   1.3670
-#&gt;    0.5550  -0.0760 -13.5542  -1.2079  -7.9003  -3.7844   5.1128  -1.3310
-#&gt;    0.3174   3.0956   2.4157  -5.5076  -4.6038  -3.0866   1.7106  14.1201
-#&gt;   -2.7781   0.9220  10.8284  -6.7518   0.9215   1.0806   0.1307  -2.5053
-#&gt;   -3.6079  14.5984  14.9128  -1.6049   2.3935  -6.3602   2.2049  13.9058
-#&gt; 
-#&gt; Columns 41 to 48   0.5827 -14.5320   0.4964   3.5702   2.3349   9.0861 -14.4840   3.3408
-#&gt;   -0.1507  -3.5503   3.5474   5.1030   1.9997 -14.5783   6.0471   6.3539
-#&gt;    0.9544  11.0549   6.6880   5.3812   0.1303   6.6303 -11.0309  -1.7320
-#&gt;   -7.6245  -0.0049  -0.9843 -15.7375 -11.4406  -6.2528   9.0378   1.0270
-#&gt;    1.6558  -8.9502   4.7940  -5.5731   4.1795   1.1932   2.1896   4.8853
-#&gt;   14.3003  15.9370  -2.4795  15.1712  -6.6201  11.4219 -18.5331   7.8164
-#&gt;   -1.0089   9.0866   1.9671  -1.7625   6.3674  -3.6731   8.3222 -14.2495
-#&gt;    4.1276  12.3908   9.8473  12.6976   1.5151  -5.9014  -0.2797  13.9435
-#&gt;   -3.9812   1.4039   1.5917  -1.9296  12.9255  -1.7455  -4.4085   2.9830
-#&gt;   -8.8001  -5.9129 -25.9769 -15.9085  -2.5395   1.6889   2.3261   4.3513
-#&gt;   -9.7226  13.9384   4.9436 -13.2980  -3.9781 -17.7245   5.9356  -6.5637
-#&gt;    2.6124  12.0644   7.9088   5.1791  -7.3222  -8.5355   7.5398  -4.6009
-#&gt;   -3.9850 -11.5542  -3.8048   4.4912  -1.0899   2.0877  -7.0894   3.5796
-#&gt;    1.3989  -2.9663  -0.3866  -8.6538 -14.9876   5.2110  -0.0306  -5.5487
-#&gt;    9.8539   7.9870   5.5961  -1.1400   4.6906   8.1461  -5.8389   3.4838
-#&gt;   -9.0481  -1.5539  -3.7405 -14.6683   2.1629  -6.8277   4.8991   3.9683
-#&gt;    2.0475   1.2916  -5.1039 -12.3087  12.4202  -4.1135  14.9501  -2.9654
-#&gt;   -0.1180  -1.2171   1.4894   8.7977  -7.5817  -7.4126  -3.9236  -3.8998
-#&gt;   -9.5031  -6.5820  12.2920 -10.5108  -2.8664 -20.0851   5.2814  -5.3916
-#&gt;   -0.9263  -0.6047   0.3055  16.5079  -7.1965  11.3492   9.1250  11.7718
-#&gt;   -5.2860  -8.5939   9.5510  19.4943   3.1645  -8.9477  -4.9520   1.6991
-#&gt;    1.5696  -4.5106  -7.2966  -3.0200   9.3829  -3.4478   4.5129  -2.2212
-#&gt;   -5.0658   8.0142  -6.0436  -2.5730  -4.6213   7.3504  -5.7104   6.6648
-#&gt;    8.2818  -0.4789   0.8039  -1.3135  -0.8698  -0.2130  10.8047   2.5029
-#&gt;   -0.7703  -2.8414   3.4043  -1.0950   0.4074  -6.2115  -7.6816   0.4794
-#&gt;   -2.3225  -6.0477  -2.5396   3.9355   2.4371  -7.9815   3.4472   0.6230
-#&gt;   -0.2495  18.1130  -7.7010  13.9634  -4.7141   5.6318   0.6220   5.1963
-#&gt;   -2.7155   4.0813   9.1636   2.9616  -1.6306 -16.3090  11.1759  -1.9021
-#&gt;   -0.7715   7.8429  -3.7323  -2.6672  -0.1565   1.9350  -9.1700  -3.5357
-#&gt;   -6.0522  -0.7853 -14.2814  -6.7987 -13.3946   4.0025  -3.2440   5.1645
-#&gt;    0.0312 -10.6605   7.6453  -4.8131   7.7196  -2.8358   3.9185 -11.7351
-#&gt;    2.9667   1.8414  -2.5485   3.8482  -5.4516  -2.3080  -3.5490   1.5128
-#&gt;    7.0054  10.0824  10.6791   4.1310   1.5800   4.6501  -9.3444  -8.8559
-#&gt; 
-#&gt; (12,.,.) = 
-#&gt;  Columns 1 to 8   0.4056   0.8876   5.1884  -4.4219   8.5446  -7.5393   5.3915 -23.8727
-#&gt;  -13.0179   1.5620  -3.5915  -2.7131  -3.9933  -4.3521  10.1967  -5.8456
-#&gt;    3.2014  -0.8459  -2.5785   4.5122   2.6316   2.7232   2.3129  11.8839
-#&gt;   -0.3937   7.5672  -2.6861   0.5795   3.6751   1.1882  -2.1438  -3.5030
-#&gt;   -0.1975  -3.9299   2.3365  -3.7749  -4.5440  -0.6195  -7.7874 -11.3267
-#&gt;    0.5530   4.9093  -2.2027   0.5943  15.6953 -11.2994  -2.4757   0.2925
-#&gt;    4.2426   2.2599   2.7186   2.4240  -0.7912  -0.1905   2.3372  10.3284
-#&gt;   -6.4727  -6.7757   8.8995   0.7204   7.3921 -12.8450  -2.2300   1.7367
-#&gt;   -1.2503  -3.1529  -1.7946  -3.4451  15.0746  -3.3943   5.3458   5.6590
-#&gt;   -3.0919  -3.5126  -1.1606  -6.4086  -2.9198  -0.4340 -16.4829  -4.7736
-#&gt;  -13.3170   5.6886  -7.6925  -4.1254   0.1656   6.3959   3.0047  -2.0345
-#&gt;   -0.7346   5.3046   1.7481   6.5122  -5.3944   0.3971   3.8081   2.4284
-#&gt;   -4.0138  -0.4230  -2.2615  -4.9632  11.9725   1.8523   0.6927  -2.8468
-#&gt;    2.1239  -2.7985   1.4439  -3.8698  -9.6730  -1.3662  -8.9721  -8.8285
-#&gt;   -1.8067   5.2545   2.3760   8.8494  -0.2310  -1.8642  13.9749 -12.2259
-#&gt;   -6.3340  -4.7220  -6.4573  -6.3743  -3.4688  -1.0710  -5.3870  -3.9236
-#&gt;    5.4538  -1.8066   3.6432   8.7471   9.4055  -1.0590  -4.7912   8.8082
-#&gt;   -0.3419   2.1850  -0.6104  -3.6220  -0.5109 -13.8229  -6.3621  -9.9481
-#&gt;   -6.3248  -6.5003  -9.1568   0.1463  -2.9171  -5.2785  -5.7665  -8.1459
-#&gt;   -5.1471  -1.6014   7.9769   7.2044   1.2455  -4.5790  -4.2525  -1.9137
-#&gt;   -9.4777  -2.4472  -6.5844   7.5689  -8.9626  10.1725  -0.4819   7.4391
-#&gt;   -5.0588   0.4856  -7.8680  -7.8254  -7.3603  -3.4479  -6.5787  -4.2240
-#&gt;   -6.3175   1.4367  -4.8325 -11.2935   6.9996  -7.8434   6.5713  -2.3569
-#&gt;    5.2386   2.6213   8.3586   9.5913   4.4172   5.0172   3.1231  -0.5100
-#&gt;    4.3717  -7.3292   5.4849  -4.2025  11.2254   5.6899  -6.7466   3.9096
-#&gt;    5.1047  -3.8638   0.7097  -0.7118  -4.1918  -6.2882   2.6035  -6.6067
-#&gt;    3.4798   4.5987   5.7985  -1.5775   4.2841  -0.0870  -1.3759  16.6445
-#&gt;   -2.5603   6.2796   4.8097   1.5983  -5.1292  -5.2941   3.8649   5.3747
-#&gt;    3.0247  -0.3667  -7.9915 -13.4677  -1.6558  -5.7772   2.9658  -5.5806
-#&gt;    0.2056   6.4838  -1.9876   1.5061  -2.9604   5.5792  -1.5471  -2.0118
-#&gt;    4.9678   2.0791  -3.4241   6.7106  -7.4939   5.6620  -5.5060   5.8144
-#&gt;    7.4880  -7.4016   0.9398   9.2326  -6.8488  -0.1389  -7.3953   2.5605
-#&gt;    1.8508   1.6521   9.8373   7.9543  -2.4097   1.6162   7.8636   2.1567
-#&gt; 
-#&gt; Columns 9 to 16   9.3458  -5.3383  -6.9169  -1.1179   4.6276   8.0892  -2.7098   0.7819
-#&gt;   12.7022   2.2205  -3.5277  -2.5567  -4.5709   3.4486  -5.2198  -6.7625
-#&gt;    0.4976   7.5913  -1.3450   8.1661  -3.8270   0.3464 -11.7264   2.7147
-#&gt;   -5.6262  -4.9153  -3.6763  -4.2444  -2.1447   8.1005  -6.2625   0.5630
-#&gt;    6.0183   3.8067  -3.2136   8.0925  -3.6133  -2.9097  -8.2116 -11.6465
-#&gt;   -4.2890   8.3766   1.1561  -2.8679  -0.6949 -11.2014  -1.7136   0.9300
-#&gt;   -0.1172 -12.9051 -12.0630  -7.8006   1.9388   6.8923   7.0537  -2.5409
-#&gt;   -4.3871   6.2528   5.7902   9.1694  -5.2986   0.6891   2.9813   2.6258
-#&gt;   -7.1151 -12.7987  -3.1885   8.0484   8.5721  -6.3817   4.2911  -5.3739
-#&gt;   -3.4557   2.9749  -1.6689   4.4339  -7.0525   1.3518  -3.6283   3.8076
-#&gt;    6.4681  -8.4703  10.0124  -1.6930   3.9444  -4.7536  -0.5863  -0.2673
-#&gt;    0.5470  -5.1996   6.0910  -2.3094   2.3837  -7.0642   6.9528  -8.6061
-#&gt;   -3.8783   0.6924  -6.2541   4.8215   2.7925  -2.3426  -3.5799   5.3236
-#&gt;  -11.5259  -2.2119   2.8391   4.2497  -0.7876  -5.0566  -4.1673   1.0804
-#&gt;    2.2423  -2.6770   0.2256   0.2402  -0.7686  -8.8361   3.6255  -3.2955
-#&gt;    1.2327  -2.9479   0.0185  -0.2724   3.9211  -3.8348  -2.3638   0.2193
-#&gt;   -0.1756 -14.5456  -4.2604   5.1419  -2.4055  -5.8657   4.1764   5.2482
-#&gt;    0.0354   4.8247  -3.1324  -3.1345   0.6753  -2.0915   3.5999   2.9391
-#&gt;    1.9002  -4.6962  -0.7557  14.3804  -0.8495   1.6277  -0.9069  -4.7358
-#&gt;    0.4428  -5.5897   1.1509  -1.2719  -5.6029   3.0212  -3.3915  15.5261
-#&gt;    5.0963 -14.0449  -5.1280   5.5360   4.0577   5.9124  -1.8230   4.1503
-#&gt;    6.1279   3.7011  -2.2646  -3.6412  -2.3712   3.4814   8.7167   6.6520
-#&gt;   -7.5818   1.0502   5.2112  -6.1256   3.6438 -11.4905   0.5201  -5.6950
-#&gt;   -3.8959 -11.7203   3.8930   2.9867   6.8903  -2.4377  -5.0380   6.2020
-#&gt;   -4.1643   0.5814  -1.6259   7.7678  -0.8730  -5.2856  -5.2236   1.9133
-#&gt;    0.9577   9.7130   4.4131  -6.5472  -1.0742   1.7868   1.2717  -9.1122
-#&gt;  -16.1288   1.1250  -3.3211  -4.5630  11.5378 -15.3848  10.9763  -7.7790
-#&gt;    1.3004  -7.8135   8.1722  -1.9154  -5.7940   2.4854   0.2321  -1.7326
-#&gt;    5.2010  -2.5615  -8.6069  -4.7058   4.3904   4.7591  10.5242   1.8539
-#&gt;    0.2789   6.8861   0.3526  -7.4350   1.7533   6.0150   3.4336   1.6937
-#&gt;    0.4081  -9.9380  -3.2647  -1.4525   7.3124   6.5546   5.2917  -3.0596
-#&gt;   -6.8740   6.5005   4.5562   5.4274  -1.7707  -8.7511   5.4926   1.3508
-#&gt;  -13.4634  -7.6633  -3.5374  12.9149   0.6773  -1.4439   1.7735  -2.0473
-#&gt; 
-#&gt; Columns 17 to 24  -6.6758  -2.3115  -1.8144  10.2396  -2.5704  -0.6360  -2.8433   8.0150
-#&gt;    2.7594  -2.9518   3.0638  -6.9506  -5.1608 -13.4237  -4.3250   1.6952
-#&gt;  -10.9816   5.6807  -6.0190   4.1413  10.4369  -2.1201  -5.2681  -4.5911
-#&gt;    1.2036   2.3427   0.4134   4.5637   0.0577  -0.7732   1.7811   5.2994
-#&gt;    8.7105   6.8545   3.1949   1.3937   0.0100   5.8397   4.4916  -1.8084
-#&gt;    1.6900  -2.7668 -11.1752   3.5525  -5.0963  10.1204  -4.3556  12.4704
-#&gt;  -25.7956  -0.5318  -7.2533   3.8221   4.5100  -9.7079   8.2379 -13.9655
-#&gt;   -1.8067  16.9299   0.9557  -7.1314   4.9027   7.3799   7.5181   2.8453
-#&gt;   -7.6389   2.6775  12.8119  10.6065  -0.2255  -3.9841  13.4117   5.8837
-#&gt;   14.5477  -8.9183 -11.4517  -0.4495  -7.8697   4.1780  -0.3095  -7.4357
-#&gt;    2.4193  -9.0142 -11.3824  -1.0346   6.7050  -5.0009  -2.4081  -3.8872
-#&gt;   -2.6307   7.8235   3.5044  -4.1360  -0.9632  -5.1071  14.9333  -1.4923
-#&gt;    4.6163  10.1197  -4.0531  -0.7208 -10.5970   1.5962  -1.3126   1.6901
-#&gt;    3.3039  -1.8267  -1.0020  -9.5606  -1.2304  19.0604   4.4279   3.9314
-#&gt;    6.3824   8.2006  -2.1487  -4.0053  -1.0760  -1.0433   2.5072   3.4743
-#&gt;   -4.5904  -2.0923 -13.8343  -1.4787   5.1855  -3.7473   8.5143  -5.3852
-#&gt;   10.2423  -1.5732  -1.6289  -1.0153  -1.3177  -0.1209  10.8594  -2.1458
-#&gt;   -0.6903  -7.2578  -8.4723  -6.7341   4.0499  15.7905  18.0459  17.1110
-#&gt;   -4.0052   1.0847   9.0756 -11.0888   1.8600  -3.4230   6.9516  -2.2243
-#&gt;   -9.0810   5.9553   6.0241   6.5003  -6.9839  -6.7588  -0.2573  -8.2637
-#&gt;   11.0827   0.5725   1.1194  -2.5153  -9.6254  -3.8436   6.0477  17.1165
-#&gt;    9.5964  -9.6391  -3.6196   4.7186   0.6795  -0.5874   8.5211   6.7352
-#&gt;    4.1385  -3.7619   3.2874  -3.3715   0.9727  -5.8309   8.3561   5.4229
-#&gt;    3.3422   3.3920  15.3035   4.5345  -9.3401  -8.9612   5.2024   6.5303
-#&gt;    1.3874  -8.3468   0.5307   0.4719  -1.0684  13.0017   3.5475   1.9411
-#&gt;    7.8383  -0.3872  -9.1837  -4.3832   5.5937  10.4747  -0.4994  -7.1532
-#&gt;   -4.1392  -4.2514  17.2107  -9.0837   2.2444  -5.3992   9.7609   1.6078
-#&gt;   -7.6977   2.7040   3.4015   6.7264   3.0917  -7.5491   6.1099   9.4676
-#&gt;  -10.2605  -2.6816 -15.5322   2.7011   8.7147   5.4040   7.2252   4.3530
-#&gt;    1.8864  -4.1129  -6.5493  -2.4918  -1.1677  -7.4674  -7.8565 -11.9294
-#&gt;  -12.2952  -2.0234   4.2440  -5.9217   0.7824   1.0463   2.1955  -2.6835
-#&gt;    8.1690   7.0150  -1.1805 -10.2385  -0.1367  12.9098   9.4430   0.8826
-#&gt;   -6.2213  11.5372  -6.7679  -2.3581   3.7334  -1.2013   6.2669  -3.6501
-#&gt; 
-#&gt; Columns 25 to 32  -6.1698  -1.6474  -2.2663  -1.8277  -3.1630  -7.4502  -2.5559  -3.7437
-#&gt;   -1.7781   4.0729  -3.7097  -7.5584   0.5931   9.5246   1.1157 -13.8558
-#&gt;   -1.0510   0.4607   3.5279  -2.8626  -5.0302   0.8810  12.5190  -3.4316
-#&gt;  -10.8903  -5.5081  -8.0885  -5.6893  10.7799  -1.7681  -4.2182  -5.0007
-#&gt;  -16.3544  -7.5200 -15.2437   0.0923  10.9689   3.3145 -12.8988  -4.5391
-#&gt;   -3.0638   2.4312   7.9346  -5.1953  13.5948  -5.0685   5.7843 -12.1177
-#&gt;   -1.0139  -0.1685  11.2930   2.2854  -3.4150  -4.4382   8.3275   3.7555
-#&gt;   -0.5502   4.5709   4.8062  -0.9945  -3.5451   1.9792  -5.8564 -10.2205
-#&gt;   -4.7891  -3.9065  -0.2994   5.3080   6.6401  -2.4488  -8.6320  -7.3804
-#&gt;   -7.6895  -9.5310  -2.8987   8.0264   7.8697   2.9807  -7.6261   4.7303
-#&gt;    2.1665  -4.8225   5.6164   6.4843  -1.2005   6.0761   3.9458   0.1784
-#&gt;    9.6569  -2.1744   0.6348   5.0585  -0.7696   2.5795  -3.0030   2.2954
-#&gt;   -2.8603   6.4339  -2.1674 -12.7680  12.1574  10.4178   2.3336   0.1953
-#&gt;  -10.0501 -11.5571  -7.4317   4.7009  12.5894  -6.4227 -16.1516   4.9827
-#&gt;    4.7383  -5.0773   8.4081   3.0991   0.4492   6.9855  -1.1470  -3.2694
-#&gt;    2.7560 -13.7740  -0.3506  12.0112   6.6388   2.4455  -8.2124  -7.5173
-#&gt;    6.0029  -3.0033   2.7446  11.7315  13.0676  -2.1083 -13.4809  -6.0123
-#&gt;   18.6509   6.1383   0.3154   6.7715  10.6101   2.0577  -4.9352 -16.9203
-#&gt;   -3.0511  -6.1246 -18.4137   2.2468 -16.5072   7.3207  -4.1353  -1.8045
-#&gt;    1.4086   2.1484 -10.5341   4.1937  13.5946   0.6065  -8.4692  -1.9187
-#&gt;    2.2332   8.3897   2.7779  -3.5148   6.3018  -3.6936  -0.6382   7.9078
-#&gt;   -3.1718   1.9077   8.9294   3.7708   1.7176  -0.6829   0.5946  -8.0137
-#&gt;    0.7258  -9.7872 -12.7972  -3.0090   3.9058   2.9654   0.6009 -10.3583
-#&gt;   -0.0457  -5.7633  -4.8096   9.0695  -2.5144   0.0088  -2.1138  10.0462
-#&gt;   -4.3601   8.4845  -4.9396   2.3666  14.8461  -6.3210  -8.5342   3.2413
-#&gt;    8.3900   7.6006  -2.1740   2.7758  -4.5522  -1.0440   3.0023  13.3651
-#&gt;    9.6933  -0.9344   9.3582  -6.0753  -4.2013  -0.1381   1.2334  -1.6747
-#&gt;    8.6889  -1.9448  -4.7591  -7.2180   1.0769   9.2945   1.1412  -4.4267
-#&gt;   11.5822   2.7258   9.6080  -5.0686  -5.5662   2.2223   2.7009  -3.7171
-#&gt;   -2.9917  -1.5529   3.0838  -7.7899  -6.3958   2.2141   2.6840   5.6365
-#&gt;   -7.9136   7.4127   1.1215   4.3973  -5.1366  -5.4354  -5.6012  12.8517
-#&gt;   -0.2890   2.1480   1.7537  -2.0575   9.3688   0.8108  -7.1489  -3.2080
-#&gt;    8.7259   3.3360   1.3043  -2.4074   2.3065   3.3359  -4.5746   3.4530
-#&gt; 
-#&gt; Columns 33 to 40   4.7875   6.5790  -4.1601   8.8983  -0.1766   0.5440   3.2310 -14.6199
-#&gt;   -9.7934  -9.6638  -1.5760  -1.4391   0.7987   6.0460   0.4374   3.8331
-#&gt;    0.4435   2.2971  -3.8048   2.0441  -5.7964  -7.5946   7.3700  -1.9706
-#&gt;    2.5682   3.2703  -1.9792   7.1821   5.7297   4.2228  -6.6716  -1.4583
-#&gt;    1.6816   6.3443  -0.4201   3.7339  -6.3113  -7.0601 -11.9902   3.3880
-#&gt;   -3.1707  -1.6879  -7.8794   3.2930  -3.6637  -7.9042  21.4070  -7.7597
-#&gt;    8.8373  -4.6912   9.4687  -4.3220  14.9845  14.5648   0.5939 -11.3703
-#&gt;   -0.9402   1.6948 -18.9320   0.3677  -3.2783  -5.8037  -4.8750  -3.2859
-#&gt;   -2.7005   6.2552   8.0638  -1.6799  -2.5848   8.9988  -7.3991  -3.9057
-#&gt;   -8.9849   2.3712  -1.5445  20.0429 -14.9939   8.6060   3.6730   7.0302
-#&gt;   -8.9012   0.7352   0.0109 -10.0589  -2.2522   5.5425   3.2543   8.2970
-#&gt;    0.6129  -6.6267   4.3504 -15.3576   3.1808   0.6659  -0.3630  -8.5245
-#&gt;    0.3662   3.0580   2.9308  15.6735  -1.4977   0.2091  -0.4242   7.4604
-#&gt;    3.4067   7.3226   9.2959  -5.0622  -0.1394  -2.0460  -7.2800  -3.5220
-#&gt;    3.5949  -0.1167  14.1191  -6.5329   1.1254  -0.8360  -2.5129  -9.0505
-#&gt;   -9.1575  -9.0673   7.1658  -5.8988  -6.3523  -0.3368   9.0066   1.5864
-#&gt;    9.3012  -5.6568  18.6036  -4.1611   2.7983   7.9850  -1.6222   4.7659
-#&gt;   -4.7167   0.8860   4.1913  -5.8586   2.8997  -0.8708  11.3583  -0.9688
-#&gt;    2.6125  -3.0008  -6.8674  -0.2859  -0.4529  -0.2620  -3.7536  -5.7605
-#&gt;    5.3727   3.8907   3.6569  -2.3436   1.5687   2.2128   2.0233  -3.6687
-#&gt;   -1.4914   2.0956  -0.3738   5.6077  -4.3581   1.7291  -4.6016   6.5436
-#&gt;    0.6189   3.3780   3.1212  -9.8942  -3.3944   4.8645   1.6326   4.3194
-#&gt;   -6.4454  -0.4978  -0.1666   5.1852  -0.0493   3.3605   6.9111  -0.7187
-#&gt;    3.3840  -7.7755  11.1717  -0.0513  -8.8061  -0.5524   0.0379 -15.5858
-#&gt;    7.1161   1.4466   1.9150   2.6924  -7.2405   1.0428  -2.0644   2.7381
-#&gt;   -6.4742  -3.6608   4.0766   6.0029  -3.5791  -4.4866   2.1043  -0.0723
-#&gt;   -2.0758  14.1644   0.6402  -6.6299   7.9994   5.0421 -13.6974  -9.9462
-#&gt;   -1.9586   2.3809   3.5113  -8.3938  -0.0427   4.7312  -4.3803  -2.7995
-#&gt;   10.3346 -11.2143  -1.5939   0.4156   2.4601   2.4226   3.6523  -7.8892
-#&gt;    2.5557  -6.4100  -8.4880   6.1494   2.6394  -3.7969   6.4694  -2.3941
-#&gt;    1.8533  -4.0851 -10.0929   4.4038   8.2359  -0.8261 -11.7268   4.0492
-#&gt;    0.5650  -1.0874   2.4905  -5.1572  -7.4639  -0.7059  -0.3785   0.8023
-#&gt;    8.1158   0.7698   1.4298  -3.2080  -2.8965  -4.5860 -18.1470  -3.3778
-#&gt; 
-#&gt; Columns 41 to 48   5.1382  -4.7008   4.8067  16.1457   6.4081  -9.4950  -3.6593  10.3389
-#&gt;   -2.5934  -1.0267  -6.8773  -2.1612  -0.8507  -4.9039   1.5473   1.8223
-#&gt;  -13.7937   3.6038  -5.6425 -10.9990  -8.2458   0.5081   0.8726   0.6666
-#&gt;  -18.8627   4.5665  -0.6620   5.7271   4.8852  -3.0230 -11.5730   2.6364
-#&gt;   10.2248   1.4528  -6.4034   1.8308   5.4820  -2.9191   4.8324   2.5589
-#&gt;  -20.5310   6.0017   7.6381  -8.2801   1.4457   4.9457  -1.6932   3.4441
-#&gt;   -6.9997   1.4677  -2.7549   9.9774  -1.8570  -3.0483  -6.0362   1.9534
-#&gt;    6.7322  17.2807   3.0715 -14.0406  -9.4018  13.6472   9.0784 -11.7356
-#&gt;   -8.8807  -3.3841   7.6394   1.3778  -0.5563   1.3800  -1.0330  10.0597
-#&gt;   -2.4160  -1.7038   4.0047   4.7304  -6.3882  -2.0191   0.2612  -3.4886
-#&gt;  -12.1856  -9.7394   5.5960  -7.0623 -12.9716  -6.5205  -4.3098  -4.0508
-#&gt;    0.1195  -4.2085  -4.5229  -3.7910   0.5356   5.9519   2.6234   0.3131
-#&gt;   -0.4382  -3.0816   5.6402  -1.2227  -7.2957  -6.7370  -4.0130  -1.1019
-#&gt;   -1.2263  -0.1793   2.2963  16.1276   2.0752  -2.2035  -5.2883  -8.0491
-#&gt;    1.3132   5.0426   6.4592   5.9031   6.9724   0.0573   6.5176  10.3403
-#&gt;   10.6342  -4.8031  -3.3886   8.2338  -6.2420  -2.8225  -0.2793  -6.2505
-#&gt;    3.3266   2.1537  15.1974   5.3039  -3.7460  -1.4555  17.3334   0.5410
-#&gt;   -0.2903  -0.6230   3.7239   6.3993  -1.9545   1.7957  11.4047   1.8758
-#&gt;   -6.6749  -7.8258  10.8803  -4.3231  -5.9142 -12.1221   3.2411  -5.0770
-#&gt;   15.8314   3.4944 -12.0616   6.6592   1.4056   2.9365   6.0582  -3.9810
-#&gt;  -10.6840  -2.3156   4.0229  12.2783  -8.3194   3.2800   2.2376  -1.9780
-#&gt;    2.8898   6.9111  -1.2271   8.0708  10.7419   8.2588   3.7482  10.9284
-#&gt;    2.1799 -10.8281   2.8671   2.2678  12.8061   8.5485  -5.6850   5.4894
-#&gt;   -6.8716  -6.9357  -1.7143   4.5239  -6.4940  -1.2922  -1.9443  -1.1773
-#&gt;    3.9284  -8.2330  -6.3981   3.4806  -6.4709  -0.1650   1.2969 -12.3393
-#&gt;    0.8716  -3.8836  -8.8192  -2.0444  -0.6779   7.9251  -2.0050  -6.7976
-#&gt;    1.7755 -15.7819  15.5392   1.7714  -0.2409   6.6567   1.9292   3.1195
-#&gt;   -3.6432   6.4804  -7.8211  -8.1969  -2.9857   9.3122   5.8874   1.1600
-#&gt;   -4.4012   0.2639  13.6348   7.7375   6.5981   0.6471  -5.5024   0.1783
-#&gt;   11.0860   8.6958 -11.4951  -0.8946   0.1291   0.2036  -8.1603 -11.1988
-#&gt;   -8.8414   5.2274   9.0182   7.3409  -0.9308  -1.5656   1.0939  -2.4567
-#&gt;    0.8440   5.3392   4.3979   7.7295   2.2854  12.2121  11.0583  -6.4554
-#&gt;   -4.0941  -0.5387   2.5027   4.3657   2.0060   5.7755  -2.2620   3.4619
-#&gt; 
-#&gt; (13,.,.) = 
-#&gt;  Columns 1 to 6 -7.7065e+00  3.8293e+00 -4.1844e+00  5.3806e+00  3.2864e-01 -4.1156e+00
-#&gt;   9.4233e+00  2.5763e+00 -2.2174e+00 -2.1785e-02 -8.2457e+00  6.7376e+00
-#&gt;  -8.5021e+00  1.2372e+01 -7.3538e+00  4.8703e+00  1.8759e+00 -2.0435e+00
-#&gt;  -5.0045e+00  4.0311e+00  5.8209e+00 -1.3053e-01  9.2024e-01 -2.3565e+00
-#&gt;  -2.0730e+00 -2.7477e+00  9.7954e+00  1.0454e+01  5.9142e+00  3.8032e+00
-#&gt;  -4.7104e+00  9.9648e+00 -5.1080e+00  9.8181e-01  5.5644e+00 -5.8342e+00
-#&gt;   1.5570e+01 -7.5510e+00 -2.7573e+00  6.9881e+00 -1.3875e+01  3.6826e+00
-#&gt;  -6.2798e+00 -1.1845e+01 -7.5414e-01  9.1074e+00 -1.3831e+01  5.8651e+00
-#&gt;   3.3059e+00 -5.3083e+00 -7.4946e-01  9.6656e+00 -4.1792e+00 -8.1834e+00
-#&gt;   1.0225e+01  8.0074e+00  7.4973e+00 -5.6093e+00  1.0047e+00  1.7723e+00
-#&gt;   1.0081e+00  5.3584e-01 -3.0932e-01 -6.3492e+00 -1.5246e+00  3.0512e+00
-#&gt;   2.3016e+00 -1.2941e+01  2.6686e+00 -1.0231e+00 -7.2848e+00  8.2816e+00
-#&gt;  -5.3689e-01  2.2100e+00  7.1985e+00  6.9955e+00 -8.2289e-01 -2.4764e+00
-#&gt;   1.3547e+01  3.0757e+00  2.1227e+00  6.7935e+00  5.2912e+00  5.9006e+00
-#&gt;   5.5944e+00  5.2280e+00 -9.5268e+00  3.5163e+00  3.0536e+00  2.4576e+00
-#&gt;   5.6763e+00  4.9057e+00  4.4172e+00  4.6057e+00 -1.0558e+01 -3.1791e-01
-#&gt;   4.1352e+00 -1.0130e+01  2.1542e+00 -3.1244e+00  6.1874e+00 -6.3792e+00
-#&gt;   9.0300e+00  1.0145e-02  1.6160e+00  5.5021e+00 -1.9584e+00  8.7389e+00
-#&gt;  -1.9823e+00  2.7709e+00  1.1220e+01 -2.5833e+00 -4.6002e+00  3.3313e+00
-#&gt;  -5.2313e+00  1.1840e-04 -1.5223e+00  3.6791e+00 -1.3561e+01 -3.5695e+00
-#&gt;   3.0319e+00  4.3396e+00  8.3409e+00 -5.6676e+00  5.3663e+00 -1.3685e-01
-#&gt;   1.4312e+01 -6.1780e-01 -6.2137e+00 -1.7115e+00  1.0808e+00 -4.4376e+00
-#&gt;  -6.2121e+00  9.9772e+00  3.3879e+00 -3.0445e-02  6.5858e+00  2.5029e+00
-#&gt;  -3.9355e+00  2.5479e+00 -5.6966e+00  5.9952e-01 -5.3002e+00 -5.8692e+00
-#&gt;   3.0064e+00 -4.8578e+00  8.3230e+00  6.3984e+00  2.4513e+00 -3.8871e+00
-#&gt;  -2.9204e+00 -5.9955e+00  8.6154e+00 -7.8586e+00 -2.6540e+00  1.3504e+01
-#&gt;   1.2779e+01 -1.2656e+01 -1.5251e+00 -4.0272e+00 -1.8264e+00  2.5948e+00
-#&gt;  -5.2616e+00 -4.5921e+00  1.2319e+00  4.6023e+00 -6.6667e+00  3.6150e+00
-#&gt;   1.3665e+01 -3.4437e+00 -3.6536e+00 -3.1000e+00 -2.1857e+00 -2.8452e+00
-#&gt;   1.4881e+00 -7.6835e-01  4.4153e+00 -2.4005e+00 -5.2620e+00 -2.2493e+00
-#&gt;  -3.4973e+00 -5.2465e+00 -3.4835e+00  2.3866e+00 -6.8984e+00  1.4069e+00
-#&gt;   6.7693e+00 -4.8388e+00  1.9939e+00  2.1066e+00  3.1124e+00  4.4439e+00
-#&gt;  -4.8687e+00 -5.8056e+00 -9.3937e-01  8.6580e+00 -3.3940e+00  1.8065e+00
-#&gt; 
-#&gt; Columns 7 to 12 -1.2134e+01  4.7417e+00  2.9874e+00 -1.1425e+00  1.1299e+00 -3.2048e+00
-#&gt;   1.5746e+01  3.3564e+00  3.9597e+00 -1.2897e-02  8.1200e+00  8.0226e-01
-#&gt;   7.5447e-01 -2.3681e-01  4.6980e+00  7.5715e+00  4.3496e+00 -9.6788e-01
-#&gt;  -7.5483e+00  2.6030e+00 -6.6364e+00  2.4811e+00  7.6250e+00  1.4472e+00
-#&gt;  -6.7267e+00 -4.7804e+00  5.5380e+00  3.1653e+00 -1.4827e+00 -4.6729e+00
-#&gt;   6.9764e+00  6.9598e+00 -1.5880e+01  1.5427e+00  6.3221e+00  1.7467e+01
-#&gt;   3.8674e-02 -6.4877e+00  9.1248e+00  1.3497e+01  5.3462e+00 -5.5241e+00
-#&gt;   2.4217e+00 -1.2003e+01  2.0455e+00 -5.5569e+00 -6.9024e-01 -3.1396e-01
-#&gt;  -1.8632e+01  3.7378e+00  9.6045e+00 -4.5290e+00  6.3721e+00 -6.6000e+00
-#&gt;  -1.2911e+00 -3.8949e-01 -3.5156e+00 -3.3530e-01  2.0286e+00  7.7299e+00
-#&gt;   7.5422e+00  1.7693e+01 -7.6898e+00 -7.5076e+00  2.7346e+00 -1.3289e+00
-#&gt;   8.1684e+00 -3.8706e+00  1.7923e+00 -9.5252e-01  4.8062e-01 -7.8629e+00
-#&gt;  -3.6798e+00  8.4876e+00  9.6021e+00  2.2266e+00 -4.4319e+00  6.1611e+00
-#&gt;   7.7945e+00 -5.7408e+00  4.7746e+00 -1.5602e+00 -4.9105e+00 -1.0417e+01
-#&gt;   4.6498e+00  2.8314e+00 -3.3910e-01 -8.3825e+00  6.6628e+00 -8.8876e+00
-#&gt;  -1.0526e+01 -9.2342e+00  1.0674e+01  1.8792e-01  1.0621e+01  1.2300e+00
-#&gt;  -7.0216e+00 -6.2256e+00 -6.1602e+00 -1.1519e+00  2.1074e+00  9.4408e+00
-#&gt;   7.9239e+00 -9.2378e+00 -2.0134e-01 -9.6779e+00 -7.4555e+00  4.2041e+00
-#&gt;   8.3319e+00  1.4533e+01  5.6011e+00 -9.7963e+00 -3.5897e+00  8.9471e+00
-#&gt;  -2.6386e+00 -1.1278e+00  2.8696e+00  2.9170e+00  6.3009e+00 -3.2128e+00
-#&gt;   5.5846e-01  1.1916e+01  3.8685e+00  5.4390e+00  7.4248e+00 -1.5721e+00
-#&gt;  -2.9441e+00 -6.8422e-01 -2.5737e+00 -7.6842e+00 -1.5313e+00 -6.2461e+00
-#&gt;  -6.2096e+00  1.2585e+01  8.0645e+00 -1.4160e+01  1.1424e+01  6.6587e+00
-#&gt;   2.2881e+00 -7.7080e+00  6.7623e-01  6.7294e+00  1.1748e+01 -1.4637e+01
-#&gt;   3.0370e+00 -1.6688e+00  7.6820e+00 -2.5509e+00 -1.2186e+01  1.7550e+00
-#&gt;  -1.9212e+00 -5.0295e+00 -1.1711e-01 -6.0723e+00  2.5658e+00  2.4951e+00
-#&gt;   5.4327e+00 -4.1526e+00  1.0461e+01 -7.2246e+00 -7.0710e+00 -7.8581e+00
-#&gt;   3.3833e+00  4.9301e+00  3.3079e+00 -6.6145e-01 -3.7382e-01  2.0080e-01
-#&gt;  -4.2618e+00  1.4066e+00 -5.8734e+00 -6.9847e+00 -1.3140e+00  1.3562e+01
-#&gt;  -3.9237e+00 -1.5447e+01  1.1209e+00  4.0880e+00  1.5113e+00 -4.0183e+00
-#&gt;   2.9609e+00 -7.4816e+00 -9.9399e+00  6.2400e+00  4.6403e+00 -1.1483e+01
-#&gt;   1.7705e+01 -8.5616e+00 -7.5033e+00  5.9306e+00 -6.9346e+00 -1.2284e+01
-#&gt;  -7.1122e+00 -4.2936e+00  1.4293e+01 -1.2469e+01 -4.0430e+00 -9.9753e+00
-#&gt; 
-#&gt; Columns 13 to 18 -5.6210e+00  9.1636e-01 -6.4803e+00  2.1472e+00 -8.9325e+00  3.8773e+00
-#&gt;  -6.2000e+00  2.3927e+00  8.5307e+00 -2.5127e+00  5.0501e+00  1.2578e+00
-#&gt;   7.5513e+00  7.6698e+00 -8.2861e+00  4.9058e-01 -8.5038e-01  9.1719e-01
-#&gt;  -1.0160e+01 -1.3417e+01 -1.3929e+01 -6.5216e+00  1.2762e+00  1.7770e+00
-#&gt;  -1.9829e+00 -1.0834e+01 -1.2301e+00 -1.4346e+00  7.0042e+00  3.7109e+00
-#&gt;  -1.3307e-01 -2.3318e+00 -4.0584e+00  1.1196e+01 -2.1499e+00  4.4672e+00
-#&gt;   8.9548e+00 -7.9725e+00  3.5983e+00 -3.4193e+00  5.8215e+00 -3.3851e+00
-#&gt;   4.4715e+00 -9.8394e+00 -3.4131e+00 -6.4139e+00  9.7247e+00 -4.6009e+00
-#&gt;   2.9918e+00 -1.8872e+01 -2.1612e+00 -3.6690e+00  1.8145e+00  1.4511e+00
-#&gt;  -1.1397e+01  8.7300e-01 -1.3155e+00  3.0746e+00  7.5464e+00 -1.9062e+01
-#&gt;   5.6199e+00  2.6841e+00  1.2416e+01 -5.3052e+00  2.7216e+00 -8.0156e+00
-#&gt;   3.0632e-01 -4.9654e+00  7.4011e+00 -6.6034e+00  6.6127e+00 -1.0381e+00
-#&gt;  -5.1006e+00  9.4251e+00  4.5521e+00  3.3838e+00 -2.5417e+00 -3.7484e+00
-#&gt;  -4.3988e+00  8.9085e+00  1.1022e+00 -1.5276e+00  7.1983e+00 -1.0297e+00
-#&gt;  -3.1512e+00  4.6678e+00  1.0875e+01 -8.6603e-01 -1.0298e+00  1.3839e+01
-#&gt;   5.8018e+00 -2.1430e+00  1.2154e+01 -9.8206e+00  6.4830e+00 -8.8135e+00
-#&gt;  -1.6851e+01 -9.3349e+00  8.1077e-01  2.0297e-01 -9.9171e+00 -4.7173e+00
-#&gt;   5.8795e+00  1.3149e+01  1.0228e+01 -8.0624e+00  2.7781e+00 -6.8271e+00
-#&gt;   1.8359e+00  8.0045e+00 -1.3868e+01 -5.0007e+00  3.3426e+00 -2.2927e+00
-#&gt;  -1.4279e+01  4.7050e-02 -3.0074e+00  7.7230e+00 -8.1804e+00  3.4077e+00
-#&gt;  -1.1533e+01  7.1694e+00  1.4709e+01  3.4914e+00 -7.1703e+00  7.9198e+00
-#&gt;   5.6526e+00 -1.0704e+01  8.0366e+00 -1.4451e+00  3.2774e+00 -1.4256e+01
-#&gt;   1.1856e+01  1.5885e+00 -4.8980e+00  2.7712e+00  8.8603e-01  4.7324e+00
-#&gt;  -3.2342e+01 -3.9844e+00  2.2891e+00 -6.2306e+00 -2.5554e+00 -6.4543e+00
-#&gt;   3.4698e+00  1.0134e+01 -1.5496e-01  2.5653e+00  1.8459e+00 -8.0244e+00
-#&gt;   7.4423e+00  1.1076e+01  1.0384e+01 -3.9286e+00  7.3162e+00 -1.4495e+01
-#&gt;   8.6689e+00 -2.2154e+00 -4.5210e+00 -4.4370e+00  3.1739e+00  9.2168e+00
-#&gt;   3.4841e-01 -9.9138e+00 -1.3067e+00 -1.5524e+01  2.4891e+00 -2.4170e+00
-#&gt;   1.7507e+01  1.0114e+00 -1.3986e+01  1.0684e+01 -1.4138e+00 -9.5773e+00
-#&gt;   8.6196e-01 -2.4543e+00 -8.0031e+00 -7.5923e+00  8.2234e+00 -3.4536e+00
-#&gt;   1.3415e+00 -1.9068e+00  1.3614e+01 -4.1779e+00  5.4349e+00  7.1960e+00
-#&gt;  -2.7454e+00  1.9422e+01  6.1375e+00 -5.0589e+00  4.4803e+00 -2.7960e+00
-#&gt;   3.1716e+00 -6.8549e+00 -1.7060e+00 -2.1514e+00  4.3417e+00  7.9337e+00
-#&gt; 
-#&gt; Columns 19 to 24 -6.2956e+00 -5.4901e-01  4.8912e+00 -2.0396e+00  5.5203e+00 -2.5341e+00
-#&gt;   1.8089e+00  3.6869e+00 -2.8780e+00  5.4926e+00 -1.3651e+00 -1.0444e-01
-#&gt;   9.0295e-01  1.6422e+01 -4.1254e-01 -1.2909e+00  7.4408e+00 -1.9770e+00
-#&gt;   9.6367e-01  1.4823e+00  3.0280e+00  1.0433e+01  8.3856e+00  9.6685e+00
-#&gt;  -2.2439e+00  2.4626e+00  1.1606e+00  1.7121e-01  5.3022e+00  8.9071e+00
-#&gt;  -8.2165e+00  1.1956e+01 -1.0781e+01 -1.3590e+00  7.3914e+00 -2.9975e+00
-#&gt;   1.0702e+01 -6.1904e+00  3.8413e+00  1.3440e+01 -6.1791e+00  1.1233e+01
-#&gt;   2.2866e+00 -4.9683e+00 -8.2548e-01 -9.4899e-02 -7.2741e+00  7.8572e+00
-#&gt;   5.2096e+00  1.8596e+00  4.5819e+00  1.9263e+01 -4.3795e+00  5.9414e+00
-#&gt;  -5.6940e-01 -3.6738e+00 -5.3175e+00  9.7302e+00  1.2996e+01 -8.1014e+00
-#&gt;  -2.9163e+00  9.5900e+00 -5.5392e+00 -6.5626e+00  9.2042e-01  3.6020e-01
-#&gt;   1.1700e+00 -6.8173e+00 -8.6923e-02 -2.0172e+00 -4.7332e-01  3.6847e+00
-#&gt;  -9.4815e+00  9.4299e+00  9.7430e+00 -7.4362e+00  2.4805e+00 -4.6113e-01
-#&gt;   2.8220e-01 -4.8713e-01 -2.7926e+00 -2.6140e+00  5.3441e+00 -6.0938e+00
-#&gt;  -1.1414e+01  1.5097e+01 -5.6874e+00 -8.7780e+00  1.2951e+00 -1.1591e+01
-#&gt;  -3.0670e+00  2.3485e+00 -5.4163e+00  9.3025e+00  1.6638e+01  1.1348e+00
-#&gt;   2.5218e+00 -3.1326e+00  1.3811e+00  6.0155e+00  5.3583e+00 -3.0425e-01
-#&gt;   2.1832e+00  6.7915e-01 -4.4559e+00 -5.4928e+00 -9.1482e+00 -8.1237e-01
-#&gt;   7.9414e+00 -6.1423e+00  1.0372e+01 -7.2223e+00 -1.0041e+00 -1.1848e+00
-#&gt;   7.7793e-01 -3.1167e+00  6.6862e+00 -8.2950e+00  7.2112e+00  8.4211e+00
-#&gt;  -5.1206e+00  9.7018e-01  1.4075e+01  4.6057e+00 -2.6982e+00 -6.8597e+00
-#&gt;   4.5084e+00 -4.6508e+00 -4.5140e+00  9.1297e+00 -9.8745e+00  6.6253e+00
-#&gt;  -1.1107e+01  3.0822e+00 -1.0251e+01  5.4596e+00  2.4995e+00 -4.7185e+00
-#&gt;   1.7931e+00 -2.5745e+00  7.2970e+00  2.0414e+01  1.9192e+01 -1.4639e+01
-#&gt;   9.8010e+00  1.3981e+00 -6.8298e+00 -6.5918e+00  2.6487e+00 -6.0698e+00
-#&gt;   3.1666e+00 -9.9418e+00 -3.5426e+00 -1.3412e+00 -5.6037e+00 -3.3889e+00
-#&gt;   3.0179e+00 -3.5484e+00 -4.6676e-01  1.1964e+01 -1.9321e+01 -2.0397e+00
-#&gt;   6.9145e+00  8.4699e-01  3.6754e+00 -6.4030e-01 -4.4482e+00  9.9256e+00
-#&gt;  -1.4845e+00 -6.0872e+00 -2.2835e-01 -5.1838e+00  4.2860e+00  1.1620e+01
-#&gt;  -1.0497e+00 -3.5490e+00 -3.8938e+00 -4.3489e+00  7.9926e+00  1.9135e+00
-#&gt;   2.7385e+00 -2.6755e+00 -1.0561e-01  8.6747e-01 -5.8655e+00  5.2962e+00
-#&gt;   3.5723e+00  2.5690e+00 -7.3527e+00 -8.2735e+00  1.8719e+00  1.4672e+00
-#&gt;  -1.9392e-01  2.1355e+00  3.0068e+00 -8.0364e+00 -2.4561e+00  2.8311e-01
-#&gt; 
-#&gt; Columns 25 to 30 -1.4136e+00  1.3096e+00  1.0036e+01  6.8944e+00 -9.0380e+00 -2.1814e+00
-#&gt;   1.5241e+00  2.5589e+00  2.8848e+00 -6.8126e+00  2.0813e+01  7.4431e-01
-#&gt;   2.7422e+00 -7.8634e+00  3.7194e+00 -3.1470e+00 -1.1753e+00 -2.6639e-01
-#&gt;   5.9330e+00 -5.4327e+00 -1.8925e+00  6.6899e+00  9.8232e+00 -3.7352e+00
-#&gt;   9.4836e-01 -5.3464e-01  1.5797e+00  1.0859e+01  6.6530e+00 -8.9498e-02
-#&gt;   2.7728e+00  2.5036e+00  2.5260e+00  4.1573e+00  7.1715e+00 -6.9383e+00
-#&gt;   3.8340e+00 -1.2192e+01  2.4473e+01 -5.3782e+00 -4.6436e+00 -2.1244e+00
-#&gt;   2.6798e+00 -1.1568e+01 -1.5426e+00 -8.7191e-01  9.5219e+00 -5.3673e+00
-#&gt;   7.8847e+00 -1.7421e+01  1.1517e+01  1.7837e+01 -7.1395e+00 -6.5690e+00
-#&gt;  -3.1272e+00  2.0515e+01 -7.9765e+00  1.1024e+01  8.5130e+00  1.1001e+00
-#&gt;  -2.1318e+00  3.8055e+00  2.2788e+00 -1.1583e+01  6.1026e+00  1.0842e+01
-#&gt;  -6.6398e+00  3.1049e+00  5.3960e+00 -6.0895e-01 -3.2020e+00  1.8943e+00
-#&gt;  -7.2396e+00  1.4151e+00 -1.4621e+01  4.1223e+00 -1.4123e+01 -3.5313e+00
-#&gt;   4.6681e+00  1.5608e+00 -8.3295e-02  1.4743e+01 -1.2464e+01 -5.9170e+00
-#&gt;   3.7853e+00  5.0755e+00 -1.1900e+01 -8.1328e+00  2.1676e+00 -6.5960e+00
-#&gt;   1.4073e+00  2.2650e+00 -1.7159e-01  7.3994e+00  1.2947e+01  3.4569e+00
-#&gt;   2.0055e+00 -9.6155e+00 -9.3354e+00  4.1195e+00  1.0159e+01  3.9691e+00
-#&gt;   3.8416e+00  6.3414e+00 -3.0695e-01 -4.7814e+00  6.1342e+00 -5.3269e+00
-#&gt;   3.8433e+00  5.3422e+00  5.0996e+00 -4.0477e+00  1.7228e+00  7.7069e+00
-#&gt;  -1.9073e+00 -1.0477e+01  2.6135e+00  1.2038e+01 -7.2946e+00  2.5532e+00
-#&gt;  -4.5468e+00  7.9480e+00 -1.3443e+01 -6.5495e-01  1.3479e+00 -1.6065e+01
-#&gt;   1.7537e+01 -5.7008e+00  1.0052e+01 -3.6535e-01  9.0234e+00 -3.2772e+00
-#&gt;   1.5129e+01  1.9626e+00  6.3434e+00  7.2055e+00  1.5413e+01  1.2947e+00
-#&gt;   9.1140e-01  3.0207e-01 -6.8198e+00  8.7094e+00  3.0556e-02 -1.2865e+01
-#&gt;  -2.3630e+00 -1.0100e+01  2.8403e+00  1.0398e+01 -2.0874e+01 -9.3871e-01
-#&gt;   8.6288e-01  1.6203e+01  6.9381e-01 -4.3842e+00 -1.0763e+01  1.6549e+00
-#&gt;  -1.1371e+00  2.7559e+00  4.2582e+00 -3.1801e+00 -5.0477e+00 -8.7710e+00
-#&gt;   1.8853e-01 -8.5718e+00 -9.5679e-01 -3.9834e+00  4.5526e+00  9.9315e-01
-#&gt;   1.7031e+01  7.1143e-01  3.1711e+00 -3.1865e+00 -1.0915e+01  4.7415e+00
-#&gt;  -5.4977e+00  3.6721e+00 -1.1467e+01  1.0444e+01  7.9935e+00 -5.3967e+00
-#&gt;  -3.1760e+00 -1.6745e+00  9.0577e+00 -3.9722e+00  2.2179e+00 -8.5000e+00
-#&gt;  -2.8735e+00 -8.7253e+00  1.5042e+00  2.4415e+00 -2.8710e-01 -6.1614e-01
-#&gt;  -3.7102e+00 -7.9358e-01  7.7241e+00  3.9987e-01 -7.9393e+00 -3.9693e+00
-#&gt; 
-#&gt; Columns 31 to 36  1.7236e+00 -1.5425e+01  4.6300e-01 -4.1755e-01  7.3907e+00  4.1355e+00
-#&gt;   1.1028e+00  3.2760e+00  7.3615e+00  4.1039e+00 -1.7122e+01  3.2252e-01
-#&gt;   3.3403e+00 -4.4221e-01  1.7216e+00  3.3724e+00  2.2567e+00  5.9628e-01
-#&gt;  -5.6596e+00 -1.4178e+01 -4.4832e-01  5.0828e+00  6.3958e+00  2.2786e+00
-#&gt;   4.5477e+00  2.2383e+00 -6.6557e+00  1.1015e+01  5.5971e+00 -1.9491e+00
-#&gt;   7.6030e+00 -1.0969e+01  1.9702e+01 -6.3508e+00 -1.2940e+00 -7.4034e+00
-#&gt;   2.7160e+00  3.8430e+00 -3.9399e+00  1.6613e+00  1.3658e+00  1.3324e+01
-#&gt;   1.1306e+01  8.6261e+00 -9.8411e-01  4.1573e+00 -8.1120e+00  1.8583e+00
-#&gt;   5.2886e-01  3.7833e-01 -7.1319e+00  3.3040e+00  1.3043e+01  1.1911e+01
-#&gt;  -1.1700e+01 -2.2412e-02  2.6802e+00  3.4133e+00 -7.5023e+00 -3.5183e+00
-#&gt;  -3.8157e+00 -1.1574e+00  1.0744e+00  1.0155e+00 -2.2457e+00 -5.6313e+00
-#&gt;  -6.7968e-01  8.8626e+00 -2.0492e+00 -7.5759e+00  3.7782e+00  1.4494e+00
-#&gt;  -5.8084e+00 -9.9403e-01  1.4376e+01 -1.6049e+00 -2.0299e+00 -2.2383e+00
-#&gt;   2.7821e+00  1.0083e+01  2.1219e-01 -9.5210e+00  1.0069e+01 -4.0610e+00
-#&gt;  -7.2156e-01  4.3751e+00  5.8299e+00 -1.7798e+00  2.9577e+00 -6.4626e+00
-#&gt;  -2.3103e+00  4.4556e+00 -6.5853e-01  5.8063e-01 -1.1257e+00  3.7927e+00
-#&gt;  -2.1016e+01  3.3368e+00  3.5595e-01  2.4987e+00  3.5600e+00  3.4326e+00
-#&gt;   3.4245e+00  9.3259e+00  1.0126e+01 -7.1381e+00 -1.4821e+01 -3.7074e+00
-#&gt;  -5.1615e+00  5.0730e+00 -1.1352e+01  9.1521e+00  3.3543e-01  7.4697e+00
-#&gt;  -3.0265e+00 -2.3837e+00  1.3253e+01 -7.4806e+00  9.3693e+00 -9.3990e+00
-#&gt;  -9.0115e+00 -3.1332e+00  1.7064e+01 -7.0058e+00  1.2841e+01  3.3063e+00
-#&gt;   1.1065e+01 -8.7549e+00 -6.3998e+00 -3.1083e+00 -2.2560e+00  2.4060e+00
-#&gt;   9.7931e+00 -3.8464e+00 -8.3139e+00 -3.1331e+00  9.9517e-01 -2.3847e-01
-#&gt;  -2.4153e+01  2.4549e+00  9.7826e+00 -2.6141e+00  1.8594e+01  7.9493e+00
-#&gt;  -2.9398e+00  4.4437e+00  6.3044e+00  2.1433e+00 -1.9960e+00  2.2209e+00
-#&gt;   4.4625e+00 -3.2650e+00 -6.0303e+00 -9.3335e+00 -1.1450e+01 -2.0169e+00
-#&gt;  -8.3086e+00  1.5343e+01 -1.1622e+01 -4.6021e+00 -9.5455e+00  1.3966e+01
-#&gt;   6.3554e+00 -4.5630e-01 -2.5583e+00 -4.1765e+00  2.7036e+00  4.3401e+00
-#&gt;   4.6608e+00 -3.3992e+00 -6.4445e+00 -1.1970e+01 -8.8417e+00  1.7842e+01
-#&gt;  -1.2741e+00 -9.4054e+00  6.7515e+00  2.5904e+00 -5.3902e+00  4.1182e+00
-#&gt;  -9.3007e-01  7.0389e+00 -4.8460e+00  3.5187e+00  4.5959e+00  2.6910e+00
-#&gt;   8.7707e-01  1.2004e+01  1.1852e+01 -1.2016e+01  2.7552e-01 -7.2159e+00
-#&gt;   9.4460e-01  9.5357e+00 -8.0741e+00  4.4409e-01  1.0114e+01  9.1878e+00
-#&gt; 
-#&gt; Columns 37 to 42 -6.3024e+00  9.2331e+00  2.1053e+00  8.1298e+00 -4.4395e+00 -1.3394e+01
-#&gt;  -1.0186e+01  4.1213e+00 -6.6050e-01  7.9117e+00 -8.1772e+00  1.0104e+01
-#&gt;   5.6658e+00 -4.5996e+00 -2.2227e+00  5.7552e+00  2.2246e+00  1.0713e+00
-#&gt;   1.7547e+00  3.9077e+00 -1.4328e+00  2.3279e+00 -1.9161e+00 -9.1311e+00
-#&gt;  -7.9968e+00  1.2395e-02 -2.0124e+00 -6.4250e-01  2.7983e+00  3.8626e+00
-#&gt;   4.4637e+00 -5.3501e+00  7.7467e+00  8.4858e+00 -2.3013e+00 -9.2966e+00
-#&gt;  -3.3978e+00 -5.0804e+00 -1.1221e+01  4.3189e+00 -2.8538e-01 -1.1074e+00
-#&gt;  -3.1456e+00  4.0720e+00 -6.7001e-01 -8.1662e+00  1.7786e+00 -1.7582e+00
-#&gt;  -8.5821e+00 -9.8434e-01  1.2431e+00  5.1320e+00  2.3866e+00 -5.2160e+00
-#&gt;  -1.2199e+01 -8.8500e+00  7.5428e+00 -1.8629e+01  3.9852e+00 -3.2104e+00
-#&gt;  -1.0143e+00 -6.4449e+00 -1.0971e+01  1.0701e+01  5.3938e+00 -1.6339e+00
-#&gt;  -5.7324e+00  3.3589e+00 -4.1978e-01 -1.9088e+00  2.9041e-01  3.9577e+00
-#&gt;  -5.4446e+00 -9.8014e+00 -2.8755e+00 -6.3510e+00  1.8491e+00 -1.0996e+00
-#&gt;  -3.9270e-01 -2.6333e+00  6.3313e+00 -1.7696e+00  6.2944e+00  1.8212e+00
-#&gt;   5.2217e+00  4.3818e+00 -3.2333e+00  1.5549e+01 -1.3073e+01  5.6230e+00
-#&gt;  -4.1843e+00 -1.0650e+01 -8.1104e+00 -1.3741e+00 -5.7625e+00  1.1042e+01
-#&gt;  -6.2159e+00  5.7307e+00  3.7206e+00 -3.4317e+00 -3.8483e+00  3.9160e-04
-#&gt;   1.5918e+00  7.8795e+00  5.3794e+00  2.8602e+00  2.8072e+00  1.0967e+01
-#&gt;  -3.3718e+00  6.6019e+00  2.5650e+00 -2.2115e+00  2.3895e+00 -4.8804e+00
-#&gt;  -8.2816e+00 -1.0824e+01  3.4398e+00 -7.0828e+00  4.6975e-01  5.0292e+00
-#&gt;  -3.0797e+00  6.9775e+00  8.3985e+00 -2.1313e+01 -5.5539e-01 -1.5363e+00
-#&gt;  -1.6065e+00  2.9044e+00 -9.1959e+00  3.0475e+00 -3.0167e+00  2.3876e+00
-#&gt;  -8.5703e-01  1.4794e-01 -2.6393e+00  4.2725e+00  1.2049e+00  4.1360e-01
-#&gt;  -2.3454e+00 -1.8672e+00  2.1116e+00 -1.8034e+01 -8.1080e+00 -1.1363e-02
-#&gt;  -1.2415e+00 -5.3392e+00  7.1647e+00 -1.7568e+00  7.0321e+00  2.8003e+00
-#&gt;  -1.5237e+00  7.9583e+00  6.5890e+00 -1.2983e+01  8.7296e+00  9.4606e-01
-#&gt;   9.7275e-01  7.9603e+00  1.7402e+01 -2.8543e+00  9.5210e-01  5.1784e+00
-#&gt;   1.8857e+00 -1.0835e+00 -6.7855e+00  2.8129e+00  3.8306e+00  2.2866e+00
-#&gt;   1.5935e+01  5.0615e+00 -1.6203e+01  5.2092e+00 -2.6523e+00 -1.4275e+01
-#&gt;   5.6711e+00 -2.6921e-01 -2.0150e+00 -6.0248e+00 -1.0321e+01  3.2117e-01
-#&gt;   6.6948e+00 -1.0014e+00  1.8786e+00 -1.9562e-01  1.9119e+00 -8.0191e+00
-#&gt;  -2.0787e+00 -8.8649e+00  1.1713e+01 -5.2966e+00  1.0532e+01  2.7974e+00
-#&gt;   3.3563e+00  8.4151e+00 -1.6328e+00  1.0389e+01 -2.2788e+00 -8.4337e+00
-#&gt; 
-#&gt; Columns 43 to 48  4.2203e+00 -1.3882e+01  7.3686e+00  4.3402e+00 -6.4337e+00  2.4311e+00
-#&gt;   5.5295e+00 -8.4421e+00  1.2677e+01 -9.7654e+00  5.8002e+00 -7.6111e+00
-#&gt;  -2.1324e+00  3.9120e+00 -1.0768e+01  7.1645e+00  8.4131e+00 -1.8667e+01
-#&gt;  -1.4008e+00 -6.2520e+00  6.2151e+00  1.7621e+00  9.4807e-01 -2.2960e+00
-#&gt;   2.1902e+00 -4.4769e+00  3.3299e+00 -8.1230e+00  4.4334e+00  3.8076e+00
-#&gt;   1.3286e+01 -1.0599e+01 -6.4395e+00  1.3741e+01 -1.0640e+01  3.6305e-01
-#&gt;   4.3350e+00  4.5732e+00  2.0489e+00  5.3847e+00  5.3636e+00 -8.9669e+00
-#&gt;   1.6071e+01  1.8958e+00 -5.9123e+00 -6.1808e+00  8.2521e+00 -1.1546e+01
-#&gt;   6.7451e+00 -5.4566e+00  1.0220e+01  5.6496e+00  1.7268e+00  5.1072e+00
-#&gt;   4.3090e+00 -8.6832e+00  3.5267e+00  2.0816e+00  4.8520e+00  1.4619e+01
-#&gt;   8.9568e+00 -2.7768e+00 -2.1286e+00 -2.4006e-01  1.4564e+00 -1.1411e+01
-#&gt;  -2.7915e+00  3.8757e+00 -1.9125e+00 -5.1591e+00 -3.6256e+00 -5.6638e+00
-#&gt;   8.4375e+00  4.2035e+00 -3.7574e+00 -2.4581e+00 -9.8935e+00  1.1406e+01
-#&gt;  -7.8671e+00  6.4153e+00 -1.9498e+00 -7.2003e+00 -5.0332e+00  9.4496e+00
-#&gt;   4.1759e+00 -5.3868e+00  8.5182e+00 -4.8315e+00 -3.9942e+00  9.3226e+00
-#&gt;   6.5260e+00  5.8214e+00  5.1324e+00  7.3820e+00  8.5672e+00  4.6077e+00
-#&gt;   1.5087e+00  4.0073e+00 -3.4293e+00  1.9788e+01 -3.6838e+00  6.8749e+00
-#&gt;   5.2789e+00  8.9882e+00 -6.5267e-01 -3.8636e+00  9.1428e+00  1.4188e+01
-#&gt;  -6.1409e+00  2.0553e+00 -1.0121e+01 -1.3175e+01  4.2167e+00 -7.2906e+00
-#&gt;  -4.0417e+00  1.2934e+00 -1.6142e+00 -1.0782e+00 -1.9889e+00  1.0359e+01
-#&gt;   2.1199e+00 -1.4933e+00  7.7877e+00  8.9683e+00 -1.7970e+01 -1.5169e+00
-#&gt;   2.8272e+00 -1.0402e+01  9.0713e+00  2.3029e+00  1.1358e+01 -2.7348e+00
-#&gt;   5.1348e+00 -1.8781e+01  8.7002e+00 -7.0015e+00 -2.5072e+00  4.2827e+00
-#&gt;  -1.9633e+00 -1.1393e+01  1.2000e+01 -5.8304e-01 -2.2610e+01 -1.5226e+00
-#&gt;   5.0294e+00  6.3532e+00  1.5947e+00 -3.0987e-01 -7.7456e+00  3.8887e+00
-#&gt;   1.3794e+00 -8.8035e+00  2.4881e+00 -1.0528e+00  3.2560e+00  8.5054e+00
-#&gt;  -1.6516e+01  1.4994e+01  2.9052e+00 -8.3299e+00 -2.3326e+00  2.1180e+00
-#&gt;   5.6776e+00 -5.4202e+00  2.6331e+00 -5.9035e+00 -1.7268e+00 -2.6480e+00
-#&gt;   9.7392e+00  8.2860e+00 -1.0905e+01  1.2701e+01  5.4691e+00 -1.4955e+01
-#&gt;  -6.6906e+00  4.4519e+00  7.0060e+00 -8.9896e-01  2.3385e+00  5.5591e-01
-#&gt;  -7.1877e-01  2.7173e+00  8.2011e+00  4.2681e+00  3.5827e+00  6.8828e+00
-#&gt;  -3.7042e+00 -2.9605e+00  3.0238e+00 -2.7666e+00 -1.8268e+00  1.2268e+01
-#&gt;  -3.0747e+00  5.9238e+00 -1.8271e+00  5.8801e-01 -1.1549e+00 -5.4681e+00
-#&gt; 
-#&gt; (14,.,.) = 
-#&gt;  Columns 1 to 8  -3.5164   3.6367  -1.4160  -4.0975   0.9639  -1.8462  -4.1789  14.6930
-#&gt;    8.0072   1.7503  -5.3146  -0.4204   6.8764   2.0414  -7.9524  12.2324
-#&gt;   -6.5291   4.9946  11.3336 -10.5581   3.8569  -2.2844   6.3746 -12.4604
-#&gt;   -0.2242  -7.5017   4.9436  -4.6596   1.2145  -3.1558  -5.9316  -7.2928
-#&gt;   -3.2594 -17.0785  -0.1295   7.0709  -1.2688  -2.8494 -17.2709   1.2272
-#&gt;   -3.6998   2.7234  16.0321  -9.7121  -4.3751   0.6424  -3.0030   0.9900
-#&gt;    5.3667  19.4733 -13.2158  -4.4837  -2.9292   6.2776   2.8057  -9.2579
-#&gt;    1.9085   0.7961  -5.1424  -4.7352   9.4109  11.1137   8.0715   4.3164
-#&gt;    3.9786   9.1573  -1.5915  -3.9721   6.5853   2.8105  -5.1650  -2.2983
-#&gt;  -12.3772 -11.9594   8.8969  -2.5702  -0.6411 -12.2294   0.1368   5.2428
-#&gt;    3.9994  -1.4334  -2.4016   6.7978  -0.2643   2.6654   1.4299   2.0340
-#&gt;    6.4346   1.4756 -16.8006  13.6452  -3.1047   6.1899  -0.4337  -2.4797
-#&gt;    1.3019  -5.3739   3.7125  -8.4061   1.8571  -3.0144  -5.0489  -6.4340
-#&gt;    2.6067 -13.4311   4.8190   4.0239  -5.6464  -8.3670  -8.2846   1.3341
-#&gt;   12.8636   1.6068  -3.2822   1.4247  -3.1683   6.0716  -5.8441   1.8720
-#&gt;   -3.9771  -4.5414  -7.1822   1.4133  -5.0695   2.8305   0.8321   5.4292
-#&gt;   -8.7474   3.8713  -4.9234  -1.8103  -4.3676   3.7990  -0.6306   4.0374
-#&gt;   -2.2389  -5.2256  -1.5404   2.2848   0.1737  -1.1414  -4.0227  12.1833
-#&gt;  -16.3646   5.3910  -5.6977   3.9520  -0.2996  -2.2186   1.1758   3.6029
-#&gt;   -1.4915   4.4172 -12.6648  -5.8431  14.2555 -17.7738   5.2982  -0.2712
-#&gt;    9.5657   0.9618   3.1295   5.1415   1.7033  -1.7185   8.3103   5.0772
-#&gt;   -7.8955   0.0624   0.6347   0.0188   1.8245   6.8816 -11.9790   3.7026
-#&gt;   -8.0121  -8.1253  -1.6098  -6.8500   1.1229  -8.2504  -7.6292  -1.3854
-#&gt;  -13.6882   2.5464  -0.5131   5.9562   0.3413  -1.6047   4.9369   2.8244
-#&gt;   -0.8381  -2.8790  11.8195  -2.7638  -0.3576  -2.8189  -6.8700  15.3085
-#&gt;    4.1169  -8.9967  -3.9228   3.7318  -8.1387  -1.1206  -5.4818  11.2950
-#&gt;   17.6907  -2.5866  -9.2065   3.2079   5.3877  -5.3042   5.6182 -12.9487
-#&gt;    1.2963   0.2776 -15.7464  -2.8772   9.8389   8.3356  -3.3693 -11.2286
-#&gt;  -12.8907  10.6822  -4.2554  -2.8740 -10.4444   7.7742   0.7778  -7.8733
-#&gt;    4.1566  -4.2775   2.0939   4.0822  -4.2914   7.1006   3.8153   0.7882
-#&gt;   13.3020  11.6568   4.1307   6.7648  -8.1812  -2.1688   8.4480   3.3808
-#&gt;    7.4760  -1.4232   0.2086  -0.3338   0.8910  -2.3695  -5.3699  -3.6025
-#&gt;    9.5920   3.1115  -6.3598   1.7514   9.8004   3.6639  -0.9600  -0.5205
-#&gt; 
-#&gt; Columns 9 to 16  -5.5929   4.5618  -7.4927   3.4289   1.3331  -1.7752   8.4566   4.9757
-#&gt;   -1.4229   8.0559  -2.0782  -0.2723  -4.6004 -10.1408  -2.5770  -3.9344
-#&gt;    4.6210  -5.9105   3.5841   0.9493  -4.5401  -0.8149  -0.5413   2.2125
-#&gt;   -8.3523  -4.9837  -7.9866  -6.6378   3.4828   0.2880  -2.5459   1.0412
-#&gt;   -4.4225  -6.1601  -5.7446  -1.5877   6.6126   1.2816  -1.2225   0.1413
-#&gt;   17.8076   4.7270   5.2505  -0.2464   1.0499  -5.7317  -1.9935   2.0331
-#&gt;   -1.9122   0.8349  13.8861 -14.5385 -16.2474  -4.2714  -3.9231 -16.5673
-#&gt;    8.8594   8.3186   6.1188  -1.3800   4.7407   0.4451   0.6599  -3.3538
-#&gt;   -2.1891   1.3881  -2.9810  -5.0258  -3.2415   8.4750   1.0803 -12.1283
-#&gt;   -2.8790 -10.9584 -13.3650   3.9468  -5.4824  -4.4396   0.1894  -2.3919
-#&gt;    3.9129   8.4664   0.1469  -1.5449  -8.9002  -5.8506   3.5564  -6.2270
-#&gt;   10.5509   6.4026   8.1164  -7.1577  -8.1755   1.8608   3.8171  -5.3652
-#&gt;    2.9167  -5.9860 -16.2346  -6.8949   5.6491   0.0032   0.0876   1.4090
-#&gt;   -3.8093  -7.6199 -12.6911  -3.6044   8.7720  -0.7719  -0.3206   5.5415
-#&gt;    6.0354   5.5389  -1.7169   2.3942  -1.3202   4.5369  -3.1961   0.7814
-#&gt;    0.9562  -4.9363  -0.4958   0.2276  -4.3065  -7.4811   0.3841  -6.8021
-#&gt;   -7.1752   6.6296   7.2296  -0.8938   1.0353   4.1042  -4.5165   0.3020
-#&gt;   -1.7306  -5.3993   0.8894  -2.0682   0.7556  -3.6872  13.5828  10.2769
-#&gt;  -15.3653   6.0799  -7.6907  17.1634 -10.9158  11.9625   1.6894  10.3460
-#&gt;   -3.7587   4.3064  -5.2800   8.3357  -2.6197  -6.0977  -5.3347  11.0742
-#&gt;   -7.9770   6.8166  -4.3991  -1.5339   5.7006 -14.2219   7.4062  -2.6463
-#&gt;   -7.9111  -3.9378   8.9507   1.3088  -1.5346   5.3095   3.4546 -11.2990
-#&gt;   -5.0476  12.0075 -15.9090  15.0254  -7.8414   7.0417   4.3592   4.2804
-#&gt;   -3.5964   7.4462 -15.3866   4.5328  -9.8158  -0.5236   4.2729   1.9904
-#&gt;   -5.0842   0.9961 -12.6483   2.9322   6.0953  -1.5123  -0.2941   5.6668
-#&gt;   -4.5444  -3.7754 -12.9401   7.0178  -0.8779   0.8966   4.1001   7.0043
-#&gt;    1.8707   1.3767   3.9174  -7.6209   4.9833   2.3590   7.8729 -11.0369
-#&gt;   -7.2535   3.8344   4.8637  -5.9557  -7.5566   4.7398   7.4064   0.2341
-#&gt;   -5.2053  -1.2035   6.2022  -2.5401  -3.0806  14.0859   1.4470  -6.6461
-#&gt;    4.2303  -1.2518   1.1730   0.5567   1.2015  -4.7660  -3.9524  -6.8054
-#&gt;   10.6729  10.8136  -0.4903  -1.2082   2.9729  -6.6538  -2.6711 -12.5401
-#&gt;    6.6330   6.9785  -8.7683   5.3702   3.9900   4.0154  -2.0155   2.9895
-#&gt;   18.9901   8.7432   0.9028 -10.9142   4.7474   1.6564  -5.8684  -8.2884
-#&gt; 
-#&gt; Columns 17 to 24  -5.3790  -0.9855   5.6169 -18.2759 -16.3836  -4.9224  11.3363   4.5390
-#&gt;   -8.1771 -11.7430  24.4947  11.6503  -3.2695  -0.3118  -4.5503  -6.8570
-#&gt;    5.3822   3.5684  -2.8405  15.8247  11.7706   7.9169   3.1145  -5.3061
-#&gt;   -1.7656  -7.2537   3.8744  -6.4333 -14.9469 -12.0526  -4.8421  -4.6510
-#&gt;   -1.6633   6.9774   4.3620 -22.2809  -6.1096   8.8112  -2.4306  -2.8509
-#&gt;    3.8061  -5.2728  -2.7712  16.1335   0.1281  -6.5065  -5.7664   0.5802
-#&gt;  -11.4734  -5.6398   4.3297   2.3805  -6.5976   0.7871   7.6001   7.9596
-#&gt;    6.5978  -2.2852   7.8332   3.0658  -2.5338   5.4978  -7.6666   1.3631
-#&gt;    9.1997  10.0393   6.3128  -7.0310  -4.6799 -11.0419   5.8593   1.0575
-#&gt;   -5.5663 -12.0870  -2.0860  -4.0233 -14.2020   3.5947  -0.3166   6.1554
-#&gt;    2.5222  -0.8534  -2.3841   7.6700   9.2888   6.1199   3.9201  -4.3974
-#&gt;    0.4061  -4.5200   6.6921  -0.4338   2.7219   3.6859  -2.1517  -2.8815
-#&gt;    0.1721   8.0156   6.2377   5.3039   0.2567  -2.9417   2.7859   2.6194
-#&gt;    4.6029  12.2062  -7.1125 -24.9851  -6.7922 -11.5757   8.9138   3.3311
-#&gt;   -2.3171  -8.9713  15.3642  -9.0105  -0.6402  -4.4201   1.7331  -2.1492
-#&gt;   -3.4820  -2.3812   6.2440  -4.5449  -3.1354   1.6829   6.4353  10.3905
-#&gt;   -3.4292   1.9171  -7.6198  -3.5421  -8.6754  -6.7137  -3.3883   2.7849
-#&gt;    5.9605   2.2090   6.4053  -8.7386  -9.4234  -2.2131   3.2512   3.9714
-#&gt;    2.7371  -0.4413  -4.3493   3.6882  -7.1502  16.4328  -9.1946  -5.2900
-#&gt;   -6.6086  -2.2985   5.1128  -6.4429  -5.5370  -6.4827   3.5535  10.9970
-#&gt;   -1.4213   5.3383  13.2044   9.5259  -7.0969   3.8296   5.8712  -0.0791
-#&gt;   -0.1843  -4.3418  -9.8389  -0.4002  -4.4666  -0.0591  -0.6841   4.7968
-#&gt;    1.3424  -9.0016  17.1681  -0.0714  -2.2550  -1.8366 -10.7331   2.3398
-#&gt;   -3.4669  -3.7810   9.7367   6.2144  -8.5013  -1.7241   1.4585  -1.6565
-#&gt;    0.9119  20.5850 -12.5548  -8.3888   8.9931  -3.6125  -0.5737   0.1818
-#&gt;   -0.3500  -1.0129   4.0909  -6.4882   3.7918   4.0418   4.3591   9.7403
-#&gt;    7.6679   5.4363   6.3729  -3.8852  -8.5735  -3.7597   2.9544 -10.1028
-#&gt;    3.8585  -4.8431   2.9133   6.5957   5.7623  -1.4144  -1.6764  -3.7551
-#&gt;  -10.3585   7.1357  -0.5083  -5.1656   8.7244  -0.2630  -6.0859   6.4932
-#&gt;  -10.3344 -13.3026   1.7382  -8.9908   4.4260   0.2269 -10.4719   2.8067
-#&gt;   -5.2721   1.6589  -3.6540  -6.3139 -10.6639   2.5981  -1.1077   4.6284
-#&gt;    0.1581   8.2012  -6.9604   0.7550  -6.3239  -3.4114  -1.5317   1.1282
-#&gt;    9.0149   3.8501   4.8679  -8.5945  -5.5783  -3.9184   2.4521  -0.5970
-#&gt; 
-#&gt; Columns 25 to 32  -6.2210  -1.0632  10.6254  -6.8330  -2.1937  -2.1070  -2.5554   6.8722
-#&gt;   -1.0755   2.9137  -1.1398   4.4118   0.4758  -6.5798  -7.4432   4.2671
-#&gt;   -6.8829 -10.1002   0.4902  -5.0363  -0.5875   7.0966  -4.4420  -6.2689
-#&gt;    1.2166   5.3162  -7.1506  -1.8251   4.4946   3.9062  -2.0706 -11.1943
-#&gt;   -3.4154   2.4285   5.7348   6.8020  -1.8707  -3.3322  -4.8005   2.7156
-#&gt;    4.6751  -0.2491  -5.5887  -6.7336   6.1668  12.6504  -6.1129  -9.9088
-#&gt;   -8.5144   3.9564   6.4367  15.3100   2.9594   1.7147   1.4241  -1.4055
-#&gt;    4.1286  -3.8172  -1.0225   0.5144  -3.4679  -2.8533  -4.6758  13.4229
-#&gt;  -20.1642   5.3597   8.0426  -1.8678  -1.5061   1.4953  -5.5915  -8.2865
-#&gt;   13.8842   8.3924   7.1239  -1.0169  18.4628   6.7325   5.0013  -1.5284
-#&gt;   -2.8245   9.7516  -0.6588 -12.6353   1.0765   8.4742  -1.4553  -7.4094
-#&gt;   -8.3626  -1.2321   1.5658   2.4255  -4.7763  -4.5383  -3.5917   2.8955
-#&gt;    8.8844  -1.9101   6.7190   2.6483  -0.8942   2.6923   7.1067   0.1883
-#&gt;    4.8005   5.2435   3.4225   5.1251  -0.9953  -5.6484   1.7879   1.9805
-#&gt;   -6.1791   0.3935   7.5717  -4.5425   1.9090  -4.3953  -7.5359   3.8934
-#&gt;   -3.0174  -3.3863  15.0364  10.1912   9.1169  10.0399   3.1390   8.6105
-#&gt;    1.9042   3.9829   2.1027 -10.7210  12.0177   9.4682  -5.1764  -2.2131
-#&gt;    1.6847  -9.2166  -0.4113   6.9802  -0.2831   4.9070   4.3973  15.3852
-#&gt;   -1.8165  -2.4708   7.5908 -11.3678  -6.8742  -2.5030  -3.5433   3.1933
-#&gt;    5.3460  -1.2400   5.8465   7.4548  11.5973  -9.3113  -6.7320   8.4282
-#&gt;   -1.0264   8.4869  -1.5835 -11.1812   4.5130   2.9057   2.2435   6.4795
-#&gt;   -1.3267  17.5572   5.7703   2.1952  -0.6874   8.1275   3.7612   7.8429
-#&gt;   -8.5016   7.3364  13.2270  -0.3477   5.2222  -2.9413   5.6974   2.0749
-#&gt;   -2.6074  -2.3159  -5.6692  -6.5075   7.9999 -14.8871 -14.4097   0.9635
-#&gt;   -1.5115  -5.5099  -3.9575   1.9949   0.3257  -4.8133   4.6057  -0.6521
-#&gt;   -1.7757  -1.1523  -1.7907   2.6488  -2.5109  -2.3777  14.5466   5.7118
-#&gt;   -4.5718   1.5242   0.0595   1.1810 -10.0224  -7.1540  -1.6676 -10.6580
-#&gt;  -13.5309   2.4733  -2.5426   3.5009   2.3707  -4.5183  -1.3079   0.9009
-#&gt;    4.3232   3.3634  14.1748  -3.0300  -5.1103   8.2594  15.8045  -0.0937
-#&gt;    9.2340 -10.8620  -7.4813   5.7556   4.8324  -0.7097   1.2115  -2.8395
-#&gt;    3.7414  -0.0898 -16.7507   3.4193  -1.0159  -4.9505   0.0832   4.8839
-#&gt;    0.1538   3.1175  -2.9419   1.4508   5.2871  -5.3669   7.3249   5.6258
-#&gt;   -8.2996  -5.2832   5.0452  -7.3682 -10.2540  -2.2762  -6.3729  -4.8946
-#&gt; 
-#&gt; Columns 33 to 40  -6.8907  -4.3424 -18.6564  -6.5747   0.8499   1.4973   0.8559  -4.6112
-#&gt;   -5.4620  15.6065  -1.0627   9.9078  -4.6156  -2.2212  -4.7855  -4.3044
-#&gt;   -8.9469   3.5484  -5.9735   1.9080   4.8943  -2.0781   2.3372   0.0882
-#&gt;  -11.6367  -7.5656 -14.1537  -6.2036   1.9798   2.4618   0.6554  -0.1926
-#&gt;   -8.5727 -16.3754  -9.5505   2.5258  -1.8459  -1.2091   1.5882   5.8439
-#&gt;   13.2425   0.0354  14.7705  -3.1169   5.5386  -9.3187   7.8481  -2.1047
-#&gt;  -13.0168   4.2000  -8.1509   8.2615   1.3545  -0.5269   4.9521  -7.8099
-#&gt;    2.7142   7.6584   9.0400   1.1934  -3.6597 -11.4678  -7.9291   0.4130
-#&gt;    0.1426  -1.1059  -0.8155 -11.0661  -1.4351   4.7290   3.9507  -1.7935
-#&gt;    7.5459  -2.8065   0.9977   4.9174 -15.0465  -4.2354   6.5824  -0.1017
-#&gt;   -6.4125  16.2317   6.7146  11.2386   9.7810 -10.3435  -0.4031  12.6095
-#&gt;    2.2521   1.2397   1.3851   7.6104   2.4934   1.9724  -9.7187   7.1258
-#&gt;    1.9720  -3.0106   6.8993  -4.4195  -3.1432  -4.2201 -13.7608  -0.3804
-#&gt;    1.2371 -18.4614  -3.6686 -12.1953   4.9874   9.9031  -4.6036  25.0248
-#&gt;   -0.6606   5.4327  -5.6909  13.7630   6.1328   0.8239  -9.8001   9.5904
-#&gt;   -4.0075   9.4304   1.0257   8.1408  -6.9734  -3.4550  -3.7521  10.5251
-#&gt;    4.5512  -5.0705  -1.1216  -1.6832   0.5390  -6.0689  -9.5145  -1.3415
-#&gt;    7.7977   0.1589   1.9629  -4.2962   0.3114   3.9007   2.1996  19.4179
-#&gt;  -22.0518   0.1199 -11.0530   5.1047   9.0494  -0.9638   0.3279   5.1530
-#&gt;    5.0492   2.4547  -1.0361 -14.1092  -6.5352  -0.1912 -13.2664   2.6271
-#&gt;    9.6594   3.4574  -1.9525   0.7571   7.5191  -2.5348  -7.1019 -10.1315
-#&gt;    7.7013   4.9133  -1.7829   6.6154  -2.2435   1.5807   9.1597  -8.4393
-#&gt;   -4.0932   2.5416   2.0889  -3.7209   1.8058  -6.1362   5.0166   1.5836
-#&gt;    1.8950  -3.0127  -7.9712  -3.2187  -8.8078  -2.5009 -14.2962   1.2943
-#&gt;    1.2425  -9.4147  15.0456  -6.9890   1.2702   0.1865   6.1020  11.8886
-#&gt;    4.3921   3.7759   7.4317  -1.9483  -1.3581   7.6178   8.0995   5.2921
-#&gt;    7.3449  -8.4433   6.9279 -10.4516   9.6367   6.8909  -0.1039   0.6578
-#&gt;  -10.8080  -2.4735  -9.1660   0.5389  -2.2211  -2.9281  -3.9350   1.0759
-#&gt;   -3.0433   9.0416   6.1990   7.7375   4.6229  -1.1571   3.0804   1.1517
-#&gt;   -2.8065  -2.1862   3.5347  10.8738 -10.1927   5.1322  -5.9649  -6.0424
-#&gt;   -3.9638   5.7148   0.2114   9.0527   6.1581  -3.1611   9.3944   3.7303
-#&gt;   10.2919  -6.2694   2.7175  -4.4998   9.9062  -3.6026  -3.0043   1.4377
-#&gt;   -7.6750   4.4300  -3.4528   9.6243   2.2941  -1.2676 -10.6246  13.9407
-#&gt; 
-#&gt; Columns 41 to 48   1.4608   0.1396   0.5564   6.6016  -6.0373 -14.0324  -0.3698   3.5015
-#&gt;   13.7397   8.4947   3.4890   5.1928   5.5288  11.5638   0.2098   1.7494
-#&gt;   -7.5417   2.1345  -3.4932   3.7325   2.0334   6.5741  -1.7155  -1.5202
-#&gt;    4.3123  -5.2696   2.7072 -14.4254 -14.2805 -13.2434 -11.8453  -0.9969
-#&gt;   11.1941  -5.5161   3.3982  -2.9459 -10.6112  -8.4628  -4.7659  -3.5250
-#&gt;    7.9894   4.2813   6.1102   1.1719   5.0829   0.7326   3.0351   6.7694
-#&gt;  -11.4716  -5.2894 -11.6754   2.7108 -14.3192  18.2111  -5.9757   0.7594
-#&gt;   13.1960   7.3637   3.0847   8.7412  -0.3544   9.3797  -2.9512   7.9134
-#&gt;   -8.5316  -1.8647   9.3956  -1.4067 -25.2899  -8.5641  -0.1490  -4.9419
-#&gt;    5.5415  -9.4443   3.1945  -4.1653   0.4547  -9.9153  -1.3897  -5.9577
-#&gt;    2.5276   3.4718   1.4383   6.9117  -1.0648  10.5546   4.5399  -4.6508
-#&gt;    4.4477   9.2924  -8.5454   9.0551  -2.6270  17.4112   9.6292  -5.0822
-#&gt;    0.5014  -7.3653  10.9144   1.7105   6.8618 -10.1564  -3.8704  -1.5644
-#&gt;    5.7107  -1.4383 -10.5158 -14.6223  -3.2541  -7.7564   0.2668 -15.5028
-#&gt;   -3.4364  -0.2516  -7.8772   3.2116  -3.0264   3.5423   4.9572  -2.3570
-#&gt;   -3.2991   1.3607  -4.7082  10.1341 -11.1344   3.5132  -0.5431  -1.2703
-#&gt;   -1.9864 -11.4166   0.8685  -7.8772  -7.4765  -5.5899  -0.5649   2.3631
-#&gt;   17.9983   2.3783  -5.6749   1.0787   1.3691  -4.4425 -14.2848   4.8670
-#&gt;    3.0872   5.9777   2.8293   1.3380  -0.2152  -4.5861  -1.7911   0.7233
-#&gt;   -8.0559   9.3178   2.1391  -9.0132  -7.4502  -6.2529  -0.0882   8.9923
-#&gt;   11.9393   2.4615  -5.3886   1.8530   3.9278  -9.0155  17.8079  -0.3481
-#&gt;    1.2226  -6.0641   2.9070   4.9460 -16.3279   4.6153  -0.2458   0.4006
-#&gt;   -4.5549  -0.2029  10.9696  16.3538  -0.9287  -1.3249 -10.4672   6.1552
-#&gt;   -0.2733   3.2047 -12.6674  -7.5409 -10.2010  -0.9739  21.8379 -10.9226
-#&gt;    1.0553  -3.7045   9.3245  -8.7081   2.4729  -5.7542  -1.9227   2.1253
-#&gt;    5.8217   7.7168  -0.2429   2.4846  10.7917  -3.7778  -5.5909   1.5932
-#&gt;    5.9314   7.3883  -8.5620   0.3581  -4.5428   3.1784  -8.8950  -3.3841
-#&gt;    3.0066  -0.7769   4.9888   5.9224  -6.9347   4.4921  -0.9893  -3.6826
-#&gt;  -13.8814  -6.1712   0.1671   8.9496  -4.4673  -4.1689  -1.4313  17.9333
-#&gt;   -7.2690  -2.1672  -8.3789   5.6867   2.7306   1.2254  -1.0055   6.7302
-#&gt;    0.6484  -0.0343  -9.4856 -10.8494  -4.6287   5.8470  11.1581  -1.0961
-#&gt;   10.9886   3.9224  -5.0731  -9.3482   7.2313  13.1391   7.4518  -5.7778
-#&gt;   -1.5595  -2.2667  -3.0429   4.2170  -6.7985   0.9657  -4.0107   0.6484
-#&gt; 
-#&gt; (15,.,.) = 
-#&gt;  Columns 1 to 6  6.1411e+00  1.2012e+00  3.1153e+00  7.7743e+00  3.2541e+00 -9.1764e+00
-#&gt;   4.4054e+00 -6.6610e+00 -1.2588e+00  4.4774e+00  1.2777e+00 -4.7747e+00
-#&gt;   3.4056e+00 -6.4190e+00 -1.0470e+01 -1.3224e+00  7.1852e+00  3.8429e+00
-#&gt;   5.9740e+00 -2.7648e+00  3.4562e+00 -1.0509e+01  1.7566e+00 -6.8144e+00
-#&gt;  -8.3474e+00 -7.1660e+00  1.3639e+01  3.1890e+00 -1.0338e+01 -3.1956e+00
-#&gt;   1.4506e+01  9.9517e+00  6.4918e+00 -1.4455e+01  1.4570e+01 -5.7897e+00
-#&gt;   1.3567e+01 -9.1613e+00 -3.7541e+00  7.3114e+00 -5.4468e+00  1.1911e+01
-#&gt;  -2.9887e-01 -9.5368e+00 -2.2441e+00  5.8618e+00 -1.1434e+01  8.9721e-01
-#&gt;  -1.7673e+00 -1.2018e+01 -1.2320e+01 -1.9735e+00 -2.6236e-01  5.9154e-01
-#&gt;  -1.0547e+00  3.9560e+00  1.2173e+01 -1.7685e+01 -8.6751e+00  1.9231e+01
-#&gt;   1.2957e+01  1.6805e+00  3.3267e-01  6.2575e+00  1.7514e+00  1.7162e+01
-#&gt;   2.9415e+00  2.7478e-01  6.5338e+00  1.7775e+01 -8.2378e+00  4.4949e+00
-#&gt;  -4.4437e+00  6.5249e-01 -1.9180e+00 -1.0160e+01 -7.1975e-01 -3.2121e-01
-#&gt;  -1.3941e+01  1.7074e+01  8.8939e+00  5.9519e+00  7.0081e+00 -6.6319e+00
-#&gt;   6.0009e+00 -7.9731e+00 -1.8817e+00  1.0784e+01 -9.4142e-01 -7.1085e+00
-#&gt;   3.4008e-01 -6.1638e+00 -5.0541e+00  1.7915e+00 -1.8934e+01  1.7161e+01
-#&gt;  -4.3582e+00  6.4512e-01 -7.7828e+00 -2.6731e+00 -9.3943e-01  3.6267e+00
-#&gt;   5.0334e+00  1.6203e+01  4.0857e+00  4.5691e+00  1.9769e-01 -4.8376e+00
-#&gt;   1.0236e+01  7.1080e+00 -4.0819e-02  1.7662e+01 -8.6366e+00  1.2132e+01
-#&gt;  -1.4437e+01 -3.5487e+00 -8.6666e+00 -8.6697e-02  5.8625e+00 -2.2120e+00
-#&gt;   1.4146e+00 -7.6626e+00  1.1082e+00 -3.2022e+00  4.9528e+00  5.3191e+00
-#&gt;   4.7776e+00 -7.5216e+00  2.8824e+00  1.1923e-01  4.9627e+00  7.0490e+00
-#&gt;   2.4965e+00  4.6869e+00  4.8636e+00 -7.8742e-01  1.1951e+00  5.0196e+00
-#&gt;  -8.6296e+00 -2.2059e+00 -1.0925e+01  9.4977e+00  2.2051e-01  2.2556e+00
-#&gt;  -1.7170e+01  1.6010e+01  9.6767e+00 -9.9231e-01  1.9987e+00 -3.5373e-01
-#&gt;   1.0832e+00  9.0020e+00  6.5085e+00 -4.4241e+00 -1.5618e+01  1.2934e+01
-#&gt;  -9.8570e+00  5.6997e+00 -4.0706e+00  5.2474e+00  4.0911e+00  1.1322e+00
-#&gt;   4.2819e+00 -1.0849e+01  7.1359e-02  1.0062e+01  6.2812e+00  8.6944e-01
-#&gt;   1.2269e+01  5.0511e+00 -3.2111e+00 -1.6680e+00 -1.6798e+00  1.0160e+01
-#&gt;  -4.0083e+00 -2.6180e+00  8.3733e+00 -4.9721e+00 -1.1904e+01  3.0680e+00
-#&gt;   1.0548e+01 -6.9571e+00 -2.1370e+00  1.0727e+01 -1.1825e+01 -7.4524e+00
-#&gt;  -8.1247e+00 -1.1662e+00  9.2885e+00  1.2215e+01 -1.7843e+00  3.6030e-01
-#&gt;   9.3856e-01 -5.7762e+00  1.2670e+00  1.3260e+01 -2.2480e+00 -3.5372e-01
-#&gt; 
-#&gt; Columns 7 to 12  2.9218e+00  1.2536e+01 -1.9047e+00 -8.1584e+00  2.3325e+00  3.9832e+00
-#&gt;   2.2695e+00 -8.1089e+00 -1.2136e+01  3.6958e+00  1.5570e+00  2.6760e+00
-#&gt;   2.9745e+00 -4.9709e-01  2.1208e+00  3.5506e+00 -5.0013e+00 -1.3100e+00
-#&gt;   8.3533e+00  1.2493e+00  2.9756e+00  1.0526e+01 -1.3756e+00  9.5803e+00
-#&gt;   9.8965e+00  7.5875e+00 -2.3657e+00 -1.5050e+00  5.7379e-01  1.8771e+01
-#&gt;   2.2456e+00 -7.5023e+00 -3.1013e+00  1.2603e+00  1.4803e+00 -7.3584e+00
-#&gt;  -2.6735e+00  6.7673e+00  1.0947e+01 -4.6943e+00 -7.9545e+00 -9.3387e-01
-#&gt;  -1.2066e+01 -6.8287e+00  5.1836e+00  4.3823e+00 -7.3767e+00  4.3197e+00
-#&gt;  -6.2801e-01  9.9074e-01  9.1064e-01  3.1407e+00 -1.4757e+00  9.3457e+00
-#&gt;   2.1655e+00  3.4964e+00 -1.3240e+01 -3.5232e+00  3.3125e+00  7.0731e+00
-#&gt;  -9.9213e+00 -5.2672e+00 -1.4565e+01 -8.4973e+00 -2.6732e+00 -2.0114e+01
-#&gt;  -6.6740e+00  7.7210e+00  2.1280e+00 -8.7417e+00 -2.1518e+00 -7.7921e+00
-#&gt;   5.1449e+00  2.4198e+00  7.7006e-01 -2.1721e-01  8.6021e+00  3.0894e+00
-#&gt;   1.1788e+01 -5.4100e+00  8.0960e+00 -3.3977e+00 -3.9753e+00  7.4800e+00
-#&gt;   1.8765e+00 -4.2533e+00 -2.2733e+00  5.0395e+00 -1.8724e+00  3.2300e+00
-#&gt;  -5.4369e-01  5.9746e-01 -2.5505e+00 -5.8269e+00 -8.8620e+00 -4.3021e+00
-#&gt;   4.9574e-01 -8.9389e+00  9.8590e+00  1.4443e+01  2.5121e+00  5.1293e+00
-#&gt;   4.4574e-02 -7.6692e+00  1.4007e+00  3.5770e+00 -3.7786e+00 -9.3450e+00
-#&gt;   2.4865e+00  5.2717e+00 -7.6370e+00 -9.3909e+00 -9.3465e+00  2.3405e-01
-#&gt;  -1.1693e+01  4.5547e-01  1.4006e+01  8.6374e-01  7.2120e-01  1.5077e+01
-#&gt;   6.3830e-01 -1.9876e+00  1.8827e+00  2.0018e+00  8.0429e+00 -1.3298e+01
-#&gt;  -4.0522e+00  1.4735e+00 -6.7797e+00  1.5274e+00  3.7795e+00 -9.1248e+00
-#&gt;   1.9437e+00  1.5379e+01 -1.1056e+01 -1.6349e+00 -1.2829e+01  1.0709e+01
-#&gt;   3.0123e+00  2.3140e+00  9.3824e+00  1.3176e+00  3.3829e+00  1.0936e+01
-#&gt;   3.9712e+00 -8.9924e+00  3.5327e+00 -4.5288e+00 -6.3137e+00  6.3386e+00
-#&gt;  -3.0625e+00 -4.6547e+00 -3.5768e-01 -9.1277e+00  1.4112e+00 -1.5012e-01
-#&gt;  -9.0523e+00  3.7856e+00  1.7989e+00  6.2832e+00 -2.5905e+00  3.7059e+00
-#&gt;  -5.3393e+00  1.4247e+01  7.4705e+00 -1.7043e+00 -1.8051e+00 -5.2747e+00
-#&gt;   1.7954e+00 -2.1038e+00  9.6809e-01 -1.9480e+00 -2.4217e+00 -8.2812e+00
-#&gt;  -7.2160e-01  3.3457e+00  1.9367e+00 -9.4227e-01 -5.4904e+00 -2.9071e+00
-#&gt;  -2.8833e-01 -4.4734e+00 -1.6095e+00 -2.5625e+00 -3.3707e+00 -1.3406e+00
-#&gt;  -5.1354e+00 -6.2129e+00  1.0319e+01 -3.3682e+00 -5.6694e+00  2.6834e+00
-#&gt;  -5.5868e-02  9.5074e+00 -1.0118e-01 -2.5412e+00 -8.2867e+00  3.2569e-01
-#&gt; 
-#&gt; Columns 13 to 18  8.8347e+00  1.4396e+00  4.0423e-01 -1.7441e+00 -7.5023e-02  4.8366e+00
-#&gt;  -2.8250e+00  6.3946e+00 -9.3475e-01 -5.3389e+00 -6.7120e+00 -2.0927e+00
-#&gt;  -9.4466e+00  1.1010e+01 -9.3707e-01  2.5019e+00 -5.3673e+00 -7.5820e-01
-#&gt;   1.2462e+01  5.2646e+00 -1.0303e+01  7.2893e+00 -6.2456e-01 -1.1898e+01
-#&gt;   8.4322e+00 -2.0249e+00 -4.9952e+00  4.6921e+00  1.3757e+00  1.5081e+00
-#&gt;   1.1657e+01 -7.0824e+00 -8.1840e-01  3.0650e+00  5.9628e+00 -1.6257e+01
-#&gt;  -5.6547e+00  8.0231e+00 -1.1312e+00  2.0000e+00 -4.9929e+00  1.1532e+01
-#&gt;   5.8648e+00  9.9423e+00 -5.9867e+00 -4.9364e-01 -9.1868e+00  2.6057e+00
-#&gt;   3.4177e+00  6.9162e+00 -2.8710e+00  1.6692e+00 -2.5146e+00 -6.0695e+00
-#&gt;   1.1698e+00 -9.8473e+00 -9.1352e+00  6.5762e+00  1.6125e+01 -1.2818e+01
-#&gt;  -1.1198e-01 -3.2185e-01  5.6983e+00 -5.9241e+00  3.5708e+00 -2.7809e+00
-#&gt;   5.1578e+00  1.1628e+00  1.7804e+00 -7.3900e+00  4.3813e+00  7.4967e+00
-#&gt;  -1.9075e-01 -6.3208e+00 -5.9200e+00 -2.8678e+00  5.7667e-01  9.3575e-01
-#&gt;  -5.1662e+00 -8.0506e+00  1.0882e+01  5.7851e+00 -1.5085e+00 -1.8302e+00
-#&gt;  -1.6578e+00 -9.5201e+00  4.1524e+00  6.5263e+00 -3.3916e+00  6.1907e+00
-#&gt;   1.5990e+00 -4.5068e+00  8.4405e-01  2.1374e+00  7.6890e-01  6.4375e+00
-#&gt;   3.1843e+00 -1.0207e+01  1.0368e+00  1.0318e+01 -7.0697e+00 -9.5794e-01
-#&gt;  -5.9021e-01  7.4545e-01  1.1504e+00 -2.6608e-01  1.7463e+00  9.2731e-01
-#&gt;   3.9899e+00  2.5300e+00 -1.2578e+00 -9.6065e-01  6.6087e+00 -6.8485e+00
-#&gt;   2.0634e+00 -3.8333e+00  4.7312e+00  5.6640e-01 -1.0761e+01  3.2408e+00
-#&gt;  -3.5653e+00 -1.7817e+01  6.9846e+00 -8.1224e-01  5.3895e+00  2.4304e+00
-#&gt;   1.1116e+00  4.3112e+00  4.3846e+00 -2.9199e+00  5.0153e+00  3.5801e+00
-#&gt;   7.9057e+00 -1.2727e-04 -5.2695e+00  4.4264e+00  1.0531e+01 -1.2101e+01
-#&gt;   2.6052e+00 -9.8268e+00  7.6544e+00 -1.9517e+00  5.9334e+00 -2.8919e+00
-#&gt;  -7.2707e+00 -6.1547e+00  5.2558e-02  1.8293e+00 -4.8107e+00  6.2861e+00
-#&gt;  -2.7363e+00 -6.6868e+00 -6.2622e+00 -4.2433e+00  1.2682e+01  4.2031e+00
-#&gt;  -3.0243e+00  3.7963e+00 -2.8952e+00 -9.1032e-01  5.1841e-01 -1.5468e+00
-#&gt;   1.4587e+00  1.1961e+01  6.5100e+00 -9.9790e+00  1.0887e+00  6.0448e+00
-#&gt;  -8.0783e-01  2.5804e+00  1.9394e-01  1.4125e+01 -8.8215e+00  3.6955e+00
-#&gt;   7.2562e+00 -2.0922e+00 -4.1749e+00  2.4479e-01 -2.6694e+00  7.5744e-01
-#&gt;  -6.7572e+00 -5.6966e+00  6.6473e+00 -3.1544e+00  3.7829e+00  3.2889e+00
-#&gt;  -9.0707e+00 -3.0105e+00  1.4849e+01  1.5335e+00 -5.9031e-01  2.5278e+00
-#&gt;  -1.0161e+01  3.1143e+00 -3.7795e+00  1.0885e+01  3.6641e-01  1.2018e+01
-#&gt; 
-#&gt; Columns 19 to 24 -1.5678e-01 -2.8430e+00  7.7438e+00 -2.2959e+00  7.5365e-01 -4.5730e+00
-#&gt;  -2.1412e+00  7.4476e+00 -7.3377e+00 -6.4009e+00  9.0126e+00  6.5982e+00
-#&gt;   2.6757e-01 -4.5662e+00  5.2942e-01 -6.5997e+00 -3.2208e-01 -1.0595e+01
-#&gt;   1.5061e+00  6.7873e+00 -3.4651e+00 -3.6654e+00  4.9538e+00 -1.3519e+01
-#&gt;  -2.5097e+00 -1.4296e+00  1.1493e+01 -1.3009e-01  5.6917e+00 -8.0933e+00
-#&gt;   1.3585e+00 -6.0838e-01  8.3252e+00 -7.5121e+00 -1.2092e+01  5.5174e+00
-#&gt;  -1.4689e+01  1.4679e+00 -3.9201e+00  7.0320e+00  2.0206e+00 -6.6903e+00
-#&gt;   1.3374e+01 -1.4073e+01 -1.8973e-01 -5.4955e+00  8.6264e+00 -9.2127e+00
-#&gt;  -4.2856e+00  9.7618e+00  5.4193e-01 -9.8837e+00  3.8149e+00 -1.3552e+01
-#&gt;  -5.1530e+00  1.5701e+01  9.5244e+00  3.9395e+00 -5.6707e+00  9.1807e-01
-#&gt;  -7.8286e+00  6.0811e+00 -6.1554e-01  6.4617e+00  8.2324e-01  4.1026e+00
-#&gt;  -9.3054e+00 -6.7414e-01 -3.2811e+00  4.0874e+00 -1.3291e+00  1.3885e+01
-#&gt;  -6.0135e+00  1.0079e+01  9.4125e-01 -6.1053e+00  1.3793e+00  8.6402e-03
-#&gt;   1.1038e+01 -2.1391e-01 -3.8249e-01  3.8128e+00  7.3565e+00  1.0462e-01
-#&gt;  -3.0908e+00  3.8431e+00  8.1564e-01 -8.9262e+00  1.2413e+01 -2.1075e+00
-#&gt;  -1.8686e+01  2.7057e+00  3.8143e+00 -1.7397e-02  4.3993e+00 -6.2289e-01
-#&gt;  -4.6669e-01  1.3863e+01 -1.7005e+01  5.2828e+00 -1.3052e+00  3.1146e-01
-#&gt;  -4.6040e-01 -2.9173e+00 -8.6883e+00  2.3938e+00  3.6502e+00  5.5416e+00
-#&gt;  -3.5759e+00 -5.3777e+00  4.1859e+00  5.6907e+00  2.6902e+00 -4.8041e+00
-#&gt;   1.5699e+01  1.2456e+00 -7.4594e+00 -2.1436e-01  3.0159e+00 -3.0897e+00
-#&gt;   3.5007e+00  2.4383e+00 -1.1885e+01  4.5385e+00 -1.0628e+01  5.9547e+00
-#&gt;   5.3922e-02  6.9363e+00 -2.8053e+00 -2.5222e+00  3.5432e-01  3.8426e+00
-#&gt;  -6.0969e+00 -1.8357e+00  1.2462e+01 -5.7737e-02  1.6048e+01 -1.0987e+01
-#&gt;   2.5441e+00 -1.0261e+00  3.2093e+00 -7.3923e+00 -8.9302e+00  1.0336e+01
-#&gt;  -6.7660e-01  1.9685e+00 -4.3052e-01  3.4231e+00  4.5800e+00 -2.8415e+00
-#&gt;  -4.9795e+00 -8.2735e-01  4.6769e+00  8.4598e+00 -8.7513e-01  7.1136e+00
-#&gt;   6.7725e+00  3.1003e+00 -1.6163e+01  5.0531e+00  8.9823e+00  1.5046e+00
-#&gt;  -9.1886e+00 -3.5220e+00  8.7880e-01 -2.3723e+00  3.1240e+00  2.6378e+00
-#&gt;  -8.6741e+00 -1.6992e+00 -3.5022e+00  5.3353e+00 -6.8154e+00 -7.0972e+00
-#&gt;  -2.7981e+00 -2.2180e+00  2.6794e+00  4.2207e-02 -5.2390e+00  4.8414e+00
-#&gt;   5.2728e+00 -1.5188e+01  5.2365e+00  7.9242e+00 -2.2300e+00 -2.1721e-01
-#&gt;   1.1958e+01 -3.5159e+00 -7.2350e+00 -1.1952e-01  4.2997e+00  6.2928e+00
-#&gt;  -9.1048e+00 -3.8001e-01 -2.0207e+00  1.3898e+00  1.0817e+01 -1.7368e+01
-#&gt; 
-#&gt; Columns 25 to 30  6.1586e-01  1.0723e+01 -1.4226e+01 -2.2350e+00  4.3100e+00 -2.2377e+00
-#&gt;   4.6700e+00  4.1365e+00  1.7161e+00  1.0891e+01  1.3871e+00 -1.0575e+01
-#&gt;   3.7608e+00 -3.0684e-01 -5.4910e+00 -4.6036e-01 -7.5868e-01  3.3461e+00
-#&gt;  -1.8112e+00 -4.7864e+00 -4.8441e+00  3.6786e+00 -1.1393e+00 -7.2146e+00
-#&gt;   6.7485e+00 -7.0151e+00 -1.2549e+01 -5.9156e+00  1.3171e+01  1.9582e+00
-#&gt;  -2.6626e+00  8.0942e+00  4.6499e+00  6.5449e+00 -1.4993e+01  1.1357e+01
-#&gt;  -6.3394e+00  6.2018e+00  5.6609e+00 -4.1641e+00  3.9466e+00 -3.6721e+00
-#&gt;  -2.7860e+00  8.1746e+00  2.5984e+00 -4.3780e+00  4.2339e+00  2.2591e+00
-#&gt;  -5.1666e+00 -8.8088e+00  1.5319e+00 -1.4501e+01  2.1763e+00 -1.1935e+00
-#&gt;   7.6523e+00 -5.2915e+00 -3.2383e+00  6.8400e+00 -5.9936e-01 -6.2848e+00
-#&gt;  -1.8862e+00  8.9497e+00  2.3507e+00  7.5264e+00 -2.5281e+00 -1.0800e+01
-#&gt;  -7.4712e+00  1.0753e+01  3.7705e+00 -5.4932e+00  1.3983e+01  2.2036e+00
-#&gt;  -7.6764e+00 -5.4469e+00  3.9233e+00 -6.2591e+00 -4.6313e+00 -8.0646e-01
-#&gt;  -1.1404e+01 -4.3964e+00 -2.3786e+00 -3.6239e+00 -2.4862e-01  2.1303e+00
-#&gt;  -2.3067e+00  9.4831e+00 -1.0368e+01  3.7508e+00  3.0886e+00 -8.1633e-02
-#&gt;  -3.8838e+00 -3.6964e-01 -1.3061e+01 -1.5662e+00  4.2924e+00 -4.8975e+00
-#&gt;  -1.0996e+01 -5.2264e+00  6.7016e+00 -7.4774e+00 -8.2021e-01 -1.0090e+01
-#&gt;  -1.3429e+01 -2.0451e+00 -1.0625e+00 -9.5406e+00 -6.7793e+00 -3.0793e+00
-#&gt;  -1.2521e+01  5.6861e+00 -4.2399e+00 -1.6970e+00  1.2026e+01 -1.0719e+01
-#&gt;  -2.7979e+00  5.7407e+00 -4.1176e+00 -5.4762e+00  2.1707e+00  1.0926e+01
-#&gt;  -1.5266e+01 -3.0576e+00 -1.0782e+00  7.4054e-01 -2.6770e+00 -1.2212e+00
-#&gt;  -3.0987e+00 -1.6582e+00  7.7346e+00  9.3959e+00 -2.8105e+00  6.6136e-01
-#&gt;   7.9818e+00  4.4954e+00 -1.2614e+01  1.4414e+01 -4.5452e+00  5.7826e+00
-#&gt;  -1.1260e+01 -6.7206e-01 -9.7642e+00 -1.1710e+00  3.4823e+00  9.3414e+00
-#&gt;   4.8880e+00 -1.4166e+01  1.5699e-01 -4.9338e+00 -5.8791e+00  6.3410e+00
-#&gt;   1.0273e+01  5.5454e+00 -4.4529e+00 -5.2210e+00 -1.8625e+00  1.5325e+00
-#&gt;  -1.0691e+00 -8.8590e+00  1.4772e+01 -7.8302e-01 -1.0871e+01  1.8737e+00
-#&gt;  -8.1977e+00  4.0050e+00 -5.2612e+00 -3.2795e+00  8.8445e+00  1.7135e+00
-#&gt;  -2.6536e+00 -6.3943e-01  8.3850e+00  6.0751e+00 -6.3359e+00 -1.0932e+01
-#&gt;   6.3043e+00  4.1522e+00 -3.2869e+00  1.1625e+01  5.2271e-01 -5.1482e-01
-#&gt;   3.6324e+00  1.0405e+01  3.8951e+00 -9.3793e+00 -2.1545e+00  3.5405e+00
-#&gt;  -2.5831e+00  4.3855e+00  7.8724e-01  2.7064e+00  1.8029e+00  1.6470e+01
-#&gt;  -6.5404e-01  8.2599e+00  6.5799e-01 -1.5549e+01  7.5403e+00  2.7031e-01
-#&gt; 
-#&gt; Columns 31 to 36 -5.9201e+00 -7.5340e+00 -3.4763e+00 -5.3898e+00  5.7264e+00  3.7439e+00
-#&gt;  -3.9307e+00 -7.9970e+00 -3.6613e+00 -1.8173e+00  1.1836e+01 -9.6959e+00
-#&gt;   3.0409e+00  3.1409e+00  3.9315e+00  3.8225e+00 -1.1152e+01  1.1445e+00
-#&gt;  -1.6153e+00 -1.3033e+01 -8.6845e+00  4.2365e-01  8.7148e-03  5.8595e-01
-#&gt;  -4.7503e+00  1.3155e+00 -1.1609e+01  7.5878e+00 -8.0054e-01 -3.7962e+00
-#&gt;  -3.4989e+00 -7.1413e+00  5.0519e+00 -6.8110e+00  1.1266e+00  7.5290e+00
-#&gt;  -1.5645e+00 -4.5282e-01 -7.6692e+00 -4.7306e-01  3.8763e+00  8.2929e+00
-#&gt;   1.8348e+00  5.5555e+00 -2.5466e+00  5.8404e+00  6.0851e+00  1.0069e+01
-#&gt;  -3.4145e+00  2.1655e+00 -1.1108e+00  5.8072e+00 -1.3568e+00  1.1286e+01
-#&gt;   3.6999e+00 -1.0533e+01 -3.7725e-01  1.9680e+00  3.4073e+00 -3.7330e+00
-#&gt;   1.7669e+01 -5.2079e+00 -3.6174e+00  1.0705e+00  8.3419e+00 -3.1506e+00
-#&gt;   8.7185e-01  1.2069e+01 -3.3660e+00 -2.5149e+00  4.9631e+00 -9.6895e+00
-#&gt;  -5.0212e-01  6.4904e+00 -2.2725e+00  2.5696e+00  6.0688e+00  2.7065e+00
-#&gt;  -8.8154e+00 -4.2433e+00 -6.0739e+00  8.1400e-01 -3.3242e+00 -8.9910e+00
-#&gt;   9.1248e+00  1.6636e-01 -9.9175e+00 -4.1452e+00  4.6817e-01 -4.0483e+00
-#&gt;   5.1608e+00 -7.5553e+00 -9.0017e+00  4.0291e+00  3.4272e+00  1.2484e+01
-#&gt;   1.1618e+01 -6.2762e+00  8.7868e-01 -2.3939e-02 -1.3047e+00  2.9953e+00
-#&gt;  -3.2704e+00  3.1781e+00  7.7302e-01 -4.2930e+00  1.0224e+01 -1.1903e+00
-#&gt;  -2.4756e+00 -7.7146e+00 -1.2481e+01 -5.3403e+00  3.2199e+00 -1.3366e+01
-#&gt;  -2.9826e+00 -3.7066e-01  4.9547e+00  5.3281e+00  5.6334e+00 -4.9327e+00
-#&gt;   2.2647e+00  3.7119e+00  1.2174e+00 -4.5663e+00  2.4894e+00 -4.0043e+00
-#&gt;   1.5470e+00  1.3000e+00  4.3999e+00  2.9317e+00 -1.0113e-01  1.3733e+01
-#&gt;   5.2532e+00 -8.6832e+00  1.0032e+00 -1.1233e+01  3.8057e+00  2.3562e+00
-#&gt;   5.5204e+00 -4.1909e+00  8.5203e+00 -2.1612e+00  1.6234e+00 -1.6599e+01
-#&gt;   1.0267e+00  9.9644e-01  2.9089e+00  4.0683e+00 -2.2161e+00 -6.9053e-01
-#&gt;   7.7579e+00  1.1461e+01  6.2183e+00 -1.8167e+00  2.4635e+00 -2.2440e+00
-#&gt;  -7.4586e+00  7.0211e+00  5.5047e+00 -6.9198e+00  5.2545e+00 -4.5697e+00
-#&gt;   3.8684e+00  3.0456e+00 -6.6785e-01 -3.5584e+00  3.4081e+00 -4.0620e+00
-#&gt;   4.2854e+00 -3.1271e+00 -7.0345e+00  4.1272e+00  1.3301e+01  1.1133e+01
-#&gt;  -8.3591e+00 -1.0407e+01 -5.3188e+00 -1.3842e-01  2.5500e+00  4.8923e+00
-#&gt;  -2.0532e+00 -6.6008e-01 -1.0025e+01  1.7499e+00 -9.7989e-01 -4.0550e+00
-#&gt;  -1.1398e+00  1.9597e-01  1.4056e+00 -1.0088e+01  1.3031e+00 -1.0344e+01
-#&gt;   5.5549e+00  9.4302e+00 -2.1735e+01 -3.6420e-01 -7.8630e-01  9.1835e-01
-#&gt; 
-#&gt; Columns 37 to 42 -1.2500e+01 -1.6597e+00  8.8187e+00 -3.0726e+00  1.0101e+01 -7.1916e+00
-#&gt;  -3.3525e-01  9.1159e+00  2.0040e+00  2.6244e+00  7.0478e+00 -2.9979e-01
-#&gt;   1.1654e+00 -1.6167e+01  5.0181e-01 -4.6441e+00 -9.0904e+00 -1.4704e+00
-#&gt;  -3.2141e-01 -6.4843e-01 -1.2843e+01  8.9389e-01  8.3534e+00  3.5396e+00
-#&gt;   8.6654e-01 -7.4135e+00  3.6055e+00  4.0188e+00  1.2734e+01 -1.4643e+00
-#&gt;  -2.4015e+00  3.7966e+00 -9.4126e+00  8.4553e+00 -4.9606e+00  6.0746e+00
-#&gt;  -1.4565e+01 -6.7877e+00  9.0795e+00  1.3152e-02 -4.6342e+00 -9.9842e+00
-#&gt;   5.3184e+00 -7.4556e+00  8.7594e+00 -6.5285e+00  1.5695e+01  4.7158e+00
-#&gt;  -1.8156e+01 -2.5426e+00  5.7639e+00 -5.4702e+00  1.3063e+01 -8.4737e-01
-#&gt;   8.3514e+00 -9.5168e+00 -3.6114e+00  2.5799e+00  3.0256e+00  3.6688e+00
-#&gt;  -1.6123e+01  1.8091e+01 -1.7613e+01 -6.4271e-01 -7.8591e+00 -8.7322e+00
-#&gt;   4.3154e-01  9.6482e+00  2.2021e+00 -2.1536e+00 -2.8521e+00 -5.6857e+00
-#&gt;  -7.9253e+00 -8.6081e-01 -1.1696e+01 -2.1102e-01 -2.4717e+00 -3.0896e+00
-#&gt;   1.1872e+01 -1.2718e+01  8.3939e+00  1.1412e+00 -3.8182e+00 -5.5725e-01
-#&gt;   4.3128e+00  1.5962e+01 -1.7030e-01  5.2630e+00 -6.1427e+00 -1.1790e+01
-#&gt;  -9.9510e+00 -5.0862e+00 -4.7179e+00  1.3627e+00  8.1574e+00  7.2007e+00
-#&gt;  -1.3578e+00  9.9023e+00  9.9474e-01  8.0218e+00 -1.8325e+00  3.3346e+00
-#&gt;   2.7851e+00  2.3657e+00 -2.7398e-02  1.7278e+00  6.6922e+00  2.6817e+00
-#&gt;   1.6744e+00 -5.4009e+00 -2.0069e+00 -5.5519e+00  3.8851e+00 -1.9882e+01
-#&gt;  -3.7923e+00 -1.8365e+01  1.6843e+01  8.5690e+00  7.9638e+00  3.5172e-01
-#&gt;   7.8980e-01  1.6670e+01 -8.6735e+00 -1.6744e+00 -1.7911e+01 -5.1538e-01
-#&gt;  -9.3167e-01  6.9374e+00  2.8467e+00  3.2126e+00  5.1753e+00  3.8667e+00
-#&gt;  -2.4374e+00  7.1102e-01 -7.6900e-01 -3.3706e+00  8.7239e+00 -1.0625e+01
-#&gt;   8.6085e-01 -1.1295e+01  4.1958e+00 -8.0042e+00 -1.2186e+00 -8.8777e+00
-#&gt;  -2.8992e-01 -1.9301e+01  7.5276e+00 -1.3628e+00 -7.1159e-01  1.6808e+00
-#&gt;   3.3970e+00 -5.1397e+00  4.9086e-01 -6.5172e+00  3.2329e+00 -5.5680e-01
-#&gt;  -2.1998e+00  9.6797e+00  3.9085e+00  1.8021e+00  2.5591e+00  6.7714e+00
-#&gt;  -1.1844e+01  1.0864e+01  2.0851e+00 -2.4612e+00 -3.6853e-01  1.3808e+00
-#&gt;   1.1712e+00 -6.9688e+00 -1.5436e+00 -1.8308e+00 -1.1263e+00 -8.5259e+00
-#&gt;   1.9034e+01 -1.2691e+01 -1.0649e+01 -8.2376e-01  2.4798e+00  1.4402e+01
-#&gt;   2.8304e+00  6.8613e+00  1.7631e-01  4.7940e+00 -5.0460e+00 -3.9073e+00
-#&gt;   1.9534e+01  1.8186e-01  4.7826e+00  2.2521e+00 -1.7059e+01  4.8119e+00
-#&gt;   2.6820e+00  3.4089e+00  7.0708e+00 -9.3430e+00 -4.5705e+00 -1.1076e+01
-#&gt; 
-#&gt; Columns 43 to 48  4.8009e+00 -2.3100e+00 -8.2075e+00  9.0234e+00 -6.1107e-01 -4.0483e+00
-#&gt;   9.7927e+00 -2.8635e+00 -4.7972e+00  3.1057e-01 -1.1890e+00 -6.0181e+00
-#&gt;   3.5285e+00  1.7852e+00 -9.9727e-01  2.0852e+00  3.1648e+00  3.1008e+00
-#&gt;  -1.8110e+00  2.7660e+00  2.8947e+00  4.3550e+00 -5.9623e+00 -8.6348e+00
-#&gt;   9.8008e+00 -1.0450e+01  1.4263e-01  1.0038e+01  6.6475e+00  3.2529e+00
-#&gt;  -1.5543e+01  1.5340e+01 -7.5227e+00  5.3337e+00  5.6205e+00 -9.2955e+00
-#&gt;  -1.1234e+01  1.1228e+01 -8.6978e+00 -6.8727e+00  5.4275e-01  3.6621e+00
-#&gt;   8.8628e+00  2.5904e+00 -4.0582e+00 -7.7262e-01  9.1437e+00 -2.7064e+00
-#&gt;   8.8122e-01  1.7788e-01  3.2033e+00 -9.7429e-01  7.2314e+00 -8.5375e+00
-#&gt;  -7.3723e+00  2.6608e+00  7.7108e-01  1.0262e+01  2.8678e+00  1.5398e+00
-#&gt;   1.9159e+00  6.1942e+00  4.6819e+00 -6.7605e+00 -3.5926e+00  6.2675e+00
-#&gt;  -6.2088e+00  1.7462e+00 -8.6675e+00 -3.3900e+00  3.6244e+00  5.8448e+00
-#&gt;  -1.2316e+01  4.5562e+00 -1.2575e+01  1.0337e+01  6.2224e+00 -1.4418e+01
-#&gt;  -2.1496e+00  2.4394e+00  3.2146e+00  2.0401e+00  5.4271e+00  4.4695e+00
-#&gt;   1.0560e+01 -3.0106e+00  7.2985e-01 -1.3981e+00 -3.9298e-01 -1.0766e+01
-#&gt;   7.1222e+00  2.8715e+00  6.6031e-01  2.1408e+00  5.5733e+00  7.8222e+00
-#&gt;  -2.0737e+00  7.5871e-01  2.8297e+00 -3.1915e+00  6.0647e-01 -1.2216e+01
-#&gt;  -5.5555e+00 -3.1444e-01 -3.1813e+00  4.6177e+00  1.6197e+00  2.0964e+00
-#&gt;   6.8484e+00 -1.2225e+01  5.3377e+00  4.3864e+00  3.6390e+00  4.4531e+00
-#&gt;   3.1482e+00  5.9825e+00 -1.4020e+01  1.0717e+01 -6.7765e+00 -1.3092e+01
-#&gt;  -2.8828e+01  7.0588e+00 -5.6404e+00  2.3838e+00 -2.3070e+00 -1.5651e+01
-#&gt;  -4.4521e+00  4.7119e+00  8.3183e+00 -4.7470e+00 -2.5365e+00 -2.2245e+00
-#&gt;   1.3423e+00 -1.8530e+01 -1.4279e+00  1.6355e+01  1.2589e+00 -1.5176e+01
-#&gt;  -5.1349e+00 -1.1752e+00 -9.8313e+00 -1.7719e+00  5.4913e+00  8.4010e+00
-#&gt;  -3.6320e+00 -7.2458e+00 -2.6546e+00  1.9690e+00  7.4371e+00  5.4733e+00
-#&gt;  -6.7690e+00 -5.0295e+00  1.6067e-01  6.3777e+00 -2.1160e+00 -6.0978e-01
-#&gt;  -7.1093e+00  3.1119e+00  3.7138e+00 -8.5700e+00  1.9689e+00 -2.0066e+00
-#&gt;  -4.9791e+00  3.0067e+00 -4.0329e+00  3.5131e+00 -7.8257e-02 -2.1218e+00
-#&gt;  -1.6076e+00  3.5492e+00  9.6321e+00 -9.0752e+00 -4.8262e+00  1.1744e+00
-#&gt;   5.5998e+00  2.7769e+00 -1.4631e+00 -5.2224e+00 -2.4303e+00  4.2283e+00
-#&gt;   8.0644e+00  8.3733e+00  1.8085e+00 -1.3090e+01 -8.4868e+00  1.1287e+01
-#&gt;  -1.5767e+00  8.7968e+00 -5.4855e+00 -6.1367e-01  4.9456e-01 -7.7481e-01
-#&gt;   8.8541e+00  5.2928e+00  2.8271e+00 -3.2569e-01  1.9041e+00 -6.1821e+00
-#&gt; 
-#&gt; (16,.,.) = 
-#&gt;  Columns 1 to 6 -1.8684e+00 -1.4766e-01  1.3525e+01 -1.0671e+00  8.0421e+00  1.1180e+01
-#&gt;  -3.4071e+00  1.9010e+00  4.7176e-01  1.0126e-01 -4.1453e+00 -9.7433e+00
-#&gt;  -4.2337e+00  5.4720e+00 -6.5856e+00 -1.1869e+01  6.0145e-01 -8.3834e+00
-#&gt;  -1.9654e+00  2.2855e-01  3.8764e+00 -3.5374e+00  1.2067e+00 -1.1689e+00
-#&gt;  -8.4461e+00  5.1046e+00  3.3670e+00 -1.1370e+00  4.3894e+00  5.2747e+00
-#&gt;  -2.7547e+00  2.1884e-01  7.1182e+00 -7.7577e+00 -1.5415e+00 -4.9214e+00
-#&gt;   1.8278e+01 -2.4060e+00 -2.7747e+00  4.8105e+00  1.8374e+00 -4.7448e+00
-#&gt;   1.3403e+00  2.6363e+00 -1.1645e+01 -1.3400e+00 -9.1005e+00 -1.1688e+00
-#&gt;  -3.4522e+00 -5.1935e+00 -3.4194e+00 -6.4154e+00 -9.7446e+00  9.9460e-01
-#&gt;  -5.5164e+00  7.7909e+00  5.7159e+00  1.1775e+01  8.5761e+00 -4.7306e+00
-#&gt;   9.9588e+00 -1.0587e+00 -6.2387e+00  9.8317e+00  7.7631e+00  1.0831e+00
-#&gt;   8.9294e+00 -7.1194e+00 -1.7293e+00 -2.3997e+00  1.8838e+00  1.6582e+00
-#&gt;  -1.2574e+00 -7.7385e+00  7.4593e+00 -1.1920e+01 -3.3347e+00 -7.9591e-01
-#&gt;   8.5276e+00  1.0692e+01  6.0651e+00  9.5834e+00  1.5466e+01  8.4468e+00
-#&gt;  -1.2824e+01  7.1593e+00  1.2602e+00 -7.0969e+00  1.4191e+01 -1.4707e+01
-#&gt;  -3.7324e+00 -3.6618e-01 -2.1996e+00  1.0805e+01  1.0217e+01 -6.2906e+00
-#&gt;  -8.5354e+00  9.0259e+00 -6.9228e+00  4.1147e+00 -1.1718e+00 -6.8423e+00
-#&gt;   3.5965e+00  6.7174e+00  6.3106e+00  3.6100e+00  2.8678e+00  1.0577e+01
-#&gt;  -5.9587e+00  1.5708e+01  7.0611e+00  5.4348e+00  6.0001e+00  1.6868e+01
-#&gt;  -1.8674e+00 -7.2686e+00 -1.3629e+00 -3.6036e+00 -2.8288e+00  5.9894e+00
-#&gt;   9.5588e+00 -5.9783e+00  1.2489e+01 -9.5533e+00 -1.2593e+01 -1.5370e+00
-#&gt;   5.0611e+00 -6.2017e+00 -1.0990e+00  4.2928e+00 -1.4533e+00  2.5336e+00
-#&gt;  -9.9759e+00  7.7356e-01  3.1047e+00  4.5059e+00 -1.8545e+00  8.5254e+00
-#&gt;  -2.8555e+00  7.5774e-01 -4.6734e+00 -1.9147e+00 -6.2242e+00 -7.5117e+00
-#&gt;   2.9050e+00  1.6507e+00 -4.5341e+00  1.0382e+01  4.2635e+00  7.0577e+00
-#&gt;   5.3112e+00 -2.2945e+00  1.4607e+00  7.8600e+00  1.4061e+00  6.0951e+00
-#&gt;   1.2049e+01 -3.9551e+00 -5.6236e-01  8.5452e+00 -1.8900e+01  3.7461e+00
-#&gt;   1.5005e+01 -7.1362e+00 -4.8445e+00 -3.0704e+00 -3.5547e+00  1.1457e+00
-#&gt;   1.3002e+01  7.6447e+00 -7.8157e+00 -1.7125e+00  8.0077e+00 -2.6252e+00
-#&gt;   5.0920e+00 -3.6717e+00  1.7916e+00  4.4889e-01  5.6752e+00 -9.5060e+00
-#&gt;  -2.7244e-01 -4.0171e+00  3.9467e+00  3.6084e+00  3.1501e+00  1.2151e+01
-#&gt;  -3.7244e+00  3.3739e+00 -3.6722e+00  5.8942e+00  5.2715e+00  2.0435e+00
-#&gt;  -1.8273e+00 -1.5823e+00  2.5105e+00 -9.3614e+00  6.9726e+00 -1.9305e+00
-#&gt; 
-#&gt; Columns 7 to 12  3.0094e+00 -1.9944e+01  9.2199e+00 -4.1236e+00  7.9477e-01 -5.5829e-01
-#&gt;  -9.2996e-01 -2.1613e+00  1.4919e+01 -8.4496e+00  8.1955e+00  1.4342e+01
-#&gt;  -9.8228e+00  1.5612e+00  4.8537e+00  3.4198e+00  3.5300e+00  6.2088e+00
-#&gt;   6.8322e-02 -3.7477e+00  2.8179e+00 -7.0389e-01 -2.0919e+00  4.3040e+00
-#&gt;   1.8218e+00 -1.0111e+01 -2.9359e+00 -1.8720e+00  5.8211e+00  7.7770e+00
-#&gt;   4.2999e+00 -8.4450e+00  2.4452e+00  1.0770e+01 -1.0544e+01 -1.7070e+00
-#&gt;  -1.2500e+01  2.3454e-01 -4.1861e+00 -7.4854e+00  9.0363e+00 -2.1330e-01
-#&gt;  -5.9183e-01  4.5566e+00  6.5519e+00 -9.1348e+00  1.0225e+00  9.7079e+00
-#&gt;  -2.3761e+00 -6.4641e+00  8.1841e+00 -5.7318e-01  6.0317e-01  1.2388e+01
-#&gt;   6.3004e+00 -1.4432e+00 -1.1656e+01  1.6576e+00 -6.4202e+00 -7.4193e+00
-#&gt;  -9.4966e+00  6.3622e+00  7.4412e+00 -2.1124e+00  6.1628e+00 -1.2935e+01
-#&gt;  -4.2430e+00  4.2747e+00 -5.0427e-02  3.2777e+00  3.0763e+00 -4.7529e+00
-#&gt;   4.5876e+00  4.7082e-01  1.8859e+00  9.2255e+00 -1.0144e+01 -3.2237e+00
-#&gt;   1.3728e+01 -1.1327e+01 -7.4635e+00  8.2156e+00 -1.0938e+01 -4.7432e+00
-#&gt;   2.5406e+00 -2.7475e+00  1.2176e+01 -5.1162e+00 -5.8617e+00  1.0418e+00
-#&gt;  -3.4461e+00 -2.8969e+00 -2.4280e+00 -9.4252e+00  3.3128e+00 -6.2812e+00
-#&gt;  -6.1341e+00  4.6496e+00 -6.9009e+00  1.8814e+00 -4.8055e+00 -1.4371e+01
-#&gt;   8.3296e+00 -1.0248e+00  6.0233e-01 -9.1593e+00 -1.0965e+01 -9.0577e+00
-#&gt;  -1.1146e+01  4.8925e+00  6.1547e+00 -5.6911e+00  3.2594e+00  2.1478e+00
-#&gt;   2.9491e+00 -2.6244e+00 -2.3602e+00 -1.7067e+00  1.2867e+01  6.9866e+00
-#&gt;   4.9031e+00 -3.8184e+00  4.5065e+00  7.9431e+00  7.9876e+00 -8.2641e+00
-#&gt;   3.6047e+00 -1.3839e+00 -3.0610e-02 -9.5192e+00  3.6270e+00  1.2379e+00
-#&gt;   5.6368e+00 -1.0192e+01  1.3779e+01 -9.3259e+00 -4.9011e-01  8.3683e+00
-#&gt;   1.7016e+00 -4.2857e+00  5.8179e+00  1.7731e+01 -2.4056e-01 -4.4694e+00
-#&gt;   7.8881e+00 -5.7162e+00 -2.1972e+00  2.4176e+00 -1.0857e+01 -7.9870e+00
-#&gt;   1.0939e+01  3.6670e+00 -3.9156e+00 -4.1783e+00  3.6431e+00 -6.2428e-01
-#&gt;   6.2709e+00  5.6536e+00  2.3202e+00 -4.0239e+00 -1.7894e+00  3.0902e+00
-#&gt;  -7.5298e+00  5.3961e+00  1.1580e+00 -3.4156e+00 -4.9876e-01 -2.7696e+00
-#&gt;  -3.4698e+00  2.3131e+00 -4.6084e+00 -6.4530e+00 -3.6590e-01 -5.0079e+00
-#&gt;   5.2807e+00  5.8571e-01 -4.9726e+00 -6.1881e+00 -4.0154e+00 -4.6596e+00
-#&gt;  -5.0770e+00  3.7546e+00 -5.0477e+00  2.8043e-01  5.8561e+00 -4.3006e+00
-#&gt;   2.0923e+00  7.7939e+00 -6.0092e+00  4.4491e+00 -6.0429e+00 -4.9322e+00
-#&gt;   1.9919e+00 -1.5663e-01 -3.9756e-01  1.7887e+00 -3.3827e+00  2.7350e+00
-#&gt; 
-#&gt; Columns 13 to 18 -5.9360e+00 -1.8245e+00 -2.8236e+00 -6.7908e+00  1.0127e+01  6.2190e+00
-#&gt;  -5.0947e+00 -5.6744e+00  2.3488e-01 -2.7034e+00  9.7043e+00 -1.7191e+00
-#&gt;   6.3105e+00  4.1589e+00  7.5243e+00 -1.3825e+01 -4.7072e-01 -2.2969e+00
-#&gt;  -6.3437e+00 -1.4535e+00  8.3249e+00  9.1984e+00  6.2328e+00 -7.9188e-01
-#&gt;  -3.1559e+00  3.4781e+00  3.1863e+00  1.6908e+00  3.4632e+00 -1.0485e+00
-#&gt;   8.0635e+00 -1.1892e+00  2.0476e+00  4.6725e+00 -1.2527e+01  8.4767e+00
-#&gt;   1.1470e+01  4.2154e+00  1.6286e+01 -1.5180e+01  1.2228e+01 -1.8405e+00
-#&gt;   8.0459e+00  7.5761e-02  6.9279e+00 -1.6449e+01  2.6666e+00  7.4848e+00
-#&gt;   1.0683e+01  7.9085e+00  8.9545e+00 -8.5484e+00  1.3577e-01  3.2129e+00
-#&gt;  -6.3425e+00  2.5162e+00  1.5291e+00  1.4065e+01  3.4508e+00 -9.7429e+00
-#&gt;  -4.8450e-01 -1.3638e+00  2.9909e+00 -1.4983e+00  7.3294e+00 -1.1253e+01
-#&gt;   6.6605e+00 -5.3380e+00 -5.1814e+00 -3.5894e+00  1.1217e+00  1.0886e+00
-#&gt;   1.0313e+00 -4.1733e+00  2.4452e-01  3.8120e+00 -4.8046e+00  7.3577e+00
-#&gt;  -1.1659e+01 -3.2439e+00 -6.9736e-01  1.5319e+01 -1.0852e+01  1.3042e+01
-#&gt;  -4.2612e+00 -4.3212e+00  3.1732e+00 -3.7711e+00 -2.0935e+00  4.6552e+00
-#&gt;   8.6718e+00  7.3055e-01  1.7207e+01 -1.0589e+00  5.2292e+00 -2.3780e+00
-#&gt;  -8.6806e+00  6.9841e+00  1.9570e+01  2.4538e+00 -7.9174e+00  4.6372e+00
-#&gt;  -7.9581e+00 -2.5169e+00  8.6734e+00  2.1345e+00 -1.0157e+00  6.4947e+00
-#&gt;  -4.8345e+00 -7.9342e+00  3.5246e+00 -3.9864e+00  1.0152e+01  4.7751e-01
-#&gt;   7.7144e-01  5.7363e+00  4.5286e+00 -1.8064e+01  9.0516e+00  4.1435e+00
-#&gt;  -1.3319e+01  6.3517e+00 -4.5950e+00  3.3305e+00  1.6889e+00 -1.5463e-01
-#&gt;   7.5183e-01 -4.9352e+00  4.9088e+00 -8.5980e-01  1.7451e+00 -2.1619e+00
-#&gt;   2.6515e+00 -8.6047e+00  2.2431e+00 -1.0112e+01  1.4079e+01  8.4864e-01
-#&gt;   8.4292e+00  4.9300e-01  1.6823e+00  5.8517e+00 -7.8911e+00 -1.5197e+00
-#&gt;  -4.3946e+00  1.0549e+00 -1.9719e+00  3.9475e+00 -1.2227e+01  8.0858e+00
-#&gt;  -5.3340e+00 -8.5060e+00 -9.8558e+00  2.2584e+00  5.1082e-01  2.5456e-01
-#&gt;  -3.3074e+00 -4.3921e+00 -4.0937e+00 -6.6596e-01 -1.5058e+00  3.8685e+00
-#&gt;   8.7555e+00  5.4638e-01 -3.0147e+00 -1.0155e+01  1.4231e+01 -3.7709e+00
-#&gt;   1.6190e+00  3.6647e-03  3.7832e+00  5.7492e+00  2.2228e+00  4.2877e+00
-#&gt;   2.1721e+00  2.7710e-01 -3.8969e+00  1.0844e+01 -5.7061e+00 -9.3995e-01
-#&gt;   1.7055e+00  3.6109e+00 -1.6147e+00 -2.2704e+00  4.1695e+00 -1.0406e+01
-#&gt;  -3.8161e+00 -8.0122e+00  4.4045e-01 -6.7354e+00 -6.0975e+00  8.3236e+00
-#&gt;   3.2929e-01  1.8431e+00  2.5784e+00 -1.4883e+01  7.7930e+00  3.9683e+00
-#&gt; 
-#&gt; Columns 19 to 24 -7.5268e+00  3.0739e+00 -5.7209e+00  3.4643e+00 -5.9756e+00 -1.2192e+00
-#&gt;   9.3178e+00  6.1341e-01  3.8599e+00  9.1067e+00  3.8392e+00 -5.5726e+00
-#&gt;   6.2547e-01 -2.0583e+00  5.8964e+00 -9.9048e+00  1.5425e+01  1.3273e+00
-#&gt;   5.3188e+00 -6.6433e+00  8.9013e+00 -1.5653e+00  2.7332e+00 -2.5712e+00
-#&gt;   7.2660e-01  7.5049e+00  1.0780e+00 -3.1281e+00 -6.9511e+00 -7.5640e+00
-#&gt;   1.1623e+00  5.6797e+00 -8.4517e-01  1.8663e+00  1.4525e+01 -4.2588e+00
-#&gt;  -3.6788e+00  7.0740e-01  3.5869e+00 -5.2371e-01  3.1667e+00 -1.1896e+01
-#&gt;  -2.9809e+00  9.7337e-01 -7.8560e+00 -9.2134e-01  1.3600e+01 -9.5273e+00
-#&gt;  -3.8693e+00 -1.2499e+00  4.2433e+00 -9.3950e+00 -6.7237e+00 -7.6876e-01
-#&gt;   1.8552e+00  1.2372e+00 -2.3881e+00  8.0670e+00 -7.4468e+00 -2.9651e+00
-#&gt;  -4.1716e+00 -4.7431e+00 -1.0297e+00  1.1070e+01  1.0140e+01  4.0286e+00
-#&gt;  -3.8449e+00  7.2667e-02  6.9501e-01  8.1294e+00  4.9778e+00 -3.4572e+00
-#&gt;   7.6907e+00 -4.6111e+00 -6.9592e+00  7.5880e+00 -2.9393e+00  8.0160e+00
-#&gt;  -3.1194e+00 -3.2233e+00  3.7037e+00 -5.8724e+00 -1.2456e+01  4.8039e+00
-#&gt;  -8.8325e-02 -1.8305e+00  6.7245e+00 -3.7756e+00 -5.7763e+00 -5.9979e+00
-#&gt;  -4.6302e+00 -7.0964e-01 -5.9018e+00  5.6902e+00 -3.6060e+00 -1.0528e+01
-#&gt;   2.2876e+00 -3.0434e+00 -7.9529e+00  2.6782e+00 -1.1646e+01  3.1496e+00
-#&gt;  -3.2801e-01 -3.7378e+00 -1.0493e+01  5.9841e+00 -9.9064e-01 -8.0748e+00
-#&gt;  -1.0794e+00 -4.5775e+00  8.5974e-01  1.2944e+01 -1.9286e+00  6.4937e+00
-#&gt;   6.7339e+00 -7.0636e+00 -4.1328e+00  2.1630e+00 -9.9488e+00 -1.0845e+01
-#&gt;   3.9262e+00 -4.0431e+00 -9.7745e+00 -6.9191e-01 -5.2872e+00  1.1717e+01
-#&gt;  -7.3929e+00  4.7978e+00 -1.1911e+01 -4.5670e+00  5.5021e-01  3.3767e-01
-#&gt;   2.8182e+00 -6.8408e+00  7.5317e+00  5.6656e+00 -2.3736e+00 -3.7998e+00
-#&gt;  -9.9837e-01 -5.0038e+00  6.1053e+00  7.8743e+00 -7.9630e+00 -6.1131e-01
-#&gt;  -6.3397e+00  2.6349e+00 -9.7098e+00  1.0105e+01 -5.1866e+00  4.7680e+00
-#&gt;  -7.4612e+00  3.2792e+00 -2.6367e+00  6.1745e+00 -3.8032e+00 -1.0991e+00
-#&gt;  -3.6866e-01 -4.3113e-01  5.0581e+00 -2.4265e-01 -4.3356e+00 -4.2670e+00
-#&gt;   3.5061e+00 -5.8322e+00 -2.2268e+00  1.0168e+01  3.5164e+00 -1.3477e+00
-#&gt;  -1.1579e+01  5.9794e-01 -2.8078e+00  5.3996e-01  3.2398e+00  2.7812e+00
-#&gt;  -2.4280e+00  4.8507e+00 -3.7711e+00  3.9562e+00  8.1723e+00 -3.7480e+00
-#&gt;   2.3320e+00 -5.4770e+00  1.2695e+01 -9.8039e+00 -1.0727e+01 -3.7466e+00
-#&gt;  -4.5659e+00 -7.6336e-01 -2.4638e+00 -2.7705e+00  1.8808e-01  3.7249e+00
-#&gt;  -4.1240e+00 -9.3039e+00  6.1160e+00 -4.7119e+00 -6.3784e+00 -3.3162e+00
-#&gt; 
-#&gt; Columns 25 to 30  2.5887e+00  2.2935e+00  1.9649e+00  3.5451e+00  7.7730e+00 -1.7670e-01
-#&gt;  -1.3803e+00 -9.6403e+00 -1.1060e+00  5.2263e-01  9.2718e+00 -2.3045e+00
-#&gt;  -7.1514e+00  2.2065e-01 -7.5313e+00  1.0995e+00 -1.1730e+01 -6.2081e+00
-#&gt;  -1.4536e+00  6.1842e+00 -3.9985e+00  2.8149e+00  3.1626e+00  4.4393e+00
-#&gt;   8.3566e+00  5.3874e+00  4.2045e-01 -7.4698e-01 -8.4653e+00  6.6114e+00
-#&gt;  -1.6220e+01  1.0924e+01 -5.7541e+00  2.9574e+00 -1.2784e+00  1.3077e+01
-#&gt;   1.0072e+01 -9.5556e+00 -5.1834e+00 -2.6780e+00  4.7677e+00  1.1810e+00
-#&gt;   2.3815e+00 -1.1907e-01  5.2789e+00  6.1286e+00 -2.4604e+00  8.4872e-01
-#&gt;  -2.5160e+00 -2.2807e+00 -4.2638e+00 -3.0930e+00 -5.8365e+00  6.1690e+00
-#&gt;   3.8296e+00 -7.2075e+00  1.1497e+01 -2.3230e+00  6.2188e+00  6.0040e-01
-#&gt;  -8.3924e+00 -1.0899e+01 -7.7562e-01 -4.1228e+00  8.8129e+00 -3.2171e+00
-#&gt;   9.6811e+00 -1.3321e+00  3.0957e+00 -8.3565e+00  6.1447e+00  1.8672e+00
-#&gt;   4.1658e-01  2.6173e-01 -1.5451e+00 -1.2132e+00 -3.8274e+00  8.7015e+00
-#&gt;  -2.5793e+00  9.6243e+00  3.7824e+00  9.2569e+00  6.3162e+00  2.5759e+00
-#&gt;  -3.7824e+00  1.9437e+00 -6.9945e+00  6.6013e+00  3.8416e+00  3.3050e+00
-#&gt;   5.8479e+00 -7.6219e+00  3.1383e+00  9.2510e-01 -2.6181e+00 -2.8247e+00
-#&gt;   6.2220e+00  3.0906e+00  8.7725e+00 -7.9736e+00  7.8358e+00  3.2331e+00
-#&gt;   3.7516e+00  4.0380e+00  1.4119e+01  6.9900e+00  7.7370e+00 -9.9313e-01
-#&gt;   1.7816e+00 -1.9872e+00  5.9134e+00 -8.5231e+00  2.8282e+00 -8.7307e-04
-#&gt;   5.7482e+00 -5.5906e+00 -4.0816e-01 -3.6816e+00  5.4810e+00  4.3547e+00
-#&gt;   1.4656e+00 -1.2978e+00  3.5642e+00 -2.2850e+00  5.3190e+00 -5.3999e-01
-#&gt;   3.7373e+00  9.5583e+00 -4.0452e+00  3.7477e+00 -9.0404e-01  8.1428e+00
-#&gt;   3.1450e+00 -2.8079e+00 -4.6845e-01 -9.9126e+00  1.4725e+00  2.2670e+00
-#&gt;   6.0488e-01  7.0981e+00  3.6024e+00 -7.3691e+00  3.3677e+00 -7.0932e+00
-#&gt;  -1.6515e+00 -2.2773e+00  5.3539e+00  1.2602e-02 -1.3188e+00 -2.7170e+00
-#&gt;  -4.2720e+00 -9.6251e+00  9.0629e+00 -4.5116e-01  9.3857e+00 -7.8525e+00
-#&gt;   2.3567e+00 -1.4090e+01  1.4367e+01  2.5887e+00  1.3665e+00 -6.0053e+00
-#&gt;   1.0195e+01  1.2377e+01  1.7317e+00 -9.8303e+00 -9.8185e-01  2.4457e-01
-#&gt;   4.6918e+00  2.3875e+00  1.2650e+01  6.2390e-01  3.8500e+00 -8.0340e-01
-#&gt;   3.2844e+00 -4.7655e+00  4.6736e-01  2.3433e+00 -2.7576e+00 -9.2043e+00
-#&gt;  -9.5269e+00 -8.7066e+00 -9.5476e+00  7.6292e+00  7.5704e+00  2.6375e+00
-#&gt;   1.3800e-02  3.5012e-01 -1.6677e+00  5.1254e+00  9.5566e+00 -6.0825e+00
-#&gt;  -6.9850e-01 -9.2740e+00 -4.4907e+00  8.1648e+00  1.5736e+00  3.5277e+00
-#&gt; 
-#&gt; Columns 31 to 36 -1.4096e+00 -5.4610e+00  2.5577e+00  1.4734e+01 -5.0446e+00  3.2328e+00
-#&gt;   5.1137e+00  9.1216e+00 -7.8775e+00  3.3966e-01 -1.0373e+00 -1.5945e+00
-#&gt;  -4.6091e+00  1.8677e+00  6.8544e-01 -3.2319e+00 -3.2252e+00 -1.4705e+01
-#&gt;  -6.6846e-01 -7.3227e-01 -1.4996e+00 -6.8364e-01 -5.6464e+00 -8.0275e+00
-#&gt;  -3.9895e-01  7.4033e-01  5.1326e+00  5.0590e+00  5.2604e-01  6.7448e-01
-#&gt;   9.9863e-01  3.2084e+00  9.3861e+00  2.7597e-01 -1.4062e+00  9.4281e-01
-#&gt;   1.4823e-01  6.2729e+00 -2.2112e-01  1.4402e+00 -5.8112e+00 -6.3623e+00
-#&gt;   4.1057e+00  8.9076e+00 -5.4154e+00  9.6999e+00  7.8314e+00 -5.1803e+00
-#&gt;  -5.1784e+00 -7.8750e+00  1.1021e+01  4.2520e-01 -1.0500e+01  1.5145e+00
-#&gt;  -1.0805e+01 -8.0081e-01 -1.3308e+00  1.0340e-01  4.4480e+00 -9.5788e-01
-#&gt;  -7.3688e+00  3.2670e+00 -4.4798e+00 -5.0912e+00 -4.9489e+00  1.0550e-01
-#&gt;   1.1037e+00  2.6704e+00 -1.1555e+00  2.5583e+00  1.9256e+00  3.7869e+00
-#&gt;  -2.9483e+00 -9.5689e+00  3.1632e-01 -3.9603e+00 -2.5554e+00 -2.7902e+00
-#&gt;   4.2042e+00 -6.9982e+00  5.9082e+00 -3.0647e+00  7.9535e+00  7.8961e+00
-#&gt;   3.6583e+00 -1.8373e+00  4.4647e+00 -9.5580e-02 -1.6832e+00  1.1353e+01
-#&gt;  -1.0082e+01 -1.8665e+00  2.3081e+00  7.1025e+00  1.9871e+00  3.6971e+00
-#&gt;  -6.7471e+00 -5.1194e+00 -2.0369e+00 -5.4193e+00  4.6686e+00  7.9556e+00
-#&gt;  -9.5882e-02  2.1174e+00  5.5369e-01  1.0547e+01  1.4830e+01  9.9312e+00
-#&gt;  -2.8182e+00  7.1967e+00 -7.2068e+00  2.8902e+00 -1.2322e+00 -1.4218e+01
-#&gt;   1.1043e+01 -4.6925e+00 -3.9115e+00  3.2115e+00  8.5972e-01  6.2885e+00
-#&gt;   3.7955e+00 -3.8116e+00 -8.3687e+00  4.8270e-01 -2.1999e+00 -1.2051e+00
-#&gt;   1.2773e+00  5.0312e+00  4.3694e+00  3.3564e+00  1.0116e+00  6.0543e+00
-#&gt;   9.2709e+00 -6.2722e+00  4.3283e+00  1.6144e+01 -1.0675e+01 -3.2221e+00
-#&gt;  -1.4062e+00 -6.2194e+00 -9.3249e+00 -6.7695e+00 -9.3819e-01 -8.5845e-01
-#&gt;  -4.4373e+00 -8.0609e+00  7.4858e+00 -2.3924e+00  1.9384e+00  6.4661e+00
-#&gt;  -2.5268e+00  7.2814e+00  5.3543e+00  5.1885e+00  3.8323e+00  5.8970e+00
-#&gt;   1.4056e+01  5.5286e+00  2.6198e+00 -2.4195e+00  8.1921e-01  1.8275e+00
-#&gt;   3.3637e+00  1.0628e+00 -4.0594e+00  3.8566e+00  2.4774e-01 -4.2937e+00
-#&gt;   7.0143e+00  4.1374e+00  7.6241e+00  8.0654e+00 -1.1274e+01  6.7890e-01
-#&gt;   4.6884e-01 -3.8017e+00 -5.9935e+00  1.9922e+00 -6.9982e+00 -1.9401e+00
-#&gt;  -1.3549e+00 -1.2055e+00 -5.0854e+00 -3.8936e-02  5.0901e+00  6.2371e+00
-#&gt;  -1.3841e+00 -1.3637e+00  2.8431e+00 -4.6878e+00  1.5813e+01  1.0143e+01
-#&gt;   1.4965e+00 -8.2697e+00 -2.6655e+00  2.5604e+00 -3.1328e+00 -1.4820e-01
-#&gt; 
-#&gt; Columns 37 to 42  4.5812e+00 -3.7560e+00  5.2754e+00 -7.6497e+00  4.0308e+00 -2.1650e+00
-#&gt;   5.1034e-01 -3.6570e+00  7.3188e+00  4.3913e-01  7.8465e+00 -8.7358e+00
-#&gt;   4.2948e-01 -3.8081e-01 -1.0295e+01 -1.1294e+01 -2.3964e+00 -1.9810e+00
-#&gt;   2.9192e+00  3.8087e+00  5.7936e-01 -8.4333e+00  1.0835e+00  3.8117e+00
-#&gt;  -9.6431e+00 -1.7431e+01  1.2983e+00  3.9097e+00  5.8001e+00 -1.2589e+01
-#&gt;   3.6394e+00  1.7255e+00 -2.1675e+00  1.6147e+00  9.1221e-01  7.1667e+00
-#&gt;   4.4026e+00  1.8468e+01 -7.8770e+00 -3.7911e+00 -6.4780e+00 -6.6981e+00
-#&gt;  -1.5611e+00  6.8740e+00  7.1020e+00 -3.8851e+00  2.3308e+00 -8.9564e+00
-#&gt;   6.5641e-01  5.5700e+00 -8.6624e-02 -8.3704e+00 -8.2052e+00 -2.3612e+00
-#&gt;   2.9536e+00 -3.8561e-02  3.6217e+00  2.3027e+01  1.6312e+00 -8.3004e-01
-#&gt;  -1.3279e+00  6.4598e+00 -1.1885e+01 -1.9173e+01  3.9224e+00  3.3208e+00
-#&gt;  -5.7886e+00 -2.5071e+00 -8.9809e+00 -3.2475e+00  5.2556e+00 -4.2559e+00
-#&gt;   1.3851e+00  8.1814e+00  7.0289e+00  7.5062e+00 -5.5757e+00  4.0470e+00
-#&gt;  -9.2318e+00 -3.7726e+00 -2.6049e+00 -3.7187e+00 -5.1426e+00  2.7971e-01
-#&gt;  -3.6830e+00 -6.5899e+00  5.5523e+00  2.3852e+00  1.3684e+01 -1.3302e+01
-#&gt;  -7.3371e+00  5.0715e+00  4.1084e+00  1.2214e+01  6.7292e+00 -8.5733e+00
-#&gt;  -6.1951e-01  6.2342e+00  1.3022e+01  1.7293e+01 -3.9369e+00  2.9165e+00
-#&gt;   1.3633e+00  5.1330e-01  4.3325e+00  1.5478e+00 -7.4928e+00 -5.0358e+00
-#&gt;  -6.6869e+00 -4.0022e+00 -8.1790e+00 -1.2048e+01 -6.8049e-01 -2.0287e+00
-#&gt;   2.7660e+00  9.2773e+00  1.3033e+00  6.1846e+00  3.3976e-01 -8.2155e-01
-#&gt;  -6.9719e+00  1.2367e+01  2.1024e+01 -5.7003e+00 -1.5388e+01 -1.0581e+01
-#&gt;   6.7371e+00  9.0502e+00  3.9023e+00  3.1597e+00 -8.7243e+00 -6.9801e+00
-#&gt;  -6.4155e+00  8.8098e-01 -4.0530e+00  2.2637e+00  9.7988e-01 -6.6191e+00
-#&gt;  -1.3935e+01 -4.9170e+00 -2.3810e+00  1.7937e+00  3.8555e+00 -7.4160e+00
-#&gt;  -1.0939e+01  8.3475e-01 -1.1251e+01  3.9114e+00 -9.1608e+00  9.5725e+00
-#&gt;  -1.0637e+01 -1.7639e+00 -6.6008e+00  6.1653e+00 -5.0299e+00 -8.8578e-01
-#&gt;   4.5978e+00  5.0808e+00 -6.8893e+00 -2.2602e+00 -7.1628e+00  1.0474e+01
-#&gt;  -6.3408e-01  8.6280e+00 -8.0533e+00 -5.7377e+00 -2.5849e+00  2.1175e+00
-#&gt;   1.0419e+01  1.4053e+01  1.6833e-01 -9.7024e+00 -6.9853e+00  2.8325e+00
-#&gt;   1.2727e+00  3.9953e+00 -3.3612e-01  6.2437e+00  9.9415e+00  5.1424e+00
-#&gt;  -2.8018e+00 -2.2976e+00 -2.4187e+00 -1.0343e+01  5.2141e+00 -4.8749e+00
-#&gt;  -1.3271e+01  6.4375e+00 -1.0524e+01  5.7403e+00 -6.2767e+00 -7.8805e-01
-#&gt;   5.6858e+00 -1.8301e+00 -4.6521e+00 -1.1225e+01 -2.2636e+00 -4.8880e+00
-#&gt; 
-#&gt; Columns 43 to 48  2.0825e+00  4.9907e+00  1.9236e+00 -6.2419e-01  7.3296e+00  6.0031e+00
-#&gt;  -5.1543e+00 -1.7060e+00 -1.2590e+01 -4.5041e+00 -6.0333e+00  8.3129e-01
-#&gt;   2.0518e+00  9.3912e+00 -4.2104e+00 -9.2941e+00 -6.7906e-01 -5.4954e+00
-#&gt;  -1.1870e+00 -4.9190e+00  5.5326e+00  5.7155e+00 -2.5322e+00  6.1449e+00
-#&gt;   3.5773e+00  1.9501e+00 -1.4223e+00  5.8473e+00  2.8632e+00 -1.5511e+00
-#&gt;  -1.8891e+01  1.7914e+00  9.5288e+00 -1.5020e+01  3.1886e+00  1.1058e-01
-#&gt;   9.5395e-01 -1.7922e+00 -9.4857e+00  1.0466e+01 -1.4195e+01  9.3693e+00
-#&gt;  -4.5003e+00  4.9413e-01  5.0566e+00 -6.7241e-01 -1.7252e+01 -2.0448e+00
-#&gt;  -3.2866e+00 -1.5250e+01  1.8232e+00  1.0501e+01 -1.0169e+01 -3.8126e-01
-#&gt;  -3.0327e+00 -3.2125e-01  5.1990e+00  5.6279e-01  4.8496e+00  1.6290e+01
-#&gt;   3.3497e+00  3.7063e+00  2.5383e+00 -6.9089e+00 -8.8328e+00  1.6036e+01
-#&gt;   7.4564e-01  2.1015e+00 -2.6245e+00  3.6182e+00 -1.2122e+01  6.8218e+00
-#&gt;  -1.0149e+01  1.9998e+00  9.3749e+00 -3.1929e+00  7.4531e+00  8.9445e+00
-#&gt;  -4.9387e+00  1.2320e+01  1.6951e+01 -1.0779e-01  5.6769e+00  5.1763e+00
-#&gt;  -4.7388e+00  3.4479e+00  7.5650e+00 -2.7795e+00  5.3173e+00 -4.1398e+00
-#&gt;   9.3911e-01 -2.6644e+00 -1.7416e+00 -1.2298e+00 -9.2823e+00  9.1314e-01
-#&gt;  -8.0143e+00 -4.2947e+00  1.1961e+01  5.1631e+00  2.2321e+00 -7.4716e+00
-#&gt;  -3.2285e+00  3.2433e+00  3.2006e+00  2.6415e+00  1.2717e+00  5.0750e+00
-#&gt;  -1.0820e+00  5.0328e+00 -7.3675e+00  2.8608e+00  4.9745e+00  5.5441e+00
-#&gt;  -1.1025e+01 -1.3057e+00  6.9830e+00  1.3261e+00  5.2002e+00 -1.7039e+00
-#&gt;  -2.0403e+01 -2.4564e+00  1.1657e+01  2.9298e+00 -8.5363e+00 -4.2101e+00
-#&gt;   3.1805e+00 -2.1571e+00 -1.3628e+01  5.6071e+00 -4.2400e+00 -4.0485e+00
-#&gt;  -1.6257e+00 -1.3405e+01 -2.2942e-01 -9.1229e+00  8.9430e+00 -3.9940e+00
-#&gt;  -1.4142e+01  1.5606e+01  5.3896e+00 -1.9620e+00 -9.1333e-02  5.7324e+00
-#&gt;   4.3915e+00  2.6265e+00  1.2058e+01 -8.7958e+00  1.6276e+01  1.0141e-01
-#&gt;   4.6129e+00  1.3550e+00  2.4493e+00  3.3806e+00  3.2840e+00  8.9192e+00
-#&gt;   9.7383e-01 -1.2270e+01  1.2017e+01 -8.4440e-01  2.3670e-02  1.0746e+00
-#&gt;   8.3332e+00  3.7801e+00 -1.1128e+01  8.3124e+00 -1.0937e+01  6.1997e+00
-#&gt;   4.1233e+00 -8.9888e+00  3.5914e+00 -9.8030e+00 -2.8249e+00  7.2485e-01
-#&gt;   1.2840e+01 -2.7533e+00 -1.1758e+00 -8.1789e+00 -3.4476e+00  6.1761e+00
-#&gt;  -3.1257e+00  6.6395e+00 -6.6192e+00  1.5209e+01 -1.1706e+01  1.4626e+00
-#&gt;  -8.4545e+00  1.4226e+01  9.2742e+00 -7.8340e+00  1.9898e+00 -3.5491e+00
-#&gt;   4.0704e+00 -1.0808e+00  8.2068e+00  7.9025e+00 -1.0983e+01 -4.6107e+00
-#&gt; 
-#&gt; (17,.,.) = 
-#&gt;  Columns 1 to 8  -0.8957   0.9133  -7.7773   0.8101   5.6996  -0.8484  -6.8629   4.4637
-#&gt;    1.1087   0.6553  -3.1044   7.5764  -3.5507   7.9569  -8.2644  11.7114
-#&gt;   -1.1347   2.7586  13.3701  -1.5455   1.3245   3.4478  -0.8979  -1.5339
-#&gt;    0.9630  -5.2434  -9.1103  -1.6996  -4.9055  -1.2485 -11.8417  -0.2401
-#&gt;    0.3419   6.7787  -7.0244   6.8135  -3.4546   2.6689   1.8302   5.6543
-#&gt;   10.4224  -0.3220  12.0285 -15.8165   9.6847   4.2263   2.7406 -11.3027
-#&gt;   -4.7621  -0.2518  -3.3584  -3.4717  -8.1826  -7.5434   4.6487  -8.3026
-#&gt;   -4.3310   1.9411   0.6276  -1.0993  12.7893   5.2697   0.2032  -8.7685
-#&gt;  -10.2758  -5.0742 -12.4506   0.6382   3.0111  12.5470   4.5550 -15.8759
-#&gt;    0.6064  -6.1836  -4.7183  -7.3365   9.5579  -6.1642   8.6902   7.3474
-#&gt;   -1.5247   7.7421  -2.8257   8.4139  -7.9206   7.5217   3.0913   3.0714
-#&gt;   -6.1678   9.9270   1.1979   5.1805   2.5185   1.4035   0.8106 -10.5005
-#&gt;    6.2093  -6.0260  -0.8201  -6.8358   5.7874   8.0379   6.9558  -1.7438
-#&gt;   -5.6664   5.7843  15.2326  -6.9789  -5.9220 -10.0735  -5.3399   2.6775
-#&gt;    0.6465  13.1741  -7.5641   7.3568 -12.3053   6.7197 -11.4561   2.8254
-#&gt;  -14.8181   3.1872 -11.0331  -1.1910   0.6160   9.3987  10.6854   0.2042
-#&gt;   14.6794  -4.0564 -18.3920 -13.8861  -7.1076   8.7193   9.0291  -7.9646
-#&gt;   -2.8500  -0.2828   5.2092  -5.0976   2.1824  -2.7569  -9.9653  -2.8342
-#&gt;   -1.9689   4.6746  -0.1039  10.3148  -0.6793 -11.2543  -4.6086  -4.9088
-#&gt;   -4.8251  -9.5551   5.1641  -4.4307  14.7803   4.2962   0.7682  -8.7219
-#&gt;   -3.0634  -9.8832  -5.2007   3.2983  13.2174  -4.4922  -8.0478   4.1470
-#&gt;   -1.5129  -1.9173  -6.2167  -2.2313  -4.5250  -6.6696  -0.8794  -2.6323
-#&gt;   -0.6099   8.4789  -4.5116   0.5574   3.4137   5.1493   0.0434  -4.5764
-#&gt;   -2.3931  -1.4582  -0.6107   1.8647  16.5004  -4.0826  -0.6031 -14.7914
-#&gt;    2.0855  -3.4815  12.2346  -7.7258   5.0392   1.7224   9.7028  -5.6914
-#&gt;   -3.0539   4.6379   6.3584   9.7894  16.1582  -5.5860  -3.6208   4.0522
-#&gt;   -2.9685   4.7054  10.6858  -2.0099   2.1936  -6.8140  -4.4934  -7.4594
-#&gt;    5.4329   2.1185  -1.0500   0.7953  -3.1590   2.3579  -0.8411 -14.4082
-#&gt;    2.9807   7.2805   2.5951  -7.1966 -18.6689 -14.4721   5.2249   4.0944
-#&gt;  -12.6689  -7.3958  -0.7287  -7.5680  -5.6277  -8.6588   1.2493  11.0575
-#&gt;   -5.6716  -9.8731  -5.4515  11.1040  -3.9794 -11.3120  -3.5737   8.4365
-#&gt;    0.7895  -2.6145  17.4044  -7.8193   9.2040 -10.3242   0.4495  -4.8018
-#&gt;   -6.2973   0.4409  -6.7365   5.2268  -4.1707   3.8894  -8.8242  -3.1853
-#&gt; 
-#&gt; Columns 9 to 16  -3.2885   7.1868  -3.6288  10.6497  -5.8207  -9.2504   0.5996  -6.2679
-#&gt;    8.9242   1.1631 -13.6276  -2.4526  -9.9044   0.6221  -1.2559   6.7333
-#&gt;   -6.3403  -5.5540   5.4020  10.5818  -1.6068   5.3700   5.2054   6.8242
-#&gt;    5.8997   2.0353  -5.2443  -2.3748   2.5640  -9.2236 -13.3658  -4.2873
-#&gt;   -3.0662  -6.4944 -10.0449   0.0567  -2.8564   0.5511   5.4053  -2.4873
-#&gt;  -12.1699   5.7151  -1.4971  -5.3178  13.8834 -15.8153  -0.5379   1.9627
-#&gt;   12.0944  -6.4249  12.4387  -2.6954  -7.3221  -1.5340 -13.2086   3.9256
-#&gt;    0.3353   5.9040  -7.9046   1.7037  -5.3985   9.8246   9.6901  16.2667
-#&gt;   -8.8456  -6.3223  10.3767  -0.2057   6.3175  -1.9689  -0.3585   2.4860
-#&gt;  -11.6030  -2.7892   0.2611   6.4165  -6.4743  -1.0813 -11.4904   6.2912
-#&gt;  -11.9947 -11.5499  -1.5780   9.5929  -2.0500  -1.1119  -6.9285   4.5575
-#&gt;    2.6109  -3.7598  -3.5858  -4.5742   2.4488  -4.5020  -4.8436   6.0760
-#&gt;   11.6930  -1.0088  -5.8371  -7.6288  11.5150  -4.6979  -3.7715   0.3019
-#&gt;   -0.5216  -8.2137  -9.2156   6.9064   7.9199 -17.5086   0.6772   0.4293
-#&gt;    4.9131  -0.8462  -3.0693   4.0244  -0.3832 -14.1126  -0.4212   0.7982
-#&gt;  -10.4033 -23.0197   0.9966  15.5363   0.9677  -1.9102   5.0652  12.9234
-#&gt;   -0.8586  -8.1821   5.3989  -0.6412  11.9570  -2.7428 -10.2142   3.3805
-#&gt;   -4.2077 -11.7031 -10.5481   1.3759   2.0872  -8.3996   1.6865   6.8127
-#&gt;    1.6553   5.3803  -5.6872   3.6074 -13.8397  14.4929  -7.3642  -9.3488
-#&gt;   13.7179   4.5823  -8.4690   6.6404   1.8533  -3.8326 -11.3071   5.5155
-#&gt;   -8.7689   8.5579  -4.0953  -1.3172   8.8104   6.5560  -7.6428   0.6612
-#&gt;   -5.3245   3.5485   8.1448  -2.5676  -2.5868   2.8139   1.4327  -5.8530
-#&gt;    2.3187  -4.3681  -9.6178   3.3058  -3.0573   3.5394  -2.1971  -9.5240
-#&gt;  -12.3424   4.5916   0.8375  12.8865   2.3569  -3.1821  -2.2357  -0.7889
-#&gt;   -6.4146 -13.2679  -1.6415   5.2290   3.2513  -4.0675  10.6704   2.8924
-#&gt;   -1.6806 -11.4765  -3.4327  -5.7305  -6.6357  -2.7733   7.7412  -0.5135
-#&gt;    4.9325  -0.9485   6.0910 -10.2686  -7.8495   3.5259   5.0494   1.6066
-#&gt;    8.6919   2.6585  -2.2197  -6.3834   6.2155   6.8981 -12.4037   4.5688
-#&gt;   -0.2002  -6.7431   3.5234  -3.4527   0.0342   7.2138  -1.0909  -1.4564
-#&gt;    8.1970   9.7817  -0.7770   1.2676  -0.8132  -2.6508   2.8823  10.5904
-#&gt;    5.7195  -0.4828  -1.1081  -3.2300  -8.5730   0.8796 -10.3381  -1.4938
-#&gt;   10.1745  -4.5769  -4.8075   4.3697  -1.2110 -12.6386  -4.3293   9.2067
-#&gt;    5.1469 -13.2271  -1.2079   3.0729   8.4260   4.2997  -5.0520   4.9818
-#&gt; 
-#&gt; Columns 17 to 24  -5.8999   3.2196   7.4108   7.1947  -0.5510  -3.1271   2.3997   4.7674
-#&gt;    0.4936   8.3201  -2.3954  -3.1007  -5.2927   0.1601   4.4317   9.3884
-#&gt;   -2.3894  -2.5043  -0.4025  -8.4702   6.7296  -2.3731   1.2254   3.0604
-#&gt;    7.3127  11.1880   9.0929  -1.3939  -6.2199   3.4980   7.4226  -1.9657
-#&gt;    3.8318   3.3214   1.3228   8.6404  -5.9535   7.3461   7.6692   1.0457
-#&gt;    4.0341  -2.3604   9.9881  -1.1048  -0.1663   3.6636  11.5069   8.7591
-#&gt;   -3.8932  -4.5769   1.3792   1.8807   7.0083   3.1790  -1.9002  -4.8432
-#&gt;   -0.8929  -9.9323 -17.5328  -6.4454   7.4211  -2.5669   1.6103  -0.2914
-#&gt;   10.5251  -6.0160 -11.3183   2.2125  -2.7469   3.8832   8.9047   1.0072
-#&gt;   -3.5021  13.1764   2.9379  -1.4159   2.1006  -9.1955   5.5083   2.7699
-#&gt;    4.9752  -1.0943   3.6100  -1.0254   0.1235  -1.9986  -5.6683  -5.9065
-#&gt;   -6.9010  -4.4505   7.1835  12.1616   0.9225   3.1036  -1.8576  -1.4376
-#&gt;    5.1202   6.7665   7.2054 -11.4368  -8.4699  -1.2849   5.6437  -8.2311
-#&gt;   -1.7426   8.5794   4.7792  -1.9822   3.0865  21.1479  -1.1927   4.2571
-#&gt;    8.0535  10.0259  -5.0931  -2.4183   2.9957  -0.4944  -1.2353  -9.9578
-#&gt;    2.6945   6.2321 -12.6139 -13.1404   9.7509  -0.8877   8.7762   3.6675
-#&gt;   20.9414   3.3724  -5.0368 -13.8545   7.2940  10.3122  -2.2138 -12.1709
-#&gt;    4.2368   5.7209  -3.1497  -2.6154  10.0762  13.3094   6.9289  15.3782
-#&gt;    4.4038  10.7216   2.3276   7.5414   2.6321  -0.0764   3.1554   5.2606
-#&gt;   -8.9477  -8.0536  -3.1174  -8.2816   7.4909   9.5979  -9.5554  -5.8043
-#&gt;    3.3452   3.1114   3.8593  -1.7458  -1.8021  -1.5792   5.8211   6.0015
-#&gt;    0.2224  -2.3015  -1.2869  12.7543   0.1396  -0.0118   3.3825  -1.6614
-#&gt;   -5.6527  17.1612   1.4775  15.6323  17.2388  -4.2212  13.4958   5.1051
-#&gt;  -15.7948  11.6434   8.2088  -1.9623  -4.0261 -10.5744  -2.2378   2.2162
-#&gt;   -2.8310  -0.2838  -0.9188  -9.2368  -0.6431  13.7799  -0.8004   0.7121
-#&gt;  -15.7932   2.6549   1.1593  15.2095   0.7136  -4.9563   0.9821   6.1003
-#&gt;   -4.5270  -0.5837  -8.4713  -0.4181  13.3623  -2.5368   0.7471   2.7603
-#&gt;    3.0683  -6.8014   3.5343   9.8722   6.0579   1.6718  -2.3426   1.0676
-#&gt;    1.5630  -0.5463   3.8052   7.9535  -3.0730   2.2589  10.4235   2.8426
-#&gt;   -8.8355   3.0659  -3.2311  -8.4564 -10.8454 -12.1449  -4.7575  -3.6315
-#&gt;    5.2165   2.0679   0.1592  -3.1625 -11.2231  -2.8802  -4.0102   5.0782
-#&gt;    3.2510   8.6525   8.5177  -6.2565   8.4862   7.2118  -1.1435  -1.0126
-#&gt;    1.8064  -1.4386  -7.9436  -1.4348   3.9283  -4.1286  -7.7260  -9.8072
-#&gt; 
-#&gt; Columns 25 to 32  -5.4628  -2.1503   7.5308   3.3416  -1.7954   1.1780  -3.7493  -3.3003
-#&gt;   -1.0213   5.6654   4.8418   2.0009  -1.9902   4.7499  12.8304   6.5692
-#&gt;    0.7484  -4.6255  -2.3978  -3.8382  -3.3574  -4.3756   1.5885  -4.7206
-#&gt;   -2.5173   7.3390  -3.3686   0.4794   0.4701  -5.5991   1.4016  -7.0661
-#&gt;    1.8592   3.3536   7.1265  -9.0401   4.1287  10.7269  -8.6423  -5.3319
-#&gt;    4.8651   1.7671  -7.1598   6.9009  -9.2020  -1.4576  16.3859  -4.2260
-#&gt;   -7.4835  -0.8836  -3.6929   0.6542 -14.5461  -8.1399  -9.1538  -0.7031
-#&gt;   -6.3771   8.4749   2.0323 -13.1979   1.0877  16.7394   3.9611  -4.8070
-#&gt;   -2.0248  -0.3795  -3.2047  -0.2990  -3.2445  -5.7931  -9.0461   4.8141
-#&gt;   -1.6221   7.3392  -4.6775   6.5222   9.2884   5.0572   7.9044  -5.2620
-#&gt;    1.5609   6.1361   0.7698  -6.0488  13.4924  -5.2303   4.9260   8.8316
-#&gt;    0.4152  -4.0871   5.9216  -3.2029 -11.9647   8.2658   0.1236  -0.7154
-#&gt;   -3.2754  -0.5865  -0.1741   3.1197  -3.3774  -0.3422   0.4075  -1.1167
-#&gt;   -4.6476   8.2276   3.8136  -4.0465  -2.2959   7.9427  -3.7256  -3.1964
-#&gt;   -2.0111  -1.2899   4.5266 -13.2836  -4.2700   4.1085  -5.8507   7.8828
-#&gt;    4.5551   8.4775   8.0101  -6.8649  11.0356  13.3461  -8.2063   9.3098
-#&gt;   -1.3610   6.1448   3.5709 -12.9520   7.4297   0.2323  -7.1805   2.1787
-#&gt;    8.3839   9.0629  13.5152  -1.1806  -4.4290  14.7960  -0.8422  -7.6898
-#&gt;   -0.3642   7.2888  11.2845  -4.6808   4.9479  -6.3021   3.7052   4.0614
-#&gt;   -3.4502  -5.4501  -4.6223   6.2483  -2.7144  -3.2435   2.6881   0.8275
-#&gt;  -13.4386   4.7699  -3.2590  -0.9974  -0.7438 -13.0981  10.6939  -0.9954
-#&gt;    2.2781   0.2653  -3.1706   8.4947   8.0836   3.6140  -4.2029   2.2057
-#&gt;    3.0733  11.4027  -8.4896   8.1213   8.0495  -0.5772   4.5396  14.4383
-#&gt;  -13.7787  -4.5253  -8.3319   1.9762  -8.9361 -12.5650  -6.5351 -11.6579
-#&gt;   -2.0013  10.3804  -2.6648   1.1347   2.8072  -1.8974 -13.8000  -5.0189
-#&gt;   -1.1011   6.9473  -4.1039   7.6070   3.3449  11.3338   0.4421  -8.0482
-#&gt;   -1.7426   3.0699  -1.5852   2.0021  -8.0732  -0.8842  -3.8849   1.4503
-#&gt;    0.7573   5.7836  -5.1206   2.2831  -3.6523  -5.8224  -0.6530   4.8816
-#&gt;    6.3512   4.6887   3.6773  -2.5802   2.1749   0.2298 -12.2780  -0.3960
-#&gt;   -1.8628   1.0463  -0.6489   5.3649  -2.0109  11.0703  -3.9464 -12.8514
-#&gt;   -7.9961   1.3866  -0.1868   0.3007  -1.6856 -13.7017   5.9260  -2.8155
-#&gt;   -4.7343   9.9148  -4.8411   2.8250  -6.0729   7.7534  15.8867  -4.5468
-#&gt;    1.9881  -1.0494   3.0680 -14.9160  -4.8246   2.6192 -13.8249   4.3926
-#&gt; 
-#&gt; Columns 33 to 40  -4.5534   1.9049   2.2301  -8.9134 -13.2018  11.8397   0.9347   0.4591
-#&gt;   -3.5408   7.6820   2.0759   4.2478  -2.1306   9.4390  -1.9514   3.2497
-#&gt;   -5.0056  -4.3806   0.9970   0.2008   6.0334  -4.0089   3.2347  -7.1693
-#&gt;  -18.5080  -7.8409  -2.5366   0.8542  -4.3869 -13.3961 -12.4722  -9.0650
-#&gt;   -0.6311   8.5667  -1.1728  -3.0434  -5.5877   6.7917   2.1702  -1.5088
-#&gt;   -4.5045  -1.1817  12.1971   7.6255   2.6387   0.6131   2.2974  10.8470
-#&gt;   10.4231 -13.3687 -16.7691  -5.7188   4.2949  -7.9806  -8.8122  -8.2690
-#&gt;    9.2289   2.6020   7.9256   7.9635   0.8455   3.5400  17.2372  -1.3279
-#&gt;   -9.8759  -9.0630  -5.4032   4.7757 -11.8442  -7.8557 -20.4439  -1.0552
-#&gt;    0.3197  -2.1982   8.3683 -12.1285  -2.5965   0.4046  -1.7927  -4.3663
-#&gt;   -1.3880 -10.5602  -5.9017  -2.8311   9.9165  10.2241 -10.0664   9.7003
-#&gt;    7.6828  -4.8946   1.1287  -0.4769   3.1082   9.7104  -0.8719   9.3676
-#&gt;   -0.6108  -3.1946   4.9776   2.4519  -6.9426  -5.3539   0.5598  -6.0925
-#&gt;   -5.3042   0.6078  -2.7642  -8.7776  -1.6695   1.2614   7.3998   0.2852
-#&gt;   -5.5151   3.9563  -1.0132   3.2041  -4.8043   9.7591  -4.9180  14.9343
-#&gt;   13.8403   0.7607  -3.5785 -11.8638  -0.1667   3.6870  -2.8488  -2.5665
-#&gt;   -8.4550  -2.3597  -7.1927  -2.9543  -0.0183 -12.3825  -9.1236  -7.9643
-#&gt;   -0.3500   5.2196   1.6427  -7.3647  -5.9223   5.6274  13.4504   2.3196
-#&gt;   -0.8689  -7.2609 -10.7439  -7.7306   4.4620   1.4265  11.3543  -4.5805
-#&gt;    6.9292  -2.7063  14.6982  -7.0158 -12.7545  -5.9994  16.6744 -22.2591
-#&gt;    4.8937  -6.9389   1.2245  -9.6102 -10.4156  11.0756  -3.4455  -7.5596
-#&gt;    7.5873   3.2074   2.4595  -5.7699   5.6157   1.1191  -9.8080   9.3327
-#&gt;   -0.8087   0.5341   1.8226   3.4934  -5.3489   7.1886  -0.8455   6.8968
-#&gt;   -3.0221  -3.8652  -4.1174 -16.9851   1.1652  -6.1352 -10.1784  -6.0780
-#&gt;    2.0403   2.9672  -5.7012  -5.8810   1.8692  -0.5237  11.9921  -2.0380
-#&gt;   11.8653   8.0732   6.5067   0.0421  -2.6303  10.0871  13.6090   7.4506
-#&gt;   -7.9146   1.9899   1.3962  14.7993  -6.1934   0.6977  -2.2894   6.6892
-#&gt;    4.0407 -12.6677  -0.4913   0.1764   5.9447   1.8964  -0.9624  -3.7136
-#&gt;   14.0619   2.8701  -7.3742   0.5896   9.1222   6.4215  -6.6302  -4.2540
-#&gt;    1.6786  -3.1052   6.1986   0.5267  -2.9252  -2.1091  -3.8343  -3.5606
-#&gt;    9.8266   3.6244 -13.2276  -3.2288  -1.4724  -6.5761   0.6919   1.6912
-#&gt;    7.6283  -6.0566   4.3949   1.1593   2.7112  -6.8121  20.1824   1.3616
-#&gt;   -3.4224  -9.0733  -2.1197   6.4100 -10.8665  -1.1762   3.2309   3.8386
-#&gt; 
-#&gt; Columns 41 to 48   3.1944  -5.2492   3.4916   4.4356   6.8751   4.3760   9.1833  -1.4286
-#&gt;   -9.1642   4.3111  -7.1116  -0.4451  -2.9630  -3.9939  -4.2681   0.8049
-#&gt;    7.8321  -5.2698   4.0509  -0.8536 -13.5536  -4.7024   0.1228  -6.5937
-#&gt;  -15.6090  -4.4942  -6.0831  -1.9443   3.4311  15.8296  14.5438  11.2437
-#&gt;   -8.5077   1.4469  -5.0803  -9.7383   1.4971   3.5996   4.0489  -4.0086
-#&gt;    2.8627   1.7702   1.7651  -6.9564   2.0692  -1.9610  -1.0979 -12.2447
-#&gt;   -7.5514  -5.2607   2.6289  -3.2219   0.4694   8.1661 -17.5392   5.0024
-#&gt;    4.2685   2.8641   3.5730  -6.1077  -3.9480  -5.0927  -8.7375 -14.3204
-#&gt;   -8.1738  -3.1351   2.4621   1.0816   3.8892  -4.8042   6.0928   6.3055
-#&gt;   -1.3933  -2.4226 -11.4983  -0.2306   5.7605  18.0419  -3.8821  11.5576
-#&gt;    2.9656   0.9795   9.4184   0.5272  -1.3693   4.8836  -6.6682  -7.6282
-#&gt;    9.5539   7.1991   7.5133  -1.7044  -8.1477  -5.1546  -8.9182   7.0162
-#&gt;   -1.5199  -1.6830  -9.3677  -1.3064   2.3877   5.7583   6.3476   3.1143
-#&gt;    2.5398  -1.2269 -11.1556   4.0165   4.7477   8.8398  12.9522   4.6047
-#&gt;   -1.9705   3.3363  10.4356  -8.9923  -1.5369  -3.2073  11.7645  -7.4794
-#&gt;   -2.8307  -6.6521   8.0105 -13.7085   4.6357   5.4463  -7.8626  -1.6915
-#&gt;  -13.2651   2.8052  -2.0827   1.5752  10.1198  -0.8197  -2.0813   7.8306
-#&gt;   -5.3472   3.4146  -2.3632  -0.6688   4.9751   3.3038   2.6746  -6.8108
-#&gt;    3.7636   0.1522  -3.0695  -0.0961   0.6504   7.2467  11.8165   6.8450
-#&gt;    2.2346   1.5209  -9.8162  10.0368   2.7487  -4.2364  -4.3300  -1.9671
-#&gt;    8.2369   2.1709  -8.3422  16.8059  -8.2435  -3.8983   5.4238  18.3885
-#&gt;  -12.7218 -10.8518   6.8189  -6.5487   8.7718   8.9600   3.7359   1.0525
-#&gt;   -1.1842   0.6400  -5.2801 -14.3687   1.3197  -7.3196  15.5994   5.9581
-#&gt;   13.7268  -0.7578   2.9104   4.0788  -8.6151  -9.6813  -2.4246  25.4784
-#&gt;    7.2466  11.7216  -2.9668   2.0328  10.8977   2.2582  -9.0059  -2.8960
-#&gt;   11.2319   7.0766   0.6424   4.3856  -0.6871   0.9848  -8.0389   0.6461
-#&gt;   -1.5800  13.8104  -8.9993   4.6309   7.6692  -9.4410   3.8508  10.6504
-#&gt;   -2.7035  -2.3572   0.2021  -3.5377 -12.1272  -0.0958  -3.9344   8.2155
-#&gt;   -2.7671 -11.0014  -2.4527  -4.9959  13.5354  17.8385  -5.1663  -8.3733
-#&gt;   -4.4979  -0.8367  -0.0149  -8.4675   3.9075  13.8436  -2.7044   0.2681
-#&gt;    9.3785   5.2808   5.3096  15.7158  -0.4866   3.1434  -7.4671  -3.6104
-#&gt;    9.8102  14.9225  -5.8216   1.2088  -3.6336   5.9941   2.7037   8.9727
-#&gt;   12.1992   2.9667   4.2158  -1.1313  -3.0329  -5.2573   4.1910   0.9479
-#&gt; 
-#&gt; (18,.,.) = 
-#&gt;  Columns 1 to 8   2.7639   9.5167   4.9895  -6.1387   3.7808   0.2192  -5.3810 -10.9075
-#&gt;   -1.4025   4.8328   1.0638  -1.6674  -3.9742  14.2987  -0.0210  -2.9820
-#&gt;   -2.9803  -4.4852   5.0743   7.4367  -0.8519 -10.1946  -0.3986   8.3512
-#&gt;    6.2243   6.6300   5.5545   2.0601  -8.0294  -4.7771  -0.8925  12.9316
-#&gt;   17.9180   7.5003   4.4401  10.4903   4.9368  12.7047  -2.3953  -8.3566
-#&gt;    9.1864   2.7902   2.9887  -6.1746  -1.0645 -15.5765  11.0697   2.3087
-#&gt;   -4.2358   5.7118  -8.3034   4.2734  -9.1646  -5.0029   3.2965  -4.1212
-#&gt;   -4.5165 -10.3084  -7.7550   6.9415   2.8986   4.6530  10.7622   0.7905
-#&gt;    7.3138   1.6046  -3.8516   1.0577   2.5517  -1.0334  -1.5495  -3.8386
-#&gt;   -6.8302   6.7344   4.2211  -0.0361 -12.1579  -1.1337  -3.5491  -4.2010
-#&gt;  -11.0679  -3.7512 -10.4915  -7.4708   1.5401  -6.1699 -11.4766   7.0921
-#&gt;    2.8687   0.0873 -13.2660   4.5316   5.4170  -4.0493   2.0258 -10.9518
-#&gt;    3.1448   0.9238  -0.6453  -6.8931 -10.0159   3.5715   2.2664  -0.1047
-#&gt;   14.9432  -2.4587   8.2097   1.3062   0.3193   0.5201  -3.8163  -2.6040
-#&gt;    2.2700   1.1425  -2.7445  -5.6329  -2.3061   6.0993   5.1187  -6.4215
-#&gt;   -3.5758   0.3435  -0.1980   7.1061  -3.7888   1.4207  -0.9326  -7.6890
-#&gt;    2.5027  -1.9327  -2.9419  -8.7511  -5.5105   8.2263  -0.3020  -5.8838
-#&gt;   10.5329   1.4515  -2.9958  -9.5455  -3.2040   8.4232   0.2680 -10.4985
-#&gt;   -5.5102   8.8796   5.1902  -0.7381  -0.9571  -8.0159  -8.6572  -1.5995
-#&gt;    0.5274   1.9936   0.6238  -3.4404   5.1759   7.7158   8.7939  -5.4514
-#&gt;   -2.4013   1.1787  -7.5370 -13.4210   1.6146  -3.2490 -23.6894   2.5978
-#&gt;    5.8812   2.8483  -5.0419  -6.3385   3.0311   0.3390  -2.7283   0.2030
-#&gt;    1.3460  15.7328  14.6294   6.4444  -2.6377  -2.3493   0.9399 -11.2640
-#&gt;   -8.4286   0.6870   1.4715  -3.7041   3.4331 -11.3449  -2.1769  -9.8178
-#&gt;    4.0793 -17.6783   5.4600   2.8805  -5.0774   3.8429  -3.4478   4.7462
-#&gt;   -2.6219  -5.5933   8.3119   3.9613  -2.7773   7.9405  -3.2745  -4.1651
-#&gt;    2.0979   9.1856  -1.9381   3.8072  -4.9375   7.5384  11.0382   1.7652
-#&gt;    1.3514  10.6603  -8.1120   0.4670   6.0176   3.2405  -1.6180  -0.6254
-#&gt;    8.7245   8.6317  -1.1497  -9.5651  -7.4556  -3.8119   3.9696   4.7354
-#&gt;   -3.8817  -5.5475  -1.3767  10.0880  -8.3033 -10.6668  11.5400  10.8974
-#&gt;   -9.0754  -6.3121 -16.1102   1.5634  10.8654   1.7987  -7.9525   5.1407
-#&gt;   -0.6645  -6.7050  -1.7356   4.2908   0.1860  -0.6135   1.8321  -1.9084
-#&gt;   -3.1475  -0.9009  -4.4690   1.5015  -1.8592   8.2385  -6.7907  -8.3100
-#&gt; 
-#&gt; Columns 9 to 16   2.4772   6.9853   5.0554  -7.1010   6.3564  -2.1751   2.0915   4.7553
-#&gt;    0.9726   7.8110  -2.9964  -1.5681   4.3726   0.5310  -0.2902  -5.6185
-#&gt;   -3.4761   0.8730   1.1033  -3.8177   9.3869   1.8351  -1.9366   0.1456
-#&gt;   -0.8326   3.0677  -2.2720  -5.5846   2.8953  -1.2542   1.3065  -2.5521
-#&gt;   -2.2788  -3.2951   9.0223   8.0911   3.4538   1.6911   5.5717   0.2435
-#&gt;   -3.8224   2.9995   3.2025  -6.8491   6.1166   3.1798  -3.9305   1.4680
-#&gt;   -8.1437  -1.0316 -12.3721  -2.3694   7.1590 -11.0265   1.9216   5.3572
-#&gt;    2.4455  -5.8715   3.6161   0.6281  13.1662 -12.1495  -0.6049  18.0797
-#&gt;    1.0635   7.9470   2.6926  -2.7691   5.4396  -5.3615  -7.3676  -0.7646
-#&gt;   -0.6392   5.8910  -0.9460  -0.8314 -10.5364  13.8411  -4.7026  -2.1743
-#&gt;    6.6901   0.6010  -4.9159  -5.3949   6.1078  -7.9671  -7.1100  -0.1997
-#&gt;    2.1689   3.6665   3.4164   7.1108   0.1495  -1.8227  -3.0289   0.0623
-#&gt;   -3.7657   0.5422  -2.0929  -4.6203  -2.3059   5.9940  -4.2585 -10.9629
-#&gt;   -1.5935  -2.4850   3.3292  -4.0908  -8.0067   9.8384  -8.4399   0.7540
-#&gt;    4.2742  -3.4736  -7.2025  -2.0734   1.6656  -3.7740   3.0785 -15.1965
-#&gt;   -0.7944   3.8598   4.3324   6.2648   5.2413  -7.1847   4.9089  -0.4260
-#&gt;   -3.4255   0.4826  -3.8941   2.5279   8.8388  12.8171  -5.0199 -11.6666
-#&gt;    3.7167   4.2522  -0.8370   4.2541   7.9790  -1.6958  -9.0686  -6.4219
-#&gt;    7.7238   4.7805   0.4762  -4.2732  -7.5977  -6.9851   4.2282  -7.5488
-#&gt;  -16.9813  -2.7374   1.3675   2.8424  -0.9552   5.9908   1.1160   2.0236
-#&gt;    0.8311  11.3594  -8.6496 -11.4927 -14.1226   8.6763 -14.9596  -5.7950
-#&gt;    5.5092   1.8082  -0.0375  -3.9476   8.3790  -3.6774  -4.0320   2.1729
-#&gt;   -6.0308  12.9847   3.7472   4.4492   1.9196  -4.6812   9.8431  -4.8811
-#&gt;   -0.8555   7.7828   2.2830  -5.7389  -2.0971   8.2863  -4.4375   2.0117
-#&gt;   -5.7209  -0.2968   1.3621   5.4267  -5.9068  -4.0007  -4.8186   2.4413
-#&gt;    9.7814   3.2556  -5.8478   6.6912 -10.3340  -4.6893  -0.3891   8.9309
-#&gt;    1.9613   4.3279  -6.0690   7.9198  -9.2568   4.6448  -1.9574  -1.4606
-#&gt;   -5.0922   2.5238   7.7959   8.9944  12.9909   0.4995  -0.6196  -4.0216
-#&gt;   10.1429  -3.1766  -6.9035  -0.7455   2.1293  -9.7152   5.3082  -0.3140
-#&gt;    0.9209  -9.7199  -2.7639   8.3404  -7.5061  -2.2077  12.0477   5.2788
-#&gt;   -0.6768  -3.8672  -8.7041  -4.0398  -2.4001 -13.5475  -4.4183  14.2935
-#&gt;   -2.2461  -4.4661  -1.7569   7.6530  -3.6616   3.3638  -6.0332   1.9089
-#&gt;   -2.7886   1.4433   0.7091   4.0929   3.4725  -6.7438   1.2681  -2.7312
-#&gt; 
-#&gt; Columns 17 to 24   3.3851   1.8491  -8.5467  -4.5405  -4.5587   2.6732  -2.7599   1.9790
-#&gt;    8.1940   1.3294  11.0396   8.4894  -7.3702   8.0175   0.0626   5.0957
-#&gt;   -4.4258  -2.8543  -2.9384   7.6887   0.8931   0.8051   1.8761  -0.8031
-#&gt;  -13.0039  -3.1194 -10.5884 -16.2938  -6.0052  -3.0101  -0.2661   3.2331
-#&gt;    0.8140   7.8326  -9.0368 -13.9403   4.7873   0.1236   8.0950  19.8561
-#&gt;    0.9751   5.1760 -12.1409   5.3148   8.2552   1.8082   3.0068   1.9162
-#&gt;  -15.5110 -16.7880  -5.3187  -7.2511  -2.0886   9.5445  -4.8413   2.3053
-#&gt;   -5.0294  10.7742  12.4335   4.6090   9.1152  10.8389  -3.7134   0.4050
-#&gt;   -5.8010  -1.8140 -12.7529  -7.3587   6.6428   7.4352  -3.6307   4.7221
-#&gt;   19.0162  -0.7988 -12.2259 -10.2333   2.5986 -11.0972  -0.3036  -3.6820
-#&gt;    5.1899   2.3718   2.9351   1.6534   5.5810   3.0026  -6.0241   3.7822
-#&gt;   -1.8551  -1.7632  12.1538  -4.9026   4.0411  10.8076  -4.6295   6.9809
-#&gt;   -5.2245  -2.5606 -11.2833  -7.3452  -3.4430  -3.4578   2.1999  -0.6423
-#&gt;    2.3738  -7.6750  -2.6908  -8.1225  -7.8125   7.2094   8.7003  -1.3648
-#&gt;    6.6493   1.5781  -8.2913   5.2276   4.2121   1.4815  -4.8591  16.0501
-#&gt;    7.0537   8.1463  -6.8701  -9.7104   7.9404  10.5724  -4.4714  11.1090
-#&gt;   13.6840  -6.1425 -18.3738  -6.0894   1.8208   2.7201   6.2710  -2.4806
-#&gt;   -0.0975  -4.3382  -9.8879  -6.9328  -1.1757   9.4569   3.5052   7.5286
-#&gt;    0.7061   4.2232   0.5715  -0.4430   4.2517  -5.8026  -2.8868  -4.9002
-#&gt;    1.2511   2.4108  -9.9578   1.7519  -5.0179  -7.8598  13.0860  -4.5497
-#&gt;    2.9756 -16.0447   4.7767  -9.0616  -4.8777   9.2915  -2.4349 -21.7847
-#&gt;    6.6630  -0.3102   2.3268 -10.0149   0.4415  -3.0719 -11.3836  13.9827
-#&gt;    1.0734  23.5448 -14.1885  -1.5917   4.0957  -8.6177  -3.7140   8.7251
-#&gt;    1.2022 -18.6504  -4.6281  -7.2867  -8.4697  11.5906  -2.0713   0.1189
-#&gt;    3.1042  -3.8494 -11.0867   0.4055   0.6254   2.5578  16.3072  -2.7552
-#&gt;    4.4808  -2.1697   3.5964  -1.6728  -3.7138  -5.4888  -9.2798  -1.7689
-#&gt;   -6.4289  -2.5715   4.1104   2.2508  -4.2537   3.5405   4.4927  -6.6407
-#&gt;  -13.9658  -3.2024   5.4410  -0.4239  -2.8007   5.5158   0.4421   6.9604
-#&gt;   -5.8114  -9.6978  -4.9136  -1.1524  -5.2480  -1.7633   1.8385   0.0672
-#&gt;    1.0896   3.8782  12.0328  -4.0481  -3.4674  -6.9956  -7.5722  -0.0195
-#&gt;   -6.9420  -6.9372  12.7527   2.9103  -2.6005  10.7231   1.4098  -9.8890
-#&gt;    5.4170  -5.4354  11.2508   8.4135  -9.2736   0.4072  13.5762  -4.5096
-#&gt;   -2.8923   3.3830   5.0346  -2.4782   5.9142   8.8269  -2.6120   6.3380
-#&gt; 
-#&gt; Columns 25 to 32   0.0205   4.9643   1.2960   5.4800   8.5825  -4.6408   1.4444  -1.0495
-#&gt;   -6.5737  -0.9815  -6.7421  -1.8886  -0.4694  -0.7801   7.1028  14.1191
-#&gt;    6.3220   4.0138   8.4868  -2.3805  10.7068  -8.9014   4.0767  -0.9640
-#&gt;    4.2186   4.5924   1.9950  -3.2594   6.8950  -0.1474 -10.1816 -11.1045
-#&gt;    2.5633   7.0689  -4.7396   0.9187   9.9804  -4.6381  -9.2280  -4.9021
-#&gt;    5.8971  -4.3776  -5.5970  -4.4280   7.8079 -12.7287   9.8350   0.9944
-#&gt;  -12.7747   2.5115  -2.6319  -4.5909   0.6613   5.1970   6.0061   8.3177
-#&gt;   -1.6409  13.2419  -5.6789 -16.9081   7.1442  -3.6103   6.5863  -2.5819
-#&gt;   -4.5254   7.0098   9.8454  -7.9016  -4.7171   3.9773   0.7421  -7.3133
-#&gt;    4.8134 -14.7660  -4.4442   5.5173  -0.3276  -3.6180  -4.5146   9.0902
-#&gt;  -11.5672  -4.2116 -14.4788   4.6223  -0.0106   0.6987  -2.0236   8.4430
-#&gt;    3.5297  -1.8249  -2.7753  -1.0305  -6.6108  10.9399  -3.6063   1.9043
-#&gt;    7.9418  -8.6407  -5.2586  -1.4298   2.5561  -5.0876 -11.8273   1.8070
-#&gt;   -0.5398 -13.6520  10.0932  -8.7402   6.9118   9.2258 -15.8188  -1.1072
-#&gt;   -7.5311  -8.7481   6.7178  -6.7533   3.9055   8.8272  -5.8145   0.0367
-#&gt;  -13.2021  -5.2121  -9.4141  -2.1197  -0.5477   4.7581  -0.8517   4.1688
-#&gt;    1.3346   3.3551   4.8791   9.5248  -5.4004  10.5782 -10.8131  -2.3465
-#&gt;   -3.2658 -10.1462  -7.5537   1.4367  -2.6066  -8.0288  -1.4869  -0.8463
-#&gt;   -5.2855   6.1593  -5.6326  13.7419  -6.6151   0.6728 -10.9939  -5.5840
-#&gt;    4.1573  -5.6993   0.5329  -6.3674   4.3796  -3.3078   4.2778   4.7140
-#&gt;    8.2873   0.7915  -7.8381   0.4974  -8.1768  12.2511  -1.4057  -3.6990
-#&gt;  -10.5332   4.2573  -7.0135   3.3450  -4.1731  -0.2403   4.7415   8.0409
-#&gt;    2.6840   1.3186  -0.3212   1.4226  10.6361  -5.5999  12.0515  -8.8935
-#&gt;   16.6018  -6.4842   7.1444   5.0024  -1.5026  13.9369 -13.5996  -7.4364
-#&gt;    2.5763  -3.7592  -2.7278  -0.2633  -0.0419  -9.5247  -6.7075  -0.2844
-#&gt;    4.4678  -6.7915 -11.9508   1.1356  -3.9916   5.5633   3.4563   4.5067
-#&gt;    2.0963   4.5714  10.8916  -9.4756   3.4999   2.7286   8.8064 -15.3072
-#&gt;    8.7781   6.1425   2.9535  10.5251  -5.6192   4.3482  -8.6036  -5.9964
-#&gt;   -9.3259  -1.2282  -0.2952  -3.4448  -2.3268  -1.7323   5.6953   4.5704
-#&gt;    5.3529   1.7714   1.6831   0.3097  -1.0398  -2.5306  -6.1373  -0.9298
-#&gt;  -13.7645  -0.9706  -8.0342   2.7802  -7.0854   5.4955   5.0612   5.1936
-#&gt;    3.8546  -9.1119   1.1678   0.0722   4.3046   8.3404  -6.3787   3.1192
-#&gt;    0.9018  -0.5630  12.2276  -6.3525   4.5277   6.8525  -3.7508  -2.5143
-#&gt; 
-#&gt; Columns 33 to 40   5.1383  -2.4837  -1.4141  -5.7984   0.3202   2.2431   8.1778   4.9440
-#&gt;    7.5057  -1.2760   0.7530  -4.6806   3.9510  -2.1182   7.0977  -5.6069
-#&gt;  -15.1417  -1.3083  -5.4594  -8.2165  10.4542   2.6353   0.7428  -1.9645
-#&gt;  -10.3230  12.4675   6.7490  -0.1253  11.6774   0.3948   8.2061   6.8831
-#&gt;   11.9432  -1.1162   6.3040  12.3100   5.4530  15.2469  12.0818   0.3958
-#&gt;    8.2155   3.9947  -8.9792  -1.3506  15.6240  -6.7361   9.0322   0.5402
-#&gt;    6.8986   8.5562  -5.4659   3.2280  -4.7835   4.5981   4.8154   7.9747
-#&gt;    8.4676   3.0723  -6.2415  11.6067  -5.3416   8.8337   8.6633 -13.5074
-#&gt;   -5.1404  -7.1930   6.0573   4.0338   3.8567   6.4831   7.3858  10.3672
-#&gt;   10.8073  17.0466  -6.4018  10.0882   3.6213  -4.7703  11.5535   1.3310
-#&gt;   -5.1817   6.1236  -2.8362 -14.9483  -1.1146  -5.5259  -3.4730  -0.0859
-#&gt;    3.0377  -0.1759 -10.0860   5.2140  -9.1414  -0.2410  -3.5086  -0.4263
-#&gt;   -5.7513 -14.9189   5.4176   4.4967  11.8767   1.2122  -2.5681   5.3231
-#&gt;    3.5997   2.3716  13.0881   8.4309   3.9751   8.1493  13.0257   7.2199
-#&gt;    3.1304  -3.8528  17.9919  -3.4239   2.0940  -6.9595   3.5969   3.3877
-#&gt;    7.9142   4.5348   4.7329   0.5066  -4.3921  -1.3601  10.7639   7.2423
-#&gt;   -3.5060   4.8020   6.8256   1.4988   1.0495  -3.6741  -7.6146   8.3754
-#&gt;    1.6073  -6.9342  -4.9045   4.6595  -3.0004  -0.9853  11.0280   0.1298
-#&gt;   -5.9702  -1.1516  -3.6068  -1.9599 -13.0181  16.6372   0.7078   1.0732
-#&gt;    3.4706  13.1934   3.6381  17.3257   2.5163   3.8170  -1.1459   6.8972
-#&gt;   -7.9116   3.1650  -5.7282  -5.3093  15.1509  -4.7559  10.1193   4.5790
-#&gt;    6.1733  11.2552   7.4118  -3.4589  -1.6616  -4.7684  -4.2669  -0.3219
-#&gt;    2.2618  -2.6119   0.8374   2.2059  -3.2179  -5.5608   9.9189   1.1965
-#&gt;   -6.6953  -1.1304 -17.5309   8.8312   0.5182   2.2583   7.2884   1.8725
-#&gt;    0.1359 -18.8669  -1.2311   3.5072   0.4222   4.8974   0.5970  -4.9225
-#&gt;   -1.8149 -11.3152  -6.4699   2.2013 -14.4938  -0.2538   3.2230  -6.4114
-#&gt;    1.3748  -6.9197  -2.1969   0.0640  -3.8683  -5.9475   8.3174  -4.7122
-#&gt;  -11.0267  -1.8070   0.3108  -0.1753   2.8982   4.6955  -6.7445   1.6743
-#&gt;    2.0620  -6.1920  -7.1108  -5.8927 -13.0216   8.3311  -3.9989   1.5352
-#&gt;   11.2957   5.5379  -8.4664  -3.8174  -9.4350 -10.3040  -5.2362 -14.7481
-#&gt;   13.0448   9.5193  -4.6771  -1.7990  -9.1255  -1.2214   5.4885   5.9563
-#&gt;    1.3056  13.5454   6.4890   6.8591  -5.5396  -2.0985   3.7828   1.3250
-#&gt;   -3.2363  -1.4909   9.0954  -4.8429  -4.8511  -4.0335  -0.9469   8.7191
-#&gt; 
-#&gt; Columns 41 to 48   6.9669  -5.2000  -3.2190  -1.0191   2.1120  -4.4909  -3.9559   5.8736
-#&gt;   10.8097  -5.9390  -0.9505   4.4599  -0.1143  -4.7481  -2.2605   0.2527
-#&gt;   -8.7247   5.8426  -1.9317 -11.2596   7.8812   0.8820   2.4157  -6.0888
-#&gt;   -0.9341  -0.7320   1.2953  -1.6729   1.5217   3.5265  -4.5357  -0.0032
-#&gt;    4.4807  -1.0969   1.3443   2.9896  -3.9546   0.3685   1.5812   9.2616
-#&gt;   -8.4439  17.1511  -0.3030  -8.3922  16.5984   6.0320  -9.1012   0.8052
-#&gt;   14.8450  13.8827   1.8740   6.3164  -8.0105   3.9321  -7.7526 -10.1645
-#&gt;   -6.0547  -3.5009 -10.0774  -9.0763  -1.1722   5.0219   1.8827  -0.2991
-#&gt;    2.4451  -0.0411  -7.1939  -9.2591  -6.6834   6.8234  -7.4659   9.4849
-#&gt;    4.8945  17.2362   5.1588  16.4691   4.8290  -4.1272   7.7989  -1.8618
-#&gt;    2.1758  -0.6867   1.2765  -4.0645   7.8758   4.4395   8.1930  -9.4271
-#&gt;   -0.0252  -3.2395  -5.9251   7.0463  -0.6145   2.7143  -7.0918  -4.4182
-#&gt;   -7.6970  10.5970   5.1209  -2.8310  -1.6084  -3.5707  -3.6112  11.3841
-#&gt;   -2.0696   5.5044   6.3489   5.5643   3.3655  -1.1913  -2.8829  15.7165
-#&gt;   -6.3951   1.0188  -0.6555  -3.8966   0.7242 -12.6917   1.7929   5.2504
-#&gt;   11.0707  15.0233   4.6116   6.3492  10.0248  -9.6052   8.3301   7.4037
-#&gt;   -0.0599   6.2314  -1.7312  -5.8794   0.7072  -0.1696   0.9178   9.8289
-#&gt;    8.9359   7.7375  11.4586  10.6073  -0.7442  -6.7872   9.7666  19.5429
-#&gt;   -2.2792  -6.6757  -1.7404   1.7494  -3.9090  -4.4396  21.8444  -2.2397
-#&gt;    5.4938   5.9476 -11.3288  -0.6589   8.7407 -11.0044  -1.5241   2.6215
-#&gt;    1.2733 -11.3973   0.9858  -4.6250   1.7525   0.5846  -2.4223  -0.6348
-#&gt;   13.8170   4.1282   5.8440  -2.7142  -4.2825   4.0540  -1.7965   3.5908
-#&gt;   12.9541   9.2010   1.7503   7.1184   0.8586  -8.3702   8.4927   6.5989
-#&gt;  -11.7143  -7.6038 -16.6942   6.8044  13.9010 -14.3428 -14.4140  -9.9911
-#&gt;   -0.6232   4.7803  -0.4861   4.2755   4.4080   4.6442  -8.2603  15.2881
-#&gt;    6.3128   4.4874  -0.5685  15.4413  -4.3615   1.8497   5.6419   0.6278
-#&gt;    4.1010 -11.3828  -1.4361   5.5389 -13.4272  -0.0704  -0.8677  -2.0701
-#&gt;    7.8729  -0.0013  -5.1101  -1.0363  -5.0268   1.1405   2.7002  -8.0662
-#&gt;    6.1756  16.5872  13.5591  -5.5747 -15.4864  18.0718  -5.9808  -8.6193
-#&gt;   -7.5350  -1.5627   2.5491   6.5842   5.5901   3.8863 -11.7480  -5.2317
-#&gt;   -1.3673  -9.6215   1.7316  -0.0201  -2.0976   6.1188  -1.6005 -19.3349
-#&gt;   -6.1232   8.0622  -6.9668   2.5948   2.4252   0.7960  -1.6697   2.5228
-#&gt;   -6.5306  -9.1254  -4.3853  -7.9630  -4.0423   8.9235  -6.8360  -4.4291
-#&gt; 
-#&gt; (19,.,.) = 
-#&gt;  Columns 1 to 8  -1.9219   6.2151  -7.9723   8.0410   8.0539   1.4463  11.2436  -0.8404
-#&gt;    6.6128   4.5412  -0.0858  -4.0557 -13.2176  -5.7479   8.0268   0.2596
-#&gt;   -9.7374   4.7627 -14.2572   4.5592  -5.9660  -2.3266   0.3891   3.8062
-#&gt;   -2.6296   5.7987   9.7269  11.1651  13.2819   9.4931  10.8493   0.4959
-#&gt;    3.8341 -14.3383  -3.7609  -1.0188   5.9949  -2.9949  -2.9681 -13.8083
-#&gt;   -3.5925   7.7720 -13.4244   3.3354  -1.7212   1.1368   4.5410  -1.4334
-#&gt;    0.0417  12.3907   3.5433   6.0599  -0.3389  -4.9288  -4.9764   7.1718
-#&gt;    0.9129  -9.7319 -11.7862  -8.6221  -6.3993  -4.8655   0.8730  -3.0932
-#&gt;    2.1292 -12.2025  -0.4890 -12.7047   7.0288   5.1016   6.1689  -0.8740
-#&gt;    8.1656   5.3881   3.9958   3.3374   3.6409   4.4966  -3.7257  -7.9144
-#&gt;    3.7283  11.9461   8.5415  -3.5704  11.0041  -8.0508   0.4103  -1.2395
-#&gt;    5.4483  -0.4633   8.3927  -3.9632   4.8526   2.7297  -5.8583  10.1534
-#&gt;    3.8819   6.1168  -6.6375  -3.8200  -0.4303  11.0505   5.1304  -2.7080
-#&gt;    1.7724  -6.8488   3.8035   3.6338  14.1085  10.7995  -9.8884  -3.6471
-#&gt;   10.5576   3.7238  -2.0956  -4.5579   0.6068  -9.3219   0.9999   1.3625
-#&gt;   -0.5139  -8.8330  -0.5853 -11.4648   3.4105   3.5541  -5.6678  -2.8639
-#&gt;    2.8101  -2.0291   8.1613  -2.5094  -2.5844  -2.2588  -1.5256   9.1854
-#&gt;    5.0183  -2.6736  -9.5332  -8.8108   4.5742  13.5034   0.6927   2.6070
-#&gt;   -2.4257   5.7814   7.2997   9.1085  22.4047   2.2107   5.6325   7.2983
-#&gt;  -13.3279   4.3501  -6.7470  -1.8188  -2.8783   0.0885 -11.0330  -6.9947
-#&gt;   -2.2614  -6.6453   5.5900 -11.4815  -9.8183   8.6205  11.4967   8.5412
-#&gt;   -1.4464  -9.0578   7.3159  -2.1486  -2.9283  -7.1088  -8.8823 -17.6863
-#&gt;    0.7271  -4.4389  -1.9123  -7.8538   9.7063  -0.3891   9.5969  -6.3907
-#&gt;    0.8210   1.5678  10.3816   1.4013   4.2139   9.9972   1.3656   5.9026
-#&gt;    2.1933  -9.3149  -1.6315  -3.0883   6.5925  13.2524  -0.6062  -8.7613
-#&gt;    7.6490  -6.4544   7.9433 -11.9607   9.4700   5.3995   0.8768  -3.8981
-#&gt;   15.4285  -0.4268   0.3277  -5.1864   0.1203  10.2461  -5.3700   8.4087
-#&gt;   -6.4447  -1.1483   5.0814   1.1639   1.8557  -0.2702   4.3733   1.6317
-#&gt;    3.5179  -1.8067 -12.7815  11.6083   7.4915  -1.2810  -1.7166  -3.0006
-#&gt;    8.9932   1.4182  10.0213   5.5874  -5.8616   7.9495  -0.6624  -2.8152
-#&gt;    8.1759  -0.0571   6.9989   0.4292  -6.0793 -11.2034   2.1217   4.1011
-#&gt;    0.5654  -3.8046  -0.7800  -1.9987  -4.2532   0.0972  -9.8609  -3.0128
-#&gt;   12.8977  -0.5714  -8.4409  -6.6145  -2.9113   1.2461   2.9227   9.0618
-#&gt; 
-#&gt; Columns 9 to 16  -0.0537   3.5203  -6.0327   8.1488  -4.0733  -1.0320   1.6348   1.1869
-#&gt;   -1.7455   8.4745  -4.1985   1.5521  -1.5325   4.3806   5.6955   0.0986
-#&gt;   -2.7095  -2.3551  -1.3266  -1.6933  -6.0836 -11.8516  -3.3973  -0.0775
-#&gt;   -0.9736   7.0103  -4.4801  -1.6628  -6.9170  -7.1509  -1.3148   5.5477
-#&gt;   -4.0244  10.8167  -5.6246   3.8867 -25.2293  -0.1626   4.9295  -1.7814
-#&gt;    0.2507  -1.6698   4.9788  12.9161 -16.4967  -5.6156   0.0620   7.5230
-#&gt;   -0.8193  -1.1562  -4.5697   5.0834   1.4184  -8.0987  -8.3402   4.0712
-#&gt;   -8.3970   1.5917  -2.9095  -0.4185 -12.6023  10.4028   2.5319  -5.1357
-#&gt;   -3.4296  12.2175 -11.6369  -2.1541  -8.6980   0.8035   2.5453   0.6151
-#&gt;    0.8072 -14.9599  16.4148  -6.2045  -5.3332  -2.0441  -1.3751   3.7715
-#&gt;    1.4639  -1.7101   2.8343  12.4789   3.7032  -5.2887  -9.1987   4.9077
-#&gt;   -7.2305  -3.6721  -0.7796   2.0327   2.9468  -3.4998  -2.0196  -0.0356
-#&gt;    1.9059  -1.6361   7.3709   2.2446   1.7817  -2.5382  -2.2523  -5.1913
-#&gt;   -2.1247   5.8113   4.7646  -2.4158   1.3118  -5.3108  -2.4789   0.8234
-#&gt;  -11.8593  15.4524  -5.8956  15.2296  -5.9163  -3.9794  11.5367  -6.7733
-#&gt;    2.1191   6.8468   2.5149  -0.6669  -9.5578  -0.8734   1.7987  -4.4297
-#&gt;    3.2060  -1.7692   3.5791 -14.8040  -4.6654  -2.1537   8.3117  -4.6184
-#&gt;    3.0838   5.5975  11.6088  11.3639   1.1784  -2.3686  -4.5642  -6.3569
-#&gt;    6.9279 -15.9540   6.5124   1.8017   2.2056  -7.4601 -12.0609   4.6467
-#&gt;   11.0227  -3.6535   0.5205 -14.4146   9.4725   3.8488   3.0179  -2.6211
-#&gt;    1.2147  -8.1402  -3.5926  -2.7432  20.7541   5.9904  -7.2942 -15.7940
-#&gt;   -7.5868   3.9234 -13.9996   2.7921  -4.3065  17.2427  -5.7518   8.1364
-#&gt;   -6.5642   0.3405  -0.0376   1.4010  -1.9131 -10.1116  -5.2953  -6.1517
-#&gt;   -5.0853 -13.3693  -0.5417 -14.6102   2.6733  -2.0867  -7.0722  -3.3004
-#&gt;   14.7107   1.8055  14.9286  -2.4846  -0.4012   5.7766  -5.2366  -2.4759
-#&gt;    3.4891  -3.0630  13.8853   8.6335   7.9663  10.6106  -8.6293  -9.6142
-#&gt;   -2.0553  -2.6452   2.0947 -10.6673  22.7084  -5.9159   9.9049   0.8010
-#&gt;   -3.8411  -6.8312   1.5836 -10.9816  -2.8986  -4.2839 -10.8524   7.6473
-#&gt;  -10.7269   3.4838   4.4464   9.1879  -4.4402  -7.1206  -0.4742   7.8972
-#&gt;   -4.6231  -2.4838   2.0384  -3.8118  -5.3796   4.4788   6.2277   6.1395
-#&gt;    6.6657   2.6972   0.4700  16.4874   8.2614   4.6667  -0.5139   1.7938
-#&gt;    5.3081  -8.1994  16.0321  -5.8476   1.8431   0.9868  -9.8172  -1.7812
-#&gt;  -19.2342   5.7861 -10.0965   4.2746   5.2664 -10.4838   3.4450  -0.2092
-#&gt; 
-#&gt; Columns 17 to 24  11.0648  -4.2616  -3.5562   0.3916  -1.8376   3.3788  -0.4423  -8.1041
-#&gt;   -0.2767   0.1320  14.2367   0.1054   6.0332   0.8694  -5.0937   0.6785
-#&gt;    0.5038   4.4818   2.3851   1.7207   1.4124  -3.3953   6.8282  -0.2989
-#&gt;    7.4905   1.2777  -4.7348 -13.0746  -2.7778  -1.6235   7.5489   2.5093
-#&gt;   12.9373   8.2684  -4.2186 -13.2319  -4.6523  -2.9009   6.1159   1.1434
-#&gt;   -9.4813   1.6689   2.9463  11.3122  -7.0985   3.3937  -0.0480  -5.5963
-#&gt;   -6.8537  -2.5387 -15.0538  -0.1292   1.5937  -1.0007  22.8233   2.9675
-#&gt;   -4.0254  14.9489   9.3981  16.5211  -0.0575  -7.7982  -7.7859  -8.5714
-#&gt;    6.5749  -2.8655  -7.4423   3.5061  10.9653   3.1836   5.9649  -4.5804
-#&gt;    1.1430  -2.8152  -1.3915  -9.3767  -0.9861  14.1244  -3.3883  -1.2622
-#&gt;    1.2876 -14.7572   6.7969   3.2172   0.1214   4.3064 -10.8975   0.6513
-#&gt;   -9.1768   0.2048   3.5234   0.2465  -7.3220 -12.4801   1.6871  -1.5608
-#&gt;   -2.1287  -9.0370   0.4298  -0.3119   6.0812  10.8881   8.2612 -11.0879
-#&gt;    2.9433   8.4038 -11.5077 -11.1560  -5.9501   0.9321   4.6843   1.5257
-#&gt;    3.8749  -2.6489   6.2686 -10.6099   2.3932   9.1694   4.8593  -5.8351
-#&gt;    6.4969   7.6292  -0.4297  -3.4883  -0.3184  20.5664   3.1195  -0.2290
-#&gt;   -0.5318   0.9904 -13.1871  -6.5101   5.7714  13.0979   7.0342   4.2031
-#&gt;   -7.3089   4.3226  -0.5126   2.8804  -5.6790  -3.4517  -4.7398   2.6306
-#&gt;    2.7750  -4.9301  -0.8741  -1.7237   2.9311  -0.1028 -12.9309  11.0712
-#&gt;    2.3975   0.7732  -0.8077   6.0351   2.3376  -4.4041  -1.5474  -9.1251
-#&gt;   -1.8936  -6.9217  -4.7895   4.7446  10.6301  11.1553  -2.1563   5.6503
-#&gt;   -6.0168   6.2457 -11.3312   7.8837  -2.8488   1.4407  -8.7688   9.7988
-#&gt;   12.4634  -2.2983  -1.1992 -10.4799  -2.5240  13.8778 -10.5038   1.0919
-#&gt;   -6.4890  -0.7032  -7.5852 -10.4909  -9.4819   8.4442  -9.1790   1.5488
-#&gt;   -1.3927   1.1406  -9.4890   1.0581  -3.6003   5.1686  -2.8764  -7.0863
-#&gt;    1.9717   7.9552   6.0285  -2.7318 -14.1447  -8.0522 -11.0288  -6.5743
-#&gt;    4.5219  -0.5576   0.1047  -6.5185   4.7981  -5.5824 -10.2698   3.6593
-#&gt;    1.1866  -5.1977  -3.0954   4.9852  -4.5273  -7.9954   0.2396   2.9124
-#&gt;   -7.3148  -7.4875  -8.6709   1.1319   4.3304  -3.7922  12.2677  -2.0505
-#&gt;   -2.8180   7.0855   1.5813  -1.9910  -3.0259   2.8090   3.9845  -2.9099
-#&gt;    0.5403  -2.3332   1.6155   3.3290   2.1611  -4.7318   3.9446   8.8050
-#&gt;   -4.6336  14.4416  -0.8647   5.3473  -6.4543   3.9093  -5.9648   3.1126
-#&gt;    7.0305  -0.2391  -3.8540  -1.8511   7.1994  -1.4278  12.7858 -15.4387
-#&gt; 
-#&gt; Columns 25 to 32   8.7410   3.3459  -2.5498  -2.3815   2.6844   4.5968  -9.4982   4.2627
-#&gt;    7.7132   2.3078  -4.8193   7.1286  -1.4498   1.7027  -1.3328   1.1042
-#&gt;   -3.0782  -5.0451  -4.6450  -1.8835  15.0243   5.4433  -4.6986   2.9013
-#&gt;    8.1737  11.5360   4.4398  -0.6946  -1.4544   1.4905  -0.6399 -11.7900
-#&gt;    5.7272   6.5737   2.4600   7.0932   4.5177  -6.1633  -7.9696   0.2826
-#&gt;    2.0985  -1.7161  -5.8898  -5.2858  -0.1799   8.3341  -2.0645   0.9876
-#&gt;   -2.0073   1.1663   0.5606  -6.8686  10.0122  -1.5827  14.7228   5.4615
-#&gt;   -7.7653  -3.6500  -1.9070  -4.9235   7.5674   1.0546   1.2127  -8.7272
-#&gt;   -1.2045   2.3907   3.9975  -6.6139   0.4313  -4.6843  11.0246  -5.7970
-#&gt;   11.7201  -0.9215  12.4491  -3.8038  -1.2794 -14.3005   1.9895   3.4754
-#&gt;  -11.7552  -4.7248  -4.9868   2.1783   5.5538   6.2059  -3.9920  -0.2307
-#&gt;  -11.1980 -10.9872  -1.5657   0.0867   4.9996  -3.1908   1.4625  -9.3178
-#&gt;   13.4062  -3.0954   9.0783  -7.8284  -0.4204  -5.7967   1.4474  -1.8467
-#&gt;   14.1800  -7.4414  10.7325   4.1083  -0.1777   1.4406   2.3926   0.1525
-#&gt;    3.9184  -2.3212  -5.1430   6.1245  -1.7567   1.4825  -4.5756  11.9628
-#&gt;   -4.0276  -5.6306   4.2715   1.7379   2.7032  -4.7683  -0.0734  10.3263
-#&gt;    1.6863   4.1963  -8.8835   5.3396  -6.6297  -7.1684  -0.3423   7.4915
-#&gt;    7.5494  -2.2477  -7.2921  -3.7746  -3.2463   4.9433   6.0280   5.4301
-#&gt;  -10.0935  10.7065  -0.3998   0.6383   6.8831   4.2477  -8.8782   2.1744
-#&gt;   18.4300  -3.8206   4.3433 -12.1532   5.3699  -3.2808  -3.8642   0.3013
-#&gt;    8.1933  -7.8306  -3.2876  -4.4331  -9.2632   9.9411   5.8863   2.2274
-#&gt;   -4.7491   9.2763  -1.9067  -2.5484  -4.8863   1.5996   7.2170   5.4375
-#&gt;   -6.4607   6.8969   6.4905   1.4494  -1.1506  -1.3511  -5.7866   6.6933
-#&gt;    1.2191 -15.4613   6.3815   4.8905   0.1053 -10.5055 -10.0863 -10.1264
-#&gt;    4.1602 -12.2516   5.5472   4.5518  -0.4509  -6.4709  -4.7143   0.1748
-#&gt;    0.1696  -9.4974   4.8859  -5.6144  -1.9537  -0.2567  10.9847 -13.9179
-#&gt;   -2.8224  -1.1403   6.6132  -2.9330  -2.4090   4.5905   3.8825   2.0847
-#&gt;   -4.2475  -2.4763   1.7904   3.2624   8.0430  -5.7229   7.4865  -7.6772
-#&gt;    0.8953   2.8100  -5.5735  -2.1053   1.3508   4.9462   8.3201   5.4806
-#&gt;    3.9424  -1.7510   7.6580   3.5026   0.2183  -2.6100  -7.4731  -9.2247
-#&gt;   -4.7280  -4.3018  -5.8259  -2.6722   3.1272  -0.3146   8.5085  -1.1770
-#&gt;    4.4550 -14.0853  -1.4137  -1.3853   3.6045   0.4965   1.6374  -0.6331
-#&gt;   -4.9527  -4.9255  -0.8300  -0.7204   8.0641  -2.4269   6.0537  -0.3613
-#&gt; 
-#&gt; Columns 33 to 40   3.9096  -0.4877   9.4965   1.9018   7.4793  -4.9901  -2.4869   4.1778
-#&gt;   -8.0365  -1.7530  -3.7598  -5.1837 -10.4958  -1.6798   3.9841  -1.1027
-#&gt;  -10.1339   1.6393   1.4836  -1.0454   7.4993   6.3342   6.1408   2.6759
-#&gt;    1.1020   5.0354  -3.0270   3.3197  13.0838   4.4074  -4.2690   4.1531
-#&gt;    4.9415   7.8312   7.5921   5.6758   4.1434   8.4302  -8.8064   1.5792
-#&gt;    6.2270   3.9901   5.8924  -0.5236  -6.5496  -6.0540  -1.9407 -10.5165
-#&gt;   10.9977   1.2687  -5.2032  -2.4548 -11.3234  -6.3458  19.4490   2.4126
-#&gt;   -7.1143   3.8627  -1.7971 -19.0001  -2.7602   0.3455  -6.1325  -4.4814
-#&gt;    0.3334  -3.1235  -6.1762   0.4386   2.2807   4.3911  -1.4278   1.2735
-#&gt;    6.3997   2.7634   3.8427  -0.0444   2.5992  -3.0002 -10.6938  -6.8102
-#&gt;   -6.0867  -4.7509  -1.8881   0.3868  -4.5747  -6.5770   4.2926  -5.4983
-#&gt;    3.9024  -5.1690   1.4043  -0.5216 -12.7850   0.4554   6.6806  -5.8201
-#&gt;    3.9665  -6.2008  -3.6598   0.5874  -2.2815   9.8810   6.9364   0.5339
-#&gt;   17.9525   4.8519  15.1140   8.7306   6.6579  13.3819  -7.5571  -1.8525
-#&gt;    3.1759  -0.5504  -0.5264   6.9310 -10.4074   4.1549   4.8557   1.7691
-#&gt;    8.0802   5.1533  -2.1663  -1.7078  -5.2725  -1.6210  -4.6366  -5.5398
-#&gt;    0.2485   4.5760   1.2154   1.2832  -0.8333  -1.5337  -7.0904   2.1742
-#&gt;   10.5528   8.2366  13.9054   2.0521  -5.4831  -1.5178 -10.2573 -10.9827
-#&gt;  -11.0724   2.6563   3.3045   3.7777   9.9910   2.1184  -6.0857   4.0128
-#&gt;    6.6553  -7.7767   3.3952 -10.0739   9.1290  -9.5721  -7.5345   7.6312
-#&gt;   -4.9541   0.8909  -2.8313 -11.8866   3.3461   1.9124   3.1322  12.0527
-#&gt;   -2.6464   1.8878  -1.1505   9.1481  -6.8794  -5.4210 -10.2410  -8.6058
-#&gt;   -1.8375   2.2391  -2.6890  -4.2819  18.6925  -9.0924  -3.4045  -0.3264
-#&gt;   -1.7975  -3.7399  -2.1383   0.7604   3.1546   1.2911  11.8209   8.0601
-#&gt;   11.6631   3.8413  10.2025  -2.8003   5.0172   2.1534  -2.1983   5.4234
-#&gt;    6.4924  -3.2318   1.0022  -4.5632  -0.9454  -1.0143  -0.2147  -0.4725
-#&gt;   -2.1088  -1.1487   7.6117   0.1122   7.3898   5.7153   8.9293  -7.4090
-#&gt;   -1.9836   1.7567  -0.7496   4.5732   6.4541   3.2367   4.4240   3.8623
-#&gt;    9.2568  -0.2233   0.6824   2.3797  -5.6565  -9.5515  11.1580  -4.8514
-#&gt;    5.3739  -0.7264  -0.9675  -0.7596  -2.4318   2.9596  -2.1286  -2.9925
-#&gt;   -3.2444   9.9163  -2.9073  -3.1904 -14.4421  -9.2668   8.3912   5.3907
-#&gt;   -2.5733  -2.7264  12.5150  -1.8215  -3.3308   3.9640   4.0528 -13.3933
-#&gt;    0.4196   4.3823   1.6297   4.8952   0.4841  14.2224   6.2815  -0.6086
-#&gt; 
-#&gt; Columns 41 to 48  -2.7672   3.7208  12.3698  -6.7071  -4.0218   2.4617  -2.3055  -3.0585
-#&gt;  -21.4549  -0.1497   0.7926  -8.9397  11.9963  11.8353   9.3624   8.6786
-#&gt;    2.1180  -1.6644   3.1430 -10.9170   0.6775 -12.2154  -5.3089  -0.7371
-#&gt;   -7.1380   2.4873   4.8889   3.9505   5.7964  -1.1769   4.5732  -1.5207
-#&gt;   14.6590   3.0606 -12.1060  -4.8512   6.9884  -3.0037  -2.8116  -2.6516
-#&gt;   -0.0327 -16.3275  10.8651   2.0523   1.0747  12.3221  -7.7520   4.9479
-#&gt;  -13.6528  19.1399   7.3297  10.3895   3.7169  -1.9209   6.1863  -8.5783
-#&gt;    4.2371  -6.7783  -0.5155 -10.9251   9.9120   2.4266   0.0703  10.8914
-#&gt;   -4.3150   5.5292  10.7424   9.0707  -0.2139  -1.8272   6.5687  -2.1927
-#&gt;    2.5072  -9.6418   0.5100   8.5378   4.0179   7.9863  -0.9619   4.2916
-#&gt;  -21.1843   9.9973   5.6876  -3.9578 -17.8302   2.0246  -5.4809  -6.1279
-#&gt;   -7.0507   8.5104  -7.2070   0.9777  -6.6302  -1.0320   0.3091   0.2146
-#&gt;   -1.7184  -3.0585   6.1165   1.4548   0.4007   5.8829   4.6264   6.0154
-#&gt;    4.3245   0.4157  10.3667   1.3591   0.8693 -13.2675  -8.8426 -16.0214
-#&gt;   -3.7482  13.1237  11.2992  -5.5070  -0.7050   4.0754  -0.9753  -2.6391
-#&gt;   -8.9956  -3.6571  -6.8314   7.9370  -0.9717   0.3781   6.4128  -1.9482
-#&gt;    0.3300   0.1405  11.3827  15.9246  -1.3659   9.9000   4.0634   0.8399
-#&gt;    4.4840   2.6771   8.4163   4.1853   8.8714   7.2794  -6.2133 -11.5971
-#&gt;  -11.4794  -8.1500  -6.4611 -14.3761  -6.8035  -3.2302  -1.7037 -11.7694
-#&gt;    1.9558  -4.2263   7.0563   4.5851   8.0044  -0.3807  11.1479  -6.8858
-#&gt;  -17.4100  -4.2479   8.4763   8.8035  -2.1215   7.1819  -5.9753   3.0123
-#&gt;    0.8830   4.8013   2.1601   6.7120   3.4061  -4.1449  -0.2736   2.3734
-#&gt;   -6.8952 -14.0610   5.7157   1.9264  -2.1557   6.5113   1.9117  -0.2940
-#&gt;  -15.1419  -2.7412  11.7976  -5.9972  -7.6600 -10.0618   4.2977  10.6392
-#&gt;   16.4489  -4.6174  -3.0824  -1.7887  -2.0365  -8.6351  -0.5409  -3.1896
-#&gt;    6.3099   6.7198 -10.1733   1.6813  -1.4718  -6.4369  -8.6549  -7.6935
-#&gt;    7.6704   1.7806   8.4127   6.7180 -12.6676  10.5540 -10.7135  -5.8232
-#&gt;  -10.4780   8.1179  -0.2585  -5.0976   0.0746  -6.3991   2.2779   2.1072
-#&gt;  -11.7143  -2.1904   2.4193   4.7612  -1.1862  -4.5601  -7.8085  -7.3502
-#&gt;   10.9926  -3.7747 -21.0389  -3.1728   6.0296   1.6622   4.3698  16.7481
-#&gt;   -9.5130  22.0948   2.1287  -6.6694   3.8841  -1.3873  -2.7266  -0.6018
-#&gt;    5.0176   6.5275   6.4011  -7.4667   2.7072  -0.2616  -8.6594   8.2475
-#&gt;   -0.8630   8.9089   4.9805  -4.7752  -2.9950 -11.7469  -1.2864  -6.6945
-#&gt; 
-#&gt; (20,.,.) = 
-#&gt;  Columns 1 to 8   1.7252 -14.8480  -1.3122  -6.8763  -8.2387   7.9475  -7.6022   7.3452
-#&gt;    0.6720   7.4071   7.5532  -0.7971   2.8195  -1.8310   2.4016  -8.9003
-#&gt;    1.3530  -1.8855   8.5455   4.6325 -12.9106   0.8878  -9.7704   1.8681
-#&gt;   -5.1689  -8.8773 -19.9643  -4.0051   5.6141  14.8097   4.9348  -5.5046
-#&gt;    1.4795  -4.1947   1.7524   3.2130   1.0674   5.9347   3.4350 -11.3493
-#&gt;   -1.8686   5.1532  -3.3560   2.3861 -15.3625   5.1979  -7.3940   1.5590
-#&gt;   -4.4031   0.9647  -6.4139   3.8370  12.3422  -0.1200 -11.5913  15.0681
-#&gt;    4.3586   4.0796   6.7243   7.3895  -9.9461  -3.2354  -4.5104   1.6412
-#&gt;   -5.2322  -4.5018  -2.3745  -0.1936   5.4458  -5.2635   2.0982   5.4633
-#&gt;   -5.8160  -4.0851   0.5779   1.3368   7.4024  13.5355  -3.1321  -3.0253
-#&gt;   -4.5513   0.6381   5.8785  -4.9006  -2.4700  -5.2673   2.9093   0.6050
-#&gt;   -1.1465   5.1678  -6.6122  -5.6476  -5.3708  -9.0402   2.4606  -1.0336
-#&gt;   -7.3619   2.6053   1.0058   5.9082  -1.6268   9.3700   5.9648   4.1062
-#&gt;    1.4162 -12.3436 -10.0557   6.4172   7.8683   1.6856   5.7705 -10.8192
-#&gt;    2.8813  -5.5029   7.0525   6.6234   0.6849   3.9709   5.0617  -5.5303
-#&gt;    1.2333 -11.5379   4.9693   4.1077   1.9133   0.3097   2.0472  -5.6508
-#&gt;   -8.2772   0.2241  -8.7338   7.9282   8.0070  -5.2584   7.6813  -9.9816
-#&gt;    2.3749   3.0609  -3.6381  -5.3386  -4.9121   4.9331   7.9149  -7.0250
-#&gt;   -5.5101  -8.7552  -3.7763  -9.5326  -0.9774   9.5581   0.9064  -6.7405
-#&gt;    9.4276  -2.0303   4.9475   4.9300 -10.9621  19.7397  -4.0587   1.1521
-#&gt;    3.7437  -3.2536  -7.9271   5.8935  -2.4158  -6.8607   7.3626  -1.5358
-#&gt;    0.4596  11.9258   4.7840  -2.2470  -2.6745  -7.4176   0.1189   1.7963
-#&gt;   13.3680  -4.7773  10.7324 -11.9909 -10.1118  11.9171  -3.7547  -8.6359
-#&gt;   -1.6105 -16.9622 -11.8356  -6.7399  -8.9411  -0.3635   5.2695   4.1697
-#&gt;   -2.0305  -3.4260   0.5036  -3.3305   0.2850  -0.9940   3.2623  -1.8830
-#&gt;   10.3675   5.2892   1.3901  -6.0093  -3.6198  -1.6077   3.6724   1.3640
-#&gt;    5.4371   5.5649   0.3218  -7.6565  10.1444 -17.7558   3.8237   0.0852
-#&gt;   -6.1258   5.8761   0.4263 -11.1105  -8.2717   0.3547   3.5265  -3.3027
-#&gt;    4.6687   5.4736  -9.1146  -6.1717   5.7742  -9.1591 -22.8003  20.0344
-#&gt;    0.8175  -3.8061  -1.8174   3.9473   5.0689   1.3440  -1.6193  -1.5220
-#&gt;    2.4585  -8.7691 -12.0945   0.8366  20.4496   0.7206  -0.6214  10.6927
-#&gt;   -3.1890   3.0298  -2.6198   6.6054  -1.4379   3.7082   0.4482  -3.7827
-#&gt;    6.4526  -2.0473  -5.8115   0.3843  -1.9456 -10.1910  -7.4822   1.6218
-#&gt; 
-#&gt; Columns 9 to 16   2.0075  -1.8353   6.8737  -5.8835  -9.1281  -1.3544  -4.4225  12.9169
-#&gt;   -3.5885   9.5508  -0.9245   1.9731   0.8805   8.7235   1.0292  10.8507
-#&gt;   -8.2275 -11.1771  10.2273   2.0740   3.5853  -0.8909  13.6437  -7.2273
-#&gt;    2.8260   8.7595   3.6574   0.7901   3.1849   3.8283  -5.7305  -8.1799
-#&gt;   -5.3732   7.5187  -0.4793   2.1950  -0.0609   2.4789   5.2191  -2.2955
-#&gt;  -14.5814   2.6835   0.2126   9.3085   6.9319   5.0317   7.2174  -3.7247
-#&gt;  -10.3726  -6.1911  14.7388  -1.9601   6.2466  -2.3601   5.1586   3.1926
-#&gt;   -4.0838  -0.5616   9.2677  -6.3993   3.1509   1.9152   5.2973  -6.0426
-#&gt;   -2.0800   7.0867   1.9968   7.9931   2.2381  -2.0733  -5.4676  -1.6084
-#&gt;    3.1215  12.5480  -4.4662   7.3541  -0.6242   6.1948 -10.9769   8.5089
-#&gt;    2.5135 -10.6599   6.0673  -4.4627  -1.7048  -0.4578   6.1901  -2.0074
-#&gt;  -10.0448  -5.5971  -0.6297  -2.3118  -2.4648  -4.6460   2.1587   8.9298
-#&gt;    2.0737  14.4843  -3.3012   1.4692   5.8397   5.2068   1.1666  -1.2374
-#&gt;    4.6504  -0.8351  -1.7413  14.1528  -7.8898   3.1197   1.4482   4.8265
-#&gt;   -9.9071   5.3138  -6.0719   4.2695  -5.8998  -5.3429   7.4863  -4.5594
-#&gt;   -2.2702   1.3094   5.7514   2.3102   9.7301  -1.5854  10.4384   5.8820
-#&gt;    8.3791  11.0479  -4.4159   3.0888   4.5970   0.6239   0.4563 -10.6319
-#&gt;    8.0629   1.7194 -10.3611   8.8563   7.8096   4.8733   9.5906   4.9349
-#&gt;    2.8421 -12.5223   2.2334   1.6354   3.9082   9.9141   5.9023   1.7413
-#&gt;   10.3661   7.0371  -4.3993   3.5816  -0.9154  -0.6573 -12.6788   4.8105
-#&gt;    8.6244  -1.8600  -6.0566   5.2145   2.0788  19.8847  -2.4929  19.0349
-#&gt;    1.6874  11.1513   4.7346  -4.2177   4.1945  -4.6585   5.6377  -1.3399
-#&gt;  -10.4497  15.3615  -9.8508  10.0222   8.9041  11.3403  -3.2093   3.7045
-#&gt;   -2.2076  -9.5991  -1.0591  -2.3317  -5.3487   4.1441 -11.2773  14.1150
-#&gt;    1.4578  -0.3811 -11.4714  -0.8540  -5.6543   4.3754  -7.8942  -5.5787
-#&gt;    8.3783  -7.2991 -14.1713  -0.4414 -14.0136   1.3609 -13.5634  17.7558
-#&gt;   -3.5410  10.3228 -15.8426  -2.1500  -5.8839  -3.6539  -0.5591  -1.3192
-#&gt;   -6.5879  -3.1056   7.3087  -1.7993   5.0630   8.3732  -0.3273   0.3013
-#&gt;   -1.2152  -5.5659  10.7817   2.0428   6.4830   2.5819  12.3449   0.9490
-#&gt;   -5.3685   7.6091  -1.8096  -6.3460  -3.0721  -4.1672 -12.4358  -0.2009
-#&gt;    3.7544 -21.7906  -0.2640  -6.4019  -7.0754  -4.0596 -11.2746  -1.8914
-#&gt;   -9.6771   0.4426   1.3056   6.1372  -8.1885   0.1691   0.0491   6.4934
-#&gt;  -10.4909  -1.9796  -6.1454  -4.2994   0.1721 -10.0820   5.6219 -11.7916
-#&gt; 
-#&gt; Columns 17 to 24   6.2403  -6.4249  -8.1908  10.7399  -5.6061  -7.4926   1.6097  -3.1384
-#&gt;   -1.7023  -0.3424  -5.3906  -0.4153  -8.4967   2.1442   4.4724   5.8937
-#&gt;   -2.3762  -3.4020   1.2481  -4.0242  -4.1924   2.2500  -5.0092  -2.0411
-#&gt;   -1.7852  -2.0201  -4.8709  -0.4924  -4.7223  -3.6793  -1.6113   6.6468
-#&gt;   13.4451  -8.7020  -3.1124   4.3127  -6.3494  -1.6710  -3.5853  -6.6836
-#&gt;    1.2745   0.5012   0.3422  -0.8683  11.6989 -10.2503   1.5824   3.0222
-#&gt;   -5.3109  10.2733  -1.9617  13.2842  -9.5726   5.3425   2.6603   4.3737
-#&gt;    0.6632   5.7470   2.0252  -8.8563   3.9526   0.6645  -8.0279  -3.8673
-#&gt;    2.2812  -4.5382  -3.7998   0.3397  -3.3393  -3.4754  -3.5906  -0.7944
-#&gt;    4.1849   1.7063   8.1227  -0.5010   6.2439 -11.5813   1.9686  10.4354
-#&gt;  -23.1889   3.4221  12.4363   2.0380   2.7121   5.9897  -0.2478  10.4659
-#&gt;  -16.0640   5.9357  -7.7839   8.4677   4.6046   2.5092   4.4376   6.5390
-#&gt;   -0.7806  -2.8931  -4.4523   2.4333  -0.4918  -9.5598   1.1114  -2.9882
-#&gt;   -2.6021   0.7366  -4.7111  14.7616  -1.2373  -4.0771   9.8829 -10.4332
-#&gt;   -6.9134   5.5952  -2.2580   8.7971  -8.7009  -1.8514   9.5014   3.6787
-#&gt;    4.6083   3.1831   3.6204   6.5515  -1.8867   0.6890  -8.3347   8.4540
-#&gt;   -0.8246   0.1753   4.1905  -1.8191  -3.4016  -4.4465   0.2784   9.3834
-#&gt;   -9.4625   0.4207  -0.2394   6.4733  10.7721   5.0009   0.0664  -3.7522
-#&gt;   -3.4893 -11.5185  10.3134  -3.4626   4.8664  -5.5804  -4.4054   1.6629
-#&gt;   12.5411   5.4096 -14.1912   5.3714  -0.8315 -14.7690   2.3156  -3.4873
-#&gt;   -3.3686 -10.2562  -7.1034   4.1918   0.7287  -1.5607  -3.0196   8.2744
-#&gt;    1.1545   7.6550   1.8296   2.6469   3.0924  13.1004  -2.8515   1.5712
-#&gt;    7.2546  -7.0381   7.1611  -1.6077   9.1094 -12.3342  -0.8523   5.4035
-#&gt;   -8.4613  -9.9953  -4.9736   8.4151   0.4528 -11.5083   9.2650  10.8377
-#&gt;    3.7070  -8.2972   4.9708   7.2909   2.5996  -2.6636   1.7842 -10.0987
-#&gt;   -6.4769  -3.1082   5.2175   1.1604   7.9138   0.5798   2.2258   0.7214
-#&gt;   -8.5734  -1.4689   1.1200 -10.7679   8.6646   5.8114   9.5640  -0.9070
-#&gt;  -12.9074   7.8855  -7.3101  -4.0278   3.4161   3.0376  -1.0465   5.4212
-#&gt;   -8.5793  14.7233   1.2222   9.3129   4.7728   6.3283   8.7740  -5.0379
-#&gt;   -2.2058  15.4178  -5.0925  -3.6503   1.4613   2.3131  -0.3692   3.4943
-#&gt;    8.1706  -3.9435   9.1629   5.5945  -6.8215   7.4580  -1.0787  -5.6812
-#&gt;   -5.3186   4.4780   3.0022  -3.9439  -0.9710  -0.5131   6.8737 -10.3874
-#&gt;    1.0521   6.9712  -9.7665   7.6458  -3.8678   7.2758   4.2704   0.9626
-#&gt; 
-#&gt; Columns 25 to 32  -3.3912  -2.5286  -9.3769  11.8274   5.6895  -2.5768   5.6313  -4.7121
-#&gt;   -8.8508   0.5556  -0.9256   9.4479   1.3776  -1.1505  -2.4085 -14.3438
-#&gt;    5.3756   1.2084  -5.9838  -4.7108   4.4129  -0.8080   3.6097   1.7453
-#&gt;   -2.4742   2.4381  11.6879  16.6098  10.9048   4.8455   0.2066  10.7231
-#&gt;    1.6231   2.0454 -15.2447   1.5519  12.4927   3.3116  -3.0623   7.8224
-#&gt;    0.7853 -10.0101  11.6750  -2.8914   1.2276  -1.2787  -8.0448  -0.0225
-#&gt;    4.4075   0.4703   0.5174   1.7624   6.7509   2.4721  17.4390 -13.1859
-#&gt;    4.7888   7.2642  -7.6150  -6.9720   7.9053  -7.4628  -5.8285  -5.2581
-#&gt;   -2.4739  -2.0684   7.5835  13.3800   3.5993   4.1092  12.2977   0.1507
-#&gt;    3.5179  -6.5455   1.9916   9.8039  -1.8997  -6.6941  -2.6712  10.4570
-#&gt;   -5.6835   5.6669   7.0577   1.8322 -22.4478   0.7882   0.0207  -9.8554
-#&gt;   -3.8837   4.8942   0.7315  -9.4436  -4.0605  -4.4749  -4.8481 -13.1065
-#&gt;    0.2180  -2.9138  12.1960  14.3663   5.7106  -5.4378   2.8187  -0.8184
-#&gt;   -5.4199   5.2720   4.3600  -5.2283   4.2522  -0.5430  -1.1804   8.8940
-#&gt;  -15.3223  13.2608  -5.3247   1.0020 -10.7603   4.4651   5.2750  -8.1206
-#&gt;    0.3000   7.1139  -9.2877   7.9337   4.2375   0.1205   9.3553  -2.0189
-#&gt;   -8.3493  -2.1566  13.5024   9.4321  -1.8889   4.1231   1.0994  -1.3551
-#&gt;   -6.8859   9.4845   9.8439  -2.9555  -0.2922  -2.5634   8.3340   2.0551
-#&gt;    0.2669  -1.1046  -7.8345   8.7597  -3.1329  -2.6745   1.9172   0.8996
-#&gt;    3.0399  -3.3020   2.3222   1.8604  10.5954  -2.9166   9.7108   0.1657
-#&gt;  -12.5563 -11.4573  14.2578   7.9171   4.5369  -5.9704 -15.8470   5.0444
-#&gt;   -3.8112   0.1902  -5.6419  -7.1455 -11.6702   0.0719   2.9366  -4.6636
-#&gt;   -5.2085 -17.0142  -9.4326   6.3836  -4.9986  -7.4897  -1.0034   4.7266
-#&gt;  -10.1889  -1.8837   1.6545   6.7245   2.5348 -10.7894  -1.2571   6.7104
-#&gt;   15.3332  -3.8025   2.7653  -0.4945   5.5042  -3.6028  -1.4748  10.1198
-#&gt;    9.9167   0.5632  -5.4546  -7.9791  -2.8987  -7.5299  -2.0083   1.5369
-#&gt;   -0.9258  -8.8398  17.8430  -7.5998  -4.7067  -0.6691  -6.0252   5.0689
-#&gt;   -4.8014  -0.0499   2.9092  -2.1181  -1.1975  -1.4554   5.2559 -10.5487
-#&gt;   -1.1453  -4.7731  -1.4323   2.0572 -11.6912   2.6600  10.6665 -13.9292
-#&gt;    7.6350   6.4468  -6.8771  -2.4935   7.6794   1.2818  -5.0658  -0.6374
-#&gt;    1.0428   8.5430  -1.5858  -8.4915   3.4612   8.5803   4.2164   3.0435
-#&gt;    0.3879  -0.5820   2.7948 -22.2690   1.8208   4.2725 -11.5252   2.5549
-#&gt;   -7.0740   4.6810  -6.3560  -1.2923  -3.3424  -6.7174   3.5648  -5.2779
-#&gt; 
-#&gt; Columns 33 to 40  -8.8727  -2.2160   1.5209   2.8552   1.3431  10.1196   6.5930  -7.6075
-#&gt;   -9.6809   8.1199  10.1258  -5.6256  -2.3656   1.2589  -0.6604  -5.7086
-#&gt;    0.5283  10.1389   9.8664   1.0007 -15.5487  -6.6794  -8.5568   1.1170
-#&gt;    5.6618  -4.4508  -6.3365  -2.4153  -0.1393   2.9306  14.8375   4.8085
-#&gt;    5.2692  -5.7820  -6.1889  -3.8294   3.2414   2.0322 -11.8571  -6.7043
-#&gt;   -1.5429   7.3862   0.2590   9.7463 -13.3216  -9.4524  -2.4742  -2.3775
-#&gt;   -8.7325  -8.8117   2.2779  11.7247   9.5705   3.1294   3.9008  -4.0716
-#&gt;    0.2832  14.0958  10.2730  -3.7528 -10.6099  -8.0350   0.5311  -1.1856
-#&gt;    5.3593   1.3372   3.5599   7.3792   1.9244  -5.8128   7.1509   0.4624
-#&gt;   -9.6803  -7.4820  -4.0167  -3.2374  12.4032   2.1655  -1.6460  -9.7431
-#&gt;    5.2260   8.9990   4.9876   1.2923 -11.8185 -17.7404  -5.9452   5.8822
-#&gt;    9.0048   2.5949  -4.6099  -2.7159   1.2899  -5.0853  -4.7839  -4.8168
-#&gt;   -0.9257  -1.3172  -4.0057  18.6537   5.0639  -7.2816  -5.8746 -13.4026
-#&gt;   12.3361 -12.2633  -9.9642   7.1387   4.9049 -10.8527   1.0424 -12.1821
-#&gt;    4.5415  -5.9787  -3.0281  10.5324  -0.0688  -0.8953  -3.2042   1.6550
-#&gt;   11.3205  -5.7753  -4.2016   5.1550   4.3101   0.7490  -8.1906  -3.1752
-#&gt;    0.8063  -4.7852 -10.6585  -1.8459  10.0371  -6.0703   7.1813  12.3244
-#&gt;    4.2498   1.4733   1.0350   0.6859   2.5420  -4.1264  -5.7497  -9.9476
-#&gt;  -10.2838  -4.2429  11.0802   3.8846  -1.7314   2.2437 -17.4209   2.5936
-#&gt;   -3.1304  -1.0010  -6.8732   3.4003  -4.6404   0.1276  16.6321  -5.9526
-#&gt;   -3.7268  14.5500   7.8635   3.8177  17.0790  -2.7806  -3.1730 -12.5561
-#&gt;   -1.4350   2.2702   3.2838   1.5676   7.0938   4.8583  -0.2889  11.6727
-#&gt;   -6.4040 -12.3580   2.0857   7.6426  -3.7487  -3.9566  -5.2483  -3.7624
-#&gt;   17.4755   1.3540 -15.2339  -6.2031   6.0624   3.5496   4.9384 -12.2513
-#&gt;    7.8512  -6.4835  -4.0205   1.2339   0.6791  -8.8671  -1.5022  -8.5548
-#&gt;    7.4479   3.0103   7.6237  -5.4770   1.0172  -0.7992  -4.5848 -12.6838
-#&gt;    1.7208   2.3288   6.5055  -4.4488   3.7851  -1.1985   6.5244   0.6033
-#&gt;    2.5369   5.2902  -0.7465  -3.6148  -3.9690 -11.8789   2.1300   2.6446
-#&gt;   -6.0415   9.4107  12.5269  14.4866   7.3232  -3.0151  -3.5655   5.4866
-#&gt;    0.5948  -7.2797  -9.0873  -8.8393   8.4712  18.9232  15.3094   3.0478
-#&gt;    0.4975  -5.4076  -0.6216   2.8208   3.2354  11.1174   9.6548   0.5842
-#&gt;    4.9779  -5.7231   1.4991   3.4900  -4.9769 -11.3501  -0.0708  -9.6416
-#&gt;    7.7293   1.5328   4.6722   9.6083  -1.4497  -8.6063   3.1760   5.3558
-#&gt; 
-#&gt; Columns 41 to 48  -7.4737  -3.7097   0.2273   1.5251  -6.7216   3.0715  -1.3159   4.1252
-#&gt;   -2.3120   5.4398  -7.0346   7.0993   3.7333  -5.9555  -1.9810   9.1459
-#&gt;    5.5385   9.0684   5.1333  -5.5072   5.6059  -1.6626  -2.3722   9.7429
-#&gt;   -8.2339  -5.0494   1.8107   6.8656 -10.6488   6.2544   3.4625   1.5873
-#&gt;    2.8210  -8.8930   1.4235  12.2725   2.5269   6.0485   5.3416  -4.4078
-#&gt;   -6.1118  16.1920   7.5638   7.7113 -17.5319  12.4622  -3.1412   1.0569
-#&gt;  -11.8614  10.4000   6.9316   1.7748  13.8646 -12.7845  -7.4400   0.2700
-#&gt;   11.6171   7.5705  -4.4600  -1.3047   6.1674   1.6963   1.0235   1.6221
-#&gt;   -1.2799  12.2896  12.3922  -6.3836  -6.4369  -2.0820  13.5463  13.5507
-#&gt;   -5.7280 -15.8311  -3.7006  12.6153 -14.3366  -4.6295   5.3723  -0.1968
-#&gt;   -2.6496   5.2628  -3.5204   0.4963   0.6744   3.5733   2.5976   0.1268
-#&gt;    4.3995   4.5005  -4.2450  -8.9770   6.2355  -2.9611   0.0960  -6.5304
-#&gt;   -2.9366  -3.5410   3.1684   0.0036 -12.5287  -1.7121  -1.5932   5.3707
-#&gt;    1.1422  -5.0778   0.7168  -2.0220  14.7918   9.4715  -1.4891  -6.4235
-#&gt;    7.1662   9.0927  -2.4983  -5.0775   6.2072   3.6193  -7.1174  -2.7793
-#&gt;    2.2160  -2.6972  -1.6987   5.8769  11.9308  -5.8990   4.3394   5.4781
-#&gt;  -12.9097  10.3348  15.0787  -7.3179  -9.9170 -10.7866  12.4551  -3.7380
-#&gt;    1.6027   4.4919   0.7682   7.6810  11.9506   5.4176   3.3725  -0.7469
-#&gt;   -5.0259 -20.1310   4.2820   1.2434   2.6299  -3.9073   2.7063  -3.4525
-#&gt;    1.6461  -4.6675   5.6383  -5.9757   0.1336   6.8219  -3.1239   0.3070
-#&gt;  -11.8755  12.2758 -13.6745   5.1485 -26.8200  -4.3314   9.9231   4.5660
-#&gt;   -4.7745   1.5823 -10.2253   4.1500  -3.7468  -3.2687  11.7311   6.6250
-#&gt;   -4.4786 -13.0181   3.9384   9.1702 -13.1641  -0.2921  -5.9560   3.9230
-#&gt;    4.7485   1.7625  -0.7967 -12.1083  -9.0915  -1.1627   0.1179  -6.3186
-#&gt;    0.3089  -6.2117   8.4654  -1.6931  12.2475   6.1479  -0.6659  -6.2620
-#&gt;   11.7807  -7.3991  -7.0008   6.3496   5.1100   2.6146  -0.1494  -8.4347
-#&gt;    6.4109  11.3296  -0.2283  -1.1396  12.0399  -6.8874   8.0937   3.8719
-#&gt;    3.1251   6.9977  -2.9502  -4.1665  -3.5759  -1.6230  -2.2060   7.5451
-#&gt;  -11.2044  -0.1795  19.1819  -2.7841   7.0736  -9.8716  -5.9783   5.3902
-#&gt;    1.1986 -13.0779  -9.4125   1.7581   6.3130   1.0702  -7.1147  -0.3977
-#&gt;   -1.2140   6.1907  -4.9632   1.1162   5.3525  11.7932  -5.2163 -14.1242
-#&gt;    2.8074   5.9518 -17.0373 -14.8777   8.7029   9.2438  -6.3362  -3.3627
-#&gt;   10.0955  11.9407   1.7070 -12.8660   9.2686  -3.9808   5.0046   2.8367
-#&gt; [ CPUFloatType{20,33,48} ]</div></pre>
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-    <h1>Conv2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv2d</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_conv2d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
-  stride = <span class='fl'>1L</span>,
-  padding = <span class='fl'>0L</span>,
-  dilation = <span class='fl'>1L</span>,
-  groups = <span class='fl'>1L</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape \((\mbox{out\_channels})\). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a>conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 2D convolution over an input image composed of several input
-planes.</p>
-<p>See <code><a href='nn_conv2d.html'>nn_conv2d()</a></code> for details and output shape.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># With square kernels and equal stride</span>
-<span class='kw'>filters</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>8</span>,<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>3</span>))
-<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>5</span>))
-<span class='fu'><a href='nnf_conv2d.html'>nnf_conv2d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>filters</span>, padding=<span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,1,.,.) = 
-#&gt;  -1.4245 -12.7542  2.2528 -0.3866  2.5493
-#&gt;   2.9826 -9.0592 -5.1611 -10.6623  2.4037
-#&gt;  -1.4955  2.5807 -1.9657 -3.9358 -4.4436
-#&gt;   0.6805  9.7785 -3.2673  4.0095  4.2091
-#&gt;   0.9847 -0.4036  5.9882  9.3795 -1.7761
-#&gt; 
-#&gt; (1,2,.,.) = 
-#&gt;    4.5145  -2.4805  -5.3284   0.8233  -6.8563
-#&gt;   -3.3530   9.9481 -13.1431  15.9801  -6.0858
-#&gt;   -4.9445  -0.8279  -8.0712   5.7594   5.3230
-#&gt;    1.0521   5.7345   6.3723  -1.1788   6.5458
-#&gt;   -0.8010  -1.7652   4.9594  -0.4293  12.3910
-#&gt; 
-#&gt; (1,3,.,.) = 
-#&gt;   1.0681 -6.0911 -0.8935 -9.1405  5.0011
-#&gt;  -8.4795 -5.8684  7.3377 -5.9550  3.0338
-#&gt;  -2.6467  2.3587  7.7660 -9.2336  0.2182
-#&gt;  -1.8283 -0.6086 -3.1678  3.0317 -4.8309
-#&gt;  -4.2962  8.5252 -0.5398 -1.8218  1.2039
-#&gt; 
-#&gt; (1,4,.,.) = 
-#&gt;   -1.3445  -0.3195 -12.8374  -1.1816  -0.5928
-#&gt;   -3.2313   1.7963  -2.3132  11.4452   1.2307
-#&gt;   -7.9412   9.7058  -0.4148  14.5645   8.4712
-#&gt;   -1.6918   5.6646   8.0784  -0.2112   5.9365
-#&gt;    6.7174  -3.6893  -4.2169  -6.5144   2.1925
-#&gt; 
-#&gt; (1,5,.,.) = 
-#&gt;   0.2206 -2.0267  1.5345 -1.5597  0.3543
-#&gt;   2.1080 -2.1205  2.0761 -0.6247 -1.4351
-#&gt;  -1.2843 -2.1713  0.8377  9.0441  7.5876
-#&gt;  -2.2599 -8.1282  2.3085 -3.2934  0.4643
-#&gt;  -0.7322 -2.1198  1.5308 -5.6029 -0.2443
-#&gt; 
-#&gt; (1,6,.,.) = 
-#&gt;    2.5436  -5.5757  -5.1579  -1.0070   2.7929
-#&gt;   -1.8420   0.6654  -3.2031   3.6631  -1.7629
-#&gt;   -6.9052   7.3092 -12.1616  -1.1287  -4.9790
-#&gt;    3.0777  -2.6246  -4.7174  -6.3964   1.4545
-#&gt;    0.1741  -1.3698  11.8108  -0.2743   0.3037
-#&gt; 
-#&gt; (1,7,.,.) = 
-#&gt;  -1.8987  2.7313  1.7351  4.1529 -2.8448
-#&gt;  -0.9446 -5.0874 -6.1593 -1.2105 -5.3511
-#&gt;   1.0798 -2.2736  1.0058  5.0419  0.3171
-#&gt;   0.1284 -7.0855  4.5112  0.5907  5.5060
-#&gt;  -2.5361  3.2895 -3.2044 -2.4517  3.0299
-#&gt; 
-#&gt; (1,8,.,.) = 
-#&gt;  -1.2208 -7.6550  0.0724  3.7119  1.7695
-#&gt;  -3.7807 -1.3733 -2.9395  3.7062 -1.3440
-#&gt;   4.4652  5.5200  0.1732  4.1136  5.7837
-#&gt;   1.0496 -4.4151  3.9518 -4.2376 -5.2899
-#&gt;  -3.7516  0.0786 -3.3617 -11.5217  2.9681
-#&gt; [ CPUFloatType{1,8,5,5} ]</div></pre>
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-    <h1>Conv3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv3d</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_conv3d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
-  stride = <span class='fl'>1L</span>,
-  padding = <span class='fl'>0L</span>,
-  dilation = <span class='fl'>1L</span>,
-  groups = <span class='fl'>1L</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a>conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 3D convolution over an input image composed of several input
-planes.</p>
-<p>See <code><a href='nn_conv3d.html'>nn_conv3d()</a></code> for details and output shape.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># filters = torch_randn(c(33, 16, 3, 3, 3))</span>
-<span class='co'># inputs = torch_randn(c(20, 16, 50, 10, 20))</span>
-<span class='co'># nnf_conv3d(inputs, filters)</span>
-}
-</div><div class='output co'>#&gt; NULL</div></pre>
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-    <h1>Conv_tbc</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv_tbc.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv_tbc</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_conv_tbc</span>(<span class='kw'>self</span>, <span class='kw'>weight</span>, <span class='kw'>bias</span>, pad = <span class='fl'>0L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>NA input tensor of shape \((\mbox{sequence length} \times batch \times \mbox{in\_channels})\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>NA filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} \times \mbox{out\_channels}\))</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>NA bias of shape (\(\mbox{out\_channels}\))</p></td>
-    </tr>
-    <tr>
-      <th>pad</th>
-      <td><p>NA number of timesteps to pad. Default: 0</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Applies a 1-dimensional sequence convolution over an input sequence.
-Input and output dimensions are (Time, Batch, Channels) - hence TBC.</p>
-
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-    <div class="page-header">
-    <h1>Conv_transpose1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv_transpose1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv_transpose1d</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_conv_transpose1d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
-  stride = <span class='fl'>1L</span>,
-  padding = <span class='fl'>0L</span>,
-  output_padding = <span class='fl'>0L</span>,
-  groups = <span class='fl'>1L</span>,
-  dilation = <span class='fl'>1L</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dW,)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 1D transposed convolution operator over an input signal
-composed of several input planes, sometimes also called "deconvolution".</p>
-<p>See <code><a href='nn_conv_transpose1d.html'>nn_conv_transpose1d()</a></code>  for details and output shape.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>))
-<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>5</span>))
-<span class='fu'><a href='nnf_conv_transpose1d.html'>nnf_conv_transpose1d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>weights</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  Columns 1 to 8  -3.4020  -3.3221   5.5997  10.0866 -12.0378   9.4439  12.0100  -6.3561
-#&gt;    4.6622   4.9088  -3.1538   0.7134  -3.2418   0.7069   9.2807  -2.7560
-#&gt;    2.9613  -1.9165   3.7265   6.6917  -0.4944   9.0744  -3.4301 -13.0656
-#&gt;    4.0219   1.4612 -11.4401  -4.4515  -4.5334  -6.5769   0.9479  -3.4467
-#&gt;    5.2708   2.1965  -0.7640  -2.2201   7.0304   2.2051 -12.2149  13.6686
-#&gt;   -1.8769   6.3726  12.6981  -0.4064 -13.3427  -3.5539 -11.5465  -6.0669
-#&gt;   -0.6353  -2.9486   0.0668   1.1492   0.7228  -3.1090  13.6239 -10.2690
-#&gt;   -3.1151   8.4682   3.0212  -6.0019  -3.4838   5.5069  -7.3848   9.1741
-#&gt;    1.6811  -2.5962   0.7999   4.6459  -6.4100  -1.2882  13.1433   2.1507
-#&gt;    1.2856   3.0652   3.7618  -5.0627  -5.5548   7.5233  -8.8703  18.5810
-#&gt;   -0.0534  -2.5442  11.2876 -14.1367  -5.8941  -0.3376 -10.4786   2.0474
-#&gt;    1.7841  -8.6978   6.1701  -6.1149   4.6801   9.7392 -12.1410   5.5319
-#&gt;    4.1689  -7.0452   3.7100  -0.1998  -0.2903 -19.5314   8.1485  -0.5425
-#&gt;   -5.4996   3.8203  -0.4553  -9.2764  11.4712   1.8233   0.8841   3.1020
-#&gt;   -1.4409   1.1196   2.3681  -8.6920   4.2522  -0.0869  -4.8010   0.3400
-#&gt;   -3.0391   2.9967 -13.2953  -9.6981  -0.5755   0.4798  11.0536   2.4582
-#&gt;   -8.9306   1.5906   4.7661   6.2675  -9.1133 -13.6243  -9.8338   6.5095
-#&gt;    4.0747  -6.3471   3.3191  -5.2998  -5.7780   6.4962   3.5602   0.5494
-#&gt;   -4.4727  10.4216 -11.3272   3.3672  -7.4187  -0.6279  22.2492   2.5181
-#&gt;    6.3450   1.5869 -10.9720  -0.7985 -15.7023  -8.8974   0.9418   4.2943
-#&gt;    4.2300   4.2705  -8.6831   5.6413  -7.9616  -9.4056   3.8547  10.5490
-#&gt;    1.4871  -2.3792   9.5268  -4.3106   9.0210  -5.5616 -18.1935  15.0745
-#&gt;    0.3383  -1.0036   2.7723  -7.4130   4.1278   7.0419  -5.3719  -7.6536
-#&gt;   -3.6353   1.6384  -6.1772   5.7951 -17.8785 -16.2790  11.7388 -15.1771
-#&gt;    5.6825  -0.6642  -5.7148   8.0170   4.5256 -10.0751  -5.0112  -7.0595
-#&gt;   -3.3480   4.8101  -4.3835  -4.3078  13.7962   5.3648  -1.8608 -11.0505
-#&gt;    0.3060   5.4332  -3.0289   3.7720   7.9775  -1.2186   3.0689  -6.2374
-#&gt;  -10.9572   8.7549   4.1430  11.1171   7.4537  -7.9009   8.3601   0.8618
-#&gt;    2.9056  -2.1716   8.9625   3.3790  -0.6524  -7.3391  18.7573   3.1784
-#&gt;    3.1966   4.2776   8.5735   0.8205  -8.6669   8.7617   3.8120  -4.7238
-#&gt;    0.6688  -2.4631   4.5162  -4.9566  10.4983   4.2963  -6.3707   2.4045
-#&gt;    1.4870   2.4361   1.9831  -5.6132   6.4245   0.8812   5.8486   2.4380
-#&gt;    5.5982   1.3517 -11.0389  -0.4521  -6.5151   5.8992   3.4627 -21.5431
-#&gt; 
-#&gt; Columns 9 to 16   5.3329  -1.2630   2.1277   3.5754  -5.1023   5.9644  -0.0256  15.5311
-#&gt;    0.1320  -5.9520 -18.1951 -13.0733  -3.9256  24.6851  -5.0745  12.8511
-#&gt;   -0.0432  -7.3288 -13.4791   2.1404   1.5252  -6.9826 -13.9457  -9.5798
-#&gt;  -16.0070  -4.0371   3.3476  -8.4853  -4.4672 -10.0733  12.9262  10.8009
-#&gt;  -20.3813  -7.0259 -10.4576  -8.0504   6.3497 -14.3200  12.0344 -12.3178
-#&gt;   13.9733 -11.9845 -17.4676   9.4152  -4.7336   7.8038   1.6975  -5.0659
-#&gt;   -2.5201   4.2446  13.2564   6.6906  -2.3874  -1.7195   0.2030   2.7831
-#&gt;   11.3460   0.2127  -2.8990 -10.1528  -9.1793  13.1459   7.6709   4.7359
-#&gt;    7.3873  -7.2720  20.6399  11.0227  -1.1562  17.6629   1.9004   4.0286
-#&gt;  -14.9912  -1.5069  14.7801   9.7501   6.6418 -12.6758   0.8990   1.7226
-#&gt;    4.3554 -11.2538  -2.1386 -30.9455   8.8646  -1.7486  -1.3369   5.8498
-#&gt;   15.2936  -5.2467  13.4738 -10.7510  -0.6811  -3.7045   2.1725  -1.7966
-#&gt;   -6.5613  10.3322 -11.0907 -22.5725   7.0308  -2.0663  11.9282   7.9475
-#&gt;   -4.9340  10.9411   1.4077  11.7134  -0.8646  11.5059   5.5536 -13.2366
-#&gt;   -0.3841 -12.5060 -10.7907  -8.0465  -8.6964  -5.3936  -3.7869  -1.3017
-#&gt;    3.0987 -11.3781  -2.1467  -2.3245  -5.4774  -2.0704  10.7995  -1.6707
-#&gt;   -3.8505  10.2883   5.1499  -2.5561   9.3952   1.4439   4.2016  10.5301
-#&gt;   -0.0898  -3.7571  15.2637 -11.1889   3.9881   7.6021  -4.0997 -13.4968
-#&gt;   13.5229   0.8679 -11.3233  25.4322 -20.1884  -0.2870  -2.3886  15.5536
-#&gt;   24.7709  -4.6339   7.2347 -18.9138 -10.4246   3.4996   0.1074   7.6424
-#&gt;    5.0590 -13.2545  -5.3252 -13.1357  -7.5967  -1.6829  -9.6901   8.2465
-#&gt;   -8.9745  -1.1905  -3.5946  -7.8038  16.0472 -10.9445  16.0494 -17.1568
-#&gt;    5.7468   2.1633   3.5435  -6.1949  -6.0812  -0.0240  -5.1476  -4.3254
-#&gt;   22.8465  -3.6399  -3.2858  17.3418  -9.0783   3.6177   2.9707  13.7459
-#&gt;    3.4682 -22.2032 -11.0163   1.2015  -1.4196  -4.1947  -0.1011  25.5689
-#&gt;    6.0909  13.4249   3.4556   9.0600   1.0097   2.0460 -10.4345  -7.3232
-#&gt;   -5.8473  -0.5750  -7.2077   3.7108  -2.7650   6.8820 -16.6877   6.0375
-#&gt;   -3.4993  -6.2754   7.5627  17.7500  -7.4868  -8.8540  -4.1517   5.3775
-#&gt;   -1.4684  -4.1773   8.1174   2.1676   0.5835  -9.8079   0.1065  10.0973
-#&gt;    4.3583 -14.2953  -8.4102  -6.0394  -1.5263   2.7660  -8.3920  -4.3442
-#&gt;   -7.1803  -7.3127   3.9185  -2.9441   9.7254   7.9097   9.9815 -12.8131
-#&gt;    1.8957   3.5981   2.2479  -5.4874 -11.6105   9.2716  15.0421  15.8466
-#&gt;   -3.7470  17.1303  15.5143  -9.5845  -2.4711  14.5061  -6.6229   3.9792
-#&gt; 
-#&gt; Columns 17 to 24 -11.4030   3.2914  -3.6824   7.3778  -3.6403  -9.2144  -3.8123 -11.4147
-#&gt;    6.9775  17.3332  15.0369  -2.2205 -12.3325   5.4442  18.4977 -19.0987
-#&gt;   -2.6600  -0.8244   0.1156 -19.6334   7.2126   3.7555   2.3947  -2.4205
-#&gt;   -5.6889 -16.5378 -10.6555   6.7101  -3.0607   1.9847   6.9351  -2.3856
-#&gt;   10.7362   8.4449   8.8638  14.8649  -7.8654   9.4950   5.8434  -7.6986
-#&gt;   -5.4969   8.2730   3.1316   0.5562  12.0991  -4.5922   3.8559   3.9451
-#&gt;   -6.8268  -2.1249  -1.2615  -8.3138  -6.4297  -8.9406  -6.5720   0.9212
-#&gt;    6.2238  14.3190  10.4240   9.2894  15.9443  -9.5853  -4.9122  -2.8612
-#&gt;  -18.1243   0.6864   1.3126   5.9148   3.8656 -15.2529   6.0997   6.0199
-#&gt;    6.0552  12.4736 -10.5636  -3.1575 -12.3052   0.3854   8.3527 -14.1803
-#&gt;    1.7624  18.4184  -8.2860   0.1208   7.5036  12.1680 -10.2531  15.1738
-#&gt;   -8.4918  -1.2095 -10.8568  -1.9252  16.0981  -2.0101 -12.8951   2.2095
-#&gt;   12.0019  -9.8840  -8.1874   7.0738   3.6993   6.1547  12.4801   2.8636
-#&gt;    4.7660   8.7987  12.7994 -11.3721 -15.2040   0.7972  -2.2404  -2.5280
-#&gt;   -9.2431  -7.3090 -11.1377  10.0527  15.0743  -5.8762  13.7034  -3.9267
-#&gt;  -18.6915   2.5403   5.3542  -1.4592  -9.7959   4.2395 -15.7803   0.5938
-#&gt;    6.5556   7.5932  -3.9556  12.9236  -1.7507 -13.1872   2.5448   9.8752
-#&gt;   -9.4816  10.2009  -2.6876  -8.0310  -1.8658   2.7470  12.3406   2.9556
-#&gt;   -2.7724  -8.6357  -3.7667   7.3440   4.1915  -7.3716   2.1982  -5.0892
-#&gt;  -14.9141 -14.8599   4.7102  19.5942  21.7712   7.9518  -7.1642   7.2634
-#&gt;  -15.5029 -10.1660   4.5629  18.4478   6.8156  -6.1980 -13.9624  -9.4698
-#&gt;    0.2529  -7.1825  -7.7222  -1.9380 -14.1539   0.2943  -9.0932   8.2087
-#&gt;  -14.1441   3.0024  14.5939  -0.2025   3.4876  -7.4976  -0.0551   5.3231
-#&gt;   -9.5177 -12.8646   0.6513   2.6348  -9.0701  -4.0028  -1.8900  15.3628
-#&gt;   -3.9713 -10.9939   8.8224  -4.6739   8.1388   1.6124 -14.6302   1.4824
-#&gt;   -9.0340  -5.0870 -10.3445  -0.1625  -3.6289   6.8809  -6.1462  -0.2733
-#&gt;    6.2609   0.8774  -2.8065   5.4479 -21.1925   5.0128  24.3217 -10.5720
-#&gt;   32.8786   1.0215 -13.9515  -1.5953  -8.0246 -11.3875  -2.2170  -4.9979
-#&gt;    2.6768  -2.8146   2.4852 -13.5925   4.2060  -4.2898  19.2806   7.1249
-#&gt;   -3.9153   1.1336   5.9213   5.0984  -2.2023   8.6069   6.0606   1.2647
-#&gt;   10.1535  10.2717  -5.9220  13.6057   3.1961  -3.0319  -3.2295   8.7145
-#&gt;   -9.5890   1.5876   0.7531   5.3205   6.7801  -2.7564   0.6690  -8.3498
-#&gt;   -4.3430  14.4685   7.5216  -2.5209  12.9735   1.8064   4.3173  10.1270
-#&gt; 
-#&gt; Columns 25 to 32   2.4853   5.6105   1.9683  13.4964   9.1434  -6.4918  -7.0492   3.6363
-#&gt;   12.9857   1.9555   4.9777  22.5779  -9.7904   4.6078   3.8744  -1.2456
-#&gt;   11.0348   9.8309  -8.5216 -13.4313 -10.1479   2.6104  -5.8196   1.3545
-#&gt;   -9.2727   3.0961  -5.3351 -15.8565  -5.0644   6.8959  14.5202 -14.9730
-#&gt;   -9.9999  -1.7592   5.7623   5.3228   2.7962  -8.4379  10.9653   9.2355
-#&gt;   -0.8570 -10.0629  -4.2377   2.0996   4.8431   1.0146 -20.8486  -6.6101
-#&gt;    5.8557  -6.6936   9.2499  -7.3945   0.5268  -4.0318   3.6466  -4.6563
-#&gt;   -6.5592  14.5206 -10.2924  12.9481  22.7132  10.5523   8.9015   7.3241
-#&gt;  -13.8000  -4.0928  -6.3584   3.6438  -4.8425   2.1957 -10.8246   2.6016
-#&gt;  -10.1104   2.7793   1.3134  -3.8754  -2.6685  16.7417   2.8038   4.5308
-#&gt;   10.1800  -6.0003  -3.2687   4.8912   0.4450   2.3451  11.0465   9.4713
-#&gt;  -18.0873  -5.6706   5.0255  10.1279   3.4458   7.7139  -6.9240  11.8126
-#&gt;    0.6084  -7.2368  11.5329  -5.1521  -3.0057   2.8228   9.8822  -0.8717
-#&gt;   13.2128   6.6350  -8.8597  -6.0699 -11.7773   6.4691  10.7185 -10.1524
-#&gt;   -1.4784  20.6837  -0.8946  -2.0686 -13.9778   6.2311   2.8580  16.2230
-#&gt;  -16.2416  -1.3529  -2.3926  -7.0074  -4.3543  -7.7845  -2.2654  -0.6351
-#&gt;   13.4708 -17.2668  -2.5720   7.8603   8.6782  -6.9270 -15.5004  12.3729
-#&gt;   -2.0224   0.1913  -6.6729   7.0807 -12.7506   8.0729  -4.5340   0.5993
-#&gt;   -6.1033 -11.7415  12.7260   1.6359   3.7216   6.8006  -9.3725  15.0121
-#&gt;   -1.7067 -11.2133  -7.2387   1.2911  -2.7941  11.3819  -5.3998   1.1760
-#&gt;   -1.6709 -11.8860   2.8899   8.1777  -6.5949  12.3433   8.4417  -1.0217
-#&gt;   12.9855 -10.4326 -10.7686   7.5307   5.8579 -14.4266  -1.1107  -4.9546
-#&gt;    4.4699  -1.8724 -19.8293  17.7525   3.2798  -7.4969  11.7519   3.4336
-#&gt;   -5.7059 -24.8347   0.4397  -2.4724  11.8311  -0.2100  -8.9870  -3.8547
-#&gt;   20.4853  -1.0191   0.5193   2.6300  -7.5003   6.4929   5.3039 -11.0795
-#&gt;    8.4373  -2.7194 -17.6829  -3.6844  -3.6820   5.9430   9.4556 -13.9000
-#&gt;    8.9509   1.9025  -6.3066  13.4222 -11.5982  -1.3947  12.7121 -10.5347
-#&gt;   -1.9653  10.4427   1.9862  -1.1744  15.0027  -4.3004   1.2407 -18.4286
-#&gt;  -19.3040   6.4675   7.5222  11.1232 -14.7564   8.5401  -0.8420   4.3079
-#&gt;    6.9177   9.0533  -0.1754  11.0561  -3.5253  -4.8426 -14.6301 -11.6506
-#&gt;   -3.2684  23.9275  -6.2449  -5.1929  10.8331 -10.7755   0.0731   0.9456
-#&gt;    9.0071 -11.7154  -0.1658   4.8980   4.7151   2.4651 -12.3172   4.1598
-#&gt;   12.2653  -2.1510  -1.6780   2.0408  -0.2796  17.0241  -6.9541   8.1348
-#&gt; 
-#&gt; Columns 33 to 40  -6.4934 -16.8743   2.3181   4.4653  -9.3090   2.1655 -13.9660  -2.1848
-#&gt;   11.0947  -3.7358   5.6452   1.4305  14.0751   3.3250   6.1680  -2.4080
-#&gt;   -0.3422  -6.5172  12.8814  -5.2839   6.2948  -7.7879   4.5184   0.8974
-#&gt;   -2.4789 -11.5037  -5.9303   3.9639  -5.6399   2.9390   2.3949   3.6339
-#&gt;   15.4083   4.3242   0.6047   0.8194  10.8756  20.4237   3.0728  14.6398
-#&gt;   27.1500 -10.0167  17.5785  -2.8886  11.4281  14.7418 -11.7986  -4.5393
-#&gt;  -13.6894  -9.4443   1.6899  -5.3305   3.8067  -8.1760   0.4252   3.7902
-#&gt;  -10.5987   7.6583  -0.4940  -4.6562   7.7836  -0.0849  -4.5291 -18.2112
-#&gt;    0.3112   9.6398 -14.1425  -3.2337  -9.8963   2.1637   1.8130  11.1850
-#&gt;    4.0215  -8.9530 -24.3401   4.7321  -7.7476   2.6174  -1.7013 -10.4683
-#&gt;  -14.1461   0.8646  23.9741   0.1648   6.7659   2.8736 -11.4991  -1.5781
-#&gt;  -17.1377  -9.4656 -25.5518  -5.3314  -9.3202  -8.2058  -2.2420 -15.0553
-#&gt;    3.4363 -11.4185  13.8324   3.8381  -1.7419  -3.1661  14.4247   0.6314
-#&gt;   -8.2500  -2.5786  -7.6117   0.1081   2.2540 -14.2212   6.6012   5.2179
-#&gt;    8.0123   5.2456  -3.2183   5.1887   7.1867   6.4124  -0.0386  -0.8770
-#&gt;    2.0467  -7.7391 -13.1609  -0.4225  -1.1648  14.8292  -0.4638  21.6506
-#&gt;    8.9941   4.8175  18.0106  -7.5279   6.6245  -1.2210 -10.3610   0.7324
-#&gt;    9.8753  -3.3676   2.6292   4.9669 -12.5947  -6.0543   4.8028   4.7117
-#&gt;   -2.4654   3.6883  -6.7979  -7.2631   1.0806 -14.5713  -2.8864  10.6796
-#&gt;   16.2626   2.1684   5.7132  -4.9697   6.9928 -11.4322   2.9932  -1.7062
-#&gt;   10.5728 -11.0543   4.9675   6.3518  -8.8985  -7.9297  -1.8789  -4.2027
-#&gt;   -8.0290 -13.1899  -1.3802   2.3988   2.5024  -4.8978 -14.7851  -4.7994
-#&gt;   -4.3272  -3.9751  -2.7541  -1.4456  -6.2994   0.6048   3.3404   4.0992
-#&gt;    2.8736  -2.6483   1.7485 -15.0721  -8.1690  -4.9127 -17.6872   1.0006
-#&gt;    3.0736 -16.2307  13.2554  -1.5358  27.9092  -7.0486   0.9153  -0.0269
-#&gt;    9.5154   4.7432   3.5428 -16.1041  -6.5499  -0.0852  -3.7778  -3.0053
-#&gt;    9.3989  18.5137  -8.8804   4.8191  -8.6373   8.8543   1.0093   6.4497
-#&gt;    2.6793 -19.7219   9.6046  -7.6926   7.9928 -10.1505   4.0265  10.5751
-#&gt;   -2.6140   7.9032 -11.6294   7.0794  -2.5575   2.4047   5.9283   5.8705
-#&gt;   -0.7595  10.1407   7.5237   4.2946  -5.0244  14.5707   5.7195   0.5241
-#&gt;   -2.7957   2.3151   7.1001   7.9507   3.6236   8.4751   2.7096  -3.8596
-#&gt;   -7.4017   3.1887  -5.4891   4.8062  -7.8596  14.7663   0.4869  16.0927
-#&gt;    8.5615  19.6132   3.6054  10.0349 -12.5893  -8.6976   6.5938  -7.1872
-#&gt; 
-#&gt; Columns 41 to 48   0.8108   1.6407  -5.6032  -3.6555  -1.6804 -13.2877   2.8539  -3.0787
-#&gt;    9.3537  -9.7476   4.8880 -20.5840  -1.6154  -5.1163  -7.4199   2.7217
-#&gt;    7.3419  20.0371   7.1091  -2.5200  16.9993  12.9213  -0.5617  18.8439
-#&gt;    0.0593  -9.0227  -4.3812   6.3282   8.6994   6.1488   9.3744   9.6400
-#&gt;   -2.0540   1.0386  -8.4654  13.4306  -8.9922  -2.8610   4.4931   4.1233
-#&gt;    3.0125  -1.0158   1.3725  -6.7613   2.7951 -11.9854  -0.5260   9.5570
-#&gt;   -4.0728 -12.2745  -5.7936  -3.0942  -2.5297  -1.8631  -3.6863   1.2608
-#&gt;   -2.7227  -0.2364 -11.1707  -2.8963  -9.0208 -14.8454 -16.7950  -5.8408
-#&gt;  -11.7613   2.5029   6.1459  11.5396   9.3762   3.5727  -1.4332  13.9619
-#&gt;    2.3293   4.6877 -13.2462   6.0601   8.1219  -2.5225   0.4609   3.6000
-#&gt;    9.2167  -1.7051   2.7683 -12.1350 -11.7119 -17.3898  -5.4030  -2.9345
-#&gt;   -0.6129  -5.9798 -14.2149  12.1874  14.2455  -5.5344   1.1349   1.4865
-#&gt;  -10.0586  -7.3004   5.1125  -7.4362  -4.4018  -7.8018 -10.0920   6.8644
-#&gt;   11.4776   1.8402   5.6009  -1.2885  -3.7582   9.2648 -13.5565  -9.7072
-#&gt;   10.5082   5.6524  -1.7752 -18.7630  10.0411  -2.7845 -14.7705   1.5436
-#&gt;   -2.3691  -8.4096  12.7298  -3.1411   7.1285   7.9916  16.4924  -9.7048
-#&gt;   -4.1282  -8.7706 -10.2554   6.2089  -3.9885  -3.1999   0.8261   2.7411
-#&gt;  -10.2897   1.9276   3.9883   4.5791 -18.2775   6.2249   4.1014   9.4091
-#&gt;  -12.8971  -9.0517  -4.5489  -7.3506   7.6722 -13.9137 -10.1596  -5.2806
-#&gt;   -2.4044 -14.9630  15.4090  -4.0349  -6.4806  -5.4862   0.1645  -1.5550
-#&gt;    7.8658  -3.3698  17.8102  -4.0142   1.9149  -8.1755  15.3992   1.7615
-#&gt;   -0.2406   5.9758  -5.8056   8.9618  -0.9571  -5.2890  -6.8692  21.5712
-#&gt;   15.4602  -1.5876  -2.0642   0.1091   6.2430   8.8998  -2.3240  -9.2328
-#&gt;   -0.7729  -7.5472  -9.4573  -1.7350  22.7880  -0.9829   8.1624  -3.5893
-#&gt;    5.4965 -19.2006 -14.7501 -10.7392   2.4455 -11.7979 -12.1449   5.0141
-#&gt;   -0.7157   6.8242  -0.0890  -6.8346   4.9808  13.5284  -0.0661 -10.2603
-#&gt;    2.8447   4.5713 -23.6320  -1.4885   0.4654  -1.6369  -1.9075   8.7763
-#&gt;    7.3281  -4.2269   0.3614   2.2044  15.0641 -16.7609  -1.8358   2.5082
-#&gt;    5.4544 -12.2758   7.0340   0.0071   4.2552 -21.7817  12.7642  -2.5611
-#&gt;    8.7109   7.3505   8.3116  -7.4865  -3.8266 -20.9745  -2.2267  -8.9084
-#&gt;   -3.0163  13.6449   0.8343  -3.1268  -9.5365  -0.4607  -1.8929  -5.1847
-#&gt;   -4.2591  -8.1355   3.6791   3.5154   0.0712  -3.0043   3.6564   7.4843
-#&gt;  -24.8125  -1.4200   0.9121 -14.5971  -7.0491   8.0285 -18.4479  -2.0825
-#&gt; 
-#&gt; Columns 49 to 54 -13.4775   6.4299   9.4011   2.9963  -4.2410  -0.7961
-#&gt;   19.5722   4.6494 -12.6057   4.8109  -9.6454   3.2013
-#&gt;    4.9691  -5.6945   0.4871  12.4633   0.6329  -1.3960
-#&gt;    6.7437  -2.2118   5.5732  -5.3188   0.2837   8.8553
-#&gt;   -0.7238 -15.7879  11.9493 -10.9455   1.9425  -4.1946
-#&gt;    9.4814   3.1128  -5.0689   4.6081   4.1684  -5.0189
-#&gt;  -15.2703  -4.2306   2.9745  15.9301  -7.8372  -1.0261
-#&gt;    8.7696   8.5561   1.7464  -7.9331  -4.4275   3.4173
-#&gt;  -11.3748 -15.1486  -0.0812   9.4316  -4.8301   5.7018
-#&gt;   13.9733 -13.0100   6.7084   1.1921  -3.3436   9.0758
-#&gt;    6.3874  17.3336  -1.5617  -2.0738 -15.3500   2.6654
-#&gt;   -7.8476   1.0076   2.4657  -0.7294   2.8429  11.5619
-#&gt;    9.7219  15.0092  -8.8457 -19.1730  -7.4966   8.3684
-#&gt;    3.6757   0.2104   2.4663  -8.7350   4.1012  -6.2780
-#&gt;    2.6837   9.3360 -14.7944  -9.6862   5.3753   1.0145
-#&gt;   -6.8848   5.7031  -8.0181  -4.1240   8.5213  -4.0727
-#&gt;    7.6754   9.4564   9.2758  -2.9939 -12.3505   1.2485
-#&gt;   -8.9874   8.3191  -2.7353   2.5287 -10.4629  -0.7473
-#&gt;    4.4643 -20.7586  -2.1356   5.4673   1.6132   0.9972
-#&gt;   -2.5125   0.2293  -4.2287  -7.8518  -2.1981   0.3639
-#&gt;   -5.5986  -2.2980   2.0952  -1.8191  -2.1499   1.8584
-#&gt;   12.0343 -11.1413   1.5269   7.1166   6.1728  -3.1503
-#&gt;  -12.9014  13.5368 -10.9415  -0.7706   3.2099   1.0282
-#&gt;   18.4152  12.3789 -15.2508  -6.6067   3.0568   7.1803
-#&gt;    0.4503  -2.8247   2.5629  22.6167  -4.7037  -2.0953
-#&gt;    1.5277  14.1460 -15.6665  -3.8909   1.8063   5.9350
-#&gt;    1.3014  -1.7635 -14.9856  -0.6496   4.3290   2.4247
-#&gt;  -13.3666  -2.6916   0.4056  14.2201 -10.1396  -2.4675
-#&gt;   -8.5456  -6.5618  10.6644   2.8039 -10.9306  -2.4231
-#&gt;    1.8607 -12.1675  -1.1500   4.7635   4.2335  -8.6470
-#&gt;  -13.6513   1.2997  -3.0154  -4.6409  12.3325  -2.6101
-#&gt;   -0.8295   4.4036  -3.5868  -3.4683  -7.4299   4.6622
-#&gt;   13.1270  11.0330   4.8870 -11.0830   1.5373   5.3535
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  Columns 1 to 8  -0.4819  -5.2206  -0.0029   2.5153  -3.8608  12.7501   0.5133  -7.9405
-#&gt;   -2.9389  -8.1096  -6.5544   5.3744  -2.0980  -2.6901  -7.6292  -2.6633
-#&gt;   -0.1633   6.8258  -8.0601  17.6487  -1.6527   9.2041   4.1098  18.9536
-#&gt;    3.0934   4.9433  -2.3287   8.7831  -0.0413   2.2191  16.2313  10.9159
-#&gt;   -3.4244  -6.5850   2.3879   2.1533   1.1951   7.9103  -7.6059   5.8994
-#&gt;   -1.0241  -3.2718   0.7590  14.0352   2.5676  28.5041 -11.1373  -7.3604
-#&gt;   -8.7309   0.6447  -4.4101  18.1409  -2.4054 -15.2883  -0.4211  -5.7780
-#&gt;   14.1064  -7.4859 -13.0060   1.2977 -12.2089   6.6004  -0.4598   6.1669
-#&gt;    1.0145  -1.5559   5.7633  -2.9374   9.2341   0.9511 -10.4202  -7.2072
-#&gt;    4.1396  -1.4229 -12.1161   1.2589  12.2684 -24.2916   1.7676   9.1974
-#&gt;    4.9893 -11.2712   0.0091 -14.8816   7.9679  -8.5449  15.5104  -7.7474
-#&gt;   -0.4274   3.6934   5.7053  -1.5316  13.9495   6.9647  -5.1044  14.6975
-#&gt;    0.4463   6.4648 -20.1534  -6.3858  -4.4117   3.8279  -0.5994   9.0915
-#&gt;    0.7436   3.0245   0.0705   3.7484   6.2527 -13.8330  -6.7148   2.5273
-#&gt;    1.7846  -1.7478 -19.3775  -3.4420   6.4562  12.0843  -7.5099  19.6084
-#&gt;    0.4382  11.7157   2.2197   0.9970   5.0869  -8.7664  10.2099 -23.0072
-#&gt;   -2.3241   7.8631   7.1129   8.5391 -14.9299  -4.3842   2.1925  -4.0727
-#&gt;   -4.6688  -5.0788  -1.5869  -0.5996   4.7212  -4.0829 -11.0372  -4.5099
-#&gt;   -1.4248  -1.5143   0.6068  -7.2181 -13.1587   9.8462 -12.2122  -0.7579
-#&gt;   -6.0846   3.5484  -1.6567 -13.2123  -1.4540  -0.3677  -2.4008 -13.0060
-#&gt;   -6.3128   3.2801  -3.9464  -4.6877  21.3952  -0.6947  16.1426   1.3038
-#&gt;    7.4999  -5.0251  -4.6848   2.9339  17.0494   2.3238   3.5144   7.9491
-#&gt;   -3.0672   4.6524  -1.9322  -0.0829   2.1092  17.6671  -7.9221 -12.8698
-#&gt;    4.1902   7.1206  15.7259   0.9299  -4.4021   2.5889   1.3139  -1.6823
-#&gt;   -5.9441   2.0650   0.7162  -0.8369   6.6083   5.7187   5.0481  -4.2100
-#&gt;    3.7929  -5.7785 -10.2156  -4.9948   2.7512  -2.8759   6.7069  -1.1763
-#&gt;   -4.2408 -15.1860  -3.0270  -4.3397  15.4940  -4.1178  -8.4906  12.2055
-#&gt;    2.0475   7.9887  11.8281   6.4205 -10.4581 -14.4355   8.4397  11.2832
-#&gt;   -7.2197   6.7202  -0.0503  -0.8183 -18.4166  -8.3775  -3.4855  11.0167
-#&gt;    3.6864 -17.1030   1.3593   1.3738   1.1387   2.0644   3.6189   2.3409
-#&gt;   -2.3272  -0.4464   0.1034  -1.5606   7.7496  -0.3080  -0.7441   1.0797
-#&gt;   -2.9679  -0.2349  -6.5420  -6.8238   9.2080  11.8530   0.5845 -19.6636
-#&gt;    2.7156   5.1943  -7.5562  -6.8587   3.4782 -10.9609  -5.8410  -6.6909
-#&gt; 
-#&gt; Columns 9 to 16   0.9220   8.9288   9.5904  22.7022   3.3025   4.3970  -6.4355 -15.6514
-#&gt;    2.2034   4.2848  -0.3982   2.9224  18.6466   0.8719   1.8450   6.8912
-#&gt;   11.0208  -8.4912   8.1046  -5.8479  -0.9123  13.0428  10.8669   9.9183
-#&gt;  -10.0539  -2.4357  -7.0667  -3.8567  -4.5919  12.8926 -11.6936  -9.3270
-#&gt;   -1.0260   1.4523 -11.5662  12.7857   8.1606  12.4949  -3.5062   6.7396
-#&gt;    1.1564   5.8108  -3.2525  -1.5163   8.1889   5.3126   1.1266   1.0732
-#&gt;    6.4986   5.7391  11.7258 -19.4050  -4.6006  10.8698   3.8123  -9.7043
-#&gt;    1.0159   9.3217  10.9731  -8.1476  15.1942  12.9909   0.4143  -4.8029
-#&gt;    0.3895  -7.5200   2.3376  -5.2167   9.5403   4.8892  -5.6347 -14.4278
-#&gt;   -2.0589   2.8281  -6.0354  16.0513  -2.9700  -4.1386   4.0437  -4.6662
-#&gt;   24.0197  -6.4195  10.7529   1.6021   0.1077  14.4642   8.9894 -17.9891
-#&gt;    9.8413  -0.3396  -1.9306   0.5483  -1.6017 -12.8183   7.7651   7.0370
-#&gt;   -0.6094  -5.3091  21.9270   4.9678  -8.2398  -1.4033  -8.7919 -17.7061
-#&gt;    1.1638  10.6985  -0.1985  -4.1491  -5.2095  -8.9683   4.3647  -3.4482
-#&gt;    0.4460   1.8941  11.2065  13.4008 -11.6229  -5.7442  -7.7553  -2.6037
-#&gt;    8.8885   9.0848 -19.6663   9.7019  -1.5179   0.8339 -22.8608   5.6407
-#&gt;   -8.5548  -2.1782  -5.0146 -13.3911  -6.6352  12.3301   0.8355 -14.2048
-#&gt;   -3.8014  10.0423  -6.4181   7.8804   0.5152  -6.4056  -5.1725  -7.1846
-#&gt;   -0.8038   4.7665   3.2067   0.5151  -6.1491  -1.9547  13.9486  -0.1191
-#&gt;  -18.4996  -4.1372 -12.6162  -6.1895   7.2162  -1.0841  -3.8087  -3.2502
-#&gt;   12.1832  -8.1580  -7.0187  14.1639   7.0291  13.9368  15.2381  -1.0285
-#&gt;   15.2792  12.0553   1.9043   8.0445 -16.4793  -3.8086  -0.2945  -4.1705
-#&gt;   16.1596  17.3198  -8.7729  -3.4102  -1.6892  -7.8405  -8.4079   2.0705
-#&gt;   -0.4981   0.7947  -0.6073 -12.2975  -6.4922  -2.6902  -6.0578 -10.3380
-#&gt;   19.1037  -0.8374  19.9664 -10.3776   5.4749  -6.8924   2.1914   5.3674
-#&gt;   -4.3390   9.8423  -7.3448 -10.9137   6.9789  -1.7818 -13.5169  -0.6241
-#&gt;  -10.0936   6.5791  13.2126  -4.6311  -3.5971   9.1622 -14.4920   5.6246
-#&gt;   12.9948  -6.7352  13.4531  -2.9093   5.2915   8.0255  11.4416   3.2484
-#&gt;    0.9402  -3.5074   7.2505  -3.3603  20.0030   9.4175 -15.4524  -4.3753
-#&gt;   -4.6485   5.3935  -5.9771  11.3638  13.2288   3.5048   4.9051   0.9133
-#&gt;    4.3863   2.0889  -6.0108   6.2971  -0.7904   2.5078  -4.6413   6.9718
-#&gt;    5.2172   7.3604   1.6021   7.8235  -4.6848  -9.3336  -7.0261 -10.7770
-#&gt;  -14.3799 -21.7400  -4.4857  14.5990  -3.0595 -10.7919   8.1404  -1.7315
-#&gt; 
-#&gt; Columns 17 to 24  -9.8048 -12.5923 -16.2473  -3.5541  -0.3008 -11.0337   3.0276  -7.1472
-#&gt;   -4.1545  -0.8647  -7.7718   4.8614  -7.1756   5.7949  12.1805  -4.2729
-#&gt;   -6.1350   8.9204   4.3775   3.1224   6.3487  11.4796   1.0545  -1.3963
-#&gt;    9.3818  11.9372  -1.6923 -14.5852   0.3782   0.3846  16.7013 -21.9739
-#&gt;   11.6835   0.9472   0.3652   8.4374   7.7478   4.7737   6.5792   4.5043
-#&gt;    3.4278   4.3648  -5.9592   5.1076  -6.1410  -8.4339  -8.7813   0.5910
-#&gt;   -6.6582   8.9154   1.6474  -4.2073  -5.1433 -21.5178  -7.6089  -7.0222
-#&gt;    1.1059   6.8018  -6.5985  -4.1589  -0.8176  -0.6786   5.7862  -3.4422
-#&gt;   -1.4974  -2.6496  -6.3141  -5.5224  -0.2532   1.6860 -24.6843   2.7009
-#&gt;   -8.3300   2.5907  -5.7009   3.2674  11.3087   0.0925   7.7410  18.3008
-#&gt;  -10.0354 -15.9220   7.3913  -2.8652  -1.9373  -2.3386   9.7288   2.6125
-#&gt;   -0.0476   6.9088   0.4586   9.2760  -9.2232   0.8646  -2.3746   2.4411
-#&gt;  -16.3246   4.8224   5.0342   0.4464   4.2872   3.5051  16.9570  -6.5543
-#&gt;  -13.5920  -3.2927  -4.3793   0.4956 -18.3107   1.6298  -1.6129  -2.4548
-#&gt;   -6.1644   0.8024  14.7420  10.1974  -2.8191  10.8615  10.0414   1.2799
-#&gt;   11.0713  -0.2822  13.3128 -22.5962  12.7363  -6.8463 -17.3946  -4.1150
-#&gt;   -3.5870  12.6684  -7.6992 -19.0780  -9.9113 -11.1350   4.2789  10.3612
-#&gt;   -3.5789  -6.3575  -5.2115   9.2524  -3.9640 -22.7487  -3.9939   5.6334
-#&gt;   -4.5564  12.8454  -5.9693  -1.4966  -7.9953   0.9102 -10.5091 -15.6979
-#&gt;    5.9805   5.0793   9.1717  -9.1791  -8.8985   4.1495  -3.0508 -17.1221
-#&gt;    3.9411  14.7797  24.7605 -10.4807   3.3607   7.8733   5.8041  -4.4127
-#&gt;    9.2486  12.7223   9.4017  -1.2602   6.9014  -2.1000 -20.2121  -3.3159
-#&gt;   13.8874  11.5645   2.7802   3.1322   5.4592   1.0001   8.0593   2.9946
-#&gt;    7.9786  14.9924   8.5748 -19.5978 -12.7893  -4.2343  -1.9550 -12.7660
-#&gt;    5.9776   9.1636   8.3088   3.4711   8.7790  -6.2560   0.1083 -11.1884
-#&gt;   -1.3666  -2.0074   4.5830  -0.8431   2.1098  -1.2609   2.7545  -8.9033
-#&gt;    5.5715   0.5770   1.2148  10.6711  15.8895  10.2264  11.4597  -3.0619
-#&gt;   -3.8137   3.3634  14.8873 -14.7357   3.2538  -8.9756  15.4470  10.8127
-#&gt;  -10.9382   0.2563 -18.7371  -5.6780   6.1646  -9.5219  11.7426   6.3293
-#&gt;    0.1631   2.7593  -2.7463   8.8819  -6.1188   8.5627 -12.4856 -10.0039
-#&gt;    2.2367  -7.6403  12.4575   3.0167   0.0456  -5.0556  -1.7143   9.3855
-#&gt;    0.3451  -2.3883   0.2954 -10.5897   4.3268   3.0021  -1.3534   2.1988
-#&gt;  -14.4107 -13.7240  -2.8676 -13.7877 -14.4132  10.0769  -9.2827  -9.3796
-#&gt; 
-#&gt; Columns 25 to 32  -6.8085  -8.6017   2.1119  -0.5937 -15.3484   8.8596  -5.8220   7.1562
-#&gt;   15.1118 -13.7286 -10.1623   9.7267  -0.6236 -10.9437  -2.7784  11.3351
-#&gt;    9.7706   6.4808  17.4938  -2.7736  -6.0615 -11.9327  10.3893   5.4409
-#&gt;  -16.6304  15.2463   1.4248   1.6986  -1.0918  -1.9998   4.9247   9.7056
-#&gt;    2.1222  -9.4263  -1.4495   2.5241   7.8925  -5.7002   3.6337  -3.5190
-#&gt;   24.7766  -7.7072   5.0106 -12.8077  -2.2998   0.5796   6.9233  -6.3570
-#&gt;  -10.3773   8.4580  -4.0006  15.5527  -3.0032 -12.0764 -11.0711   5.2015
-#&gt;    5.7345  12.7130  -1.4539 -16.6609  -2.8527  -2.0592   8.1273  -9.8065
-#&gt;    7.9213  -7.9519 -10.3330   4.7381   7.5159  -2.1260   4.8071  -3.1184
-#&gt;   -4.1126 -12.7701  11.8306   4.2474  -5.2006   2.6578   4.2070  -0.5555
-#&gt;   -8.1335   9.8278  -3.4931   6.4433 -25.9378  12.2491  -2.0956   1.9224
-#&gt;   -9.6807  -3.5407   4.7496   5.5022  -6.4779  -8.4542   8.9948  -2.2052
-#&gt;  -19.2651  -5.2954  -2.5868  -0.8298  -0.2517   3.7779   8.2183   4.5539
-#&gt;   -3.4994 -12.8477  17.2146   1.4502   2.7379  -8.0086  -0.6183  13.2688
-#&gt;   -6.5192   0.0200  19.2129  -5.5829  -0.6446   7.5521  -9.4717  -4.0767
-#&gt;   -0.1924  -9.1071  -9.6005  -0.0226   5.8651  15.3660   7.3478   4.9196
-#&gt;   -7.4130   6.5431  -6.9433  -5.3040  14.1182   9.5369  -4.2912  -6.6774
-#&gt;   -2.9045  -6.7124  -2.7740  15.1553  -2.9931  -5.8485  -0.0076   1.1254
-#&gt;   -6.6412  -6.9020  12.3054  -6.6301   1.7703  -1.3016 -17.2159   5.1338
-#&gt;    1.2423  11.7999  -1.0971  -2.9499 -14.2456   1.4208   2.8311  -2.1426
-#&gt;    9.8558   3.3286  -6.0330   1.5924 -17.0395   1.6937   1.5088  14.2872
-#&gt;  -13.6782  -2.6559   9.6998  -7.6153   4.1168   8.5486  -5.7462 -11.3712
-#&gt;   -1.0461   3.1661   0.9723  -0.1603   8.1947  -0.1903  -2.0659   1.9872
-#&gt;    1.2356  -5.5363  -2.5218   4.4223   6.3811   9.0688  -2.0909 -11.4505
-#&gt;    0.1803   9.0605 -11.5272   2.4784  -6.5612  -0.9182   4.5369  12.0906
-#&gt;    0.9670  15.9536   1.1850 -10.7306   1.7331  -3.5623  -3.2337   0.0780
-#&gt;    2.1487   0.4831 -11.7601   0.4493   8.4097  -8.6628  -6.1674   6.6803
-#&gt;  -12.5104  -2.1950   1.0655  -2.1045  -9.9182   3.6660  -1.0077  -3.0266
-#&gt;   10.3879  -4.1284  -5.2046   3.7640  -1.6218 -13.5922  -0.0236   9.4374
-#&gt;   30.6151 -10.4498   1.9584 -13.9154 -17.9564   2.5540   1.7984 -13.5876
-#&gt;    6.1135  -3.3587   4.7755  -7.8638  -4.8419  11.8515  -8.4928 -11.5208
-#&gt;   -2.2833  -0.1637 -13.6969   0.5208  -4.3343   9.7485  13.0213   2.9965
-#&gt;   -7.2476   2.0487  -1.7963  -4.0775  -2.5360  -4.1925   0.1009  -4.5751
-#&gt; 
-#&gt; Columns 33 to 40  -2.2380   8.5559  -5.3574   3.9011   6.8494 -13.2887  -1.3763   1.2354
-#&gt;    3.0555  -2.9720   1.5243   9.0502   1.9101  13.1961  -6.1384  -3.1684
-#&gt;   -7.3028   2.0933   6.0631   4.2544  -9.3460  11.8531  -2.6913  -4.8134
-#&gt;    9.5156   3.1706  -3.9208  -2.1002   9.5816  -5.8240   2.8750  10.7120
-#&gt;   -8.0500  -4.9300  -5.1968   5.7922   2.3075 -14.6487  14.0825  -8.4532
-#&gt;   -4.2872   4.4361  -9.0631  23.6798   7.8985 -10.2105  -1.5070  -8.9764
-#&gt;    2.7651  -9.2720  -5.0431   2.4528  -7.3981 -11.0051   5.6896  10.0866
-#&gt;    0.1144   4.3722 -16.8620   9.5580   0.5869  12.7837  -5.9188   8.1464
-#&gt;   -5.8672   1.1694  -1.9444  -2.9742  -9.3270 -11.8932  -7.8279 -11.7872
-#&gt;    1.7626   6.6941  10.4813 -13.6764   2.8596  -8.5942 -16.0595  10.5953
-#&gt;   21.6362   3.0477   4.8127  -1.9326  19.5973   7.3324  -7.9649  -9.8971
-#&gt;    7.5853  -0.8151  -0.8230  -7.8966   0.6527   3.7587  -6.2060  -7.5402
-#&gt;    3.3989  -4.9643   9.2965  -6.4633  10.8497   2.2541  -9.1115   4.5233
-#&gt;    2.8878  -8.9345  -3.1467   9.9046 -15.6538  -3.2851   0.7502   5.4513
-#&gt;   14.8621  -1.1065  -1.7157   8.0387  -1.7380  14.0130  10.3726   6.7905
-#&gt;   -7.1412  -3.9666 -13.4998  -1.2814 -11.2630  18.7226  -2.8956  18.7293
-#&gt;    8.6632  10.9309  -3.0818   3.1086  10.5533  -5.1691  -3.9426  -4.3740
-#&gt;    3.9342 -18.1656  12.2228   4.9261 -14.5196  -5.0080   9.1050   0.2222
-#&gt;   -9.5120  -4.8606   6.8878   7.3667  -9.4149   3.3709  13.2423   1.4056
-#&gt;    1.9436  -4.4698   1.2865   5.6216 -17.1033  13.1808  -0.0951  -9.0469
-#&gt;  -10.8797   3.1092   9.0160  -1.3826   1.8526   7.0632 -11.5050  -4.6841
-#&gt;    4.0211  -8.2939  11.7602  -8.7567   0.6896  -5.8630  -8.5215  -2.8009
-#&gt;   10.9789   5.2504  -4.0149   1.6084 -19.9733   8.4035 -11.8587  12.0032
-#&gt;   -1.7481  15.3985  -4.5329   4.9446  -3.5283  -8.7917   3.9513  -4.3308
-#&gt;   -8.3930 -21.3731   3.0285   6.8246  -4.9782  10.4994  -2.3267   4.7095
-#&gt;   11.4066   0.6951  -0.9972   3.4559  -2.6510   6.9732   1.6735  21.5091
-#&gt;    8.3481 -12.7215  -0.9001   3.2570   3.5659  -0.1153   2.1793   6.2822
-#&gt;  -12.4261  -8.7786  -0.4329   7.2962  -5.3862   2.6605 -11.6414  -1.7750
-#&gt;   -1.8166  -9.1530   3.1636  -2.1578  -1.8015   2.4225  13.8183  10.3135
-#&gt;  -11.3429  -3.4252  12.7994   6.7096  -6.6971   2.3818  -3.2512  -6.6221
-#&gt;   -1.6620  -2.6742   0.1637   7.8182  -5.9893  11.5357  -9.3134 -17.6894
-#&gt;   -1.9012   3.6935  -2.6552 -13.8588   3.3953  10.5430   1.1457  -1.6239
-#&gt;    3.1291   7.4067  23.5298   8.6963  -0.2826   8.4151   6.2040  -4.1558
-#&gt; 
-#&gt; Columns 41 to 48  -3.1363   7.3544  -5.3423  21.9039  -1.4420  10.2161   4.4932 -12.0389
-#&gt;  -14.1041  17.2067  11.0677   2.9500   0.5418   8.1772 -11.2283   1.7841
-#&gt;    9.3775  -5.6499  -3.7570  -6.1523  -3.0654  12.7029  -7.6766   3.1684
-#&gt;  -14.4615 -14.4262  -8.8674   4.9137  13.3862  10.0671  -5.2368   1.7981
-#&gt;   -3.1554   0.3789   0.8130   0.0190   7.6217   4.1427 -12.8618  -0.1358
-#&gt;  -10.5835   6.6097   3.6207  12.9302   1.2838  -7.0782  -0.8255  -6.7387
-#&gt;   -2.1045   1.9887   8.4957  -2.4092  14.9181   7.3300  -4.6289  -5.0099
-#&gt;  -10.8884 -10.2908   8.4855  -3.8827   2.7233  -0.3036  -6.1666 -19.7676
-#&gt;    7.3152  -5.1568  10.9097  -8.3887  -0.5458  -0.1820   2.8254   2.9475
-#&gt;   -6.1307  -0.2383 -26.3784   1.9305  -1.7310  12.4772  -5.9847  -5.8463
-#&gt;   -8.0316   0.7484  10.9591   6.7322   8.4529  11.4079  -1.3539 -21.4052
-#&gt;    0.8046  -5.7605 -14.7639 -10.6698  -8.2835   4.9069   1.6418  -7.5691
-#&gt;   -0.1441  -6.0972   7.5696  -2.2854  15.2401  -2.5021   2.7712   0.9315
-#&gt;   -8.4309  -3.5781   1.5312  -7.2183   3.7684  12.3533  -7.4360  -3.6979
-#&gt;    1.3081  -9.1062 -10.9158   0.8641   7.4869   8.3131  -4.4609  -9.5696
-#&gt;    1.6417  -9.1959   3.1554  21.2603  -5.1166  10.5320   3.7212  -3.2197
-#&gt;    4.5389   1.9457   4.4451  16.3994   3.5741  -4.6571  -4.0474  -2.3826
-#&gt;    5.1297   7.8768  -8.7989   1.2365  -0.8035  14.8657  -1.5166   2.1960
-#&gt;   -5.5949   2.4311 -12.4298  12.9970  -8.9063  -0.4423  -6.1634  -3.0376
-#&gt;    9.5492  -8.8751  -8.5154  15.5427  16.4931  -5.3865  -5.8158  -2.7917
-#&gt;    7.7901  -9.8998 -20.7473   2.7747  -6.4682   9.0566 -13.1231 -11.4694
-#&gt;  -12.3843  -8.5634   3.5880   3.4265  -1.6278   9.8123  -3.9558  -6.3625
-#&gt;   -8.5465   0.3669  10.9545   0.0538 -31.6006   8.7117  -2.9243  -2.6953
-#&gt;  -15.1314   6.7208  -0.0664   6.8320   1.0457 -14.2291  12.4592  -5.2388
-#&gt;  -10.5041   1.5691   6.9511 -13.5531  17.6497  -6.9941   7.6501   1.2557
-#&gt;   -7.3582   4.6629  -5.2947 -10.3137  -6.0007 -11.0059   0.3809  -2.1057
-#&gt;   -1.2976  16.3320   0.5808 -29.5155   3.8300   6.0922 -10.3215  13.2572
-#&gt;   -1.3692   7.6790  -2.5385 -10.3082  12.3751  -2.3040   9.9964 -15.5451
-#&gt;   -0.7694   9.0607   3.9911  -9.5284  14.6629  -3.6646  10.7148  -3.6341
-#&gt;  -24.0645  10.9705  -4.9396   7.1644  10.8988   5.3295  -7.8901   3.6073
-#&gt;   -4.2969  -0.4734  -2.4455  13.5691  -9.0603  -6.5910   2.2965   4.0957
-#&gt;    3.8535  -6.8195   6.8174  -1.0543 -12.0561   5.7382   5.6158 -12.4215
-#&gt;    7.2680 -15.4868  -1.0820   5.4146  10.3428   4.1904  -6.8178  -5.0218
-#&gt; 
-#&gt; Columns 49 to 54  -5.4966   4.3610  -3.1538  -1.2931  -1.9057 -10.2110
-#&gt;    7.3548  -5.2083   0.3370  -7.3086   5.1919  -3.9028
-#&gt;    1.8305  -1.1567  -8.7825  -0.4367  -3.6235  -5.8356
-#&gt;   -4.7541   7.1628  -0.7194 -10.0009   1.7585   1.5027
-#&gt;  -15.3772   8.9647  -9.0400   7.3545  -5.8981   8.3576
-#&gt;  -11.2623   0.4739  -3.4062   0.5766  -1.1190  -6.9952
-#&gt;   10.2340  -1.4894   9.4347  -4.9607  -1.4775   2.1482
-#&gt;   -8.3996  -9.0917 -10.1142   1.5123  -3.1730  -0.4410
-#&gt;   -3.3901   5.6836   7.7788   1.7784  -2.1055  -6.1990
-#&gt;  -13.0869   2.6456   7.4809  -4.9600  -0.1278   3.9787
-#&gt;    1.2147 -14.2896  10.6475 -11.4537  -4.2922  -9.7600
-#&gt;   -2.1622  -3.5125  -0.8263  -5.0042  -2.8507   2.7770
-#&gt;  -15.2335  -2.2717  -7.5016  -1.2329   1.3644  -0.5270
-#&gt;    9.0287  -7.2195 -14.0937   2.0112  -2.8852   8.4057
-#&gt;   -6.5483  12.7553  -9.1046   1.3183  -3.0489   1.2316
-#&gt;    8.9171   2.4454  -3.1406  -0.8452  -3.0388   5.0780
-#&gt;   -8.0307  -4.8983  -3.7974  -2.0752  -6.7438   7.1591
-#&gt;    3.8050  -8.7271  -1.3800  -4.7771  -0.3398  -1.3886
-#&gt;  -11.7553  10.7943   0.9459   6.9986  -0.9770  -3.5349
-#&gt;    2.7490   3.3282   8.0601   3.4355   1.8605  -5.8076
-#&gt;    2.7776 -11.3273  10.1910  -9.4382   0.1475  -8.8791
-#&gt;  -11.6640   4.6689  -9.3334   5.0207  -5.6725  -1.0805
-#&gt;    4.8995  -6.2917   2.2296  -0.7147  -8.1342  -2.6591
-#&gt;  -10.2629  -4.5991   0.8952   0.9093  10.6944  -1.9790
-#&gt;    7.3398 -21.2451   3.6049  -2.7504  -1.5583  -5.1873
-#&gt;    3.7885   7.0916  -1.7625  -3.5409  -4.8971   4.6294
-#&gt;    5.1073  -2.0471  -0.0095   2.9273   0.1626   2.9369
-#&gt;   -7.2647 -14.0054   3.8258   3.3260   8.5812  -6.3849
-#&gt;   -2.3203  -7.4033  -3.5734   2.6714  -2.3590   3.7384
-#&gt;    1.5883  11.3498   9.0052   7.2625  -3.4268 -11.4209
-#&gt;    4.9088  14.3874   2.8020   0.2484   2.3005  -0.9292
-#&gt;   -4.7410 -16.2666   1.6729  -2.1344  -0.5127  -1.1410
-#&gt;    8.6789  12.3184  -2.7760  -7.8175  -3.6750   4.5928
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  Columns 1 to 8  -4.1930  -4.2938  -8.0492 -16.1209  -3.5431 -16.9353   3.6549 -16.3226
-#&gt;    2.0634  12.4639  -6.7176   4.5199   4.1660 -12.6230  15.7908 -13.0765
-#&gt;    0.7939   0.9953  -6.9306  11.6025   7.4693  -7.5668  11.3821  -2.0572
-#&gt;    2.8244  12.2678  -3.4168  -1.7151   8.2090  -5.3083 -13.6367   6.0393
-#&gt;    2.6262   7.3891   5.8154   1.7653   4.0755  31.8661   3.8790   4.5940
-#&gt;   -1.4368  -2.4654  -0.2267  15.0589 -16.8031   1.8162  20.2713   0.9348
-#&gt;   -1.1332  -5.8031  -2.3842 -12.1341   4.1885 -10.8732  -5.2933  -4.0929
-#&gt;   -2.2673   3.7238  13.5108  15.0286   4.2366  -5.0028 -19.4132  -0.6896
-#&gt;   -5.7678  -5.1759  -4.0971  -7.1286 -11.8967   6.5170  12.1592  -3.0949
-#&gt;   -1.6882  -1.6721   9.1632   1.6115   5.5952  19.6032 -10.0981 -14.6816
-#&gt;    2.9134  -0.4012   8.3009   5.4625  -0.7142 -16.6623 -15.5116 -15.9213
-#&gt;    4.1479  -5.9330  -0.3079  -2.2435 -32.6861   6.6801 -15.3196  -4.4335
-#&gt;    2.5314   8.3939  -3.0095  -2.5680   7.1230  -6.4127 -15.0353 -15.6675
-#&gt;   -0.2210  -7.3752  -2.5114  -2.8594   0.3443  -6.0536 -13.0363   4.6189
-#&gt;   -2.7052   7.2422   2.7381   0.4341   8.7789 -11.9617 -15.6283  -6.7623
-#&gt;   -3.3685  -2.8176   3.6247 -14.7462  -1.3084   5.4775  18.7384  -2.4179
-#&gt;    2.3477  -5.5858   8.1684   7.8423  -1.5581  -7.1784  16.4660  12.8485
-#&gt;    1.9556  -0.7883   2.0491  -5.6521   2.0258   6.0386  -3.6705  -5.2138
-#&gt;    2.0203  -7.8045   0.9171  -1.3053 -13.8989 -14.5418  11.9457  -9.3766
-#&gt;    4.8615   6.3854   5.4163   7.0596  -9.1315  -3.8524   3.9689   3.9628
-#&gt;    2.6069   3.8186   9.9940   4.3394   6.8537  -3.4462  13.5649 -13.7966
-#&gt;   -0.4352  -4.8231   8.7437  -7.5278 -10.8580   3.0095  -8.6159   4.9880
-#&gt;   -0.8947   3.2055   2.5879  -7.2642   0.3748  -6.4874   6.5245  13.2762
-#&gt;    0.8444   0.4116   5.5033  -4.8638 -19.3270 -11.7894   4.0989   3.4386
-#&gt;    5.3317   3.0724  -1.9510   2.9267   3.7724 -11.5613  16.3503  -5.0854
-#&gt;   -1.7861  -3.4821   0.4333  10.7412  -3.3958  -8.5369  -3.0370   9.9645
-#&gt;    2.3875   7.9833  -1.4948  -0.5889   4.7385 -11.4760   7.9666   3.7245
-#&gt;   -2.6735  -9.3640  -2.6331  -1.0881   7.1601   5.4990   9.8436  -0.1357
-#&gt;   -2.5386   4.2169  -4.1966   3.3202   0.7131  -3.0641   5.5399  -1.8628
-#&gt;   -2.3504  -5.8159  -2.2859  -1.1824 -13.9452  10.2136   0.4261  -2.4565
-#&gt;   -0.6191  -0.3615  -1.1782  -7.4217  -8.7476   6.0040 -19.8930   9.3176
-#&gt;   -2.7044  -4.1761   6.9519  -9.4281  -4.2231  -3.7208   9.2337 -15.6565
-#&gt;    0.8752  -1.6728   5.2760   7.6203  10.1084  -7.1332  -5.7176  -8.5470
-#&gt; 
-#&gt; Columns 9 to 16  -1.3751  -4.6718   7.3163  -4.2272  12.6638  -2.2706  22.3544  14.7884
-#&gt;  -17.4905 -11.2657   9.5052  -3.1713  -7.5956   3.7764   9.9466  -3.8770
-#&gt;   17.7851  14.4638   2.2805  -2.9703   2.1310   1.2383   9.6480  -4.6290
-#&gt;   12.7306  -4.4880 -12.3419   5.5879  -9.1243   5.1035  -6.5518  -1.8256
-#&gt;   -7.0213  10.0898   0.9359  -2.2754  -0.8967   3.5106  -1.8343  13.3342
-#&gt;   -5.7105  -2.6554  12.0819   2.4828  -9.6719  12.4730   0.0195   6.6412
-#&gt;    9.9782 -17.3660   6.0132 -15.7503   1.9145 -11.5812  13.9218   7.2288
-#&gt;  -12.8559  -9.1790  -3.5515   7.9658   1.8167   1.9882   7.4664  -3.3135
-#&gt;   -5.4854  -4.6593  14.7058  16.9274   2.1959  -4.0901   5.5985  -6.5418
-#&gt;    6.9887   6.7250  13.6766 -10.8270   9.5203  -3.0196 -15.0874  20.0360
-#&gt;   -7.9077  -9.4513  -0.8396  -0.6632  -2.3429  -5.6054   3.0491  -6.5713
-#&gt;    0.0626  -2.1586  -1.9358 -11.1933   0.6719  16.6357  -9.4569  -6.9966
-#&gt;  -15.8013 -10.2705  -8.0154   5.7952   9.1501   5.0046  -7.6896 -22.1871
-#&gt;   -6.2963   9.5553   4.9791 -10.5206   5.9284   3.2635  -0.0806  -1.4651
-#&gt;    9.5112  11.1536  -3.3476 -10.0706  13.3855  -9.7142  -4.9835  -2.9197
-#&gt;  -16.9403  -3.6381 -11.1996  -8.8482   5.7720 -13.0883  -1.7720  -7.8983
-#&gt;    1.1808 -29.7958  -1.6217   0.2144  11.8355  -4.9172  14.5603   0.8325
-#&gt;  -13.8267   7.4284  19.3828  -7.0147 -10.4835   3.4233 -10.3264   4.8940
-#&gt;  -14.9396   3.0823  -2.3867  -1.3608  15.5699 -16.2471  15.0608 -20.7246
-#&gt;   -2.7182 -16.4000  -5.5439  -4.2584  -0.6977   0.1262 -19.6594 -23.1513
-#&gt;    7.1535   7.1394  -3.2191  -1.3613 -10.2780  -5.1404  -2.8785  -2.0390
-#&gt;   -8.2808  10.1002   2.9142  14.5043  13.0980  -6.8247 -12.6182  10.2535
-#&gt;  -11.0635  -0.5313 -12.4435  -7.3602  -3.1639  -6.9580   5.0292 -19.4873
-#&gt;   15.2656 -10.6572  -9.6755  11.0557   4.7636  -0.9666 -11.1211  -2.5062
-#&gt;    4.1579  -8.0778 -10.8777   4.0926   6.6726   2.6812   7.5614 -27.8926
-#&gt;    6.1098   0.1846   5.4939  -6.4260   4.8834   6.1523  -5.2224  -1.1672
-#&gt;    2.1530  -7.9274   5.5197   4.7026 -12.5497   7.2099  -4.3758   3.8331
-#&gt;   10.0518   0.2685  -7.9241  -7.0586  -7.5226  11.0818  19.4186  23.0082
-#&gt;   -9.8596  -8.8434   9.9212  10.0419 -14.5056  10.0515  17.1173  -2.3899
-#&gt;  -11.0053   8.2552   6.6327 -13.5537   3.6395   8.0894 -19.4818  27.0947
-#&gt;    3.1117   3.5740  -1.6299   0.0573  11.5230   2.5039  -5.6034  19.5031
-#&gt;  -14.6715   2.7438  -3.5536   1.2380  10.5156  -6.8564   5.1934 -13.7403
-#&gt;   -0.0594  10.1835  -0.9862  -2.6569  13.4508   0.1535   1.9503  -9.3566
-#&gt; 
-#&gt; Columns 17 to 24   2.1648   9.9118   2.7669  37.0798   6.1554  -0.9674   2.2145 -22.7400
-#&gt;   -1.9847  15.1123  -6.1441   0.9552 -15.1720  18.7342  -5.8578  -0.5343
-#&gt;   14.4967   7.8643 -22.9665 -13.4702  11.5459  -8.5376 -19.1667   0.3537
-#&gt;  -11.2231  -6.1155  -3.2519  -4.4614   8.2612   1.5561  -6.3060 -12.0597
-#&gt;    8.6151  11.8901 -12.8979 -14.7739 -12.6495  11.6654  15.9824   8.7371
-#&gt;    9.9124   1.9871   0.9960   3.4667 -24.8880  -6.4308  -2.5527   5.0941
-#&gt;   -2.7828   6.3362   3.9042   2.4375  13.7409 -10.7666  -2.9679  -3.2584
-#&gt;    9.5973   0.6512  -8.4749   3.0273  -5.2330   3.1956   5.3670  16.2812
-#&gt;   14.2044   3.3525   1.5754   8.0550  -9.4856   8.5634   4.5889  -0.0931
-#&gt;   -7.8997  -0.3781  -6.0813 -12.3311  15.4703  -3.3157  12.7185   4.9055
-#&gt;   -5.9339  -2.7463  -3.1515   2.7804   7.8179  -3.4383 -17.9781  -3.6307
-#&gt;    8.4237   0.6782   6.5379   2.7824   6.7653   8.1552   3.7292  -9.6535
-#&gt;   -0.4395   1.3288  -6.4139 -12.6609  14.5188   3.1010 -15.3666  -4.0688
-#&gt;   -3.3196   9.0274  -1.2820 -10.8813  -0.2054  -2.2556  -5.1097  13.6087
-#&gt;   -4.6788   6.3323   7.1277   5.8492  -3.0102  -2.0614 -10.8959   8.5586
-#&gt;    9.8584  13.0345  -1.7015 -11.0904  -4.9500  -5.3458  11.9340   0.5475
-#&gt;  -12.4314  -3.6081 -13.2721   5.2850   8.3172 -31.0106  11.5076   8.5326
-#&gt;    2.4899   4.4806   9.1995   1.0325  -2.4253  -2.5370  -4.1346  -5.0383
-#&gt;    1.0775  19.3360   0.5598  27.1162  -0.7628 -12.5944  30.4839   1.9405
-#&gt;  -17.2410  -5.0430  10.6785   7.0410  -9.6584   1.4408  -6.5145  -6.6291
-#&gt;   14.9644   5.9138 -11.7722  14.0470  -2.2471   7.8040   2.7462   5.2156
-#&gt;    0.7162   6.4672  -8.4394   1.1383  22.0631 -11.5370 -16.6585  -6.2486
-#&gt;   -6.6241   7.9182 -10.7970   4.2958 -15.1758  11.3678  -7.5028  -9.0273
-#&gt;    2.3674 -19.9666  14.5254  12.0053 -15.5009  -0.5880   6.6127   1.1500
-#&gt;    4.5254  10.9125  -2.2966  -5.9631   3.4624   3.0108 -15.1200  -0.6346
-#&gt;    2.1486   1.0356   0.6908  -1.0763   0.4338 -16.0986  -2.1404   6.7595
-#&gt;   -5.7633 -10.7579  18.2628  -2.3337 -10.4704  -0.1686   2.3580  10.8218
-#&gt;    9.7607  -4.3554 -11.3628  -8.1869   7.0975  -2.6700  18.8758   7.0897
-#&gt;   10.2672  -7.9668   5.1197  -0.4113  -4.2827  -5.3678   8.4160   4.7576
-#&gt;    2.9898  -5.2576  14.0701  -2.8236 -12.9971   1.6569 -19.2059  -4.0859
-#&gt;   -5.0436  -4.3163   5.1428  -6.3913   6.4500   4.4555   4.5713  -2.0779
-#&gt;    7.4635   9.5475  -2.0701   2.3935  -7.6614  -3.4064   2.2909  -3.3473
-#&gt;   -3.0359 -18.4094  -5.9897   3.5837  15.4239  -7.9032 -13.3763  14.3111
-#&gt; 
-#&gt; Columns 25 to 32  -3.4821   1.5919  -0.5404   6.6345  10.2549   5.1496  -4.6597  -6.3812
-#&gt;    6.6718   4.5763   8.5384  -7.8661   4.0971   1.3491  14.2791 -11.4972
-#&gt;   -1.4053  -3.6598 -12.7860  -2.9403 -11.6251  10.8033  -0.0073   4.2307
-#&gt;   -1.5744  -5.7959 -12.2524  -4.3580  -3.5845  14.3454   6.2108   5.3134
-#&gt;    1.8314   8.3284   1.8034  -5.2064  -7.8224  -5.3731  -4.8582   6.9603
-#&gt;   -0.8277  -0.7005   4.1211   9.4683  -8.6101  -2.3929  11.7292  -1.9720
-#&gt;   -5.9606  -4.3736 -14.4081  -7.6530  -1.3490   3.5621  -1.3992   2.9611
-#&gt;    9.2808   1.0782   9.8240   0.6300   0.9681  -0.0713   4.9551  -0.0518
-#&gt;   -4.1896   0.4045   4.4909   1.5669  10.6281 -10.2800   0.4696   4.3616
-#&gt;   -9.0439  -7.2937   5.7169  -3.0721  -7.3751  13.5537  -8.6710  -0.0292
-#&gt;   14.9836   5.4560  15.3617  -5.9226   5.1746  -2.2856 -13.7751   0.6819
-#&gt;   -4.0140 -14.0268  -8.2175  -0.9519  -5.1625   8.0537   1.5979 -14.5411
-#&gt;    7.1920  11.9737   6.2407  11.6686  -8.2055  -7.3287   5.8900 -15.2964
-#&gt;    7.6898  -2.8854   4.5457  -9.4811  12.2688   6.4499   9.9586 -11.7068
-#&gt;    5.1114   3.4677   4.1876  -1.6112 -13.0650  -4.0395   7.5988 -13.1688
-#&gt;   -7.8363   4.9015  -0.7028   6.8719   1.7890 -17.6106  -4.4946   8.8973
-#&gt;   -0.2351   1.0507  -3.4648  15.7866   7.7076   6.3321  -6.0999   2.5981
-#&gt;    3.6089   1.2441  -3.7658   8.3332  -4.1737  12.7334   0.3043   5.4791
-#&gt;  -10.1327   2.4970  11.5853  -8.0003  -4.0257 -10.2915   4.6891 -17.9207
-#&gt;   -5.4563   5.3216   0.3391  10.5032   7.2889 -10.1855  -2.1307   6.0210
-#&gt;   10.4668   4.2693  -8.5078 -16.0902  -1.1436 -10.8844  14.0803   8.7199
-#&gt;  -19.3162   7.6029   4.3524 -12.8139 -10.6980   3.0332  -8.0925   0.1107
-#&gt;    7.3866   6.3433 -14.7157   5.4192  -3.4251  -4.4213  11.0647  -4.8345
-#&gt;  -11.3755  -6.2598   1.1291  -7.9356   2.8415  -1.8066  12.3747   2.4658
-#&gt;   -3.7016   5.2315   5.0243  -9.4980  -6.1606   2.7696  -0.2799  -2.0557
-#&gt;    5.9827 -11.4304   1.7460   2.8687  -2.9336  -5.9753   2.5853  -1.3301
-#&gt;   11.7251 -17.9440   7.4188  -8.2807   1.0440  -2.0800  -5.7646 -13.8108
-#&gt;   -7.1919   3.9141  -5.1279  -5.6996   5.3954   7.4172  -0.5486  -0.5860
-#&gt;  -10.5670   2.7208  -2.9277   5.2154  12.3865   8.3219   4.6989  -7.3337
-#&gt;   -1.6000  -0.2491  17.8286 -11.7855   7.8799   0.4044  -4.6315  -9.4158
-#&gt;    5.3294   7.2001   2.7448  -2.1285  -0.7591  -4.9822   3.1356  12.5329
-#&gt;    2.4237   6.8455   5.2112  14.3163  12.7004   2.2962  -6.3547 -13.9957
-#&gt;    6.6262  -8.1785   1.7888  13.5563   8.6386  -6.9859   5.2224  -6.0477
-#&gt; 
-#&gt; Columns 33 to 40  -3.6295 -10.2917   8.5892   3.1696  13.3036  17.3819  12.9586  16.5758
-#&gt;    6.6092  -2.9969  -2.8927  -7.7109   6.3966  -2.1101  13.8204   9.3526
-#&gt;    6.2442  19.8402 -12.3389  -2.4286  -7.1243   1.7260  -0.3617  -4.9136
-#&gt;  -16.9625  -8.9939   5.3354   2.2880   5.0129  -5.3740 -10.2874  -7.6332
-#&gt;    0.8666   7.5960  11.6284   5.5943   8.8814  -3.9681  10.1872   1.2560
-#&gt;    7.5882   1.7635  12.9804 -10.7055  17.0646  -7.4957  20.8100  -2.4768
-#&gt;   13.9405  -0.7564  -4.5942  14.6832  -2.5138   5.3676  -1.4905  12.1335
-#&gt;    2.9576   7.8570  -9.4772  -5.0125  -2.1230  13.9095   9.7698   5.8164
-#&gt;   -2.9063   5.8186   2.0244   3.5474  -4.7886   4.2385  -9.2499  11.0428
-#&gt;   -6.7743   1.2483 -22.3652  12.2323  -6.4695   3.3480  -3.6016  -3.1109
-#&gt;   12.4404   3.2073 -14.3595  10.2491   0.6184  25.1737  13.3845   4.2837
-#&gt;    1.0335 -11.1925   4.4010   5.3148  -8.2017  10.8889  -8.4609  -5.2009
-#&gt;   -6.7986 -13.8239  12.3816   2.5796   4.2697  12.8967   8.4663  10.4730
-#&gt;   -4.0010   3.5506 -11.4775 -10.9847  -7.6262  -9.2315  -0.3651  10.1988
-#&gt;    3.9863  -6.7942   5.1925  -1.6966  14.6266  23.8630  -1.1480   5.1540
-#&gt;  -14.0801  -6.0060   9.7818   8.0171  -4.6193  -6.8966 -16.4738   3.5937
-#&gt;   20.9683  -6.2488 -15.1516   5.0018   7.3859  -5.7246  -4.2427  -1.6580
-#&gt;   10.7246 -11.7779 -13.4427  -7.4993  -6.9058 -10.1685  11.2852  10.3569
-#&gt;   10.5054  -3.9206   3.8638   4.7846  -2.0697   3.5954  -3.6378  11.3385
-#&gt;    4.1252 -11.0834  -1.7460   6.2007  -6.0273   7.5279 -10.2783  -1.9535
-#&gt;    3.4406  -6.6389  -4.1109  -5.3326  -0.1168   3.0863 -13.0025 -10.2368
-#&gt;   12.7000   9.8563   1.2965  17.9106  13.2486   6.4561  -0.0430   0.6004
-#&gt;  -11.6515  -9.6040  13.1283   0.5972   8.4389  -4.5280 -21.0795 -11.8676
-#&gt;    8.8663 -12.8994   6.6525   6.1411  -1.8591   1.8417 -14.5053  -1.8609
-#&gt;   -7.7876  18.1655   5.5346  27.1514   2.6053   6.6444  -2.3142  -2.3360
-#&gt;   -1.3490 -13.5940 -13.1572   2.1202   7.4358 -10.2670  -3.8431 -13.6691
-#&gt;    1.8399   2.3093  -1.5086   2.7331  22.4825  -5.7358   0.2116   1.8048
-#&gt;   -6.8692  -2.9631  -5.2463  -1.3727  -2.9430  -6.2948   9.1915   7.2304
-#&gt;    0.3341 -14.8332 -13.3900 -11.2991   1.9816  11.1011  -5.7825  29.1227
-#&gt;   15.4083  -0.7331   0.6251   6.7672   8.9791   1.1342  16.4126  21.4550
-#&gt;    5.3967   7.2457   4.4598   4.8386   5.5386  12.5095   4.0675   0.1107
-#&gt;  -26.0174 -10.9731   1.7895   6.7177  -2.6839   8.1280  14.7717  -3.1816
-#&gt;    6.0219   3.8545 -16.8521   1.3949  -6.7502   8.1508  -0.4747  10.3703
-#&gt; 
-#&gt; Columns 41 to 48  -2.0943  14.1358   5.2628  22.5514 -18.9663  -0.7048  -0.7734  -6.8586
-#&gt;    3.5579   1.3429  -0.2211  -0.7780  -1.8308   3.6201  -6.9235  -8.0446
-#&gt;  -10.3418 -18.6290 -12.2164  -1.8177  -9.1018   6.8737  -4.6316 -10.3998
-#&gt;   -2.1535  -3.7097  -8.9061  -5.3074   5.4677 -16.7918 -11.1065 -13.1747
-#&gt;    5.1731   6.9404  -1.3071   8.5775   5.0367   4.8396   3.1104   8.4906
-#&gt;   -6.2020 -16.2004   4.8414   5.8877  -4.9806  -4.3998  -0.0188 -12.7874
-#&gt;    1.2019   1.9996  -9.5571  10.5973   1.7739   2.4475   0.8500 -10.6539
-#&gt;    1.1911   4.3050  13.6223 -10.0130  -3.8862  -0.8930  -4.5866   2.7503
-#&gt;   -3.2063   0.0720   6.5406   9.4520  -1.7510  -3.7581  -6.8393   7.2631
-#&gt;  -10.0268   7.1053  -4.5637  -7.9629 -17.0981 -11.8952  -0.6961   3.3416
-#&gt;    7.7641   1.5028  -6.5552  -6.2409 -16.4655   0.8441  -4.4734 -12.9904
-#&gt;    1.2510   7.7808   6.3974  -9.8847   0.0787  -3.4884 -12.9718 -12.0862
-#&gt;   11.8159  -9.0116  -2.0621  -1.1946  -2.5243 -12.1487  -2.8661  -4.0730
-#&gt;   14.7862   5.9178   0.6501  -9.3364  -4.0615   8.7970   0.6572  12.1467
-#&gt;   -6.4953  18.9466   3.8013   5.1661 -13.4450  12.7503  -5.3354  -8.4281
-#&gt;  -10.8090  11.5462  -1.9622  14.5664   1.6406  -3.1981  -2.2539   6.9770
-#&gt;    3.5918  -6.4713  -0.6301  13.2689 -11.6568  -6.3536  -6.0580  -4.6613
-#&gt;   -7.5931  -3.5884  -2.2018  20.6279   2.4437  -1.6212  10.4710 -11.8318
-#&gt;   23.5669  -5.5143  21.2536  10.1749  -4.4157  25.5596  -1.2231  13.3097
-#&gt;  -11.8193   6.4634   4.1492   7.5405   7.6260  -0.1190  -9.1554 -12.3418
-#&gt;   -0.4148   6.1904   1.0594  13.6151  14.6106   3.2841  -2.8350   3.4250
-#&gt;    3.5107   1.6208  -6.3237 -15.4603  -5.2671  -3.8217  -0.4310  -0.4421
-#&gt;   -9.0398  17.7742   8.0316   1.6539 -11.8560  -5.5559   0.5970  -7.2548
-#&gt;   -8.1339 -12.0789   1.5030  -7.6398   0.5048  -7.0274  -9.5390  -2.3658
-#&gt;    7.3572   0.7943 -15.4211 -13.1274  -2.3047  -4.8176  -0.9751  -2.6167
-#&gt;   -4.3912   6.9678  15.7213  -7.8044  -3.4204   6.1412  -3.5023  -3.4635
-#&gt;   -0.4161   0.3529   5.1244 -13.6240   3.6193   9.6268  -0.1192   5.9276
-#&gt;    8.3979  -8.7823   5.2884  -0.8518   5.4551   8.0964  13.7187   5.8702
-#&gt;   -1.2982  -1.0433  -3.9384  18.8391   3.8674  -9.9159   5.1703   4.9355
-#&gt;   -5.4425   4.2834  10.4256   1.1455   7.1868  -0.5773  -9.5653   4.9853
-#&gt;    6.8816   8.0096  10.7152  -3.5045  -6.2312   2.3285   2.0168  -1.2305
-#&gt;   -3.2355   1.8947   8.5151  10.5126  -6.0269   0.3725  -5.8767   0.2457
-#&gt;    7.8995   4.6941  -1.1446  -4.0774  -3.8205  -1.6506  -8.6854   0.1603
-#&gt; 
-#&gt; Columns 49 to 54  -7.5786  -6.0751  -1.7626  -4.5013  -3.3668  -3.8615
-#&gt;    3.7414   2.2096  -1.6060  -1.7807   5.3725  -2.1578
-#&gt;    2.0870  -6.6615  -1.9610  -3.1591   8.3221   4.7112
-#&gt;   -0.8236  -0.4252   0.6816  -4.9913   2.1837   0.6129
-#&gt;    8.2050   9.5457  -7.1727  -0.7535  -5.8072   3.0399
-#&gt;    3.2751  -0.4321  -4.1820   4.7442   1.4371  -0.4434
-#&gt;   -9.7479  -7.5958   2.2332  -7.3946   2.4205   0.5376
-#&gt;    6.9937  11.0040   0.3333  -2.5405  -1.1652  -4.2575
-#&gt;   -4.5529   6.8868   2.4849  -0.1791  -1.7426  -3.3353
-#&gt;    3.4190  -4.3534   1.1246  -6.4373   1.9196   0.9237
-#&gt;   -1.1376  -3.9930  -4.1098   1.4407   2.5957  -4.2096
-#&gt;    3.4737   2.1364   0.8750  -1.9192  -3.6025  -4.0939
-#&gt;    0.1571   3.1012   4.4755  -2.3259  -2.4102  -3.0718
-#&gt;    4.4098   1.8252  -0.6708  -4.8814  -5.7913   3.1760
-#&gt;    6.5828  -3.4017  -6.3739  -5.8187  -5.2798   0.1928
-#&gt;    7.5506  -7.5372   2.1939  -1.3638  -5.7928   1.7419
-#&gt;    2.9977   1.7618   9.2132   7.2704   1.9816  -0.5879
-#&gt;    4.2801  -0.9015   9.3269   0.3706  -2.5626   0.4424
-#&gt;   -7.4979  -0.1236   8.1475  -7.2954  -6.3724  -3.3619
-#&gt;   13.4201   7.6125   8.8104   3.8601  -3.7596   0.8458
-#&gt;    6.2017   4.3398   5.2157  -1.2586  -7.3754  -3.9266
-#&gt;    1.3138  -6.4390  -6.3847  -2.5865   0.7655   4.7840
-#&gt;    4.0197   1.7975  10.0595  -3.3986  -1.2426   3.1468
-#&gt;  -11.5967   8.7534   4.4135   0.3158   1.5442  -2.3150
-#&gt;    2.8117  -4.7478  -7.9741  -8.1770   9.2287   2.2994
-#&gt;    7.5280   1.6121   5.1598  -2.1733  -0.9190   1.5696
-#&gt;   -7.8544   6.4922  -2.8451  -5.3751   5.3834  -0.7857
-#&gt;    3.7249 -12.0811   3.1027  -1.8758  -1.8687  -1.1863
-#&gt;    2.7261   1.1560  -4.7327   2.8734  -0.7383  -5.6295
-#&gt;   -1.4595  -2.1498  -4.8049  -5.1187   1.4256   0.8033
-#&gt;    8.4719  -7.6745  -1.1570  -1.2413  -4.8077   5.0042
-#&gt;   -4.0323  -0.6491  11.4553   0.4311  -4.8471  -2.8728
-#&gt;  -13.5257  -0.1274   5.1082   1.7050  -0.3404   1.5809
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  Columns 1 to 8  -4.2814  -0.9928  -6.3250  -2.1468   1.5893  -0.1107  -0.3478  -2.5690
-#&gt;   -4.5912  -0.7841  -7.3830   0.4865 -13.4365   8.8657  -5.5422  -1.4158
-#&gt;   -0.9003   4.3861  -0.5063  11.5701 -19.2629   6.1588  13.2475  -3.5314
-#&gt;    8.9280   4.5447   1.7445  -5.8586  11.9142 -12.3538  -5.4583   2.3470
-#&gt;    3.0439   1.9802   6.4492  -6.8405   7.5266  -9.4528   0.5469  -1.1861
-#&gt;    0.9831  -5.1110  -7.9394  -0.2652  -4.3542   2.8440   9.3760   1.3132
-#&gt;   -3.9534   7.0481  -6.5829   7.9464 -11.5846  10.8620   3.2126   4.8576
-#&gt;   -1.7461   1.5196   3.2618  -0.6441 -12.7095   0.5623   1.2715  -6.1720
-#&gt;   -4.2921  -1.1264  -6.1237   0.5789  -1.9632   1.0937  -2.1667  -5.2376
-#&gt;   -1.5062   5.8473  10.3813  -3.1029   7.7080 -15.0788  -6.7675  -0.7083
-#&gt;   -2.0822  -4.5379  -7.1992 -10.6396  -5.6484   5.9305   6.4131   4.9896
-#&gt;   -3.1570  -4.1702   2.2387  -6.7174   5.0959  -6.3532 -13.2667   7.2639
-#&gt;   -3.3436  -2.7883  -3.0615   1.1381 -11.9659  -9.8985  -0.4151   2.4652
-#&gt;   -2.6957   2.5030  -1.8442   5.7424   2.5167   6.3293  13.2339   1.7207
-#&gt;   -3.2400   0.6813  -2.6747   0.3859 -12.6634 -14.3495   2.9569   1.4342
-#&gt;    2.8652   1.0444   5.5808  12.7845   8.4653 -11.6578  19.4486  -2.2291
-#&gt;    5.0276   4.0755   0.9046  -1.5058 -17.1550   4.1865  -4.0057  -4.7463
-#&gt;    1.9473   0.8288   2.4925   5.4847   8.3818  -2.8047   5.9484   0.1989
-#&gt;    5.9925  -7.0113  -5.4275  -0.5087  -4.3847  14.2030   3.4832  -6.1293
-#&gt;    5.0400  -4.5351 -10.9847  -7.5161   1.9359  -6.0338   1.9983  13.4167
-#&gt;    1.4772   5.2028   3.8514   2.4530   7.9827  -1.4913   5.7961   9.9785
-#&gt;   -0.5456   1.5560  -1.3027 -13.8389  -1.2489  -0.1050   6.9702  -8.6840
-#&gt;   -2.5184   1.4788  -9.0941   2.0005  -2.4838  10.2320   1.1519  -5.7467
-#&gt;    5.1392  -1.4691  -7.0037  -6.1645   7.8981  -0.2776   0.1753 -11.1557
-#&gt;   -3.7912   3.5770 -14.5816   6.2232   0.2479  18.1888 -13.1904  -2.2443
-#&gt;    1.6099   3.2369   9.2255   0.6171  10.9835   5.5523   2.0693  -5.2416
-#&gt;   -3.4939   0.2865   4.1363 -10.6005   8.7497   4.1534 -10.5140 -11.9144
-#&gt;    3.2897   1.0218   6.6864  12.5689   2.3676   7.9885 -10.2161   1.2930
-#&gt;   -1.4450   4.9212 -11.9445  -1.2991  16.2595   0.0175  -9.1012 -11.8757
-#&gt;   -2.8267  -8.4401  -8.1498  -6.4326   7.1976 -15.5500   6.6901  -2.6834
-#&gt;   -3.4825  -8.1806  -2.1333 -16.9153  -0.3077  -6.6839   7.3645  -5.2896
-#&gt;   -3.3669  -4.3666  -3.8859   9.8369  -6.1755  -3.5137  -5.7803   3.3139
-#&gt;    1.2799   0.1712   0.4645  11.8776 -12.4482 -31.1680  -0.9364   4.3990
-#&gt; 
-#&gt; Columns 9 to 16   7.5556   0.9503  -4.1515  -1.7091  -8.8934   3.0020  -0.7908 -11.2742
-#&gt;   -2.8899   2.3534  -9.3264   9.9079   1.5249  -1.5301  -0.6600  -5.5362
-#&gt;    4.2049   2.7425  -5.2712  17.1109  -3.3822  11.7646   0.6467  13.4775
-#&gt;    7.1356  -7.4585   3.8696  -4.3935   6.2941   3.8395   7.8643   3.8099
-#&gt;   25.3339 -15.7911   5.6148  -8.6942  12.6028   5.5771  -0.9985   8.1309
-#&gt;    1.2086  -2.2314   3.2339  -9.4745  -1.9318   2.3319 -10.0888  -7.4205
-#&gt;   -8.4327   4.0183 -11.6087  -0.8532   0.9907   7.8800 -10.1926   5.5849
-#&gt;   -7.2541   4.1477   4.4964   3.4301   1.8144   5.2678  -9.5849   3.5256
-#&gt;   14.8126   7.0383  -9.2279  11.4214  -8.4555  11.7790   3.9250   4.2546
-#&gt;   -0.2038   0.4956   5.6271  17.2422   3.4549  21.2041 -14.0893   9.0832
-#&gt;   -5.0772  16.0759   3.4007   7.6380   0.2486 -11.2148   7.3970  -4.2384
-#&gt;    2.5268  -6.0831   8.6829  -4.7677   3.7255  -0.6817   1.5917   6.6164
-#&gt;    1.3830   4.7304  11.2663   3.1119  -6.1452  -8.6502   3.5458   4.2314
-#&gt;   -6.4222  -4.4665  -5.3657 -12.2609   0.4897  -0.4835  -8.9000  -1.9559
-#&gt;    9.2684  -3.4328   8.3752   5.1490  -6.3485  -0.0821 -12.0316  -0.0749
-#&gt;    2.4943   4.1517 -20.2558  -7.0651  -8.8395  -2.1914   8.6668   1.9817
-#&gt;  -19.0777  -6.6924  23.4983 -15.0311   2.4787  -1.6277 -17.8973  15.2943
-#&gt;   -2.5235   0.0060  -1.6143 -15.6307  14.5766  -6.0868 -10.0141   1.6058
-#&gt;    3.3122  10.5995 -13.1453 -16.9765  -5.0313   4.4488  -9.1534   2.8923
-#&gt;   10.3218  14.5946   2.8905  -2.7832  -1.5993   0.4322   4.8817   3.8644
-#&gt;    7.1675  23.0007   3.6840   4.6965  -2.4768   1.8314  20.4653  -8.5894
-#&gt;    7.9079   6.0228   3.5809  -5.7187  -0.7004  -0.5707   2.7965  15.6104
-#&gt;    5.4505   2.4822  -5.2771  -3.4954   3.2701   2.1681   7.9177   7.7022
-#&gt;    5.0304   8.6729 -10.3565   3.6704 -18.2723  12.9627  -4.9947  -5.5513
-#&gt;   -1.7056  13.3363 -15.7573   9.9531  -7.9418  -0.4604   0.0761   3.4076
-#&gt;  -12.8338   0.2334   7.4548  -5.7402  -2.4206   7.3497  -7.7487   6.9471
-#&gt;   14.6118  -6.1001  13.8037  -1.6199  -5.1633  -0.5073  -2.2048  -1.9187
-#&gt;   -4.1629   8.3504  -7.4656  10.5670  -7.8399   4.7926  -8.8730  -0.4069
-#&gt;    3.9484  -6.9035   1.5564   3.0990  -7.8450   3.3026  -6.5571  -9.2804
-#&gt;   19.0746  -0.1347 -15.5799   6.3705   3.1539   7.5290 -11.3685 -15.6369
-#&gt;   -6.9263  -4.9007   5.0781  -0.1490   3.9841 -13.4460   0.4719   1.0777
-#&gt;   -0.1617  10.4963  -4.3485  -3.7952   6.7318 -11.9180   4.6326   1.7147
-#&gt;  -12.0514  -3.0709   4.0683   9.3400  11.9945   8.2751 -17.3297  -6.4765
-#&gt; 
-#&gt; Columns 17 to 24 -18.5431  -7.2467  -2.1888   1.4468  13.5736  16.3870   5.4183  12.6853
-#&gt;   -7.5680  20.0193  12.7224   9.3565  11.0996  -8.0080   0.0394  -1.7001
-#&gt;   10.6259   2.5089   6.6300  -1.7975  -6.1460 -10.1886  -2.0635  -7.0110
-#&gt;    7.2354  11.2542  -0.0624 -21.6368  -8.8893 -15.4952   0.9672   6.2642
-#&gt;   -8.1167  15.6667  10.3195   6.6947  -1.7131   3.1816   3.3514  11.6446
-#&gt;   -4.7835  -8.8592  -1.3281  14.0944  18.0586 -15.9571  18.1663 -17.1892
-#&gt;   -1.8263 -13.4149  -0.1356  -1.8739  -4.7672  25.3649 -15.1191   2.0606
-#&gt;  -19.9534   1.5255   3.3970  12.4343   5.9920   4.2786   9.1741  -3.7390
-#&gt;    0.0707  -5.7771  -5.5050   1.7327   2.1867   3.0563   0.3864   7.4616
-#&gt;    5.3929  -3.7625  -7.9790   0.5764 -34.4987  -0.0026 -10.5214  13.1395
-#&gt;   -0.9534   0.2775  30.2686  10.3520  18.6599  14.5629  -9.0326  -6.9237
-#&gt;    5.6702 -13.5301 -19.4285 -12.9179 -14.8922  -7.3377  -0.1385   7.7447
-#&gt;    6.4202  21.8046  21.2044  -1.5585   3.4727   7.4153  11.9231   7.0996
-#&gt;   -0.6430  -1.9038   6.9139  10.3206   2.8880   3.2982   8.0143  -3.2991
-#&gt;   -6.4054  15.3655  -4.1955   5.5846   2.0637   1.8493   0.6728   5.4217
-#&gt;    9.5364  -4.7927  12.3312   8.1107  -6.2731  -8.8036   4.7018  27.4013
-#&gt;  -20.6867  -2.3423  -1.2976  18.6948  12.9973  16.1323 -18.9161 -11.3342
-#&gt;   -3.6722  -3.8782  15.4733   9.6689  -5.8265  12.2826  13.6032  -2.6629
-#&gt;   -3.4141  -8.9852  -2.4286   2.5016   0.4346  -5.7922  -3.4047  16.6654
-#&gt;   -2.6916  -0.3887   2.6413  12.8351  10.5345   5.8159  -5.0782   8.6941
-#&gt;   -9.7662 -17.2759   3.0965   0.5703   5.9018 -11.7873   2.6515   4.6911
-#&gt;   -0.6048  -5.7382  -5.0305   2.3694  -9.5541   7.7798   1.4498  -8.4205
-#&gt;   -1.2836   5.6164   4.8220 -11.4307   5.7866 -10.3455  -2.8623  -0.4544
-#&gt;   15.2078   0.8712  -2.9165  -2.4730   6.8601 -18.6322 -17.4785   1.9733
-#&gt;    9.4349  -2.7726  -6.5827 -11.2047  -6.9441   9.5381   4.0998  -5.9581
-#&gt;    5.5522  12.7413  -7.5371  -7.0737   3.0071 -10.3697   0.0802  -0.0540
-#&gt;    9.1696  27.8370  -3.1698  -3.3912  -0.3053 -11.4314   1.5094  -9.3088
-#&gt;    9.0033 -15.0044   9.0648  -4.5580  11.7064  -4.9846   6.2351  -9.4519
-#&gt;    4.8476   3.0312  -3.1720   8.2694   8.0651   1.5876  -3.4739  13.2249
-#&gt;    2.9785  -6.8381  -0.8522  12.7236  10.4859  -1.0775  10.8023  -0.7909
-#&gt;    2.3852  -1.6217   4.1906 -16.2859   4.5290   4.0605   7.2554 -11.3662
-#&gt;    5.7548  -8.5325  15.7518  14.9931   1.6744  11.1153  12.6375  17.8654
-#&gt;   -1.6258  -0.0871  -6.5129  24.0142  -1.1732   2.3960  -2.6927   7.4124
-#&gt; 
-#&gt; Columns 25 to 32  -9.5471   5.2504 -12.7695   2.3644 -13.8618   0.2774 -15.6364 -14.1579
-#&gt;   14.7131 -11.0395  -0.4385  -5.2621   6.3454  -4.8161  -2.6302  -0.2614
-#&gt;   -7.2549   8.4048   6.0721  10.9949  -5.2534   8.4186  -1.1710 -12.5879
-#&gt;    4.1679  -3.5380  11.0071  16.2490   3.2509   3.6150   4.0472   9.6936
-#&gt;    4.0278  -2.9144  -3.3443   2.0031  -1.0520  -0.0441  -1.3249   9.3437
-#&gt;   14.5443  -9.3212  16.9836 -11.4537  13.3687  -3.3269   6.3070  13.4147
-#&gt;   -8.2076  12.3010  -8.5888   7.3520 -10.7655  -5.2091 -14.3411 -14.1986
-#&gt;    5.9523 -10.0569  -3.8595 -20.2493  -2.0627  -0.4181  -3.0065   5.6296
-#&gt;    4.7760  -8.0083  -5.4019 -12.8869  -9.9989 -11.7036  -6.5765  -0.7335
-#&gt;  -16.0199  -1.7111 -25.0884   1.3993 -12.2556   6.1053  -0.3440   6.8919
-#&gt;    9.3518  12.2459  -6.7242   6.7622  -2.8774  16.9616  -9.0211   2.1739
-#&gt;   -5.5053  -6.3817   6.5013  -0.9767   1.2809   4.4648 -12.0254  -0.6119
-#&gt;    2.6438   1.2746  10.4572  -0.5747  -1.2947   0.3566  11.5671  -1.6072
-#&gt;   -0.1143  10.6304  10.2569   1.4319  -1.9019  -6.9403   7.7606   1.8653
-#&gt;   -9.6573   1.9309  -6.6948   8.2459   4.2051  17.1861  10.4649   1.9986
-#&gt;  -11.1705  20.2875 -12.0803  22.2812  -2.8107   0.6885  -1.7844  -6.6154
-#&gt;   15.1302  20.6313  -4.4599  -4.0068  -8.7788  -2.6910  -3.3488   5.8401
-#&gt;    3.8432   1.3941  10.2156  -1.8910 -10.1370   2.5800   0.8677  10.4740
-#&gt;    1.8179  -2.9557  -8.5651  -6.9542   0.5963 -11.8578  10.6642  -7.3410
-#&gt;    1.2753   2.4480   6.3933  -0.3294  -3.7209  -1.8414  11.2670  11.3281
-#&gt;   -5.6177  -8.7114   2.9465   1.2682  -0.8144   2.9600  -3.4893   7.5283
-#&gt;   -9.1247   8.3640  -6.3848   5.0155 -11.7959   7.0195   1.2110 -14.9573
-#&gt;    0.1508   9.9349  -3.5400   2.1387   1.7781   7.0856  -2.3553  -0.8642
-#&gt;    0.3437  13.0693  11.1754  -4.3952   3.2404  -4.1229   4.9823  -6.3456
-#&gt;   -7.9140 -17.3896   7.1403  14.7661  -4.2565   5.6876   3.3273 -12.8727
-#&gt;   17.9979  -9.7022  -9.3277  -6.2117  -1.1411   0.4867   2.3489   8.7640
-#&gt;    8.3089 -16.6858  -6.2112   0.1621  13.9036   3.2464  -3.4327   2.4497
-#&gt;    8.6450  -6.3349   3.1143   5.5876  -3.0286 -13.7050 -14.6632  -6.2764
-#&gt;   15.2174  -0.0023  -2.7854 -12.1750  -0.9458   5.4222  -6.7874   6.7810
-#&gt;   -1.8121   3.9559  -0.2908  -8.4434   7.7751  -6.4052   5.9881   9.5435
-#&gt;   -6.5729   7.2560  -4.5695  -5.1728  -6.4014   0.6282  -7.5125   3.0794
-#&gt;   -7.8699 -14.9691  -7.3728   2.1914   9.6109  -8.9311   3.7203   2.3840
-#&gt;  -12.7572  -3.9377  -0.1697  -9.3399  -7.2752   0.8100  24.8372   5.3728
-#&gt; 
-#&gt; Columns 33 to 40   0.0255 -10.5537  12.0398  -2.8798   7.8527   6.0653   3.7556  -2.7993
-#&gt;    0.5793  -1.3766   4.5411 -14.1848 -13.6158  -5.8394 -14.9293   2.6083
-#&gt;    7.9137   4.0285  -4.4772 -14.9357  -7.8080 -11.7395   1.2601   5.3019
-#&gt;   -1.7825  -2.5223 -17.1768 -11.1701   7.6567  10.8567   4.5283   4.2274
-#&gt;  -13.8521  -5.9386   1.3900   4.5375  -5.3543  -8.5911  -1.7874  -5.5076
-#&gt;    2.4048   2.1830   0.2718   8.9167   5.0220 -14.0045  -3.6629  -0.6883
-#&gt;    9.7812   0.5064  11.6264  -2.6978   4.6974  19.1449  10.3248 -10.5954
-#&gt;    5.1291  -9.7703 -10.5945 -12.9645 -19.7945 -21.6079 -14.2834 -16.6200
-#&gt;    8.8441 -13.6686   3.5471   3.8791   8.3426  12.2786   3.8017   1.5322
-#&gt;    7.4526 -11.7824 -17.0089 -11.6105  -4.3543 -18.8413   9.0462   9.6838
-#&gt;    7.4071  -2.8717   2.9983 -27.7138 -21.8610  11.0050  -5.8977 -15.8267
-#&gt;  -14.3504  -5.6956 -15.2953   0.2299  10.2585  -3.9242   7.9970  -4.3053
-#&gt;   -2.5948   2.7677  -6.3130 -15.4906  -9.5032   2.0528 -10.4810  -0.7177
-#&gt;   -4.7284   5.8592   0.1817   7.3320   5.0945   2.8817  -4.5427   1.0050
-#&gt;    3.5236   1.4551  -2.4442  -9.9123  11.4148  -8.4898   6.6037  14.7087
-#&gt;   -6.7383  -5.6641   8.5320  11.6816  16.0395   7.4078  -0.7981   9.2914
-#&gt;    9.1674   9.1387   6.6702  -5.7627  -4.7178 -10.5185   6.3161 -32.7938
-#&gt;    1.7054  10.5128   3.9919   2.8157  -5.9017  -0.5674   6.1011  -0.6632
-#&gt;   -3.0844  -4.5578  -0.2523  10.1596  25.0276   9.6281   7.0218   5.0987
-#&gt;    3.8584  -4.6755   6.0791  -3.4412  11.7337  11.5995  -2.2210  10.4436
-#&gt;    5.5665  -8.1250 -12.8798 -17.9741  -1.4146   8.0804  -0.7542   6.4626
-#&gt;   -5.1206   8.7158  -1.5439   3.9449  -3.9907   1.5403  13.9952  -0.7947
-#&gt;  -10.5157  10.2331   2.9008 -15.0729   0.6510  15.7860  -2.1538 -10.6885
-#&gt;   -1.8739  -0.4793  -4.8961  -7.4751  20.4304   5.5089 -13.9037  16.6051
-#&gt;   14.3429  -7.1883   3.9712 -14.7365  -5.5607  14.3891 -19.7455  -4.2297
-#&gt;    8.5246   8.6258  -1.2779 -10.6972  10.4836  11.9329 -12.2021  -1.9701
-#&gt;    4.0135   5.0804 -19.6892  -4.7816  -4.4228   2.4131  -5.3044  -0.3084
-#&gt;    8.7885  -7.6300  -4.3651  -1.5497  -7.8559   2.6720  -8.4111  -6.2689
-#&gt;   12.3964  -7.9888  -2.3543  -9.3764   6.2574  -3.8785  -6.2403  -0.8317
-#&gt;    2.9739   5.7846   2.3233  13.3836  13.2524  -2.6907  -8.2679  11.2288
-#&gt;  -10.4153   6.7377   9.0617   2.7913  -4.5044   4.6413   5.1831  -5.1458
-#&gt;    1.9140 -11.7563  -0.8589   0.5466  -8.1280   2.8368   1.2742  -0.5698
-#&gt;   10.6163  -0.1833  -7.7692  -1.7128  11.4946 -14.0834  -1.2917  17.7301
-#&gt; 
-#&gt; Columns 41 to 48  -9.3097  -3.2294 -15.2823  -5.5916  -2.8869 -13.3814   4.3317  -3.0247
-#&gt;   -4.2290   4.1951 -16.7100  -1.5207 -11.2070   6.4024   0.4190   0.2200
-#&gt;   -2.0386   6.9385  13.1174  18.1421  -1.5071  12.8023 -18.7490  -1.8599
-#&gt;   18.4593   8.4568  -3.1086  -7.4198   9.0273  -4.0757   4.0843   6.8361
-#&gt;   26.2047   9.1452  -0.9051   9.4615  14.5867  -8.7027   6.8597 -11.0876
-#&gt;   -0.3914   5.8634  -5.8186   8.2457  -6.0187  -4.2411   1.5618  -1.6414
-#&gt;  -12.6927  -1.9998  -0.9853 -10.8415  -3.9114   2.6062  -2.6440   3.7274
-#&gt;   -8.8539   3.9519   5.2844  -1.2765  -4.1111  12.9090 -15.8774  11.8727
-#&gt;    7.6303  -2.2731   1.3181 -12.4971  -4.4182   1.9453  -7.3281  11.1393
-#&gt;   -3.2201   3.8184  -4.6067 -16.7003  -2.2369  -3.9596   3.5951  10.0822
-#&gt;  -11.6579   7.8313   5.3211 -15.1023  -0.3087  -3.9228  -1.2938  -3.4461
-#&gt;    0.7949  -6.5761  -2.5533  -1.4342  -1.0680  10.8438  -3.1406  -3.7439
-#&gt;    6.3776   5.9789   3.4295  -4.9996   1.1147  -6.0050  -1.0939   0.7998
-#&gt;   -4.8944   7.6342   2.6140   0.0859 -17.4067   1.0536  -2.9926  -1.2391
-#&gt;   -4.8344  -6.5907   4.3126   7.8370   1.4042   0.2332  -9.0687   7.1153
-#&gt;    3.2358  -7.2652  -2.9834  13.5405  -2.6637  -2.5203  -2.3874   8.4274
-#&gt;    7.9810  13.7403   0.1694  -1.3022  -0.9424  -3.1207  10.9842   4.0421
-#&gt;    1.8911   3.1536   6.8938  -4.1200   4.2714  -0.1230  -1.1179  -4.7350
-#&gt;   -2.2938  -6.0231 -19.0117   1.5719  -9.5748  12.8228  13.1441  -3.3493
-#&gt;   10.7481  -6.3431   0.3737   7.7536  -4.7909   7.0765  -3.1651   4.2243
-#&gt;    3.6017  15.1340   6.0504  10.1029  -7.4420   0.1014   6.7481  -5.4837
-#&gt;   -0.0784   9.3293  15.6520  -4.0878  -3.7600   0.3677   3.1342   3.4903
-#&gt;   -3.8704   6.5227  -6.3015   0.5103 -21.8646   3.0479  -1.3854  17.2001
-#&gt;    8.4214   7.6997   0.9864  -0.9435 -11.7146  -6.1746  15.6330   0.4350
-#&gt;  -16.5340 -10.1568   8.4983   5.9920   3.1088   1.7066 -13.9209  13.9806
-#&gt;    1.5558   1.8379   3.9420   1.7257   3.1951  -3.2493  15.4737   9.2361
-#&gt;    6.3086  -2.5628   0.3350 -10.9559  17.6020  -5.5011  10.6129 -12.3235
-#&gt;   -0.3650  14.8840   1.9107  -6.1086  -6.6737 -12.7069   0.8940  -2.2039
-#&gt;    3.1929   6.2128  -7.8411  -7.8573   6.2594   2.4828 -18.5858  18.8544
-#&gt;   -5.7802   1.7379 -10.0281  -8.3402  -3.4336  -2.4688  -6.2941   1.4532
-#&gt;  -19.7309   0.3456   1.5423   5.0810  -5.3819 -20.2688  -8.8805  -5.7137
-#&gt;    6.3231  -7.2561  -6.8232  -7.2471  -6.2084  -0.7173   7.4032  -4.7070
-#&gt;    5.8310 -15.3420   3.5827 -11.7139  -1.6032  21.9959  21.4625  13.3878
-#&gt; 
-#&gt; Columns 49 to 54  -5.6312  -5.8757  -1.9303 -12.3841  -8.7887   1.6929
-#&gt;  -10.4864   6.1126  -3.5091  -8.9068   4.0623  -5.7620
-#&gt;   -2.1884  16.6332  -2.0371 -12.4439   0.6066  -4.1242
-#&gt;   -1.3575  -0.4263   5.1404  -0.1741  -6.8442   4.2203
-#&gt;   -6.8127  -9.8460   2.3364   2.2290  -3.7343  -0.1920
-#&gt;    2.8453   5.7171   5.0623  -6.8731   1.0511  -9.9707
-#&gt;   11.2657   2.0124   0.5337   7.2810  -8.1656   1.9357
-#&gt;  -13.1813   5.9682  -7.3149  -6.4285  11.2959   2.0956
-#&gt;  -17.6844   0.2337  -5.4424  -6.6757   1.5245   2.8099
-#&gt;    4.5482  -7.7945  -8.2763  12.8818   2.0741   2.1411
-#&gt;    9.8887   6.6724 -12.0848  -1.7216 -12.9660   6.9057
-#&gt;    1.4679  -3.5013  -7.9730   9.9813   5.7734   5.4036
-#&gt;    0.0230  -6.8706  -9.6006   2.2370   3.7218   3.7930
-#&gt;    3.4625  -5.7614   3.5262  -4.9120   5.7077  -7.1647
-#&gt;  -22.1792   0.2916  -6.5840   4.3210   4.0772  -5.2424
-#&gt;   -1.7010  14.3890  -3.7071   4.8871  -9.9836   3.4531
-#&gt;   12.5006 -18.7379 -15.4905  10.0722  -0.3006   4.2005
-#&gt;    8.7680  -3.9162  -5.9903 -12.7046   1.0095  -4.6932
-#&gt;    3.3805  -3.4063   7.5696   8.5019 -12.2275  -2.6869
-#&gt;   -2.7318  15.9727   2.4262   7.0770   5.8128  -1.3519
-#&gt;    5.4811   8.5442   2.1813  -0.3263   1.0540   0.7265
-#&gt;   -0.5873  -2.8766   5.1717   6.7857  -7.5141  -0.8182
-#&gt;  -10.3046   7.6129  -2.9870  -1.9915   4.7210   5.7012
-#&gt;   -4.2336  -2.6930  -4.2447  19.3978  -0.2569  -2.1045
-#&gt;    7.5336  12.7859  -2.0850   4.0781  -0.6318  -2.1356
-#&gt;   10.8794  -2.4958 -10.9086  -4.9001   8.2499   2.8629
-#&gt;    3.9111  -1.8168  -0.6243   1.4135   9.6943  -0.6005
-#&gt;   -1.1399  13.8082   2.6008   4.6557  -4.7420   3.8188
-#&gt;  -22.6820   3.4946 -10.7565   3.0645 -14.6814   1.9716
-#&gt;   -1.3526  18.4295  -4.0918  -5.1161  -7.1546  -3.4542
-#&gt;    6.5281 -10.4227   6.3853  -9.8121   0.4639   6.8855
-#&gt;  -11.3997  11.3466  -8.5666   2.8764  -6.5039   2.7487
-#&gt;  -11.9130 -10.0625 -17.8755  -1.7348   8.8254   2.1121
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  Columns 1 to 6 -1.7639e+00 -4.3283e+00 -6.1132e+00 -4.5967e+00 -1.3640e+01 -1.1114e+01
-#&gt;   6.2992e+00 -2.0702e+00 -4.1359e+00  2.8116e+00 -4.3190e+00 -3.1170e-01
-#&gt;  -2.3507e+00 -6.2577e+00 -1.5493e+00  1.3407e+01 -1.5534e+01 -4.6453e+00
-#&gt;  -5.6948e+00  1.0951e+01 -1.0501e+01  1.1419e+01 -6.5027e-01 -1.2957e+00
-#&gt;  -7.3840e-01  5.2193e+00 -8.9245e+00  1.3058e+00  1.1172e+00  2.1565e+00
-#&gt;   4.3255e-01 -3.9143e+00  9.0325e-01 -1.2378e-01  4.6417e+00  1.8861e+01
-#&gt;   8.4166e-01 -2.4222e+00 -5.6610e-01 -1.0372e+00 -1.3386e+00  4.0038e+00
-#&gt;  -6.4126e+00  1.5800e+00  1.0187e+01  1.4854e+00  3.1206e+00 -2.6316e+00
-#&gt;  -2.1530e+00 -2.6822e+00 -2.9371e+00 -2.0850e+01  4.0258e+00 -1.0592e+01
-#&gt;   3.1439e+00 -2.6710e+00  3.8607e+00  5.0322e+00 -4.1489e+00  7.5615e-01
-#&gt;  -1.9739e+00 -2.0847e+00 -2.0033e+00  7.8632e+00 -5.4920e+00  1.4451e+01
-#&gt;  -4.1063e+00 -2.3122e+00  4.5584e-01  1.1010e+00 -6.3852e+00 -1.0550e+01
-#&gt;   1.4112e+00  1.2267e+00 -1.3281e+01  2.8764e+00  1.1144e+01 -1.7067e+01
-#&gt;   3.8010e+00 -1.3544e+01  1.5016e+01  2.2447e+00 -3.9226e+00  1.1308e+01
-#&gt;   3.1114e+00 -1.3672e+00 -3.5322e+00 -1.2939e-01  1.6929e+00 -3.8027e+00
-#&gt;  -4.2248e-01 -2.5009e+00 -2.1471e+00  7.6074e+00 -1.3740e+01  1.5843e+01
-#&gt;   4.4462e+00  1.2335e+00  4.7030e+00 -2.0860e+00 -1.8409e+00 -1.6971e+00
-#&gt;   3.9578e+00  2.2063e+00 -2.9810e+00 -9.5369e+00  3.4277e+00  3.7505e+00
-#&gt;   6.7714e+00 -1.4172e+01  1.7516e+01 -1.5937e+01  6.6506e+00  7.4282e+00
-#&gt;  -1.0196e+00  7.7461e+00 -8.8981e+00 -2.3861e+00 -2.0546e+00  1.9540e+01
-#&gt;  -2.2792e+00  2.7639e+00 -9.2889e-01  9.9078e+00 -5.5229e+00  8.4585e+00
-#&gt;  -6.0199e+00 -7.7380e+00  2.9657e+00 -5.1898e+00 -7.7473e+00  6.1726e+00
-#&gt;  -7.5183e+00 -2.1791e-01 -3.6168e+00  7.1331e+00 -8.3211e+00 -1.6825e+00
-#&gt;  -4.4690e+00 -1.6420e+00  7.0870e+00 -1.5348e-01  2.7785e+00  6.6276e+00
-#&gt;  -1.9135e+00  2.4460e+00 -5.8359e+00  6.8601e+00  5.1187e+00  1.0962e+01
-#&gt;   1.8956e+00 -8.9603e+00  1.6548e+01 -3.3380e+00 -7.9856e-01 -5.2454e+00
-#&gt;   5.1149e+00  1.5148e+00  6.1622e+00 -1.8812e+01  1.1046e+01 -1.2177e+01
-#&gt;   7.3048e-01 -5.8034e+00  5.7465e+00  7.0270e+00 -1.8593e+00  1.0197e+01
-#&gt;  -2.5703e+00  6.9681e+00 -1.6737e+01  7.6986e+00 -2.8168e+00 -1.5091e+01
-#&gt;  -2.0857e-01 -6.4343e+00  1.1316e+01 -6.0384e+00 -1.2292e+00  1.7320e+01
-#&gt;   3.6944e+00 -3.7830e+00  3.0928e+00 -9.2586e-01  7.7732e+00  7.7317e+00
-#&gt;  -3.0843e+00  2.5282e+00 -4.7313e+00 -1.4229e+01 -1.1720e+01 -1.3563e+00
-#&gt;   1.5097e+00  1.5640e+00  1.4943e+01 -1.8254e+01 -1.8201e+00 -9.0214e+00
-#&gt; 
-#&gt; Columns 7 to 12 -8.9350e+00 -6.9566e-02 -5.6295e+00  6.3098e+00  6.5924e+00  1.5812e+01
-#&gt;   6.9294e+00 -1.8698e+00 -1.1017e+01  5.8959e+00 -9.7153e+00  1.0373e+01
-#&gt;  -6.3429e+00 -4.9665e+00 -8.7360e+00 -1.8737e+00 -1.3440e+01 -1.2811e+01
-#&gt;   9.2107e+00 -4.0876e+00 -3.6253e+00 -5.5159e+00  6.9354e+00 -5.5825e+00
-#&gt;   1.0459e+00 -3.1881e+00 -3.5681e+00 -5.8470e+00 -6.6196e+00 -3.3472e+00
-#&gt;   3.0375e+00 -8.3935e+00  4.3932e+00 -8.1822e+00 -1.0728e+01  2.9207e+00
-#&gt;  -3.0152e+00  5.6034e+00 -1.9826e+00 -7.2026e-01  3.4574e+00 -1.9772e+00
-#&gt;   7.5832e+00 -1.9544e+00 -5.5401e+00  6.5496e+00  9.0706e+00  9.9699e+00
-#&gt;  -9.0796e+00  5.6546e+00  2.7854e+00 -2.7277e+00 -6.5753e+00 -9.2038e+00
-#&gt;  -1.0118e+01  1.3518e+00 -4.4730e+00 -2.5590e+01 -1.4456e+01  4.5737e+00
-#&gt;   3.4095e+00  7.9163e+00 -1.5486e+01  2.7615e-01  8.1601e+00 -5.4015e-01
-#&gt;  -8.5736e+00 -1.4262e+01  7.4116e+00 -4.7590e+00  1.1589e+01  1.5461e+01
-#&gt;   1.3783e+01 -2.6828e+00 -4.0919e+00 -2.9714e-01  6.5563e+00  9.9284e+00
-#&gt;  -5.2984e+00  5.7420e+00  3.9504e-02 -1.3003e+01  2.5343e+00  5.0881e+00
-#&gt;   8.5388e+00  3.6475e+00 -1.8820e+00 -2.0523e+00 -4.3577e+00  2.1410e+00
-#&gt;  -1.5965e+00 -8.5463e-01  1.4233e+01 -3.6424e+00  9.4730e+00  3.0286e+00
-#&gt;   1.6834e+00  3.7803e+00  1.4193e+01  1.1496e+01  9.0453e+00  2.2642e+00
-#&gt;  -5.6557e+00  9.8126e-01  3.2697e-01 -1.4071e+01 -1.0720e+01  1.2564e+01
-#&gt;  -7.9788e-01  1.0412e+01  1.3838e+00 -4.1120e+00 -6.1826e+00  1.9529e+01
-#&gt;   1.2302e+00  7.0254e+00  9.7709e+00 -2.8979e+00  4.4358e+00 -4.6303e-01
-#&gt;  -1.2715e+01 -5.9507e+00 -9.4371e+00  3.1804e+00 -1.2967e+01  2.4200e+00
-#&gt;  -4.9812e+00  8.7052e+00 -5.3166e+00 -7.2073e-02  2.3611e+00  6.4684e+00
-#&gt;  -6.7415e+00  9.7168e-01 -6.3866e+00  1.0914e+01 -1.4116e+01 -6.4288e+00
-#&gt;   4.0926e+00 -2.0470e+00  8.7645e+00  6.6774e+00  1.8183e+00  3.4800e+00
-#&gt;  -2.1283e+00  1.4827e+01  1.1738e+01 -7.7184e-02 -8.1251e+00 -6.7463e+00
-#&gt;   2.2788e+00 -1.0609e+01 -2.4556e+00 -4.0298e+00 -2.5518e+00 -4.6358e+00
-#&gt;   1.3014e+01 -3.3797e+00  4.3573e-01  1.4571e+00 -1.5221e+01 -7.3141e+00
-#&gt;  -2.4233e+01  2.1460e+00 -4.5529e-01  5.0510e-01  1.5762e+00 -7.2303e+00
-#&gt;   5.4054e+00 -1.9065e+01  8.3893e+00 -2.0747e+01 -5.1684e-01 -7.1609e+00
-#&gt;  -6.6588e+00  1.0436e+01 -8.9602e+00 -1.9745e+00  2.3018e+00 -1.2898e+01
-#&gt;  -8.6910e+00  9.3456e+00  1.8359e+00  3.9617e+00  1.1066e+01 -1.3349e+01
-#&gt;   2.2998e+00 -6.9085e+00 -6.6095e-01 -1.8927e+00  3.3494e+00  2.6099e+00
-#&gt;   1.7146e+01  1.5218e+01 -6.7461e+00  2.7815e+00  1.3875e+01  2.8589e+00
-#&gt; 
-#&gt; Columns 13 to 18 -2.5921e+00  9.6303e+00 -4.0978e+00 -6.3144e+00 -1.7628e+00  4.6008e+00
-#&gt;  -1.4007e+01 -1.5917e+00 -3.3578e+00  3.3296e+00 -8.3357e+00  1.8685e+00
-#&gt;  -9.0918e+00 -8.6139e+00 -1.0047e+01  3.6508e+00 -4.2898e+00  2.6359e+00
-#&gt;   9.8389e+00  7.5857e+00 -9.5771e-01 -1.6056e+01  3.6413e+00  1.1782e+00
-#&gt;  -1.2620e+01 -5.3541e+00 -8.1617e+00  6.3586e+00  3.2467e+00  1.0340e+01
-#&gt;  -1.4099e+01  1.1359e+00  4.0920e+00  5.8359e+00  4.7557e+00  2.1503e+00
-#&gt;   1.1784e+01  5.6302e-01  1.4390e+01  2.8176e+00  8.2875e+00  6.2427e+00
-#&gt;   1.6017e+01  7.3131e+00  1.6915e+00  1.1573e+00 -1.4525e+00 -9.0169e+00
-#&gt;  -6.7376e+00  6.3004e-01  3.7769e+00  1.1633e+01  7.2102e+00  5.7358e+00
-#&gt;   8.1079e+00 -2.3420e+00  1.2319e+01 -2.3412e+00  1.6579e+00  5.1757e+00
-#&gt;   1.8059e-01 -1.3849e+01  6.3714e+00 -8.4150e+00 -8.2502e+00  1.8219e+00
-#&gt;   2.1508e+01  5.2464e+00  1.1173e+01 -2.7531e+00 -1.0828e+01 -6.5018e+00
-#&gt;  -7.5913e+00 -5.7307e+00 -5.4725e+00 -5.8707e+00 -4.1794e+00 -1.6605e+00
-#&gt;  -3.2330e+00 -1.1277e+01 -3.5753e+00  1.3951e-01 -6.1838e+00  1.0851e+01
-#&gt;  -1.9681e+01  4.1062e+00 -9.4758e+00 -1.1426e+01  6.0292e+00  5.0747e+00
-#&gt;   1.9173e+01 -7.7379e+00 -1.9713e+00  2.0153e+00  5.8121e+00 -1.4840e+01
-#&gt;  -6.8901e-02  5.5945e+00  5.7723e+00 -4.3883e+00  7.2223e-01 -9.3176e+00
-#&gt;   2.6848e+00 -7.1888e+00  5.5684e+00 -4.6232e-01  9.4176e+00  6.7185e+00
-#&gt;  -2.4015e+00  1.5118e+01 -7.2321e+00 -1.5982e+00  5.0375e+00  5.8909e+00
-#&gt;   4.0168e+00  2.0581e+00 -2.3786e+00 -1.0786e+01  1.5289e+01 -1.2678e+00
-#&gt;   1.2221e+01  3.5300e+00  1.0519e+01 -2.5316e+00 -6.0389e+00  3.5330e+00
-#&gt;  -5.8762e+00 -2.8819e+00  4.8930e+00 -1.4999e+00  7.3704e+00  7.3484e+00
-#&gt;   3.8088e+00  8.0301e+00  7.0761e+00  9.1928e+00 -4.7236e+00 -2.4320e+00
-#&gt;  -5.2715e+00  7.3693e+00  1.3077e+01 -1.0364e+01  2.0970e+00  1.4131e+00
-#&gt;   2.3777e+01 -1.8499e+00  1.1237e+00  1.1998e+01  1.6895e+00  4.5195e+00
-#&gt;  -7.6300e-01  3.4632e+00  5.0195e+00 -1.3742e+00  6.2651e+00  4.8471e+00
-#&gt;  -1.8603e+00  6.7856e+00 -5.2399e+00 -5.9896e+00  8.8089e+00  1.3915e+01
-#&gt;   6.2870e-02 -2.8958e-01  1.4425e+01 -9.6279e+00 -1.2682e+00 -6.9679e+00
-#&gt;   8.7124e+00  5.7259e-01 -1.1119e-01 -2.7168e+00 -4.0965e-01  9.9774e-01
-#&gt;  -2.2156e+01 -4.6847e+00 -2.9286e+00 -7.8457e+00  1.4073e+01  3.8880e-01
-#&gt;  -1.1880e+01 -9.5431e+00  4.2382e+00 -4.0751e-01  1.0304e+01 -3.7266e+00
-#&gt;   1.0079e+01 -3.1204e+00 -7.1118e+00  3.2029e+00 -6.1856e+00 -2.6642e+00
-#&gt;   6.8710e+00  4.9260e+00 -6.6788e+00 -8.0686e+00 -1.6855e+00 -7.1562e+00
-#&gt; 
-#&gt; Columns 19 to 24  1.0661e+01 -6.7058e+00  1.5007e+00 -1.3736e+01  5.1623e+00  1.7691e+00
-#&gt;  -4.5375e+00  2.1905e+00  6.2607e+00  1.3751e+00 -7.1106e+00  7.5050e+00
-#&gt;   6.1609e+00  9.8227e+00 -4.6799e+00  1.3780e+00 -2.3364e+00  1.1728e+00
-#&gt;  -1.4755e+00 -2.1337e+00  8.4183e+00  1.1437e+01 -3.0945e+00  4.4640e+00
-#&gt;   6.5003e+00 -1.5958e+00  1.2718e+01 -1.0630e+01 -3.6972e+00 -1.6157e+01
-#&gt;  -1.5179e+01  8.1092e+00 -1.2509e+01 -5.5731e+00 -5.4389e+00 -4.4766e-01
-#&gt;   7.6010e-02 -1.2714e+01  4.8865e+00  1.9211e-01  1.8152e+01  6.3459e+00
-#&gt;   6.7229e+00  1.7700e+00  6.1512e+00  9.9995e+00 -5.3709e+00  1.5119e+00
-#&gt;   3.6173e+00 -8.5659e+00 -2.1395e+00 -3.8007e-01 -7.0149e+00  2.3719e+00
-#&gt;   6.7197e+00 -1.2068e+01  2.0456e+01 -1.3012e+01  1.7636e+00  3.3615e+00
-#&gt;   1.2768e+01 -2.2042e+00  7.6418e+00 -1.3228e+00  1.5068e+01 -2.1357e+00
-#&gt;  -8.0088e+00 -3.5447e+00  3.1663e+00  8.3359e+00  1.7119e-01 -2.9674e+00
-#&gt;   8.4079e+00  1.2736e+01  5.9723e+00  5.6105e+00  4.4824e+00  4.8617e+00
-#&gt;  -1.1179e+01 -8.7469e-01 -3.2031e+00 -3.1058e+00 -8.5296e-01  1.0843e+01
-#&gt;   7.1239e+00 -7.8795e+00 -7.3123e+00 -5.5709e+00 -8.0940e+00 -9.3772e+00
-#&gt;   3.6454e+00 -1.2010e+01  1.3955e+01  3.1990e+00  3.1844e-01 -2.6529e-01
-#&gt;   7.3153e+00  1.4953e+00 -1.0762e-01 -4.1387e+00  1.5016e+01 -7.2972e+00
-#&gt;  -6.9275e+00  5.1156e+00 -3.8005e+00 -4.7224e+00  3.6294e+00 -1.8127e+00
-#&gt;   7.0181e-01 -5.5262e+00 -6.1950e+00 -2.2352e+00  2.1245e+00 -1.5853e+01
-#&gt;  -3.1780e+00  9.1862e+00  2.1000e+00  1.7467e+01  1.1237e+00 -1.3626e+00
-#&gt;  -5.4429e+00 -7.7771e+00 -3.6973e+00 -4.3483e+00 -1.7117e+01 -7.5708e+00
-#&gt;   2.2050e+01 -4.5891e+00  5.2975e+00 -8.9670e+00  1.3237e+01 -8.1066e+00
-#&gt;   7.2863e-01 -5.5539e+00 -1.0969e+01  7.7435e+00  2.0750e+01  6.0225e+00
-#&gt;  -4.2573e+00 -2.2853e+00 -1.6509e+00 -2.7979e+00  9.4483e+00  3.5425e+00
-#&gt;  -7.6306e-01 -1.6146e+00 -1.7647e-01  9.7838e+00  1.2560e+01  1.1978e+01
-#&gt;   3.3929e+00 -4.2719e+00 -1.1677e+00  2.3590e+00 -1.0142e+01 -9.6464e+00
-#&gt;   1.4320e-01 -1.5324e+01  3.1126e+00  3.3207e+00 -2.4504e+00  6.5046e+00
-#&gt;  -1.2608e+01 -5.7396e+00 -7.6074e+00 -2.7813e+00  2.6001e+00  1.9047e+01
-#&gt;   1.8657e+00 -8.5694e+00  1.1288e+01  9.5233e+00 -7.3646e+00  8.5608e+00
-#&gt;  -9.7254e-01 -3.8673e+00  6.5777e+00 -2.1686e+01 -8.2752e+00  2.6071e+00
-#&gt;  -4.3436e+00  6.4063e+00 -1.5188e+01 -1.0822e+01 -8.6963e+00 -5.6848e+00
-#&gt;   4.1897e+00 -1.0023e+01  1.2088e+00  6.0416e+00  1.4004e+01  4.6875e+00
-#&gt;   4.5768e+00 -1.7082e+00  1.3180e+00 -1.4084e+00 -6.7442e+00 -5.5770e+00
-#&gt; 
-#&gt; Columns 25 to 30  1.3226e+00  1.5740e+01  2.2149e+01  2.8212e+01 -6.6869e+00  1.5589e+00
-#&gt;  -2.3018e+00 -2.6353e-01 -8.4783e+00  2.2821e+00  9.4461e-02  1.4321e+01
-#&gt;   4.0182e+00 -1.7871e+00 -1.5348e+00 -2.5222e+00 -5.5564e+00  4.9781e+00
-#&gt;  -1.7394e+01 -1.4695e+01 -6.8803e+00  5.4470e+00 -1.1275e+00  6.5338e+00
-#&gt;  -4.6072e+00 -3.7721e+00 -5.5945e+00 -1.0690e+00  2.9885e+00  4.8373e+00
-#&gt;  -1.8188e+01 -5.6459e+00 -1.5634e+00  9.5936e+00  3.4533e+00  4.6806e-01
-#&gt;   8.3153e-01  1.2096e+00 -5.2792e+00  3.6154e+00  1.0479e+01 -5.2420e-01
-#&gt;   9.4961e-01  4.8344e+00  1.4512e+01 -1.9542e+00  1.0138e+01  1.9964e+00
-#&gt;   1.3541e+01  2.6203e-01  1.5379e+01  8.3621e+00  1.4950e+01  7.4927e+00
-#&gt;  -9.6937e-01  8.3582e+00  3.2399e+00  3.7212e-01  1.4123e+01  1.6857e+01
-#&gt;  -3.9481e-02  1.3974e+01 -2.5741e+00  1.0163e+01 -3.7311e+00  8.6161e+00
-#&gt;   1.1020e+00  6.3313e+00  2.3024e+01  1.6278e+00  8.9968e+00  4.9953e+00
-#&gt;   3.3722e+00 -3.7714e-01  3.8283e+00  1.3050e+00 -1.6145e+01 -1.0091e+01
-#&gt;   2.8619e+00  9.5725e+00  8.2407e+00 -1.3655e+01  1.4456e+01 -7.0267e-01
-#&gt;   5.1508e+00 -4.6781e+00 -2.3416e+00 -1.3427e+01 -4.6782e+00  2.1503e+01
-#&gt;  -1.5419e+00  3.4845e-01 -1.2967e+01 -1.1099e+01 -4.1621e+00 -7.9843e+00
-#&gt;   2.4265e+00  1.3129e+01  1.3109e+00 -2.3909e+00 -1.6090e+00 -6.0352e+00
-#&gt;  -5.3263e+00 -8.9699e+00 -4.6849e+00 -8.9473e-02  4.8969e+00 -1.1078e+01
-#&gt;  -1.3667e+01 -1.1663e+01 -2.2251e+00 -1.2584e+01 -1.6623e+00  3.6203e+00
-#&gt;  -8.6841e+00 -6.0825e+00 -8.1758e+00  4.7498e-01 -8.3269e+00 -7.4194e+00
-#&gt;  -1.8053e+01  7.8156e+00 -1.3081e+00  1.5730e+01  1.7296e+01  1.5245e+01
-#&gt;   4.9510e+00  9.8875e+00 -6.8280e+00 -3.6904e+00 -1.8776e+00 -8.0786e+00
-#&gt;  -3.5968e+00  8.4006e+00  1.6145e+01  4.5911e+00 -6.6937e+00  1.2195e+00
-#&gt;  -1.2580e+01 -9.4243e-01 -2.9820e+00 -2.6423e+00 -1.3148e+00 -6.0163e+00
-#&gt;  -7.4871e+00 -7.5926e-01 -1.5403e+01 -5.3236e-01  4.0709e+00 -1.3234e+01
-#&gt;  -4.8966e+00  7.7397e-01  1.3278e+00 -2.0840e+01 -7.2712e-01  2.9837e+00
-#&gt;  -9.2283e-01 -8.4179e-01 -1.0574e+00 -2.3313e+01  9.4690e+00  3.6086e+00
-#&gt;  -4.5212e+00  8.4711e+00  1.6506e+01  1.3033e+01  4.5948e+00 -6.4962e+00
-#&gt;   1.3801e-01 -6.6971e+00  3.1851e+00  3.3268e+00 -8.9086e+00 -5.9442e+00
-#&gt;  -1.9564e+01  1.2552e+01  2.4011e+00 -5.1524e+00  8.5682e-01  1.9653e+00
-#&gt;   6.8867e+00  5.8133e+00  7.9853e+00  8.3451e+00  7.4290e+00  1.3178e+01
-#&gt;  -9.5930e+00  3.9846e+00  7.9441e+00  9.3300e-01  2.5904e+00 -2.7199e+00
-#&gt;  -1.4954e-01 -1.1575e+00 -2.7382e-01 -1.8721e+01  1.0818e+01  1.6041e+00
-#&gt; 
-#&gt; Columns 31 to 36  6.9137e+00 -3.5176e+00  6.5545e+00  1.8444e+00  3.1205e+00 -5.4108e+00
-#&gt;  -7.6151e+00 -1.0234e+01  3.4714e+00 -6.5538e+00  3.0220e+00  2.2023e+00
-#&gt;  -8.8864e+00  8.2983e+00  1.4388e+01  8.3669e+00  1.0084e+01  1.5412e+01
-#&gt;  -2.4555e+00 -7.0719e+00  4.0196e+00  5.6480e+00  4.5945e+00  8.3736e+00
-#&gt;   3.5312e+00  1.0270e+01  8.9921e+00  1.4576e+00  1.0617e+01  2.6715e+00
-#&gt;  -6.1361e+00  1.0208e+01  6.5051e+00 -1.0694e+01  2.0462e+00  2.3320e+00
-#&gt;  -1.0747e+01 -9.5578e+00 -3.3911e+00 -9.9083e-01 -4.7383e+00 -3.6644e+00
-#&gt;   1.4026e+01  1.7863e+00  3.8205e+00  6.1602e+00  3.1983e-01  1.0855e+01
-#&gt;   1.6929e+01  2.8555e+00 -5.0579e+00  2.0350e+00 -9.0138e+00  9.2670e+00
-#&gt;   3.7052e+00  1.1268e+01 -2.7157e+00 -4.5859e+00  8.7106e+00  7.9450e+00
-#&gt;   5.5109e+00  1.6378e+00 -8.4175e+00  1.2399e+01  1.1540e+01  4.1396e-02
-#&gt;  -1.2295e+01  7.8289e+00 -7.7395e+00  6.7758e+00 -9.5825e+00 -8.8592e-01
-#&gt;   1.1292e+01  1.8595e-01  1.0260e+00  1.4979e-01 -9.1601e+00  8.5649e+00
-#&gt;  -2.3984e+01  3.2865e+00  9.6090e+00 -8.1193e+00  6.1263e+00 -3.3199e+00
-#&gt;   8.7470e+00  1.8516e+00 -2.5790e+00  5.1210e+00 -1.9271e+01  1.6993e+00
-#&gt;  -7.7618e+00  5.7447e+00 -1.4343e+01 -7.0549e+00 -4.4797e-01  2.1781e+01
-#&gt;  -1.1861e-01  8.6165e+00 -7.8584e+00  1.1260e+01 -6.1033e+00 -7.9278e+00
-#&gt;  -8.1973e+00 -2.3669e+00  1.8301e+00 -9.7057e+00 -7.2424e+00  1.0921e+00
-#&gt;  -7.1213e+00  9.7527e+00 -1.7444e+01 -1.5597e+01 -1.4746e+01  7.4671e+00
-#&gt;  -8.9020e+00  5.4278e+00 -6.5688e+00 -1.3439e+01 -1.6678e+01 -2.1812e-01
-#&gt;  -1.0231e+00  6.8600e+00 -1.2712e+00 -6.5202e+00  5.1126e+00  2.2325e+00
-#&gt;   2.3321e+00  2.0368e+01 -1.3110e+01  1.6556e+00  1.1359e+00  1.0868e+01
-#&gt;   1.8892e+00  2.6442e-01 -6.5557e+00 -1.2648e+00 -6.0188e+00  9.1587e-02
-#&gt;   4.7778e+00  7.1101e+00 -2.2089e+00 -1.2507e+01 -1.1834e+01 -5.2271e+00
-#&gt;   6.5519e+00  3.6455e+00  1.3480e+01 -1.6155e+01 -3.8592e+00  1.0915e+01
-#&gt;  -1.1086e+01 -9.5750e+00  2.3776e+00  2.5093e+00 -5.7327e+00 -6.2574e+00
-#&gt;  -5.1196e+00 -1.3722e+01  1.3314e+01 -3.8946e+00 -9.6720e+00 -7.6980e+00
-#&gt;  -5.6943e+00  3.4296e+00 -2.3878e+00 -5.2717e+00  1.3485e+00  1.2648e+01
-#&gt;   7.3212e+00 -8.7664e+00  4.1799e+00  4.0226e+00 -1.1350e+01  2.2416e+01
-#&gt;  -9.3072e+00  1.2376e+01 -1.6107e+01 -1.8800e+01  8.1665e+00  3.9629e+00
-#&gt;   1.0313e+01  1.1733e+01 -9.7604e+00  1.6498e+00  7.0625e+00 -1.7680e+01
-#&gt;   1.8900e+00  6.7329e-01 -1.6446e+00 -6.8673e+00  1.0626e+01  6.9103e+00
-#&gt;  -1.2253e+01 -1.2969e+01 -1.0370e+00  8.2871e+00 -1.2701e+01 -1.2418e+01
-#&gt; 
-#&gt; Columns 37 to 42 -1.3153e+01  3.4491e+00  2.2724e+01  1.9831e+00 -1.3741e+01  9.5685e+00
-#&gt;  -6.4227e+00 -1.7090e+00 -8.6992e+00 -7.6755e+00 -6.7372e+00  3.1734e+00
-#&gt;   8.0086e+00 -2.1805e+01  3.0719e+00 -1.6749e+00  2.6287e+00 -1.3152e+01
-#&gt;   1.8171e+01 -4.9509e+00 -2.0939e+01  6.9043e+00  1.5881e+01  5.6567e+00
-#&gt;   4.1863e+00  6.5807e+00 -1.2938e+01 -9.6686e+00 -1.2928e+01  1.2250e+01
-#&gt;   1.4993e+00 -1.7178e+01 -9.4984e-01  3.7480e+00 -1.9024e+01  1.3271e+01
-#&gt;   6.2692e+00  1.1574e+01 -1.8215e+01 -2.1191e+01  2.0364e+01  3.6126e+00
-#&gt;  -7.6624e+00 -8.9270e+00 -1.0389e-04  1.4433e+00 -8.2330e+00 -2.5686e+01
-#&gt;  -5.1587e+00  8.3752e+00  8.4417e+00  6.8448e+00  6.3259e+00 -5.0207e+00
-#&gt;   3.6936e+00  2.5182e+00  3.2318e+00 -1.4826e+01 -8.1209e+00 -1.9062e+01
-#&gt;  -4.8439e+00  5.7488e+00  5.1016e-01 -1.0295e+01 -5.3832e+00 -1.6348e+01
-#&gt;  -3.1710e+00 -2.4417e+00 -3.1887e+00  1.6286e+00  8.8977e+00 -8.7057e+00
-#&gt;   1.0303e+01 -1.6435e+00 -8.2582e+00  1.6669e+01 -4.7704e+00 -1.3304e+01
-#&gt;   5.7954e+00  8.2571e+00  1.7538e-01 -6.1136e+00  1.0502e+01 -6.3529e+00
-#&gt;   1.5298e+00  1.0044e+01 -1.2730e+00 -1.6982e+01 -8.3509e+00 -1.9682e+00
-#&gt;   9.2806e+00 -5.6739e+00 -3.3008e+00  6.2423e+00  1.2135e+01 -2.0286e+00
-#&gt;  -1.1612e+00  1.0099e+01  1.5367e+01  7.3764e+00  5.0782e+00  2.9992e+00
-#&gt;   3.8830e+00  7.2015e+00  1.5877e+01 -5.9342e-01  6.4535e+00  6.7703e+00
-#&gt;  -2.2565e-02  1.0786e+00 -5.5719e+00 -5.4338e+00 -3.2415e+00  1.2260e+01
-#&gt;   1.8108e+01  1.2870e+01  6.1683e+00 -2.5972e+00  2.1895e+01 -1.1829e+01
-#&gt;   6.3334e+00 -1.0733e+01 -6.8986e+00  3.1314e+00 -2.3219e+00 -4.5420e+00
-#&gt;   5.1179e+00 -2.7076e+00  9.9742e+00  5.6114e-01 -1.4195e+01 -1.5554e+01
-#&gt;   3.7764e+00  6.4509e+00 -6.3118e+00 -3.1187e+00 -4.0794e+00 -1.1283e+00
-#&gt;   7.8021e-01  1.4461e+01 -4.1445e+00  2.7584e+00 -3.5464e+00  4.4641e+00
-#&gt;   1.8207e+01 -1.9987e+01 -2.3994e+01 -8.2485e+00  1.1963e+01 -1.1089e+01
-#&gt;  -3.8658e+00  1.8228e+01  1.9360e+00  6.3785e+00  1.0093e+01  1.7838e+00
-#&gt;  -1.2469e+01  2.6283e+01 -1.3855e+01 -1.1569e+01 -1.0398e+01  6.2715e+00
-#&gt;  -3.6807e+00 -9.8778e+00 -1.3110e+01 -8.4631e+00  4.3761e-02  6.7079e-01
-#&gt;  -1.7556e+00 -1.2893e+01  7.4841e-01  6.6402e+00 -2.5439e+00  4.9879e+00
-#&gt;  -2.2650e+01  4.1290e+00 -3.5871e+00 -7.1791e+00 -9.3301e+00 -7.7389e+00
-#&gt;  -7.2566e+00 -1.4262e+01 -1.2660e+00 -4.4009e+00  7.9739e+00 -5.0741e+00
-#&gt;  -9.4557e+00  3.6270e+00 -1.4217e+00  8.9967e-02 -3.5302e+00 -5.3342e+00
-#&gt;  -6.6430e+00  1.8517e+01  1.6317e+01  1.3659e+01  1.3198e+01 -1.6029e+01
-#&gt; 
-#&gt; Columns 43 to 48 -1.4218e+01 -1.1261e+01  8.3986e+00  2.9706e+00 -6.3916e+00  5.5533e+00
-#&gt;  -2.3102e+00  2.0152e+01 -9.2693e+00  7.1224e+00  4.1302e+00 -1.0215e+00
-#&gt;  -3.4843e+00  1.2134e+01 -1.6020e+01  1.6759e+01  7.0993e+00 -2.1776e-01
-#&gt;   3.5498e+00  4.5103e+00  8.0835e+00 -1.0191e+01 -3.6019e-01 -1.7265e+01
-#&gt;  -6.0870e+00  4.5764e+00  2.9551e+00  8.7098e-01  1.5460e+01  1.3404e+01
-#&gt;  -1.8965e+01  9.5369e+00 -1.5184e+01 -2.8496e+00  6.5097e-01  1.5635e+01
-#&gt;   4.0963e+00 -8.2608e+00  2.2973e+00 -2.0702e+00  4.4568e-01  2.4888e+00
-#&gt;  -1.0599e+01 -4.6582e+00 -3.4182e+00  8.0025e+00 -7.7630e+00  1.0803e+01
-#&gt;   2.9712e-01 -6.4755e+00 -1.8007e+01  8.0548e+00 -5.0537e+00  2.3730e+01
-#&gt;   4.1087e+00 -1.7578e+01  4.4802e+00  4.3932e+00 -1.9422e-01 -9.7674e+00
-#&gt;  -3.2010e+00 -1.1195e+01  1.7481e+01 -6.6046e+00  4.4173e+00  3.5258e+00
-#&gt;  -3.3872e+00 -1.8305e+01 -3.3755e+00 -1.0790e+01 -1.0050e+01 -9.4535e+00
-#&gt;  -4.7096e+00  1.0400e+01  4.4654e+00 -3.8650e+00  1.9342e+00 -1.7321e+01
-#&gt;  -9.4228e+00  8.1513e+00 -1.3215e+01 -2.0789e+00  1.2351e+00 -1.3591e+01
-#&gt;  -4.9130e+00  5.0590e+00 -3.4248e+00  1.1890e+01 -7.5417e+00 -4.2509e+00
-#&gt;   2.5993e+01  5.8945e+00  1.1839e+01 -4.5489e+00  1.7621e+00  3.3316e+00
-#&gt;  -3.1706e+00 -4.8533e+00  3.4894e+00 -8.0006e-01 -1.1835e+01 -3.8124e+00
-#&gt;  -2.1618e+00  1.4440e+00  6.9685e+00  4.2273e+00  1.3473e+01  9.2615e+00
-#&gt;   9.1131e-01  1.1156e+01 -1.1159e+01 -1.5213e+01 -1.4027e+01 -5.6771e+00
-#&gt;  -1.0091e+00  4.9464e+00  8.0003e+00  1.2916e+01  2.1394e+00  1.0779e+01
-#&gt;  -2.8188e+00  4.0183e-01  1.0724e+01  1.8182e+00  1.8075e+01 -2.4964e+00
-#&gt;   2.6894e+00 -4.5097e+00  5.3388e+00  6.2943e+00 -7.5091e+00 -1.0143e+01
-#&gt;  -3.4449e+00  4.8348e+00  2.7362e+00  1.1391e+00  2.5120e+00  3.3457e-01
-#&gt;  -5.1743e+00  3.3184e+00 -8.9793e-01 -5.7021e+00 -8.6061e+00 -1.2820e+01
-#&gt;   2.6471e+00 -1.5069e+00  6.4044e+00  5.7222e+00 -2.2341e+00 -4.1491e+00
-#&gt;  -6.0838e+00 -2.7069e-01 -2.7864e+00  6.5246e+00 -5.3389e+00 -2.9782e-01
-#&gt;   2.3306e+00  1.2695e+01 -3.1929e+00  8.1350e+00 -6.3068e-01 -1.2087e+01
-#&gt;  -4.9103e+00 -1.0235e+01  8.3555e+00 -1.6253e+00 -2.9271e-01 -3.1100e+00
-#&gt;   1.3922e+00 -1.1338e+01  1.1563e+01  2.7692e+00  8.0650e+00  1.2959e+01
-#&gt;  -4.2165e+00  2.8165e+00 -1.2201e+01  4.0674e+00  8.7586e+00  7.0882e+00
-#&gt;  -2.1583e+00 -9.2601e+00 -1.3659e+01 -4.1608e+00  7.6412e+00 -8.2027e-02
-#&gt;   3.6218e+00  3.1677e+00  1.5279e+00 -8.9615e+00 -5.4873e+00 -4.9342e+00
-#&gt;   4.4645e+00  4.5815e+00 -2.5408e+00  9.3274e+00 -9.9373e+00 -2.5018e+01
-#&gt; 
-#&gt; Columns 49 to 54 -1.0018e+01 -1.1171e+01 -1.8493e+01 -3.2740e+00 -6.9994e+00 -1.2666e+01
-#&gt;  -1.4477e+01  4.4245e+00 -6.1400e+00 -2.1536e+00 -3.9000e+00 -1.0086e+00
-#&gt;   2.7638e+01  2.1042e+01 -8.5341e+00  1.2444e+01  5.9801e+00  4.3587e+00
-#&gt;   2.3543e+00  2.9245e-01  2.3125e+01  1.0619e+01  2.7030e+00  9.9232e-01
-#&gt;   1.0752e+01  1.6781e+01  1.3759e+01 -1.5949e+01 -5.4920e+00 -2.1970e+00
-#&gt;   6.0310e+00 -2.2471e+01  5.2957e-01 -8.0441e+00 -7.0046e+00 -8.4130e-01
-#&gt;  -1.0907e+00 -6.6864e+00 -3.7117e+00  2.2300e+00 -3.4953e+00 -6.7303e+00
-#&gt;  -1.3436e+01 -2.4374e+01 -1.5342e+01 -3.6882e+00 -1.0596e+01 -6.2659e-02
-#&gt;   5.4434e+00  7.7001e+00  3.1077e+00 -3.1572e+00 -1.2236e+01 -2.2595e-01
-#&gt;  -1.1376e+01  1.0785e+01 -6.2180e+00  1.4487e+01  7.8710e+00  6.1228e+00
-#&gt;  -1.3575e+01 -2.2334e+01 -1.4282e+01  1.1473e+01 -1.0628e+00 -8.4188e+00
-#&gt;  -4.5127e+00 -4.7241e+00  1.0653e+01  5.3931e+00 -6.0533e+00  3.8398e-01
-#&gt;   2.6724e-02 -1.0962e+01 -2.0880e+00  4.1984e+00  5.2069e+00 -1.3422e+00
-#&gt;  -1.5984e+01 -2.5604e-01 -1.9016e+01 -3.8826e+00  3.1061e+00  2.6660e+00
-#&gt;  -1.4306e+01  4.5640e+00  5.3182e-01 -1.8699e+00  1.6807e+00 -1.4108e+00
-#&gt;  -1.6832e+01  3.2941e+01 -5.4155e+00 -5.8067e+00 -1.5757e+00 -3.4861e+00
-#&gt;   1.9777e+00 -1.2564e+01  2.5453e-01  1.3376e+01 -7.4173e+00 -2.7721e+00
-#&gt;  -3.6310e+00 -1.0588e+01  2.6847e-01 -1.0441e+01  1.2414e+01 -7.7004e-01
-#&gt;  -1.2195e+01  4.9213e+00 -2.3104e+01  1.2320e+01 -1.4927e+01 -7.9862e+00
-#&gt;   1.1807e+00  2.3189e+00  1.4310e+01 -5.8723e+00 -1.3048e+00 -3.7160e+00
-#&gt;   8.7451e-02  1.5662e+01 -7.1258e+00 -5.2072e-01 -5.7004e+00 -3.5567e+00
-#&gt;   1.0911e+01 -5.2645e-01  3.2068e-01  1.5779e+01  1.6338e+01 -7.8552e-01
-#&gt;  -1.9024e+01  9.7262e+00 -2.3585e+00  1.3129e+01  7.5439e+00 -4.2555e+00
-#&gt;  -5.7801e+00 -6.6805e+00  3.7651e-01  2.0527e+01  3.6207e+00 -8.2320e-02
-#&gt;  -7.2179e+00  2.0666e+00 -9.9681e+00  5.5207e+00  4.1326e+00 -1.4112e+00
-#&gt;  -1.6013e+01 -4.2240e+00  5.3135e+00  4.5096e+00  4.5357e+00  6.0342e-01
-#&gt;   6.0501e+00 -8.2517e+00  1.7974e+01  7.3948e+00 -4.3243e+00  3.0166e+00
-#&gt;  -4.3929e+00 -4.5378e+00 -6.5814e+00 -3.6391e+00 -2.6622e+00 -5.6004e+00
-#&gt;  -1.3750e+01 -1.1605e+01 -1.8236e+00 -2.3015e+00 -1.3830e+01  1.0253e+00
-#&gt;   1.1217e+00 -3.3303e+00 -1.9645e+00 -7.9449e+00 -5.1005e+00 -7.4454e-01
-#&gt;  -3.0886e+00 -3.6884e+00  8.1092e-01 -1.1272e+01  5.7310e+00  6.4692e-01
-#&gt;  -2.1388e+01  7.8607e+00 -9.3771e+00  4.0420e+00 -5.4895e+00 -6.3162e+00
-#&gt;  -8.6226e+00  5.8287e+00 -1.2998e+01  2.2333e+00 -2.6422e+00  1.7339e+00
-#&gt; 
-#&gt; (6,.,.) = 
-#&gt;  Columns 1 to 8  -0.5921  -7.3050   2.9918   2.9297  -8.4131  11.9221   8.6648  -2.5337
-#&gt;    0.3617  10.9339   3.5230  -1.5499  19.8571  10.2776  -9.7786   3.6116
-#&gt;   -0.8737   1.3364 -12.6465  -9.4850  -5.5985 -21.2450  -7.2572   2.7530
-#&gt;    0.4939   5.6862  -0.3541  -6.6885  -9.6455  -3.0015  -4.9913 -10.4938
-#&gt;    0.3231  -0.8629   2.0309  -4.5730   2.5879   4.7678   2.7074   2.7076
-#&gt;   -2.4548  -7.8034   2.6649   2.5601  -7.5308 -13.1132  -3.7407  -4.3662
-#&gt;    1.4125   8.8545  -6.1698   4.6130  -1.7855  -3.4146 -13.5172  10.6570
-#&gt;   -6.1669  -0.3787  13.2487   4.3056  14.7512  11.2855   8.9941   9.3375
-#&gt;   -0.0356  -7.5243   0.4170  -0.3375   1.3880  -4.2005   5.4275   6.1914
-#&gt;   -4.2257   2.0514   6.8795   2.4746   3.1756  -8.1853   0.8485  -0.9941
-#&gt;   -2.3872   4.6179   2.1956  14.0589   0.1142   9.1170  -2.6500   0.4194
-#&gt;   -2.5715  -0.0142  -5.4799   3.0350 -10.5895  -6.2304   5.2874 -18.6043
-#&gt;   -0.3822  -0.3993  -2.5486  18.4505  12.7305  12.1497   4.0170 -10.2228
-#&gt;   -2.4343  -1.8346  -4.0418  -8.8045  17.3669 -11.9327 -23.0847  10.9830
-#&gt;   -3.7712   1.9584   1.8275   1.0021   3.6088   9.3534  -3.1897 -20.8539
-#&gt;    2.1421   0.9360 -10.1721  -5.0737  -4.8002   7.1658   5.3652   6.8194
-#&gt;   -0.1418   4.1803  -7.7792  12.9840   1.4901  -3.8547  15.3994  -4.6085
-#&gt;    5.6991   2.3156  -4.3586  13.8094   9.1187  -2.0185  -0.2382   8.3535
-#&gt;    5.9933   0.0165   3.6880   4.9763  -5.4149 -12.5846  12.0472  -1.2298
-#&gt;    5.1535   6.7640   1.3667  11.1300   2.1325  -7.0033  16.9778   5.6279
-#&gt;   -1.1282   9.5823  -0.6723  -0.5968  -6.9834 -10.6563  -0.8894  -9.1415
-#&gt;   -9.0755   3.8074   6.3679  10.7905  -5.2306 -14.6898  -7.3457  -0.8321
-#&gt;   -6.0802   4.4655   0.3181   2.2318  -3.2989   1.9357 -10.1002   3.1692
-#&gt;   -3.9741  -0.8675   6.9667   5.3399  -7.9617  -2.5516  -7.5788  -6.4984
-#&gt;    1.5684   3.9719  -3.6338   8.4350  21.6891  -9.9079 -11.0771   9.7129
-#&gt;    1.7671  -5.9607   2.0439  -5.3243  -3.2971  -4.5455  -7.9488   9.5991
-#&gt;    3.1236   4.2033   5.0688  -5.5244  11.7720  12.6320 -22.9717   2.8605
-#&gt;   -5.3981  -4.5133   1.4431   5.4165 -11.8354  -5.8880  -9.9610  14.1250
-#&gt;   -2.6390  -0.8280  -7.1450   0.1168   4.1226  -5.1870  21.7742  -3.5029
-#&gt;   -3.7520   1.0903  12.4338  -7.0709  -8.2280   3.4598  -7.1212   4.0686
-#&gt;    0.8093 -10.3504   8.1887   3.8252   2.6143  13.2474  -7.7255  -0.7997
-#&gt;   -1.7830   2.4117  -3.5747   4.4797  -0.1327   7.2824  10.1355  -2.5867
-#&gt;    1.0746   6.7265   3.0171   2.3948   2.6841 -13.4296  14.0522  -1.0665
-#&gt; 
-#&gt; Columns 9 to 16  -2.6314  -2.5828  -3.2876   1.0380 -12.2031  19.7218  -9.8598   0.4530
-#&gt;   -5.0586  10.1790 -13.6894   2.0574  -1.7498  -1.4304   2.3208  10.8284
-#&gt;    0.0291 -19.8916  -3.2155  -2.3621   0.0733 -10.0715   4.8575   1.1604
-#&gt;    0.4619   3.3918  -0.4088  -3.5013  -2.5040  -0.2875  -5.5786   0.9883
-#&gt;   -3.2142  -9.7898   4.5775   0.9242  -6.0632  11.1692  -3.8238  10.4866
-#&gt;   -8.6818  -5.7562  -5.1412  -0.2732  -3.6182  -3.3576   4.7100   4.5477
-#&gt;   16.1602  -6.6932   8.3765  -0.1665  -4.4954 -10.5942  -7.8055   3.1544
-#&gt;   -7.5272   7.7576  -3.0995   0.4644  15.4102   1.6683  22.9862   5.5311
-#&gt;   -1.4461   3.3700  13.0938  -1.8491  -7.7227  -1.7516  -8.6129  -9.4606
-#&gt;    5.4752  -0.3912 -11.9422  17.4190  -3.3731  12.7631   8.2073  18.4190
-#&gt;   -6.4412   4.4376 -10.7075  -2.0951   0.4077  10.8879  -1.9762   3.9636
-#&gt;    1.5958   1.1878   0.7888  -0.0093  12.7069  -2.6678   2.5561 -13.0708
-#&gt;   -6.6537  -3.9234  -9.2058  -4.2411  13.2523  -1.2436   1.1798  -0.5627
-#&gt;   16.8092   2.7200  -2.6699   4.3059   2.0476 -10.0011   7.2998   5.7776
-#&gt;   -4.7084  -8.9365 -19.7462  -5.1007   3.7879  -4.4307  -6.0738   6.6045
-#&gt;   -0.0381  -6.4804  -0.6123   4.9970 -14.3221  -7.1558  -2.0993   0.5433
-#&gt;   13.9554  -0.4741   8.0989  13.8078   3.9816  -7.7183  -4.2339  10.5959
-#&gt;   12.7808   2.9985  -2.4498  -0.8486 -11.5980   1.3365   2.9022  -0.5457
-#&gt;   -2.1596  -7.7915   0.7798   4.9604  -8.2090   1.1784  11.5680   5.5788
-#&gt;    0.9681   6.6312   9.2087  -5.2140  -8.1446  -0.2739  -5.7397  -8.0219
-#&gt;  -10.7900   6.6136   1.6608  14.6679  -9.3718  15.1641   5.3037  15.4307
-#&gt;  -20.9792  -6.8224   2.0661  17.3165  -6.8921   4.5759  11.6353  -8.0799
-#&gt;   -9.1321   7.7614   4.6809   6.4817   5.7039  -1.0356  -0.4405  -1.5950
-#&gt;   -8.8131   9.6483   1.6895  -0.1432  -6.4518   1.8541 -10.6957  -6.1720
-#&gt;  -17.3797  -0.0985   5.8981   5.3693  -3.0382  -3.6525   9.1044  -0.2727
-#&gt;  -11.7464  -3.3346  -7.0558   4.4520   9.6325  -0.7452  15.3251  -0.2187
-#&gt;   -0.4724  -7.5185  -4.4106  -2.2756   7.8341   1.1788   0.1676   1.8439
-#&gt;   -7.1170  -7.2860  -0.3977   1.2359   2.1901   0.2328  14.6747  12.4345
-#&gt;    7.0033   2.4001 -16.6258  -6.0039  -8.8479  -4.1982   8.6088   3.8066
-#&gt;  -12.3674  -9.9047 -12.2357  -3.5581  -9.8226   6.5446   2.7209  -6.1199
-#&gt;   -6.5267  -5.5217   5.9473  -5.6399   5.0627  -3.4595  -0.7283  -3.0445
-#&gt;   -7.3148  -8.1246   3.8053   3.8291  -7.3210   9.4116   0.5184  -1.9287
-#&gt;   15.4440   0.2234  -5.3904  -0.8191   1.5237  -0.4519   4.7297   0.0810
-#&gt; 
-#&gt; Columns 17 to 24  -6.2127   8.5899 -10.0677 -13.2735  -4.9979  -8.8968   4.4713   1.3222
-#&gt;   -2.0306   6.7927  -7.8522  -6.2002  -1.8883  -4.1407   4.9463  -4.3372
-#&gt;   -5.5899   2.1305   7.7127  -7.1682  -3.0100  -4.9739  -2.5173  -3.0036
-#&gt;  -13.0921  -7.0004  -8.4307  -2.4247  -0.1872  -0.3123  -1.2053  -3.1284
-#&gt;  -10.8146   9.6992   1.7139  -7.3966 -12.1348  -6.8353  -7.4684  -0.3972
-#&gt;    6.4366  -3.0071  -0.4269  22.9486  -8.8380   5.4169   5.7751  -8.9626
-#&gt;    4.3075  -5.1820   1.0205  -8.5488   7.8897  -7.7990  -2.3989   6.0176
-#&gt;    9.0273  -7.7313   1.3264 -13.3473  -3.3692  -1.8882  -3.2317  -8.7708
-#&gt;   15.4291 -12.4500  12.4810  -5.5295  -4.5205   6.8580   3.9878   1.2861
-#&gt;   -3.2822 -16.8745  -0.1470   0.6761  -1.9855  -0.1507  -2.7189  -8.1266
-#&gt;    1.1568  -3.3803 -19.2922  -3.2479  -0.2770 -15.1418  -7.3453  -1.3753
-#&gt;   11.6638 -14.9403  -3.3577  -0.1677  -6.7605  -2.6042   3.2432   1.4242
-#&gt;    0.8641 -18.0617  -8.8385 -11.5461  -4.7462   5.3244  -5.9223  -4.7006
-#&gt;  -11.8066   1.3436   6.3498   4.6773  18.0154  15.3355  -3.2530  -1.3931
-#&gt;    2.9396 -11.6634  -0.8682 -10.3404  -1.4323   0.2711   4.9805  -8.1190
-#&gt;  -18.3323  12.0614  18.9068  -6.1701   4.3865   3.2425  -3.4594   4.4439
-#&gt;    1.1836  -0.8268 -13.2845  -3.2542  -0.4099  -9.3526   5.8996  -0.4461
-#&gt;    8.1394  -7.3584  -1.9840  -7.0159   0.2276  -7.4663   1.3635  -7.9515
-#&gt;   -4.2297  -0.7278  12.2203  16.2547  -8.5689  -8.4590   0.2145  -6.9938
-#&gt;    1.7611  -4.8812   0.2981 -10.3694  -4.5060  -1.0138   8.3311  -3.8651
-#&gt;  -11.3138  13.0053  -9.3314  -5.5806 -12.3644  -3.9440  14.5821  -9.0346
-#&gt;  -14.4377  -5.5794  -8.2150 -10.2906   2.9362   5.9677 -14.4528  -7.8647
-#&gt;   -4.8385   2.6813 -12.5125 -21.6845  19.3500   0.7031   1.4329  -5.0047
-#&gt;   12.2072  -9.5406  -1.3944  -5.8068   6.0823  10.6510  -0.5395 -12.9039
-#&gt;   -0.0747  -1.3369  -0.9013 -15.9850  17.5126  11.3719 -12.9760 -11.2027
-#&gt;    5.6698  -3.3631   1.1364   2.3347  -0.4283  -7.0738  -2.1670   9.4380
-#&gt;    1.0329  -6.0996  -2.2627   1.8367   2.4655   2.5983  -6.3759  -7.2178
-#&gt;    5.9541  -4.3570  18.0037   9.6993   3.8362   4.6500  -7.4757  -8.0718
-#&gt;   10.8640 -14.7272   1.5116   0.5722   7.6089 -10.0782  -7.5853   9.4062
-#&gt;   12.3544  -1.3992   7.3210  12.9861  -1.7531   0.5038   0.2530  -0.3521
-#&gt;    3.9097   6.3581  -0.4656  11.3001  -1.1144  18.0090   8.7483   1.4711
-#&gt;  -18.5693   0.8387   8.9763  -2.7255  -0.9358   6.6932   3.8568  -0.2258
-#&gt;   12.3387  -9.0341  -5.2910  -2.5514  -8.9520 -25.9220   2.3972   8.8284
-#&gt; 
-#&gt; Columns 25 to 32  13.9203  -5.1411  -3.7397 -13.0389  -5.5351  -5.4166   3.3249   5.4221
-#&gt;    6.5402  -1.2898   2.6110  -0.3121  10.9936  -9.4124 -12.9698  -3.1218
-#&gt;    3.4431   8.9098  -5.3384  10.0593  -7.5491   5.1608  -0.1100  10.5544
-#&gt;    2.4914   8.3584  -0.1032   2.7726  -2.6764  -6.2074 -18.8197  10.4107
-#&gt;   14.3670 -17.7966  -1.4818 -15.3990  -1.3299 -18.2614 -10.7544   7.3100
-#&gt;    4.8918  -3.2175 -10.3728 -19.5243  13.3467  -6.1767   3.1792  -8.4939
-#&gt;   -4.0701  -4.3451 -10.9726  -0.9357  -5.2505  15.5701 -11.3169  -3.8568
-#&gt;   -5.1610   0.9718 -10.0749  -0.5979   3.1039   1.0060  -0.3319  -4.3893
-#&gt;   11.5095  -3.0051 -11.0122 -19.5825   1.7032  11.8966 -11.3749   0.9719
-#&gt;    5.8776  16.4904 -10.4353   5.8625 -14.9989  16.8020 -24.9150  20.4281
-#&gt;   -2.9029  -8.7309 -10.8258  -1.6222   0.0148  -5.3680   3.5372  -9.7834
-#&gt;   -2.5692  19.5724  -8.8038  14.4747  -1.6114   3.0450  -4.0873  15.8688
-#&gt;   -0.4460   0.3128   6.5217  -8.4194  -6.4576 -17.7028  -2.7387  11.1212
-#&gt;   -2.6805  -1.1609   8.9434   0.6905   6.3071   2.9359   6.0291 -10.4703
-#&gt;   -4.0706  -3.6596  -1.1569   1.0802  -2.7839   1.2536  -3.4713   2.4045
-#&gt;   -6.2090   2.7344  -9.7826  18.1193 -16.8997  -5.2478  -4.7747  14.4298
-#&gt;   -2.5025  -4.0757  -8.4496   5.6488  -6.4029  -6.9049  -2.1848  -8.2183
-#&gt;   -8.2289   0.1056  -5.8257  -0.0926  -7.8139   2.7514 -19.7037  -5.8178
-#&gt;   -4.3903   5.0865   8.2017 -22.5354   5.2763   4.0586   7.7214 -20.7393
-#&gt;  -12.0764   0.2824 -13.6420  -6.6536  -6.0297  -1.5481 -18.1568   4.0864
-#&gt;   -9.5272   3.5378  -7.7141  -1.9937   1.9049 -11.5997  -4.8943  -3.4524
-#&gt;   11.1257  -2.9809  -6.5860  -9.6681 -13.4429   3.8299  -0.5542  -8.3702
-#&gt;   -1.2551  -1.0210  -6.7044  10.2648   1.1264   6.8658   4.3591   0.9758
-#&gt;  -16.7861  11.3792   2.4883  -5.5172  13.9477   5.1821   6.2736 -12.6968
-#&gt;  -15.1680   3.6357   6.1374  -3.2717  -5.6775   0.6134   6.4856   0.1380
-#&gt;   -9.8328  -6.2255  15.8622  -6.1597  -4.2320  -0.1612  -5.6116  -0.9880
-#&gt;    4.9614  -4.1519  14.6066  -6.5710   5.4998   1.6499 -11.3658   0.4047
-#&gt;    0.5486  15.5492   8.4415   1.5766   8.0746   6.9292  -8.5531 -11.5407
-#&gt;  -11.4112  10.0323  -2.1236  15.3491  -5.6997   1.7658 -15.9353  -5.0595
-#&gt;   18.4395   0.7091  -9.3505  -9.8701  10.0363   5.0088 -13.7493   1.1558
-#&gt;   -0.1568   0.4374   2.5258   7.0329   2.7381  11.1021   4.3213   7.6974
-#&gt;    5.5163   2.9471  -8.6853 -12.7224   1.0474  -8.2495  -8.1296  -2.8440
-#&gt;   -7.1946   0.1787  -0.0368  -9.9593  -7.8838   1.5171   0.1479  -0.1033
-#&gt; 
-#&gt; Columns 33 to 40  -3.7911  12.1518 -12.4860   2.6569 -10.7482  23.9582   3.6793   6.9927
-#&gt;   21.6640  -1.7124  -3.1777   0.3407  -9.8257  -1.4930  -6.0599   2.7004
-#&gt;    5.9089  -9.6381   0.2861   3.9356  -3.2325  -0.4193  -3.6428   6.4882
-#&gt;    5.5402  23.2455  -2.1077  -1.2085   5.3946   9.5776   3.7294  -0.7982
-#&gt;   -4.9266   9.6740 -21.0425 -11.9165 -13.8099 -18.7955  -2.4415   3.7892
-#&gt;    8.3979  -4.3224  -4.4753 -14.2303 -20.8875  -0.1966  -0.3839  -3.0956
-#&gt;    1.3473  14.9265  -7.5943   1.9822   8.1808   4.5570  15.4761   9.9853
-#&gt;    2.5585  -2.5019  10.3923  -1.2145   7.6382  -0.0601  -1.8602  -4.4900
-#&gt;   19.0909 -16.8279  15.4652  -6.7328   2.4564   5.9226   7.1813   1.9175
-#&gt;  -18.3534  18.7507  17.1995   1.0777   6.6634  -6.4457  10.2317  10.7236
-#&gt;    9.5872   8.8461  11.2826   5.8554   3.5950   0.7347  10.1858   2.0207
-#&gt;    8.3156   0.0495  11.1532  -7.1496  12.4271   0.5085   3.7157  -4.9383
-#&gt;  -13.8465   7.9507  16.1755   9.0361   6.2507  -2.0187  13.0574 -11.3785
-#&gt;   -8.3752  -9.2702  -8.8078  11.7558   4.5974  -9.8872  -7.7214   1.3391
-#&gt;   -8.3076   5.9218   7.8956   4.1186  -8.9959  -4.5177 -12.1228 -17.7053
-#&gt;    0.9367   3.2685  -5.0240  -3.3188   1.4855  -2.1235  -0.6587  31.0940
-#&gt;  -10.5558   9.3237  -5.0276  18.2213   7.1087   7.4319   8.1322   0.8563
-#&gt;   -6.8857  -0.5189   5.1926  20.6016   2.5555  -7.3398   4.1618  -7.5910
-#&gt;    2.1677   8.9296  -6.0163  -3.7822 -18.4205   0.8807 -11.3650  -4.4115
-#&gt;   18.2442  -4.7516  19.0846   7.2391   6.8901   0.7916  -5.0943 -12.4589
-#&gt;   15.1377  -6.9018  -2.9680  -6.6185 -12.6120   7.0639 -14.1244  -5.4742
-#&gt;  -20.5676  10.2792   9.1764   7.8115   5.7623  -7.6038  22.0978  -3.4538
-#&gt;   -7.3811   6.8090   2.1523   9.8327   9.1900  -0.3134  -6.6946  -2.0403
-#&gt;  -11.5477   4.7825  16.9129   1.8906  21.5221  -1.0988  -0.2649   1.9224
-#&gt;    4.6545 -11.7158  -2.7977  -0.5938   0.6002  -8.3269  -1.1153  14.7561
-#&gt;  -10.1349   4.2950  12.2134   5.9309  10.0026  -6.6870   4.0859 -10.5939
-#&gt;    3.0391  10.0342  -3.5942  -0.3481  -9.8188 -11.0153  -9.2263  -5.0465
-#&gt;   -1.7542 -10.3302  -0.9362 -28.4955  -8.6106  -4.8442  17.1029   3.9272
-#&gt;    3.9016   3.0031  -0.0232  -0.4625  -3.8010  -2.2967  -0.3972   1.4114
-#&gt;    0.2260  -1.5307  -2.1923 -23.5221  -4.9030 -11.5876  -9.6034 -11.2109
-#&gt;   -7.5214 -14.8817  -9.0939  -8.4155  -5.6686  -3.1537   3.9214  -5.6939
-#&gt;   14.4805  18.6970   4.9151  -3.6566 -11.2894   2.2530 -10.2839  14.5644
-#&gt;    6.7170  -3.5367  13.9275   6.9395   8.2710   5.5950   2.8168  -9.6009
-#&gt; 
-#&gt; Columns 41 to 48   3.6754   5.7341   0.5890  -3.1034 -22.7892 -10.6951  -3.0795  -7.1552
-#&gt;   11.7361   6.9683  12.4311  -7.3974  -2.5010   5.3156  -2.6036  14.1821
-#&gt;   -3.4822   0.6920 -12.7487  -2.6508   0.1997 -14.0354  -3.6570  -7.3534
-#&gt;   -3.0321   0.4996  -2.8123   6.1408  -3.8226  -1.4761 -15.8022   7.3362
-#&gt;   17.8252  -4.5703   2.2880  -1.6378  -4.1593   2.0968  -8.1251   2.7922
-#&gt;   14.2517  -1.5537   5.2673  -3.3111  -5.7111   7.5999  -1.8471   7.3347
-#&gt;   -0.0767   4.7225 -15.3590   3.9876   8.8358 -14.6376   4.5659 -13.9173
-#&gt;  -10.5326  -0.6201  -1.0925  -5.2530   8.5617  -0.2840   5.1392  12.5474
-#&gt;    0.1083   4.8166 -12.9342   3.3716   0.1382 -16.7899   2.7613 -18.7108
-#&gt;   -4.3559 -22.8177  -6.8771   8.1785   3.2880 -11.4257  -1.7631 -23.9149
-#&gt;  -19.1731  20.8131 -10.0138   9.1790 -17.1966  -3.9221  -9.1246   1.7852
-#&gt;   -1.3843  -6.1908 -10.4012  -6.6296  -2.7767  -5.2155   7.0629  -0.9354
-#&gt;   11.3710  -8.0150   1.5301  -9.4331  -3.4338   6.9458 -14.1129   5.0649
-#&gt;    8.8009  -5.1358   5.1320  -1.3880  21.7187   9.3955   4.8864   3.5573
-#&gt;    9.7325 -15.7635   5.4257  -4.1241  -9.7555   2.4939  -0.4845 -15.5254
-#&gt;   -4.2966  17.4133  -1.3929  14.4502  11.8713 -20.1299  12.7981  -5.0820
-#&gt;   -2.8510   3.0062   7.9507 -12.5768   7.2383   5.9030  -9.3479   8.6700
-#&gt;   14.3601 -16.2156  -9.9655   9.7940  -9.6406   3.5138  11.7669 -14.3414
-#&gt;    3.3443  -4.8443  10.3752   5.7092  11.7739  12.7506 -13.2347  10.3294
-#&gt;  -14.3205   1.1702  -4.0863   0.8304  -3.6081   4.3165  12.4251  -2.1380
-#&gt;  -16.9572  15.0581  -5.4490   5.5524  -4.1872   3.8895  -4.4594  10.0068
-#&gt;   -7.3660 -15.2241  -7.6776  -1.5092   5.7293  -9.8835 -18.7441   8.3754
-#&gt;   -3.1077  -4.3220 -16.5266   1.0002   4.5959   3.3605  -2.2249   0.0460
-#&gt;   -2.1842 -12.4134 -19.3114  -1.2431  14.2194   4.6354   2.3228   5.2780
-#&gt;   -2.6347   8.0248 -18.4699   3.0250  18.2152  -6.9530  -7.0196   0.7275
-#&gt;    8.5633 -15.3786   9.7878  -2.7362   3.2654   5.7009  -6.8312   9.4549
-#&gt;   29.1375 -10.1737   8.6765  -5.5657  -6.4003  10.9071  -8.9669  12.8262
-#&gt;   -8.2957   2.4049   1.7137  29.9000   0.2209  14.1602 -13.8773 -16.9040
-#&gt;    4.8131  -5.1538  -6.4099  11.4370  16.0962 -24.3848   4.7490  -3.8665
-#&gt;    3.3349  -7.1038   8.9801  20.5595  -0.1310  -1.6054  -1.8755   1.3486
-#&gt;    3.0458  -8.5429   8.7590  14.1337 -11.3169  22.5329  -2.0260 -14.0578
-#&gt;  -13.6518  20.9156   9.0751   4.2498  -7.2825 -11.3926   3.6335   0.0939
-#&gt;   -7.4630   7.7766   6.1473  -9.0679  -8.4699 -14.8582  12.8660  -0.5680
-#&gt; 
-#&gt; Columns 49 to 54   7.0604 -12.3663  -0.5226  -6.8936 -10.8041  -0.2461
-#&gt;   -2.6014  -8.4759   5.7232  -1.6245   3.9158  -4.3509
-#&gt;   13.3360  -7.6029   0.0345   5.1574   4.6278  -5.9920
-#&gt;   10.6612  -6.0840  -0.2245   1.9504  -4.5212  -5.8024
-#&gt;   -2.7472  -3.1279   6.1993  -2.7118  -9.8399  -3.1373
-#&gt;   -8.9446   1.9264 -11.5309  -5.9857   2.8370   1.1483
-#&gt;    6.3232  -3.0606  -0.1252  -1.0598   0.5540  -2.3036
-#&gt;    6.9565 -10.3953  -9.0305   4.0211   1.6107  -2.2318
-#&gt;  -10.1198  -1.8121  -1.8612  -0.9103   1.5333   0.1453
-#&gt;    9.9625  -7.8325  11.6999   1.0087   0.4057 -11.2952
-#&gt;   14.7862  -8.8966  -8.8422  -0.8916  -4.1096  -2.5748
-#&gt;    4.6805  -1.8486  -1.3795   7.1425   0.0207  -1.8725
-#&gt;   11.5180  -1.4500 -12.4168   0.9895  -2.0305   0.5197
-#&gt;  -19.2600 -13.6909   0.4791  -3.2383   4.8340   0.1858
-#&gt;   11.2362  -9.6844  -2.5856   3.1203  -3.8219  -2.0940
-#&gt;    1.8190   2.5725   7.9023  -3.3964  -5.7698   0.0544
-#&gt;    3.1076 -15.6543   5.1738   4.3797  -0.4473   0.7765
-#&gt;   -5.3943   0.8383   2.0102  -2.9610   7.1186   2.7515
-#&gt;   -0.7008  -0.1363   1.3645   5.7854  -8.8053   3.0049
-#&gt;   -0.7166  16.4983   3.5903  -1.9196   4.1170   5.5047
-#&gt;    0.1315  -0.6503  -5.1142  -5.3348   4.4149   1.4026
-#&gt;    0.2050  -8.2407   2.5395  -1.0077  -7.9856  -0.2476
-#&gt;   16.4718 -12.0561   3.9728   6.1531   5.1271   0.5295
-#&gt;  -11.9285  -7.5084   4.3974   9.2665   0.4728   0.7307
-#&gt;    8.9156  -3.7103   3.7676   1.5391  -1.9950  -6.7001
-#&gt;    0.8849  -8.8481   6.9022   0.4013   6.7156   1.5966
-#&gt;  -10.0360  -9.8862  21.2081   6.1466  -1.5443  -3.1707
-#&gt;    4.3326   1.5116   5.0717   2.6726  -3.9813   3.1357
-#&gt;   -5.0521  -3.7416  10.8626   7.0063 -11.6324  -2.4871
-#&gt;  -12.6678   5.7080  -0.8921  -5.6726  -2.9415   4.0978
-#&gt;   -1.8998  -9.9931 -13.5315  -2.1760   3.5571   3.7361
-#&gt;    2.5907   1.0858  -3.5744  -3.8948  -2.9187   2.5515
-#&gt;   -5.0308  17.8387   1.6618   1.6537   7.1909   4.4350
-#&gt; 
-#&gt; (7,.,.) = 
-#&gt;  Columns 1 to 8  -3.3248   2.4817  -5.8836 -10.2751  -2.4382  -2.9074  -3.4906  -1.0779
-#&gt;    0.6741   0.3772   0.8488  -0.8105  -0.3163  -7.4203  -2.5587  -3.6270
-#&gt;    6.6328   1.5998   6.8579   5.3515   4.4896   6.0720  -4.3402  20.4938
-#&gt;    0.2424   8.7026   3.1746  -1.0717  22.7176   1.2056   9.0388  -9.0423
-#&gt;   -0.9179   8.3895   4.8578  -3.1193   7.7504 -10.2406   7.3355  -1.8219
-#&gt;    2.4174  11.9795  -4.1046   6.8838  -3.3487 -10.1572   6.7633 -12.6019
-#&gt;    2.2541  -3.0341   6.3052  -8.5356   1.3544   5.2649  -2.7966   7.1300
-#&gt;    1.4672  -1.1532  -8.8590   6.9215   0.1295  -3.4548  -0.1219  -1.6862
-#&gt;   -0.3353  -1.5306  -0.1661  -1.0408 -12.8391   7.1590  -1.7939  21.0872
-#&gt;   -2.9293 -12.7689   5.3169  -1.3962  -4.6597  12.0093   5.9790   0.0856
-#&gt;    5.1974  -1.9752   8.2457  -1.1977   8.1092  -0.9547   0.5115   1.2933
-#&gt;    5.4066  -2.9115  -2.3137  12.3646   0.2206  -1.7345  -3.1952   8.1150
-#&gt;   -1.9160   2.0189   8.5495  -6.4555  12.2363   7.5438  -2.7970   5.9333
-#&gt;    0.2436   0.4232  -6.2445  -3.0283  -5.9772   4.2241   4.7554   1.2021
-#&gt;   -1.8063  -3.9256  -0.1361   8.1327   6.1233   2.3600  -5.0601   8.1541
-#&gt;    1.9766  -2.5985   9.7859   1.8768  -7.6177  -2.6617   7.0800  -5.3256
-#&gt;   -3.6428  -4.5808   0.1712   2.1232  -0.7481  -6.8851   4.0386   2.8114
-#&gt;   -1.0312  -4.2223  -1.1118  -0.0368 -12.4716  26.5331   5.2723  -3.3355
-#&gt;   -5.4607   8.7482  -1.6231  -1.5422  -3.3247  -2.8790 -13.0558  -7.4059
-#&gt;    4.6959   0.2964   4.1579  12.9135   7.3936   9.6438   0.7461  -1.2772
-#&gt;    4.1746  -1.9723   1.2772   3.7845   1.4201 -10.8224  10.3062  -5.7867
-#&gt;    6.9554  -6.9743  10.0568  10.8695   7.6095   3.6476  10.7561   6.1449
-#&gt;    8.5364  -1.4418  -3.4683   2.4936  15.2091  -1.6301   1.5145   4.1711
-#&gt;    5.0922   2.5292  -3.5444   9.9262   9.2920  -4.0266  10.1120   1.3076
-#&gt;    6.6187   2.3226   2.0332   1.4356   8.2641  -0.3188  -7.5887  11.8264
-#&gt;   -5.6842  -2.2210 -12.9193   2.2378   1.1706  -5.9336   3.5181  -0.2156
-#&gt;   -5.7310   0.1057  -5.6990   7.6357  -0.0794  -7.0096  -0.6174   6.0681
-#&gt;   -2.2574   2.1389   1.9388 -12.5128   2.3850  -5.7819   5.6038 -14.4425
-#&gt;    0.2119  -5.4000   0.1037  -1.7351  -5.1923   0.2799   2.9136   0.2913
-#&gt;    7.1292   1.1158   1.3726   8.4350  -6.4590  -0.4877   3.8310  -6.1884
-#&gt;    1.4067   6.2037  -8.8822  -0.3785  -8.5548  -8.5225  -4.3624  -2.0978
-#&gt;   -0.5408  -5.1605   4.0256  -2.7424   6.0295   2.1399  -8.0476  -7.7257
-#&gt;   -0.3146 -11.4148  -8.8329  10.2222   0.2849  21.9203  -6.1961  -1.3277
-#&gt; 
-#&gt; Columns 9 to 16   6.2796  -3.8979   6.7823   5.8974   1.7733  -0.4172  -1.3487  -3.7556
-#&gt;   -2.0428 -13.5110   5.4141   1.2338  -3.6599  13.0327  -0.8439 -12.8924
-#&gt;   -7.5565   4.3240 -10.5101   6.6608  -9.9193   7.4676  11.7745  -5.0774
-#&gt;   -8.0773  -6.3498  16.1852   8.4571   4.3552  -0.9393  -1.7985   4.3135
-#&gt;    1.8295 -18.2823  -0.6122  -3.6352  -2.5280   8.9567  -7.1343   4.3055
-#&gt;   17.3829 -12.8736  -1.5392 -10.0692  -3.6356   6.4004  -0.9817   4.0210
-#&gt;  -12.0572   9.7172  -2.9157   5.0965   1.4001   1.0645   3.8370  -7.2873
-#&gt;  -11.4245   2.3202  -3.7599  -5.3383  -6.0854   4.4083  11.2368  -4.5463
-#&gt;   -2.5193  16.8083  -6.5902   3.9740 -10.1788   7.4226   1.1710 -15.0063
-#&gt;  -15.4408   0.4141   3.5219  13.5428   2.8120  18.7524   0.5033   4.2042
-#&gt;   -1.3896  -4.5844  -4.0523   3.5960   3.5094   6.0490   5.7652  -2.0596
-#&gt;  -13.7614  20.1056   2.9430   6.1152  -6.6606  -6.7494  -9.6962  11.0388
-#&gt;   -0.1941   2.1928   7.3363   5.6780   4.9649 -10.6139  -2.0720   7.7383
-#&gt;   -9.4652  -3.9015   3.7036   3.7406  -0.6674   9.2443  10.0916  -0.2630
-#&gt;   -4.5360  -1.2954  -3.6108   7.2701 -13.6694   2.0035   0.4035  -0.3786
-#&gt;    4.4990   1.3470 -10.9959  -0.9675  -2.3193  -9.5329  15.7426  -9.3156
-#&gt;    6.6497  -8.0498  -7.0756 -13.3864  12.0961   9.8454   0.7985   8.2227
-#&gt;    5.0233   2.1976 -11.4795  -6.7238  -5.0116   8.3116  -1.5374  11.5673
-#&gt;   -2.0652 -23.2564  23.2151  -8.4361   6.7169 -17.3659   7.6327   1.0586
-#&gt;    1.4237   7.7798  -8.2511 -12.2505  -3.0598   0.8088   5.5957  -1.1046
-#&gt;    1.2324 -20.9002  -2.3342 -13.5759  -7.0779  -4.1098   8.5572   0.7346
-#&gt;    5.7063  -2.7921   1.6015  -4.1777  -3.8561  -0.6750   3.0211  -1.3393
-#&gt;   -1.0792  -7.2742  -4.7205  25.3029  -0.2891  -7.0533 -13.9249  -1.5288
-#&gt;   -7.9404  -7.1863   7.2877  -8.4942   8.6121  -3.9966   5.7318  -6.7021
-#&gt;   -7.7793   7.2377   2.8921   7.2314 -14.2062 -18.3991  -0.3790  -2.0332
-#&gt;    1.2827  -6.3264  12.2993   0.4631  -0.7200   4.3927  -1.8274   8.3615
-#&gt;   -6.5969  -6.3746  26.2187 -11.9883  -1.1077   0.3525  -7.9839  -5.4867
-#&gt;    1.5979 -16.9486   0.0058   1.6271  -1.9871   9.5204   2.7001  -6.1456
-#&gt;  -18.8674  15.9347  -8.1854  17.2909  -2.8524   6.8098   5.4276   4.3217
-#&gt;    8.5079   4.3248   4.2280 -20.8879  -1.6165   9.8705   2.7717 -11.6015
-#&gt;   19.2414   4.8321  -2.4099  -9.4010  -3.3887  -0.5499  -7.6193  -3.1140
-#&gt;    1.6795   4.1662  12.4592   4.0307  -0.2005  -4.4186  -5.0507   0.6601
-#&gt;  -12.3052   8.3305  -1.7184 -13.3377  13.1152   8.1130   9.7581  -7.7416
-#&gt; 
-#&gt; Columns 17 to 24  -3.2699   2.6249   2.0462  -7.6699  -1.7015 -16.3228   0.0562 -19.3075
-#&gt;   -1.7291  -1.6662 -17.1192   0.6693   7.5752   2.9815  -3.3067  -9.1143
-#&gt;    2.5111  -8.5699  -6.0001  19.3233   8.4588   7.6521  -1.7174  21.9405
-#&gt;  -10.8917  15.1707  -0.3122   9.3993   8.3662  14.5547  22.3711   5.5297
-#&gt;   -8.4872   0.9569   1.1113   1.1561   0.8639   8.3584  -1.7952   3.5359
-#&gt;    2.7947  14.2569  -1.4649  23.7249 -11.7622  11.1499  -1.5804  -2.9791
-#&gt;   14.3194  -4.1446   3.9175  -8.5501  -1.8492  -6.2369  -5.9697  -0.0608
-#&gt;   -0.8419 -14.2269  -2.5502  -2.2500 -12.5195   1.6914 -16.3398  13.8673
-#&gt;   10.8871  -3.4872  -1.7039   1.9547 -12.1608   2.4552  -4.1310  -0.0602
-#&gt;   -0.6112  -6.5130   2.5349   4.0264  -0.1101   7.9535   1.0875  18.6967
-#&gt;   -5.6084 -21.6910  14.6061 -13.6325  15.4865  -7.6574   8.7736   3.0390
-#&gt;    1.3909  -6.7269   7.5603  -2.8187  12.5269  10.9461 -14.1496  -4.0736
-#&gt;   -6.0101  -6.4017  -2.3547 -14.8420  16.5460   3.0710   4.1724   6.2011
-#&gt;    5.7721  -0.5868  -6.8293   1.5099  -5.9225 -10.6736  -7.3887  10.3437
-#&gt;   -5.0794  -2.6174   8.0498  -7.1813  16.5096   5.6253   5.3930   8.5105
-#&gt;    0.9877   2.1175   3.6347  -6.8569   0.8494  -5.8977  16.4877  -4.5800
-#&gt;   -3.0547  -6.0362  -1.1060  -6.2753  -4.3403  -6.7938   0.7282   3.7845
-#&gt;   10.8267  -0.4947   1.5760  -5.4020  -8.7880   5.4126  -3.7768   3.8335
-#&gt;   -4.4493  -6.1158  -1.6434  -7.0184  -0.9918 -20.0914   1.4096 -10.1040
-#&gt;   10.6199  -9.1828   5.5857  -0.4304 -10.2106   3.6463   6.0428   6.3926
-#&gt;   -4.7965   2.2072   0.9320   5.7174   6.3965  -9.5274   4.1025  -9.8490
-#&gt;    1.5350 -14.3588   9.0187  -6.0929   4.4356   8.8360   9.2793  11.5666
-#&gt;    4.7380   4.2363   4.5677 -18.9179  10.1341   8.4035  14.4339  -0.5983
-#&gt;    7.5650   6.6210   4.7237  -4.3575   0.3310   5.9229  17.2370  -0.6861
-#&gt;   10.9183 -11.4886   6.3929   9.6597   5.5371   1.0323  -1.0683  -3.0937
-#&gt;    5.6690   4.6933   2.9593  -2.4036  -9.1496  -1.9458   7.2576  -0.8581
-#&gt;    1.1716   5.2141 -15.1759   2.9869   9.2018  -2.4565   0.9985  -9.8015
-#&gt;   13.3044 -11.6907  -6.7319  -1.1465  -8.9318 -10.6592 -11.2060   1.6812
-#&gt;   -7.3380  -1.6048  -3.5023  -1.8812   8.0469  -7.3219   0.3288  -0.9382
-#&gt;   13.8334  -7.2696   5.7064   0.5290  -8.0053  -3.1905  -2.3381   4.7869
-#&gt;   -9.7596   2.9291   0.9557  -3.8178  -5.5092  -0.7109 -10.4581  -6.5202
-#&gt;   -2.8069  -0.7047  -4.7937  -9.1351   4.4530   2.2077  13.3472  -2.7550
-#&gt;    3.9466  -4.2792  -2.1776 -15.6933 -18.5317  -3.3090   1.4984   8.0754
-#&gt; 
-#&gt; Columns 25 to 32   4.4840 -13.0888   8.5711  -9.7300   3.8015  -8.2237   8.6201  -7.3003
-#&gt;    6.5900  12.3176   9.0123 -14.5995  15.9172  -6.8306   3.5181  -2.5099
-#&gt;   18.4091   3.6605  -4.0795   2.9074  13.6253  -7.5262  19.0508  18.7751
-#&gt;    3.8240  -3.6905  -6.0818   4.0993  -2.7649   7.5930  -4.5105  -6.4166
-#&gt;    0.4316  -1.9274  -8.4682   1.9504  -7.0814   1.7261  13.6480  11.2571
-#&gt;   11.5199 -12.9580   6.3912   3.0446   4.7877   4.4198   6.4831   3.2611
-#&gt;   -2.5982 -10.3750  -9.8170  -6.8726   1.3715  -3.8160   0.3180  -3.6297
-#&gt;  -14.9105 -18.4475   3.9712  -3.6647  -7.7598   8.4168  -0.7855   0.2818
-#&gt;   -6.3061   6.6833  -3.6013 -10.8893   9.7435  -4.6612   1.0187   3.8400
-#&gt;   14.3347  -3.3765 -15.5776   4.7295  12.0156  -5.0148  22.0035 -14.2353
-#&gt;    1.2952  -3.1072   4.2476  -2.8018 -12.6230  -1.4437  13.9418  -6.9662
-#&gt;   -8.7029   4.2593  -4.0309   7.6206  -4.7034  11.4133   3.0641 -14.0801
-#&gt;   -4.4237  18.3676 -15.8120  -0.5417 -14.7940   5.6294 -10.4246  -5.0745
-#&gt;   12.9649   0.2970 -10.0714  -1.7145   4.1559  -4.1365 -22.3864   0.9568
-#&gt;    2.9719   4.3021 -13.9943   2.0198   5.5296   5.6144  16.9061   5.9810
-#&gt;  -13.1698  -1.3431  -1.0867 -11.0371 -18.4299 -11.3619 -10.0345  -3.0149
-#&gt;   -2.6937 -14.5829  18.5762   3.2974 -19.5758  -4.9856   5.2770 -17.4897
-#&gt;    5.3877  -1.5563  -6.3256  -8.5460   1.6645  -8.4808  -3.5336  -1.9241
-#&gt;  -11.2611  -9.6050  -7.9232   6.8785  -5.2474  -9.3079  -7.0895  -6.7728
-#&gt;  -21.6035   0.1725  -1.5698   1.5195  -1.2575  -6.6937  -9.4594   0.7861
-#&gt;   17.7225 -13.7377  -0.5056  -4.3554   8.6485   3.6338  12.7679  -5.1801
-#&gt;  -13.1987   2.3589   2.7389   8.7827 -14.5646  -2.2190   6.0027  -4.5147
-#&gt;  -18.8600   3.7588  16.7576   0.1047   3.3141   5.5343  10.1905 -13.2553
-#&gt;  -16.6215  -8.6144   7.3575   6.6289 -11.3795   9.5852  -7.2163  -8.5739
-#&gt;    4.6352   7.3419  -3.2505   2.3928   2.1961  -4.4246  -0.0123  -0.2957
-#&gt;  -12.7679  -1.9870   1.0357   5.7957 -10.0690   2.6528   4.1737  -7.0356
-#&gt;    7.0145  10.3468   1.7367  -4.6643   7.4847   5.2197  -4.6776   3.6985
-#&gt;   13.3335  -3.9153  -7.0064   3.4329   0.6104   4.5883  -1.7054  -5.2740
-#&gt;   -1.0016   8.3642 -10.8177   3.6652  -8.4200  16.2961 -11.7937   2.7594
-#&gt;   -2.6048   8.9218   1.1763   8.1646  16.4586  -0.3546 -10.5154  22.5108
-#&gt;   -5.9356  -2.6336  14.8012  12.8165  -4.2616  12.9473   8.9774  -7.6498
-#&gt;   -7.8090  -0.5478   0.9706  -2.8553   3.6330 -13.4676   1.4434   3.2652
-#&gt;    2.0089  10.4774 -12.2131   7.4535  16.8252 -10.5730 -17.7032   8.8748
-#&gt; 
-#&gt; Columns 33 to 40   2.7448  -6.5698   2.2107  -8.0348  11.1315  -2.9297 -18.5890   2.4805
-#&gt;   11.6054  -1.7582   2.9016  -5.4564   5.2269  -4.3980   1.3241  -3.9149
-#&gt;    7.2655   9.6418   6.6235  13.9766  11.7431   5.0866  15.4316  10.8956
-#&gt;   19.4075   2.0404   1.5440  -4.2929  -4.8512  11.3078  -0.6978   2.3330
-#&gt;   13.8757  -7.1019  19.6982  -9.1706  32.6911  19.2408   5.6990   7.8961
-#&gt;   -7.6885 -10.9749   5.8043  -5.3356  11.6102  -2.4343  -0.7013   8.0407
-#&gt;  -10.8828  -0.4218   4.8956   2.0259  -3.4672  -6.5780 -14.8963  -1.5209
-#&gt;    3.5376   2.4990   0.8338  12.5955   6.0659   2.9109  17.6276   2.0713
-#&gt;   -9.7914  -2.2717 -12.5832   7.3850   0.3479   4.7274  -5.5711   5.7904
-#&gt;   19.7647  -9.8320  17.0081   3.7720   5.7112   8.2893   4.4943 -23.8148
-#&gt;    1.3287  21.7345  -9.3590  16.0078 -19.4302  -9.8252   4.2135   8.4878
-#&gt;    2.2967   0.3079 -19.9102  12.8718  -9.1642   5.1655   5.0684  16.9821
-#&gt;   22.4333  10.2583 -13.3461  -5.6963 -17.9623   8.4045   9.0629  -5.4719
-#&gt;   -4.5671  -6.2131  -7.6639  -9.0325   5.2024  -9.3594  -8.7210 -12.3095
-#&gt;   20.9062  -0.9230  -7.1702   0.2086  -7.9969   2.8683   2.0706   1.9410
-#&gt;   -9.7770   4.9727  -1.9263 -23.1064  -6.8906   0.0961  13.3180  -3.0139
-#&gt;  -10.6876 -10.5392  18.5871  -0.5603  -1.5558  -1.9573  -0.8930   6.9356
-#&gt;    6.6284  12.8952  -3.0792  -5.3177  -0.3978   6.6976  -4.5672   4.2146
-#&gt;   -8.4513   5.4829  -6.0572 -26.5950  -8.7982  -9.6006 -12.8505   4.7843
-#&gt;   -1.4631  19.0366  -8.2251   2.2435 -10.9802  -4.5644 -10.8094  13.1314
-#&gt;    4.5922  10.3286   0.0092   3.7143  16.5110  18.6228   7.6342   8.3945
-#&gt;   14.2098  -4.8536   3.4394   3.1155   8.0134  11.9984  13.2984  -6.8541
-#&gt;   -6.0745   2.5484  -2.2949   1.0228  17.5016  -8.3310   4.6793   5.4480
-#&gt;   -2.8074  -3.5256 -10.2963   0.8372 -10.5791   1.3980  -2.5388  10.5860
-#&gt;   -9.5019  27.2387  -6.2512   2.6855   1.2857   2.8775  10.8098   0.4801
-#&gt;    1.5698  -4.2545  -7.8157  -2.5048  -4.1617  -0.8027   4.9608  -0.7514
-#&gt;   11.6930  -1.9166   1.5741   2.4195  -6.1576   1.2383  -3.8020   0.2751
-#&gt;  -15.3269  -3.8273   1.4994  13.4972   6.0693  13.4974  -7.4485  -6.3433
-#&gt;    9.7782   5.4965   6.7106   7.0247 -20.0425  11.0496  -6.1337   3.6130
-#&gt;    0.1228 -21.1744  -5.3426  12.3208  -7.5330  -4.8275  -7.4898  -4.7354
-#&gt;   -4.4498  -0.4257 -17.1606  -4.7013  -0.9034  -2.9276   5.7993  -2.9146
-#&gt;   -4.7729   3.3291  -6.4782 -17.4497   1.6472  -9.7971   0.6640  -7.3147
-#&gt;   -5.3067   2.1648  -8.6127  13.5061 -17.1456 -16.8773  -6.7784 -10.5913
-#&gt; 
-#&gt; Columns 41 to 48  -6.4859 -12.8556  -8.2343   7.3396   1.3524 -15.8957   4.7175 -24.1313
-#&gt;   -2.9631  -5.6619 -12.3883   0.3856   8.7190  -0.7716   1.3164  -9.2367
-#&gt;   20.8606  12.3054  -3.1525   1.5176  -2.4657  11.0004  -2.8057   1.4363
-#&gt;   -9.5120   3.6917  10.5489  -5.2715  -4.7576  17.3026  -2.0666 -11.2814
-#&gt;    0.5791   0.4635  -9.2662   3.3490   1.5569  -4.3128   2.1297  -6.2249
-#&gt;   -8.7455   7.5960  -7.7777   6.0059  13.5665   4.2219   0.9894  -5.4343
-#&gt;    4.5408 -16.5838   6.2956  -9.4008 -10.6636   1.4536  -0.2344   1.3090
-#&gt;  -10.2224 -12.7407   1.4192 -17.3845   5.7662   5.6823 -10.2792  -5.9355
-#&gt;   -5.5347   2.2507  -0.0774   1.4039   4.3226  -5.2607  -2.1064   1.7886
-#&gt;   13.4875  -6.1741  19.6522  15.3213  -4.6700  -3.4189  11.5752  -4.7768
-#&gt;   -7.1482 -17.6998   4.9129  -0.4092 -11.1532  -6.8143 -16.8973  -8.0337
-#&gt;  -10.9271  -1.3525   3.1397  -6.3334 -17.8930  12.6149  -4.4812  15.5305
-#&gt;  -17.6932  -9.1321  12.5453  -6.1991 -10.6697  -6.5734  -7.9668 -12.1605
-#&gt;    2.9171  -3.4602   3.5744  -8.0134  -3.5252  -2.5591  -6.0676  -1.9463
-#&gt;   -9.3306  15.0952  -0.7581  -2.9558  -9.6076 -10.3469  11.4266 -14.2205
-#&gt;    1.8668   3.2753 -12.0584  -8.7809  11.8024  -8.9362   7.3010  -7.4704
-#&gt;   -9.0343  -2.0468   2.5671  10.6097  -6.9096  11.2965   1.1858  11.4443
-#&gt;   -8.3199 -11.3897  -8.6271   8.9525  -5.3198 -13.9905  -3.6821  -6.1750
-#&gt;   12.0289  -6.1635   5.5488 -13.5697  -6.4517  -0.3587   1.3628  -2.7413
-#&gt;  -17.2091  -5.6923  -4.8504   3.7806  -7.2495   4.5916 -11.9105   0.0139
-#&gt;   -6.7073  -5.6931   0.2037   4.7052   2.9357  -2.6843  -2.1262  -8.6269
-#&gt;   12.2844   4.2822   8.0277  -5.3412  -1.9398 -19.0604   5.2809  -4.1721
-#&gt;   -6.8332   4.4561   4.6166  -2.5745   0.1558 -16.0018   1.0518  -1.4797
-#&gt;   -7.5971  -4.7100  20.5918 -20.2603  -1.7351  -3.6051  12.1930  -8.0277
-#&gt;   15.7728 -16.6209  -2.5946  -0.8703  -0.9030  12.5972  -9.5671   5.8994
-#&gt;    0.5949  -0.5895  18.0075   5.8372 -12.0265  -1.0628  13.8607  -0.6650
-#&gt;    3.9531   6.6170   0.9229   2.6102   4.0805  -7.7084   4.7866  -5.4621
-#&gt;    5.9965 -11.6592   0.1691   3.9341  -2.5681  -1.3690   8.8693  -1.2484
-#&gt;   -3.4041  -0.5177 -11.4659  -8.2807   4.9572   4.6057  -2.7430   3.3594
-#&gt;  -11.1110   5.1209   0.9075 -14.0492  13.8778 -10.0091  -1.4123  -8.6733
-#&gt;   -8.8167  -1.8789   2.6629  -2.8927   1.3574  -3.8857   0.2279  10.6436
-#&gt;    1.8897 -16.9212   0.5364   7.2175  12.3675  -0.1585  -8.5489  -4.7305
-#&gt;  -15.3692   7.6241  25.1584   1.7370  -2.8614   6.5374  -8.8300  -3.7905
-#&gt; 
-#&gt; Columns 49 to 54   9.7412  -4.7033   2.1253  13.9574  -8.2095   0.2648
-#&gt;   10.7819   0.4497   8.3670  -3.5690   4.6679  -1.7777
-#&gt;    5.3281  -9.7906  -2.2448  -4.0005   5.5152  -0.2714
-#&gt;    5.2704  17.2318   0.0453  -9.4181  -0.6278  -0.8195
-#&gt;    1.9360 -11.2269   5.3541   0.7119  -6.1596   2.7401
-#&gt;    9.4323 -20.7417   7.8966   4.0778  -0.0750  -0.4017
-#&gt;    2.7970  12.8043 -12.1614  -2.0960  -4.2098  -1.1390
-#&gt;   -8.8252 -10.5031  -3.4642   1.8819   2.1273   3.1027
-#&gt;    3.7916   0.0906   3.9408  -0.6601   4.7451  -2.8109
-#&gt;  -11.4899  23.1233  -2.0594  -0.2774  -2.4963  -0.0932
-#&gt;   -6.8828 -10.3972  11.8882   4.3727  -6.9167   3.7834
-#&gt;  -11.8583  -0.8508   5.8367  -8.4136  -2.7583   3.7790
-#&gt;   -1.5500  10.7037   3.3483 -11.3571   1.4331   2.6669
-#&gt;    0.3870  11.6860  -2.4483  -5.7054   4.4647   2.7550
-#&gt;   -4.6169   0.0701  11.7378  -4.9458  -2.6528   1.8438
-#&gt;    6.3040  10.1576   1.0204  -3.2159  -7.2186  -0.0034
-#&gt;   -8.5095 -16.9835  10.0629   1.6555  -6.0150   2.2001
-#&gt;   15.0897   6.9875   1.7919  -7.9321   7.8906   0.1551
-#&gt;   -0.0331   0.1360  11.5330  -4.6317  -8.1376   7.2461
-#&gt;  -10.8348   2.4773   2.5264   1.4305   0.9814  -3.7109
-#&gt;  -13.5211  -8.0313  10.1245  -3.3612   0.5363   4.4643
-#&gt;    1.7721   6.4298   0.7854  -5.0664  -5.5873   1.1033
-#&gt;   14.9193 -14.5945  11.7856   1.7492 -11.9945  -3.1030
-#&gt;    7.9484   8.1269   7.7267  -2.7987   1.7264  -4.0858
-#&gt;   -4.6936  -3.9849   3.7247  -3.0523  -9.5926  -2.8702
-#&gt;    3.4013  -0.9906   2.6591  -6.5120  -1.1841   0.9572
-#&gt;    6.9358  -0.7580   8.0148 -10.3013   6.3752  -8.1062
-#&gt;   -1.4095  -5.2164  -8.2729   2.1605   0.1339  -1.9743
-#&gt;   -7.5405  -3.3800   0.9432  -5.6965   4.9030   0.8907
-#&gt;    9.0935   4.4262 -14.8614   8.0662   8.5447  -8.2317
-#&gt;   -0.0674   0.2606  -7.4586   7.1030   0.7133   2.6842
-#&gt;    9.1829  -5.1901   6.2216   2.0078   0.8703   0.2288
-#&gt;   -1.9942  13.9877  -4.9041  -8.4396   8.0163   3.6284
-#&gt; 
-#&gt; (8,.,.) = 
-#&gt;  Columns 1 to 8  -2.5193   2.0742  -3.7081  -1.9921  -6.2333  -3.0474   2.6398   5.0528
-#&gt;   -4.6642   2.8744   7.1202   4.0893   4.4526  16.9637   1.0590  -1.6731
-#&gt;    1.6832   3.4108  -6.4444  -7.2372   0.9911  -3.1259   4.4429   4.5822
-#&gt;   -1.5461  -9.2288   3.8914  12.6471  -8.3693  -2.9074  -7.1498  -1.3730
-#&gt;   -0.8998  -2.4569   4.4137  12.6417   5.5160  14.2270  -0.1057  17.0182
-#&gt;    4.7249   8.1304  -1.8147   1.1212  -6.9818   3.6627  -3.7037  18.7453
-#&gt;    1.7128   8.5744   4.9717 -10.4654 -11.6219  -5.6615  11.6851   0.5319
-#&gt;   -1.0221  -4.9959   2.5011  12.1780  23.9450   1.4176  14.1033   5.2451
-#&gt;   -3.4797  -0.4410   1.1407  -0.8786  -7.1093  -3.7629 -10.4710 -11.0519
-#&gt;   -3.4061  -5.9145  -1.9550  15.8363  12.5324  -4.7849 -17.4260  11.0253
-#&gt;   -1.0038  10.0643  -0.0327  -2.4089  25.5182   5.2832  11.4999   7.8814
-#&gt;   -2.3409   4.3861  -5.2236  -7.6659   3.7767  -3.0251 -12.0483  -1.2759
-#&gt;   -6.2174  -1.0805  -7.0878   8.1238  21.5761   5.7706  -9.8967  -7.9048
-#&gt;    2.3738   4.2506  -1.5686 -13.3733  -2.4392  -4.4171  -2.3013  -8.1733
-#&gt;   -0.8407   1.1356   0.8859  17.5424  14.2075  -7.4244  -5.5142   4.0945
-#&gt;    5.0010 -16.5608  -5.5332  -9.1448   6.7308  -6.9976   7.9401  -5.2133
-#&gt;    6.9894   4.5544  -4.6856 -11.6005  -0.1531   4.5906   5.4727   6.4359
-#&gt;   -3.5102   5.2286  -2.4596 -11.9002 -15.7956   6.8671 -11.8295  -4.7360
-#&gt;    5.0326 -10.9050   0.2507  -5.0598   6.9716  -5.5180  13.7045 -10.5254
-#&gt;    5.1134  -8.1674   6.3303 -12.7284  -5.0472   8.6418   1.7839  -1.9928
-#&gt;    1.1242   3.4845  13.0800   4.5502   8.8635   4.8516  -3.9514   3.0905
-#&gt;   -0.0068   4.6616  -4.7090   4.3378  11.4875 -20.5933  -7.1657   7.0286
-#&gt;    0.7661   2.4756  -5.0572  -4.4167   6.8676   2.7290  10.4479 -18.1952
-#&gt;   13.3813  -5.9982  -7.1473  -0.8059  -2.2736  -6.8136  -4.0109 -21.4300
-#&gt;   -1.7219  10.7669   9.0112   3.2865  15.6637   2.5642  11.3249  -3.7144
-#&gt;   -0.4806 -10.0418  -8.8418  -2.7037   0.8515   2.3430 -10.1040  -4.0096
-#&gt;   -3.2653   2.3049   9.2431  10.0881  -2.5151   9.1572  -6.2206   1.3909
-#&gt;    8.8680  -5.0265   3.3244   1.1374  -3.6810  -9.2164   5.8279   6.0719
-#&gt;   -2.9681  -0.3926 -17.2432  -4.7971  -1.4827  -0.5445   8.7422  -4.4027
-#&gt;    2.4027   1.1250   4.6400  -1.0687   8.5955  -9.2736   3.8656   9.5360
-#&gt;    2.7482   8.7508  13.1990   5.6817   8.8133   4.2370  -5.0173   8.3154
-#&gt;  -10.1275  -9.1509   1.3828   4.6060   2.6574  -2.5437  -3.9383   0.4901
-#&gt;   -5.0119  -2.1409   4.6772 -10.9940   6.2159  -1.0546  -7.8472 -10.6390
-#&gt; 
-#&gt; Columns 9 to 16  -3.9939 -10.1231  -1.7760  -8.2353  -4.4813  -9.6758  -2.5104  12.5144
-#&gt;    9.8652  -3.6447 -10.1556  -3.4383  10.6498  -1.6296  -2.2005   3.5460
-#&gt;   19.2141  -6.3664   4.3963   1.7060  12.0528   3.7848  -4.5901   7.6934
-#&gt;   -4.0919  13.0456   5.1246 -13.8922  -1.0435  -1.8617  -1.2937 -11.8237
-#&gt;   -6.6306  11.9140  -8.4073  -1.6125 -11.4719  -1.2195   9.0346 -10.0093
-#&gt;   -1.0861  -8.0166  -6.3122   6.3034  -8.3774  10.8648  15.2445 -10.9617
-#&gt;    4.6972  -1.7473   0.4801  -2.9267   2.5578   0.6059  -2.9180   1.8847
-#&gt;    4.6901   4.7859  -0.3032  -6.5932   1.1439   8.3620   0.4234  -0.7066
-#&gt;    5.9010 -23.7958  -4.2506  -8.7121   6.8013 -14.3295  -0.0627  -0.5038
-#&gt;   -1.7831   6.0950  14.7406   9.4905   9.0613  -5.8375  16.8017 -15.2032
-#&gt;    2.0836 -12.2233  14.9367 -12.8193   5.0824  -2.7596  10.3489  11.8278
-#&gt;    5.5511  -2.0500  12.6091   6.4044  -1.0069 -15.4218   8.1831  -6.2691
-#&gt;    2.6557  15.6789   3.6620 -11.4620 -10.0354   5.1807  10.7045   2.8125
-#&gt;  -11.8965  14.1707  -5.1728   0.8851  -7.7714  10.6989   0.8355  -2.6908
-#&gt;    3.6545  -3.6128  14.6525 -17.2103   4.9038   8.6556   6.9777   1.9191
-#&gt;    3.5981   1.4622   0.0435 -28.8899 -14.0851  -5.2308 -11.4120 -17.0375
-#&gt;  -16.8008 -12.9334  -3.1367   3.0786  -3.2857  -8.6857   7.8341  -6.7888
-#&gt;    2.1152   3.7797   1.4790  -3.6138   1.0385  -1.2166  -8.4552  -9.8133
-#&gt;   -8.0721  11.8458  -4.9682  -9.4686 -12.2920   3.8444  -5.4699   2.3400
-#&gt;   10.4745  -3.3726   7.5779 -13.0954   2.0010  -3.7568  -3.4697  -0.3330
-#&gt;   13.4053   3.7033   1.7217  -1.2383  -1.6357 -12.2637   0.6980  -3.8945
-#&gt;   -1.3205   7.9204  -2.0118  -3.9371   0.4916   9.4818   1.6657 -11.5132
-#&gt;    3.0416   2.2088   9.9163   2.0323  10.9761   6.9918  -6.8843   2.0976
-#&gt;   -2.1986  -1.1039  -2.5731   0.0256   1.3178   7.7661   7.9824 -12.0968
-#&gt;    4.6729   5.1111  13.0433 -12.6273  11.3182   4.8499  -5.0008  16.4894
-#&gt;   -9.6652  15.3778  12.5188  -4.2726  10.1390  -6.4001  -5.5764 -15.9101
-#&gt;    1.5931   3.2260   2.2857  12.5908  12.0530  -5.9100  -2.1154  -2.0768
-#&gt;    4.3152  -1.7097   8.8216  -0.8328   4.3849  -0.1489  -2.0748   3.5123
-#&gt;    0.5976   2.1782  15.4537  -6.0885  14.6092 -12.6133   2.2310  -4.2046
-#&gt;   15.8351   0.4301 -13.7024   0.7211   7.8922   0.5023   7.9240  -2.9977
-#&gt;  -17.1230  -7.1100 -12.6164  -5.3802   7.2192   9.8078   4.8259  -1.5951
-#&gt;   12.2493  -1.6791 -10.4620  -6.4817  -4.0155  -3.4648 -11.1977  -8.0670
-#&gt;    8.6633  -6.6052 -13.9898  -6.5534  -1.2267 -14.5473  -0.4302  13.3984
-#&gt; 
-#&gt; Columns 17 to 24  -5.6202  14.8153  -6.9213   0.2904  -1.5795   7.4237 -10.3013 -10.9729
-#&gt;  -11.1150   5.0450 -24.3616  12.3308  15.8825  10.8293   9.8308  -5.3949
-#&gt;    1.2365  -0.4655  15.6610   3.4104  -5.4976 -11.4440   4.1550  -1.1699
-#&gt;    3.5161  18.4776  14.6736   2.8891   3.2794  -3.7633  -8.1552  11.3333
-#&gt;   20.9661   7.2968  -7.5412  -5.6893   7.8234  -2.8752  -3.8425   2.9802
-#&gt;    2.8222  11.3314 -14.1172  -9.8654  -4.5503  16.5374   5.4962  -0.5073
-#&gt;   -3.6793   1.9594  17.8935 -24.0708  -4.7980   0.7308   4.6970   5.0104
-#&gt;  -12.7489   5.4472  -8.2672  -9.0991  15.3294   6.4179  19.4601  -3.1991
-#&gt;    0.9249  24.2136   2.5948 -10.6380   3.7737  -6.7576  -1.9976  -5.3479
-#&gt;    8.9262  -1.5332  12.4095  15.7161  -4.6269 -16.1444  -7.3548 -13.6074
-#&gt;   -0.2277   5.1871  -5.3498   1.1324   5.0683  17.6698   9.6113  -9.8043
-#&gt;    8.9290  -8.1930   0.1636   7.4272   0.6158   7.3963   0.9435  -8.4701
-#&gt;   -5.2607 -25.0452  10.9097  11.6916  15.1256   7.8027   4.7674  -1.5646
-#&gt;    0.4591  -7.1015   3.0161   4.3905 -10.3063  -3.2351  -1.0819  -5.9332
-#&gt;    5.4616   4.3969   9.2781  18.0379  -2.2336 -27.3042   5.6611  12.3312
-#&gt;   24.6435   2.9490 -12.2307  -8.4190  -6.5932  -1.1561  -1.4300  11.0922
-#&gt;   -0.7842 -14.4210   9.0596  -2.0410   1.1028  18.3922  -0.5173 -20.2342
-#&gt;   -5.4909  -8.5370   8.7973   7.2331   9.8952  12.1991   1.5158  -6.6579
-#&gt;  -18.0107   4.1794   5.1994  -0.4510 -23.7803  15.2960  12.6819  -1.7537
-#&gt;    5.1664  12.6723   1.4728   3.6295   0.5660   7.9974   5.2269   0.9230
-#&gt;   -7.2167  14.6838   3.3760  24.5103 -10.7561  17.7518 -15.4175 -12.0886
-#&gt;    6.7945 -18.4892  17.7768   7.4137   3.7278  14.1091  13.6089 -11.2851
-#&gt;    8.3737   9.2821 -13.9302  -8.4823   1.6804   2.0114  -1.9007   5.4060
-#&gt;   -6.9811  -3.2592   1.1388   2.8326   9.1765   9.3046   1.3775   2.2703
-#&gt;  -10.0412  -0.4415  -2.6176  -9.3117   0.9906  12.9362  22.3034  -1.0037
-#&gt;    0.2937  -7.7650   0.1338  -0.5640   3.2965 -12.8018   1.9346   1.0695
-#&gt;   -7.2628   1.9120   4.5161  -7.6110   7.9618  -8.5009  -1.1358   0.6147
-#&gt;   -1.9677  -9.3075   3.5389  -0.5726 -10.3630   0.3208   0.0221 -18.5801
-#&gt;   -4.4291   1.5478   0.4308 -13.4074   2.5952   3.4190  -7.5277 -12.4009
-#&gt;    1.4239  14.2755  -5.9780  -6.2143   9.7713   4.3076   5.3872   9.2880
-#&gt;   12.9149  -4.4352 -18.1719   1.5704   6.6809   7.1756  11.4798  16.1524
-#&gt;    0.2872  20.2832  -1.1479  -4.0496 -10.2029   4.9701  -8.3954 -19.1564
-#&gt;   -4.9315  -9.5369  20.7877  22.7228   2.0153 -10.3838   4.0319   3.5024
-#&gt; 
-#&gt; Columns 25 to 32   4.7791   9.7077   9.2054  -2.7164   5.6398  -1.5409   2.8834 -13.8638
-#&gt;  -14.8345   1.3875  -2.6569  -1.8193   3.8540 -15.3751   7.0353  -6.6969
-#&gt;  -15.8817  14.7059   6.6842  -5.2638   5.1058 -16.6650  19.5683 -11.4908
-#&gt;    1.0607  -6.6884  -7.8010   7.1819   7.2983  -3.1962   0.7173  -3.8589
-#&gt;   11.4602  -4.1368   5.6405  16.0743   4.6759   6.5215 -17.4408  -1.8663
-#&gt;  -21.2316  -0.2846   7.8149   0.0095  15.5707  -3.7569  14.6790  -7.2756
-#&gt;    0.1669  -5.8723   6.1753  -0.3766   6.7111  -3.6686 -14.3516  -8.1315
-#&gt;   -9.0732   5.3072  -8.3696  -4.0122 -13.1107   7.6841  12.4141  -9.3331
-#&gt;    0.9592  -3.1495 -10.8994   8.5236   1.8557  -4.5910  -6.1125   1.2540
-#&gt;    1.1048   1.8194  -7.1857  -5.4076 -17.1184  -0.7875  -0.6749   9.4790
-#&gt;    5.0604  -1.6987  -5.1211  -2.9309 -10.4524  23.4438   5.2112  -2.1768
-#&gt;    2.5733  -4.4505  -1.4908   4.6839 -16.1708  12.2185  -0.1755 -12.4080
-#&gt;    4.2385   1.1800  -2.3862  -7.4027  -2.9908   1.0700  17.5984  -7.6354
-#&gt;   -7.8440  -7.5879   3.6717  -0.2627   9.6219   0.2351 -17.4053   3.8882
-#&gt;   11.9069  10.1671  -8.6410  -7.2133  19.4292  10.3369  21.7181 -11.3119
-#&gt;   10.5030 -12.5481  27.7833   8.1734   7.5850  -6.0950 -12.1099   4.9553
-#&gt;    7.4591   9.6849  -7.1136  -4.0662  -0.8277  -1.0526   4.3297 -10.5124
-#&gt;  -13.7402  -0.3343   6.3075   5.9719   6.8418 -11.0248   0.5371   0.1221
-#&gt;   18.4076   1.2548 -23.9158  -5.6746  24.5436  -7.6671   5.6770  -3.2191
-#&gt;    3.9931  -5.7273 -25.3302   8.4547   8.7571  -1.4628  -3.1169   5.9926
-#&gt;    3.7220  -9.1228   1.3672  -2.3291  15.0565  14.6801  -3.6912 -14.2512
-#&gt;    7.4535   4.8481 -10.2407   0.1237  -8.8577   4.2619  -0.6230  -2.0698
-#&gt;    2.5938  -7.0308  14.5178 -19.5931   4.0759  -7.4966  -3.7671 -12.8844
-#&gt;    8.0506  -2.7224  -3.4744  -6.2983  14.5593  -5.9857  -9.3803   5.1594
-#&gt;   -0.8315  -2.5488 -11.4162   2.2635   0.8348   0.5231   3.7723   7.4795
-#&gt;    3.2450  -2.0317  -4.6894   1.9585   9.5709  -4.4705  -1.0216   2.1743
-#&gt;    6.3712  12.0639 -14.0917  -4.7382  14.4409  -0.9921  -0.3463  -6.7818
-#&gt;    1.7531  20.6774   9.9255  -8.3220  -3.7141 -14.8564  -5.8050  -1.8621
-#&gt;    9.6614  -5.2416   3.1039   8.7113  -1.1126  -3.9662  -1.0181   9.5873
-#&gt;   -0.7345  -3.0745  -5.9742   4.4747  -1.3239  -6.7344  -7.1989   3.5918
-#&gt;  -12.5575  -4.2413   3.2333 -11.1054  -2.3005   4.4785  -3.1820   0.5434
-#&gt;    3.0887  -3.2298   9.3098  -6.7163  -3.8078   4.9373  15.1195  -5.8149
-#&gt;    3.7932   4.9239  -2.5954   1.0078 -12.2426  -9.3398  14.8513   9.7156
-#&gt; 
-#&gt; Columns 33 to 40  -7.8233  -9.7842 -10.4799  -3.5506 -17.3716  -7.1844  -1.4159   7.7997
-#&gt;    9.7794   2.0835   6.1126   7.2902   4.8744   0.4896 -14.8422   4.3866
-#&gt;    6.7955   1.5658   0.4971  -6.4392   1.4921  17.9961  -1.4227   3.0743
-#&gt;    6.6056  14.7367   5.5244  -9.3334  -6.4033  17.8978   0.3428 -21.8344
-#&gt;    1.1338   4.8969  15.5306  14.2781  16.2390  -7.6773  16.0759  -4.1957
-#&gt;   -5.2089 -11.3833   8.7755  16.9890  -2.7173   4.2470  -8.7495  14.2428
-#&gt;    6.4049  10.3754  -1.6804 -13.8808 -12.0450   0.8421  -1.4850   8.8613
-#&gt;    2.2942  -1.1848  -1.2863  -3.9356   9.6689  19.4051 -20.8587  -9.1414
-#&gt;    0.1992  -0.0530   8.1335  -2.8002  -3.5920 -12.4475 -11.5015   9.2813
-#&gt;   -2.8260   1.9593  24.0351  -6.5243  -6.4060  -1.5575  16.6019   2.6187
-#&gt;    0.7293   2.9068  -6.5737  -5.9843  13.5482  -5.3027   8.1619  -1.5142
-#&gt;   -4.4483 -15.5399   0.3453 -19.5931  -9.8596 -11.2519  -3.1922  -4.4109
-#&gt;    3.1559  -1.3568   0.1837  -0.6868   0.8367  10.0688  -2.2109 -22.1234
-#&gt;    1.7594   8.7483  -0.0330 -11.3810  -8.4217  16.5842  12.9036  -1.2103
-#&gt;   -8.0281  -8.7093   3.2851  -7.6428 -13.0462   6.5693  14.1121 -14.2416
-#&gt;   -1.1935   3.0437   0.1397  10.7483   0.3801  -0.3606  12.0643 -23.1945
-#&gt;   19.6280  11.3876  -4.0757  15.3446  10.4412   0.3363 -12.9435  18.2520
-#&gt;   -2.6819  -5.0899  -0.9045 -13.4161   2.7827 -11.6094  14.9193  15.8317
-#&gt;   -6.8539 -14.7565  -8.1981  -1.1949 -24.6289   2.2832   3.4538 -16.6920
-#&gt;   -7.1952   4.8237  -1.2552  -1.6680   9.3217   4.2961   2.1714  -2.8507
-#&gt;  -10.4690  10.2360  -7.6615   5.9058   2.8977  -2.6628   2.6884   0.9068
-#&gt;   -4.0276  -6.7258   3.1917 -19.4778  12.2685 -13.2598   1.3788  -7.9438
-#&gt;    5.7275   9.2501   3.9554   0.3331   2.6841  -5.6564   7.4015  -5.9041
-#&gt;   -1.2056 -13.0406   7.1534  -3.7298  -8.6677  -9.4427  -4.6725 -12.8452
-#&gt;    2.2872   2.3318  -1.0446   5.3130  -4.3572  14.7173 -16.3878   4.4235
-#&gt;    6.0222  -9.1257 -10.0928  -0.2591  -5.8325  13.4987   4.7509   6.3784
-#&gt;   13.7760  -7.3529  -1.2094   8.4638   0.7262   1.3399 -12.3616  10.4236
-#&gt;  -11.5588   6.6586   7.6932   0.4572  -7.3679   7.6941   3.1586  -4.3217
-#&gt;   19.2644 -16.8464 -10.2940   0.9402  -3.0294   0.3463   2.0001  12.5031
-#&gt;   -9.7039 -18.9529   8.7975 -10.9976   7.9514  -5.8859  -1.4219   8.7000
-#&gt;   -8.2907   5.5317  -0.7847   3.0357  -4.9111  -1.6778  -6.9723  -9.1618
-#&gt;   -8.4116   3.5519   6.5900   7.0345   7.8455   4.3377  -7.0939 -17.9069
-#&gt;   -1.3985  10.7238  -8.8682 -16.9020  -3.5137  18.6713  -3.8448  -5.1413
-#&gt; 
-#&gt; Columns 41 to 48  -6.4643  -2.5388 -21.4616  -5.6040  -6.3191  -1.6342  -4.0569  -2.0379
-#&gt;   -5.6680   1.4724  13.2840  -3.4921 -10.2399   0.2641  10.3352   3.8171
-#&gt;   12.6115  -1.7108 -13.5216 -10.8210 -10.4264  -7.7969 -10.3062  -8.7279
-#&gt;   10.4648   2.8866  -5.8904 -16.2398   7.4473  -6.0535  14.6214  12.9427
-#&gt;   -0.5917  -9.7757   5.0393  -2.0081 -14.6688 -15.5372  -7.2909 -15.1629
-#&gt;   -1.6890 -10.6030   1.2818   5.6617   5.6431 -21.3628   6.0934 -21.0824
-#&gt;  -15.9358   1.5402 -26.5112   9.5584   3.4395  -2.0985 -14.3276  16.3301
-#&gt;   -4.0987  -5.7655   3.5041  11.3578   1.1506  -7.4525   9.1520   4.6732
-#&gt;    2.9761  -1.2580 -17.1514   9.7909   1.7526  -1.2143  -7.4833   4.6656
-#&gt;  -34.0663   7.0265  -0.9760  -7.9130  -7.6047  -2.2309   1.1039   4.0653
-#&gt;   -5.2336   4.9508  -1.9094  -1.9472  -0.4502  -3.1149  -1.5659  21.3993
-#&gt;  -19.2430  10.7173   0.4560  -5.2085  -1.6233   0.4360   4.8870  12.3292
-#&gt;    0.5092  18.0340  -8.6627 -14.0658   2.8487   3.7132   9.6075  17.8677
-#&gt;    1.7973   1.6369  -3.5604   8.4947   1.1297   7.1048  -1.6887 -10.7250
-#&gt;   -6.4333   6.9952   2.9784  11.2297 -15.2731   7.4938   3.9207  -7.3343
-#&gt;   -4.6724  -3.6897  21.7221   7.7212 -12.2005   4.3440  -5.2160   2.5832
-#&gt;   -0.8814 -13.4018   1.4543   8.2097   3.0870   2.8957  -2.8904  19.4127
-#&gt;  -17.6998   1.7737 -11.9489   2.6230  -0.8362  -9.3559  -3.6307 -13.1814
-#&gt;   -0.4040  -2.7175   9.9510  11.9625   1.8166   0.0588 -17.0698  -5.7016
-#&gt;   -7.6151   7.6112   5.0353   2.8595   9.1448   6.6662   9.9320   0.1998
-#&gt;    6.6930  -2.8096   8.1582 -23.3403   4.0149  -0.0831  -1.9450   5.3090
-#&gt;    9.8380   1.8321 -15.9085 -17.9951 -10.9483   1.8701  -5.8176  -3.5743
-#&gt;    9.3034 -15.1221 -14.4197   3.4409  -0.1845  10.0440   7.2038   6.9657
-#&gt;    7.3433   7.9461  -2.1134  -0.6867   5.0906  -5.7845   8.6582   4.1319
-#&gt;   -8.9065   9.6049   6.0332  -6.8178   1.4591   5.2779   5.0914   6.3659
-#&gt;  -11.3646  -8.0048  -1.1809  -4.7319  -6.3755  -6.5568  -6.9608   5.1983
-#&gt;    4.3447 -22.8404  11.1539   8.0251  -1.9617  -5.5055   0.2847  -3.4197
-#&gt;   16.8622  -0.0450   4.2667 -14.3625  -4.1209  -5.6725 -10.9154  -5.5301
-#&gt;  -16.7231 -18.8290   6.8705   4.6012  -6.2427 -25.3542  -3.7881   3.4049
-#&gt;   12.9233  -0.4702  -9.5153   4.5197   5.6491 -10.5623   4.6353 -23.7695
-#&gt;   10.1386  19.8835  11.4105   6.4405  -4.3931   3.3104  11.3250   4.0869
-#&gt;   -8.0696  -2.3744  -4.2164   4.0229   9.0065  10.9019 -11.6374  -2.6199
-#&gt;    0.5046   7.4333  -7.3450   0.3356  11.8692  15.1388  -4.2314  16.5826
-#&gt; 
-#&gt; Columns 49 to 54   3.5972  18.2749  -4.7586   3.6296   0.3607   8.6015
-#&gt;    2.0428   3.4814   5.4453   4.2697   0.4042   3.8950
-#&gt;    1.7291   1.5574  16.8792  -5.3084  -0.4315   4.4522
-#&gt;   -7.6349   6.2060  -5.6430   0.3628  -0.1090  -4.9678
-#&gt;   -1.6757   3.3553  -2.0791   9.3839   2.1083   5.0346
-#&gt;   -9.3989  14.7985  -0.7696   4.3401  -8.5039   1.5701
-#&gt;    0.3240   4.0606  -2.8000   2.0324   2.4145   3.7744
-#&gt;   -2.3418   4.3597   9.5879  -3.8425   1.8605  -3.1328
-#&gt;   -9.6384   2.8427   0.3154 -13.5424   6.0735   3.9423
-#&gt;    2.7057  -1.6304   7.9158 -13.7631   2.2500  -4.9022
-#&gt;   15.0102  14.7068  -0.3661   4.1686   0.5645   4.0027
-#&gt;   -0.2919   5.6041  -4.0488  -6.8466   5.3191  -7.7658
-#&gt;   -2.9402  14.0371  -5.4205   2.6821   6.7547  -1.5828
-#&gt;   -3.1571 -13.8576   6.9405  -4.0459  -3.8991   3.3396
-#&gt;   -4.2456  -1.1746  -0.7794  18.8350   5.0391  -2.2222
-#&gt;    2.6344  -8.7018  12.7860   1.3861 -10.0942   4.4054
-#&gt;   -0.6345  -9.0174 -10.8328  -1.6377   3.2380   2.2593
-#&gt;   -9.7855   5.2557 -11.5096   2.5707   2.0966   7.4030
-#&gt;   -2.0640 -11.9835  12.2130 -15.5415   4.8031   2.8852
-#&gt;   -3.8418  -0.1652  -3.7828   2.6067  -1.8318   0.5663
-#&gt;  -13.3231  -9.2703   1.5388  -2.2590  -5.1580   6.0511
-#&gt;    5.1561   2.7740   4.6100 -16.3253  -6.3282   7.9442
-#&gt;    6.8917   6.0949   1.5524  -9.4631  -4.2104   2.9825
-#&gt;   -1.2133   3.5938 -10.8773  -1.9219  -3.9599  -3.5902
-#&gt;   -7.0418  15.3227   6.5206 -12.1712  -5.6557   0.2419
-#&gt;   -3.5968  -6.1455  -5.4279   3.5267  -0.4374  -3.4031
-#&gt;   -5.3538   9.0563   1.9151   6.3954   1.9907  -4.3894
-#&gt;   -3.0861   6.2220   3.8168  -5.0016  -1.8433   6.8036
-#&gt;  -13.2281  11.5181  -4.2948   6.5032  16.2834  -0.1578
-#&gt;   17.1929   9.3506  -5.7669   1.0761   5.8214   2.3497
-#&gt;   13.9171  -1.6056   0.2667   2.3196 -11.3400   0.1823
-#&gt;   -5.9167  -1.6086  10.3681   3.9454  -1.6798   1.8314
-#&gt;    4.0623 -16.8163  -1.3544   4.7902  15.9488  -2.5542
-#&gt; 
-#&gt; (9,.,.) = 
-#&gt;  Columns 1 to 8   1.1975  -1.2398  -1.1140  -0.4007  -7.6894  -7.7668  -4.9110   0.3874
-#&gt;   -2.0079  -4.1931  -2.1942   8.3943   5.5565   2.9180  -9.4828   4.0615
-#&gt;    2.3774  -8.7220   6.3585  -2.4744  -4.3597 -21.7627  11.6707   5.1222
-#&gt;    2.9137   6.1560  -3.0131  -1.6986  -4.3185  11.3390 -11.4204  -1.6090
-#&gt;   -4.4234   9.3519 -14.3655   0.4714  -2.4198  11.2250  -0.8828  17.0621
-#&gt;   -1.2452   1.7493  -7.0586   1.9896  -2.0212   3.4949 -24.5502   9.4198
-#&gt;    1.4979  -1.9569   3.5107  -7.7533   9.6295  -0.6350  11.7325 -24.0503
-#&gt;    0.1361   2.3522   9.9037   2.4987  -7.3834 -16.0749  -3.5865 -17.2965
-#&gt;    2.3907  -6.5865   1.3081  -8.0257  18.2305  -4.9180   3.0163  -8.8809
-#&gt;   -6.3246   3.5597   3.1556   2.0748 -22.5611 -11.2376   5.8873   6.6697
-#&gt;    3.9019  -4.3745   9.7155 -16.5032  -5.1125 -15.6230   6.6706 -25.0054
-#&gt;   -1.7383  -8.0914   8.7910   5.2512   0.6146 -20.6273   8.8196 -10.7820
-#&gt;    0.0126  -1.1759  -0.6623   9.9538  -2.1906  -0.6913   0.1033  -6.6710
-#&gt;   -2.9732   1.8734   2.9771   0.0380 -21.4319   0.4342   1.8939   4.2588
-#&gt;    2.9036  -6.2506   3.4026  -4.8426 -11.2883  -8.8573   6.6336  -6.0067
-#&gt;    4.9290  -4.6536  -5.9262   2.9032  19.6155  -0.1082   5.0408  19.7874
-#&gt;   -1.0095   3.8923   3.6943   0.4899   8.4123   2.0245  -0.6544  -4.3576
-#&gt;    1.6349   0.0770  10.0165   2.5779  -3.2742   1.0889   6.8993  -9.0178
-#&gt;    2.8319  10.4926  -0.5778  -3.4108  -0.6758   9.6593  -0.1572  11.0647
-#&gt;   10.8234   6.2000   6.6863 -10.1281  14.7570  11.9094  -5.0639  -5.8587
-#&gt;   -0.6484   0.4529   1.0131  -7.5977  -4.8991 -11.4535 -18.1432   6.6691
-#&gt;    1.5181   4.7733   9.3450   6.0604 -14.2982  -7.5637   8.1757  -9.4277
-#&gt;   -0.5035 -14.4148  -5.1629  10.1066   4.2851 -18.0413  12.8186  -1.6068
-#&gt;    7.5480   0.2490  -5.1833   3.2923   8.7902  -7.5551  -8.0653   2.4492
-#&gt;   -3.4262  -7.7450  12.8316  -3.4365  12.6299  -6.2849 -11.3101  -5.7181
-#&gt;   -0.0236   6.2767  -0.2204   2.9886  -8.4727  13.8220  -6.2735  -0.8397
-#&gt;   -8.0756  -1.0202   5.9199  -0.0587 -16.7772  21.7973   6.4815  -6.3890
-#&gt;   -5.3436  -4.9510  -1.9499  -7.5289  -2.5556  -1.5822  -1.6402 -14.9113
-#&gt;    1.8190  -3.5587   6.7261  -5.0426  10.0783  -0.3129  -8.3220  -5.4908
-#&gt;    3.8357   1.9682  -6.8254  -7.4561   1.4260   3.1438  -9.9417   4.8008
-#&gt;   -3.0639  -0.7477 -12.4322   7.8253  -0.4780   2.8818  -1.9868  -9.3309
-#&gt;   -1.7829  -5.6081  -0.4569  -1.3581   5.1148  -9.0049   5.4972   1.2022
-#&gt;    6.9496   5.6042  17.3907  -1.9601   2.1745  -5.6899  10.9068  11.5198
-#&gt; 
-#&gt; Columns 9 to 16 -17.3711 -12.9928   3.2901 -15.1752  -1.5514  10.3804  -6.0575  -5.7734
-#&gt;    4.6119 -19.8687   3.8588  -4.3210  14.0870  -4.4967 -11.2424  18.6381
-#&gt;  -11.8382  -0.7039   3.0366   4.9311 -11.3027  -7.4788  15.4230   7.6634
-#&gt;   19.1101 -11.0164  -4.8428  -1.2549  10.5324  -3.2340  -1.7188  -5.3316
-#&gt;   -0.7459  -1.2086  -5.9901  -0.9203  -6.1969   0.7095  -4.0238  19.3639
-#&gt;   -0.0751   8.7539   9.1530  -2.8213   2.0529  -1.4694   3.0654 -14.4758
-#&gt;  -13.1996   6.1162  -2.1352  -3.7159  -7.4341   4.0106  -2.6182   2.4459
-#&gt;   -1.7496  -4.0543 -16.2818   7.5484  -9.6444  19.5541   6.4399  -0.0436
-#&gt;    0.6540 -10.4300 -14.8093  -3.3458  -7.5036  -3.4038   2.9628  -3.7620
-#&gt;  -12.2336  -3.7282 -10.2374   0.4582  -1.5777   6.4288  14.7661  15.4012
-#&gt;   -8.5588   3.9641   7.0492 -22.2405  20.8335  -3.8610   9.2778  -6.2139
-#&gt;   -2.6226   7.1552  -5.3703   1.3597   4.3568  -7.0808   7.1658 -23.9945
-#&gt;    2.3516 -10.7932 -12.1660   0.2726  -6.5457   3.6207   7.8481  -0.1697
-#&gt;  -13.0105  11.6846  -1.0039  13.2130  -8.2659 -14.1765 -13.0109  11.4957
-#&gt;    1.8079  -5.7656   4.5115  19.4263  10.7865   9.4973  -3.3427   6.2776
-#&gt;    5.3061 -18.1478 -10.7017  -2.8879  -8.7543 -12.0937  -3.6480   7.9167
-#&gt;    1.6903  29.3763  -1.3741 -10.5327  -5.8438  -5.4007  -1.8660 -16.7540
-#&gt;   -9.2364  -0.6770  11.1444   2.7539  -1.7139  -3.7675  -9.1076  17.4619
-#&gt;  -17.5186   9.4659 -10.3318  -5.2399  11.9510  14.0091   6.0915  -9.9244
-#&gt;   22.8848   5.3730  -6.7007  -5.8932  12.5665  -5.9278  -5.8602   6.5889
-#&gt;    8.1040   2.1473   5.8895   0.7757  32.2080   1.1852   3.6303  -0.2835
-#&gt;  -22.0536  -8.1105  -5.2643   5.5950 -10.2206  13.4802  13.0991  -1.0854
-#&gt;   11.7985 -10.6667  10.4807  -2.5544  -1.2562  14.3751  -4.0616   2.8359
-#&gt;    3.2431   2.5250   3.9259  -3.9233 -19.5617   1.4743  -4.2915  -4.6315
-#&gt;   -9.0622 -13.8465   7.0939  -1.4632  11.4788   8.0314   7.1271   4.9558
-#&gt;   10.4974   2.8016  -3.0044   5.8171   6.6871  -0.5668   3.7981   8.9111
-#&gt;    7.5844  -2.0526   2.9415   6.7946   4.0922   7.1109 -17.8537  12.8409
-#&gt;    8.6288   7.6210   3.4370 -22.6726 -18.2157  11.8873  19.0300  -2.8070
-#&gt;    8.5402  -6.6355   0.7787 -12.7052  -5.2719  11.1716   5.6612   2.3374
-#&gt;   -2.5872 -11.4895   1.3166  -0.3656   3.0527  11.5654   1.8374   6.6581
-#&gt;   -9.8683   7.9084  -9.1966   4.8316   3.6363  -8.8287  12.5186  -8.0488
-#&gt;   -4.6945  -5.3836  -0.0190  -6.9191  16.7716   3.6244  -7.7551  -8.3506
-#&gt;   -6.7007   5.3990  -4.0925  20.4380   7.3323 -10.3048  -0.8245  -3.7948
-#&gt; 
-#&gt; Columns 17 to 24  -4.6344   9.8339   2.5297   6.8117  13.3151  -7.1821  -6.6767  -0.0172
-#&gt;   -3.4772   2.0396  14.9551  -8.4582  -2.7791   7.0603  -5.2847   8.0093
-#&gt;   -5.3824  13.0748   1.5425   2.2757  -2.3508  14.4779 -14.3930   1.2384
-#&gt;   -7.0085 -17.4764   0.4750  -0.1999  -7.5433  15.4842  11.7387   2.9776
-#&gt;   17.1677   2.4123   6.4029   2.6913   7.6612   0.3592   3.6135  -6.4987
-#&gt;   -5.3700  -0.1783  10.9539 -17.1706  -2.0350   4.1051 -17.1668  -2.6029
-#&gt;    1.5010  -2.3540   1.4813  13.6143  -9.4859   4.5107   2.2634  -9.7841
-#&gt;   11.5847  -6.3802   1.8878  -9.8852  -7.3464  -0.5203  -0.8940   0.5471
-#&gt;   18.5743   1.0590   2.9609   3.3293   3.5869   6.2283  -9.2043   2.3795
-#&gt;    4.1767  -1.7605 -11.3802  -0.5865   7.8135  -6.9812   1.0162  10.2456
-#&gt;  -15.8616   7.6204  -8.4168   7.6492  -8.9636  -2.9288  13.1910  -0.1943
-#&gt;   -1.1855  -2.5161 -16.1825   2.9432   6.9627  -1.8941   1.8202  11.0087
-#&gt;   -1.8356  -0.4324   6.0256   2.2957 -15.5257  14.0642   6.2270  12.8129
-#&gt;    0.8032 -13.8984  -3.2603  -2.4013  -2.3220  -4.8148   7.2350  -3.8735
-#&gt;   -6.9933  -2.9864  -6.3564 -14.1842  -6.4163   6.6726   2.0448   9.3606
-#&gt;   -1.8290   8.0418 -19.2705   2.0612   9.0959  -2.1745   5.0004 -13.8161
-#&gt;   -7.7377  14.6564   6.4312  -2.0344   2.4712  -2.4577  13.9262  -1.9439
-#&gt;   12.7181   1.5941  10.7739   4.8782   2.4335   2.8360   1.5782  14.1582
-#&gt;   -7.6028  13.1236 -11.4974 -19.0733  20.2128  -6.6458  -2.7676  -0.0749
-#&gt;   -1.7151   7.0791  10.2799  -4.8853  11.2483   3.3129   1.8797   4.0104
-#&gt;   -2.6039   5.8851 -20.2782  -5.4882   2.7440  -4.6487   6.0318   3.6723
-#&gt;  -11.6696   6.9944  -6.9048   5.6354   7.4811   1.1098  -0.2223   0.4078
-#&gt;   -6.1247  -0.3623  -6.6419   8.8517  -3.4674   5.2302  -1.3889  -8.6816
-#&gt;   -8.9044   1.3065   1.5638 -17.3409   7.8677   8.9325  -5.3082  21.7797
-#&gt;  -14.2809  -9.7267  -1.9414  -7.3090  -9.9456  14.9514 -17.0950   2.5775
-#&gt;    4.4569  -5.8721  12.0697  -6.1482  -2.2238   7.0624  -5.6663  -2.5693
-#&gt;   17.5576  -7.9505   6.5649  -2.7650  -9.3318   3.3320   3.9829   8.0490
-#&gt;    1.2434  -8.5197  -8.1784  -5.1863  -0.2853   0.4195 -11.2633   7.0829
-#&gt;   22.2755   4.8318   3.0687  -4.1995  -2.4253  16.1788 -20.5545   7.0368
-#&gt;  -11.7527  -4.7581   4.2222 -12.1603   6.1406 -12.3324 -11.6035   0.2889
-#&gt;   -1.6692 -11.6614 -13.5768  -1.8077  -7.6895  -6.9542   7.4546 -10.8698
-#&gt;    3.8157  12.5881 -10.2511   6.8567   5.3444 -16.6095   6.2280   1.5643
-#&gt;  -19.4063   1.5522   7.2770   1.3584   0.3552 -12.4477  12.3885  10.4627
-#&gt; 
-#&gt; Columns 25 to 32  -9.5769   2.4625  -4.2659  -1.1244  -9.6604 -21.8320  -8.6931 -16.9367
-#&gt;   10.7441  -2.1736  -8.5456   8.3532  -5.9532  11.1422   3.8743 -15.7459
-#&gt;   10.5707   5.9120  -1.6166   4.5801   2.5701  -3.9070  -0.3070  13.7188
-#&gt;   -8.7126   5.2126 -21.6582   5.2874  14.4752  10.0755  22.2482 -14.5742
-#&gt;  -18.3199  12.9979 -10.7505  16.5473  -5.1269  -4.5079  -8.3278  -9.5051
-#&gt;  -10.8635   5.6270   5.4557  27.4302   0.8201  13.6504  -8.6767  -5.4259
-#&gt;   -3.4086  -1.1095  -0.0361  -3.0832   8.3095 -13.2503  -1.9285  -6.9786
-#&gt;   24.6086   4.5951  -7.9285   0.6195  -2.0287 -11.4892   8.9497   5.6496
-#&gt;   -7.9044 -10.4854   8.1525  -5.3832   8.1807 -10.4097  -5.6736 -16.2380
-#&gt;   -7.9985   0.1954   4.6654  -4.2638  20.1632   1.0771  -8.4827  19.1884
-#&gt;   17.1190  -4.0988   7.0600  -7.9747  -7.9464  -2.0233  -4.3403   6.2999
-#&gt;    2.4927  -7.3519   0.7382 -10.9118   7.5890  -3.7200 -14.0395  -1.4388
-#&gt;    1.3877   2.6994 -10.6244 -17.7301   9.0477  -1.1237  20.8489 -10.0757
-#&gt;    2.1575  -3.2954  -3.9481  16.9882   5.9757  11.3388  -6.8171  12.3640
-#&gt;   -5.7286  -0.5980 -14.4089 -15.1805  -0.0011  -7.8112   8.1191  -3.5735
-#&gt;    9.6932  10.6950  10.3304   3.5469  -1.8773  -0.4794  -5.1628   3.8472
-#&gt;   -2.4541   3.0765  -3.3206  -1.6310  -2.6567  -7.7168   5.1882   8.6982
-#&gt;  -12.8336 -15.5716  -0.7187   8.0754   3.9385   8.3554  -8.1032   3.5514
-#&gt;    5.5786 -14.5399  21.7838 -14.4555   5.5795  -0.0965 -10.7571  -0.0937
-#&gt;  -22.1829 -14.6992   1.7042   8.9775 -12.5094  15.3044   5.2975  -0.2269
-#&gt;    2.9834  -5.9687  -5.1473   0.7876   1.8317   7.6425 -12.5784  -3.1962
-#&gt;    4.2260 -14.4335  -9.6942  -5.1841   5.0552  -6.1023  -2.8069  17.3816
-#&gt;   18.1181   5.0467 -19.5156  -2.9655  -1.5503   9.2741  13.3782   0.7498
-#&gt;   -4.5867  -4.4932  -4.9179 -10.4347   9.3939  -3.9583  14.3968  -3.5493
-#&gt;   25.9232 -12.9552  -7.6197   1.3831  -9.2296  19.3003   3.3880  -9.9745
-#&gt;    6.2639   8.3370  -3.7196   3.2851  -0.0649  17.0393  14.7158   2.1852
-#&gt;    1.7578  -9.7828 -18.7070  13.0029  11.1053  -7.7852  20.3358 -24.4372
-#&gt;    0.4453   5.8993  14.0123   9.4855   1.0015 -14.3425  -3.1427   6.4256
-#&gt;   -0.9933  -7.4320  11.4095   6.2291   3.5597 -11.1464  13.2829 -14.4998
-#&gt;  -12.4312  -7.5292  20.2893   9.3842  -6.9078   4.2495  -8.9122  -0.8833
-#&gt;    1.9873   7.6905  -9.3570  -5.9532 -10.3786  -5.2514 -19.1197  20.7845
-#&gt;    5.9731 -11.4575   8.3881   0.0517  28.7392  15.9121   2.5811 -11.2345
-#&gt;    6.2376  -8.9132  15.5614 -17.9054   5.8949  -4.4550 -15.4754  -0.6651
-#&gt; 
-#&gt; Columns 33 to 40   8.8274  -2.0494  11.3735   2.4166   5.8753   0.9227  -2.9541  -5.9154
-#&gt;   14.6587 -15.0344  -8.1114  -3.8675 -12.6707   6.5043   2.4011   1.4694
-#&gt;   12.8222  -2.0889  11.7450  14.9423  -2.8694  12.1342  12.4036  10.3000
-#&gt;  -13.2216  -4.1742   2.8572   7.0496  21.8543   6.7175   8.3689   4.1604
-#&gt;   -0.2787   7.4410  -4.3835  -4.1375   6.2870  -5.0722  12.2639 -13.0750
-#&gt;   14.8516  -7.6058  16.2324   2.6191 -10.6812   0.8444  12.9380  11.5148
-#&gt;   16.3133  17.3926   0.2803  -8.7089   5.4235   1.1392 -10.6105  -7.8981
-#&gt;  -22.6307  -3.0687 -13.5976   3.8321 -19.1732  -5.1593   0.2230  -3.0170
-#&gt;    9.8544  15.0199   6.9769  -2.4057   1.3690   8.9038 -13.2167  -1.0254
-#&gt;  -18.2837  -8.2476  -4.7673   4.8838  14.9280   3.6832  11.9774 -14.9842
-#&gt;   -2.9671 -21.3483  13.4773  -8.2653  -2.2556  -2.9402   0.7005   4.5175
-#&gt;  -10.7969  -7.6531  20.3559  -8.7176  15.1757   6.2287   6.7490  12.1439
-#&gt;  -12.8511  -0.4756  -1.3749  -4.4966   5.8188   3.0535  -8.2877  -3.8175
-#&gt;    1.3197   5.7884  -9.1576  15.3510   0.0286   1.0393  -3.7706 -12.1922
-#&gt;   -0.1025 -28.8027   4.3628   0.6398  -1.8094   5.4907   3.2245   0.5442
-#&gt;    2.0075  -5.6178  10.6544   3.2493   0.1864   0.6155   2.1873  -4.5126
-#&gt;    4.9906  -6.0231   0.2108 -22.5401  -4.0671  -8.9950  -1.8902   4.2314
-#&gt;   21.0038  -5.2987  -4.6289 -12.2208   2.3124   3.6595  -6.6480   1.9168
-#&gt;  -12.9012  -3.3060  -1.7020  -1.8652 -17.5854   5.4782 -12.8724  -0.3183
-#&gt;   13.4157 -17.6442   9.3061 -13.0933 -14.0007  -0.0940 -11.1198  13.1313
-#&gt;   -3.3653 -26.4918   4.9906  -9.7203   3.5228  13.5853   0.7401   8.1041
-#&gt;  -11.2516  13.0149  14.5271  10.0730  -1.7234   1.7355  16.3706 -16.5040
-#&gt;    1.0220  -3.6162   2.6692   0.5590  -7.6241  -2.4108   6.3428   6.5638
-#&gt;    5.0940  -0.0694  11.0811   3.5004  -3.5242   3.4591   1.2344   8.0370
-#&gt;    1.3809  -3.5537   5.7799   5.8406 -18.5895  14.5387  -6.3814  -1.4145
-#&gt;   -5.2832  -9.9453  -2.0621   5.0152  -0.8117   2.7958   8.3679  -2.8486
-#&gt;   -3.4829   0.8273 -11.6870   0.7553   4.7476   1.3692   2.2515  -3.9707
-#&gt;    0.2804   4.0596  10.2204   7.2813   2.0398   1.6096  -9.2500  -2.7174
-#&gt;   23.4933  -6.5115 -11.5276 -10.2504  13.7994   9.1442 -13.1773   5.7294
-#&gt;   12.5285  12.1677   8.2291   5.0004  -0.4306  -6.9078   8.2193  -3.2564
-#&gt;  -11.3853   5.8968   4.1864  -2.0609   3.4350 -12.6994   0.3758  -2.5303
-#&gt;  -10.9426 -12.2642  -0.4888   0.3123   1.2148   4.6199  -7.1471   4.0117
-#&gt;    2.3575 -11.2653 -15.5099   1.1284  -6.2209   0.3092 -17.4598  -0.8969
-#&gt; 
-#&gt; Columns 41 to 48 -15.0982  -3.1641 -11.5331   2.7484   5.2684  -8.4730  -3.4940 -10.7082
-#&gt;    0.6976 -22.8528  -2.4819  -3.5093  -9.1276   6.4026   0.9419  -0.8509
-#&gt;    7.2296  -1.3453  22.8778  -1.6704   8.2158   1.2705   3.7712 -14.2437
-#&gt;   11.3039  10.7791   0.7054  -5.0214  -7.2238   2.6059  10.7593  -3.2558
-#&gt;   14.7552  -0.6161   4.5493  15.1339   0.5638   7.0355  13.4353  -4.9595
-#&gt;   -0.5938 -10.2857   2.4860  -0.6320   7.6495   5.9285 -13.3669   6.2465
-#&gt;    9.6788   1.5769 -11.8858  -0.8934   3.4447 -21.4495   9.5003   3.6249
-#&gt;   -4.7844  -2.4145  -3.2683   4.9814  -4.6650   8.4679  -7.1884  15.3933
-#&gt;    0.6942  -6.5546  -9.2951  14.2461   2.4155   3.4753  -4.2303  -4.9865
-#&gt;   -8.0985  18.5906  18.4073   2.9648  -3.2881 -17.2604  -5.5474   3.3556
-#&gt;  -16.9723  -2.9256  -7.8427   4.5080  -7.8752 -11.7270  -2.5444   9.1492
-#&gt;  -16.0411   3.3623   2.0852  -9.0993  -2.5859 -10.8925   2.5955   2.4356
-#&gt;   -6.1881 -12.5620  11.0662 -10.9062  -4.3516  -6.7282  -2.1957   5.2102
-#&gt;    3.8346  -3.6723  -0.6606   4.7380 -12.7817  10.3440  -5.6791   0.8007
-#&gt;   -5.7009   8.6002  18.4995 -13.4326  -7.6281  -0.2299  -0.8093 -11.5298
-#&gt;   -7.2815   9.0089  -1.8208  -1.0079  -8.7824  13.8996  -4.4841   8.7618
-#&gt;   -3.3436 -10.7400   2.6921  -8.3071   9.6350  -7.0577   3.8494   6.6270
-#&gt;   -3.1201  -1.4005  -1.2335   4.9910  13.5354 -12.0149   1.3193   4.0018
-#&gt;    1.8656   0.7464  -5.5016 -11.7265  -5.6981   9.5529   9.1353  13.3796
-#&gt;   -1.2917  -6.0425   0.2696 -12.8005  -3.5424   5.1456   3.8755  -2.0848
-#&gt;    6.8537  13.1599  15.3077   1.5987  -8.4261  15.7216   2.7301  -8.5690
-#&gt;    3.3192  12.8480  24.7916 -11.0170  -3.9709   6.9850  -8.2116  25.8752
-#&gt;   -1.4302 -10.7771   9.6759   5.3653 -18.1811  -7.9057  -8.1210   5.3092
-#&gt;    3.8571   9.6986  -8.3416  -5.6875  -5.2164  11.1976  -5.0036  12.1279
-#&gt;   -4.3177   1.4917  13.7937  -5.3674   1.5354   0.0726   2.8281  11.9119
-#&gt;  -15.6567   4.7850  14.7015   6.5166  -9.5498  10.7624 -10.2419   2.9574
-#&gt;   -5.1239   0.9761   1.7468  13.2061 -12.6672   9.8714 -10.8477  -5.6109
-#&gt;   10.4034   6.8093  -0.1542   8.1070   3.2255   1.2537  -6.0933   3.6642
-#&gt;   -8.8527  -5.2336  -0.6565  -8.2328   9.3072  -9.1219   1.9044   2.2948
-#&gt;   -3.0176   2.0131 -17.7462   3.8071  -3.1126   9.2685 -18.2617  13.0078
-#&gt;   -8.2006  -8.1164 -10.0005  15.9031 -15.3337   5.6334   0.2218  -7.8126
-#&gt;   -0.6995  -4.0868 -15.9673  -1.0390  -1.8955  -4.5538   3.8172   5.5747
-#&gt;   -7.4443   2.5282   9.1631  -9.9060  -6.0557   3.4871  -1.9564   6.2611
-#&gt; 
-#&gt; Columns 49 to 54  14.7934  10.7032   3.0428   4.3052  14.4522   0.1353
-#&gt;   -4.1295   0.7517  -1.0725  -5.9963   5.7713  -5.0256
-#&gt;    4.0433   5.1771  -3.2108   1.1541  -0.8420  -9.1398
-#&gt;   -9.9673  -8.8738  -1.8942  -1.4834  -6.3817   7.8222
-#&gt;  -13.3712  -5.3652  13.8375   1.6535   1.9068   1.6758
-#&gt;   13.4982  -1.0825   0.9072   2.3625   6.1872  -4.6543
-#&gt;    1.9236   6.6417   3.4439   6.0731  -3.9129   1.2775
-#&gt;   12.5134  -4.8576   5.8881   6.7770   6.0290   4.6036
-#&gt;    2.6090  -0.7607  -0.1944  10.1795   7.8796   3.7345
-#&gt;   -0.3608  -5.8855  -0.4970   3.8679   1.0226   4.3410
-#&gt;   -5.4470   7.1426  -0.9355  10.4627  -3.5240   3.6359
-#&gt;    7.6488  -7.2293  -6.7642  15.3302  -8.7879  11.8737
-#&gt;    3.5748   6.9353  -4.0357   1.7857   0.1649   3.5149
-#&gt;    7.7534  -1.6446  15.1873   1.0002   6.1079  -4.5628
-#&gt;   -4.6717  -4.5044  -3.0102  -2.6753   2.4820   2.4006
-#&gt;  -10.4784   5.0278  -4.1785 -10.3842   1.0259  -2.0809
-#&gt;    1.2513  10.3777 -12.1033  -7.1036  -6.7072   7.2739
-#&gt;    2.7570   0.3887   3.0678   5.8539  -5.4389  -6.2017
-#&gt;    3.5110  -6.3346 -17.5973   0.9396  17.2846  -2.5351
-#&gt;    1.3420  -0.9772 -14.5203   2.0036   2.2666   4.5572
-#&gt;  -10.2011  -3.1082  -9.0771   0.0843  11.4800  -1.3659
-#&gt;    2.7997   7.7928  -0.2684   3.6770   4.1407  -9.8394
-#&gt;    5.7998  -6.8508  -4.6347  -1.5305   1.7878  -0.6218
-#&gt;    5.2803   8.2373 -12.2639 -14.8658   6.6198  -0.1940
-#&gt;   11.6090   8.1991  -4.6967   0.9464  -0.5578  -0.7397
-#&gt;   -3.0316  -1.1493   4.8180   2.7201  -7.7325   2.1080
-#&gt;    7.6878  -0.9196  -0.9955  -2.6530  -2.2799  -1.6075
-#&gt;    4.3545  22.3996  -2.0364  -0.0578   1.9645   1.0939
-#&gt;    0.2818   4.3390   0.4123   7.4185  -7.2953   4.3082
-#&gt;   -6.5838  -7.2752  22.0966   8.3256   0.0846  -2.2953
-#&gt;   -0.6201  -4.3393  14.9479   1.3762  -1.9033   2.7198
-#&gt;    2.1361  -1.3838  -1.5741  -4.5331   5.1471   2.4475
-#&gt;   -4.7353  -3.5813  -2.5246  -0.7377  -3.5806  -0.9274
-#&gt; 
-#&gt; (10,.,.) = 
-#&gt;  Columns 1 to 8  -1.8500 -12.2675   1.4892  -7.1764   2.8354  -5.8378  12.8481   5.6656
-#&gt;   -1.1577   0.5383  -2.5248 -11.6310   8.1116   0.1921  -1.9742  -7.5366
-#&gt;    1.4824  -1.2159   0.6557   0.5767  14.7386 -13.6151  -5.3813   8.2099
-#&gt;    4.6639  -7.2431   6.0229 -11.7378  -6.4094   9.5070   4.7361  -5.2824
-#&gt;    6.8163   4.9981   2.8543  -3.3842  10.4626   5.2760   4.8904  -4.0745
-#&gt;    1.1682   8.4392  11.5753 -14.2050   6.5186  11.5484  10.8122   1.4898
-#&gt;   -1.8258   1.4089   6.9804   4.3547  -4.4739 -12.7371  21.1777   1.0594
-#&gt;   -4.5790  -4.0777 -10.2127  -2.3566  -8.4689   7.7534   4.8805   4.2048
-#&gt;    2.5802   4.5797   2.6721   2.8278  -9.1001   1.9560   0.0806  -8.2095
-#&gt;    3.1398  -4.2600  -3.9862  -7.2621  -5.5050 -16.6525 -12.6482  -4.4031
-#&gt;   -4.5828   2.1145  -1.4471   2.8078   9.0766   0.2921  11.3616  16.4376
-#&gt;    0.8457  -9.4431 -16.5262  -8.0672  -7.7677  -2.9037  -1.5686   6.2334
-#&gt;    3.0319  -5.4088  -2.7331   1.4707   1.6673   9.7579   2.5652  -6.9566
-#&gt;   -4.7804   8.1184   8.0742  13.6175  12.1578   0.7685  -7.6160  -0.9307
-#&gt;    2.2419  -3.2220   3.6841  -2.1644   8.4578   2.8451   2.8823  -0.2854
-#&gt;    4.5086   2.8474  -3.5571  -8.7192  12.4858  15.0887   1.8407   2.8965
-#&gt;   -5.4173   1.3649   6.7848   0.2605  -3.3994 -18.4237   8.8473   0.7316
-#&gt;   -0.9389   2.8991  10.4100  -4.9842  -3.8332  -8.0460  -5.1748  -6.3293
-#&gt;   -5.2253  12.6549  -9.8596   4.6462  -3.8878   1.9781  12.5557   6.4403
-#&gt;    2.2612   7.0087   2.0771 -12.5669  -8.6309  11.7383  15.1539  -7.2659
-#&gt;    1.3714  -5.7800  -5.0473  -6.9958  10.1594  -1.2386   8.3225   8.1150
-#&gt;    0.9239  -3.0967  -8.9633   8.8797  11.6718  -3.8222  -4.4639  14.2213
-#&gt;    4.6836  -5.2329  -1.6285  -9.1274   6.9853   5.9279  -1.0487 -11.6224
-#&gt;    2.0499  -0.3689  -4.0227   2.8012 -11.1128  -4.5234   7.6860   0.6552
-#&gt;   -2.5586   0.1792  -3.6759  -7.1461   9.8904   8.3477   5.1319   1.8757
-#&gt;    0.1599   3.7062   0.3903   3.6076   3.1018   3.3453 -11.0709  -3.8549
-#&gt;   -3.0774  -0.5135  10.3292   8.1960 -12.5844  -5.3556  -0.5532  -1.0248
-#&gt;   -3.9124   2.7457   2.7111   3.2968  -0.5090  -3.9839   5.1643  16.5431
-#&gt;    6.3972  -5.2741  11.6109 -12.5734 -12.3844   5.5059   2.6992   2.1771
-#&gt;   -0.8587   8.7423  -0.7683   6.9076  15.5605  16.0487   4.4676   6.1374
-#&gt;    0.9435   4.4355  -0.8689  11.7017  12.4502  12.5783   3.5812 -20.7519
-#&gt;    0.3436   3.0918  -2.2111 -10.3139   0.8019  -3.9671   6.2100  11.5562
-#&gt;   -3.6844   3.7193  -6.7148   5.7169  -4.8891 -13.3637 -23.8774   2.2370
-#&gt; 
-#&gt; Columns 9 to 16   4.9145  -6.0721   7.7146  10.5538  -8.4413   6.4439 -12.8841  -6.6100
-#&gt;    2.6133   1.1197  -4.7463   1.3765   6.7308  13.4183   0.6573   9.6976
-#&gt;   23.7829  -3.9357  -8.5177  -0.8124   4.0652  -9.0011  -2.1937  13.2107
-#&gt;    1.9626   1.1916   0.2588 -13.6375   6.1413   2.5194   2.9095  -6.3250
-#&gt;    3.9238  -1.2172  -5.0347 -15.8092   7.8051   5.3026   3.9615  14.8559
-#&gt;   -4.3524 -15.6227  -2.7966   3.8273  -0.0957  10.4034   7.6753  16.0185
-#&gt;    0.0641  21.7018  -2.7384   0.0584   5.2902   0.8238   2.2139   5.1473
-#&gt;   -1.6556 -20.0234   3.9523   5.7937   4.7890  -0.6384   5.2002   8.5340
-#&gt;   -0.5821  16.2645  -0.1885  14.6969  -3.1680   9.0324  -3.7846  -8.7105
-#&gt;    2.7144   3.0629  13.7043  -2.6407  12.9891  -9.3664  -7.5100  -5.4292
-#&gt;  -24.6937  -1.3630 -10.9943   2.4038  -4.9863  -9.0954   5.8939   3.9086
-#&gt;   -1.7070   4.2584  13.2341   4.6520  -2.8907   4.5812   5.7238  -5.1814
-#&gt;   -2.5448   5.3008  -8.6184   0.3666   1.6529  15.2911 -12.8152 -14.8737
-#&gt;   -4.8208  -0.5189  -8.5980 -10.1726  14.1816  -3.6141  -8.6742   6.6223
-#&gt;    7.4390 -10.3070  -6.6040  -2.8309 -20.5618  -3.8598  -8.4416  10.2294
-#&gt;   16.9588   0.7044  -1.0466 -13.1469   1.6360   1.2870   5.0114 -12.7230
-#&gt;  -19.2117  -3.5752   4.9981   0.2389   0.7434   0.3358   7.8417   7.0483
-#&gt;   -1.9847   7.3675   2.5835  -5.0991   5.5215  -0.2240  -8.8273 -20.6662
-#&gt;   -5.0049   0.2266   9.0363  10.1649  -7.9133   5.5165   7.6121  -2.4820
-#&gt;   -7.9860  -2.0977  -1.3178   4.1911 -10.8242  -1.0916  10.6230 -20.6504
-#&gt;   -9.6594   5.5636  -5.5112  -7.3372 -12.8611  -0.9137  13.0654  -6.8811
-#&gt;    2.7879   2.7062  21.0970   9.4824   7.3445 -16.1385   7.7272   1.3046
-#&gt;    3.2371 -14.1730  11.1436  -2.8708  -1.8910  -5.3189  -3.6189   1.2137
-#&gt;   -9.6461   2.7362   6.9843  21.6926  -4.3479  12.8534   6.1525   2.1206
-#&gt;   -7.3817   5.6223  -3.9876   0.4446  -3.0767   3.7663  14.6100   2.7813
-#&gt;    2.7759  -0.6100   4.6858  17.1738  -1.1455 -13.7499  -8.2681   6.1157
-#&gt;  -10.0088  18.0462   2.9224   1.1051  -5.2682   0.6095   2.2258  13.4844
-#&gt;   -0.4106   7.1385  -2.6845   2.3948   0.6804  -0.9568  13.7818   7.0911
-#&gt;    1.3601   6.1807   2.5651  -4.6246 -12.0232  16.2925  -6.4541 -12.6192
-#&gt;    0.0367   2.9608  -2.2894  -0.3915   4.3545   0.9612  -4.2341   4.0421
-#&gt;   -1.1083 -11.3478  -3.7562   7.3843   9.0587   0.5682  12.2225   5.5262
-#&gt;   -3.3381   2.1199   2.8611  -2.4743   1.6596  -4.2909  -1.1444  -0.6014
-#&gt;   -2.4636   5.2086  -3.4735  12.8289   3.9967  -6.4613  -6.9887  -3.3523
-#&gt; 
-#&gt; Columns 17 to 24  -2.1559  -9.8576 -10.1948  -3.3122  -6.3155  -3.8948 -27.2719   1.7505
-#&gt;   -2.1521   4.0064   1.9136   0.1647  -6.7919  -0.0733 -11.1800   0.2256
-#&gt;    5.4232  -4.5297   3.4226 -16.6109 -19.3669  -9.9671  -5.0840 -15.8884
-#&gt;   -5.3707 -10.3328   5.7534   5.5549   3.2900   0.9707   4.3669 -11.6294
-#&gt;    7.7867   1.9023   1.4675  -0.3012  -4.2364  -6.3151  -4.3149  -6.9864
-#&gt;    3.6924  23.2753  -4.5692  -2.5359  12.6196  -1.1250  -2.7867 -14.3799
-#&gt;   -6.1620 -16.6808  12.7803   6.0621  -6.5813   7.0458  -1.4725   6.4389
-#&gt;    5.2466   9.6241   6.2911  -9.4166  -1.6638   2.7221  -9.2076 -17.8687
-#&gt;   -6.7425   1.1051 -14.0518  -6.3564 -10.5235  -9.3756  -0.7154  -6.3670
-#&gt;    8.2304  -9.2772  -6.8307  -2.4571 -14.8917   7.5416  11.0721 -16.5973
-#&gt;    2.9845   1.1880  -0.3566 -12.2831  -6.6397   0.1049 -11.6885  -0.2269
-#&gt;  -12.6444  -3.8359   7.2372  17.3424   5.5750 -11.8784  -3.4308  -7.5790
-#&gt;    7.8830   0.4781   6.5014  -7.0082  -0.6729  -4.2275  10.9963  -6.1631
-#&gt;   -9.7303   7.3071   9.3475  -4.6312  -9.2856  13.2649  12.1456  -4.6776
-#&gt;    6.8102  -5.6296  -6.5209  11.0740  -4.4415   6.2017  12.8256   2.7447
-#&gt;    0.1775   7.5466  15.1378  -9.5714   5.3693  14.9650  -2.3706  14.5969
-#&gt;   -7.0732  -1.2279  12.9900  -2.9203 -10.7722  22.7616 -17.4422  11.7573
-#&gt;  -10.4201   9.3671  -9.3441  -5.9673  -8.6098   0.9277  14.7462  14.2034
-#&gt;    3.3381  -2.6992  12.5822  16.8852  10.4079  20.1637  -1.0201  11.6586
-#&gt;  -16.2020   7.1365   2.6194   8.6313   5.2558   3.0275  -0.6936  14.9872
-#&gt;   -7.5766   3.1303   1.1693   0.2474 -13.7310  -4.9695 -10.1676   7.6483
-#&gt;    3.8422   4.3058   5.7670  -9.5064  -3.9193  -1.3834  -3.9565 -11.8401
-#&gt;   -8.2808 -16.4307  -0.9879   5.5589  21.0677   3.7365 -14.3544   9.3748
-#&gt;   -4.1544  -3.5511  -8.4676  11.4364  -0.8777  -2.8934  20.3865  -4.6941
-#&gt;   -4.5382   3.6219  21.8238   5.5826  -4.4377 -15.2030  -1.1862  10.3909
-#&gt;   -8.7293  -0.9134  -5.4663   9.6777  20.8804   8.1244   7.1723  -5.3494
-#&gt;   -6.1536  -3.7883   1.4498  16.5608  -5.6539   1.7384   3.9536  -4.9169
-#&gt;    7.5200   5.2810  -6.4937 -11.0208  -6.0932  -0.1089   6.8844 -22.2866
-#&gt;   -5.2039  -1.2965   2.1826 -11.7864 -10.5727  -3.0584  14.8570  -6.6752
-#&gt;   24.2666  15.9634  -8.7593   6.7360   3.2166  -1.8886   6.6456  -9.3214
-#&gt;   13.3195  10.2136   2.3852  -4.4087   4.6114  -6.0370   1.9381  -4.4393
-#&gt;    3.6607  -6.6867   8.8165   2.2749   8.4605   8.1030 -18.8902   9.9795
-#&gt;    5.5831  -8.2335  -8.7191   7.0844  -4.3951   5.3666   1.9027   6.8024
-#&gt; 
-#&gt; Columns 25 to 32 -10.2224  -1.9389 -19.3736 -10.7482  -9.7533 -11.8404   8.3034   4.0973
-#&gt;    9.3930   8.2273   1.8095   8.1928  -0.4073   0.4799  10.8521  -7.8852
-#&gt;   -8.6524  -0.3236  -5.2522   1.7153   2.0514   1.4288  -2.5463   5.1395
-#&gt;   11.2489  14.8124  -4.4783  -5.2268   3.3807   0.8141   1.5575   1.4992
-#&gt;    4.3262 -14.4228   5.4898   6.6534  -7.0818   6.8338  11.2666  -2.7705
-#&gt;   15.2712  -9.0649   1.6693   6.1079  -4.4173   7.4819   5.5009  -3.8136
-#&gt;   -7.6070   4.7240   1.3422  -3.4018   0.3073   0.2174  11.3154   2.4306
-#&gt;    3.5969   1.9192  -6.2191   2.7220  12.3226   2.7691   6.2103   3.4041
-#&gt;    2.2569  -2.6670 -11.0406  -3.9329  -3.3825  -7.0046  -7.4850  -0.6617
-#&gt;   -9.4944   8.7426  -3.6296   5.0845   0.4671 -12.5662   6.9672 -17.9908
-#&gt;   -9.6233  18.1501  -4.4963  -3.2665  10.2044   4.6927  11.9656   6.9270
-#&gt;  -12.2610   2.0286  -4.2413 -23.7392  -7.8217  10.3566  -1.0038   7.4171
-#&gt;    6.0435   7.3772   7.6686  -9.8636   6.8095  14.6836 -13.4730   3.4181
-#&gt;    9.1964 -12.5802  -0.7783   3.0091  -4.8010   6.1394  -1.1459  -1.1744
-#&gt;   -0.5279  -5.5851   2.2629 -13.5109  -6.9919  -4.3634  -0.1308  13.8073
-#&gt;    0.5349   5.5183  -2.0564 -10.8775  17.3398 -14.5798   3.8909   3.6974
-#&gt;   -0.1317  -9.2980  26.0642  -1.1933   3.6307   3.8268  -0.8533   1.9078
-#&gt;    7.0466  -6.3736   1.8694   6.0220  -8.7652  -2.9689  -6.6833  -4.6140
-#&gt;   -2.0061   0.6532   4.6247 -11.8469 -19.5675   2.8180  -6.9292   1.4644
-#&gt;   16.5723   6.6351  11.5395  -7.5450   2.8206  13.2409  -0.5340   8.8756
-#&gt;   -2.6678   2.6012 -21.0468  -9.6533   7.2493  -4.3256  -3.6277  -4.0059
-#&gt;    9.1129  -6.6308  -4.1212  -2.0461   3.7792   4.0394   1.8128  12.2187
-#&gt;   -2.8475   1.8910   0.6765  -6.2240   0.1040   7.3775  16.0001  16.5972
-#&gt;    9.2151   1.6988   6.7297   6.8434  -4.6075  -5.4027  10.6723   4.6200
-#&gt;   -2.5280   9.8955   2.0369  -5.2786  11.0871  18.8807  17.2888 -10.0791
-#&gt;    5.8986  -9.1404   5.0503 -10.1183  -3.1722 -11.0779   1.4438   5.4589
-#&gt;   12.6305 -10.9764   8.0426   9.5701  -7.6651  -2.2229   5.7638  -2.1522
-#&gt;   -3.3263 -11.8235  -8.4992   5.9554   5.4361  -1.5795  -0.9457 -22.3206
-#&gt;    1.3393  -3.7732   1.6990  -8.4276   2.6964  -8.5071  -1.2511  -7.1547
-#&gt;   18.0202 -14.7667  -9.5110  17.7941   0.7772  -6.9105   1.5280   3.0158
-#&gt;   -3.5361  -3.0091  -2.2453   4.7789   0.9857   6.9009  -9.4692  -0.3711
-#&gt;   -2.0192  11.5536  -7.7319 -13.2447   8.5858   0.3289  -4.9393  -0.4135
-#&gt;   -0.8826   1.2346   6.4182  -1.4927  10.8155  -7.6332 -23.9371  10.4665
-#&gt; 
-#&gt; Columns 33 to 40   5.1319  10.1390   4.2346  24.1695   4.6489   1.7460  -0.9835  -2.0338
-#&gt;   -4.1671  -8.6674  -5.3342   5.5455  -2.9394  12.7071  -8.4014   0.6553
-#&gt;    3.7389   4.9442   9.2769   1.2523  -0.2772   5.1782  -7.9768   6.9962
-#&gt;    5.0574   1.9856  -0.0201   7.3269   6.2790  -2.5767  12.9263  -3.7182
-#&gt;    5.3231  -6.2901   6.9165  12.9884  -8.4718  -0.8653 -18.6610  -4.3692
-#&gt;    3.5551  -9.0450 -13.4135 -17.8318  -6.6085   3.6141  -3.8519   1.1129
-#&gt;    5.4421   6.1592   3.5464  -6.3049  -9.8598  -2.9978   3.3122 -10.3513
-#&gt;    0.5685   3.7157  -4.7719   0.5936  -2.9502   6.6800  -5.2828   6.3479
-#&gt;    6.0839   2.8477  -1.2380   7.2542  -4.8610   6.6942  -6.3328   6.1477
-#&gt;    2.6931   1.2125  -5.1100   7.9270  -8.7259   4.3895   7.9249   7.8616
-#&gt;   15.7830  17.0342   6.1084  -2.8490  12.5228   7.3003   3.7822  13.5545
-#&gt;    0.3258   1.6821  -2.1845  -2.6203  25.8428   8.6013  15.1667   8.2342
-#&gt;   15.3455  10.9665  21.9293   1.6005  15.8667  -7.6271   3.2396   5.3830
-#&gt;   -1.4143 -12.5001   3.7871   5.2451 -11.8829   1.0236 -12.2732  -8.1600
-#&gt;    5.5329  13.3028  12.6074  -2.5878  -5.5941   0.5562  -5.8103   8.5753
-#&gt;   -5.4010   2.8003  13.1058  19.3734   6.7127 -16.1937 -15.4596  -6.1702
-#&gt;   11.4986  -5.2856 -13.0817 -10.2939  11.4369  -3.0693  -0.0985   4.5245
-#&gt;   -2.5235   5.8303   6.6791  -2.3794  -9.5811 -10.0748   8.3251  -4.6420
-#&gt;    1.7611  -4.7982  -7.4201  -7.8791  -0.7761  -3.6252 -11.5709   8.2978
-#&gt;    6.2354   6.5989  -1.1647  -2.9024   6.3859  12.8372  -2.0742   9.0044
-#&gt;    9.0628   5.6401 -11.0643  12.9377  18.7022   8.8266  -2.4438   3.1865
-#&gt;   23.3604   9.2657   3.3970  -8.7062  -6.8717  -2.8046  -9.5589  12.2281
-#&gt;    7.7669  -1.8073  -2.6762   0.1816   5.0852   4.8972   3.4828  16.7533
-#&gt;    2.4932  -5.4963  -7.9120  -6.5047  -6.5406  -1.9329   4.1788  14.8234
-#&gt;   -5.3505  11.9495   4.3920  -9.8675   2.2816  -1.4681  -1.5793  -4.4033
-#&gt;   -0.0136 -14.8078  -1.1221 -16.8436  -9.0973  -4.0831  12.3706   6.2880
-#&gt;   -4.9696 -10.1979   6.6652   0.0189  -6.0832  -0.6317  11.0303  -3.7822
-#&gt;    2.4485  11.1872  -3.5009  -0.9902   5.9351  -5.1629  -8.4620  -3.1051
-#&gt;   -8.4239   6.5650 -10.2312   3.3654  13.2505 -10.9408   9.8782  -0.8419
-#&gt;    5.4874 -10.8403  -4.4037   1.2643 -24.7471   8.9535  -7.6901   8.3772
-#&gt;    5.0610  -1.1343  -7.9979 -22.3048 -11.4194   0.6497   3.4250  -2.3344
-#&gt;    3.3309   2.1163   8.4426   7.2083   5.7778  -5.4108  -3.7758   4.2746
-#&gt;    1.7990 -10.0215   9.5204  -1.5806  -6.8457  16.0498  16.1067   9.9897
-#&gt; 
-#&gt; Columns 41 to 48  -6.9693  -1.0025  -2.2134  -6.3170   9.0948  -1.7361   3.5968   8.4893
-#&gt;    9.0647  -6.9542  -7.0945  -1.4679   2.0589  -9.2315  -2.5960   8.8961
-#&gt;   18.2541   4.5262  -3.1089  16.8859 -13.0824  -4.6550   8.5792 -12.8205
-#&gt;   -3.7154   1.2275   7.4125  16.2874  -3.3527   6.2874 -22.8437   6.6985
-#&gt;  -14.1493  -9.6810   7.3502  -0.9728  -4.7288   7.1871 -15.1053 -10.5077
-#&gt;   13.8225   2.4936  -7.2103   5.7296 -16.9057  -1.8765   2.7273   8.8092
-#&gt;    3.1865   0.7119   4.8623   4.4978   4.3561   1.6170  -2.1706   4.1135
-#&gt;   14.0796 -14.1702  -0.0491  -7.3907  -9.0390   8.4160   4.0368  -2.7216
-#&gt;    7.1919  -5.8208   1.9014   2.4011 -11.4695  -2.2117  14.5299 -13.9922
-#&gt;   10.7657 -22.0877   5.2620  -2.4118   3.3959  15.3418 -11.0841  -5.3904
-#&gt;   11.6456  -1.1783  -8.3226   2.3509  -0.6836 -13.7478  -7.1503 -14.5361
-#&gt;   -0.5059  -4.8058   0.7666   1.7865  -8.0511  11.0405   8.4096   2.4818
-#&gt;   -4.4413   3.3785   5.6490   7.4721  -9.8648  -2.9037 -16.5969   0.6057
-#&gt;    4.2330  -1.8095  -0.6214  -7.2959  13.4904  -1.4184   2.1635   4.2052
-#&gt;    1.2887   8.2307  -1.1710   1.8860   0.3145  -4.8631 -12.7748   4.6425
-#&gt;  -24.7373   5.7072   3.6388  -2.1579  15.7320   5.0618  -1.7981  -2.8525
-#&gt;   -0.9135  -2.1425   2.2898   0.3204  -4.8585  16.6259  -2.6304   7.3839
-#&gt;    9.4863   3.0734  -7.4235  -6.9697   3.0771  -3.3978  -7.5634   5.9072
-#&gt;  -13.1391  -4.9937   3.1145  -6.9414   7.7810 -15.6575   8.0662   0.8046
-#&gt;   -5.0893   1.9083   7.1953  -4.3467  -4.0151 -14.1832  -8.5426  -3.7625
-#&gt;  -12.2095  -5.2868  -9.4353  -7.6174  -3.7417  -6.3568   6.3544   9.2610
-#&gt;    5.8789  -0.9547  10.4168  -0.1659  -1.3439  -0.9558  -4.9626  -2.8213
-#&gt;   -7.4984   9.9267 -12.3913  -4.7315   3.0514  -1.9298   3.0110   1.7664
-#&gt;    1.2642   1.1877   9.4511 -10.4580   1.3191   1.1447  -4.2584   6.1499
-#&gt;    3.0520   0.5348  -4.0596  10.5863  -5.7892  -6.0434  23.2972 -13.2471
-#&gt;    5.4167  -4.1570  -2.4775  -3.5897  -2.3363   2.6694  -7.3785  20.1467
-#&gt;    4.0000 -10.9761  -7.9503   2.4366   5.9045   1.8082  -3.2834   9.0453
-#&gt;   -5.6440 -19.9693   4.5354  10.0798  -2.7590  13.7042  -4.3610  11.5292
-#&gt;   -0.2855   6.5701 -12.6034   2.9921  -6.5323   8.9753  -4.6547  -1.9325
-#&gt;   15.3504  -6.2978   8.3363  -3.5428  -4.6572 -20.3701   5.8639   1.3350
-#&gt;   14.6105   2.5691  -1.6272  -3.5143  -4.6873  -4.8473  -5.4974   9.2217
-#&gt;   -9.4423  -5.2204   0.9414   1.1194  -4.0145   8.6794   1.3478  -6.8422
-#&gt;    4.2236   3.9177  -5.4846   6.1013  12.8284 -15.4554   1.7544  -2.1606
-#&gt; 
-#&gt; Columns 49 to 54   2.9515  11.1788  -3.2162   4.2199  -8.7510  -0.4698
-#&gt;   -8.2546   1.5481  13.9941   4.7996  -1.6358   4.6425
-#&gt;  -15.1375 -10.0061  12.1848 -10.7892   6.9691   9.1690
-#&gt;   -8.3217   9.2873   2.2899  12.8599  -4.5756  -2.8647
-#&gt;   -5.6257   2.1360  -7.2058   5.5706 -12.1383  -2.8691
-#&gt;  -14.0469  14.2210  -5.4011  -5.3964  11.1551   6.4461
-#&gt;    4.0566 -14.2672   2.0819   6.3743  -7.8146   4.9855
-#&gt;    5.4035   4.6837   5.1133  -3.5482   1.2490  -3.3770
-#&gt;   10.7789  -0.9129  -0.3367  -9.4738  -1.1590 -11.5141
-#&gt;   16.8008  17.2733   0.8854   7.7945  -4.7109  -2.0671
-#&gt;    3.8238   2.6856  -7.5157   0.3261   0.8370   6.8454
-#&gt;   11.6991  14.3613  -2.0897  -8.6265   7.9744 -12.4370
-#&gt;   -0.9094   4.4014  -0.0017  -6.7637  -8.4603  -2.3209
-#&gt;   -4.5636 -17.2242  14.9351   0.6099  -4.3880   2.3146
-#&gt;   -5.6729  -0.3560  -1.6172  -2.3183   4.5545  -2.9117
-#&gt;  -14.9504   5.6278  -2.6968  16.1884  -7.8824   1.1250
-#&gt;   -2.8782   0.6749 -10.2187   6.9900   4.3208  -7.8982
-#&gt;   -7.1419  -0.9489   8.0797   4.8775  -7.9447   5.2716
-#&gt;   -4.6478  11.8023   6.1724  -2.8810  11.0626 -10.1709
-#&gt;  -22.1819  -6.6293   0.3290 -10.5104   1.3671  -1.2411
-#&gt;   -9.3875  -0.2260  -8.4689   4.5091   3.1796  -0.9633
-#&gt;    1.8798   8.7790 -14.9757 -10.7423 -10.7118   6.8752
-#&gt;   -7.9856   3.4475   9.6813  -5.8087  -8.3653   1.1603
-#&gt;   -9.5759  12.2168   5.5431   9.9552  -3.9754  -2.4323
-#&gt;   -6.0945   1.9327   9.2131 -14.2666  -3.1957  13.9342
-#&gt;    2.7968   8.6528   4.2096  -2.2798  -4.6244  -0.5481
-#&gt;  -11.0635   3.2611  -0.0629   8.3418 -13.8785   4.8067
-#&gt;    0.7133  -0.3508  -4.4718  -4.4463   0.5524  -0.9650
-#&gt;   -2.3695   9.1491  -1.5032   5.4229   4.6288  -6.0755
-#&gt;   -8.3196  11.3067 -17.8152  -6.7723  -4.0960   3.6489
-#&gt;   10.1988  -0.4874  -8.1949  -7.2947  -3.0724  -1.9477
-#&gt;   -0.2603   7.2710   0.2193   7.4526  -1.4843  -6.8860
-#&gt;    6.5177  -7.9602  -2.2444  -4.4818   7.0015  -6.3210
-#&gt; 
-#&gt; (11,.,.) = 
-#&gt;  Columns 1 to 6 -1.1317e+00  6.1338e+00 -2.5576e+00  5.9676e+00 -7.4076e-01  9.4375e+00
-#&gt;  -7.3830e-02 -2.3643e+00  3.0892e+00  1.0282e+01 -5.4639e+00  3.9930e+00
-#&gt;   3.3214e-04  2.0503e+00  4.5830e+00  1.0551e+01  8.7468e+00  3.8177e+00
-#&gt;   3.3866e+00 -5.1552e+00  4.1583e+00  3.2091e+00 -2.3299e+00 -5.8536e+00
-#&gt;   6.2066e+00 -6.4345e+00  1.1225e+01 -8.7157e+00  2.7315e+01  3.5272e+00
-#&gt;   5.2782e-01 -7.7228e-01  1.2892e+01  2.9275e+00  2.4857e+01  1.3086e+01
-#&gt;  -4.5641e+00  6.9366e+00 -5.5966e+00  4.0318e+00  2.0407e-01 -2.2197e-01
-#&gt;  -6.0645e-01  2.1406e+00 -1.2474e+01  5.8910e+00 -6.4486e-01 -1.0130e+01
-#&gt;   2.4036e+00  6.4696e+00 -2.9297e+00  1.4889e-01 -5.1351e+00  2.0036e+01
-#&gt;   2.4509e+00 -4.6423e+00  1.2510e+01 -7.9065e+00  1.7127e+00  7.7833e+00
-#&gt;  -1.8669e+00 -2.2378e-01 -1.1977e+01  1.7257e+01 -2.8992e+00  1.4406e+00
-#&gt;   6.5174e-01  2.4414e+00  4.4012e+00  1.2432e-01 -1.6017e+01 -9.1771e+00
-#&gt;   3.8106e+00 -4.8021e+00 -4.3709e+00 -2.5357e+00  1.1032e+01 -3.5777e+00
-#&gt;  -2.6805e+00  6.9364e-01 -1.1436e+00 -9.2016e+00 -7.6174e-02 -6.5264e-01
-#&gt;  -2.2612e+00  1.5984e+00 -2.7703e+00  1.1094e+01  6.4745e-01  9.4065e+00
-#&gt;   9.2892e-01  6.2321e+00 -1.2967e+01 -9.5107e+00 -1.0817e+01  9.0930e+00
-#&gt;  -7.3795e+00  1.5975e+00 -1.2150e-01 -3.0975e+00  3.5803e+00 -5.5984e+00
-#&gt;  -1.2106e+00  1.9328e+00 -1.8825e+00 -5.4533e+00  5.3003e+00  6.1767e+00
-#&gt;  -2.4310e+00  5.6214e+00  1.8692e+00  3.5003e+00 -1.2203e+01  8.0433e+00
-#&gt;  -3.4388e+00  4.1665e+00 -2.6879e+00 -7.4793e+00 -1.2343e+01  6.3606e-01
-#&gt;  -6.2031e-01 -4.2820e-02  7.7809e+00  2.0739e+00 -1.6821e+01  1.3318e+01
-#&gt;   1.9448e+00 -2.3956e+00  1.0637e+01  2.0620e+00  2.3290e+01  3.6094e+00
-#&gt;   1.3678e+00  4.8461e+00 -6.7862e+00  1.4485e+01 -6.2401e+00 -2.1292e+01
-#&gt;  -1.5920e+00 -2.2623e+00  1.0545e+01  4.8266e+00 -1.5302e+00  1.5400e+01
-#&gt;  -3.2247e+00  4.9733e+00 -9.9709e-01  5.8495e+00 -2.0707e+01  1.5160e+00
-#&gt;   1.8802e+00 -8.1139e-01  6.7797e+00  7.3137e+00 -1.5696e+00 -9.7589e+00
-#&gt;  -7.0924e-01 -2.7850e+00  1.0743e+01  2.0128e+00 -1.5353e+01  5.6173e+00
-#&gt;  -2.7223e+00 -2.1392e+00  4.9239e+00  3.2763e+00  6.5702e+00  1.8642e+01
-#&gt;  -1.7339e+00  4.2232e+00  5.1695e+00  1.1145e+01 -1.3100e+01  1.1496e+01
-#&gt;   1.9581e+00  9.5253e-03  1.1015e+01  6.2118e+00  1.5442e+01  2.2033e+01
-#&gt;   4.2693e-01 -4.3243e+00 -5.6036e-01  3.3147e+00  1.5325e+01 -5.5071e+00
-#&gt;   4.4514e-01  1.2299e+01 -1.2557e+01 -2.5392e+00 -1.0628e+01  1.0729e+01
-#&gt;  -3.7966e-01  6.4409e+00 -2.7755e+00  1.4843e+00 -8.9353e+00  9.9412e+00
-#&gt; 
-#&gt; Columns 7 to 12 -7.4730e+00  2.0751e+01 -3.3958e+00  2.6758e+00  4.4874e+00  1.8151e-01
-#&gt;  -8.1720e+00 -5.1799e+00 -7.6785e-01 -7.8041e+00 -5.7666e+00  4.0806e+00
-#&gt;   1.1350e+01  3.0439e+00  1.9052e+01 -1.0943e+01  9.1097e+00 -1.1105e+01
-#&gt;  -5.6714e+00 -2.5151e+00  8.7902e+00  2.8472e+00  8.1658e+00  1.7552e+01
-#&gt;   1.8718e+01 -1.5498e+00 -5.3623e+00  8.4875e+00 -1.4073e+01 -8.4271e+00
-#&gt;   6.6532e+00 -5.9449e-01  6.3128e+00 -3.1373e+00  1.4934e+00 -1.7128e+01
-#&gt;  -7.5091e+00  5.9089e+00 -9.2488e+00  9.9894e+00  8.3955e+00 -3.4173e+00
-#&gt;  -8.7730e+00 -8.3483e+00 -6.8946e+00 -8.4918e+00 -6.3129e-01 -1.2386e+01
-#&gt;  -9.2449e+00  7.6322e+00 -3.6825e+00  5.4819e-01  5.9616e+00 -9.2523e-02
-#&gt;  -1.3567e+01 -1.3246e+01 -1.3746e+01 -1.1919e+01  1.5351e+01  1.0218e+01
-#&gt;  -1.5419e+01  8.9556e+00 -2.0728e+01  1.4247e+01 -3.9596e+00 -9.7159e-01
-#&gt;  -3.5671e+00  3.0351e-01 -7.7662e+00 -2.2717e+00  9.0648e-01 -5.1115e+00
-#&gt;  -7.8641e-01 -1.0749e+01  1.5721e+00 -6.4231e+00 -6.0582e+00  2.1061e+01
-#&gt;   1.4822e+01 -7.7038e+00  5.6132e+00  6.1003e+00  8.4411e+00  5.5134e-01
-#&gt;   1.2431e+01  1.2471e+01  1.5351e+01  1.1560e+00  4.8041e-01 -5.9624e+00
-#&gt;  -1.0521e+00  4.1734e-01  1.0865e+01 -1.5865e+00 -3.6401e+00  1.0007e+01
-#&gt;  -5.6002e+00  4.7122e+00 -9.6763e+00  7.3865e+00 -6.2773e+00 -8.3137e+00
-#&gt;  -8.2807e-01  2.7915e-01 -4.3664e+00  2.1685e+00  7.1691e+00 -1.3004e+01
-#&gt;   2.3468e+00 -1.0451e+01  4.5994e+00  2.8744e+00 -1.3851e+00  1.4872e+00
-#&gt;  -1.7459e+01 -8.4318e+00  4.5601e+00 -5.2780e+00 -1.0380e+01 -9.0515e+00
-#&gt;  -2.0191e+01 -1.4388e+01  2.1117e+00  4.1778e+00 -1.0476e+01  1.2955e+01
-#&gt;   4.5808e+00 -2.8696e+00 -7.1686e-01 -2.2109e+00  6.6905e+00  2.1717e+00
-#&gt;  -1.3009e+00 -5.3445e+00 -8.3054e+00 -1.4240e+01 -1.2072e+01 -6.2068e+00
-#&gt;  -1.0861e+01  2.4532e+00  1.0979e+01 -2.1877e+01  1.6600e+00  1.6845e+01
-#&gt;   1.0075e+01  8.5808e+00 -8.6895e+00 -1.5783e+01 -3.4212e+00  1.5032e+01
-#&gt;   2.2885e+00 -4.7538e+00  1.9113e+00 -2.4164e+00 -5.2331e+00  2.5980e+00
-#&gt;   5.1351e+00  8.3195e+00 -7.8503e+00 -1.0181e+01 -5.1749e+00  2.0852e+00
-#&gt;   3.4162e+00 -1.1397e+01 -8.8680e+00 -7.7502e+00  8.5635e+00  7.2276e+00
-#&gt;   8.5823e+00  7.7476e+00  5.7402e-01 -1.1198e+01  3.2618e+00 -6.1977e+00
-#&gt;  -6.6543e+00  5.0230e+00  2.0462e+00 -4.0047e+00  2.1203e+01 -1.0742e+01
-#&gt;   1.0152e+01  5.0613e+00  3.9177e+00  6.2237e+00 -7.3390e+00  4.5057e+00
-#&gt;  -2.9892e+00  9.9566e-01 -8.6391e+00  9.0331e+00 -1.6127e+01 -1.6381e+01
-#&gt;  -1.2423e+01 -1.4253e+01 -3.5978e+00  1.7549e+01  2.6094e+01 -1.4565e+00
-#&gt; 
-#&gt; Columns 13 to 18 -5.1473e+00  4.5880e+00  4.8012e+00 -8.4028e+00  1.5088e+01  1.0221e+01
-#&gt;  -3.2512e+00 -1.1164e+01 -4.0296e+00 -7.0618e+00  2.3093e+00 -1.4806e+01
-#&gt;  -9.6944e+00 -1.1380e+00 -1.1066e+01  5.2585e-01 -7.7388e+00 -7.1898e+00
-#&gt;   2.8689e+00 -2.6427e+01 -2.9465e+00 -1.8296e+00  2.7431e+01  4.0603e+00
-#&gt;   6.3855e+00 -7.1397e+00 -9.6824e+00 -8.7082e+00 -3.4153e+00 -1.7853e+00
-#&gt;  -5.3972e+00  2.1880e+01  1.5222e+00  6.1476e+00  2.2162e+01 -1.2239e+01
-#&gt;  -9.6486e+00 -2.6183e-01 -8.6121e+00  9.5640e+00  1.1261e+01 -3.2259e+00
-#&gt;   1.9952e+01 -7.3524e-01 -1.2615e+01  3.4120e+00 -1.4355e+01  5.7569e+00
-#&gt;  -7.0494e+00 -4.4634e+00 -1.1620e+01  1.2135e+01  4.0268e-01  3.1175e+00
-#&gt;  -9.9722e-01 -2.2224e+01  6.0879e+00 -7.4062e+00 -1.2521e+00  1.3005e+01
-#&gt;   1.1049e+01 -6.1986e+00 -2.4649e+01 -1.1320e+01  1.2182e+01  1.4877e+00
-#&gt;   1.1714e+01  2.3894e+00 -2.3889e+00  1.1883e+01  4.1728e-01  1.3972e+01
-#&gt;  -1.0449e+00 -1.5324e+01 -2.2083e+01 -7.5406e+00 -1.1010e+00 -1.4601e+00
-#&gt;  -2.6073e+00  4.7571e+00  4.4276e+00  1.4720e+01  1.3987e+00 -3.5035e+00
-#&gt;   6.9581e+00 -9.1628e+00 -2.0643e+01  5.9547e+00  3.9371e+00  9.6763e+00
-#&gt;   1.9253e+01 -3.2973e+00  1.6978e+01  3.7807e+00 -4.6436e+00  6.7588e+00
-#&gt;   1.7045e+01  2.9996e+00 -1.2368e+00 -2.1496e+00 -3.1666e+00 -3.4969e+00
-#&gt;  -2.3435e+01 -8.1208e+00 -4.5308e+00 -6.8805e+00  1.5370e+00 -7.5341e+00
-#&gt;  -8.5797e+00  2.9779e+00  3.0616e+00  4.3426e+00  5.5836e-01 -7.6244e-01
-#&gt;  -6.4696e+00 -8.8340e+00 -2.5160e+01 -4.6706e-01  4.2074e+00  5.3148e-01
-#&gt;  -2.1948e-01 -1.9291e+01 -5.8630e+00 -4.6582e+00  2.4120e+01 -5.1676e-02
-#&gt;  -7.8430e+00 -1.9142e+01 -1.5423e+01 -3.4274e-01  1.9277e+00  9.9320e+00
-#&gt;   1.7546e+01 -1.6262e+01 -3.4166e+00 -6.8749e-01  8.9572e+00  1.1679e+01
-#&gt;  -7.2126e+00  6.5993e+00 -3.9654e+00 -3.8250e+00  1.3641e+01  2.6073e+00
-#&gt;   1.2132e+01 -5.4133e+00 -1.7881e+01  6.7747e+00  1.2147e+01 -7.2982e+00
-#&gt;   3.1260e+00 -8.1447e+00  2.5938e+00  3.9689e+00  3.5938e+00  7.3284e+00
-#&gt;   7.2366e-01 -6.3262e+00  1.0782e+00 -8.3334e+00  2.1853e+00 -1.2104e+01
-#&gt;   6.2242e+00  8.9742e+00  9.1868e+00  3.0963e+00 -1.3469e+00 -1.5387e+01
-#&gt;  -9.5708e-01 -1.1402e+01  4.7870e+00  1.5015e+01 -1.3496e+01 -1.7116e+00
-#&gt;  -2.5014e+01 -1.7064e+00  5.2445e+00  4.9305e+00 -3.9577e+00 -4.7781e+00
-#&gt;   1.3073e+01 -1.3389e-01 -8.8147e+00  8.7060e+00  4.8878e-01 -3.6539e-01
-#&gt;   2.4386e+00  6.9157e+00  1.8024e+01 -8.7318e+00  1.0557e+00  8.8830e+00
-#&gt;  -3.7047e+01 -7.3004e+00  1.0108e+01  5.1653e+00 -1.2043e+01 -7.7882e+00
-#&gt; 
-#&gt; Columns 19 to 24 -1.1527e+00  9.7728e+00  9.2399e+00  7.1674e+00 -5.7296e+00 -9.0559e+00
-#&gt;   1.3867e+01 -1.2516e+00 -1.9645e+00 -4.7086e+00  4.0830e-01 -6.7910e+00
-#&gt;   2.2121e+01 -2.3452e+00  7.5375e+00  1.8737e+01  2.8337e+01  3.8018e+00
-#&gt;  -8.5485e+00  2.1165e+00 -5.1741e+00  1.5001e+01 -3.3457e+00  7.0586e+00
-#&gt;  -8.2962e+00  1.3145e+01 -1.0026e+01  1.8084e+01 -2.6586e+00  4.2716e+00
-#&gt;   5.7663e+00 -4.9370e-01  2.2318e+00  9.8645e+00 -8.5401e-01  1.8233e+00
-#&gt;   7.6503e-01 -1.4121e+01 -3.8389e+00 -6.6990e-01  7.2910e+00 -1.1946e+01
-#&gt;  -7.6945e+00 -1.7956e+00 -7.7401e+00  1.4362e+01  5.9470e+00 -7.0761e+00
-#&gt;   6.6104e+00  2.5208e+00 -1.4744e+00 -4.5850e+00 -4.0104e+00  6.8822e-02
-#&gt;  -1.4605e+00  1.8892e+01 -1.1577e+01  7.6078e+00  1.7155e+01 -6.3099e-01
-#&gt;   1.6647e+00 -1.6810e+01  4.4022e+00  1.1439e+01  1.2791e+01  6.3887e-01
-#&gt;   6.9808e+00 -3.1197e+00  1.0340e+01  6.6192e+00  7.2275e+00  5.4393e+00
-#&gt;   6.1483e+00 -1.1329e+01  5.2164e+00 -9.9997e+00  1.8280e-01 -1.7751e+00
-#&gt;   4.1831e+00 -1.2170e+01 -2.0623e+01  6.2708e+00  1.3587e+01 -4.3501e+00
-#&gt;   9.0078e+00  9.4333e+00 -4.0343e+00 -1.1860e+01  5.8675e+00  1.4908e+01
-#&gt;  -1.7740e+01 -1.1639e-01 -1.0863e+01 -1.2342e+01 -1.3941e+01  1.2133e+00
-#&gt;   3.6700e-01 -5.9114e+00 -1.5040e+00 -1.6791e-01 -1.3691e+01 -2.7075e+00
-#&gt;   1.0049e+01 -1.3508e+01 -5.0169e+00 -3.2451e+00 -1.7390e+01  1.5393e+01
-#&gt;   2.7555e+00 -1.2159e+01 -1.0982e+01  1.4569e+00 -1.4459e+00 -3.3818e+00
-#&gt;  -3.3839e+00 -2.7826e+01  5.1412e+00 -2.9038e+00 -1.9081e+01  7.1817e+00
-#&gt;   1.3215e+01 -6.5614e+00  1.2565e+01  1.2649e+01 -3.2289e+00  1.1356e+00
-#&gt;  -3.3892e+00 -5.6640e+00 -4.1005e+00  2.1143e+01  6.6527e+00 -3.3958e-01
-#&gt;  -5.2809e+00 -2.3998e+00 -4.0944e-01  7.6645e+00  9.1217e-01  7.1201e+00
-#&gt;  -6.3011e+00 -7.9490e-01  3.2921e+00 -6.7857e+00 -9.5898e+00  9.0010e+00
-#&gt;   5.4313e+00 -2.0263e+01  6.8917e+00  1.1567e+00  6.5042e+00 -7.5448e+00
-#&gt;  -6.0591e+00 -7.2527e+00 -5.7149e+00  1.0375e+01 -2.2299e+00 -2.6686e+00
-#&gt;   1.2329e+01  4.7704e+00 -9.7814e+00  3.5172e+00 -9.1709e+00  4.2209e+00
-#&gt;   6.9782e+00 -4.2750e+00 -3.2572e+00  1.1786e+01  1.2225e+01 -1.5089e+01
-#&gt;  -3.2112e+00  9.4004e+00  1.1233e+01 -6.5508e+00  6.4754e+00 -2.0153e+00
-#&gt;  -1.0910e+01 -1.7054e+00  4.9555e+00  1.9528e+00 -1.5414e+01 -5.9557e+00
-#&gt;  -6.0952e+00  1.2520e-02 -6.9353e+00 -3.0301e+00 -4.0288e+00  6.9027e+00
-#&gt;   8.6263e+00  4.6778e+00  8.9106e-01 -5.4085e-01 -7.0310e+00 -1.0575e+01
-#&gt;  -2.8492e+00 -1.1904e-01  2.1156e+00 -1.1266e+01 -1.6489e+01  6.7803e+00
-#&gt; 
-#&gt; Columns 25 to 30 -9.3624e+00  5.4876e-01  7.9810e+00 -7.9080e+00  7.6107e+00 -1.1416e+01
-#&gt;   1.5819e-01  3.3266e+00  1.9523e+01 -3.4094e+00 -3.4305e+00  1.2334e+01
-#&gt;   5.6357e+00  8.5313e+00  1.1254e+01 -9.2716e+00  1.6875e+01 -6.5014e+00
-#&gt;   1.5484e+01 -1.5222e+00 -1.0905e+00  5.4825e+00  1.2155e+00 -5.9257e+00
-#&gt;   1.3077e+01  1.2633e+00  4.3155e+00  5.4851e+00  1.1052e+01 -5.2909e+00
-#&gt;  -4.9876e+00  1.3343e+00  1.0314e+01 -8.6529e+00  1.4487e+01 -7.6075e+00
-#&gt;   5.8356e+00 -1.0098e+01 -8.7084e+00  2.3044e+00  4.4664e+00  7.3290e-01
-#&gt;   8.8100e+00  7.0501e+00  7.4193e+00  1.3621e+01 -1.0852e+01 -9.3415e+00
-#&gt;  -4.0239e+00  9.4036e+00 -5.5394e+00 -4.4665e+00  2.0939e+00 -7.4361e+00
-#&gt;   1.5025e+01 -6.1462e+00  2.4271e+00  2.1596e+00 -3.2672e+00 -8.1053e+00
-#&gt;  -1.1610e+01  3.4071e+01 -3.7669e+00  3.3210e+00 -6.1154e+00 -2.5193e+00
-#&gt;  -1.0500e+01  1.0087e+01 -1.6890e+01 -3.1656e+00  2.7390e+00 -1.7209e+01
-#&gt;   7.7159e+00  1.8313e+01  5.4162e+00  9.3780e-01 -2.6906e+00 -1.2155e+01
-#&gt;   6.7944e+00 -1.1847e+01 -1.5220e+01  8.5966e+00 -4.2744e+00  4.2166e+00
-#&gt;   9.3977e+00  1.0826e+01 -4.1726e+00 -9.3062e+00  8.5787e+00 -3.1371e+01
-#&gt;  -8.1277e+00  1.2554e+00  3.1502e+00  1.7895e+01  5.3301e-01 -8.0715e-01
-#&gt;   6.5867e+00  2.9961e+00 -6.5871e-01 -7.9394e+00  1.8592e+01 -8.3739e-01
-#&gt;  -6.8152e+00  8.8629e+00  1.2846e+00  9.4264e-01  1.0569e+01  1.2390e+00
-#&gt;  -3.7623e+00 -1.0827e+01 -5.3371e+00  6.2339e-02  1.4118e+00 -5.5352e+00
-#&gt;  -5.6711e+00 -1.4266e-01  9.7493e-01 -3.9305e+00  6.5719e+00 -1.0596e+01
-#&gt;   8.9836e+00  1.1699e+01 -1.0697e+00 -2.4942e+00 -7.4618e+00  5.3027e+00
-#&gt;   4.7000e+00  1.0295e+01  6.0547e-01 -3.1094e+00 -9.0754e+00 -7.5085e+00
-#&gt;  -5.3227e+00  1.3015e-01  1.7044e+00 -1.7423e+00 -5.3645e+00 -1.1395e+00
-#&gt;   1.5663e+01 -1.7152e+01 -6.4840e+00  6.6437e+00 -1.3947e+01  9.2633e+00
-#&gt;   6.0797e+00  8.3147e+00  3.4008e+00  1.0620e+01 -2.1545e+00  1.6187e+00
-#&gt;   3.9714e+00  1.8376e+00 -8.6624e+00  1.5170e+01 -1.5158e+00 -6.9418e+00
-#&gt;   9.5862e+00  5.2367e+00 -1.3220e+01  1.1621e+01 -6.2098e+00  6.8650e+00
-#&gt;   1.9166e+00  3.3546e+00 -1.0390e+01  1.4918e+01 -3.3603e+00  6.2150e+00
-#&gt;   3.1671e+00 -7.4798e+00 -1.1725e+00  9.4404e+00  9.5865e+00 -2.9694e+00
-#&gt;   1.9898e+00 -4.1024e+00  6.3809e+00  8.0951e+00 -1.9674e+01  8.8272e+00
-#&gt;  -5.9686e+00  2.0873e+00 -1.1349e+01 -1.5983e+00 -1.0487e+01 -5.0705e+00
-#&gt;  -1.0124e+01  1.8218e+01  6.1705e+00  4.9353e+00 -3.9051e+00 -7.2721e+00
-#&gt;   4.3517e+00  4.9827e+00  4.1858e+00 -5.5498e+00 -1.7824e+00 -1.3690e+01
-#&gt; 
-#&gt; Columns 31 to 36 -1.3546e+01 -6.3510e+00  4.4880e+00 -7.4601e+00  5.7447e-01 -8.6156e+00
-#&gt;  -6.1000e+00  1.6707e+00  8.0940e+00 -2.1624e+01  2.5567e+00 -5.0916e+00
-#&gt;   7.0156e+00 -7.9431e+00 -7.5156e+00 -1.2734e+01  1.2541e+01  4.7910e+00
-#&gt;   1.3774e+00 -4.0157e+00 -9.6722e-01  1.5100e+01  9.2232e+00 -6.6463e+00
-#&gt;  -1.4879e+00 -7.8858e-01 -1.6185e+01  6.6241e-01 -8.2372e+00  4.5778e+00
-#&gt;  -4.5803e+00  1.8085e+01 -1.0201e+01 -1.9039e+00 -7.1335e+00 -3.7417e+00
-#&gt;   8.8416e+00 -1.2932e+01  1.1269e+01 -1.1926e+01  9.5754e+00 -2.2987e-01
-#&gt;   1.0771e+01  3.0325e+00  4.6577e+00  7.8041e+00  1.5533e+00  3.6967e+00
-#&gt;  -3.9544e+00 -3.3679e+00 -1.4698e+01 -8.1534e+00  5.1518e+00 -3.3583e+00
-#&gt;   3.7434e+00  6.7088e+00  1.3542e+01  1.3842e+00  2.7321e+01 -3.2139e-01
-#&gt;  -4.0020e+00  3.8528e+00  1.0798e+01  1.6384e+01  1.3302e+01 -9.4720e-01
-#&gt;   1.0970e+01 -9.7171e+00 -2.9361e+00 -2.7184e+00  4.5595e+00 -1.8924e+00
-#&gt;  -9.1193e-01  1.1704e+00  8.5453e+00  8.4031e+00 -1.9880e+00 -8.7509e+00
-#&gt;  -1.9218e+00  5.1523e+00  1.2030e+01  5.9250e-01  4.6513e-01  1.4705e+01
-#&gt;   1.2687e+01 -2.0923e+00  6.3140e+00 -1.4024e+01  1.3129e+00 -9.3168e+00
-#&gt;   1.9984e+00 -1.1818e+01  3.6047e+00 -1.8089e+01  1.9418e-01  1.2791e+00
-#&gt;   8.9388e+00 -1.2062e+01  4.0897e+00  5.8645e+00 -6.9730e+00 -7.4363e+00
-#&gt;   3.4557e+00 -2.7324e+00  4.8019e+00 -9.0891e+00 -2.4316e+00  3.1782e-01
-#&gt;  -9.2512e+00  8.3469e+00 -4.9844e+00 -9.7439e+00 -8.0951e+00 -1.0859e+01
-#&gt;   2.3731e+00  3.0708e+00  4.7446e+00  1.0031e+01  1.2930e+01 -1.3181e+00
-#&gt;  -7.5118e+00  2.8330e+00  3.8269e+00 -5.7691e+00  2.7319e+00  9.6237e+00
-#&gt;  -4.9822e-01  3.5407e+00 -4.0840e+00 -3.0588e+00 -6.6711e+00 -6.3745e+00
-#&gt;   3.5504e+00 -7.8062e+00  1.8326e+01 -8.1473e+00  7.5067e+00  8.8182e+00
-#&gt;  -7.9597e-01  8.1412e+00 -1.6907e+00 -1.0880e+01  4.1364e+00 -1.7070e+01
-#&gt;   3.2947e+00  2.7745e+00  5.2721e+00  6.4839e+00  9.4522e+00 -2.6182e+00
-#&gt;  -1.4109e+00  1.4061e+00  1.2470e+00 -2.3468e+00 -7.3066e+00  1.6836e+00
-#&gt;   1.1539e+00  1.0565e+00 -1.1750e+00 -1.1603e+01 -5.3273e+00  1.9695e+00
-#&gt;   5.0062e+00 -8.7447e+00 -1.4044e+01  4.4016e+00  6.3763e-01 -9.3230e+00
-#&gt;   3.9013e+00 -2.4847e+01  9.0986e+00 -5.8871e+00  8.1606e-01 -1.5515e+01
-#&gt;  -1.1558e+01  3.2944e+00 -3.6297e+00 -1.2562e+01  3.9875e+00 -9.3170e+00
-#&gt;   5.5988e+00 -5.4421e+00 -1.5319e+01  6.2700e+00 -9.3855e+00 -1.7126e+00
-#&gt;   6.0868e+00  2.6230e+00  5.1495e+00 -1.2472e+01 -7.2877e+00 -2.8857e+00
-#&gt;  -1.1704e+01  4.3418e+00  9.1137e+00  5.8814e+00 -7.2834e+00 -5.6158e+00
-#&gt; 
-#&gt; Columns 37 to 42 -1.1038e+01 -9.4436e+00 -1.2189e+01 -7.8501e-01  6.5149e+00  1.6466e+01
-#&gt;  -1.7351e+01 -1.1245e+01 -1.2572e+00  7.2691e+00 -1.4777e+01 -8.7542e+00
-#&gt;   5.4625e+00 -7.9935e+00  1.3270e+01  1.6019e+00 -6.8235e+00  3.9497e+00
-#&gt;   1.1942e+01  4.4764e+00  2.1590e+00 -1.6536e+00  6.6310e+00 -1.3702e+01
-#&gt;  -5.6392e+00 -6.1500e+00 -8.4036e+00 -3.0322e+00 -4.5520e+00  5.1926e+00
-#&gt;  -5.0150e+00  2.2452e+00 -1.4490e+01  4.5497e+00 -4.5616e+00 -3.2219e+00
-#&gt;  -3.3302e+00 -6.7524e+00  2.2330e+01 -1.0670e+01  1.0383e+01  1.3371e+00
-#&gt;  -1.7405e+00 -1.3303e+01 -1.1521e+01  5.4908e+00 -1.6579e+00 -1.4085e+01
-#&gt;   1.0415e+01  7.3891e-01 -4.1275e-01  1.0531e+01 -2.0819e+00  4.2700e+00
-#&gt;  -2.3512e+00 -6.6763e-01  4.2068e+00 -1.3040e+00 -1.8057e+00 -8.3000e+00
-#&gt;  -4.4982e+00  1.5905e+00  1.0633e+01 -7.0164e+00  1.2260e+01  1.0875e+00
-#&gt;   5.7505e+00  4.8071e+00  4.1287e-01 -1.3970e+01  6.7895e+00 -4.7549e+00
-#&gt;  -2.7405e-01 -8.5214e-01  1.0335e+01  4.8550e+00  1.4208e+01  1.4088e+01
-#&gt;  -9.4388e+00  9.0875e+00  4.8428e+00  1.2839e+01  8.8319e+00  7.6958e+00
-#&gt;   9.2601e+00 -1.5140e+01 -9.4969e+00  1.0561e+01  5.3677e+00  1.1381e+00
-#&gt;   1.7443e+01 -1.9189e+01  1.1321e+01  5.5307e+00  1.7030e+01 -6.5051e+00
-#&gt;   4.0160e+00 -8.7837e+00  3.3643e+00 -3.8546e+00 -9.0935e-01  1.0686e+01
-#&gt;  -1.6384e+01 -1.5310e+00  1.5033e+01  1.3236e+00  1.6160e-01  1.3139e+01
-#&gt;  -1.0101e+01  1.7874e+01 -2.3895e+01  3.4825e+00 -1.2525e+01 -1.9395e+00
-#&gt;   2.1283e+01  7.1411e+00  1.0875e+00 -5.8616e-01  9.7979e+00  7.8337e-01
-#&gt;   8.4271e+00 -5.7823e+00 -1.4712e+01  4.0464e+00 -4.4023e+00 -3.9672e+00
-#&gt;  -3.5424e+00 -8.4049e+00  5.6957e+00  2.0068e+01 -4.3230e+00  4.8100e+00
-#&gt;   6.1007e+00 -4.0186e+00  1.2627e+01  7.1385e+00  1.3838e+01 -1.7519e+00
-#&gt;   5.5231e+00  3.5180e+00 -1.4350e+01  1.4422e+01  8.0808e+00 -1.3469e+01
-#&gt;  -7.9620e+00 -3.5839e+00  1.9825e+01 -1.4483e+01 -6.8880e+00 -5.3600e+00
-#&gt;   5.7460e-01  1.3915e+01  1.9113e+00  4.3465e-01  2.3210e+01 -7.4884e+00
-#&gt;  -1.0917e+01 -2.1559e+00  1.0245e+01  7.2238e+00 -1.1342e+01 -2.3520e+00
-#&gt;  -4.0703e+00  4.6827e+00  7.5163e+00 -1.0181e+01  7.8456e+00 -2.6150e+00
-#&gt;   4.4259e+00 -1.4128e+01  1.9743e+01 -1.3354e+01 -2.6735e+00  1.2199e+01
-#&gt;  -6.7443e+00  1.0965e+01 -1.2785e+01  9.3634e+00  1.6914e+00 -7.8264e+00
-#&gt;  -9.2579e+00  6.2744e+00 -6.1033e+00  1.5891e+00  5.2159e+00 -1.6728e+01
-#&gt;   3.6757e+00 -4.8500e-01  7.3705e-02  3.6072e+00  1.4813e+01  4.3598e+00
-#&gt;   1.4336e+01  1.0344e+00 -2.0588e+01  3.1367e+00 -1.2827e+01 -1.3424e+01
-#&gt; 
-#&gt; Columns 43 to 48  6.9651e+00  7.5815e+00  1.2595e+01  1.3403e+01 -1.6853e+01  5.2587e+00
-#&gt;  -2.5691e+00  2.0272e+00  1.2418e+01 -5.3198e+00 -1.2871e+00 -3.2529e+00
-#&gt;   3.9849e+00  9.4138e+00  5.3921e-01  1.0588e+01  1.3211e+01  4.9531e+00
-#&gt;   8.3807e+00  4.8643e+00 -1.2668e-01  1.0459e+01  6.5560e+00  4.5429e+00
-#&gt;  -4.0033e+00  9.6132e+00 -6.2190e-01  1.1221e+01 -3.4928e+00  1.2902e+01
-#&gt;   4.1005e+00 -1.0279e+01  2.3105e+01  1.2248e+01  1.8693e+00 -2.9177e+00
-#&gt;  -2.0353e+00  1.3293e+01 -6.4480e+00  5.2561e+00  9.0189e+00 -7.2915e+00
-#&gt;   7.1263e+00 -1.0902e+01  1.1063e+01  4.7045e+00  6.7028e+00 -1.5299e+01
-#&gt;   4.4184e+00 -2.6906e+00  8.4152e+00 -2.4045e+00  9.4426e-01 -1.7981e+01
-#&gt;  -7.2220e+00  2.2200e+01 -1.2649e+01  1.7762e+01 -1.0952e+01  3.9127e+00
-#&gt;   5.4471e+00  4.0611e+00  1.7023e+01  6.8594e+00 -4.1441e+00 -4.1229e+00
-#&gt;  -9.6938e+00  7.7109e+00 -9.2577e+00 -1.2252e+00  5.6339e+00  1.3623e+01
-#&gt;  -1.6778e+00  3.4654e+00 -4.4261e+00 -4.1301e+00 -6.6414e-01 -6.3843e+00
-#&gt;   5.3305e+00  2.0061e+00 -2.0067e+00 -1.4961e+01  2.8382e+00 -9.3593e+00
-#&gt;  -1.1324e+01  7.9800e+00 -9.3554e-01 -5.5449e-01 -8.3770e+00  1.3572e+01
-#&gt;  -8.0037e-01  7.4726e+00 -5.6056e+00  8.8582e+00 -1.5331e+01 -9.5262e+00
-#&gt;  -1.6174e+01 -4.3381e+00  1.9891e+00  6.6610e+00  4.9117e+00 -1.3336e+01
-#&gt;  -1.1890e-01  5.5596e-01  6.8062e+00  6.4606e+00 -1.4445e+01  4.2518e+00
-#&gt;  -1.4105e+01 -8.2379e+00  4.3005e+00 -1.5418e+01  1.0378e+01 -6.8396e+00
-#&gt;  -9.3498e+00 -6.7595e+00  6.4541e+00  1.0873e+00  3.4295e+00 -2.1746e+01
-#&gt;   7.2333e-02  7.8964e+00  1.4539e+01  6.7998e+00  1.4312e+01 -7.5116e+00
-#&gt;  -5.7760e+00  5.3283e+00 -3.8552e+00  1.9946e+01 -1.5278e+01  1.5928e+01
-#&gt;  -7.5951e-01 -7.7022e+00 -6.5470e+00 -5.0391e+00 -2.7167e+01  1.0447e+01
-#&gt;   3.4111e+00 -2.5768e+00  1.6794e-02  4.7421e+00  2.4847e+00 -3.8183e+00
-#&gt;   3.7589e+00 -1.5461e+01  1.1627e+01 -1.1597e+01  2.3019e+01 -8.2968e+00
-#&gt;  -2.6569e+00 -2.6220e+00  2.0674e-01 -7.9169e+00  2.6260e+00 -2.3865e-01
-#&gt;  -1.3413e+00 -6.3797e+00  4.5610e+00 -1.9869e+01  7.4186e+00  1.2396e+01
-#&gt;   2.4078e+01 -7.2091e+00  3.3010e+00 -4.5880e+00  9.1582e+00 -7.8798e+00
-#&gt;   5.8286e+00 -1.2677e+00  1.1683e+01  2.4940e+00 -7.6162e+00 -4.1181e+00
-#&gt;   8.9973e+00  1.4170e+00  7.1925e+00  6.1964e+00 -3.7068e+00  1.0188e+00
-#&gt;   2.1209e+00  1.2287e+00 -7.3702e+00  1.2771e+00 -9.5324e+00 -8.3532e+00
-#&gt;  -6.5647e+00 -5.5182e-01 -4.8383e+00 -1.3195e+01 -4.1809e+00 -6.8671e+00
-#&gt;  -2.0425e+01  8.0334e+00  6.0133e+00 -1.9198e+01  1.4579e+01 -1.0223e+01
-#&gt; 
-#&gt; Columns 49 to 54 -7.2190e+00 -1.0073e+01  6.4659e+00  1.2493e+01  2.3346e+00  2.7821e+00
-#&gt;  -3.8281e-01 -2.2221e+01  1.0597e+00  4.3999e+00  5.9141e+00 -1.5121e+00
-#&gt;  -8.0859e+00  3.9550e+00 -4.5062e+00 -2.7541e-01 -7.2299e+00 -7.3062e+00
-#&gt;  -1.2573e+01  7.0394e-01 -3.7095e+00  6.9351e+00  2.3355e+00  4.2401e-01
-#&gt;  -1.5239e+01 -5.7799e-01 -5.6978e+00  1.3292e+01 -2.2482e+00 -2.4419e+00
-#&gt;   5.2419e+00 -2.9351e+00  2.8904e+00 -2.9526e+00  1.6841e+01 -1.6054e+00
-#&gt;   1.2346e+00  5.5824e+00  7.6284e+00  3.9365e+00 -3.2672e+00  3.5293e+00
-#&gt;   1.6857e+01 -1.3910e+01  1.2852e+00  3.9804e+00  5.5320e+00  8.0857e+00
-#&gt;   6.8917e+00 -4.0023e+00 -7.2896e+00  1.5376e+01 -4.5392e+00 -5.8749e+00
-#&gt;  -1.6275e-01  1.2503e+01 -7.3538e+00  5.9774e+00 -1.1087e+00  2.0782e+00
-#&gt;  -1.1065e+01 -1.6621e+01  1.2406e-01 -7.4272e+00  4.1952e+00  7.5047e+00
-#&gt;   1.1819e+00 -5.6837e+00  2.5869e-01  3.8084e+00  7.7077e+00  3.5379e+00
-#&gt;  -1.3110e+01 -1.9613e+01  5.2154e+00 -3.1618e+00  3.5303e+00  2.6663e+00
-#&gt;  -3.2388e+00 -6.0232e-01  6.8353e+00  3.4902e+00 -2.0934e+00 -4.0454e+00
-#&gt;  -1.4695e+01  2.4236e+00  6.4834e+00 -8.7571e+00 -5.6855e-02 -1.7786e+00
-#&gt;   6.9134e+00  9.1073e+00 -8.8762e+00  1.8565e+01  7.4678e+00  1.3947e+00
-#&gt;  -2.5009e+00  2.0542e+01 -1.3876e+01 -1.1444e+00 -2.6779e+00  2.1771e+00
-#&gt;  -1.4500e+01 -5.0176e+00  5.4481e+00 -3.2757e+00 -2.2231e+00 -4.8648e+00
-#&gt;   7.7591e+00  1.9461e+01 -1.0867e+01  8.9869e+00  1.2776e+01  3.1926e+00
-#&gt;  -1.0345e+01 -1.7714e+01 -2.5051e+00 -6.5919e+00  6.6145e+00 -2.6338e+00
-#&gt;  -2.9368e+00 -1.2460e+01 -1.5806e+00  1.3673e+01  1.2103e+01 -1.0196e+00
-#&gt;   7.4144e+00  5.1670e+00  6.5157e-01  8.2214e+00  2.8969e-01 -4.0820e+00
-#&gt;   6.0764e+00 -8.6176e+00 -7.6420e-01 -3.7994e+00 -2.0069e+00 -2.2817e+00
-#&gt;   1.2473e+01 -2.9325e+00  1.2033e+01 -5.0790e+00  4.5708e+00  3.7162e+00
-#&gt;   1.6254e+01 -5.1644e+00  1.5308e+01 -4.3444e+00  4.8734e+00  6.4241e+00
-#&gt;   1.3303e+01  9.1464e+00 -5.4646e-01 -6.8950e+00 -2.3717e-01  2.3831e+00
-#&gt;  -8.0684e+00  7.2510e+00 -3.3224e+00 -1.9240e+00 -7.4004e+00  7.9264e+00
-#&gt;   1.8539e+01 -1.6550e+00  6.1599e+00  3.3643e+00  5.9530e+00 -2.5016e+00
-#&gt;  -7.3350e+00  1.3896e+00  3.5709e+00 -1.8369e+01  1.0499e+00  2.2174e+00
-#&gt;  -1.0695e+01 -1.1400e+01  1.3130e+01 -3.9909e+00 -8.6411e-01 -8.8215e+00
-#&gt;   4.5670e+00  9.5896e+00 -4.1087e+00  9.7049e-02  1.1100e-01 -5.2238e+00
-#&gt;   4.7555e+00 -4.3153e+00 -1.7688e+01  1.7190e+01  8.5564e+00  7.2813e-01
-#&gt;  -2.2178e+00  2.6892e+00 -3.9653e+00 -4.5148e+00 -1.1481e+01 -4.1458e+00
-#&gt; 
-#&gt; (12,.,.) = 
-#&gt;  Columns 1 to 8   4.1572   3.1550  -4.5066  -4.3966   0.3681  13.7396  -9.8935   3.8527
-#&gt;   -2.1753   1.6009   4.1352  -5.0619  -9.8773  -5.4267   0.4332   7.6715
-#&gt;    0.5442   3.8844  -3.2638  -8.7271  -2.0219  -1.0003  -8.7802   3.2420
-#&gt;   -3.4595   7.7164  -5.6496   0.3647   5.2847  17.9490 -13.4993  -9.7302
-#&gt;    2.8375   2.7401  -0.0320   4.1434  -5.7695   7.6593   2.0797   6.3549
-#&gt;    7.7948   4.9919   2.9437   5.6431  -1.0399   4.5085   5.1377   8.8229
-#&gt;    1.2156   0.6778   0.3048   2.4615 -11.8932  -0.5845  -4.5962   1.5282
-#&gt;   -1.1901  -5.7463  -2.6973   1.6894  11.0464   2.8514   1.1103   3.5480
-#&gt;   -1.2291  -3.4844  -5.0994  -4.0559  -3.7739  -9.6346   5.5820   2.7121
-#&gt;   -5.6083  -6.8008 -16.5468   4.8717  -6.5090  10.2817  13.9602  -5.4753
-#&gt;    1.4881  -0.1116  12.4634  -0.2185  -4.6746   1.3887   4.5608  -7.3166
-#&gt;    3.0457   4.2886  -0.2731   0.7791   6.8208  -6.9506   5.7391  -4.7759
-#&gt;   -3.7184   6.8204   7.9618 -10.4932  -3.3702   9.7914  -3.7035  -7.7186
-#&gt;    2.2507  -0.4025   3.5082  -1.9928  -3.8383   4.6852  14.3333  -0.3636
-#&gt;   -5.0853   0.4707  11.3558   6.5361  11.7461  14.7619  -8.9268   2.1032
-#&gt;    1.2178   1.1843   3.1466   8.4804  -1.5468  -7.6015   2.6589   1.4342
-#&gt;    1.8668   1.0229   4.2260   3.6026 -13.7013   4.1824   6.7998   2.7540
-#&gt;    1.0079  -7.0481   4.4371   1.4256 -13.3230   3.6913  11.5527  -2.3304
-#&gt;    6.0447  -0.8599  11.8712 -10.0378  18.7619  -0.6587  -4.1770  -1.6276
-#&gt;   -3.3841   2.5566  -1.0242   1.3781  -3.5386   2.8717 -17.6559 -15.9866
-#&gt;    0.8171   2.0557  -3.7927   3.3639   8.8911  13.5833   9.3517   0.6749
-#&gt;    4.6844   2.6273   1.8706  -3.8131  -1.6117   2.9076   8.1218 -13.7154
-#&gt;    4.3276  -1.1191   1.6236  -4.7895  -0.5551  -3.0250  -0.0153  -3.1731
-#&gt;    4.0228   8.7746   1.5787  -3.5167  17.4147  -2.3440  -6.4586 -11.5840
-#&gt;   -4.8699   7.6210   0.5728  -2.0027  -1.5587   8.3082 -12.7090  -3.2201
-#&gt;   -0.8797   0.5242  -1.6811  -6.3808  -0.6943   4.5576   8.1417   4.5895
-#&gt;   -4.6121   1.4084   3.9895  -4.3361   3.4157  -9.0769   2.0686  -1.4477
-#&gt;    4.0930   2.8935   0.9401  -3.7161   3.5047  11.9064   6.3834 -10.2932
-#&gt;   -6.5860   3.5663 -13.5360  -1.9644  -1.2082   5.7908  -0.7756   2.4446
-#&gt;    9.6996   0.9501  -3.4183   2.8111  15.6074 -14.8543  -7.2326   1.7045
-#&gt;    0.8306   2.2533  12.9535   3.3290  -9.9182 -11.2494   2.2316   3.0476
-#&gt;    0.7666 -14.2525  -1.6530   7.2452   5.0492  -2.5074   1.5907   4.8450
-#&gt;   -0.6008  -8.0231  -5.3108   2.9114  16.4067  -0.1859  -4.7346   4.3219
-#&gt; 
-#&gt; Columns 9 to 16  -2.5989  19.3196   7.4478  -7.6892  -2.1664 -10.4449   1.8263  11.6938
-#&gt;   -6.4266   6.7023   4.6939  -4.7260 -15.5371   9.8345 -15.1092   0.0464
-#&gt;   -7.0146  -8.5847   5.2452   4.4266   1.7816  -3.0670  -9.9828  -5.3326
-#&gt;    7.6460 -17.2616 -12.4030  -5.3674   5.9444  -2.3093  -4.7330  -9.2392
-#&gt;   -2.7759  12.0043   4.0179   0.7124   9.5738  -9.8513  -0.1610 -19.7876
-#&gt;   -5.9480  15.0897   7.9791   5.5983 -10.9633  -4.7206  -3.6612  -0.3144
-#&gt;   -1.4184 -11.7217  -0.5225   1.9119   1.9999  -0.7733   4.4021   4.1195
-#&gt;  -12.4361 -10.2281   3.8942  -0.5820 -12.7387   1.5944  -6.0138  -0.4281
-#&gt;    7.0889  -0.2168   9.9802   2.0844   0.1809   4.3920   3.6275  12.7628
-#&gt;   -8.0704   5.1168 -15.3324 -17.4488   7.4944 -13.1773  11.4167  -0.1773
-#&gt;  -10.2837  -9.2453   1.9945  -8.0543  -7.2474  -2.9846  -4.3786   6.2989
-#&gt;  -11.1527  -6.4683  -8.1909 -10.1569  11.2587 -10.9237   8.2038  -2.3894
-#&gt;    8.5683 -21.2833  -5.9609  -3.2890  -4.0639  -0.0971   1.0503   0.9199
-#&gt;   -7.5451   5.5467 -14.0297  17.4662  12.0563   0.5547  -0.9491   0.1231
-#&gt;    7.8096   1.8895  -1.6090 -10.1475   8.6151   2.7483   7.7948   1.8355
-#&gt;   -1.6110   3.8760   0.3370  -3.2066   7.4635  -4.5550   7.4625   1.5404
-#&gt;   -9.8545   2.4652  -2.9049   0.2660  -1.0614 -10.0108  21.3865  -5.4643
-#&gt;   11.3801  -2.3973  -0.7019   9.8669   2.5230  -5.6358   5.0970  -1.5985
-#&gt;    5.4459  24.4662  -8.2863   8.5212  12.8124  -2.9869   1.2998  -2.4445
-#&gt;    7.8268  -6.5135   0.5097   3.1459  10.4015  -4.6661  -3.6812   8.9389
-#&gt;   -2.4176   8.1669  -3.7062   3.2369  -3.6993   0.0616  -5.5032  -6.2056
-#&gt;   -0.0027  -1.8052  -9.5077   3.3441   3.4654 -12.8194   7.9062   4.3636
-#&gt;   -4.1467   3.3337 -14.9374  -3.9820  -2.9017   3.4750   0.7142  -3.4938
-#&gt;   -3.4918  -5.0057  -2.3045  -5.9854   1.6011   8.9060  -1.1377  18.2808
-#&gt;   15.2889  -5.9912  -9.7999  -9.3224  -6.7170   5.6690 -20.5101   6.6038
-#&gt;    7.9076  -5.2190  -1.3830   6.8114  -0.4127  -6.8535  -9.5364  12.9799
-#&gt;   11.9984  -1.8997  -1.4562  -6.3578  -7.3459  12.1111 -15.9151   1.1304
-#&gt;    0.1989  13.3849  -3.9429  -0.3293  -3.5202   5.1030  -6.2169  -7.5652
-#&gt;    0.7378   1.4919  24.2678 -14.2524   7.4111  -6.6276   1.3939  -7.6841
-#&gt;    3.5211  11.6763  13.3942  -1.2435  -1.9457  -8.3661  -8.3402  17.0338
-#&gt;   -1.1142   8.1604   5.1348   1.0463   6.0320   0.4729   4.5285  -6.0714
-#&gt;    5.7136  12.1639  -3.8108  -2.0531  -4.6664  -1.9786   3.5241  -2.6565
-#&gt;   17.5460 -19.8679 -10.2227   2.5279  -6.7701 -12.7822   5.9416  16.1450
-#&gt; 
-#&gt; Columns 17 to 24  29.3803  21.0593   2.4398  -8.3361  -5.0943  -8.2372  -1.9046 -15.1606
-#&gt;   -2.3811  -7.9804  16.3421  12.2823   0.7129  10.6408  -0.7800 -20.5265
-#&gt;   -2.1533  -5.1089  -3.2246  -6.7496   0.2396   6.5642  -7.6011   5.9210
-#&gt;   11.1715   4.4590   0.4038   0.9078  15.9790   4.0143 -10.4392  -6.8453
-#&gt;    3.6084 -16.9220  -3.9541   2.8535   1.3044   9.8356   4.2817 -15.6346
-#&gt;   -7.6317  -2.3667   5.3161 -13.6099  -7.0559   1.4077  13.0176   1.3548
-#&gt;    5.3048   1.4686  -4.8772   4.0396  -8.1212   1.9633  -0.4062 -13.6653
-#&gt;   -4.9060   5.8354  -7.9457  14.5750   7.9935   6.4702   2.0638  -0.1489
-#&gt;   -2.4456  11.1937  -2.5014   5.4907 -19.0260   7.5919   7.3644   4.0066
-#&gt;   -0.3308  18.6458  -2.6960   3.5419  18.4469   4.3812  -0.0362  -5.6436
-#&gt;    3.2659  11.4403   0.4002   5.4679  -5.0049  15.7757  16.6276  -3.2206
-#&gt;    3.1916  19.6969  12.1973  -5.1785   1.5728   1.1582 -19.5696   4.9540
-#&gt;   12.0712   3.1860  14.3339  -2.6020  -0.8813  14.2604 -18.0712   1.5172
-#&gt;    0.9900 -12.8885 -11.0410  -0.9553  16.6436   3.9902   4.8515  -5.5373
-#&gt;    0.5127  -6.5831  -3.0334  -3.4530  10.2651  16.0154 -21.5420  -3.2067
-#&gt;   -8.3967  -3.8054 -18.7354  -1.2475   7.0778  -5.7305  -8.9100  -2.0095
-#&gt;   -1.3965  -2.5869  -0.7215  12.7646  -8.8131  -2.7458   2.8946   6.6474
-#&gt;    6.4845  -0.7294  -4.1424   1.0055  -4.5549   6.0394  -2.3143 -21.4748
-#&gt;  -12.7323   3.5429  -5.9706  -8.2062   0.2033  -6.0935  -5.6745  -5.2023
-#&gt;   -2.7099   2.6261  -5.0237  -8.3651  -0.9513   8.3181  -7.2639  10.6739
-#&gt;    3.6652   0.9774 -19.0680 -16.3772   6.7902  10.7902  12.4168  -9.1380
-#&gt;   10.2838  11.1546 -11.8998  -6.7962 -17.1916 -10.9840   7.4722   3.7983
-#&gt;    9.4781  -5.0210 -10.8234  -2.1772  -7.4097  12.4207  14.0205   5.0680
-#&gt;  -15.2869   5.4221   0.6145   2.5127   3.3684 -10.1709 -11.8266   5.8640
-#&gt;    0.9789   7.3604  -0.8906  -1.4485  -6.5204  -1.0505   4.6188  13.3519
-#&gt;   -5.8854  -4.7830  -8.7135  -5.2810  -0.2085  -0.8188 -22.8253   6.2247
-#&gt;    2.5235  -9.7718   5.8694   5.8893  -4.3028   3.2510 -15.5529  -1.6740
-#&gt;  -18.4022  10.3055  -9.5055  -6.7339  -2.5956   0.7797  -0.7558   6.1142
-#&gt;   -2.1909   1.9745  15.1615   3.8378   3.5793   2.8107  -4.5250  -5.0017
-#&gt;   -6.7314  -2.8286 -15.0256 -10.1786   1.6298  -3.3869   7.7571  -1.9086
-#&gt;   -3.9579   4.3367   8.3764  -3.3401   0.7328  -3.5471   1.9222   5.1989
-#&gt;    4.9139   6.7396  -0.5182  -0.4557  -1.9667  -2.7505   4.4973  -0.8652
-#&gt;   -6.3184   0.4973  -8.4255  21.0409  13.9487   4.4283 -15.6547   4.0643
-#&gt; 
-#&gt; Columns 25 to 32  -7.0700  -2.2752  -2.4708   4.3807  -0.6088  -0.1357  -0.1242  -5.3550
-#&gt;    2.3627   2.0000  -8.0063  -2.8736  -0.6957   3.1555 -13.7914  -7.4795
-#&gt;   24.6417  14.2237   9.6533  15.4911  11.8384  -1.7683 -12.8823   1.5207
-#&gt;   13.7444   3.0188   1.8199  -0.3484   9.8769   0.7477  -3.9068 -17.0296
-#&gt;    7.8777  13.5908   3.2489  -9.0720  11.0010  -1.5481   4.9561  -8.8683
-#&gt;  -10.2512  -7.3435   3.8370  -0.9608  -6.5072  -1.2125  -9.2089 -17.0151
-#&gt;  -14.3400  17.8502  -5.2937  -5.1514   7.1703   7.7565  -7.1908 -10.2785
-#&gt;   -0.1426 -12.2060 -17.6858 -13.2004  -8.1184 -10.6869  -7.6454   1.3618
-#&gt;   -0.6236  15.2537   6.2818   3.8249   2.6856  12.2199  -4.8745   6.8597
-#&gt;   -4.1744   7.8153 -14.4090 -16.4069  -4.0757   7.5133   4.2493  -0.4026
-#&gt;   -4.8623  -1.0765  -2.3635   2.7958   4.3313   2.6221 -15.9258   2.4883
-#&gt;   -4.0122  -4.0043  11.2495  -3.1960 -17.7412  -2.1601   4.3011   4.5657
-#&gt;   -2.7860  -1.5812   3.6151   5.5257  -2.8109   3.3872   4.5836  -7.8568
-#&gt;    2.4628  14.3817  -8.6524 -16.6014  -7.4157   6.1581  -4.1584   2.1448
-#&gt;    3.9527   1.4082  -0.6558   0.6174  -5.6619   7.7423   2.6632 -12.2375
-#&gt;    1.3161  22.8639   3.1718   0.3832  13.6813   0.4158 -13.0023   9.7144
-#&gt;  -14.0734   3.0637   6.2071  -7.9170  -5.8216   9.0431   0.6759   3.6643
-#&gt;  -11.0519   5.2946  -5.0427   2.3767   2.9223  -1.3251  -3.9481  -7.2359
-#&gt;  -19.8609 -10.4722   8.1226  -7.9884 -17.0258  13.7505  -9.8042  -4.9323
-#&gt;    8.6805 -11.5465  11.5185  16.1384  15.1593   1.1486  -4.1157  -8.3731
-#&gt;    3.7376  -6.3409  14.9493   0.9931   6.8925   4.4898  -3.8031  -5.5801
-#&gt;   -6.6815   9.7004  10.9293   0.7936  12.8492  -3.3698   1.8947   2.8017
-#&gt;    9.2102  10.5120   5.8735  10.6206  -3.0231 -19.2863  -3.6359   5.8456
-#&gt;   -8.0366  -9.7948   2.1921   6.3495 -10.4704   8.9099  -0.2478  -9.0128
-#&gt;   -1.9995  -6.6490  -9.0658  -4.9389 -10.5287  -9.3144  -1.8650   7.3177
-#&gt;   -3.5557  10.1131  -9.9067  16.4329 -18.2947  -8.8187  -9.9033  15.6427
-#&gt;   -1.3156   0.2888  -3.6578   4.8072 -13.1548  13.8459  -0.0489  -0.0223
-#&gt;   -2.7713  -0.8188 -14.5864  -0.3359   5.2085   8.0382   2.6063  -3.6212
-#&gt;  -13.0374   7.6872   1.8541 -20.4777  -6.3205   5.8555  -0.5691 -17.4678
-#&gt;   -5.8484   4.4523   3.1636   0.6892   8.0908   2.4863 -21.4659  -4.5101
-#&gt;   10.1152   2.4967  -5.9616   1.5557   1.6616 -10.1489  -0.9126   5.6770
-#&gt;    0.0101   0.8066   1.2145  -3.5670   5.0305   3.1087  -9.9816   0.6888
-#&gt;   -4.3210 -13.8052  -1.8556  -0.1874  -4.0169  11.3596  -3.6502  11.6739
-#&gt; 
-#&gt; Columns 33 to 40  -2.3036  -3.3691   9.5908 -18.1037  -4.8269 -11.2068  -7.4217   4.9088
-#&gt;   -7.5114  -1.7772   6.0881   1.8344   1.6680   1.3232  12.2514  -4.6218
-#&gt;    0.3793 -16.3323  -5.8262  -1.7772 -15.8569   9.4506  -6.2598  -4.5962
-#&gt;   20.6516 -11.8546  -8.2994   0.6123   1.0906  -8.9916   6.3291  -4.8664
-#&gt;    3.7170  -1.4675   8.1931  -7.0169  14.4007  -0.8141   7.8405  12.8106
-#&gt;   -4.8230  -9.5403  29.2750   5.6262  -2.7883  -1.0143   5.9038  -3.9718
-#&gt;    1.4539   4.2893  -7.3459  -3.7831  -6.1927  -1.7856  -3.6120   6.8626
-#&gt;    1.2634 -13.7302   6.1503  -0.6537  -5.8861  11.8277  -6.7535  -6.6320
-#&gt;    5.0972   5.1133   2.2470  18.4447  -9.6223  -6.7254   1.2146  -2.8518
-#&gt;   -8.1007  15.8782 -11.7765  -7.6373   0.1222  -5.6664  -0.8367  -2.1414
-#&gt;  -24.0802   8.5695   7.0602 -13.3643   3.6767  -2.6671 -14.2106  -3.2118
-#&gt;   -9.3315   9.3711  -4.4886 -10.7962 -12.9782  -0.3384 -12.8933   7.7461
-#&gt;   -0.4542 -14.3748  -6.4565   5.1144  -1.2486   5.4416  -7.5801 -11.2791
-#&gt;    7.9720  12.7332  -7.8140   7.9353  -0.5352 -14.4564  13.4031   2.3444
-#&gt;   -2.9023  -5.5180   3.6935  -6.7412   4.0769   5.2571  -8.6425   1.4228
-#&gt;    9.3886  -2.1200  -9.5610  -3.0876   4.9347  -0.0463  13.4955   1.5561
-#&gt;  -10.0864   4.3146   2.2588   0.3789   7.9083   6.7046 -19.4276  -0.1729
-#&gt;   -3.7228  -5.4053   5.6259  -2.5112  -3.3916  -9.3768   2.5861  -3.6670
-#&gt;   -8.3131  22.4520  -5.5324  14.8947   4.0459   1.0668   3.8767  15.9845
-#&gt;   10.6624  -7.9231   3.7128  16.8563  -5.6617  -9.6811  13.4380  -9.8125
-#&gt;    6.0718  11.8967  -1.0762  13.4506   4.2229 -12.4771  17.0558  -5.2211
-#&gt;  -20.6407 -13.6729   4.4115   0.6139  -0.6867 -11.1066  -0.8855   4.7593
-#&gt;    4.6098 -11.8867   1.4141  -7.3806  -4.8596  -9.6417  -3.9757  15.8459
-#&gt;   -4.0810  -5.2717  -1.5935  12.8730  -4.7129 -12.5477   4.9496  -5.7003
-#&gt;   -9.0203 -16.5495  -1.3243   4.2678  -3.1199  -3.0172  10.2255   0.0476
-#&gt;    8.3088   0.8002 -10.5493   7.2809   7.3031 -12.7229   5.0744  -2.5678
-#&gt;   -0.0917  -4.9262  -1.9527   6.1414   4.2634  -9.2493   8.2614   2.1846
-#&gt;    1.7460   9.8205  -6.5230  -1.2608  10.3139  -4.1761  20.2528  -1.0744
-#&gt;   -0.0333   2.7427   1.7422 -11.0656 -14.0675   6.6688  -6.8935  12.0527
-#&gt;   -1.3355  -4.5053  19.3091   3.6446  -2.8758  15.9818  15.6617  -5.7114
-#&gt;    9.3720   6.0302   5.2258   9.6560  21.4952  -3.7397   6.5655  -2.2531
-#&gt;    0.3509  -7.2390  -3.5894   2.1297   8.6248   0.3894  -1.1847   6.3256
-#&gt;   16.8180   0.6922  -9.5850  20.3198 -14.4303  16.2089   6.5277 -18.4146
-#&gt; 
-#&gt; Columns 41 to 48  -5.5679  -8.6081  -8.0902  -2.3805  -8.5475  -8.4168  -4.8824  -5.7833
-#&gt;    4.0595 -16.3360  -4.1835   3.3160   3.3572   2.7984  13.1903  -6.5470
-#&gt;   -0.4326   4.6159   4.2730  13.2616  -8.4737  -2.8781   3.3074 -22.0892
-#&gt;  -10.2334   1.0213 -12.6329   3.2288   1.0174  16.4717  -4.7265  -7.1711
-#&gt;    2.6984  13.3488  -0.4922   8.6229  -1.1540   2.2107 -16.1245   6.5459
-#&gt;   13.2591   4.2410   1.6568 -16.5004   0.0612  -0.0220  -4.0564   7.6267
-#&gt;    5.5270 -11.8214  -5.7829  -4.2030   2.3848   0.5941   3.9594   2.0171
-#&gt;    3.0210 -13.0941  -2.3634  -9.9677  -8.2493   0.6385   2.2707   7.1209
-#&gt;    0.3835   9.9049  -8.1180   2.5193   6.0950  -3.4228  -8.6802  -3.4071
-#&gt;  -14.8652   2.5523 -13.8848  -2.4438 -17.5680   5.1551  -6.0983   5.7363
-#&gt;   -0.3636  -9.5434   6.6261 -15.5970 -16.3822   9.1635  10.4183  13.6935
-#&gt;  -17.0243 -17.7539   0.7823  -9.0645  -3.6644   5.4163 -20.6828   5.2965
-#&gt;    2.6849  -0.7907   4.6839  -1.8846  -1.7117   1.7874  12.0648   2.4275
-#&gt;    2.4699  -0.2666  18.5632   1.4087  -0.0995  -0.7709  -2.9453   3.9932
-#&gt;    0.1762   5.4777   0.6819  -0.9940  -2.8452   0.1313  -1.6933   1.7692
-#&gt;    5.8747   3.5269  -4.7942  13.1234  17.7471   3.3322   0.0272 -12.6943
-#&gt;   -3.8804  -5.4489  -4.3028   0.3182  -1.2519  -0.1378  -0.4836   7.5351
-#&gt;   -1.9209   2.3229   9.3837  -0.1610   2.1095   1.8840  -2.2196   2.6474
-#&gt;    1.0414   5.3646   4.1529 -13.7585   2.1469  -8.2172   1.5278  -1.3461
-#&gt;    4.7474   7.7419  -4.2232  -2.9790   2.6028  11.7683   5.0573  12.1592
-#&gt;   -0.2405  -6.8901  -6.7682  -5.8678   4.2005   1.9841   8.6559  -4.5057
-#&gt;   -1.2836  -0.2987   4.8265 -11.3561 -16.9850   5.6906  -0.1987  -3.5414
-#&gt;    2.2855 -22.5573  -3.6183  -7.7413   2.1702  -0.3297  10.2890 -10.0452
-#&gt;    9.1019  -1.4041  -7.6587 -10.5322   7.3408  -1.8476   1.8352   9.2809
-#&gt;   -3.0254 -19.3939   2.9031  -5.0413  -0.5864   1.2921   9.3375  -5.9431
-#&gt;   12.5148   2.0075  -0.2538  -5.5401   0.0722  14.1209  -7.1014 -10.4872
-#&gt;   -2.0042  -2.0891   7.3077   4.7568   0.4073   5.7876  -2.2974   0.7460
-#&gt;    7.1813   3.2225   4.5206  -4.7238   2.7332 -12.2813  -0.4506   6.6161
-#&gt;   -5.1340  -1.9006   7.8807   0.8306  -3.0331   2.2583  -4.8123   2.5919
-#&gt;   13.7315  10.7276   6.4005 -18.2191   6.5490  11.3154  -8.7752   6.5745
-#&gt;   10.4508   7.4712  -1.1541   2.9978  -4.9505   2.5607   3.7927   9.4961
-#&gt;   -6.0113  -2.1309  -4.4679   4.5042  14.6421  -8.0012  -0.6941  -4.0408
-#&gt;  -10.3356   1.7303  -2.3093  -2.1821  -3.3962  -6.3504  -3.0143  -5.0890
-#&gt; 
-#&gt; Columns 49 to 54   3.7028  -5.3984  -7.2504   3.5743  -2.0806   0.5751
-#&gt;   12.1668   6.1669  -6.1812   3.6463   3.3529  -6.7342
-#&gt;    8.9305  -8.1106   1.6829  -7.0498  12.2821   0.7014
-#&gt;  -12.9991  -1.8517   1.5025 -13.3371  -6.1018  -2.0436
-#&gt;    5.7798  16.7180  14.1488  15.0374   6.9144  -1.2463
-#&gt;   15.6522  -7.1147  -0.0076  -1.5716  -1.5619   1.6527
-#&gt;    1.7976  -5.7376  -5.2971  -4.6991  -8.3851  -3.3931
-#&gt;   10.3691   1.1987   5.7356   3.6660   9.2503   2.6187
-#&gt;   -0.3318   2.4105   1.0697   2.2521  11.0653  -2.2324
-#&gt;    9.6187  -2.6086  13.6892  -3.5353  -4.4068  -8.5400
-#&gt;    4.5587  -1.4578  -6.8961  10.3375  -5.3423   4.9340
-#&gt;  -14.8051 -21.1778  -0.7188  -6.9369  -1.9433   1.9391
-#&gt;   -0.9412  -1.3267   3.2347  -5.1730  -1.5281   3.1124
-#&gt;   -3.7073  10.7073   2.4221  -3.1157   5.8246  -3.0667
-#&gt;  -12.6677  -5.1099  12.6047   3.4251   0.3412  -1.9780
-#&gt;  -18.5174   9.8403 -12.8212   0.1559  -3.1616  -5.2931
-#&gt;   -5.7297  -5.7785   6.5037  14.3051   3.0407  -1.9526
-#&gt;   -4.4879 -12.5228  -3.7703  -9.1680   4.1433  -0.2310
-#&gt;  -12.4090  -1.0133   1.1724 -11.0895  -0.5221   4.0127
-#&gt;  -13.9902   0.6041   1.0424   3.2013   3.8282   3.5761
-#&gt;  -13.2814  10.0989  -9.5517   8.7239   5.4135   2.6473
-#&gt;   -3.4769   1.8335  -5.0481  -6.3952  -2.4743  -0.9681
-#&gt;   -0.2805   4.4979   0.4671  -0.3169   0.9148  -3.1718
-#&gt;  -12.3895 -15.8857  -6.9057  -0.9688  -9.4475  -4.1693
-#&gt;   13.7822   5.8705  -1.4621  -5.8589  -7.7540  -1.2116
-#&gt;   -7.1986  -9.8913  -0.0784  -1.6460   0.6375   0.0523
-#&gt;    3.2873   8.4524  -1.0413   1.7511   0.0769  -8.5522
-#&gt;    5.2746   7.9060   5.6162  -1.7566  -1.9279   3.6406
-#&gt;   -3.7238  -0.7052   4.9525  -1.3668  -0.1907  -0.9673
-#&gt;    3.3874   6.0467 -10.1865   5.9089   6.1908   1.6329
-#&gt;   -5.0828   1.4947  14.2537   4.2755   3.2765   7.6570
-#&gt;    6.9575   7.2992  -4.9129  -3.4510   6.1976  -0.5195
-#&gt;   -9.0647   4.0719  -0.5388   2.0023   6.0227   0.4775
-#&gt; 
-#&gt; (13,.,.) = 
-#&gt;  Columns 1 to 6 -2.6062e+00 -4.3707e+00 -2.9811e-01  4.3589e-01  1.5397e+01  4.6062e-01
-#&gt;  -6.2996e-01 -7.3165e+00  4.6707e+00 -8.2601e-02  9.6304e+00  3.3606e-01
-#&gt;   1.3809e+00 -3.5218e+00  1.9881e+00  1.1558e+00 -8.6363e+00  9.1554e+00
-#&gt;   1.6974e-02  5.0054e+00 -6.8452e+00 -3.1190e+00  1.3955e+01  6.2273e+00
-#&gt;   1.9571e+00  1.3817e+00  1.6321e+00 -8.6768e+00  5.3425e+00 -1.2017e+01
-#&gt;   5.0125e+00  3.9706e+00 -4.2010e+00 -5.0664e+00 -2.9579e+00  9.1803e-01
-#&gt;   2.4793e-01  3.1793e+00  7.6094e+00 -1.1938e+01 -6.7936e+00 -1.8367e+00
-#&gt;  -3.9437e+00 -3.4335e+00 -4.5680e+00 -5.5616e+00 -9.3392e-01  1.0972e+01
-#&gt;  -2.4160e-01  7.6076e-01  6.8666e+00  3.4657e+00 -1.0472e+01 -8.4885e-02
-#&gt;   3.7571e+00 -9.5575e+00  1.7940e+01 -2.2989e+00 -1.6150e+01  1.6316e+01
-#&gt;  -3.3958e+00  3.8162e+00 -2.9509e-01  7.9579e+00  1.1417e+01  1.8571e+01
-#&gt;  -1.7186e+00 -7.0585e+00 -1.6685e+00 -2.5157e+00 -7.6799e+00  1.0829e+01
-#&gt;  -1.5902e+00  6.3546e-01  5.8865e+00 -2.0034e+00  1.8138e+01 -4.0877e+00
-#&gt;   1.3875e+00 -2.0654e+00  9.3684e+00 -1.5096e+00 -9.4302e+00 -5.3708e+00
-#&gt;   6.3164e+00 -3.6365e+00  1.5465e+01  6.3207e+00 -2.8025e+00 -1.1927e+01
-#&gt;   6.2168e+00 -8.1461e+00 -9.6584e+00  5.3062e+00  6.7869e+00 -2.7349e+00
-#&gt;   2.7672e+00  5.0833e+00 -4.8200e+00 -1.1742e+01  1.0573e+01  1.7128e+00
-#&gt;  -1.1691e+00 -2.3669e+00  4.7088e+00 -1.3453e+00 -1.8542e+01 -1.9606e+00
-#&gt;   2.1901e+00 -4.3852e+00  3.0568e-01 -6.0946e+00  3.3535e+00  9.7909e-01
-#&gt;   7.6115e-01 -3.4993e-01 -1.0360e+01 -4.7331e+00  5.0237e-01  9.8493e+00
-#&gt;  -1.0635e+00 -4.8464e+00 -5.9197e+00 -5.5022e+00 -4.5157e+00  2.8959e+00
-#&gt;   1.9095e+00 -7.0489e+00  8.3986e+00 -9.6939e+00 -1.0240e+01 -2.0445e+00
-#&gt;   2.4967e+00 -1.1039e+00 -4.2037e+00 -5.8748e+00  7.5974e+00 -2.5886e+01
-#&gt;   9.4028e+00  8.4745e-01  3.8798e+00 -6.8698e+00 -4.0743e+00 -1.9555e+00
-#&gt;  -5.5476e+00  1.0495e+01 -6.3592e+00  2.7364e+00  6.1971e-01  1.6896e+00
-#&gt;   2.2141e+00 -8.3249e+00  3.3319e+00  6.4410e+00 -1.7688e+00 -1.4042e+01
-#&gt;  -2.1594e+00 -1.6089e+00  1.4742e+01 -3.4196e+00 -9.5342e-02 -8.5690e+00
-#&gt;   3.0553e+00  3.0184e+00 -2.4505e-01 -2.2310e+01 -1.5522e+01  2.5040e+01
-#&gt;   1.2341e+00  1.2788e+00 -1.1930e+01 -7.0543e+00  2.6344e+00  2.6866e+00
-#&gt;   3.8040e+00 -7.8671e+00  1.0132e+01 -5.5875e+00  9.0280e-01  1.3698e+01
-#&gt;   4.2916e+00 -1.3154e-01  5.9577e+00  8.8623e+00 -6.8182e+00  1.9860e+00
-#&gt;  -7.0091e+00 -1.1074e+00 -4.8171e+00 -1.3336e+00  6.5406e+00  7.6249e+00
-#&gt;  -4.0053e+00 -6.4096e+00  1.3976e+01  2.6083e+01 -2.6773e+00  5.3731e+00
-#&gt; 
-#&gt; Columns 7 to 12 -4.4508e+00 -1.0719e+01  5.6114e+00 -7.8810e+00 -8.5917e-02 -1.4250e+01
-#&gt;   2.2682e+01 -2.4690e+00  6.4273e+00  1.3835e+00  1.2497e+01 -7.2212e+00
-#&gt;   3.6726e+00 -1.2377e+01 -1.2504e+01 -7.5896e-01 -2.8995e-01  1.3495e+01
-#&gt;  -9.7911e-01  1.0288e+01 -2.9875e+00 -4.8412e+00 -8.5469e+00  7.0236e+00
-#&gt;  -1.5592e+01 -2.0905e+00 -8.4166e+00 -2.4852e+00 -1.0022e+01 -4.7755e+00
-#&gt;   1.5539e+01 -1.4104e+01  5.1379e+00  5.5906e+00 -4.4355e+00 -1.9978e+01
-#&gt;   8.3205e+00  3.4012e+00 -3.3323e+00 -3.4893e+00  9.5129e+00 -5.7582e-01
-#&gt;   6.0660e+00  3.6640e+00  3.7347e+00  1.6991e+00  6.1389e+00 -2.2895e+01
-#&gt;  -1.7098e+01 -1.6100e+01 -2.8639e+00 -4.6973e+00 -2.9368e+00 -9.4122e+00
-#&gt;  -8.1592e+00  1.1826e+01 -5.5137e+00 -9.2987e+00 -1.1178e+01  6.0844e+00
-#&gt;   9.0713e+00  8.4860e-02  3.8737e+00 -4.8435e+00  3.8536e+00 -1.9147e+01
-#&gt;  -2.5960e+00  8.2106e+00  1.0392e+01  5.5962e+00 -4.8304e-04  4.3146e+00
-#&gt;  -9.4830e-01  1.0101e+01  8.9650e+00 -3.0334e+00 -9.6035e+00  6.5060e+00
-#&gt;   1.6177e+01  9.7631e+00 -9.0524e+00 -1.0748e+01 -3.5107e+00 -1.2707e+01
-#&gt;   1.4002e+00 -1.7671e-01  9.7703e+00 -2.8474e+00 -7.3979e+00  2.6105e+00
-#&gt;  -8.5095e-01  4.8138e+00 -3.3246e+00  1.4117e+00  5.4377e+00  6.3159e+00
-#&gt;  -5.0819e+00 -3.8304e+00 -5.1966e+00  2.5615e+00 -4.3747e+00 -2.2119e+01
-#&gt;  -4.6736e-01 -2.1650e+00 -1.8615e+00  3.0847e+00 -6.1604e+00  2.2098e+00
-#&gt;   6.5203e+00  6.4175e-02  7.2084e+00 -9.1702e-01  9.3829e+00  1.3258e+00
-#&gt;  -7.7354e+00 -3.6039e+00  1.1178e+01  1.2516e+01 -4.0992e+00  7.1953e+00
-#&gt;  -1.2224e+00  5.3515e-01  7.0703e+00 -2.1913e+00 -4.0987e+00 -1.1921e+01
-#&gt;  -3.4407e+00  1.7221e+00 -5.7678e+00 -8.8442e+00 -1.7698e+01 -9.4619e+00
-#&gt;  -2.1951e+00  6.5299e+00  9.6918e+00  4.4663e+00  4.2183e+00 -1.0834e+01
-#&gt;  -1.1213e+00  5.8653e+00  9.0337e+00 -5.9284e+00 -5.8489e+00 -1.2275e+01
-#&gt;   4.4208e+00 -2.8730e-01  1.4607e+01  2.8656e+00 -6.0403e+00 -6.7662e+00
-#&gt;   3.3748e+00  4.2005e+00  2.9317e+00 -3.1021e+00  4.0428e-01 -3.9336e+00
-#&gt;   1.6360e+00  1.5945e-01  4.8705e-02  8.0540e+00  3.8195e+00  1.8276e+00
-#&gt;   8.2536e+00 -3.4388e+00  1.7017e+00 -9.2341e+00  3.5076e+00 -1.9357e+01
-#&gt;  -9.6651e-01  3.0886e+00 -2.7190e+00  3.6139e-01 -6.2650e+00  9.7132e+00
-#&gt;   9.6548e+00 -9.3487e-01  2.3420e+00 -1.6861e+00  2.1434e+00  3.9071e-01
-#&gt;  -4.2411e+00  2.4719e-01  2.5220e+00  2.0897e+00 -6.6626e+00 -1.0491e+01
-#&gt;   1.5349e+00 -6.2035e+00  2.0649e-01  7.0437e+00  1.9467e+01 -1.2488e+01
-#&gt;   1.1531e+01  7.4099e+00 -1.0665e+01 -3.0429e+00 -1.8355e-01  1.9221e+01
-#&gt; 
-#&gt; Columns 13 to 18 -7.2215e+00 -1.2466e+01  3.8875e+00 -3.1187e+01 -1.8820e+00 -9.8043e+00
-#&gt;   1.0160e+01 -1.3504e+01  1.4771e+01 -1.4528e+01 -4.2073e+00 -5.5348e+00
-#&gt;  -9.1119e+00  5.3482e+00  1.3323e+01  5.4014e+00  1.7196e+01  2.6325e+01
-#&gt;   1.2345e+01  1.4534e+01 -1.2310e+01 -2.9245e+00 -9.6374e-02  1.4419e+01
-#&gt;  -6.8554e-01  1.3767e+01 -7.4645e+00  2.4616e+01  1.5067e+01 -1.2008e+00
-#&gt;  -1.3373e+01 -7.0494e+00  7.9411e+00  2.1153e+00  1.6237e+01 -2.0302e+00
-#&gt;   2.9546e+00  2.0125e+00 -2.7761e+00 -1.6772e+01 -7.7785e+00 -9.7904e+00
-#&gt;  -6.9824e+00 -2.1940e-01  3.2571e+00  2.9216e+00  1.2126e+00  8.0006e-02
-#&gt;  -5.4498e+00 -4.9196e+00  1.1187e+01  5.1527e+00  1.9753e+00  3.0449e+00
-#&gt;  -1.3457e+00  2.6631e+01  1.4009e+01  1.7411e+01  3.4817e+00 -1.9325e+01
-#&gt;   3.3080e+00  3.2052e+00  7.4808e+00 -1.7963e+01  4.4081e+00 -1.9835e+00
-#&gt;  -2.7025e+00  1.3963e+01 -1.2789e+01 -1.0477e+01 -4.5054e+00  1.2705e+00
-#&gt;  -4.5097e+00 -2.9934e+00  1.3589e+00 -3.8047e+00 -3.1732e+00  2.2551e+01
-#&gt;  -4.1483e-02  1.6719e+00 -1.0017e+01  1.3831e+00 -6.1051e+00  5.1663e+00
-#&gt;   1.5727e+00 -9.1886e+00  4.7481e+00  3.3472e+00  8.1091e+00 -2.0038e+00
-#&gt;   6.0485e+00 -1.9831e+01 -6.4921e+00  2.8981e+00 -4.1119e+00 -6.7681e+00
-#&gt;  -6.8174e+00  3.9641e+00 -1.1153e+01 -5.9886e-01  5.9244e+00 -2.0832e+01
-#&gt;  -5.3595e-01 -4.9143e+00  6.8717e+00  7.2858e+00  3.9336e+00  4.0872e+00
-#&gt;   5.3218e+00 -8.4871e+00 -1.1606e+01 -2.0003e+01 -4.0211e+00 -3.2907e+00
-#&gt;   2.4451e+01  2.9314e+00 -4.8548e+00  3.3499e+00 -8.4804e+00  3.6517e+00
-#&gt;   2.4940e+01  2.8697e-01  2.3707e+01  4.7336e+00  7.7435e+00 -3.7751e+00
-#&gt;   8.7888e-01  2.9596e+00 -3.8963e+00  7.0351e+00  1.1121e+01  3.1353e+00
-#&gt;   2.4905e+01  1.4502e+01  1.8234e+01 -1.5387e+01  5.8433e+00 -2.4481e+00
-#&gt;   3.5728e+00 -4.5417e-01  6.2881e+00 -6.6215e+00  1.0147e+01  2.4268e+00
-#&gt;   1.1416e+01 -1.2934e+01 -6.9195e+00 -1.8671e+01  1.7468e+00  1.6605e+01
-#&gt;   5.0634e+00  4.9109e+00  6.2954e-01  1.8549e+01  1.1480e+01  9.4800e+00
-#&gt;   8.5113e+00  9.5946e+00 -2.4934e+00 -1.2386e+00  6.5197e+00 -4.4872e-01
-#&gt;  -1.0283e+00  1.1514e+01  1.4278e+01 -1.1361e-01  2.7421e+00 -1.8346e+00
-#&gt;  -1.2757e+00 -8.9337e+00 -1.1270e+00  2.9207e+00 -2.3644e+00  1.8114e+01
-#&gt;  -5.0240e+00 -6.2460e+00  6.4540e+00  9.4477e-01  1.7941e+01 -3.5975e+00
-#&gt;  -1.7203e+01 -3.8834e+00 -9.9946e+00  1.2757e+01 -7.5244e+00 -5.0964e+00
-#&gt;  -4.9218e+00 -3.5652e+00  4.8170e+00 -1.5382e+01  6.2901e+00 -6.6095e+00
-#&gt;   1.0685e+01 -7.6947e+00 -5.7102e-01 -3.4373e+00 -1.3529e+01 -2.3021e+00
-#&gt; 
-#&gt; Columns 19 to 24  3.1064e-01  1.3296e+00  6.9204e+00  3.7004e+00 -1.7284e+00  2.0716e+01
-#&gt;   2.0790e+01  1.4616e+00  2.7505e+01 -7.2388e+00  1.5786e-02  6.9126e-01
-#&gt;   1.0581e+01 -1.7723e+00  9.6236e+00  1.9184e+00 -5.2805e+00  1.5687e+01
-#&gt;  -5.7501e+00 -7.3766e+00  1.1645e+01 -4.0178e-03 -4.4610e+00 -7.2275e+00
-#&gt;   1.7828e+00  1.7582e+01  5.8483e+00 -4.5242e+00  9.1879e+00  1.2343e+00
-#&gt;  -1.3040e+01  1.9152e+01  1.7854e+01 -2.2584e+00  9.7227e+00 -9.9071e+00
-#&gt;   8.9136e+00 -5.0119e+00 -6.4950e+00  8.7433e+00  3.9568e+00  3.7096e+00
-#&gt;  -1.7137e+01 -1.2895e+01  1.8839e-01 -2.5101e+00  6.5838e+00 -7.2495e+00
-#&gt;  -9.5594e+00  4.6319e-01  8.4608e+00 -3.3752e+00 -6.5722e+00  8.5134e+00
-#&gt;  -2.5239e-01 -1.0364e+01 -1.2930e+01 -1.0081e+01  8.8183e-01  6.8254e+00
-#&gt;   5.9544e+00 -2.0597e+00 -4.0415e+00  1.2797e+01 -5.3929e+00 -5.9649e+00
-#&gt;  -1.8878e+01 -4.4225e+00 -1.8592e+01 -1.7749e+01 -1.2777e+00  8.3341e+00
-#&gt;   1.3246e+00 -7.8867e+00  1.6048e+01  3.5616e+00 -1.4724e+00 -9.1582e+00
-#&gt;  -2.1751e+00  6.0518e+00 -9.0192e+00 -3.6502e+00  1.5900e+00 -1.8973e+00
-#&gt;   2.3712e+00  2.9974e+00 -2.9508e+00  2.0286e+00 -1.1525e-01 -1.4763e+00
-#&gt;   6.0138e+00  2.3865e+01 -3.6094e+00  1.4144e+01 -6.2450e+00 -4.7181e+00
-#&gt;   5.5814e+00  2.3154e+00 -2.2676e+00  6.5733e+00  1.4871e+01 -6.5997e+00
-#&gt;   1.7997e+01  8.4965e+00  1.4226e-01  1.4369e+01 -8.0124e+00  5.9209e+00
-#&gt;  -5.3190e-01 -2.2827e+00  9.4504e+00  2.0171e+00 -7.0356e+00 -4.6499e+00
-#&gt;  -1.0701e+01 -6.4898e+00  2.1693e+00  1.6731e+01 -4.8116e-01 -2.1572e+01
-#&gt;  -1.1891e+01 -2.3196e+01 -3.1955e+00  6.5347e+00 -6.6946e+00 -4.1393e+00
-#&gt;   7.1322e+00 -3.6311e-01  1.1460e+01  1.1992e+01 -7.8459e+00  3.2243e+00
-#&gt;  -8.4836e+00 -9.3285e+00  5.7073e-01 -4.5595e+00 -1.6823e+01  1.5271e+00
-#&gt;  -8.2417e+00  8.1881e+00  4.8223e+00  3.9892e+00 -1.0645e+01 -6.2780e+00
-#&gt;  -1.0094e+01 -5.2064e+00  1.0419e+01  1.3790e+00 -7.3533e+00 -7.4610e+00
-#&gt;   3.3009e+00 -7.5447e+00 -8.2611e+00  1.7270e+00  2.4465e+00 -8.8734e+00
-#&gt;   2.4527e+00  7.6458e+00  8.2508e+00 -1.3474e+01 -3.8805e+00  1.0117e+01
-#&gt;   6.3997e-01 -1.2868e+01 -9.0349e+00  4.5110e+00  1.4000e+01  2.4209e+00
-#&gt;  -1.4346e+01  2.2485e+01  9.7042e+00 -9.6664e+00  5.3486e+00  1.3158e+01
-#&gt;  -2.3950e+01  2.3018e+01  1.2625e+01  7.0952e+00 -1.4772e+00  7.8866e+00
-#&gt;   6.4253e+00 -1.6112e+00 -1.6500e+01  1.2504e+01 -8.9509e+00 -1.1392e+01
-#&gt;   5.5386e+00  1.2324e+01  5.9609e+00  4.4823e+00  3.9842e+00 -3.9776e+00
-#&gt;  -7.1607e+00 -5.3085e+00 -7.4927e+00  1.1713e+01  2.4886e+00  6.0927e+00
-#&gt; 
-#&gt; Columns 25 to 30  1.1905e+00  9.2564e+00  8.4011e+00 -1.4008e+01 -2.2149e+00  1.4555e+01
-#&gt;   8.2458e+00 -3.6192e+00  1.8653e+00  1.3913e+01 -2.9621e+00 -1.0967e+01
-#&gt;  -1.4442e-01 -1.1303e+01 -3.3406e+00  1.3429e+01 -1.2511e+01 -1.1717e+00
-#&gt;   2.3614e+00  5.5158e+00 -2.0338e+00 -8.6833e+00  1.2675e+01 -8.9783e+00
-#&gt;   1.0602e+00  1.2215e+01  4.5165e+00  5.2571e+00  3.0055e+01  1.3851e+00
-#&gt;  -5.2535e+00  7.5715e+00  1.6265e+01  3.0038e+00  2.4472e+00 -4.8796e+00
-#&gt;   7.1299e+00  6.5217e+00 -1.4980e+01 -9.4806e-01  7.4960e+00  1.9995e+00
-#&gt;  -1.3304e+01 -4.0140e+00  6.6101e+00  2.9585e-01 -9.3196e+00 -3.1752e+00
-#&gt;  -3.3242e+00  2.1568e+00 -6.2000e-01  3.4941e+00 -5.2199e+00  2.0151e+01
-#&gt;  -9.9894e+00 -1.0902e+01  1.0654e+01 -1.7679e+01 -1.2660e+01  6.0098e+00
-#&gt;  -1.3889e+01  1.9576e+00  1.1860e+00 -1.1171e+01 -1.3791e+01 -1.0639e+01
-#&gt;  -6.1164e-01 -1.0909e+01  1.1147e+01 -4.5249e+00 -3.0281e-01  6.5899e+00
-#&gt;  -4.5480e+00 -6.5308e+00 -1.2006e+00  2.8115e+00 -1.9017e+00 -9.4097e+00
-#&gt;   5.9669e+00 -1.7982e+00 -1.4532e+01 -5.9495e+00 -2.5304e+00 -1.2414e+01
-#&gt;  -1.0876e+01 -5.7287e+00  5.3469e+00  1.6801e+00  9.2713e+00  1.0578e+01
-#&gt;   1.7264e+00  5.3849e-01 -1.2903e+00  8.1888e+00  3.7258e+00 -3.3342e+00
-#&gt;  -1.8725e+01  1.4106e+01 -6.2716e+00 -1.1847e+01  6.1652e-01 -2.9134e+00
-#&gt;   8.7115e+00  7.6535e+00 -2.8972e+00  4.8379e+00  1.8466e+01  1.0392e+01
-#&gt;   1.4353e+01  5.3371e+00  9.8800e+00  2.3162e+01 -1.9053e-01  9.9579e+00
-#&gt;   1.5265e+00 -4.3711e+00  6.2081e+00  1.3386e+01  1.1279e+01 -5.8588e-01
-#&gt;   1.8803e+00 -3.5812e+00  1.2188e+01  6.1393e+00  3.4254e+00 -1.7166e+00
-#&gt;   5.4301e+00  8.8909e+00  1.5000e+01 -4.4408e+00  4.6507e+00  5.3364e+00
-#&gt;   5.1883e-01  1.7006e+00 -1.3186e+01 -1.1312e+01  1.5490e+01 -1.3521e+00
-#&gt;   1.6066e+01 -1.4190e+01  1.3474e+01  6.0885e+00 -2.3042e+00  4.1153e+00
-#&gt;   6.0177e+00 -4.8943e+00 -9.1682e+00  2.2650e+01 -8.5258e+00 -1.1940e+01
-#&gt;   1.4176e+01  7.3576e+00 -1.8716e+01 -2.1392e+00  6.0326e+00  6.9501e+00
-#&gt;  -6.3807e+00  4.4097e+00 -7.4600e+00 -2.1766e+00  1.6495e+01  4.9714e+00
-#&gt;   3.7208e+00 -4.0258e+00  3.7124e+00 -5.3390e-01 -1.0266e+01 -6.1115e+00
-#&gt;  -3.1088e-01 -7.3121e+00  1.0660e+01 -2.1144e+00  1.0072e+00  4.7281e+00
-#&gt;   8.8917e+00 -6.8797e+00  2.1487e+01 -2.6049e+00  6.5320e+00 -3.0211e+00
-#&gt;  -2.6496e+00  1.1716e+01  1.7983e+00 -7.2344e+00 -5.1294e+00 -7.7372e-01
-#&gt;  -1.4292e+01  3.7342e+00 -5.5911e+00  6.6557e+00  4.0170e-01 -1.0908e+00
-#&gt;   1.3191e+01 -2.1300e+00 -1.3433e+01  6.2814e+00  7.2158e+00  6.2202e+00
-#&gt; 
-#&gt; Columns 31 to 36 -1.6154e+00 -9.1858e+00  1.8920e+01  1.5687e+01  1.1896e+01 -7.3203e-01
-#&gt;   7.2346e+00  3.4743e+00 -8.8314e-01 -6.9140e+00  7.2218e+00  5.8382e+00
-#&gt;   1.5175e+01  9.2540e+00 -1.1194e+01  2.6353e+00  2.0553e+01  8.5817e+00
-#&gt;  -1.1778e+01  6.4902e+00 -7.3078e+00 -5.6672e+00 -1.4229e+01  1.1699e+01
-#&gt;   1.0707e+01 -2.3196e+00 -3.0214e+00  2.1243e+01  1.0331e+00  6.8585e+00
-#&gt;   2.8343e-01 -4.6489e+00 -1.1248e+01  1.1070e+01  1.3132e+01  7.8884e+00
-#&gt;  -6.8908e+00 -1.1892e+01  1.4144e+01  1.0829e+00 -8.8748e-01  7.6056e+00
-#&gt;  -6.2994e+00  1.9171e+00 -7.9355e+00  1.8466e+00 -2.8274e+00  3.5433e+00
-#&gt;   1.2903e+00  1.0286e+01  1.2701e+00  6.6254e+00  1.6019e+01 -7.6350e+00
-#&gt;  -1.4009e+01 -6.7545e+00 -6.1735e+00  1.2374e+01 -9.3128e+00 -7.6453e+00
-#&gt;  -1.4326e+00 -6.2614e+00  1.4926e+01 -4.9822e+00 -5.8032e+00  2.4760e-02
-#&gt;  -7.5850e+00  8.2675e+00 -8.1907e+00 -4.6144e-01 -5.0627e+00  9.6049e+00
-#&gt;   5.6877e+00  1.1537e+01  2.9792e+00 -2.0537e+00  4.2452e+00 -6.3273e+00
-#&gt;  -1.2834e+01 -1.8024e+00  8.6549e-01 -3.1154e+00  4.1869e+00  3.0224e+00
-#&gt;   3.9790e+00  1.0663e+01  4.2102e+00  4.7223e-01 -3.9674e+00 -7.0309e+00
-#&gt;   7.6305e+00 -5.9425e+00 -8.3972e+00  1.2813e+01 -9.4647e-01  4.8360e+00
-#&gt;   4.3604e+00 -1.1983e+01 -5.3785e+00  2.0006e+01 -1.6303e+01 -6.8876e+00
-#&gt;   2.2030e+00  5.7114e+00  1.4817e+01 -9.6710e-01  9.0731e+00  6.4650e-01
-#&gt;  -7.3722e+00 -3.5506e-01  1.5525e+01 -1.0375e+01  1.2695e+01 -6.2413e+00
-#&gt;  -1.7975e+00  1.8097e+00 -7.8472e+00 -1.5908e+01 -8.2060e+00 -6.0428e+00
-#&gt;   9.9683e+00  1.7077e+00 -1.1363e+01 -8.1168e+00  5.3511e+00  4.4540e+00
-#&gt;   1.4410e+01  1.9146e+00  1.6563e+01  1.7463e+01  1.3741e+00  1.0814e+00
-#&gt;   1.3802e+00 -2.2742e+00  4.2488e+00 -1.5005e+00 -1.1515e+01 -1.7863e+00
-#&gt;  -7.9534e+00  8.7031e+00  4.2792e+00  2.6312e+00 -3.0902e+00 -6.6125e+00
-#&gt;   5.4406e+00 -5.0950e+00 -2.8125e+00 -1.2614e+01  1.1890e+01  1.2638e+00
-#&gt;  -4.4719e+00  7.3877e+00  8.1554e+00 -2.4093e+00 -1.4528e+01  5.2519e+00
-#&gt;   1.6184e+00  9.0614e+00 -8.4227e+00 -3.1052e+00 -2.0296e-01  3.5554e+00
-#&gt;  -9.5703e+00  1.3301e+00  3.7286e+00  2.8050e+00  6.4989e-01  9.3371e+00
-#&gt;  -9.8709e+00  1.1560e+01  2.9675e+00  1.4487e+01 -4.0428e+00  2.8211e+00
-#&gt;   9.3672e-01  1.2070e+01  1.7712e+00 -3.3721e+00  1.6670e+01  9.8884e+00
-#&gt;  -2.9677e+00  1.0290e+00  1.3528e+01 -8.7619e-03  2.5838e-01 -1.1710e+00
-#&gt;   8.9856e+00 -2.9730e+00 -2.0381e+00 -1.7402e+00  9.3474e+00  5.8531e+00
-#&gt;  -1.1960e+01  1.0427e+01  2.7212e+00 -2.4805e+01 -6.0701e+00 -2.3155e-01
-#&gt; 
-#&gt; Columns 37 to 42  2.3551e+00  2.8254e-01 -1.1665e+01  1.7962e+01  7.1944e+00 -8.6301e+00
-#&gt;  -5.1290e-01  1.5914e+01 -8.8460e+00  1.2676e+00 -1.0045e+00  4.1471e+00
-#&gt;   2.4212e+00  3.6791e+00 -4.4580e+00  7.0602e+00 -3.2891e+00  8.7503e-02
-#&gt;   4.5004e+00  6.2879e+00 -3.7965e+00  1.9069e+00  1.3446e+01 -4.4839e+00
-#&gt;  -5.1836e+00  9.0714e+00 -7.5423e+00  1.2139e+01 -6.2302e+00  1.3618e+01
-#&gt;  -5.1213e+00 -2.5511e-03 -1.1132e+01  1.3130e+01 -8.0554e+00 -1.9431e+00
-#&gt;  -1.1304e-01  1.1816e-01  1.1183e+01 -2.9611e+00  1.9307e+00 -4.9811e+00
-#&gt;  -1.9727e+00 -2.7378e+00  4.3960e+00  1.0611e-01 -1.5771e+01 -5.4088e+00
-#&gt;  -6.5417e-03  1.3796e+00  2.3670e+00  3.0055e+00  3.7841e+00  6.6448e+00
-#&gt;  -1.2963e+01  1.7213e+01 -3.8310e-01  9.9490e+00  1.6941e+01  7.8735e-01
-#&gt;   9.0629e+00  6.7033e+00 -1.1191e+00 -1.2285e+01 -8.5853e+00 -7.1021e+00
-#&gt;   8.6261e+00 -1.4844e+01  7.7848e+00  5.9360e+00 -1.2793e+00 -8.0916e+00
-#&gt;   1.9426e+00  5.7485e+00  4.5625e+00  3.5710e+00 -3.4899e+00  5.4297e+00
-#&gt;  -1.6300e+00 -5.6257e+00  6.3301e+00 -9.9483e-01 -5.5015e+00  5.1788e+00
-#&gt;   1.5092e+00  5.4696e+00 -6.5080e+00  8.4216e+00 -1.3069e+01  9.1284e-01
-#&gt;  -2.9043e+00  1.4306e+01  1.2545e+01 -4.7026e+00  6.0981e+00 -3.0577e+00
-#&gt;  -1.0515e+01 -5.0341e+00  1.2239e+01 -4.7907e+00 -8.2145e+00  1.1944e+01
-#&gt;  -1.3624e+01  3.9631e+00  4.6644e+00  5.1475e+00  1.0083e+01  8.1282e+00
-#&gt;   1.2754e+01  2.2002e+00  2.2674e+00  9.9364e+00 -1.9048e+01  6.8678e+00
-#&gt;  -1.1469e+01 -2.6605e+00  6.7220e+00  2.7421e+00  2.9391e+00 -7.8607e+00
-#&gt;  -9.1263e+00 -4.2605e-01 -3.1193e+00  4.1201e-01  5.9093e+00 -3.7242e+00
-#&gt;   4.3492e-01  1.6703e+01  1.8933e+00  1.0388e+01 -3.9648e+00  9.6682e+00
-#&gt;  -6.5283e+00  6.9940e+00  3.5836e-01  1.0435e+01  2.0850e+00  6.0742e+00
-#&gt;   3.6456e+00 -8.3410e-01 -4.1807e+00  1.6062e+01 -1.2603e+01  4.0980e+00
-#&gt;   1.9093e+00  1.1560e+00 -1.2015e+00 -1.3981e+01 -3.7128e+00 -1.5283e+01
-#&gt;   4.6255e+00  1.3613e+00 -3.6477e+00  6.8440e+00  1.5089e+01  2.7323e+00
-#&gt;   2.5454e+00 -2.4250e+00  5.7142e-01  1.2832e+00 -4.1213e+00  7.7301e+00
-#&gt;  -2.2818e+00 -4.5911e+00 -8.0004e+00  5.4017e+00  6.9397e+00 -4.4472e+00
-#&gt;   5.9080e+00  5.4615e+00  7.4687e+00 -1.1346e+01  7.1468e+00  6.7478e+00
-#&gt;  -8.7994e+00  3.6490e+00 -9.4112e+00  1.6115e+01 -1.2792e+01 -1.2321e+00
-#&gt;   5.7208e+00 -9.4100e-01 -1.0808e+01 -2.5667e+01 -7.3912e+00 -3.4910e+00
-#&gt;   1.4610e-02  6.7326e-01  1.8092e+01  1.3894e+01  1.2513e+00 -2.4718e+00
-#&gt;  -2.9346e+00 -2.5387e+01  2.1041e+00  1.1642e+01  1.6377e-01 -8.7306e+00
-#&gt; 
-#&gt; Columns 43 to 48  6.0765e+00  1.9426e+00  2.1378e+01 -7.0996e+00  7.6049e+00 -1.2627e+01
-#&gt;  -8.7915e+00  3.7595e+00 -3.4068e+00 -4.6356e+00  8.2426e+00 -1.0481e+01
-#&gt;   9.9701e-01  1.1628e+01  1.9693e+01  3.4146e+00  2.7501e+00 -7.8397e+00
-#&gt;   8.2143e+00 -1.3494e+01 -1.7255e+00  2.1432e+00  2.5326e+00  1.2651e+01
-#&gt;  -4.3147e+00 -6.6335e+00  8.6426e+00  3.2619e+00  1.8617e+01  9.7667e+00
-#&gt;   5.8516e+00 -4.9707e+00  1.4035e+01 -3.0433e+00  1.7249e+01 -1.1004e+01
-#&gt;   4.2333e+00 -8.3095e+00  1.0355e+01  3.0997e-01 -3.0244e+00  4.9975e+00
-#&gt;  -2.3170e+00 -1.0687e+01  1.1045e+00 -1.0873e+01 -2.6739e+00 -8.3461e+00
-#&gt;   5.5015e+00 -2.6629e+00  7.2765e-01 -2.3036e+00  5.9124e+00 -9.9039e+00
-#&gt;   8.7012e+00 -6.7180e+00 -7.7484e+00 -1.5399e+00 -1.0615e+01  8.3269e+00
-#&gt;  -2.5045e+00 -5.1550e+00 -5.8759e-01  1.2857e+00 -5.6821e+00  5.1657e+00
-#&gt;   7.4956e+00 -1.4951e+00 -2.0118e+01  3.6027e+00 -6.2781e+00  8.0361e+00
-#&gt;  -9.7192e-01 -8.8169e+00 -1.5444e+01  9.9050e+00  2.3860e+00  6.0856e+00
-#&gt;  -8.4678e+00 -1.6628e+00  1.8102e-01  7.6190e+00  8.0565e+00  8.7291e+00
-#&gt;  -4.1898e+00  7.1304e+00 -6.3211e+00 -1.4560e+00  1.5261e+01  1.8896e+00
-#&gt;  -3.2270e+00 -6.5553e+00  2.2792e+00  9.0918e+00 -6.9268e+00  4.5429e+00
-#&gt;   4.0965e+00 -7.3246e+00  3.9424e+00 -9.8837e+00  9.2395e-01 -3.9479e+00
-#&gt;   2.3486e+00  1.4029e+01  1.0366e+01  6.2082e-01  1.5957e+01 -6.8508e+00
-#&gt;  -1.0596e+01  2.4390e+00 -7.3902e+00 -4.0068e+00  1.0597e+01 -4.9538e+00
-#&gt;  -3.9977e+00 -2.2323e+00 -1.3684e+01 -3.8367e+00  1.4138e+01 -1.1361e+01
-#&gt;   7.8296e+00 -3.7739e+00 -7.5797e+00 -3.2207e+00  6.9956e+00 -1.8756e+00
-#&gt;  -1.0864e+01  1.0258e+01  5.5019e+00  4.0855e+00 -4.2594e+00  4.3806e+00
-#&gt;  -6.4795e+00  3.8035e+00 -2.6296e+00 -2.1659e+01 -7.7870e+00  6.2800e+00
-#&gt;   1.4623e+01  2.5867e+00 -5.6446e+00 -3.8611e+00  3.9633e+00  4.1464e+00
-#&gt;   2.4262e+00 -2.7001e+00 -5.0452e+00 -1.2438e+01  7.7156e+00 -1.4993e-01
-#&gt;   4.8443e+00  1.2883e+01  1.4702e+00 -1.0819e+01 -1.0732e+01  4.5385e+00
-#&gt;  -2.1977e+00  1.0503e+01 -1.2471e+01 -9.0478e+00 -2.5773e-02  8.7670e+00
-#&gt;   9.3786e+00 -7.1389e+00  5.9266e+00 -6.2217e+00  3.9112e+00 -5.5195e+00
-#&gt;   1.0829e+01  3.7420e-02  6.0090e+00  1.4852e+01 -1.0276e+00  1.1629e+00
-#&gt;  -7.0855e+00 -3.4965e+00  2.0524e+00  4.3003e+00 -1.0924e+01 -4.9521e+00
-#&gt;  -3.5143e+00 -4.8006e+00  4.2495e+00 -2.2892e-01 -1.3384e+01 -3.2395e+00
-#&gt;   4.5305e+00 -1.1471e+00 -5.3179e+00 -6.0206e+00 -2.7610e+00 -2.6290e-01
-#&gt;  -8.0060e+00  2.8811e+00 -1.2304e+01 -7.9505e+00 -1.0847e+01 -8.8666e-01
-#&gt; 
-#&gt; Columns 49 to 54  6.9918e+00  1.1415e+01  7.3463e+00  3.4970e+00 -6.3744e+00  7.2531e+00
-#&gt;   1.5587e+00 -8.3923e+00 -1.2521e+01  1.3874e+01 -3.0895e+00  3.4757e+00
-#&gt;   9.7287e+00 -3.7505e+00  1.8671e+01  6.2799e+00  7.2133e-01  9.4306e-01
-#&gt;   7.0267e-01  2.2506e+00 -1.2273e+01 -7.6467e-01  1.0749e+01 -2.8490e+00
-#&gt;  -3.3843e+00  1.3641e+00 -7.7701e-01  4.2679e+00  1.2741e+00 -2.6319e+00
-#&gt;  -6.1039e+00  2.7864e+00  4.8254e+00  7.6110e+00 -8.1359e+00  5.0819e+00
-#&gt;  -6.7253e+00 -2.4049e+00 -1.5910e+01 -7.5328e+00  9.5726e+00  1.2298e+00
-#&gt;  -7.8743e-02  1.7332e+00 -6.8354e-01  9.7407e-01  4.4324e+00  2.4144e+00
-#&gt;   3.6192e+00 -8.2890e+00  8.7725e+00 -9.4400e-02  1.0856e+00 -1.1768e+00
-#&gt;  -1.0326e+01  1.3570e+01  5.0734e+00 -7.9736e+00  4.7324e+00  4.6693e+00
-#&gt;   1.3592e+01  1.3703e+01  1.2816e+01 -2.6361e+00  6.0505e+00  1.9993e+00
-#&gt;  -1.1924e+01 -9.1573e+00  3.7050e+00 -5.4849e+00  7.3894e+00  1.7366e+00
-#&gt;   3.0010e+00  4.1978e+00 -3.0271e+00  8.6210e-01  2.0225e+00 -4.0211e+00
-#&gt;   3.3704e+00 -1.2866e+00 -1.2977e+01 -4.2512e+00 -2.0291e-01  3.1466e+00
-#&gt;   1.9533e+01  8.4132e+00  1.0321e+01 -5.0441e+00 -6.2485e-01  8.1918e+00
-#&gt;   3.9019e-01 -1.3533e+01 -4.3155e+00 -4.5624e+00 -6.6800e+00 -8.9501e-01
-#&gt;  -5.2861e+00  6.3203e+00 -2.2759e+00 -6.3074e+00  3.9645e+00 -3.0979e+00
-#&gt;   1.6318e+00 -2.7828e+00 -9.8574e+00 -4.9496e+00  3.0409e+00 -3.3491e+00
-#&gt;  -1.1995e+00 -8.9800e+00 -1.6317e+01 -7.7787e+00 -7.8512e+00  1.3099e+01
-#&gt;   9.0806e+00 -5.7894e+00 -2.9962e+00  7.7519e-01 -3.4554e+00 -2.7491e+00
-#&gt;   1.1665e+01  1.2088e+01  8.4430e+00  4.3418e+00 -3.6730e+00  4.5800e+00
-#&gt;  -7.2690e+00  4.0666e+00 -6.5989e-01 -6.4804e+00 -3.8509e+00 -1.1787e+00
-#&gt;   1.9705e+01 -5.0232e+00 -9.7709e+00 -2.7367e+00 -3.9608e+00  1.5696e+00
-#&gt;  -1.1437e+00 -1.4886e+01 -1.3385e+01 -6.8008e+00 -2.7705e+00  1.2364e+00
-#&gt;   3.3381e+00 -4.9985e+00  3.2555e+00  3.7648e+00  5.9351e-01 -3.1181e-01
-#&gt;   4.7446e+00 -1.8028e+00 -1.1697e+01  8.1703e-01  6.0735e+00 -4.2170e+00
-#&gt;   2.5526e+00 -9.1244e-01 -2.9548e+00 -4.7373e+00  8.6076e+00 -6.6187e+00
-#&gt;  -1.7904e+01  5.1095e+00  2.9816e+00  1.0241e+00  5.9741e+00 -2.6460e+00
-#&gt;  -9.1649e+00  9.2247e-01  3.7666e+00  3.3803e+00  8.3384e+00 -1.8900e+00
-#&gt;  -2.2076e+00 -5.1772e+00  3.1815e+00  1.4127e+01 -4.8023e+00 -1.2827e+00
-#&gt;  -7.9658e+00  3.9217e+00 -2.1097e+00  9.8457e-01  5.9244e+00 -1.6563e+00
-#&gt;   1.0894e+00 -1.0426e+01  4.0543e+00  1.6925e+00 -3.0131e+00  1.1340e+00
-#&gt;   1.8677e+01 -1.0037e+01  4.8771e+00 -3.7183e-01  8.1584e-03  2.1907e+00
-#&gt; 
-#&gt; (14,.,.) = 
-#&gt;  Columns 1 to 8  -2.6238  11.8783   0.8925  -3.7231  -5.0516   1.6538  -5.9199  18.7068
-#&gt;    7.8435  -3.9434  -1.3295   8.3002   1.2205   2.9473   8.0339  -5.4749
-#&gt;    4.3803 -10.7208   9.2517   2.1335  -3.2798   3.3970  10.5122  -7.5539
-#&gt;   -4.2547   9.6126  -6.4633  -5.9365   0.7896   3.6289   4.2488   0.0226
-#&gt;   -3.0356   5.9674   6.1138  -4.0501   5.9567 -16.4409  -0.6888   7.0459
-#&gt;    0.3571   3.9212  -2.2558   4.4714  14.6701   3.1947   1.7444  -8.7094
-#&gt;    3.0020  -6.4921   5.4898 -11.2936 -12.1431  17.7027  12.7318   1.9923
-#&gt;   -5.8093  -1.5309   1.7975  -0.2513  17.8399   6.4280  -8.6627  -1.1173
-#&gt;   -3.1436  -7.7415   7.9453  -7.1321  -5.5170   9.0956 -17.1787   4.2891
-#&gt;    0.2932 -11.3901   8.1851   4.4926 -18.6953   1.4956  -6.6958   6.8371
-#&gt;    4.0949   6.6922  -4.4207   7.5454   0.3782  21.0362   7.4569  12.4457
-#&gt;    1.4472   6.6942   1.7071   1.7456   4.3777   3.9410   3.4285   4.8532
-#&gt;    4.5536   1.1671   0.8985  -3.1928   8.6904   4.7448  13.9574  -1.8595
-#&gt;    5.9249  -7.0154  -7.8933  -3.0425 -14.6642   6.3479  16.4404 -12.6958
-#&gt;    2.4718   0.3073   8.3639  11.3497   5.7182  -2.1276  11.8651   9.7011
-#&gt;   -4.6603   1.1062  -5.8494   0.4471  -1.1675  -4.1252 -20.8909   9.2567
-#&gt;   -0.9010  -2.7324  -1.0717  -7.0152  -1.8226   5.5027  13.3772 -10.9183
-#&gt;   -2.5321  -4.1751  -4.7631   4.9156  -4.5450   6.6197  -5.3219   2.0480
-#&gt;    4.8902  -3.2995   1.2206   5.9603  -4.7108   8.5920  13.6459 -16.3888
-#&gt;   -0.7764  -2.1973   1.9781  -0.5887   4.4350  19.9665  -9.2995 -12.3690
-#&gt;    3.8444   1.1218  -2.2348  12.0605 -10.9903   3.4640 -12.6543  -6.5925
-#&gt;    2.9978 -10.4016   8.7841  -2.3913  -8.0730  15.0223   2.3267   8.3592
-#&gt;   -0.3261  11.8721  -4.0328  -4.8160  11.5940 -13.8267   3.5840  13.5172
-#&gt;    2.2055  -6.6852   1.9335   4.6340  -2.0750  19.9283  -4.1770  -7.4510
-#&gt;    1.5787  -2.9546   5.5056   5.4384 -11.6134  13.3892   9.1247 -16.7689
-#&gt;   -0.6800   1.4436  -4.3099   4.5047  -2.8974  -0.1452   5.7282  -2.4906
-#&gt;    1.8271   1.9714   3.5180  16.7946 -15.9720  -5.1964  10.2636  -4.4475
-#&gt;   -0.8774  -3.4560   3.7254  -6.5001 -16.1136  -5.3056  -2.9647 -12.5853
-#&gt;   -5.2820   0.9751  -2.0444  -3.2253  13.7849   8.1987  -7.9910 -11.4557
-#&gt;    8.7980  -6.5356   0.8811  11.3009   1.7633  10.6825 -13.3427  13.3426
-#&gt;   -0.3452   5.9570  -4.8606  10.3751   1.0491  -4.7892  -4.1652   6.4604
-#&gt;   -7.6320   3.3687  -0.8680  11.1396   5.6979  -9.7531  -9.9701   6.8967
-#&gt;   11.2428  -9.9424  -7.7266   5.1306   0.1823  11.5363   8.3488   9.2988
-#&gt; 
-#&gt; Columns 9 to 16  -6.5640 -16.8324  -2.5135 -15.2725  11.0887   1.6720   9.1507  11.9344
-#&gt;    1.3315  -3.3275   5.8554   2.4879  12.8081 -13.3258  -2.1245 -15.3369
-#&gt;    9.2915   1.2857  -0.4652   8.6267 -11.2639   3.8440  -5.4902   7.9065
-#&gt;   10.9011   9.6164   9.0021   2.6696 -11.4753 -11.3324 -12.9106  -8.0244
-#&gt;   -8.7772   7.3953 -16.9045  12.3426   3.2054   1.3483   2.3117   7.3114
-#&gt;    5.7725  -0.2586  -0.3988  -2.8541  10.2902   7.2558 -10.1375  -2.4596
-#&gt;  -10.6618 -13.3491  22.4513   6.7673  -1.6677  -6.7362   4.0646   5.4914
-#&gt;   -0.8330  -5.1916   3.2200  -9.6477   1.1526   2.0484  -8.8311   3.2760
-#&gt;    1.9121 -12.5371 -12.5249  -1.0135  -0.6063   6.2196   1.3053  15.5459
-#&gt;    8.6576  -4.5518  -1.0973   9.1771 -13.1983  -5.6226  -6.2221   4.6125
-#&gt;  -12.6237   4.4356  -2.6561 -15.0769   1.7772  -6.3228  -2.0625 -23.7451
-#&gt;    1.8600  -9.8089   0.9141 -10.2187 -11.8230  -5.7400  -4.0034   3.8038
-#&gt;    7.0826   3.2229  -2.7508  -1.0741 -19.4595   6.5132 -12.0106 -21.7387
-#&gt;   -8.7402  -5.8943   7.0301  11.1748  -4.2414   0.1278  -4.7647  -6.6994
-#&gt;    0.9269  -4.4729  -4.7335  -2.6257   3.5268   6.2070 -13.7916  -5.1290
-#&gt;    8.5613  17.6003 -11.8321   7.7075  12.7723  -7.4812  -3.0580  14.4725
-#&gt;   -2.4148   9.7545  10.0080 -12.0363   8.7914   8.6769  -0.3989 -14.6427
-#&gt;  -14.2281 -10.9337  12.5246   5.8334 -10.8535  -6.2768   8.0523   1.7364
-#&gt;   10.2100   8.6015  -3.0827 -15.2468   6.0830   0.4693  -0.0068 -10.1824
-#&gt;    1.8204   0.7117   4.8043 -14.4683  -1.7941   4.4718  -8.5715  -2.2855
-#&gt;   -3.1891  -1.7652  -7.3373   0.5339   7.6947 -12.7089   7.0854  -2.0435
-#&gt;   12.2364   6.9312 -23.0938   8.2634  -2.4660   0.6953 -11.9887   5.1123
-#&gt;   -9.0066   4.8706  -3.6332  -4.8646  14.1875 -15.0182  -8.3200  -0.0731
-#&gt;    3.4651  10.3846   9.9883  -6.0879  -3.5761   9.1407 -30.3162  -8.8140
-#&gt;    6.8014   2.8475   7.7587   4.6261  -5.2023   1.7644  -0.3276  -5.0580
-#&gt;   11.1385  -0.2623  -2.6296  -4.2339  -4.9253  11.2058  -4.3409   0.1656
-#&gt;   -1.1930  -1.2313   4.9747   8.9148  -1.3095   0.0342  -2.9062  -1.2148
-#&gt;    3.9552   8.8681  15.4971   8.2771  -4.7788   5.1857   1.3791  -3.5184
-#&gt;    5.9258   6.7401   9.8774  -1.5188 -18.2287  15.7711 -10.5262  16.7764
-#&gt;    6.6260  -3.6193  -2.1345 -15.6112   4.9347   8.2844  -9.9135  20.3202
-#&gt;   -5.4815  -5.3884  -6.1015  -6.5294  -3.6950   7.8741   9.1964  -5.7604
-#&gt;    5.1284   6.6819  -5.5568  -2.1891  12.3301 -14.6859   2.1697   8.7688
-#&gt;    5.7211  -5.6898   5.9201   3.8020  -0.1532   7.6367  -2.6378  -3.8263
-#&gt; 
-#&gt; Columns 17 to 24  17.2162  -1.0256   0.3160   9.1143  17.1363  -1.3541  21.4942  -8.8959
-#&gt;   -1.6111   1.2781  -4.2943  -0.6229  16.8211  -7.9216   8.6065  12.1781
-#&gt;   -0.8209  -2.4793  -8.8136   5.9557  -4.6458  -0.8195   1.6090  11.2300
-#&gt;   -0.7142   4.9930  -5.0219  -3.7000   3.7168  -5.0477   1.6013   5.1240
-#&gt;   -8.6605   9.5751 -15.4673  20.2371   4.1877   6.7997   2.7201  -4.8030
-#&gt;   14.8340   0.7137  -9.7539  15.1111 -12.7802  -5.7420  18.1997   1.4683
-#&gt;    4.8375  10.5737  -1.1101  -8.1436   7.0220  -2.2019  -6.0531   9.4921
-#&gt;   -0.4797  -8.2946  12.0737   4.3743  -1.0046  -7.3678   4.0435   1.7135
-#&gt;   17.0869   1.8302  -9.2051   9.8319   9.5798  -4.1869  -3.2089   3.1069
-#&gt;   -9.1501  14.7134  -0.1222  -5.9786   0.5594 -13.2447   1.2198 -16.6489
-#&gt;   -6.6266  -3.7841   2.7556   2.1921  -4.2507  -6.9551   1.6990 -13.9904
-#&gt;   10.6776  -4.3731  14.7331  -2.5494  -8.4224  -3.0052   8.1488  -9.0466
-#&gt;   -2.9238   1.8008  10.9250  13.4293  -1.3097   6.0970 -13.5679   1.0154
-#&gt;   -5.5360   9.4548  10.0026  -2.7483   3.3054   3.3223 -24.4331  -5.0983
-#&gt;   -5.6022  -7.7728   9.7798   3.2697   1.8226   6.7998  -6.2884 -14.4242
-#&gt;   -7.0311 -14.5360 -12.2548 -10.1017  17.7785   8.8423  18.2342   2.0704
-#&gt;   -8.5932   3.1678  -1.6530  10.6109   3.9482   3.9559   5.9577   4.6200
-#&gt;    0.7201   6.6205   2.2715   1.6009   2.4846   7.5578   0.7408 -10.3616
-#&gt;   29.7357  -3.5619   6.7981  -6.9153   1.3582  22.0497  -8.5276   7.9016
-#&gt;   14.2183 -10.6373  -6.9347 -10.4930  -8.3676 -11.1041 -12.5324  -7.5684
-#&gt;    7.9008  -6.0833 -16.9075 -17.5317   4.8789 -13.9661  -0.2523  -9.7644
-#&gt;    8.3539  13.0751   6.9067  13.5112  -2.5454   2.0223   0.2630  -9.8352
-#&gt;   -8.0456  -3.8018   4.4865  -5.1340  -5.0478 -16.9724   7.0718 -14.9987
-#&gt;   13.0746  -4.4279  15.3927  10.6201  -2.6597  -4.0475  -5.7461   5.4223
-#&gt;   -2.9602  -0.5390  -4.2766  -8.9225  -5.1651   2.6063   4.3691  10.3583
-#&gt;   12.5557   9.8150   6.0555   2.3921  -2.9749   5.3745  -3.6572  -5.0218
-#&gt;   -1.9358   5.7645   1.6134 -10.5809   4.6881   7.1877  -9.7034  10.1510
-#&gt;    9.5752  -5.5870  -2.7688   4.4489  -2.6627   0.1766   1.6358  16.9810
-#&gt;    2.3303   9.4308   8.8819  -4.7596   9.1520   8.7668  14.7653   8.3154
-#&gt;   11.5554 -13.3208  -2.6425  -4.8044   4.5992  -6.0542   3.2261   5.6698
-#&gt;    2.8681  -3.8410   7.4793   4.7572  -1.5797   2.1672  -1.0145  -8.5678
-#&gt;    7.1557  -5.0937 -16.0382  -1.4187   6.0535   4.8755   4.8610  11.8604
-#&gt;   -5.1276  -9.4482  -3.9525  -1.7504   7.6267   9.7652 -18.0320   7.3410
-#&gt; 
-#&gt; Columns 25 to 32   0.6111   5.6545  -5.2971  -1.0631  -9.2790 -10.1167  -3.3029  -5.7115
-#&gt;   15.4247   0.3559  18.1724  16.5061   0.1328   4.8931  -4.2506 -11.2141
-#&gt;   -3.3621 -13.4533   6.1243  -3.5549 -13.5957   5.8946  -4.2510  -6.3117
-#&gt;    8.4188  -7.5057 -10.0475   7.7460  -0.6696 -18.0734  -6.7418  -0.9915
-#&gt;   11.5985   7.7500   8.7506  12.2161   7.4869  15.5229   4.2245   8.8741
-#&gt;    7.5014  -3.4227   3.4777   6.8134   3.9367  12.6515   8.6806 -10.7973
-#&gt;   13.2889  -6.3263 -14.0624   2.7057   6.4050   0.7787  -3.6430  -3.4126
-#&gt;   -2.7828  -4.2337  17.3814  -5.4685   5.4631  -6.0047  -7.0551  -7.9644
-#&gt;  -10.1523 -19.8004   3.1142   2.3791   2.6251   1.9667   4.0674  -2.6634
-#&gt;    2.2634   7.8115 -13.8005   2.4605  -2.5231 -12.3954 -10.1548  10.6785
-#&gt;  -10.8571   8.6133   5.8541  -3.6097  -7.0526  -4.6336  -7.7150 -15.8500
-#&gt;  -12.6168   3.6125 -22.8687 -13.4924  -7.3051 -11.5659 -25.1099  -7.9429
-#&gt;   -2.5755   1.7162  -4.1635   0.9539  -5.9834 -10.5638  -7.7962   2.6002
-#&gt;   16.8002   2.1687  -2.4155  -5.9968  -3.6535 -10.9185  -5.2093   0.6571
-#&gt;    7.1487  -0.5283 -10.7036  -0.7206   2.8906   1.6641  -8.2856   5.6759
-#&gt;    2.1368  -3.4978  15.9114  -2.7854  14.3077  -0.0578 -17.3051  -1.6723
-#&gt;  -10.9012  -3.9015  -0.0096   9.6851  -5.9416  11.0818   0.9194  -1.8745
-#&gt;   19.1697   9.7780   2.3429  20.1149  14.0093   3.3148   9.6412   3.6358
-#&gt;    6.1683  11.2597  -3.4364  10.1281  16.5130   7.2994   7.1227  -1.4084
-#&gt;   -7.0838 -26.1737  10.1968  22.0486  15.3771  18.9434  12.0112   2.2520
-#&gt;   -1.0419  -1.1514   0.7052  13.0610   2.5244   8.5831   3.9154 -10.4972
-#&gt;   -9.5742   4.6485   4.6333  -9.0890 -10.2397   7.7269  -2.7564   8.2024
-#&gt;   -4.1830  -7.0895   9.6400 -15.3076   0.8698  -1.2905 -11.1087 -13.9310
-#&gt;    7.5570  -4.3838 -14.0130   2.4811  10.1804  -1.3575  -3.2907  -0.2027
-#&gt;    2.7269 -10.6058   7.9974  -4.9379   7.8756  -6.8058  -0.3576 -20.3921
-#&gt;    5.2413  11.1823   8.3447 -14.9203   2.5993  -2.2673   3.7601  -1.8624
-#&gt;   10.7358   4.6675  -5.1472   3.7548   1.1681  -8.3695  10.6106   1.8642
-#&gt;   18.2347  -9.9881 -11.0905  -0.5762  -0.4119 -15.3485   8.6801   2.3573
-#&gt;   -0.1054   9.9349  -2.5311   6.4289   7.7317  -8.0345 -14.7359  -0.0432
-#&gt;    2.8279  -8.7827   2.4973   8.6630   7.7821   6.5034  14.8062   7.2044
-#&gt;   -0.8898 -12.6488  -1.7311  -0.9481  -5.6131 -10.8209   5.9850   5.6000
-#&gt;   -0.5120  -6.2767   3.4345   3.3944   4.1379  -8.1213  -4.9709 -13.3534
-#&gt;   -4.3926   5.6430   6.5672   2.9556   2.4645   0.5793  19.1855   6.8028
-#&gt; 
-#&gt; Columns 33 to 40 -12.7068  -0.5995 -10.2830  -6.5177  -5.4939   6.9878   1.6794   3.2696
-#&gt;    2.5050  -3.0383 -11.9659   1.3407 -25.4523  -5.0358   9.9187 -17.9480
-#&gt;    2.8786 -15.6111 -15.7897 -14.3599  -9.0282   4.3896  -6.2996  -6.6764
-#&gt;   -7.9210   2.1520 -17.0518  -2.5083  17.2559   1.8010 -14.3769  -0.7283
-#&gt;   -3.9591   6.9741  -7.2993  -0.5098  -0.4753 -14.7053  -1.8370   6.0504
-#&gt;   -2.4684  -6.9593 -10.7885   3.1902   2.2050 -10.9056  -0.5907   7.1610
-#&gt;   -5.6503  -6.8836  -2.0963  -1.9444  10.7094  11.0482  -9.1741  -1.3379
-#&gt;    6.6145   1.6350  -8.7555  -3.8572  -4.3096 -11.3442   2.6332   3.7266
-#&gt;   -1.6752 -18.8033   3.9202   3.3283   1.7535   3.6631  -1.8261   9.8509
-#&gt;  -10.8463  -0.3222  -8.3365 -11.0714   1.4986  15.6982   7.9214   0.8114
-#&gt;    9.1322 -13.1751 -15.8976 -20.6217 -19.7542  -5.5728   6.4571  -0.3891
-#&gt;   -6.1403  -6.0859  10.0259   2.0744  -1.6766   1.8659   2.3070  13.9918
-#&gt;    6.3527  -6.4215   2.1437 -24.4945 -23.0602  -4.0357   4.7003  -2.6402
-#&gt;    2.7640   3.3456   1.7440  -2.7541   2.3401  -0.2216  11.4625   0.3415
-#&gt;   -6.0708   6.4138  -7.3087 -17.7666  -4.3697   7.0222   5.5512  -9.1406
-#&gt;   -0.5803  -5.7986   4.3799  18.6993   4.5708  -8.9367  -0.8985  -4.1488
-#&gt;    8.4109  13.1291  -4.8652   2.0031  -1.3100  -5.9746   0.6959  -6.1546
-#&gt;    3.8865  -5.4227  -0.4404   0.2942 -12.4006  -3.5516  10.9739   1.9960
-#&gt;    4.1870   5.9976  -0.1284  14.2193  10.6148  -8.6946   2.7818   0.3185
-#&gt;    9.6609  -3.3068  -0.4315   6.8060  -2.4403  -3.9810  -9.3174  -7.4440
-#&gt;    5.7255 -10.6981 -20.5788  10.1588  -2.7952  13.5274  22.3568 -12.2839
-#&gt;   11.3456  -8.6163 -18.2230 -14.5291 -10.1835  -4.6185  14.7018   6.2425
-#&gt;  -11.5411  -9.6744  -9.3670  12.3973   7.1775  -4.3015  -3.0272  -1.6116
-#&gt;    6.9090  -4.8299  -5.6281  -2.0099   5.9776  -3.3530   6.6569   4.4821
-#&gt;   20.0445  -5.6473  -7.8195   7.5109   1.9037 -10.2060 -13.1566   8.9487
-#&gt;    1.3328   4.6773  -5.5429  -4.6635  17.8086  -3.8605   7.5663   3.3084
-#&gt;   -5.5956   8.3572  -6.1388  -3.9054   1.8223   2.6309  -0.8776 -12.4144
-#&gt;   -2.9377  -2.4588  -3.1834  -8.0888  21.7707   0.8852  21.1612  -1.5778
-#&gt;   -1.9606  -7.8054  -0.7293   1.1737 -11.4728  -4.8130  -6.9979   1.9099
-#&gt;   -6.6295  -9.7812  -4.4242  -1.4821  -1.3510   2.0906  -0.0607  -2.2525
-#&gt;    1.5984   9.4679   6.6469  -7.8682   1.7121  -9.0514   9.0038   4.6284
-#&gt;   -8.9381 -13.4566  -1.8990   9.2627  -3.8908  -7.3679  -3.7604   6.3054
-#&gt;   10.8886   5.5588   9.2468   4.6783  -0.6972   5.1804  -5.0808  -1.3935
-#&gt; 
-#&gt; Columns 41 to 48  11.0239   6.3301  -9.2157 -10.4194  -2.9345   9.6454  -3.6707  12.3306
-#&gt;    8.7695   1.9312  11.3873   3.8990  -9.5887  10.6301  11.2288  -4.4983
-#&gt;   -1.4126   6.0519   1.3898 -18.8315  10.4306  12.2817   5.7536 -17.0016
-#&gt;    0.0302   8.4723  -3.6095   2.0469   4.0978   2.3113 -15.8933  -2.5055
-#&gt;   -9.9771  16.2105  -3.5728  -1.8448  -4.7291   7.7512   3.6485   8.8928
-#&gt;   -1.2777   0.8446  13.3517   3.0282 -14.8297   0.3516   7.7505  29.2833
-#&gt;   -6.6641   7.5892 -17.6258  -4.8631  12.2647   7.1886  -2.0765   5.9543
-#&gt;    4.7598  19.5294  10.8221   4.8241   9.1093 -14.0456  -4.3863  -2.2128
-#&gt;  -10.6359   5.5046   5.8159 -10.9740   5.1084   2.6907   9.3021   7.1622
-#&gt;   -8.4091   4.5744  21.2438 -16.0079   4.3214  -1.2119   6.6280 -27.0967
-#&gt;    1.2084  -4.2264   2.6369  13.4531   3.7247  -2.5579  -4.6476  18.5287
-#&gt;    6.9118   1.8652   5.2999  -7.1491   6.4423 -16.8015 -10.2055  -2.1866
-#&gt;   -1.4490   0.0429   3.8267  32.2182  -3.6503  -0.8246 -15.6116  11.2331
-#&gt;    4.4434   3.4587  -0.4308  -6.8910   2.6519  -4.0971   1.3791  -4.0739
-#&gt;  -13.9448   5.9630  16.8411   6.5202 -19.8434  -8.7286   5.7579   5.9338
-#&gt;   -5.3805   2.9925   1.6704  -2.4455  -5.7979  -1.2561  10.9728   2.3459
-#&gt;  -12.2296  -4.5646  -6.4440  12.5408   5.7211 -10.0894  11.8713   9.3158
-#&gt;    6.0258   8.6538  -7.6233  18.5302  11.3319   3.9087  -7.3663   6.0215
-#&gt;   12.1897   3.9040  20.5892  -2.3057  -7.4233   0.1569  -4.7905 -11.6820
-#&gt;    0.8225   1.2938  -4.3060  17.9566   3.5484  -7.4808  -7.8523  16.2301
-#&gt;    6.0882  14.9522  10.0352  -6.5007 -11.6924  -8.2829  -3.9503  -2.7733
-#&gt;   -4.4879   9.2618   5.6824   3.3541  -6.3387  14.9462   4.3813   4.4002
-#&gt;    5.9893  -4.2074 -21.8310   5.9468   8.3680   1.7531   2.1788   3.0668
-#&gt;    3.6283   3.5127   1.6745  11.3424   8.1867   7.1896  -9.2418  12.8458
-#&gt;   11.9704  -5.0289  -9.7905  -0.4666  19.4838   5.4463 -18.8463  11.6277
-#&gt;    3.0076  -5.4115  -0.6507  14.1134  -9.9044   3.6120  -5.8264  -4.5279
-#&gt;    6.0468  -0.2920  -8.7858   7.1293  -5.5691   9.8187  -5.4861  -1.3694
-#&gt;   -9.9383  16.4657  -9.0299  -8.3493  -0.6598   8.2678  -9.1653  -5.0429
-#&gt;    3.2870  -7.1200  14.7736   2.3252   8.8182 -11.1188  -1.7767   5.5161
-#&gt;    4.3135  -6.3075  10.0661  -2.8670 -10.3514  12.2481   3.4264  15.6479
-#&gt;   -6.5393   2.3954  -5.2652   3.3911 -12.5974  -0.0836  12.9232  12.2688
-#&gt;    0.2117   9.9287   4.2564  -0.5921   0.9085 -14.1971  -7.8865  12.5912
-#&gt;  -13.4646 -14.3913   7.6568  12.2769   4.0565  -4.5363  10.4026  -9.8247
-#&gt; 
-#&gt; Columns 49 to 54  15.0374   4.0324  -1.9407   9.9144  -7.8143  -3.3324
-#&gt;    1.8250   2.1453  -4.0559   1.2571   3.7153  -7.7585
-#&gt;   10.7650  14.8666  -3.4810  -2.8114  -0.0103  -9.6001
-#&gt;   10.6687  -3.1054   5.3376  -7.1514   8.1193   4.2568
-#&gt;   14.4687  -0.2313  21.9394  -2.2141   0.6054  -3.4184
-#&gt;   14.6922 -13.2473  -1.9772  -9.4493   1.8138  -3.2435
-#&gt;    8.1377   8.4131  -9.6667  -0.3913  -0.1425   1.3768
-#&gt;    0.4933  -5.7813  -7.6812   6.9690  -1.4657   6.3824
-#&gt;    9.0113  -9.3362  -2.7714  17.0670  -8.8431   0.6424
-#&gt;    7.2630 -10.3309  10.4459  11.4186  -4.3974  14.2050
-#&gt;   -6.3801  11.0032  -5.1077   2.7058  -4.3680   0.3892
-#&gt;   -5.2654 -23.6549   5.4283   7.3484  -9.5084  10.5216
-#&gt;    2.9438   1.9191   9.2455 -16.1758   6.3500   0.3547
-#&gt;  -13.5085  -0.3253  -0.4914  -7.8236  -0.0467   5.6963
-#&gt;    3.8486  20.0579  11.2338 -13.5011   1.5078  -0.6715
-#&gt;  -14.0068   8.7052 -10.0169   1.5570  -4.3212  -0.9272
-#&gt;   -7.3696  -9.1589  -7.0195   2.0455   1.2589   4.5426
-#&gt;    6.6578  -0.1092   9.7081 -17.8701  -1.3115  -2.8081
-#&gt;   -1.2548   7.0838 -10.6523  10.0498  -7.6591  -0.3992
-#&gt;    1.5438  -3.2565  -7.0791  -5.9863   2.0846   0.8411
-#&gt;   -3.3861   1.7179  -1.8790   4.9099  -8.9661  -2.6552
-#&gt;    8.7539   3.1641   3.9409  -5.0259   6.6639  -5.0562
-#&gt;   -9.4193 -26.4359  -8.8356  -2.0270   3.0264   0.9615
-#&gt;    2.3660 -16.7656  -6.1448  -2.6935   4.6605   0.0575
-#&gt;    9.8351  10.3817 -21.7519   6.2417  11.2304  -2.2447
-#&gt;   -5.5273  -3.6530  -5.7465 -12.6337   5.9185   2.5897
-#&gt;    3.2803  -2.5585  11.6188 -11.3109   8.8087  -6.6643
-#&gt;    3.3733  -4.4194  -8.1316   3.2067  -3.2420  -4.3532
-#&gt;   -6.3447   5.4526  -2.6013  10.3003  -5.7003  -3.3330
-#&gt;   15.6256   5.8662   1.0219  -4.8760  -2.4393  -6.3335
-#&gt;    1.8660   4.2073  -2.0864  -4.0173   0.5807   6.8610
-#&gt;    3.8490  -9.1900  -8.5033   9.4489  -8.2958  -0.2930
-#&gt;  -14.8697  11.2416   9.4341   2.3456  -6.1609   5.3453
-#&gt; 
-#&gt; (15,.,.) = 
-#&gt;  Columns 1 to 8   1.0883 -13.6513  -0.2133   1.8009   3.9161 -11.6073  -0.2295  -2.1861
-#&gt;    1.9985   6.2509   2.2288  -4.0482   9.4895  -6.3833   8.2834  12.3534
-#&gt;   -2.5297   1.0430  -8.8658  -0.5182  10.4926  13.7469  11.6099   3.6924
-#&gt;   -5.1540   2.1113   9.7580   6.0179   7.1496  -0.6682  -9.9985   7.7728
-#&gt;    4.0693 -11.8046  -1.2025   3.0068  -1.5765   4.0780   4.7880  -2.1547
-#&gt;    2.6474  -4.7372  -6.6610   2.6041  13.8240  -1.8557   9.6549  -7.2122
-#&gt;   -4.6676  -7.2728  -0.3055   8.7490  -8.5551  -3.7049  -2.7000  -1.0440
-#&gt;    2.4796  -5.8722  -0.8089  -3.6665  11.1341   8.0360   1.6684  -1.5712
-#&gt;   -2.4944  -3.5698 -13.2276 -10.2569   1.0370 -12.1596   7.5572  -0.2155
-#&gt;    5.2487   1.1269 -15.1657  14.4077   7.4754  24.0975 -13.5825 -10.3345
-#&gt;   -1.6340  -1.0680   0.4577  12.2506   4.1539  19.6165  12.3870   6.5560
-#&gt;    3.4380   1.3043   1.8387   3.7928  -7.2403   5.1083 -16.0055   2.2461
-#&gt;    3.6507   7.2334   3.8925  -7.4961  -5.6780  15.9332   5.7024  12.3805
-#&gt;    1.8125   0.4176  -0.0541  -2.8339  11.0337  -0.0279   0.0701   7.5802
-#&gt;    4.0356   4.1231  -2.2994  -8.5604  13.9954   9.8758  13.2578   7.1555
-#&gt;   -1.5065   6.5901  -7.8061  -7.1423  -2.0012  -6.2893   2.7559 -18.5544
-#&gt;    3.6522   5.8547  -1.8999   0.8719 -22.4154   0.5671  -4.3517  -9.0350
-#&gt;    0.6728  -0.9445   0.0594  -7.6364  -7.4456  -2.9993  16.2292   6.2892
-#&gt;    0.7949 -13.7723   1.2556  -6.9499  -9.3814  -2.3489 -18.6996  -3.0578
-#&gt;   -6.6654   3.2165   1.6263  -9.4282  -2.4634 -12.2318  10.6105   3.1403
-#&gt;   -6.0526  -4.2152   4.2594   5.7431  11.8459  -1.3239   8.0447   1.8969
-#&gt;   -1.6844 -10.8475 -17.8336   0.2219   9.1449  10.3928  -9.5304  -0.5534
-#&gt;   -5.1786  -1.9082  18.8871   2.2231  18.5890   4.2133  -3.9351 -18.2931
-#&gt;   -4.0084  -3.3132  13.3179 -14.1484  -3.0700   1.5621 -10.9091   0.1753
-#&gt;   -4.4713   7.1477   0.0612   4.1754   9.5400   7.0849   6.9342  -5.9359
-#&gt;    1.5532  -2.2195 -10.8981  -4.1009  -1.1116   6.8872  -2.9563  -7.4930
-#&gt;    1.4559   6.5806   3.2414  -7.0536   9.2951  -7.3517   0.9998  19.6585
-#&gt;    0.0429  -5.2700  -4.9120  13.2296   3.2047   3.2075  -3.7749  -4.3614
-#&gt;   -1.9150   6.9785  -4.1241  -5.5429   0.2014   0.9295   2.6048  11.0862
-#&gt;   -2.0567 -10.2148  -2.4953   2.7840  10.8825  -5.8349  20.9365  19.2432
-#&gt;    3.2445  -1.6501  -0.6124  -4.8188  -4.9717  14.1445  12.5350   0.0430
-#&gt;    0.9140  -0.5960   3.7858  11.5954  -5.5139 -11.9720  -5.7306 -10.5027
-#&gt;   -3.9701  -0.8151  -6.9184  -0.0661  -9.4594 -20.8165   6.3012  14.4666
-#&gt; 
-#&gt; Columns 9 to 16   8.1431  -3.0178  -0.5363   0.6678 -25.2406  -2.9713   3.9762  -7.7693
-#&gt;   10.0827  11.1786  -7.8375 -19.6413  13.2021 -13.2463   7.7809   6.5016
-#&gt;   10.5872   4.4375   4.2342   5.8741   9.4069 -14.2884   0.9204   8.4212
-#&gt;   11.9454 -11.1852 -22.4909  -2.1885  21.9187  33.1263  -7.9021 -16.5353
-#&gt;    3.9914   6.6987   5.1267   6.7116  -4.9118  15.8233 -10.2852   6.3374
-#&gt;   -7.7593  -1.4322  25.2502  -7.7409  -8.5150 -10.3848   1.6264  -2.8392
-#&gt;   -5.5525   2.4592  -4.6981  -4.3050  13.1640  10.5330 -10.4717 -19.0436
-#&gt;    1.4257  10.0176  16.1069 -14.2144 -13.1585  -3.3751  -8.2131  16.0931
-#&gt;  -10.2763  -6.3821   2.7695  -3.6538   5.6730 -17.9593  -6.5663   0.1877
-#&gt;   -6.3633  17.7181  -4.6822  -1.3337 -19.6218  13.0417  18.6251  -6.3712
-#&gt;   21.8455   2.5614 -10.2896  -6.0615 -17.4769  12.1958  -0.8616   3.6535
-#&gt;   -6.5174  -5.3700  -2.8310   3.2623  -2.6173   4.1281  -4.3831  -5.5104
-#&gt;    8.1035  -6.9454 -17.5913  -6.7784  16.6167  -5.7966  12.1388   7.2030
-#&gt;  -13.9267  -6.5409  -3.6985 -13.4278  24.0866   1.8773  -6.7192  -5.3016
-#&gt;    0.8591   6.1854  -4.1516 -21.2393  -1.7393   9.3169  19.8108   5.2736
-#&gt;   -0.6292   1.1988 -10.2492  25.2490  -9.3837  -3.8218   1.0765  -7.6519
-#&gt;   -4.4478  -7.9218  -0.0693   4.5087  -8.2218  -3.4812  13.3156  -7.7731
-#&gt;   -9.2627 -10.4391   1.7387  -5.9904   8.7306  11.2186 -11.1882   9.0069
-#&gt;    7.2575   1.3033  -1.7556  -6.5652  11.9147  -9.0567   8.4190 -14.7762
-#&gt;   -4.8597  -3.4468  -4.5055  11.8021  27.3027   2.1298  -3.1886  -5.1535
-#&gt;    3.1227  -0.7431  -2.4790  -4.1893  -1.7766  12.8459  14.4906   2.7969
-#&gt;    3.6164   3.0033  -8.1034   4.3791  -6.6245   8.8456   5.1083   4.0641
-#&gt;    7.9981   3.1624  -5.4385   1.0971   1.9352   7.1007  -3.8727   5.0028
-#&gt;   -0.4185  -4.6503 -11.2607  -9.7846  12.8877   6.5613   2.4134 -13.2733
-#&gt;   19.5945   4.4291 -21.4412  -8.0839  16.8210  -4.4509   5.5216  -7.9720
-#&gt;   -8.1950  -5.5676   8.2075  -5.2201   4.0412  -8.2001   7.4591  -2.1440
-#&gt;   -8.7324   4.6364   3.2411 -30.6622  23.7650  -0.3315   6.6229   9.6569
-#&gt;   10.4355  -4.7070   7.1092  -8.9431   5.6161   2.0974   0.9384  -0.5486
-#&gt;   -1.5238   4.3390  -4.7691   5.7860   2.7868 -12.5967  -9.0385  -2.5035
-#&gt;   -8.1675  18.3459   2.4130  -5.6824  10.5261 -22.4041  -6.8622  21.2248
-#&gt;    1.4159   0.4470  -0.2261 -15.9559 -10.8112   5.1948 -19.3447  12.1516
-#&gt;    7.2652  -0.6015  -1.3659   9.2375 -12.6496  -6.7433  -0.1772  -6.9838
-#&gt;   -9.9182  -9.0766   4.0743   0.5172   5.9142  -6.6260  15.9056   6.7418
-#&gt; 
-#&gt; Columns 17 to 24  -0.1783   7.9413 -11.8590  12.0938  11.7503   3.9021 -10.9821  11.5344
-#&gt;   -3.7389 -15.8895   0.1017   3.8653   1.7080  -0.9352   2.9333  -2.2568
-#&gt;    0.1800   0.3521   7.3771  -4.4123   2.5437   7.7247  -8.4899  -0.8335
-#&gt;  -18.5749  -1.0792  10.5733  -2.3351  10.3508   0.5997  -8.2631   0.9713
-#&gt;   -6.8135  -7.6268 -10.8502 -11.9286  -8.3616  -1.1564  -8.3715   6.2610
-#&gt;   -1.1473  -5.4414  -7.8587  11.8309   5.7112 -14.4187  -4.9764 -20.6384
-#&gt;    4.2899 -11.7472  19.3064  -3.1007  13.9645   1.0451  -2.2210   7.6517
-#&gt;    0.8576 -10.0361  -4.1235  -5.8419   5.9033  -3.3373  -3.3839 -12.2856
-#&gt;    3.9671   2.2257  -6.7027  -0.9268 -10.7361   7.5745  14.5609 -12.9599
-#&gt;   -6.5120 -26.3198   2.7245 -13.8127   2.4841  18.7492 -22.4913   7.2500
-#&gt;    7.1032 -23.1548   6.1670   7.0558   2.0435 -14.0802 -12.0205  -3.1796
-#&gt;    5.0577   2.1066  -0.4149   6.5739   6.4752  -4.0694  14.5186   4.7151
-#&gt;   -8.7884 -13.0395   5.9145 -16.3405   2.4929  11.0508   8.1160  -8.6110
-#&gt;   -9.7962   4.6801   5.7665   1.8636  -8.3646 -10.2172 -13.0652  -5.3477
-#&gt;  -13.1023   2.0782  -2.7891   4.5935   1.0453  -0.4730  -8.8137  -1.8413
-#&gt;    4.5932   7.2090   0.4703   7.3054 -16.7800  21.2898   2.1898  10.1931
-#&gt;    7.4948 -19.7627  -3.3179  -9.9253   4.6076  -8.6530   0.6264   1.1175
-#&gt;   -4.2903   3.9628 -11.1441   4.9707  -4.2456  -3.4579 -12.8071   5.5555
-#&gt;  -16.0894  32.2028  -4.5910  13.3315  -0.3863 -14.1517   9.3140  -2.1675
-#&gt;   -5.7198   3.1941   0.4223   7.2465  -2.4244 -14.3077  12.4841 -10.5792
-#&gt;   -3.5778 -12.3273  -9.8244  18.8030  -8.5429  -5.0645  -2.3328 -15.7069
-#&gt;   -6.0088  -8.0710  -1.1329  -0.8154  11.3627  24.4455 -19.0639   2.6823
-#&gt;   -6.6312   6.9885 -11.9896   5.5543  -7.4037   3.5247 -10.3736   8.7830
-#&gt;   -6.1513   7.4569   3.3442   8.9769  -4.1168   3.0734  10.9385  -7.3666
-#&gt;    7.6150  -2.5113  10.4698 -18.6772   8.8486   2.1977   8.9417  -2.4677
-#&gt;   16.4041   1.5359   5.6459 -12.4040  -3.7871  -8.8165   1.3589   2.3659
-#&gt;   -8.6904   0.4551  -6.8809   4.2659  -7.1439   3.9548  -0.3245   4.7779
-#&gt;    8.0215  -5.7699   7.2912 -10.7024   5.3209   3.1126   8.6581  -4.2902
-#&gt;    4.4659   8.8207   4.4490  -8.9990   5.0328 -10.6082   6.6292   9.4492
-#&gt;    3.5390  15.0517  -3.8489  14.0990 -11.2758   2.4672  -3.2885 -11.4459
-#&gt;   10.2022   8.3853  -0.6533   2.7785 -14.1767   0.2649   4.6376   8.3297
-#&gt;   -8.4626  -6.8197 -14.5050   9.6040  -2.0667   6.9838  -6.5402  -5.9906
-#&gt;    4.9072  -0.5606 -15.8751   5.5458   1.5741  -6.4481  10.0041   5.4857
-#&gt; 
-#&gt; Columns 25 to 32   5.1303   5.2122  -9.0443 -17.3435 -15.6165 -12.8346   0.7560  -3.9576
-#&gt;    9.2853  25.9662 -12.7428  -5.7182   7.5804  -9.2585   0.6351   1.9896
-#&gt;   -9.2542  16.7926 -18.4939   9.6153  -9.9836  -3.0133  -6.7379  10.1117
-#&gt;   -8.5450   9.6756   9.0875   2.2929 -17.3695  18.0980  -1.0384  16.4189
-#&gt;    4.5714   0.4396 -10.0045  12.7580  -7.9960 -11.9238 -19.3069  -4.0089
-#&gt;    7.4968  11.2079  -2.5493  -5.7386   1.5994  -3.1058   0.1909  -2.0720
-#&gt;    4.2286  -0.4640  -6.8048  -2.5686  -8.5246 -13.7803   1.3745   6.1672
-#&gt;   -4.4464  -4.9489  -3.7804 -22.7702   2.3866   2.6622  -7.9037 -17.4625
-#&gt;   19.0069   3.2947  -7.2880  -9.3150  11.7494  -3.8472   0.1815  -7.7672
-#&gt;   -7.2085  -5.0770 -19.5573  21.2984  -6.7350   4.4747 -19.9877  -5.6625
-#&gt;   -7.7921  -2.9634   2.9252   0.3129  -8.5658  -6.7695   5.7646 -12.8793
-#&gt;    9.8014 -10.2934   8.8807   6.3223 -13.6931   8.7320  11.6924 -13.3465
-#&gt;  -11.3146   2.0235  15.6337   2.3617  -8.2081  10.1346  15.0629 -10.9812
-#&gt;    5.8808 -10.2525   2.3238   0.6669   3.7398  -3.0457  -6.3221   7.6590
-#&gt;   -7.5559  -9.5817   4.7616   4.6179   9.3016  17.0188   2.5056  -5.0334
-#&gt;   -5.9316  16.2364 -11.6992  -1.0198   1.7946   1.7542 -14.1235  11.9924
-#&gt;  -12.4835  -0.6314   5.8761  -6.1876   1.5307  -0.7696   2.6828   1.9569
-#&gt;    3.8233  -6.2594  -3.0510   3.4905   5.7083  -2.3449  10.0599   1.8122
-#&gt;   11.2623  -2.4792  21.8768   2.9447 -12.9389  -2.4562   8.1196  -5.9313
-#&gt;    3.3974   5.1587  16.5915   1.2528   2.9424  18.2364  13.3012   7.2241
-#&gt;  -22.2727  12.3062   2.9334 -18.1311  -8.6015  -4.5627 -16.5469  -7.0431
-#&gt;  -13.9829  -0.1226  -2.2359   8.7736  -6.5571  -4.5218 -15.2744   2.8178
-#&gt;   -9.6548  11.2495 -12.7127   5.4785   4.5308   8.6679  -0.9504   5.6837
-#&gt;   -3.2021  -3.7166  21.5739  -3.7581  -2.6107  17.6935   8.5974   6.0939
-#&gt;   -3.3750 -10.4161 -11.5010  -0.7212  -4.0496  -9.2535   5.2862   0.1380
-#&gt;    1.0005 -21.5667   5.0194   9.3062   8.9942  -0.5514   1.0409  -1.0624
-#&gt;   11.5474  -3.6643   2.2632 -10.0752  26.0833  -3.6200  -8.5439  -6.0761
-#&gt;   -9.9302 -11.9761  10.5713  -1.7341 -19.7985 -15.5271 -20.2384 -10.0938
-#&gt;   -0.0631   2.5240  -6.3220  -1.8061  -3.1283   2.5712   1.3781   7.5900
-#&gt;   14.1589   8.2687   7.2193  -0.1783  10.4161   4.9365  -7.0206  -0.1133
-#&gt;   -1.4851  -7.4542   6.7481   3.9673   2.3519  12.6119 -10.1484  -9.8909
-#&gt;    0.3536  17.3712  -7.7706  -9.0948  -0.5194   9.5401  14.2717 -17.6860
-#&gt;   10.9244  -8.8958  26.3675  -3.7598  13.2513   7.5628  20.9188   7.4289
-#&gt; 
-#&gt; Columns 33 to 40   3.0689  10.6830   0.4532  -0.4838  -0.4299   0.2516  -6.4023  -5.9971
-#&gt;    2.3677  -2.4086   3.4197   2.6584   8.3074  -0.8844   2.6699  20.1955
-#&gt;   -0.2473  19.4904   9.5576  15.1257  10.0466  -6.8473   1.0817  -6.9867
-#&gt;   -3.0839  10.6226  -7.4908  14.3042  -4.7792  -4.4134   6.6427  -2.1414
-#&gt;  -11.8221   5.0038  -9.4220  25.0238  -5.1609   9.9701  -1.4302  -2.4254
-#&gt;   -3.0475  18.5491 -11.4661   0.6060 -12.2090   1.4983  -0.2139  -7.5513
-#&gt;   17.4098  -3.9796   7.5888   3.6898  -3.4088  -4.3812   7.6371  -5.2967
-#&gt;   -9.0990   2.7568  -6.9639  13.3664   4.6870   3.6382  11.8887  -3.1614
-#&gt;    1.9245  -0.5073  -2.0715 -13.6016   4.3853 -12.1042  -2.8687   1.5666
-#&gt;    5.1317   6.5544   3.9332  11.5968  11.0494 -10.1883  15.8772  -6.4212
-#&gt;   20.4573  -3.1614   7.3323   2.4851  -2.3496   0.5352  11.0916   9.2170
-#&gt;   14.6608   0.9580  -9.2194  16.2904   2.8315  -3.2757  15.3104  -3.4864
-#&gt;   -1.3125  -4.6803   4.7116  14.6555  -8.5839  -7.9635  14.2071   9.9080
-#&gt;    1.0802 -25.2680  -1.7853  -1.2369  -4.3934  -2.2559  -8.0893  -2.2389
-#&gt;    4.3586   1.2297   2.7565   3.2206   2.4893   7.3944  -2.2629  -8.9441
-#&gt;  -19.4203 -18.4878  12.3137 -12.5646  17.4905  -5.2604 -12.4287   6.0072
-#&gt;    7.8434   7.4955 -19.7156  -3.5258 -10.8456   0.6406  17.3496 -11.0315
-#&gt;   -6.3648 -12.2556  -3.8733   6.3867 -17.5353  -1.6444   2.1416   3.1229
-#&gt;   -6.0311 -15.7606  -1.4276 -13.5864   8.4976   4.2344  -2.9031   7.9949
-#&gt;  -11.4193  -7.3020  -7.6962 -15.6833 -10.4076  11.6489  -2.2371  12.7586
-#&gt;   -4.2476   5.6443  -2.5243  -7.8190  15.2216   1.0256   1.6605  13.9583
-#&gt;   12.5711  11.9341  12.0830   8.0048  -9.5232  -5.8642  -2.7317   0.3039
-#&gt;    7.4139   5.1758  -8.3210  -1.1513   4.9197  -1.7578  -5.5266  16.4828
-#&gt;    9.8795 -15.3196   3.1234 -11.0654  -5.0491   3.5039   2.9127  -6.5662
-#&gt;   -2.3470  -1.0789  12.6208   7.6499   7.1384  -7.9832  -3.6383  16.4744
-#&gt;    3.3038  -7.9061   3.7889  -0.2815   2.7064  -1.0950   2.8509 -13.4831
-#&gt;   10.6526  -5.4884   2.0499  15.5656  -0.4055   3.3537   6.8548  -3.5352
-#&gt;   -8.3463   3.4767  11.1559   1.0161   7.3049  -1.4117  -4.8500  -9.3901
-#&gt;   -4.9456  -3.9004  -2.0650   2.5997  12.4161  -9.3428   6.7366 -18.2491
-#&gt;    5.7380  -2.8731   9.2048   1.4347 -16.9276   0.0849  -7.2009  -9.2984
-#&gt;    1.5917   6.7733   3.2060 -10.8227  -6.8318   0.7219 -11.4016  -4.6355
-#&gt;   -3.4748  -3.5406   4.9675   0.6146   0.0023  -8.9758   1.5299  20.7252
-#&gt;    4.6285   1.9218   0.0187  -9.5405  -1.9712 -15.1803   8.6066  11.9646
-#&gt; 
-#&gt; Columns 41 to 48  -7.7357   1.8710 -10.3429   2.7219  -6.7990 -21.6120  18.7227 -12.7015
-#&gt;   13.6193  -1.8609  -4.5644  -5.9749  -4.2938  -0.8229 -10.9025   5.9986
-#&gt;  -20.4579  -3.8101  -5.0066  14.0796   4.0944  -2.6659  -0.6588  23.1675
-#&gt;    5.0140   3.2363   2.0032  -4.9218  -0.1874  11.2389  -0.3849  -4.2254
-#&gt;   -3.2780  -5.4297   7.5262 -16.4735  -7.9717   5.0588  -4.9824 -18.4087
-#&gt;    0.2802   5.4039   0.5018  -3.2006 -20.0813  -2.8971 -14.2868  -8.5616
-#&gt;   -0.8961   1.0392   4.0800  -4.8382   3.6434  -8.1507   6.8250  -4.5358
-#&gt;   13.1276   1.9661  10.6157  -2.2100  -1.8674   1.5272  -1.9318   2.4651
-#&gt;  -11.9074 -10.8727   0.2838 -15.6077   3.2437  -1.3454  -6.3587  10.3484
-#&gt;   10.4106   5.0825   1.0416  -5.5122   5.3467   6.6739   6.3978   4.0207
-#&gt;    4.0942   7.5176  14.9415   8.0864   0.6323  -6.9692   4.1368   1.3912
-#&gt;    5.6413  -8.4734  -5.8596   4.2755  12.5099  -0.1837  16.5984   4.5206
-#&gt;    5.5017  11.9418  -2.4176  -5.6410  13.1576 -13.0575  12.1843  22.2793
-#&gt;  -12.4225  -5.6317  11.8326   3.2874 -15.0111  11.6552  -5.5975 -12.0861
-#&gt;    1.5665  12.9163  -1.4707 -13.1655  15.3771  -2.0438   9.2375  -9.1089
-#&gt;    4.2325   1.0132  -3.2061 -21.6375  -3.5771  -5.4030   4.6671 -14.4973
-#&gt;   11.3992  10.3182   3.1644 -10.6940   0.4284  -4.0059   5.1565  -5.4210
-#&gt;   -2.5621  12.5505  -2.2309   6.6104  -8.9617  -4.1724   7.9038  -0.1967
-#&gt;    4.9072   5.9168  -4.6431 -16.4186   5.0440 -16.1875   4.9723 -13.7108
-#&gt;   16.9675  23.1500  14.9422  -0.8200  12.5455   2.6321  -7.1201  -1.0218
-#&gt;  -13.4913   1.8989   8.5363  13.1362  -1.6046  -2.4002  -5.5467   8.3550
-#&gt;   -6.0555  -8.8139  -1.2231  -7.8785  10.7005   1.0163   3.8144   1.7067
-#&gt;    5.1270   2.1468   5.4242  11.1889   2.3096   9.9851  23.4633 -10.1023
-#&gt;    6.8518  12.8204  -9.9666 -22.3843   6.9509  -0.3129  12.7661  -4.9824
-#&gt;   -2.1232   6.4515   2.8604  -1.3084  -0.9072   7.3665  -5.0734  11.3607
-#&gt;   -0.9809   1.6749   5.8178  -5.8912  -2.2741  10.8250  -2.1849  -8.5851
-#&gt;    7.8139   0.8496   1.0825  -5.9561  -9.1703  10.0648   6.2888  -1.5190
-#&gt;  -10.9344  -8.8192 -10.7541   6.4524  -0.8203  -6.1767  -5.9425   8.3565
-#&gt;    2.8342   2.2174  -3.4083  -9.9165  11.9706 -12.1254   3.3837  -1.2207
-#&gt;   -0.5685  -5.8857  13.3020  -4.0964  -3.7232  -9.7884  -5.0951  -8.7649
-#&gt;   -4.5537  -3.9051 -14.0524  -4.4882  -1.5416   2.9180  -7.9985  -4.7121
-#&gt;    8.9533  -6.4130   5.8598  -8.5057  -9.4889  -8.2710   5.4517   1.4639
-#&gt;    2.2609  15.6570  15.0149 -12.1152  15.9188  -4.9992 -15.5633  17.7933
-#&gt; 
-#&gt; Columns 49 to 54  -0.5411   7.1052   9.4594  10.9215   5.5259   0.7943
-#&gt;   11.7087   3.8304  12.0716   3.0630   7.5253  -2.7870
-#&gt;   -9.2347   3.7338  12.9016   1.3008   8.7954   7.2356
-#&gt;    0.5419   2.7705   4.5868  -3.7591   0.2899  -2.7223
-#&gt;    9.3156   1.3716  -2.0760  12.5960  13.5953   5.4191
-#&gt;    0.3376   7.6152   9.0261  -2.0834  -1.7178  -3.4096
-#&gt;   -6.4776  10.5584   0.8119   5.7581  -1.9608  -2.1551
-#&gt;   -3.3886   5.0565  19.4814  -4.5326   2.2841   0.7456
-#&gt;    1.8117   1.9668   1.5877   4.6283   5.6492   0.2275
-#&gt;   -2.2264   6.4265  11.0862  -1.4248   4.8906  -5.6389
-#&gt;    6.0374  23.3539  11.9514  17.9640   2.2568   2.5039
-#&gt;    1.2750   1.9622  -8.5067  -8.5523  -1.1474  -3.0614
-#&gt;  -12.7579  11.6931  13.0823   6.4555   6.0657   0.7070
-#&gt;   -6.8894  -5.0586   3.6188  -2.9407  -0.0728  -2.3439
-#&gt;   -0.0851  -0.0606  11.1079   8.3460   1.8776  -1.4109
-#&gt;   15.6769 -15.9328  -7.6688  -4.1366   0.2916  -0.7854
-#&gt;  -10.6747  -1.1922   4.2041   4.2644   1.3655  -1.0362
-#&gt;   -6.8981  -2.9297   7.8518   1.6363   8.5611   1.2419
-#&gt;  -12.9863   7.3051  -4.8493 -19.1433   1.2166   1.0407
-#&gt;    1.5197   6.0635   1.5461   4.3352  -1.5117  -1.4877
-#&gt;   -2.9734  -9.7322   3.7673   3.9460   0.0012   3.0688
-#&gt;   -1.5797   6.9453   5.7439  -3.0841   6.4749   4.2427
-#&gt;   18.2602   5.7800   9.6597 -12.6414  -4.3140  -0.2225
-#&gt;   -2.9976  13.0294  -7.9751  -4.3372  -7.4589  -5.9672
-#&gt;   -5.6187  20.1582  -8.1042 -15.1826   2.5816   1.9494
-#&gt;    4.2394  -4.1858   0.7183  -9.0479  -2.4883  -0.5623
-#&gt;    4.2908   9.0149   2.4474 -11.5626   3.6926  -1.7235
-#&gt;   -7.5453  -3.8337  -2.4619   0.7690  -0.8882   4.5212
-#&gt;   -4.0639  12.7878  -1.3349  -2.4716  12.2182   1.0366
-#&gt;    5.8412   8.9345  -2.8294   1.9000  13.1815  -2.6563
-#&gt;   19.1665 -12.7434   3.1815   0.8826 -11.2455   4.5177
-#&gt;    1.7249   7.4413   2.2909   3.8886   5.2707  -3.4697
-#&gt;  -12.4170  -8.2452  11.6806  10.2876   2.6011  -2.5649
-#&gt; 
-#&gt; (16,.,.) = 
-#&gt;  Columns 1 to 8  -2.7600   0.8228  -2.0277  -1.7346   6.6018   3.5911   5.9707 -15.8408
-#&gt;   -1.0356  -3.1119  -3.7687   5.2955   6.4183 -11.4737  -2.5740  -1.9347
-#&gt;    0.0251   6.3542   0.3483   7.8743  -8.6603   4.9565   3.4808   5.0205
-#&gt;    0.6988  -4.2615  13.1348   7.7283  -0.0323  -1.4919 -10.6331  -0.5465
-#&gt;   -0.2628   2.6682   9.6581   0.8211   8.9183  -2.4679  -1.1408   4.1061
-#&gt;   -0.2870   5.3620  -0.7777 -10.8287 -11.4375  -5.7923  21.4034   9.3686
-#&gt;    2.6499   3.9091   4.5398  -2.6128   1.6460  -2.3636 -15.2839   2.9387
-#&gt;    0.7565  -5.0869  -2.4769  -7.4229  10.6096  10.1590   3.2740   4.3854
-#&gt;    3.7169   6.2653  -2.8990   1.4417   0.3912   1.0326  10.4313  -1.7326
-#&gt;   -8.6562  -4.3874   0.0968  -1.4058   3.8052  15.4445  -1.7759 -16.1631
-#&gt;    4.9437  -0.9907  -9.0754   8.1477  19.9468  14.8628   2.0657   2.2561
-#&gt;    0.3993  -1.1479   1.4673  10.0646   0.0969  -1.4894  -0.1987   5.0744
-#&gt;   -0.2226  -7.9190   5.1014  19.8592  -0.5034   3.2824  13.8569  -4.6129
-#&gt;    1.1945   2.2951  -2.7683  -1.6343  -0.8375 -11.3309 -12.1549   7.0546
-#&gt;   -5.7531  -7.7927   0.6448  19.4201   7.5457  -2.0517   0.8932 -16.9676
-#&gt;    4.5334  14.9188  -3.3128  -2.6326   3.9922  -2.8078  -6.5845  -2.7399
-#&gt;   -4.4235   0.0444   6.1885 -10.0805  -1.0864   6.0184  -4.9241  17.5056
-#&gt;    0.2843   4.5429  -8.7860   6.0604  11.6351  -0.3558   6.5782  -5.6461
-#&gt;   -2.6664   1.9679   0.4705  -7.5153 -10.1342   8.8167  -1.6443  -2.9601
-#&gt;    3.9495   0.5234   5.8723  13.8322   7.9356   2.3470  19.6186  20.0600
-#&gt;   -1.3229  -0.8434   3.3744  16.6394   9.2946  -2.7097   9.9895   9.7557
-#&gt;    0.5403   5.0280   2.7740   1.1861  12.8938   3.1603   6.8896  -1.9588
-#&gt;    6.5978  10.4064  -6.8601   7.0437  16.6060  -3.6179  -1.7362  -8.8715
-#&gt;    1.7710  -0.8880   6.2348   2.3731 -10.3349   7.2290   0.3359  -6.9582
-#&gt;    4.2897   1.4855   6.1162   0.5887 -19.7970   1.1345   5.0447   4.4233
-#&gt;   -1.2467  -9.6489 -10.5180   6.0826  13.8489   0.2058  -9.3231  -9.8914
-#&gt;   -0.7229 -13.3892   4.6428  17.4410  -3.0543  -8.8277  -9.6066  -7.4319
-#&gt;    0.9406   2.0896  -0.3145  -9.2503 -20.6873 -13.6898  -2.6450   2.2762
-#&gt;   -1.9395   0.4147  -1.5328  -8.3068 -16.9304  12.9400   9.2310  -3.5199
-#&gt;    1.1742  -0.8757  -1.9737 -11.8690  -5.6613   5.8969   9.7664 -11.6756
-#&gt;    4.2570   2.6710  -8.9099 -10.7239   3.7910  -9.6373  -3.8045   0.6776
-#&gt;    3.6583   5.7429  -8.4086   4.3649  10.6601   0.6530  -9.9878  -0.1374
-#&gt;   -5.6628 -17.9922 -13.9409  10.0442  18.9254   6.1977 -13.5856  -2.2349
-#&gt; 
-#&gt; Columns 9 to 16  -6.4433 -28.5535  -8.4678 -21.0924  -7.9601  -0.9871   5.7613  15.7314
-#&gt;    2.2651   1.1432  -0.0060   4.3725  -6.5027 -11.7589  -7.4047  -5.1816
-#&gt;    0.8129  10.7154   5.0142   6.0147   5.9449  -7.8546   9.4278  14.0504
-#&gt;   22.5031  20.3193   5.1889  -9.7143   0.4322   3.0996 -13.7099  -1.1477
-#&gt;   16.2837   4.6688  16.8815   3.9505  14.1607  -2.4931   6.2618   5.2700
-#&gt;    7.0820 -12.4342  -4.1717  10.8858  -0.7170  -7.8231   0.9063   9.0388
-#&gt;    9.6803   2.5920 -11.7640 -13.8831   0.6475   2.9737  -3.5288  -0.3977
-#&gt;  -17.0527  -0.7860  -4.7970  14.2684  -1.8839 -13.8384  -1.8371 -14.7168
-#&gt;   -9.5485 -10.1838 -15.8219  -8.5915  -1.2254  -9.7282   7.0587   2.1915
-#&gt;   -3.6906  11.5401   6.7378 -10.6812 -10.8956   0.0756   5.2729  -9.4501
-#&gt;   -1.6229  -4.5235  11.1057  -1.2444  -7.3074   4.3085 -22.4215  -7.7367
-#&gt;   -1.5138 -10.8669  -8.5174  -7.7812 -10.3199  -4.2651  -7.2740   7.1272
-#&gt;   -0.2644   6.7244   3.5009  -3.5305  -0.7732   7.4118 -13.7026  -3.0189
-#&gt;   -0.4762   5.0448 -12.7251  -1.7414   1.6350 -17.3606  -4.0200  -7.6996
-#&gt;    4.6702   7.3795  17.3680   2.2459   7.5771   2.5479   3.4456   8.8597
-#&gt;   -6.4341  -1.7076   4.9719   7.1185   6.5498  -8.9925  28.7168 -12.2344
-#&gt;    2.9709  -3.6002  12.7195  -1.4692   2.6666  -5.9747   3.4872  10.2245
-#&gt;    5.2273  -8.6926   1.5180  -5.5708 -12.5371  15.3410  -4.9092  -4.1608
-#&gt;   -6.4535  -6.6394  -1.2431 -10.7386  10.0858  -5.9873 -12.6475  -3.6647
-#&gt;    2.7371  -6.4281   1.0213  16.3977  -2.8056   7.0478  -8.8185  -9.7340
-#&gt;   -2.4879   4.4497  14.1758  19.5632  -3.3521  -2.3127 -11.0467   6.3970
-#&gt;    0.6058   8.8421   9.0106   7.2495   6.5951  -3.8589  11.4994  -7.5164
-#&gt;    6.7865   6.2353   8.1907   7.6234   3.1150   1.4976  11.4351 -16.0164
-#&gt;    4.0496  -3.4824  -3.4160   6.5648  -7.5814  -4.4073 -12.0241  14.2624
-#&gt;    0.1432   8.2659  -2.4322  20.5865  -1.9699  17.5434  -7.0749  -7.3575
-#&gt;   10.3326   1.7551  -8.4061  -2.8189   2.3182   6.7862   0.2197 -12.4157
-#&gt;   13.8967   8.0922   0.7879  -1.1207   3.7886   3.8039   0.2478  10.7420
-#&gt;   -3.9853   9.9988 -16.8136  16.0818  -6.8891   0.5372   9.9837   1.1991
-#&gt;   -2.0636 -11.4404  -8.3132  -2.9374 -10.9020   5.3206  -1.8545   9.4123
-#&gt;    1.3698  -3.6526   1.0447   5.9050   3.6637   0.8032  -3.5903   9.8564
-#&gt;   -7.5786   0.0139   7.2327 -10.8007   9.2494  -7.5937   5.4437  -7.6017
-#&gt;    5.5106 -14.9470  -6.3485  -8.3104  -1.6446  -7.4743  -3.0488  -5.7098
-#&gt;    2.2644   8.0629  -3.1950 -14.0017   0.2142   6.3026 -20.2186   4.4888
-#&gt; 
-#&gt; Columns 17 to 24   9.9742  -0.2531 -15.6457   6.2607 -22.2418  -6.7331 -15.5286   1.8447
-#&gt;    1.5195   3.8391   1.4582   3.1028  -0.7016 -11.2556  -6.8686  -7.9591
-#&gt;    8.3227   9.6670   3.9340  -5.9822   3.3853   2.7057 -11.0117   5.0772
-#&gt;    5.5696  19.3540  14.9333  -6.3624  -1.7119   4.8719   4.5336  -3.4169
-#&gt;   -6.3202  -1.2739  -9.5770  -4.3426  -1.6039  -6.6722  -1.5124   7.0582
-#&gt;   -6.3374 -16.2618  -8.0484  12.6392   0.5299   9.0170   8.0919  -3.1053
-#&gt;    3.2551  -2.0668  -4.4835  -1.1432  -2.1991  -5.9773   9.2238  -0.3498
-#&gt;  -15.6604  -5.5140   3.4495  -4.5196   7.2550  -1.5833  -4.9158 -15.9783
-#&gt;   -4.7168 -11.6055  -2.3180  -4.3674   0.2962   4.1998  -8.2350   0.1807
-#&gt;  -14.0287  -4.2366  20.8949   8.6563 -13.6998   5.9285 -20.1278  13.0222
-#&gt;    1.1605  -6.6426  21.4767   4.2036  -7.5729  -4.1276 -11.5397  -2.8267
-#&gt;   15.2990 -16.3575  21.8949   0.5246  -6.9700   2.5184   2.9599 -15.2682
-#&gt;   -7.2147   3.1709   7.9658 -21.1614   5.3455 -11.2661  14.1126  -1.6688
-#&gt;   -1.0060   2.5308  -7.6433  -4.4140   6.5125   7.9431   6.6688  -1.7291
-#&gt;   -5.9115  -4.8028  12.3106   0.8792 -11.9624   3.6755   3.1587  -4.7038
-#&gt;   -9.6190  13.4994   6.2126   3.2239  -0.9379   6.0440 -18.5207  12.9122
-#&gt;  -16.7487 -24.2596 -13.7723 -10.1156  17.5785   6.3269  -0.3396  10.5154
-#&gt;   12.8811  -8.5544  -4.5990   6.8287  -7.9660   0.0625   3.6040  10.1197
-#&gt;   -6.0928 -18.3566  27.9042  12.1358  -0.8489   1.0912  -4.2955  -6.1728
-#&gt;   -9.3754 -18.3659   3.7127  12.7966   9.3833  19.0118   6.1746 -18.6876
-#&gt;   -2.6520   6.3344  13.6026  -4.7860  -8.6042  16.8175 -12.6926   0.8305
-#&gt;   -8.8930   3.7205   1.2881 -11.9197  17.4398  -4.2117 -13.5347  14.0767
-#&gt;    0.6665  -5.4405  -8.3948  -3.1627  11.5938  -0.8574 -11.6271  -1.0534
-#&gt;   -3.6281 -20.6814   7.6216  22.5142   5.3597  16.4960  -1.1877 -14.3613
-#&gt;    3.9009  -0.2643  -0.3020   7.1729  -4.1281 -11.5189   0.8285  -0.1679
-#&gt;   19.8296  -2.8968 -12.7355   4.0225  -4.6721   0.9204   8.3452  -4.8176
-#&gt;   -4.0395   2.3905  -6.8203  -0.3130   0.6592 -10.1466   1.3462   8.7574
-#&gt;   -0.0633  -0.6905   2.3094  -5.1639  -9.0047  -2.1210  11.7625  -1.5816
-#&gt;   -3.8899  -6.9879   7.0226   7.3180  -3.5224 -14.9245  -5.6905 -12.7178
-#&gt;    2.7955 -11.2511   7.8878   0.7465  -6.5599   8.1076  -7.6395   0.2875
-#&gt;   -7.1098   2.9919   7.4077 -16.2305 -12.3277  14.1097  -2.9456  -1.6255
-#&gt;   -9.6867  -4.7450  -0.3207  -9.3477  -7.4208   3.2773  -5.1424   6.0123
-#&gt;    6.4256 -15.7727  10.2929  -4.8571  -0.9532  19.6670   5.8706   7.4721
-#&gt; 
-#&gt; Columns 25 to 32 -15.3096  -6.5745  -7.2860 -10.4511 -12.6838  -9.6600   3.7903   1.1023
-#&gt;    5.6280  -1.8632  -1.9495  13.9092  -1.9274  -1.1578   3.3848 -11.8815
-#&gt;  -12.2191   1.6302 -15.7711  -1.5789  -4.9539   9.6367   3.0890   8.3050
-#&gt;    2.3652  -1.3521   0.4177 -14.5621   0.3608  -9.0927   4.3835  12.5708
-#&gt;   -0.8662   7.1923   8.8625   8.1636  10.5547 -12.6599   7.5320  -2.7925
-#&gt;    1.0215 -11.5500  14.2001  -0.3131   7.6136   9.0712  13.7593  -8.6053
-#&gt;  -18.4014  -1.2169 -21.4761  -6.5254  -3.7506 -22.3941   4.6563  15.7101
-#&gt;   -4.7805   8.5062   8.5504  -5.3809   4.6945   2.4564  -6.8784  -0.2480
-#&gt;   -1.7646  -7.2681 -13.7119   7.8265 -11.1437 -10.7238   0.3141   4.2206
-#&gt;   -9.9369   2.7507   8.4793 -15.9703  -5.9975   4.0100  -9.8299   4.4676
-#&gt;  -13.7744  -1.7668  -4.4184  -6.6285  -6.7300   6.6238   2.3076  14.0860
-#&gt;   -6.4844  -9.1079   1.1174  -9.5696  -9.5338  -6.6838 -11.1989  10.4365
-#&gt;   -6.3117  -7.8827  10.9008  -6.3362  -4.7734 -18.8958  -4.3183  -7.5403
-#&gt;   -2.1042  -0.1211   2.0555   7.4067  -5.0056  -5.6900   5.5657  -3.7629
-#&gt;    1.7712  14.1396  10.5162 -11.6767  -7.3099  12.0747  15.9133  -7.7371
-#&gt;    1.0672 -13.2745  -4.7301  -1.9732  -1.7331  10.7086  -3.3371 -15.7881
-#&gt;  -13.4721  12.1576  15.0808  -5.9852  -0.6459   5.5672   4.5189   1.4557
-#&gt;  -13.3895 -10.7102 -10.9340   0.4054  -3.8861 -21.9622  -4.5765  -0.8450
-#&gt;    3.7542  -0.6770  -2.7888  -0.2146  -5.0326   7.8067 -17.7644  -5.5473
-#&gt;    9.3491  -8.4031  -5.5353  -4.6355 -11.3979   2.4843  12.8314   1.3125
-#&gt;   12.8375  -7.6936  -3.0961  -3.0499  -4.4907   1.5318   4.0505  14.9585
-#&gt;   -6.7828   0.1375 -11.3906 -36.7822  -6.6735 -10.4578 -13.0275   4.6941
-#&gt;   -5.9329  -2.5092  -6.0033   1.9017 -11.0034   5.0022   6.1476   7.8637
-#&gt;   -1.4242   1.2150   5.6915  -9.5969  -3.3265 -11.7057  -2.3849   0.0219
-#&gt;   12.3987   0.2080 -11.2412  10.5484   7.9582  -3.2455  -8.0237   9.9753
-#&gt;    6.8234   5.2590   4.4278 -15.1414  -9.5791  12.0810   1.5970  -2.6499
-#&gt;    6.5514  11.9095 -12.5986   7.6343  11.6594  -6.3636   5.2376 -10.9462
-#&gt;    4.0786   1.9670   4.2688   2.6642   2.7381  -6.4994 -14.8093   0.5476
-#&gt;   10.1922 -11.1408   6.9625  -0.0056   5.9063 -15.8966  12.5186  -5.7160
-#&gt;   12.2368 -11.8022   0.4133  -5.1157  -1.0613  -2.0009  11.8055   0.5434
-#&gt;   11.9709   3.0553  14.6753   4.0227  -0.9326  11.0080  -1.8812  -8.1353
-#&gt;   -9.1725 -12.6282  -6.5129   5.5643   3.7450   2.5952  -7.6919   0.8718
-#&gt;    8.3638   8.7733   8.9684  -8.6622  -3.3424  18.2683   2.3889  -1.1773
-#&gt; 
-#&gt; Columns 33 to 40  11.1856  -3.2133   4.7681   8.5269  11.2416   6.0435  16.8551   2.9847
-#&gt;   -5.9053   9.3756   3.4682   9.9250  -1.8598  -1.4956   2.6643   3.4739
-#&gt;   11.3500   8.8299  -9.2754   0.9621   1.4411 -18.8435  -5.2095  -9.4149
-#&gt;    5.9444   0.1722 -11.6036  -8.8749  -1.1789  -8.3403  -3.8570   0.0271
-#&gt;    1.4253  -1.3891  10.5744  -6.9424   6.8760  -6.0207  -0.8259  -8.5892
-#&gt;    7.6890 -11.3101   3.7501  -9.2827 -14.4832   4.9891   1.8406   2.0874
-#&gt;    4.2880  -0.0469 -10.2911  13.1784  13.1831  -5.8181 -15.9914  12.7404
-#&gt;   -6.7236  -3.0588  15.0207   0.7335   2.8261   8.2799   4.1364   7.9349
-#&gt;   -1.0402   3.4433   0.2642   4.1856  16.5457  -3.9979   1.3308   1.3616
-#&gt;    0.5575  13.7648   2.8039 -21.9601   4.5285  -5.3861   1.1788 -17.1079
-#&gt;  -10.9231   6.7653   7.4018  13.8980  -8.8238   5.6408  12.0012   5.1252
-#&gt;   -3.3577  -5.3622   3.3010  -0.1772  -1.3159   7.7737  17.5155   7.7231
-#&gt;   -2.3196  10.5039  -1.0930   9.8211   7.5022  11.2936  17.4869  19.0920
-#&gt;  -12.3956  -5.4078  -9.1664  11.7405   9.0759  -0.6769   2.3385   2.5926
-#&gt;    9.8586   8.5238  -1.0977 -10.1858  -8.7758   4.9225   7.8713  -9.6061
-#&gt;  -10.6838   0.9227   2.8648   2.5495  -1.7431   2.0586   8.3699 -14.5217
-#&gt;  -11.5291 -16.0488   6.7215   4.8138  -2.8357   7.7111  -1.9383  18.9276
-#&gt;    0.3355   0.8368   2.5149   7.1675  16.4151 -14.8666   4.4832   1.4998
-#&gt;    2.8727   5.2853  -1.8622   9.5344  12.0547  10.7263  -7.2828  16.3877
-#&gt;   -1.2290   3.7722  -8.5199  14.4043   4.1647   1.5279  13.7910   9.2969
-#&gt;    2.0865  10.7595 -14.0428  -5.3399 -12.6697   0.3238   0.6859  -2.7572
-#&gt;    8.5948   8.9297   5.5949   0.7949  12.3164   6.8865   3.8063 -12.5449
-#&gt;    3.9390 -14.1598 -11.1637   3.2651  -2.2288  -3.4904  13.5260  -7.6032
-#&gt;   -6.8061   2.2664   0.2583   1.5896  -0.3508  -1.5248   5.4790   9.3369
-#&gt;    3.3596   3.2788  -9.8761  10.4238   3.2109  -9.4448 -19.5449   9.0529
-#&gt;    5.8543  -9.2212   0.1432  -2.6527  -8.5208   6.1969   5.4095   4.4358
-#&gt;   -2.2037   5.4121  -0.0989   2.9186  -2.8578  -3.6378  -3.6911  -3.4446
-#&gt;    3.5897  10.9323 -12.3107  -2.4495  -0.0585   2.3369 -10.9323   9.6458
-#&gt;  -16.7077   3.8851  10.9110   8.2285  -0.0442  -9.2399   3.4641  14.3273
-#&gt;    6.8456  -1.3216  -9.9909   3.3078  -4.4122   8.4045  10.3149  -7.9406
-#&gt;    4.3354  -3.5246  -1.2337  -6.5421  -5.3814  10.9157   2.9830  -6.4742
-#&gt;    0.6325  -0.8887   5.0589   6.4651   8.1906  -1.3228   3.6275  -3.1130
-#&gt;   -0.2744   8.4436   3.0486   5.8562   1.4216   7.0257   8.9337  15.6253
-#&gt; 
-#&gt; Columns 41 to 48   9.0247  13.3126 -12.6453  -5.6765   6.4971   0.4884  -5.2214 -19.1446
-#&gt;   -2.9086  -7.2185   4.0202   5.2726  -3.6936  -1.3687   2.4671  -5.4026
-#&gt;   -3.6742   7.0420  19.8668  -5.9150  16.6346   3.4925   2.4266  -3.7340
-#&gt;    5.6792   5.0479  -0.5947   0.1768  13.9977   7.2268  -1.7114  -8.8035
-#&gt;   -0.7816  -4.9165  -3.3806   6.1368 -18.8432  16.9812  -4.2045  -1.8059
-#&gt;    5.5661   3.8603  -6.6612   0.0961 -17.6977  -0.9973  10.5065  -2.3938
-#&gt;    9.4290   6.0583  -2.2102  -5.5096  11.5940  -5.3050   1.7050  -8.5504
-#&gt;  -10.8333   1.9431 -15.9623  -4.7673  -4.2958  -3.0052   0.1534   1.2412
-#&gt;   -7.5497   4.7395   1.5957 -15.6991  -7.0785  -4.5058   7.6090  -5.9557
-#&gt;   -2.7687  -4.5106  -3.3210   0.5556  -6.5119   0.9775  21.2342   0.7942
-#&gt;   15.4265 -17.3593   0.1879  -7.2387   7.5115 -11.9338  -6.8426 -15.4984
-#&gt;    3.2303  -2.8260  -5.9144   4.1136  10.1781  -3.6281   5.0763  -1.6742
-#&gt;  -20.2512  -7.6791   2.6995  -7.7236   5.4933   1.8741 -14.8098   5.2208
-#&gt;    5.7526  -9.8191  -1.9964   8.6155  -3.3612   1.7848  -2.2402   0.8492
-#&gt;    5.2461  11.8922   7.0627   3.9715 -13.7655  -6.9683   0.7010  -6.6267
-#&gt;    2.6352  -5.9603  25.2384  -1.8749  13.5300  11.1147  -1.6457 -16.2835
-#&gt;   -2.6916 -13.4457 -12.4253 -15.5623 -13.3052  -5.9919  -2.6454   3.8536
-#&gt;    9.1920  -8.2337 -12.6354   9.2501   2.3989  -8.9053  17.4592   4.8868
-#&gt;  -11.4651   3.7972   9.7453   5.2887   5.1445   2.2791  -8.8361   4.1618
-#&gt;  -14.4576 -14.5104 -11.4553  -3.3639  -2.7846   8.2429  11.6642   2.8306
-#&gt;    2.6709  -4.0189  14.2845  -7.3687  -3.9882   5.6073  12.3499   1.5911
-#&gt;   -5.6708  -5.5378   5.4239  -6.2941  -1.5243  13.9581  -9.0365  -5.3781
-#&gt;   18.1499  -9.5575  -8.4790 -12.4590  18.8048   7.5273  -6.1964 -10.4868
-#&gt;   -8.1602   0.4580  -6.2898  -8.2462   1.0166   5.4268   3.9140   3.3766
-#&gt;   -9.0139  -2.6806  16.3949 -11.2774  14.0488  -9.2312  -9.8103  -5.2890
-#&gt;    5.6534  -8.2521  -1.9260   8.9428   3.5507  -6.7169   4.6469  10.4421
-#&gt;    6.9031  -7.0395  -5.3966  14.4229  -2.9282  -3.9595   5.6886  13.5041
-#&gt;    8.8224   5.9935  11.7428  -2.5996   0.0723   4.3939  11.7865  -0.9889
-#&gt;   -6.0435   9.3397   3.1448  -2.0408  22.3885 -12.8522  -0.1779  11.1044
-#&gt;   13.1240   9.9574   0.3613  16.9265 -14.5009  -4.2775   3.8377  -7.4184
-#&gt;   13.1363  -5.3682   1.8631  -2.2287 -20.0610 -13.1830  -7.8798 -16.1525
-#&gt;    3.2613  -4.1543   9.0060  -7.8207   2.2249   3.4243   7.7213  -4.9296
-#&gt;   -4.4573   3.3603  -7.6442   0.5591  -4.8655 -18.5126  -6.3259  16.6873
-#&gt; 
-#&gt; Columns 49 to 54  -4.0006   3.9729 -13.1225   2.7504  -1.3509   0.5778
-#&gt;    5.7187   7.9505  10.9506 -10.1734  -4.6613   8.6627
-#&gt;    6.2525  -4.8867   6.5035  -1.1276   0.2939   7.0762
-#&gt;    0.0970   1.0255  -5.7373   1.4382   2.9648   1.0311
-#&gt;   -6.4082  14.7010   1.4877  -1.8780  -6.8536   5.3383
-#&gt;   -6.4262   5.4705   9.1379   2.3787  -3.4948   1.2208
-#&gt;   -4.5435  -1.2446   5.5469  -6.3166   4.4507   1.4723
-#&gt;    9.5924   0.5975   5.5595  -3.7893  -5.0470  -3.3063
-#&gt;    2.0133   4.0132  17.9548 -12.3098  -3.5258   0.7865
-#&gt;   -3.2688  12.6520  -6.0444  -9.5267   6.8003   4.9790
-#&gt;    7.4359  -5.4875  -6.1759   1.7151   0.6699  -3.1205
-#&gt;    0.2353   4.7986  -2.6493  -5.5781   6.7683  -7.7219
-#&gt;    8.3835  -6.5963  -7.1284   1.4208   7.3565   0.7387
-#&gt;   -7.7134  -0.6035  -2.8617  -1.2956  -0.9364   4.8564
-#&gt;   -4.6944  -6.3077 -11.3861   7.0755   1.8616  -3.6070
-#&gt;  -13.1052   6.3904  -0.0317   1.4950  -9.8441   5.7674
-#&gt;    2.9768  -2.7306   3.6861   4.1955  -2.4109  -0.7457
-#&gt;   11.8408  10.6378   0.5189  -2.5637   9.8434   7.4999
-#&gt;  -24.8580  -1.3494  11.0525  13.0056  -4.5345  -1.2215
-#&gt;   -4.9311   0.6363   6.6874   0.7675   5.5509  -7.3650
-#&gt;    0.8382  -0.4241  -0.4592  -3.2214 -12.8065  -1.5604
-#&gt;    0.0864   1.2269  -0.3867  13.4719   4.4660  11.1220
-#&gt;    8.6196   1.8098  -1.2862  10.2855  -3.5849  -1.0396
-#&gt;   -3.4387   5.7835  -0.5967   4.9575  10.9079   0.2686
-#&gt;    6.2836  -6.5648  15.8435  -9.4663  -0.9155   2.7777
-#&gt;    1.0279   3.7031   6.5479   3.6665  -0.0319  -1.2968
-#&gt;    2.1223  -9.6593   3.3715   3.2274  -8.0837   5.7017
-#&gt;    1.7811  -9.2882  -4.0152  -7.1935   3.4040  -2.8249
-#&gt;   -0.7402  -4.9798   8.0377  -9.1249   0.6037   2.1465
-#&gt;   -8.7432  14.0640  -1.8938   4.4842   0.8327  -0.6453
-#&gt;    6.4187   8.1467 -15.0601  10.3513  -1.1677  -9.2789
-#&gt;   -4.0064  -1.1963   4.7671  -0.6940  -0.4981   1.3207
-#&gt;   10.6053  -5.8935 -13.4943  -4.6510   7.1316  -2.3230
-#&gt; 
-#&gt; (17,.,.) = 
-#&gt;  Columns 1 to 8  -3.9564  -2.1750   0.3340   8.4630   8.1481   7.7816  -1.3062  -2.9915
-#&gt;    0.9839   0.5990  -5.8968   5.9612   8.2964   9.4655 -15.0413 -14.0716
-#&gt;   -2.2180   3.1751  -3.7681  -3.2927   1.6947  -9.8105  -1.9209   9.3263
-#&gt;   -0.6515   0.9319  -1.1107 -23.1428   0.8931  16.7023   2.9233 -12.4468
-#&gt;   -2.8578  -5.2455   8.4474   1.4460   9.9896  10.1463   4.2384  -2.4548
-#&gt;   -4.5548  -9.7338  -6.8885   6.8955  22.6769 -18.1877  -3.8313  -3.8911
-#&gt;    1.4960   8.0942  -0.4051  -6.8817   3.9238  14.8585  -7.3363  -2.8628
-#&gt;   -0.0432   4.4386   3.3405  10.1920  13.1560  11.0111  -4.2126   4.5319
-#&gt;    1.8920   7.2401  -7.8179   4.3049   4.7857   4.2115  -3.0862  -7.6410
-#&gt;    2.2662 -11.8599   2.4468   4.7625  -8.8832   4.2344  -0.5726  -1.6442
-#&gt;    1.8519   6.4384   2.3476  11.0174  -2.2971   8.8660   3.7699  -3.4492
-#&gt;    0.1992  -1.9375 -16.8702  -0.9815   2.9452   4.6698   8.7698  13.1808
-#&gt;   -0.7985   1.4321   0.7968   0.5426   0.7012   1.4141   5.4290 -12.5983
-#&gt;    2.4852   0.4866   1.3816  -3.3952   4.2360   5.2807 -13.5710  -6.3570
-#&gt;    0.3518   5.1583  -8.4673  -1.0688 -13.0577  -4.0151   8.4281  -6.8947
-#&gt;   -0.9826  -2.1080   5.9670  -1.0343   4.6946   5.6516  -1.9370 -11.4675
-#&gt;    1.5725  -2.8861   7.6391   4.4971   5.1302  -3.2393   2.0739   8.5438
-#&gt;    2.1404   2.6312 -15.2044   2.4766   9.1825 -13.6544  -6.1287  -7.3355
-#&gt;   -3.8904  -2.0880   8.3195  -8.5080  -9.0082   0.3313  13.1167   5.3202
-#&gt;    1.7031   3.1110  -9.0205   6.6821   8.8906   3.3616   8.0850  -3.8192
-#&gt;    1.0845  -4.8848   2.3401   1.5741  17.1633  17.2813  10.5647   5.3349
-#&gt;   -0.8367  -1.2819   3.8005   0.1361  -2.0242   2.7506  11.0772  -7.8608
-#&gt;    0.6915  11.9058  -1.5937 -13.1715  -3.5037   7.4473   8.7507  12.5144
-#&gt;   -3.0307   7.6102   3.6896 -17.8909  -1.4021 -11.7788   6.7564  -9.3313
-#&gt;    5.8190   2.0999  -3.6135   3.1024  -1.4481  18.8193  -3.9052  -3.5867
-#&gt;    3.6812   1.9317  -7.4330 -20.7271 -15.3926   3.3655  18.1572  -5.6471
-#&gt;    7.3120   6.2384 -14.8585  -6.3624   2.1335  -5.9107  10.7311 -14.2100
-#&gt;   -0.5347  -7.5949   8.4517   9.1283   9.8177 -10.5817   9.1578   0.8383
-#&gt;    2.8407  -4.0335  -9.6747   3.2892  -6.2259   6.1474 -15.0136   5.5848
-#&gt;   -7.1095  -5.9698  -7.7557   8.1749   5.2028  -7.3851   0.7095 -16.9859
-#&gt;    2.4204   6.2923   0.1449   4.9637  -3.2872  -8.5218   3.6038   3.4741
-#&gt;    0.4293  -2.5566   1.3666   1.4526   5.1957  12.2543 -11.7564  -9.5850
-#&gt;   -2.7063  -1.9760  -6.3588  -1.8847   8.4661  -2.9726 -13.1356  -7.2825
-#&gt; 
-#&gt; Columns 9 to 16   0.6611  -1.7291  -5.7305  -4.3868  -0.1751   7.5019  -9.5681  10.9814
-#&gt;    0.3289  -3.1582   1.9621  -5.7409  -4.4326 -11.5398 -10.2710   2.4720
-#&gt;   14.2533  -4.0807  -4.8408   0.3751   6.2341 -16.1900  14.9274   3.9260
-#&gt;   -7.7629  -6.4644  -5.5858  13.0363  -8.3703   6.3579  19.5210   5.6899
-#&gt;    7.7352   1.0961  -2.8332  -8.0160  -8.9810  -6.9620 -11.3984  -5.9339
-#&gt;  -12.4202  11.1434   7.5548   8.7035  -4.2278   4.0134 -13.0019 -14.4224
-#&gt;    5.2139 -20.0238  -2.9087  13.5230  19.3688 -14.4811   2.1954  10.0536
-#&gt;    2.6761   5.2841   9.8645  12.4693  -7.9252 -12.3587  16.4807 -12.8070
-#&gt;   -3.1832   3.3912  -5.2241   2.6736   1.8939   4.7112  -5.6006 -11.7534
-#&gt;   13.2216   4.2327  -0.7458  -1.1902  13.7424  -5.1098  11.1027  -1.1009
-#&gt;   -7.6741 -20.5464  15.0679  10.5614  -9.2688  -6.6599  -3.6438  10.3012
-#&gt;   -5.6790   6.5053   2.6527  13.0496   7.0162   6.3982  12.8826  -1.9579
-#&gt;   -2.3842 -11.2741  -1.6434  -2.8160  -5.9173  -5.5913  22.3190   9.4059
-#&gt;   -0.2974  -9.0226 -10.0476   4.6998   9.0848  -6.6572   6.2459  -2.0540
-#&gt;    3.8765   4.0878  -6.0271  -6.3631   1.7719  -1.5823   3.0158   9.6532
-#&gt;  -13.3969   7.4170   4.0168 -23.1251  -5.9243   4.9882  -9.3127  14.6076
-#&gt;   -6.1326 -20.6444  13.5982   3.0767  -3.9287   1.7505   3.0020  -3.4937
-#&gt;    5.4608  -2.9088  -5.6074   2.2697  -3.0629   0.8215 -13.8057  -1.3287
-#&gt;  -15.2621   4.3553   3.7479   8.5446   4.0778   2.0000  -1.2421  -8.1701
-#&gt;  -15.9818  -2.0304  13.2152   3.3705  -3.5933   6.7885 -12.3021  -8.6255
-#&gt;    0.6934  16.9297  14.9805  -9.9497  -7.4336   1.6928 -19.3659   6.8751
-#&gt;    9.2163 -10.9949   1.9788   6.2232  11.4685  15.6835  16.1974  -9.6962
-#&gt;   -6.1698 -10.5955  -3.9036   6.5612   4.8139   1.7988   5.3628   9.2321
-#&gt;   -3.1260  -1.9474   2.0254  13.0893   4.9594  10.5355   7.0550   3.0255
-#&gt;    3.8034 -20.1387  18.5108  10.5472   3.5416  -4.6177  14.6198   1.5012
-#&gt;    1.5823  -3.8952   5.8315  -0.7658  -6.4961   6.5447   7.7128   0.4808
-#&gt;   25.5171  -8.0952 -11.4933   3.7758 -15.1173  12.9088   1.1464  -2.6088
-#&gt;   11.7631   3.2464  -1.1892   4.1453 -12.3689  -2.5019   6.4111   2.6671
-#&gt;  -11.6276  -5.4697  16.0085  -0.4427 -13.5896 -12.8003   3.3718  -3.7528
-#&gt;    4.5674   5.7873  -2.3900   9.3148  -7.3938   4.3323 -14.5391 -13.2242
-#&gt;   -5.8534   4.2191  -7.0184  -0.4450   2.8462   3.6699  -0.7983   6.9112
-#&gt;  -12.2287   9.0597  -1.8718 -12.1529  -7.4464   6.4928  -4.2111  -0.2613
-#&gt;    3.5069   9.2525  -3.9487 -10.3923   2.5662   2.1483  -2.1171  -3.2136
-#&gt; 
-#&gt; Columns 17 to 24   9.2547  22.9430  11.4132   6.0274   0.5464   8.7991  -8.4071  -2.0184
-#&gt;   -2.7625   0.7433 -10.9601   3.8743 -15.5283  -7.7058   7.8654  -8.3200
-#&gt;  -20.3581   8.1769 -12.2070  -1.0100  -7.7311  -0.5646  18.5353 -16.0002
-#&gt;  -26.9896  -7.6620   5.7793   1.4793  -2.0733  -8.0682   1.2973   9.1480
-#&gt;   10.0409 -11.3006   1.3578  -0.1428  18.3400  -1.0448  -3.6329  -0.8159
-#&gt;   10.1467  18.3533 -17.7315  -9.0752   9.3450 -12.5926  -0.4323   6.5433
-#&gt;   -1.0315  19.7110   2.3451   9.9766 -11.5812   3.3626   3.9648  -5.1736
-#&gt;    3.5122  11.1471  -5.3666  -1.2245   8.4996 -15.4668   3.7436  -5.2678
-#&gt;    2.1273  15.5804  -0.4965   9.2396   1.1915  15.2501   2.3877  -6.4292
-#&gt;   -2.9319  -9.9991   1.3899  -4.0226   8.3397 -13.4436  10.6216  -7.9296
-#&gt;   -2.2457   5.1920  -5.8527   5.5700  -8.1282  -3.5494  -6.1458   8.8459
-#&gt;   -1.6178   4.0936 -13.6546  18.1428   0.7029   0.4806 -16.8094  10.9766
-#&gt;  -19.8841 -13.3471   0.5405  24.5565 -20.0386 -10.1274  15.1765   8.9601
-#&gt;    6.2276   0.6277  10.9804  -1.7583  -3.5120  -9.9417   9.6675  -8.8506
-#&gt;   -0.7090  -1.7329  -8.2980  24.9049 -10.7699  -6.7546  -0.0351  -1.3831
-#&gt;    0.8067 -14.2512   7.2843  -3.1263   6.6421   3.9858  -1.5425   3.3606
-#&gt;   -1.3532  12.4415 -24.4659   7.8378  -4.9142  -9.1678   1.1738   0.5523
-#&gt;    3.8918  10.6614  -7.5607   7.1670 -12.3355  -6.1884  10.2749  -5.2356
-#&gt;    3.1145   9.3927   0.8137  16.9215  -7.0354   4.1898   7.8400 -10.5039
-#&gt;   -1.7219   4.8508  -3.5239  11.6332 -12.1153  11.7422   1.7892  -2.5279
-#&gt;   -5.1107   2.8773   9.6036   2.9153   4.7025  -2.8247  -2.2097  -2.9586
-#&gt;    0.0817   3.7106   5.2893   1.8493  -6.7302  -8.1872  18.1146 -10.1718
-#&gt;  -13.3711  -4.3220  -7.5589  -1.1619   5.0401  -4.3154  -7.0122  -1.4504
-#&gt;    0.2229 -16.9503  -0.9717   0.7504  -4.3032  -6.9120  -4.4770   0.0658
-#&gt;  -13.2325   1.8090   6.7857   4.5068  -8.4655 -13.9790   8.4423   4.8313
-#&gt;   -5.2018  -3.5124  -0.9221  -8.8837   9.0268   5.1363  -3.6054 -10.8499
-#&gt;   -6.0790  -2.4467   2.0870  -7.2727  -5.8087   4.5968   8.3957  -6.1957
-#&gt;    8.9134  -1.9985   5.1378   0.8188  -6.9649  -1.3846   2.6627   5.7891
-#&gt;    6.4608  12.0974  -5.0705   7.8107  -5.1174   1.2706   5.6036   3.3479
-#&gt;   17.3613   6.6375   0.3784  -9.5727  -2.3533  10.9019   4.6846 -13.0937
-#&gt;   -6.0872  -2.1833   9.1082  -4.3480   8.2524  -9.3588   4.9362  10.0528
-#&gt;   -3.5193   1.7675  -3.9030  -3.7520  -4.0829   1.8997  -3.1216  -2.6997
-#&gt;   -1.5569  17.9994  -3.6929  -5.3393  -2.4811  -2.6512   1.0326  -7.0996
-#&gt; 
-#&gt; Columns 25 to 32   1.5006   5.3622  -9.8282   3.7656  -1.8869  -4.5760 -10.6600  -2.8923
-#&gt;   13.2862  15.4644 -12.3208  -4.8793  -6.2516   6.3929   5.1939  -6.7089
-#&gt;    2.6143 -14.9410   0.1438 -20.1110   9.6666   3.8671  -2.1553   2.5630
-#&gt;   -6.3714  -1.2902   0.1016 -10.2468  -3.2783  -1.4219  10.3089  -7.0954
-#&gt;   -6.1566  14.3145  -3.9457   6.3523 -13.0054  25.9881  -0.1258   4.5220
-#&gt;   -6.8454   4.0777  -9.0710   1.6716   4.1434  -0.9201   2.5194  -0.7888
-#&gt;    8.6136  -6.3188  11.1751 -25.7065   0.9291  -5.6595   5.5869 -15.8436
-#&gt;  -12.7620   0.8641  -7.2586  -0.8560   0.1366  -7.9327   3.3187   1.7080
-#&gt;    3.8265   3.6348   7.8461  -5.2458   3.3240   2.3149 -14.4692  -9.7450
-#&gt;   -4.8530   3.2028   3.0385  19.5245  -7.3373 -10.6954  -2.4104  -7.0479
-#&gt;   -4.0237  -5.7182  -1.5463  -7.3727  -7.7311  -3.6811   5.2818   4.0086
-#&gt;   -1.7809 -12.0441   5.9113 -10.8349  -0.4946 -27.0969  -9.9146   0.2161
-#&gt;    6.5975 -12.6987   7.7444 -12.1161   3.9306 -13.0361  -7.0078  -0.9961
-#&gt;   -3.5869  11.6639   7.5600   6.2125  -1.3754  19.9969   2.7238   3.5648
-#&gt;   -0.8151  -7.2731  14.8690   1.2319   6.5598  -8.2061   3.3908   7.6806
-#&gt;    1.0771  -9.7330   4.1180 -27.8674  -1.6663   9.8298 -16.7224  15.3097
-#&gt;    0.5255   1.0295   4.4848  -4.3293   1.9520  -5.9217   7.0232 -16.2566
-#&gt;    0.6632   3.0677  -7.1702   2.2203  10.5391  -0.7854   2.8086 -11.4457
-#&gt;   -4.3824   1.5234   2.4268   9.0864 -19.4765   7.3395  -2.2102   1.5304
-#&gt;   17.5002  -7.7123  -5.5016  -4.5998  -0.2509  -3.7938   0.0792   3.8531
-#&gt;  -17.9782  -5.4882  -5.4073   0.9324  -8.3121  17.2457   6.0731  22.1387
-#&gt;   -3.7666  -4.4166  -4.0334   0.6218 -24.4114  -9.5214 -15.9607   6.9100
-#&gt;   -4.5491  -7.9836  10.4585  -5.2301   5.9357  -7.6842 -24.3837   8.8376
-#&gt;    8.1528   0.4903  -9.5858   0.2715  -2.9929  -8.1600 -14.8309  -2.0877
-#&gt;    7.2739 -16.4320   0.9519 -15.6578  -2.7708 -14.9751   7.5233   1.7894
-#&gt;  -10.3110   8.8207   0.0843   4.9900  13.1409 -13.6418  -1.5619 -17.2298
-#&gt;   -0.5353  17.4012  -5.6550   8.0321   1.6942 -11.9237   3.4942  -6.8178
-#&gt;   -2.9776   7.9576   0.0321  -5.3567  20.3315 -11.1687  10.6648   7.2925
-#&gt;   -0.5574  -0.0196  17.8414 -18.0917  18.5991 -11.0587   3.9364 -12.2255
-#&gt;   11.3686  20.3430 -17.2331  18.4076  -6.5433   4.2008   6.0029   6.5729
-#&gt;  -11.2404  10.5785   4.5425   7.3969   8.5018   1.9350  -0.9698  10.6371
-#&gt;    6.4822  -8.1925   2.4144   2.4401   0.7112  -1.6499 -16.9816  -4.0549
-#&gt;   19.1052  -0.9343   8.3421  11.9296   0.6105   0.7327   0.0250  -5.1755
-#&gt; 
-#&gt; Columns 33 to 40  -6.5850  -5.0773   6.3118  15.0973  -0.5502   4.3400   3.4192  14.6276
-#&gt;    1.1622   2.3969  -1.4657  -2.0045   4.2705   1.6276  -6.5929   2.5342
-#&gt;   -0.0319  -2.5215  -8.3896  -9.5590   2.6645  -0.1957 -20.1142  -0.7856
-#&gt;    1.4295  -6.8483 -16.1640 -17.0104 -15.8533  -1.2251   8.3162 -12.2741
-#&gt;    7.1446   5.6403  11.5997  -0.7001   6.5897 -10.5679   2.1498 -12.5468
-#&gt;    5.7014  -5.8133   7.9449   6.8725   6.1787  -0.0533 -17.7036  -5.3075
-#&gt;    0.5708   2.3781 -14.5894  -5.9678  -2.5364   1.2698   4.1905   4.8995
-#&gt;   20.6719  -0.7989   7.4239  -5.1275   0.8732   8.3747  -3.4850   7.6076
-#&gt;    7.6485 -16.9352   2.3385   5.0865   2.6090   3.4965  -1.1418  -1.6453
-#&gt;    5.3659  -5.2591  -6.1329  -1.7674 -11.7298 -14.2515  -7.9252  11.6046
-#&gt;    2.9386  13.6584  -8.2997   7.5774   6.3866  -4.5452   7.3455   0.9142
-#&gt;   11.2478 -14.3765  -8.4142  -3.6341  -7.5309  -4.6225   3.2880   8.8770
-#&gt;    3.0462   5.6754  16.1329  -2.2828  -4.8174  -8.5776   9.5801   5.9432
-#&gt;   -9.9101   7.0106   8.5593  -2.7154  -3.7886   5.4330   7.2692   6.3363
-#&gt;   -0.2714   7.1433  21.0996  -5.7681  -3.9418   8.9294   9.1753   3.0718
-#&gt;   -3.1855  -7.1989   0.4626  -9.9850  -9.2727   3.6411 -16.0222   0.9230
-#&gt;    1.7516  -1.1384 -14.6472  11.3800   3.7163  -9.7168  -2.2569  13.9210
-#&gt;   -3.3187   0.1049   8.9842   9.0442  -9.8074  -8.4282  -1.5862  10.1758
-#&gt;   -1.0068   3.3115  -6.6873   5.6944   2.0790   0.9845  17.7626  14.2245
-#&gt;    9.9264  -1.6299   1.4067  -2.8134  -6.5286   9.0202  16.2849  -6.5024
-#&gt;   11.5164  -3.9032  -9.1244   5.6236 -13.4104   1.6207 -10.6343  -3.2739
-#&gt;    3.2590   0.7286 -12.6900 -11.1551  -1.6464 -15.8375   2.5332  11.3976
-#&gt;    1.3172   4.9167  -3.4154 -17.9529  -2.0245   8.6425  -3.1319  10.6447
-#&gt;    8.9093  -9.8215  -5.2518 -16.9391 -10.1703 -15.5681  13.9857  10.8547
-#&gt;   15.9969  15.1478 -27.6967  -4.1772  12.2745  -1.4362  -2.0673  -7.0506
-#&gt;    3.1156  13.3563  -3.0787  -5.4497   2.7611   2.3933  -5.8129  -7.5821
-#&gt;   -4.4838  14.3311  11.0973  -4.1163   8.3509  -7.1998   2.1250  -8.6256
-#&gt;    7.7065  -2.9739  -7.6449   3.6572   3.0542  -3.0501  -5.6467  -4.7536
-#&gt;    9.7601  -9.2893   6.0662  10.7223  11.1158   1.3322   4.7204   2.4538
-#&gt;   -4.2137  -3.6257  12.3550  -1.6244  22.8372   2.2156  -4.8342  -9.1679
-#&gt;    0.2651   3.4816   0.5713   9.5898  -2.5732  18.1316  -1.1690  -9.4446
-#&gt;   -1.6046  -5.3780   6.8993   5.8801   2.2593   4.7100   0.2463   5.3924
-#&gt;  -13.4878 -13.6431  11.0184   8.7678  -4.9206  -4.4775  -1.9417  16.9638
-#&gt; 
-#&gt; Columns 41 to 48   8.6551  10.4519   7.9291  13.8997   1.8668  -1.6043   1.9434  -2.1377
-#&gt;    3.3320  12.7206   4.2761   2.3560   6.2496   7.2874  -8.4157  -4.1285
-#&gt;  -11.5937   9.0032 -12.8571  -2.1690   3.2982  -4.2379  -1.9933   0.9548
-#&gt;    1.3208   8.7496   5.2816  -9.9804   2.7302 -10.0206   9.2736 -16.6418
-#&gt;    1.0729   2.5670  -2.0660  -4.2186  -3.6386  23.7397  18.3713  -2.6481
-#&gt;   -3.2932  -8.9020  -2.0195   6.9485   6.6723  -4.8891  21.6666  -1.0807
-#&gt;   -9.5522  21.6407  -0.2283   1.3620   5.3692  -0.7265  -7.3097  11.9479
-#&gt;    4.9933   2.3160  -3.5705   9.8861   4.3870  10.0233   5.3920  -0.5825
-#&gt;    3.4108  21.8096  -8.3828 -11.1975  -7.4322   1.3373  -0.3020  14.1589
-#&gt;   -4.3338  -2.8410   2.8090   1.7132   3.0374   3.6779   5.1479 -14.6570
-#&gt;    2.3638  -0.8785   9.9729  10.8688  -1.7978  -3.7004  -3.4883   2.3861
-#&gt;   15.9219   2.2568   8.1168   8.6758  -7.4442 -11.2989   2.1762  -4.0554
-#&gt;    2.0319   8.9099  -1.6475   4.5734   4.3266  -5.0295 -10.5274  -0.3953
-#&gt;   -5.4611 -19.9380 -10.9371 -16.2107  -1.8682   2.8439  -2.8542  17.0833
-#&gt;   -0.7665  -1.4657  -0.0538   6.1402 -12.1098   4.3941  13.5485   0.3044
-#&gt;    6.8930  -3.9569   9.6675 -33.9468   9.3299   9.3368  22.0730  -3.2071
-#&gt;    9.1725   6.1162   6.8208  11.6832 -10.2048  -4.8389  -1.0842  -6.2548
-#&gt;    4.3431   3.8659  -2.5337   9.3559   4.5738  -2.2331   4.9442  15.2937
-#&gt;  -11.4453   0.5970  14.6913  16.5632 -10.4714   3.3774   9.0893  19.3929
-#&gt;    8.6497  10.2178   5.9237 -13.4987 -12.7213 -12.7975   4.8305  13.8987
-#&gt;    4.3810  -0.5928  20.6702 -13.9781   4.0600  -0.9179   6.2806  -4.2307
-#&gt;   -6.7046  -4.8900  10.5003   9.9367  -2.5928   6.7169 -13.7944   6.3832
-#&gt;   -0.5911  -1.3710   4.6327  -2.6586  -1.0563  -6.8648  -6.6050  -2.4838
-#&gt;   -9.6484   6.0549   8.4907   5.1168   6.7080  -7.9792   3.6444   5.9602
-#&gt;  -18.1100   4.9150  -5.5376   1.3725  15.8850  -5.5013  -4.3132  10.5980
-#&gt;    6.5777   0.8678   5.4414  -2.8914  -2.0563  -3.1857   6.0596  -9.0594
-#&gt;   -9.8612   7.3149   1.5047   7.2234  -2.3040  -2.8732  -6.7493  -5.0459
-#&gt;  -20.7815 -11.2549   7.7902   5.7473  19.1433   3.4235   7.8965 -12.4819
-#&gt;    1.9060  -4.3340   0.2200   0.3047   8.8122 -11.9605  22.5921   0.1737
-#&gt;  -13.8396   0.9494   9.2263  -2.1471 -10.4394   4.2459   9.6474  -0.0864
-#&gt;    8.1214 -24.1654   4.1252  -0.3191  -7.8179   3.0945   0.6513 -20.6012
-#&gt;   -7.3897   5.1610   5.9527  -7.0695  -1.8891   7.4710   1.3475   4.5161
-#&gt;   20.4947  16.3493   6.5883  15.8103  -0.4968  -0.4535 -10.8660  -0.7745
-#&gt; 
-#&gt; Columns 49 to 54   4.7291  10.1652  -5.6773  12.7955  -2.1041   2.5295
-#&gt;  -10.5497  -7.3830  11.4977   6.9053  -3.8007   3.3671
-#&gt;    1.7491  -2.8164  -0.6597  -4.9736  -1.5763   0.6793
-#&gt;   13.8645   0.6514   4.0921  -2.7400   2.6539  -4.3940
-#&gt;   -9.9973 -18.1512   7.8621  -2.2308  -2.1382   3.0686
-#&gt;   10.8372 -19.3341   0.0621   5.4040   0.5853   3.4360
-#&gt;   -8.0477  -3.1838  -3.7408   2.9222   0.3916   3.0910
-#&gt;   11.2880 -11.8127   5.1948   2.2088  -3.4512  -2.2888
-#&gt;   -5.0024   3.1674   7.2646  12.5243   2.1487   0.9226
-#&gt;   -6.5208   2.7543   8.3292  -4.2445  -0.9989  -5.2819
-#&gt;   -1.2295   1.2352  -7.7689  -3.3089  -5.6123  -3.5922
-#&gt;   -3.9773  13.5292  11.4135  -1.2383  -0.1691  -2.4527
-#&gt;   -1.4408  12.8675   5.1608  -6.8725  -1.1264  -2.7465
-#&gt;    2.5489   7.2445  -1.5537 -12.8999  -0.8376   5.2725
-#&gt;   -3.2709   7.7874 -16.4884 -10.6657  -5.5077  -0.5093
-#&gt;   -8.9901 -10.6175   1.7309   8.6190   1.8863  -0.7332
-#&gt;   16.4565  -2.8528   0.8248  11.0336   0.4719  -0.6393
-#&gt;    7.6868  -0.4770   6.0767  -1.2162   2.2070   4.5727
-#&gt;   -6.6703   6.0225 -16.0654   1.4037  13.5640   3.3643
-#&gt;    5.7481  -4.1105 -10.2818  -8.0174   6.5896   1.1784
-#&gt;    4.0036   0.1622 -11.9668  -3.2025  -7.5823  -0.5538
-#&gt;   14.3516   7.4796   6.0024  -3.1746   4.0530  -1.2337
-#&gt;   -8.7131  -9.3996   3.5070  10.2931  -2.0188  -3.9518
-#&gt;   11.8766   4.2882  -6.7578  -0.1501   6.2587   2.3747
-#&gt;   -8.4131   5.1908   8.7007  -6.5497  -2.1157  -3.9311
-#&gt;   -0.5218  -2.9840   7.9941   5.3123   2.1362  -1.0405
-#&gt;    1.9784 -14.4657   6.3987  -1.8653  -0.5736  -0.6155
-#&gt;    1.9679  -6.4982  -4.9482  -4.3896   0.4129   0.7930
-#&gt;   -6.6492   0.5752   0.4292   1.0786   4.8807  -2.8983
-#&gt;   -1.1541 -16.0653  -3.7585   0.2108  11.2106  -3.9913
-#&gt;    2.5961   1.2070  -4.5854   7.6319  -0.1376  -2.7891
-#&gt;   -3.9781  -0.6245   4.5813   8.2781   1.6482   0.4033
-#&gt;    8.2112  27.1459  -3.3064  -2.3218   8.2511  -1.1553
-#&gt; 
-#&gt; (18,.,.) = 
-#&gt;  Columns 1 to 8   2.5607   6.9746 -14.1296   0.9879   2.6957   2.0773   4.0569   7.6320
-#&gt;    2.0279  -6.3915  -6.1660   4.2069  -3.0013   8.0295   4.6555  -6.1799
-#&gt;    2.2788   1.4274   4.8585   5.3711  -1.4711   3.8277   2.1119   1.0961
-#&gt;   -4.7835   7.1279   0.2024   8.3965   6.8299   5.1099  -2.1958  -7.8061
-#&gt;   -4.2354   6.1462  -7.8338  11.2821 -18.7821   4.6748   4.1598   9.7906
-#&gt;   -0.0890  -0.8984  -4.3897  -2.5490  -3.1948 -10.8970   2.4381   3.0767
-#&gt;    5.4540  -4.8405  -0.2467   5.3936   0.4950   7.6109   5.1184  -8.6069
-#&gt;    0.1136  -3.4569  -0.3141  -4.4563   1.3956  -4.0810  12.2848  -5.7893
-#&gt;    4.6330  -0.8335   9.6137  -7.7943  14.9346  -7.2111  13.4078  -0.3464
-#&gt;  -13.1192   5.2048 -19.0951  10.8160  -7.9359  11.4851  12.4676   3.2496
-#&gt;    9.5632  -8.1853   9.1061   0.2368  -1.5559   5.1073   3.9712   2.0836
-#&gt;    3.8734   8.4211   6.1599  -1.9157   4.4105  -1.1158   7.4251  -9.1286
-#&gt;    3.4042  -0.7714   2.1339   0.5915  12.3357  12.1531   2.1991   6.1418
-#&gt;    4.2000  -5.4897  -0.4437   5.1582  -8.4982  -5.7659  -2.1947  -7.8379
-#&gt;   -5.7188   0.2737   3.2424  -0.1404  -1.0221   1.1312  -3.6877  -6.6509
-#&gt;    2.6941   9.0053   8.7822  -0.5280  -0.9135   6.0112  -4.6837   3.2148
-#&gt;   -7.5229  -2.9931  -5.2529   6.8444  -2.2849  11.4543   4.8640  -2.3117
-#&gt;   -1.2585  -4.7316  -6.2314   7.0137   5.9990   4.2574   7.2187   2.8474
-#&gt;   -0.0381   3.0835   7.2017   2.0438   3.5235  -9.6141  -8.1712  10.2354
-#&gt;   -0.1686   4.0443  14.8337   1.2233   7.8716   8.9073  -1.2283  -3.6574
-#&gt;   -2.3591   2.6389  -8.6433  -0.2226   6.0189   5.8549  -5.2662  -9.2750
-#&gt;    1.8691   3.8149  -2.8912   9.2927  -2.4133  28.5934   0.8719   9.0709
-#&gt;    7.8411   1.8858   4.2028   8.4542  -1.6993   7.2325   1.0374  -1.2375
-#&gt;   -0.6677   0.0723   3.8231   3.3630   9.4829  -2.8955  10.7143  -3.9745
-#&gt;    3.0136  -1.4578   7.7040 -21.0847   7.7592  -6.3645   1.8535  -1.5144
-#&gt;   -2.0941  -0.4756  10.8358  -7.2873   0.7832  -0.6439  21.1079   0.9633
-#&gt;   -2.6418  -5.7361   2.2575   2.7963   0.0441  -4.1455   8.0605  -8.2170
-#&gt;   -2.0062  -7.8279  -2.9152 -14.2405   6.6760  -9.2052   4.2084  -3.7371
-#&gt;   -6.4684   7.4392  -1.2449  -8.1779  15.5428 -12.8811   7.1791  10.7447
-#&gt;    7.7662  -6.9488 -10.3117   9.1695 -11.6423  -7.3212   5.4344   0.6809
-#&gt;    2.0510  -0.4432  -1.3262  -9.6389 -16.3804  -7.7018   0.3208  -7.3703
-#&gt;    5.3213  -2.6322   3.6646  13.7486   7.7162   5.6818  -1.3432  -0.5053
-#&gt;    0.8791  -9.2443  -7.2707  -0.2304  20.3032   9.8317   9.6250 -10.1012
-#&gt; 
-#&gt; Columns 9 to 16  -4.5631   5.1183  -6.5873   2.5864 -13.0604  -3.7079  -0.1472 -14.0302
-#&gt;   10.5207  -3.4548   0.0213 -12.5632  -1.9765   0.7397   1.0502 -14.3804
-#&gt;    1.7403  -2.5348   3.6687   3.4880   6.5924   2.4608   1.0571  14.4004
-#&gt;  -10.7352   4.1994   9.6608   8.1966  -5.4956  -8.7275  21.4381   6.5063
-#&gt;   -2.2137  10.7183  -0.5602   3.5739  15.0872  -4.4756  19.1920   3.0844
-#&gt;   20.7183  -2.9069  -0.5897  11.6284  12.9918   7.6901   6.2409  -5.0766
-#&gt;   -1.5262   9.5505  -0.7460 -13.1111   4.0809  -4.6347  -0.3764 -11.3669
-#&gt;    7.7300 -20.8454  -7.2050  -2.7180  -0.8120 -16.8468  -8.3851  12.0489
-#&gt;    8.3314   4.2619  -9.2442  -5.3318   4.8112   1.4236 -15.0520  -4.2371
-#&gt;  -11.4070 -15.8329   6.1977   5.8492  23.4668 -26.3524 -20.6411  16.6209
-#&gt;   -2.9822   0.0039 -14.6921  -1.6131  -9.7715   6.9130  -9.5214   5.9409
-#&gt;   -1.8553 -13.0294   7.7852  -7.4594  -0.9728  -2.9310  -3.3730  -8.5026
-#&gt;    7.9183  -7.6343  -8.1593 -11.5273  -6.6659  -2.3622   4.3799 -11.2580
-#&gt;    8.0696   2.8184  -7.8542   3.7025   7.0463   9.3979   0.4284  -9.1644
-#&gt;   -0.7683 -16.7744   7.4818  13.6590   2.9464  -2.7925   7.6585  17.2907
-#&gt;   -5.9354   9.4640  -0.0331  -8.6923  -7.9953  -4.0715 -18.7022  18.8704
-#&gt;   11.6171   1.2253  -1.4392  -1.0766  13.1144  -1.8598   2.0302 -11.9955
-#&gt;   17.8037  -6.6855  -7.7911  -7.7429   2.3707   6.5755   0.8404 -30.1255
-#&gt;  -14.1880   8.8086  -2.0741   9.8366 -14.5066  -5.8028  10.6345   1.8623
-#&gt;    7.0657  -5.7928   4.5935   3.7881 -12.6765   0.7599   0.7667  -8.1307
-#&gt;   -9.8663   0.0087  -3.8280  12.0016  -2.3426  16.7331  -0.9584   9.7906
-#&gt;   -7.6962 -13.4335  10.8253   3.1614   2.9295 -12.1049   4.5444  12.5585
-#&gt;   -3.7655 -11.2215   3.4094  -0.9042   0.8314  -6.3878  10.5762 -13.1252
-#&gt;    0.3088  -3.4879   2.1341   6.2670  -1.6223   4.0110  -8.9762  -4.2188
-#&gt;  -13.9215  -4.0414  10.3232   2.4070 -25.9877   1.9130  11.4148  14.1799
-#&gt;    7.1045 -15.0884  -0.7175   8.2548   4.1089   2.0066  -1.5233  -6.4924
-#&gt;    9.1680 -10.4133  -0.0213   7.8832 -10.3921   5.1182   7.6320  -8.8360
-#&gt;   -0.4146  -0.6267  -1.7585  13.7173   4.7417 -16.1120   2.0723  13.0010
-#&gt;    1.0019  -2.0173  10.5827  -6.2699  -0.5893   3.8501 -16.6195  -5.4172
-#&gt;    1.1983  -0.8233   8.7631  -7.1645  -0.1655  14.4653  -7.1852  -4.0160
-#&gt;    6.6419  -5.1391 -13.4419  10.4963  -3.4981   5.4455   0.1359   7.7816
-#&gt;   -2.5987  19.3758  -9.2576  -7.4659  -3.4268  -4.4332  -9.2202   2.6208
-#&gt;    1.1092   6.4135   1.4041  -0.6589   1.4991  17.4271 -15.5688 -10.8539
-#&gt; 
-#&gt; Columns 17 to 24 -14.0499  -9.2301   4.6178 -12.1654 -10.4940 -14.9348 -10.3188 -15.8765
-#&gt;   -5.0896   6.5854   5.8955  -0.3807  -5.1324  -8.1650   8.4002  18.2566
-#&gt;    6.8861   5.6475  -2.3168  19.3992  12.5710  18.5720   3.7670   2.0277
-#&gt;   -2.3150  -4.9691  16.4158   4.6050   3.7941  -1.6848  -0.0707  14.9633
-#&gt;    4.3277   8.5186   7.0522 -10.9459   2.9571   8.4020  12.0613  -2.2034
-#&gt;    1.3741 -14.6005  15.2397   1.4461  -5.2323   1.8152   6.5879   9.1821
-#&gt;  -23.5782   3.0067  -4.4155   3.7511  -8.9802   5.0483 -11.8720 -10.8192
-#&gt;   -5.4766   1.4439  -2.8193  -3.1127  -0.5990  -3.9683  -2.6294   8.6897
-#&gt;  -17.9679   1.0021  13.4446   0.4511 -12.8746  -5.9012   6.6143  -5.2433
-#&gt;   -2.4562   8.4748 -14.3385   1.3596 -13.4721  19.2884  11.9488 -12.8471
-#&gt;   -5.0182  -3.9533   6.1100   2.4614   7.6117  -8.2385  -9.1687  -4.1848
-#&gt;   -0.9416  -1.1214   6.1312  -3.7930  -7.9747   0.7652   7.2406  -9.0999
-#&gt;    2.1331   1.0731   1.9103  -6.7984   4.9211  -8.7538  -9.4855  19.3423
-#&gt;   -6.6425   5.5058 -19.0789  -1.3226  -9.9483   2.9978 -11.5738 -12.7144
-#&gt;   22.3950  -4.0488  -0.8207  -8.0033   7.4107  -8.2981  -9.3266   8.0467
-#&gt;    0.4300  13.8113 -11.0246   0.4638  -3.4565  -4.1980   8.9884   2.1561
-#&gt;  -16.8640  -2.8841   9.5304  13.5982  -1.9989 -12.2808 -25.1453   3.4016
-#&gt;    1.0043   4.3256  15.3428 -22.3627  -0.4690   3.7045  -8.7921 -21.8550
-#&gt;    9.4900 -10.3237 -11.8943  -1.7426 -16.7271  -4.3310  -7.9813   8.7600
-#&gt;    2.3243  10.4892  14.4375   8.4814  -1.7071 -10.4577 -13.7453  16.4870
-#&gt;   11.4275  11.5315   9.1766   1.1925   5.8463  11.9297  -1.3267  -5.1440
-#&gt;   -5.4154 -12.7976   3.9966   4.7142   3.3056  -3.3443  17.5935  -1.3520
-#&gt;   -0.5399   3.5335   9.8616   4.8747  -8.7346 -11.2709   7.7776  14.1519
-#&gt;   -4.2190  -7.1317   4.3000  19.4864   4.1439 -27.4932 -18.8294  31.3441
-#&gt;   -6.4756  12.1541  -7.7001   8.0917 -10.2014  12.0076  19.7851  18.4771
-#&gt;    6.6035  -2.3357  -8.2838  -0.2955   3.0437  -5.8357 -10.5802   6.6189
-#&gt;    5.5020  -1.6457  11.1207 -14.2527   6.4448  -4.5146  14.5626  13.3791
-#&gt;  -11.7707  -1.1044  -5.9843   1.9228  -2.0126  15.8836  -6.3773   3.6230
-#&gt;  -11.1561  26.4027  -5.4763  -9.2815 -18.2669 -13.2601  14.0414  -1.4215
-#&gt;    9.7437  -6.3374  -3.7827   1.2515   2.7432  -0.2726  15.2545  -0.3416
-#&gt;   -1.0962 -18.1567  -5.2681  -3.5869  12.0039   1.9421  -0.4965   1.8151
-#&gt;   -3.5703  -4.4185   1.1990  -9.3860 -11.3799  -0.2286  10.6982  -3.3336
-#&gt;   20.0706   7.1216  -5.8278   2.5037   2.2430   7.8958 -15.7277 -17.2480
-#&gt; 
-#&gt; Columns 25 to 32 -12.6164  -5.5227   3.3738  -7.7724  -1.3513 -13.1344  -1.8423 -10.5999
-#&gt;   -7.7614   1.3194   6.5926 -11.0403   5.8749  -7.0442  -2.8857 -13.7875
-#&gt;   -5.6418  13.6040  15.7084   4.2462   0.0273   7.8405  -6.0071   3.9232
-#&gt;    2.0098 -15.4760  -7.7359  17.4150  10.1245  10.7928   5.2850  -7.3605
-#&gt;    4.1568   3.6256  -4.4117   5.2454  -1.9307  13.9355   7.5479  -5.8780
-#&gt;   -0.7214   2.7908   0.0422   3.2726 -10.8040  -1.1433   6.3433   8.1570
-#&gt;    5.9387  -1.4444   4.4409   2.3125   4.8917   6.6608   6.8507 -10.1453
-#&gt;    4.0297  -5.2313 -11.1971 -11.1062 -11.3422   1.8800   0.8086   2.8431
-#&gt;    1.2705  -6.2244  13.4794   1.6952  -1.3918  -3.3439  -2.2457  -2.2022
-#&gt;    3.1278   4.5350 -11.8367   1.9002   9.1251   7.3062  18.4287  -8.7532
-#&gt;   11.9196   4.9068 -11.9644   0.5387  -7.2879  -6.7366  -4.2140   0.1065
-#&gt;   -4.8093  -7.0073 -15.9521  -1.7806   0.7536 -10.0960   3.8251   6.1963
-#&gt;   12.5222  -7.7790  -5.4257   3.5361 -14.3582  -9.2485 -10.3866   0.9246
-#&gt;    5.8661   7.3125  -1.6674 -15.4720  -7.8211   0.0553  -3.0261  -5.4769
-#&gt;    8.5631  -3.0298   5.0931 -14.2535  -2.7308   4.7637 -12.9328   1.2227
-#&gt;    0.9701  -5.0306  -8.6260   2.6639  -2.0640   5.8678   7.5410   0.6262
-#&gt;   13.0993   6.4507  -3.5369   4.2464  10.5157 -10.5690   3.7306  -2.9943
-#&gt;   12.7029   7.0040   8.1756 -12.9181  -9.8865   1.7449  -7.7109 -11.2053
-#&gt;   -8.9517 -15.6580  -2.9539 -13.8343  -6.2445  -6.9098   1.2464   7.7131
-#&gt;   12.2824   0.4825  -2.6862   4.0484   4.6104  -0.7151  -6.3125   2.8250
-#&gt;  -16.4684  -5.4266 -15.1137  -2.0149   5.3232  -2.3350  23.8941  -2.5292
-#&gt;    2.9732   2.0121  -1.7388  10.2766  -4.9831   5.5735  -2.1185  15.3902
-#&gt;    5.9049  -8.0606  -4.1575   1.0379   1.1659  -4.2641  -5.2791  -2.2312
-#&gt;   17.6206 -17.3926  -7.7816   6.8851  -1.1693  13.0605  -7.5593  -3.1737
-#&gt;   -4.5904  -5.2606  -7.4634   3.8984   3.7105  -4.2570   1.6705  10.0136
-#&gt;   11.2688  -1.0472 -10.1374  -6.3868  -0.8456  -0.2371  -0.0685   2.2586
-#&gt;   -3.4568   0.3863  12.0084 -17.2600   5.4638   0.0448  -8.6760   4.8005
-#&gt;   -7.8276 -12.3191  -8.1353   2.9102   1.6305  -0.6341  -6.4973   9.6748
-#&gt;   -0.9094   6.0636  -1.2511  -8.8310 -11.3165 -11.4738  -7.6921   0.0382
-#&gt;   -3.9363   8.3536  10.1510   6.3995  -7.3344   7.2877  -8.8098   2.3534
-#&gt;   13.2600   1.2157   0.1989   3.2163   2.0424   7.5502 -11.0900  -2.8139
-#&gt;    3.7853  -7.0866  -6.7222  -3.3313  -9.3813 -19.0453   5.9031   0.2403
-#&gt;   -1.9067   0.7783   6.6928   2.2250   4.9033   0.2429  -6.1527  -5.2947
-#&gt; 
-#&gt; Columns 33 to 40  -2.4963 -12.0615  -3.8403   5.6308   1.3862  18.2785  13.1978   4.3638
-#&gt;   -0.5380 -17.3218  -4.2802   3.2202  -3.5401  -3.3281   4.5367  -0.6111
-#&gt;   -3.6672  -8.6141  -2.6048  -4.0159  11.2167   2.8940  27.1257  -5.7564
-#&gt;    1.1450   8.3296  -4.3134   2.9535  -6.9490   2.5427   7.5542  -2.5177
-#&gt;    4.8331  10.3720   7.7204   5.3816  -8.0562  14.4892   2.7030   4.3382
-#&gt;   10.9224 -16.7071  -3.4392   5.2322  -5.9346  12.7750   1.5090  -2.3236
-#&gt;    4.3575   5.2881 -10.4576  -4.9684  -2.6286   8.3619  12.1299  -9.0484
-#&gt;   -3.8783 -19.5700  -6.9274  -1.1657  -8.1007  -3.9363  -9.5394 -15.2302
-#&gt;   -7.4823   3.7065   4.4783  15.8209  13.5708  -0.7414   7.5003  -5.8651
-#&gt;   -4.0828   7.2917  -6.1476  -6.2215 -10.8367 -29.1704   7.0965  -4.3779
-#&gt;   -3.7543 -18.3421  -6.9295  -1.7624   1.9466  18.3790   6.7286  -9.4716
-#&gt;   -6.0741  -4.7458  -6.8814   0.1374  -7.8405  -5.8696 -13.0560   3.2316
-#&gt;    0.3924  -0.3753   4.4285   9.8612   2.6743   9.3134   4.9983  -1.2121
-#&gt;    4.9567  16.6752  15.0920 -10.1518  -7.4992  -5.9469   6.0850  -6.1996
-#&gt;   -5.1738   1.5095  20.3648   8.9442   0.0690  -0.6032  -2.1198  14.1641
-#&gt;   -0.1823  13.0101  12.3413 -13.5551  25.8879  -4.8766   0.8634   4.8214
-#&gt;  -11.1621  -3.1138  -4.7965  19.7102  -8.6528   8.5797   5.3243  -0.4905
-#&gt;    0.1567  13.0723   3.2299   8.1610  -2.2069  14.4454  -1.2265  -0.9771
-#&gt;   -2.7016  10.2370   8.1160 -14.9621  -3.4660  -6.2858  -1.6375  -6.1504
-#&gt;    3.1280  -3.1622   6.3066   8.5522  11.1640   8.3880  -2.5466  -8.4901
-#&gt;  -12.9352 -12.5071  -3.4761  11.1557   2.2152 -18.9739   4.2128  -1.7393
-#&gt;   -0.7611   5.3916  12.9196  -4.0772  -3.3279   6.1576  16.6274   3.2993
-#&gt;   -0.5279  -2.2681  15.6924   3.8534   6.9753  -5.1574 -12.1019 -13.5017
-#&gt;    0.8567  -0.2477   6.4208   3.2609   5.3720  -2.5902  -5.1892  -4.3388
-#&gt;    5.5256   1.0134  -3.1614 -12.6981  -6.2453 -16.2891  14.4755  -8.5702
-#&gt;    5.7194  13.9965   7.0678   0.8601   4.1388   2.2067 -19.0934 -14.1139
-#&gt;   -5.3955  15.1607   2.7352   6.9302 -19.8468  -0.8902  -3.2664   9.7877
-#&gt;    4.8691  -3.4704 -16.1161 -13.1538  -4.2152 -17.2861   8.2224  -3.0720
-#&gt;   -8.4361  12.1531   4.8409  -1.8901   7.9468  -0.9986  13.3304  -5.5278
-#&gt;   -0.6711  -9.2623   2.3526 -10.0311   6.4094  11.1725  -9.4261  -8.4669
-#&gt;    7.8779  -8.4922   4.3165   0.9563   4.1135  -2.5278 -19.6349  14.6098
-#&gt;    2.8421  -0.6718  -0.9401   7.5104  11.1172  -3.6945  -5.5918   6.7426
-#&gt;  -17.4358  -1.0163 -11.8569   1.2131   2.5577 -13.3417  -3.7991   7.3093
-#&gt; 
-#&gt; Columns 41 to 48  -4.8599   2.6258   4.2118  -4.7008   0.8856  -6.5538  11.5994  -5.1639
-#&gt;    6.0339  12.6308   4.2303  -1.5510  -2.2765   1.0141   8.4032  -4.2419
-#&gt;    7.4749  10.1366  -2.6453 -16.0801  -0.5412   1.3314   5.8703   3.6497
-#&gt;    4.8017   2.4280   8.1144  16.8306  -2.5457   4.4047  10.4657 -11.3151
-#&gt;    4.8720 -10.8692   0.0563  -1.2955  -1.2443   0.3756   2.8516  -1.2045
-#&gt;    4.2688  -6.6407  -0.2963   3.0330  -3.7769  -4.0510  16.8208   1.1271
-#&gt;    1.2935  18.8656   5.6419   5.3326  -3.4669  -2.4636  -9.9359   6.1396
-#&gt;    8.4172   1.3720   2.8684   5.9897  -0.4171   8.5162   5.0548  11.0252
-#&gt;   -1.5487   5.2366  -4.4980  -2.7876   1.5731  -4.4802  -6.2787  -8.1704
-#&gt;    0.9424  -9.8419   2.6910  19.0791  16.1382   0.7982   7.4255  -7.9084
-#&gt;    2.9283   8.5499   4.9756  -1.3817   5.9132   6.4634  -1.9403  16.6418
-#&gt;   10.3331   2.5474  -1.0919  13.7433  12.5200  -7.9750  -2.0745   0.1925
-#&gt;    0.0483   6.3512 -12.3006  14.0028  11.8383   8.0255   2.0476  -5.6738
-#&gt;  -17.4118  -4.3146  10.3572  -0.9958  -5.8432   2.4149   1.7537   1.6160
-#&gt;  -11.0484 -19.6526   2.6042  12.7398   1.4604  -8.0123   3.8555  -8.8930
-#&gt;   -3.8554   7.9560  10.4438  -5.1162  -5.1829   1.9227 -14.0399  17.5698
-#&gt;   13.4644  -5.7811  -3.6335  -1.9411  -0.2288  11.6834  -0.0382   3.4937
-#&gt;   -8.0552   9.7868  -9.6309  -4.0772   3.8782   5.2165  10.0244  -2.8953
-#&gt;  -20.0431   0.4752  14.2114   1.9949   4.4700  -6.1587 -12.4599 -14.2717
-#&gt;   -9.3724 -10.2040   7.9993   3.1210  -4.4391   7.4048 -16.0405 -11.8128
-#&gt;  -14.0010   2.9755   5.3850  -9.1366  -0.9312  -1.8272   0.0552   3.4090
-#&gt;   -9.2022  11.4239  13.8775  12.9496   9.0037  11.9003  -8.8200  -0.9369
-#&gt;   -6.9932  15.3172   7.0752   2.2881  12.5888  -3.1097   3.6890 -12.2569
-#&gt;    1.3302  -1.8616  -1.5761  17.8451  11.3989  10.6031  -2.0086  -8.9089
-#&gt;   12.4090  14.4565   6.9083   4.9359  -0.7136   5.6534  -6.8646  14.6534
-#&gt;    0.3398  -2.2524  -8.1239  -3.2364   6.5924   6.4631  -1.4810 -18.9798
-#&gt;   -2.5437   7.1043 -12.4989   1.6299   2.1949  -1.6248  12.1827 -12.1584
-#&gt;    2.4820  -3.1486   4.5259  -2.2337 -10.4129  10.2621  10.2763  18.5918
-#&gt;    8.9560  -1.3295 -13.4641   3.2299  -7.9888  -0.9474   2.4413   2.5917
-#&gt;    3.3603   9.0026   4.5196  -5.0809   2.8492   7.0954  -0.4340  -3.5645
-#&gt;   -4.4718  -4.4454   4.8480  -2.7909   2.1337  -7.3107   9.3300   4.6300
-#&gt;   -2.1803   7.4953   1.8675  -3.1712  -4.6992  -1.0978  -5.5272  13.1550
-#&gt;   -6.7267  -2.2257  -3.0134 -13.1032   6.0791  18.4276 -14.5529 -19.8907
-#&gt; 
-#&gt; Columns 49 to 54  -9.3083  -4.9879   3.5250  17.8403  -5.2355  -8.3191
-#&gt;   19.2539 -16.3934 -13.5419  -1.7015 -13.0288   0.7355
-#&gt;   13.3954 -18.8596 -10.4664   7.5595   1.6909   2.0267
-#&gt;    0.3320   2.6962  -1.4246  -3.3028  -0.0711   1.2782
-#&gt;    9.7353  -6.1720  11.0718 -14.4437   0.2359  -1.3292
-#&gt;   15.8777 -19.9309  15.6018   0.4667 -11.2962   3.9278
-#&gt;   -8.0989   1.7797  -6.7000  10.2614 -12.0839 -10.3892
-#&gt;   -4.8805  -5.4823   6.1991  -6.1439  -7.9516  -3.8970
-#&gt;   -6.9577 -14.1020   2.2303   6.8141   0.0739   3.6898
-#&gt;  -18.4505  -0.1985   0.6938   8.8728  -1.9524  -8.6458
-#&gt;  -24.5340   1.9533  -1.2644   7.7443 -17.8894  -1.1214
-#&gt;   -7.5853   0.4062  14.8496  -6.6129  -3.8831   4.4356
-#&gt;   -3.5281  -2.0888 -10.2234  -8.4183  -4.5941  -2.2781
-#&gt;    4.8694  -0.8738   9.2721   3.2927   3.1281  -7.8472
-#&gt;    3.0850  10.5751  14.8325  -5.4971   4.1103   3.4981
-#&gt;   -3.7079  16.5214 -10.0756  -9.1875   6.2263  -3.6299
-#&gt;   -6.3303  -9.8588   9.3464 -11.2450 -10.0330   3.6850
-#&gt;    3.2447 -12.6623  -2.7574   9.4376   0.9754  -8.9210
-#&gt;    0.8641   8.9737   8.3496   0.6925   1.9333  -1.3602
-#&gt;   -6.7453  -2.8543   9.0958  -8.5059  -6.1639   4.0483
-#&gt;    2.2621   6.7403  -4.8489  10.0781   4.3774   8.1313
-#&gt;   -8.0037   3.8853  -1.6224  -7.6566   5.4460  -8.9146
-#&gt;   -5.8331   5.8020   8.4844   4.8933  -3.3294  -7.1449
-#&gt;   -3.0564   6.0150  -8.7402   4.6850  -9.9888   1.2059
-#&gt;    5.8078   7.0987 -15.3538  11.5644  -5.3291  -5.0300
-#&gt;   -7.2983   6.6452   7.6335  -4.4609  -0.3657   1.9627
-#&gt;    2.4581  -2.1630  -5.9264   7.0184  -3.3512  -1.2382
-#&gt;   -9.0492   8.1621  -7.5449   6.0633  -6.8605  -3.4128
-#&gt;   -3.6001   1.9962  -9.7999   3.8463  -1.3292  -1.0618
-#&gt;    5.9551   3.6702  -1.3667   1.6401  -3.3458  -2.4944
-#&gt;   -7.8482   6.4090   7.3487 -13.1679   8.5474   5.1589
-#&gt;   -9.0406  -8.5377  -4.3133   1.8595  -5.3351  -4.2301
-#&gt;  -14.7113   7.2848   2.0980  -7.2598   2.3349   1.0087
-#&gt; 
-#&gt; (19,.,.) = 
-#&gt;  Columns 1 to 8   0.0661   2.0870  -4.6073  10.7420   1.7087  -1.0909 -15.3439 -16.6719
-#&gt;   -0.5374  -3.3481   2.2978  12.5929 -10.7261   2.3849  -6.6198   2.6438
-#&gt;   -3.4113   2.4719  10.8696   4.1195  13.8379   8.2492   3.7199   7.5426
-#&gt;    6.4516   3.3398  -1.3456   7.1062   2.1130  12.6511   6.1955   8.2585
-#&gt;    4.3290   5.1431  -0.4975   3.6898   5.9168   2.1959   4.0997  -1.6909
-#&gt;   -2.2095   3.8595  -2.5837  -1.8355   2.0210  11.8178 -10.8966   4.4905
-#&gt;   -2.7892  -5.9924   1.4792   0.9659   7.3357  -2.7857   3.7550   4.2172
-#&gt;    1.9647   7.9820   5.8700  -1.5222   8.1556 -14.2715  -2.0164  -8.4088
-#&gt;   -1.6567   0.2108 -11.0930  -7.9711   9.3247  -6.6707   2.9812   6.6656
-#&gt;    7.4041   0.5846  -4.6290   4.2312  -4.4900  -9.2067   7.3987  -4.6601
-#&gt;    0.4951  -5.3060  11.7260   0.6383  -0.4678   3.7606  -4.1986  -9.3302
-#&gt;    3.9703  -3.5357 -10.9399   9.6253   4.9187  -3.1735   6.8044   3.5341
-#&gt;    2.1171   2.7352   7.7885  -0.1875 -15.3752   4.9835   0.5201   6.5499
-#&gt;   -3.3548  -3.6572   0.2343  -4.1942 -16.1350  -8.6317  -0.7110   1.8420
-#&gt;    4.2630  10.1363  -6.5080   0.8007 -12.0840  -2.8793 -10.2017   0.9950
-#&gt;   -4.8164  -5.7017   4.7921   1.0376   4.5839  14.0084   0.9316   8.6032
-#&gt;    2.0466   5.7244   5.3481  -6.0730  -6.4422   7.5577  -9.3951   2.8504
-#&gt;    1.3160   0.5282   1.9091  20.4261 -10.6944  -6.7989  21.6403   3.9993
-#&gt;   -5.6429   3.6753 -12.4878  -9.2022   6.6272   6.3457 -14.5031  12.0212
-#&gt;    1.6041  -2.1937  -1.8675   1.5892   4.0034  10.4335  11.8147   1.8587
-#&gt;    3.4906  -6.2077   1.6521  13.6342  -3.2880   2.9842  -4.2357  -4.4091
-#&gt;    6.4870   0.9943  -4.6885  -5.8235  17.5423  10.5543   2.8233   6.5479
-#&gt;    4.3041   2.6745 -10.7250   1.4238  -0.5107   6.5318   1.5424 -10.4993
-#&gt;    2.8709  11.1223  -6.4623 -15.8296   4.5900   8.2280   0.3481  10.8483
-#&gt;    1.7094  -2.8385   7.6055 -10.7252  20.3783   0.0377  -6.1318  16.2998
-#&gt;    1.0807   6.8585  -4.4373  -3.4871  -4.9250  14.2394  -4.1355  -5.8829
-#&gt;    4.4285   1.8721 -10.5060   7.9557 -17.7927   0.4192  -1.2802   8.3501
-#&gt;   -2.4024  -1.2423  -5.0667 -11.3335   1.8600  -9.0971   2.6932   3.4292
-#&gt;    6.9339   2.2565  -3.7678   8.4306   6.0353  -9.4589  -3.4477  11.3125
-#&gt;   -5.0831  -9.1336  -4.9803   3.5654  -4.5181   4.4948   3.4993  -9.8636
-#&gt;   -1.8793  -1.5664  -9.7130  -8.9309 -15.8161  -5.8428   4.7911 -15.1145
-#&gt;    1.7358  -2.8950   2.5695  -0.2471 -11.5910  -6.5262  -3.8825   3.5185
-#&gt;   -4.2313  -3.9300   8.1479   8.4043  -3.2443  -7.4102  -1.1939  -3.0861
-#&gt; 
-#&gt; Columns 9 to 16 -14.5229  -0.5221  -4.0028  -0.6670  -4.2043  -6.8517  -5.9773   6.2712
-#&gt;   13.1730  -9.5314 -16.7005  -3.9893  15.6219   4.2733  -8.5564   2.8200
-#&gt;   -1.4021 -12.6414   7.5238   9.8075   2.9862   6.7640  10.9484  16.4513
-#&gt;   -2.4392  -6.5409  -0.4394  12.3174   6.5697  -5.2688  -4.9627  -3.9066
-#&gt;   -4.2615  -1.9863   1.8109  -6.7185  10.9289  12.0465  13.7484   3.2392
-#&gt;   -4.0111  -1.2893   3.9965  -9.7185  -9.3648  11.9407   7.3060  -4.6579
-#&gt;   -6.3518   2.6972   4.6105  -5.8780  -6.9098  -2.1342   5.0624   5.0121
-#&gt;  -13.3864  -9.8627 -15.9170  -6.4542   4.3105  13.2634   0.6690  -6.1696
-#&gt;   -7.0720   6.9382   6.7931   5.3477 -10.4445   0.6382  -7.4693  -5.4939
-#&gt;  -10.4596 -20.0235   8.3511  -5.8628  13.4418   0.9751 -24.5757   2.3339
-#&gt;    0.3435  -2.6199 -14.0294   7.1253   6.5185  16.6940  -1.1956   9.9666
-#&gt;   -0.8052  -4.6435 -15.8025   3.6298  -0.6910  -4.7757 -13.5109  -7.0752
-#&gt;   10.7700  -8.3105  -5.2766  14.4042  18.0837 -14.3077  -5.2396   2.9447
-#&gt;    5.2136  -3.4292   4.0591   2.5982   1.4418  -0.9509  11.8095   5.1084
-#&gt;   15.7795  -4.7347  -9.7260   7.5471   5.9221  -1.6401 -10.1610   1.2956
-#&gt;   17.9495   3.2875  -7.9680  -5.5814  -5.5111  20.7161 -13.5210  -4.9810
-#&gt;   -5.2688   9.0856   4.0819  -7.7769   1.4222  -9.2273   8.1000  -3.7608
-#&gt;   -0.2106  11.0416  -1.3179 -19.2363  -5.8926   2.9846 -13.1271   1.2171
-#&gt;   -8.4863   2.0671   7.9729 -26.7622  -5.1558 -14.7023  24.4968  -4.3100
-#&gt;    8.3090  17.0396  -5.0076   0.1587  -5.5868   1.3541  -5.2473  -4.1158
-#&gt;  -10.0219  -6.2793  -0.7600   8.2667   1.5450  12.0203  -2.7433   6.7243
-#&gt;    5.3582  -9.4872  24.5012   3.5453  -1.6444 -10.5597  -0.8993  12.4566
-#&gt;   25.0367   8.9784   4.6153  11.1161  -0.0891   2.0517 -10.1829   7.9633
-#&gt;    5.9544   7.8624 -10.7688  -5.6359   3.7458  -3.8202 -16.6039  -5.4710
-#&gt;    2.7363 -11.9839  -0.0358   4.5835  -1.1990   7.9047 -12.7141   5.1853
-#&gt;    4.1849   1.0468   3.5519  -4.8514  -4.7483   6.2087  -4.8072  -3.1982
-#&gt;   11.8752   0.1184  -3.4783   9.0304  -4.6290  -0.9458   7.8092  -2.4116
-#&gt;  -23.9764 -15.8296   5.4353   8.8544   6.6921  10.4586  -1.9372  14.7501
-#&gt;  -12.1185  -5.5914  -5.1872  -6.6415  -5.0929  18.2293   0.2109  -4.1157
-#&gt;   -3.3317   1.4693  13.2215   1.6420  -7.4775   7.1878   4.8392   4.6031
-#&gt;    5.7095  -2.0732  -1.8588  10.7490  12.6668   5.8450 -13.0340  -7.6094
-#&gt;    9.0943   9.0383  -0.6199  -5.5686  -2.0095   3.2286  -0.8752  -8.0283
-#&gt;   -9.9480  -3.7369   4.3289   8.1302 -11.2930 -22.4813   4.6999  -6.0936
-#&gt; 
-#&gt; Columns 17 to 24  -7.2027  -4.3215  -2.4805  14.0343  -0.9771  18.9370  -2.2089   2.7098
-#&gt;   -7.5369   0.3143 -17.7361   8.4265  -5.6399  -0.5021   7.2602  11.7183
-#&gt;   -0.7897   3.8883 -13.5923   7.3244  -0.3112  -5.7606   8.1349   1.1541
-#&gt;   -4.8079   4.1582  -1.3007 -12.3623  -2.5716   4.2553   6.2719   5.5389
-#&gt;    7.4594  12.4947  -4.7664  -8.1268   1.4181   4.6286   0.3460  -0.9715
-#&gt;   -7.6867   7.6405   0.3472  -0.0875 -16.7176   0.1489   5.1191 -13.8962
-#&gt;   -4.5829   1.1572   5.7628  10.2142  -2.8579  -6.7554  -9.1809   6.8710
-#&gt;    1.1283   8.3864   5.7537  -4.4541   1.7167   3.2979   2.0539  -0.0868
-#&gt;    1.9166   7.6191   2.4352   6.3947   3.0019   5.7788  14.4023   5.1220
-#&gt;   -7.6569   4.5022  -7.0041  11.1468  -1.8114   8.5264  21.7881  -5.0825
-#&gt;   -6.2318 -15.8723   8.9363  -2.3026  -9.0056   8.1640  -2.8235   7.6381
-#&gt;    4.2155  -3.6361   5.6981  13.4142  -1.4106  12.0651   7.3586  -2.5334
-#&gt;   -1.6408  -9.3910   2.5057  11.2112   4.2209   3.6600   8.7178   0.8427
-#&gt;   -6.7312  -6.2364  -3.8634   7.2856   1.1006 -12.1964  -4.7433  -2.5927
-#&gt;   -1.6386   4.4874  -5.3657   3.5608 -13.3070   9.1983 -14.4308   0.9504
-#&gt;   17.9480  20.2520  -4.2543 -30.2902   8.4886  -6.0533 -11.5624   2.8277
-#&gt;  -15.8247   5.8234   2.3457  -2.7481  -3.4655   9.0546  -4.3807  -4.1461
-#&gt;    0.2437  -4.4123  -5.7487  26.7690  -5.2205   3.9269  -3.9043   4.5450
-#&gt;   -5.0377   6.8580  17.9810  -5.8084  -9.3878  -8.7645  -8.3495  -0.0333
-#&gt;   12.4369  14.9357  13.6914  -1.2852  -6.5353   1.0605   0.9745  -2.3313
-#&gt;    9.8879  14.5174   0.5207  -2.0841   2.8942  -4.5310   0.8931   5.0559
-#&gt;    7.4051  -0.1309   0.6463  12.0319  13.9228   7.0609  -4.1507  -8.7256
-#&gt;    3.1115   7.5723 -10.7306   6.8215   8.7360   7.9605 -14.2740   6.9435
-#&gt;  -10.0632  -1.3987  16.6388   3.8100  -1.1464   1.3141  -2.3137   5.0114
-#&gt;    3.5673  -0.3788   3.6390   3.9929  -3.1701 -14.1997   5.0965   1.0794
-#&gt;    1.0113   6.2859  -2.6307   3.3654   2.2969  -0.8998  -3.8108 -13.9863
-#&gt;   -2.4067  -2.0689  -4.7709  14.5199  -3.9075  -4.4323  -2.2848   8.1093
-#&gt;   -3.2368   4.6910  20.2371  -3.9725  -2.5554 -15.3724  17.8786  -6.3150
-#&gt;  -17.2483   4.4429  13.1265   2.1070 -11.4819  -6.5347  13.1397   8.4146
-#&gt;    1.2916   2.6728  11.7801  -5.3437 -23.3577   0.9813   6.3883  -4.4032
-#&gt;    7.5110  -6.4364 -12.9539 -12.0735   2.3789  -3.7731  -0.7608   2.7620
-#&gt;    5.4479  12.2678   5.2552  -8.4726  -0.5800  17.9091  -0.7791   4.7922
-#&gt;    2.5819  -4.9861  -7.4595  -3.8470   5.3301   9.0399   5.2287  -4.9826
-#&gt; 
-#&gt; Columns 25 to 32  -7.8774   2.6498 -13.8988   5.3320  -2.7436   0.7916  -4.8953 -14.5906
-#&gt;    3.0889   5.4066  -0.7508  11.1608  -3.1031   0.2377   4.3819   9.7777
-#&gt;    7.1777  11.8661   9.5654  11.0096  -0.8775  -2.8590  -2.6277   6.4566
-#&gt;    5.8378   0.6885 -11.8813   7.4822  -1.7105  -4.9233  -2.0216  11.4635
-#&gt;    1.5974  -0.4697 -11.3996   8.9952   3.6836  -1.3551  -4.3991  -9.6687
-#&gt;    9.4206   0.0263  -9.1251  -4.0991 -13.4403   9.3163  -3.4029 -11.3583
-#&gt;   -1.8128 -10.3213  -4.3188 -11.3246  15.5292 -20.1270  -7.3940   3.9942
-#&gt;   12.2203  13.0746  -4.6047  16.1340  -1.2232  -2.7045  11.0019   4.3209
-#&gt;   -9.4066   3.8284 -16.4901  -0.6981  17.9182  -2.8674  -1.4318  -8.8721
-#&gt;    5.0602   0.7335  -1.3449   4.9916   7.9064   6.6367  -3.2292   3.1456
-#&gt;   23.3969   2.4774  13.7975  -2.5203   0.4199  -8.1588 -16.8146  10.5575
-#&gt;    8.1445 -15.7061   4.1919   0.6718  10.9566  18.7119  -0.3449   6.1520
-#&gt;   19.4841  -2.6999   9.9494  -5.2172   1.3215  -4.3190   7.6451   9.1263
-#&gt;   11.7690  -3.2818   2.2618  -2.2971  -7.5070 -19.3064 -16.5669  -0.4919
-#&gt;    9.4068   2.9167  -3.0312  -7.3198  -8.1139   0.1336  -3.9340   2.4457
-#&gt;  -12.3145   2.3976 -18.2498  -5.5297  -8.5890 -20.7637 -17.1243  -0.9415
-#&gt;   -0.8780  -9.2357  -3.1543  -8.3020   7.7872  18.3741   8.2457  -1.3556
-#&gt;    2.6158 -12.2016   4.1929  -0.6305 -11.8363  -5.6102  -8.7957  10.7673
-#&gt;   -9.5323   1.7510 -11.8923 -15.1492  -0.2186  -5.2074   3.5406  -6.9237
-#&gt;    2.3634   7.3899   7.7188   5.9298   5.2293   9.4318  -9.0633  -3.3740
-#&gt;   -8.1738   4.0598  -6.1196   3.6954   3.0014   1.4290 -18.8511  -5.9424
-#&gt;   16.8872  -9.9958  -8.3935  -3.7157   7.1598   5.7714   2.3132  -4.4835
-#&gt;    7.5837  -1.0954 -14.3790   9.5336   8.6663  23.4963   4.0614  -9.3293
-#&gt;    2.6958  -9.1072 -11.8496 -18.4155  -3.5330   1.2976  21.0049  -0.1854
-#&gt;   16.7958   2.1822  -0.1945  -5.7035  -0.4971  -5.0871   0.1646   7.6334
-#&gt;    9.3417 -16.4935  -8.1022  -4.6961   7.0947   3.1559   5.9331   9.1617
-#&gt;   -0.7480  -5.6334   5.2597  -1.9788   6.7996   6.6183   1.1151   4.7238
-#&gt;   -4.4043  -2.5835  -7.1748  -7.1668  -7.3857  -1.5953  11.4600   1.4418
-#&gt;    2.3836  -6.6339   8.1108  11.5360  -9.5108  -5.0481  -0.6337   9.3861
-#&gt;    5.4245  -7.3071  10.5473   2.2855  -8.9524  -0.4773   3.7582  -3.1280
-#&gt;    7.8064   1.4466  -1.0701  -8.5581  -7.8486  -1.8195  10.4682   6.8910
-#&gt;   -5.7067  10.7126  -7.4152 -10.8945   4.2867  11.7918 -10.6647  -1.9097
-#&gt;  -10.6668  -0.9252  18.2804   9.0472   7.0465  -1.5913  12.8763  27.1001
-#&gt; 
-#&gt; Columns 33 to 40   5.7455   6.6440   5.1333   4.7285  -1.6834   5.0350   3.9150 -17.7006
-#&gt;   11.7404   6.5461   2.4671 -12.3163  13.5755 -10.0508  -3.0414  10.8468
-#&gt;    3.6799   6.2751   6.4304   9.3852   2.1241  -1.4965   9.9268  -7.5506
-#&gt;  -10.1119  -0.1169   8.2761   2.0735  -0.1477  -5.2596  -6.3169  10.4333
-#&gt;   12.9747  -4.2239  -2.5881  -3.0490  -3.3919   2.6676  -2.8306  -9.5791
-#&gt;   -1.0797  -5.4019   8.9874   1.8165  -2.1101  -3.3293   3.8067 -12.6139
-#&gt;    3.8811   4.7945  -3.1199  12.0190  -4.7596   4.7737   0.9804   0.4693
-#&gt;    2.4565  -7.9521   2.3429  -5.2873 -12.9427  10.9659 -15.0013  -7.1101
-#&gt;   -5.5978   3.1955 -10.7353  -1.0786 -10.8347   7.7753  20.6141  -4.6179
-#&gt;   -1.1696  -4.4682 -17.1717   4.6908   2.6737 -17.7222  12.9036 -16.4125
-#&gt;   -5.6823   3.2488  -2.9932   1.4742   3.8839   3.3448  -6.6176  -0.1332
-#&gt;    1.6909  -0.9830   1.4849   2.5375   8.7023   4.8094  -5.4601   3.9626
-#&gt;   -0.0101 -18.6811  -2.2098  -1.4401   2.5943  -2.8895  -6.7554  10.7765
-#&gt;    7.4341  -7.6575  -1.9956   0.0643   6.2121  -9.9250 -12.4929   0.0233
-#&gt;    4.6947  -8.5145   2.8676  -6.7678   9.1153  -6.0277   7.6457  -1.8492
-#&gt;   11.1328  20.4074  -5.6098   6.6665  -0.7981   1.2724  15.1802  -0.9214
-#&gt;    2.0065  -2.3728 -14.9299   2.0527 -14.7214  -4.9942   2.2960 -22.2593
-#&gt;   14.2102  -9.1958   8.0552  -2.4999  14.9135   6.7224   7.4968   6.4790
-#&gt;    6.4927  18.7886   4.2979   7.0072  -2.4203   3.2747  15.5394  -3.1449
-#&gt;  -16.4752  -4.0446   9.6663   0.0741   6.4353   8.0745  11.1700  16.6273
-#&gt;  -11.2346  10.0982   4.5003   9.6972  16.6675   3.7906  11.1921  19.3909
-#&gt;   -0.0752  -8.9597   7.1216  10.6773   9.0734  -1.8238  13.3034  -7.5144
-#&gt;    4.4552   0.3448  16.5402  -3.7280   6.8191   4.7079 -19.7900   1.1728
-#&gt;   -7.0114   2.6842   7.0941   4.6588   3.2530   1.3216  -5.3847   0.3160
-#&gt;   -9.6568  10.7907  -2.1031  -5.4678  -2.0892   2.4734 -13.5568  16.2275
-#&gt;    3.6866 -12.6844   6.4373  -7.4610   2.5999   3.6523  -4.9072  -0.9833
-#&gt;   -1.4028  -2.3964   4.9332 -16.9734   8.9631   0.4252 -10.0117  14.2061
-#&gt;   -1.3318   0.6834  10.6612  15.5611 -18.6887   8.0852  -3.3422  -8.6237
-#&gt;   -3.7186  17.7336  -8.4589 -11.8589   3.0339 -13.9344  -5.5465  -4.2690
-#&gt;   -9.0489  12.7589  11.5065  -8.1725   9.3506  -9.6406  20.5683  12.9234
-#&gt;    1.2827 -18.9516  -9.4060  -5.5130 -10.1460   1.8165   3.5622   4.3031
-#&gt;   -6.1336  10.4952  -1.2057   6.1629 -14.9448   0.5742   0.6286 -11.1229
-#&gt;    6.5139  -3.4662 -11.8141   5.8008   4.1351  -5.0735  15.8385  14.6608
-#&gt; 
-#&gt; Columns 41 to 48   6.8717   0.4956 -14.3386   4.0665   0.3111   1.5549   3.4762  -2.3765
-#&gt;   -3.2883   0.8859  -7.3604  -3.1445  11.7350   7.3363 -10.8573  -5.6039
-#&gt;  -12.2208  -8.8874 -11.8103  10.7145  -6.7213  -4.0803  -1.8709  14.4826
-#&gt;   15.1173 -11.8789 -19.5492  -6.3355   4.7767   6.6108  -4.7783 -12.7828
-#&gt;    7.5438  -1.8056  -2.1763  -7.0966   6.1658   2.2195  -1.9033 -13.8860
-#&gt;  -28.6977  -2.9050 -11.2426  -2.4787 -10.0373  -5.8662  -1.4906  -6.3392
-#&gt;    4.1027  -5.9814 -15.7848  -8.6481  17.2998  -2.2789  -0.3419   4.1023
-#&gt;    0.2101  -3.3844  11.0947  10.5551 -10.8368   4.1187  11.3361  -2.1678
-#&gt;    6.4559   9.0105  -2.3534  -7.3225  11.3458 -11.0947  -0.4950  -2.7021
-#&gt;   -5.5081  19.2495  16.5199 -10.3530   8.3342  -8.2288  -1.4452  11.6316
-#&gt;    3.0834 -19.2167  -0.6479  -9.2428 -10.1175   3.1683  -1.1822  -5.3715
-#&gt;   17.3620 -13.6356  -1.1088   3.7350 -10.2756  -0.7587   5.5819  -7.6160
-#&gt;    7.4541  -0.6003  -7.6399  15.1987   0.6462 -11.2607   1.4354 -15.5447
-#&gt;  -12.1904  -9.3055  -0.3962   2.2344  -5.1785  -7.0760 -15.2977   7.8062
-#&gt;    9.9892  -0.5285  -5.7702  -3.4043 -16.0180  -5.9788  -4.0657 -13.7180
-#&gt;    3.3979  -4.5464   0.8961 -11.4948  -2.9463  12.8273  -3.9407   0.6880
-#&gt;   -6.6844  -2.3675  19.7265  -0.2686  -3.0068   2.6255   6.3293  -8.1204
-#&gt;   -3.9341   5.9373 -11.1447  16.2908  14.0079  -5.2415 -11.3333  -5.6728
-#&gt;   11.5560  19.5111  -4.9086 -13.5659  -4.1485   5.8032   8.6332   1.3511
-#&gt;   14.0230   8.7109 -17.2117   4.1641   6.0624  -2.0114   0.5608  -2.4461
-#&gt;    1.6661  -1.7901   0.1398  -6.3618  -4.4165   5.2161  -9.9295  -4.0391
-#&gt;   -3.0789   9.3793   1.4541 -14.2595  -5.5718 -12.3557   4.2358  -6.9776
-#&gt;   14.0946  -7.2803  -5.5474   8.6142 -22.9943   4.2800   1.4009  -9.3573
-#&gt;   -2.0369  18.5606  -2.9841 -14.5378   0.0169  -3.8161   6.2173   1.5810
-#&gt;    0.9066 -13.2584 -23.7553  -1.7454   1.2128   0.0721 -13.4338  10.5636
-#&gt;   -5.8314   5.7328  16.2426   7.6736   4.7098   5.0128   6.6982  -3.2705
-#&gt;    3.9518   3.5159  14.4532 -11.0990   5.2647   5.2695  -6.8609  -8.3433
-#&gt;  -11.4290  12.0189  -7.0796  -3.2063   0.8512 -11.2049   8.0210  17.1849
-#&gt;   -0.6719 -12.9099   9.2717   8.5632  -1.0645  10.9416   2.6660   7.0102
-#&gt;  -21.5674  -1.3753  -1.4188 -16.0427   1.5738  -5.0667 -14.3844 -10.6357
-#&gt;   -6.6261  -1.6589  -1.9138   0.7473  -1.0875  -8.0374 -12.2569  -9.8620
-#&gt;   11.4408   0.5490   4.5819  -5.0449  -9.5908   1.9694  -0.7852  -1.1756
-#&gt;   -5.0704  -3.7003  20.9848  12.7139   9.4592  -4.6868   1.2998   5.9470
-#&gt; 
-#&gt; Columns 49 to 54  -0.5748  -4.5555 -10.8098 -16.4562  -9.4050  -9.0289
-#&gt;   -2.9917   5.0203  -0.0712  -5.9687  -1.5708  -3.1563
-#&gt;   -7.4584   4.3009  -4.2490  10.3081   0.4498  -1.5306
-#&gt;  -11.8850   4.9189  12.3221   1.7307  -2.7227   2.2419
-#&gt;   19.9869   3.0221   8.9799 -13.7498   0.8445  -2.5968
-#&gt;    9.6091  11.1714  -5.0368  -1.5593  -1.4665  -4.0156
-#&gt;    2.7914   3.4869   0.3828  -3.8038 -15.7783   5.4642
-#&gt;  -10.8918 -10.5758 -15.0479  -1.8494  -8.8932   2.3320
-#&gt;   -5.2602  26.0897  10.0177   1.5342 -10.6252  -5.2099
-#&gt;    0.4459   3.8729 -12.8055   6.9281  -9.5037   7.5632
-#&gt;  -15.4440 -17.5008  -4.9405  -0.4203  -9.5606  -0.7487
-#&gt;    4.6954 -12.0699  -4.1469   6.8135  -7.5413   1.3218
-#&gt;  -11.8005 -20.5872   8.5660   6.0337  -0.5637  -0.4580
-#&gt;   10.9600   8.3493  -7.5067  -4.3243  -1.8352   3.3864
-#&gt;   -1.8891 -14.6502  -3.1400  -1.1797   1.9789  -6.0581
-#&gt;   -0.8843  13.4892  -3.9104  -3.8655   3.5878  -2.0615
-#&gt;   -3.2622  -8.2185  19.0636   8.5473  -2.9303   2.6972
-#&gt;   18.2717  12.9653  -1.0615  -1.6554  -6.8827  -1.3077
-#&gt;    5.3455 -12.0823  19.9503  -0.1489  -5.2718 -15.3229
-#&gt;   -3.5211  13.4520  12.5929  12.9836   2.7761  -1.0501
-#&gt;  -12.5893   6.0336   8.0014   9.1742  -0.7295  -9.0924
-#&gt;   -6.9101 -13.5712  12.0009   3.2590   6.4000  -4.5511
-#&gt;   -1.6348  -7.4601   0.4337   4.7588   9.8812  -1.1603
-#&gt;   -5.9463 -19.7875  14.2054  17.8117  -4.5226   1.1551
-#&gt;  -19.7725   1.2070 -18.8778  10.5806  -4.7235   2.0076
-#&gt;    9.4470   8.0468  -6.5413   9.1266   2.3448   1.6579
-#&gt;    0.9234   0.2097  10.9888  -6.6968  -0.1578   4.3325
-#&gt;   -5.3451 -10.5795 -13.9319  10.3420  -1.7897  -0.2628
-#&gt;    6.0273  14.1968 -11.6920 -13.5771 -15.1813   2.7092
-#&gt;    2.9078  -7.5316   5.8178  -0.7885  -6.7810  -2.4528
-#&gt;  -12.2499  -3.0318  -8.6281   3.3185   5.6072   1.1875
-#&gt;   -2.0903  -0.1257   7.2532   4.3324  -4.7236  -3.5618
-#&gt;   12.0835   2.9512  -3.1610   4.1818  -2.0375  -1.7722
-#&gt; 
-#&gt; (20,.,.) = 
-#&gt;  Columns 1 to 8   0.9634   2.9054   2.0280   0.9189  -6.4232  -5.7542  -9.3369  -9.4553
-#&gt;    4.5672   3.7111   3.7066   5.9267  -0.6864  12.6602  -5.8560  -2.4003
-#&gt;    0.7205   0.0446  -2.2689   5.8117  22.1745   5.5192  -4.3460   6.6715
-#&gt;   -5.2751   3.0791   3.0141  13.1725   9.1727  11.0314  -2.7076   4.2019
-#&gt;   -0.3381  -2.7149  -6.9338   2.1877 -13.7303   8.5995   1.9520  17.8981
-#&gt;    3.8129   2.6922   3.6013  -3.9171   0.2538  -4.7650 -10.7429  15.3260
-#&gt;    2.1073  -6.8836  -5.5644  -5.9350   6.3535   4.3816   4.6377  -8.5890
-#&gt;   -0.9676   2.1016  -6.5235   3.7079   9.8374 -18.9226  -6.9162  -8.6566
-#&gt;    0.9272   5.9761  -7.8295 -11.2318  -8.6840 -12.0523  -7.7165  -1.9227
-#&gt;    3.1633  -0.6661 -15.9515   4.1866  -8.4479   6.6912   4.5978  -7.9695
-#&gt;    2.7949   9.4808   0.9553  12.5949  -3.1201  -7.4123 -15.8925   5.6232
-#&gt;    4.2864   5.8762 -12.2021   6.7776  13.3619   3.1155  -7.5858  -3.8421
-#&gt;    2.3456  10.0452   9.2094   1.8449  -2.1219  -2.8128  -9.1632   3.3231
-#&gt;    5.9459  -3.7359  -6.0873 -11.5273  -8.5499  -5.7116   6.4149   0.9913
-#&gt;    5.4854  13.6941  -1.5428   2.1372  -8.7366   5.5064   0.7355   7.6556
-#&gt;   -1.3673  -3.5262  -0.8866   7.7530  -1.7932   7.1557  17.0147   7.6296
-#&gt;    0.1278  -6.1950   8.8458  -0.5334   3.4483 -10.5832  -7.3074  -4.2234
-#&gt;   -0.3131   0.2556   1.3867  -4.9017 -20.6380  -8.3494  -1.9160  12.7939
-#&gt;    0.0194  -5.0326   9.1343 -17.2951   5.0315 -12.4627   5.2050  -1.6706
-#&gt;   -7.1430   4.2376   7.2568  -3.0648 -14.9885  -6.6781 -11.2307   4.1359
-#&gt;   -1.1308  -7.7014  -3.1501   5.5313  -7.2261   0.6720 -13.7219  -3.4307
-#&gt;    6.2246   6.6550 -10.6407   5.9809  -5.9552   4.9377 -16.5920   4.9279
-#&gt;    5.2053   1.7866  -6.5271  -4.6019   1.6510  17.6510  -2.2651   1.8420
-#&gt;    0.8663   9.1539  -1.4542  -0.8731  -2.5950   3.5243  -0.6555   1.1245
-#&gt;   -1.9791   3.5419   5.9834  -9.8937  18.0920   2.3795  -4.3769  -8.2027
-#&gt;    1.7868   3.7935   0.0938 -11.1536  -7.6730   1.1435  10.3988  -0.4725
-#&gt;    2.0324  -0.4208   1.9411   1.8871  -8.2293   2.6894  11.5805   1.6412
-#&gt;    0.4438 -14.8460 -12.1168   0.2009  11.1124  10.3287   9.2303 -11.9898
-#&gt;   -1.5656   3.3544  -5.1336   7.6605  -1.2730   2.4206  -1.9576  15.4645
-#&gt;    7.1753   8.6167  -6.0015  12.3782 -12.6040  -3.3040  -8.4096  11.1630
-#&gt;    3.7726   8.5869   7.3108  -0.6893 -13.3300  -7.4910   6.7100 -10.9408
-#&gt;    0.3618  -6.2233  -0.3090   0.2551   5.3228  -7.1730  -1.0942  -4.9292
-#&gt;    0.6760   2.9897  -0.7960  -4.9380   9.4857  -9.7733  -0.3975  -3.5060
-#&gt; 
-#&gt; Columns 9 to 16  -0.3528 -18.7653   7.0435  -9.9835   9.3941  -6.8891 -13.3515  -7.1563
-#&gt;    9.0952   8.6011   1.2668  -9.1561   0.8553  -6.8187   1.3878   9.1749
-#&gt;   10.0583  -0.5176   2.6805  -2.8268  14.4047   1.2901  -7.4274   5.9308
-#&gt;    2.5606  -2.3930   0.9152  -9.1982  -0.6520  -7.0930 -16.4701  -8.5099
-#&gt;   -6.4240  11.0266  -5.4208   4.7472  17.9172  11.4619  21.2685  10.2914
-#&gt;    1.2828   1.2361   9.3198  -2.3012  -5.2747  -1.6889   7.6035  -1.0180
-#&gt;   -0.0098 -17.3844  -2.9865   1.8961  16.6893 -16.0707  -7.5292  -3.2148
-#&gt;  -11.9183   2.3945  -0.3817  -5.8060  -0.0419  -6.4491   8.5498  17.9618
-#&gt;   -0.9774  -6.1870  -4.4644  -2.9705  -0.7221  16.1461  17.5302   7.3668
-#&gt;    7.3146 -18.4526  -3.1441 -12.3026  -3.2399   9.4261   8.7369 -15.4927
-#&gt;    1.1085   7.0115  -1.7958   3.3254  -6.5173 -13.0431  -7.1503  -0.6413
-#&gt;  -15.4432 -14.3227  -8.2454 -14.6730  -4.0966 -11.2139  -9.7555 -10.2727
-#&gt;    3.2183   0.3818  10.3659 -15.7526 -15.1088  -6.4920  -9.3339  15.0169
-#&gt;    7.1764  -1.6178 -14.5388   4.1186   9.9932 -16.7825 -10.9561   8.0374
-#&gt;    0.7261   6.2009  16.6323 -13.3227   4.3394   0.7261   1.1469  -7.3071
-#&gt;   -9.0125   2.7999  -2.2346  -2.4866  -6.3923  -9.7040   3.5252  -9.3499
-#&gt;   -0.1593  -0.9330   5.3952  -4.7230  -6.3432 -17.2021  19.2089  15.4327
-#&gt;   18.7820 -17.4396   7.7112   5.2793   0.4740  -3.5334  -0.8678   5.0354
-#&gt;    8.5496  -7.2946  -9.2081   3.9974  -9.6320  12.9309  -6.9349  -0.0051
-#&gt;    6.2330   0.5770  16.3997  11.3157  -6.6251   0.3781   8.6329   2.1115
-#&gt;   13.1605   1.9359   1.3569   7.5658   5.3983   9.1330  -2.2144  -1.4111
-#&gt;    0.9172  -5.2295 -14.2919   0.8418  -4.9884   1.3017  13.2486  -6.4562
-#&gt;   -7.0415   5.7133  -3.2740  -4.3947   6.4831 -12.5295  -2.5346  -1.9723
-#&gt;    0.2293  -9.8406   3.7857  -3.6403 -24.6059  -7.7481   4.4684   7.8942
-#&gt;    7.6518  -6.1819  -0.3050   8.5395  -3.2536  -3.9042  -6.9719  -5.9310
-#&gt;    9.6662  -4.6163  -1.4281   3.7566   0.2317  -5.4764   5.2830   1.7840
-#&gt;   20.7692  -1.6747  -3.1844  -1.4818  10.7447  -4.5715   5.3021  12.3937
-#&gt;   -6.1274   1.0090  -6.1134   8.9748  -8.0927  12.0754  -1.8817   0.1093
-#&gt;   -6.0967  -5.5313  10.0572  -1.3197   3.7234   2.4340   4.5234   0.0586
-#&gt;    1.4688   5.3720   3.1331   9.0772  -2.3665  -9.1102   4.4327  -1.8023
-#&gt;   -3.3310   2.3040  -0.5238  -3.1363  -4.6502   2.5840   8.8475   3.9712
-#&gt;   -4.8120  10.7661  -3.7970  -2.6687  -3.9549  -3.0774   2.6182   8.8237
-#&gt;   16.7270  -2.7630   3.7300  12.7656  -1.9952  -3.1738  -8.1384   6.0455
-#&gt; 
-#&gt; Columns 17 to 24  11.5892  -0.2728   8.4075  -1.8184 -16.5459  -6.6476  -6.3005 -10.0667
-#&gt;    0.4814   7.4972  -5.5029   7.8899   7.7882   6.3943  -0.2864  -3.4995
-#&gt;    2.8804  -6.4668   5.3023   8.2374   1.8347  -6.9581 -15.7810   9.5242
-#&gt;   -2.6345   2.0587   6.3508  -2.5067  10.2373   1.0135   2.7717  -1.2770
-#&gt;   14.2639  14.7022  14.0204   0.8496  10.0495   1.2820  12.0707  -8.1895
-#&gt;   -3.1108  12.6950   0.5816  -7.2854 -15.6064   3.8450 -10.7822   7.0789
-#&gt;   11.1832  -4.3921  10.5762  -1.1231  -7.9124  -4.8597  -1.4349  -7.5702
-#&gt;   -5.6664   0.1829  -4.7565  -4.2005   6.2213  -2.8875   4.4787  12.3057
-#&gt;    7.6605   5.8865   7.5207  -0.7985  -2.0213  -2.1935  -9.0650  -0.3978
-#&gt;    1.7073  -9.6411  -3.0185  17.1335   2.1259  -1.0289   4.1738 -12.7388
-#&gt;    1.5541  -5.4857   9.8765  -0.2684  -3.7987  -6.0497   7.1121  -2.1093
-#&gt;   -9.7563  -5.8546 -14.4142  -1.7841  -1.6321  -1.0778   6.9409   1.5343
-#&gt;   12.7979  -5.9762  -3.4179   2.1657  17.6211  -4.8368   0.6637  -3.9078
-#&gt;    4.5147  -3.9925 -22.8998   5.0741 -15.7986   6.3361  -6.4118  -4.4952
-#&gt;    2.6356  -8.5295   1.1862 -16.4905  -6.9654   2.0917  -4.4601   4.3032
-#&gt;   20.3166  -1.1649  -3.2119  -4.9867  -1.2354  11.6627  -5.6509  -4.0059
-#&gt;    4.2441   1.5520  15.5362 -11.6739   0.2336  -6.1023  17.0866   1.6924
-#&gt;   25.4717  -9.8235   6.4096   5.1837  -1.7949   1.9554  -5.8003  -0.8346
-#&gt;   -0.7024   3.3963  -3.9411  -3.4217   0.2069   2.0893  -0.6163   7.2792
-#&gt;    5.5544  19.6081   9.4267  -6.1667  -0.8230  21.0126   6.1238  13.5394
-#&gt;    3.7275   6.5172  -0.1587   3.0996  -5.0465   4.3856  -8.8381   8.0903
-#&gt;   19.7904  -5.5217   8.4574   9.0471  10.1579  -9.6914   2.7508  -4.3895
-#&gt;    6.2928 -12.7422  -6.5924  -6.4773  -4.1818  12.3375  -3.6144   7.0405
-#&gt;   -1.9626  -9.2472  -4.0606  -6.8675  -1.2287  10.2811   5.5641  10.4904
-#&gt;   -0.8000  15.3576 -10.6701   1.8836  13.0945   1.6516  -4.6144   4.5468
-#&gt;   -8.2348  -3.7837  -3.0739 -11.4138  -3.4150   5.3596  -0.9196  -0.6152
-#&gt;  -24.0155  -3.3579  16.0109  -9.1620  13.6733  -5.6613   4.3812  -4.6505
-#&gt;   -4.3027 -11.2836   5.7270  -2.9997  -1.3630  -0.3054 -14.1254  -2.2912
-#&gt;   -4.2765   5.6276 -13.3565   0.9494  -4.4014 -16.2501   7.6118  -1.7980
-#&gt;   -2.7964   4.0236   3.1346  13.0703  -9.6880  -0.9096   0.5720   2.2912
-#&gt;   -2.8452  -0.7228   1.9449  -0.3865  -9.6672  10.0935  -4.5110  -0.8281
-#&gt;    8.2624  -8.6777   3.5967  -1.4703  -3.5103  -7.0783  -7.2823  -7.4595
-#&gt;    6.3897  -9.6244   2.3066   5.1464   9.2502   6.3181   2.4316   8.5156
-#&gt; 
-#&gt; Columns 25 to 32 -15.8110 -12.1970 -15.2574  -3.2981   4.7455   6.6074   2.1516  -7.3887
-#&gt;  -17.4384   9.2832 -16.7063  12.4132 -11.6303 -21.9994  -4.7605   9.1897
-#&gt;    1.8282  18.1867 -11.6403   7.0779   8.2815 -27.4744  11.6449  14.8819
-#&gt;    5.4548   6.0500  -2.7272 -10.5117   3.8961  -1.2482   0.1248  13.3173
-#&gt;    3.2690   0.4115  17.2574  11.7655 -11.7039   6.5329  -8.2356 -13.8566
-#&gt;    2.2982   0.4827  -4.3781  10.1196 -17.5762   6.9687  -2.5759  16.4260
-#&gt;   -4.3147   6.2150   2.5204 -14.0670 -12.4262  -6.0157   1.8618  -9.8316
-#&gt;   -1.4443   3.3273 -13.0649  12.9333   0.9001 -14.1340   8.2764  -1.6331
-#&gt;  -10.5235   7.6076  -7.5732   5.2962   4.2997  -0.3421  14.9253  -7.0718
-#&gt;   -4.0856   1.5858  -1.1881  -0.8423 -14.6153  -9.6988  17.1395   4.5909
-#&gt;    4.3463 -12.1220   9.6022  -5.4231  -3.9096  -3.1861  -3.2202   0.2925
-#&gt;   13.3286 -20.6847 -11.1342  10.5814   9.2763  13.1262   7.2787  -0.3493
-#&gt;   -6.0065   2.1222  -3.0643  11.3851   6.5564  -3.6623  -5.5590   8.4227
-#&gt;    0.1301   3.8785   5.5948   9.8434  -9.1751 -15.7813  -7.6671   1.0386
-#&gt;  -19.2942   8.0651  -0.0087   7.3015  11.8701 -10.9713   0.7923  10.2816
-#&gt;   -2.7928  -5.3619  17.9895  -3.0354   8.4816  -8.1104  -7.0173  -7.9132
-#&gt;  -15.7056   1.6223  -0.8999  12.8020  -8.7978  15.6144   7.7261 -22.2109
-#&gt;    2.8758   1.7478  10.6486 -12.8703   1.2071 -13.0178   2.7170  10.0030
-#&gt;   -6.2508  12.2752  -7.2457  -6.6801  11.2416   2.6189  -0.3493  -2.5662
-#&gt;   11.7202   2.8366   3.3661   3.2955  23.3569   3.6227  -3.3600  -8.7394
-#&gt;    9.7829   2.4378   1.3584  10.2482  17.2495   0.9512  -3.8454  -0.8195
-#&gt;    8.1317  -0.1454   9.5315   4.8167  12.9298  -6.8838  16.4635  -3.6226
-#&gt;    7.5095  -3.3446   0.4924  -1.0479  16.4331   5.2741  -1.7138  -5.8186
-#&gt;    3.6361  -4.0731   1.9771 -10.0517  10.4233   7.5763  -4.2404  22.7209
-#&gt;    2.0658   4.5465 -13.1287   7.8482   2.7867  -9.8514  -0.8178  -0.2279
-#&gt;   -1.8871   1.5438  -6.2686  -4.7394   3.0026  20.1133  -0.7314   8.9699
-#&gt;  -10.1506   9.0179  -6.7535   6.7010  -3.8900 -10.7159   9.2182   3.3215
-#&gt;    5.5916  14.7326  -3.2673 -11.2151  -3.8569   5.3457   6.7796  -3.0115
-#&gt;  -14.9657   8.3632 -21.1271  -3.3621 -18.5749   2.2125  11.0425  -0.1594
-#&gt;    1.8552 -10.7924   1.9515   3.9940   0.9291  -8.1563  11.6894   0.4968
-#&gt;    0.2897 -13.5349  12.5144 -27.2187  11.3958   4.3448  -1.5597   1.3620
-#&gt;    6.7060  -3.7328  -2.5251   4.4328  -9.6322  10.6299   1.3826  -2.2151
-#&gt;   -5.1594  11.1859  -1.1205   4.3690  19.4124  -4.9983   7.2642  11.8382
-#&gt; 
-#&gt; Columns 33 to 40   8.2286   2.9604 -11.1216  -1.1809 -15.2254  -4.9473 -17.4953  -2.0959
-#&gt;    7.5757   6.1849   0.6202   4.7760  -7.8025  15.5799  -7.3202   3.5549
-#&gt;    1.1497   0.6833  21.3249   4.4118   3.9060   4.7668  -0.1381  -1.8258
-#&gt;   -2.3173   9.8114   7.7596  18.5655  -1.1849   3.5061  -8.0041   4.6083
-#&gt;   11.7690   9.8272 -13.0118  14.1786  15.3396  17.2561  -2.9785   4.1998
-#&gt;   -1.0393  15.4080   1.1870  -5.4395  -9.2893  24.0823 -14.6739  15.0279
-#&gt;   12.8480  -8.8250  -1.5909  -1.1529   0.4304 -16.6304  -6.8503 -18.3497
-#&gt;  -14.2702  -7.3806   5.2498 -13.9300   6.0236  -8.6176   5.2986  -1.9341
-#&gt;   -1.1582   7.1503  -8.9847 -14.9106  -0.2512   8.7891  10.8684 -12.4882
-#&gt;   -3.2958 -12.7202   1.1362   7.0235  18.6477  -2.8588  -0.8726 -34.2020
-#&gt;   -6.7653   2.8123  -3.2707   6.1739  -4.0019  -8.5064   3.0593  20.2928
-#&gt;    2.2640  -4.5305  -0.8626  -4.8723   5.2774 -10.3823   0.9369  -4.7744
-#&gt;    5.4367 -11.1542  -0.6401   5.0743  -9.8577   3.6135   5.5268  18.2589
-#&gt;   -3.1103 -19.3509  -3.7718  -4.1066  -1.6514   7.4965  -1.0566 -21.0162
-#&gt;    8.4199  -9.7014   8.5946   8.7080  -2.5667  14.1000  13.4759  11.5891
-#&gt;    5.6181  -8.6737  -7.0056   4.9266   2.9907   0.3056   3.3313  -2.6671
-#&gt;   11.7368   9.2298  -1.5042 -10.5526  -2.6188  -3.2823  -1.0887  14.7550
-#&gt;   14.0543  -5.8167 -15.3091  -2.1286   3.7647   9.2753  -3.9368  -8.0835
-#&gt;   -5.5951  -1.5638   2.8636 -16.4836  -8.5870   2.0436  -8.0729  -8.7561
-#&gt;    0.2441   0.4300  -1.2715 -10.4177  -8.7255  10.1337   0.8760   7.0470
-#&gt;   -9.4848  12.1149 -11.6033   9.3358  -9.3404  17.0986  -8.2953   5.2780
-#&gt;   -1.6147   6.2028   7.1567  -4.5965   8.6469   3.2320 -11.1085 -11.7893
-#&gt;    4.4104 -15.8345  -2.9759   3.4570  -4.1426   9.0023   5.0001   8.7948
-#&gt;   -5.8295  12.7541  -3.9523   2.8162  -1.3069   3.6578  -0.9305  -0.4833
-#&gt;   -4.4898   0.7731  17.1622  -4.9687   8.5828   4.9526  -4.4410  -0.0513
-#&gt;   -3.6318 -16.3428   1.2826  -7.2022 -11.9219   6.8317   6.7217  -6.0796
-#&gt;   -6.6996   8.1062  -7.7651   5.7561  -7.3611  21.9361  10.5441 -12.4743
-#&gt;  -27.4978   2.3199  10.2100   8.0227  -1.9133 -10.0061 -26.5963 -24.8240
-#&gt;    3.3373   3.5906 -12.8014   1.5928   3.0418  -5.8258 -13.7717 -10.2352
-#&gt;  -10.0227  12.6295  -1.2964  -3.0949  -3.2930   3.5954 -13.2243   0.5023
-#&gt;    1.9949 -14.3813   3.4358   8.4460  -7.6783   3.0830  17.1628  -0.0988
-#&gt;   -6.7689   3.4012  -5.6975  -7.1131  -9.1701  -0.2167  -0.7232  15.4454
-#&gt;    8.4549   1.5957  13.1272  -2.5949  -1.0234  -7.2361   6.1677   1.1390
-#&gt; 
-#&gt; Columns 41 to 48   2.1392  -1.9641  -6.5577   9.0191  -6.0389  -0.4015  -3.6972  13.1820
-#&gt;   -9.0414   0.3087  -5.6368   6.0475  -4.3511 -12.0953  -7.3339   2.6292
-#&gt;   13.0542  16.1815  12.2371   6.8561  -4.6816 -13.7815  -8.0119  -9.2350
-#&gt;   10.9908  19.3968   9.8387   3.5585   9.3970  -2.5355   3.3145   7.4983
-#&gt;   -5.5746  -3.0665  -3.5611 -15.9283  -0.1809  -7.5387   4.3997  -1.8286
-#&gt;   -2.1346  -1.6237 -20.0253   6.6491   0.7470 -13.5761   9.5076  -5.2479
-#&gt;  -11.8257  19.8523  23.5760   3.2160   7.7544   9.3425  -8.4498   3.2829
-#&gt;   -0.5800  -9.5919   2.3906   5.4744   4.2977   2.9208   1.2201   7.5842
-#&gt;   10.6044  -7.7185 -11.5573  -4.1912   3.7236  -3.6209   2.1662   0.0088
-#&gt;    2.8296 -22.7754  17.3094  -4.9904   5.4060  21.7234  -9.8392  17.2495
-#&gt;    2.7088  -0.8770  15.3457   2.8346   1.0113  -3.8746  -9.5788   0.4352
-#&gt;   -5.2969  -6.8956  14.9219  14.2731   4.8066  14.4404  14.0879   7.5167
-#&gt;  -15.5413   0.4554  -1.7614   3.9611  11.4463  -5.2233  22.7256  -6.4855
-#&gt;  -16.9976  -4.4817   9.1893  -4.0083  -4.9344  -6.4946  -9.2576   0.3401
-#&gt;   -4.1094  -4.5742  -8.3689   1.0468 -22.3212  -3.4063   8.7816  -8.6158
-#&gt;   21.2817  -2.0718   7.2347   4.5538   1.5226   8.6524 -23.5999   9.7294
-#&gt;   -5.9618  13.9725  16.2653 -12.1212   3.3885  16.4777   6.4411  -5.7662
-#&gt;   -5.6278   2.0313   5.4166   7.2967   8.3041  -7.6323  -1.9660  -3.1574
-#&gt;   -4.3413   0.4590  -5.1617  -7.4920   0.9292   8.2247   1.1348  -3.7986
-#&gt;   23.6771  14.4883  -4.7164  13.6130   2.2934   5.0937   0.4653 -20.5987
-#&gt;   -7.9488   5.7798   8.3971   0.9646   3.7739   5.8724  -6.0482   5.4690
-#&gt;   -0.3480   4.7370  26.9145  15.1657  28.2415  19.4371   7.0924   3.7508
-#&gt;   -0.3853 -12.5266   7.0661   5.4242   6.6343  -0.6110   4.7062  20.1748
-#&gt;   -1.5220   8.1788   5.5780   4.9807   1.9349  10.6204  16.8929  -3.1397
-#&gt;    4.1067   4.5605   3.0939   3.1818  14.6641   5.6888   7.0544   6.7392
-#&gt;   -4.7623 -22.2312   0.9539  -3.5595   2.4508   0.3514  10.2319   2.7251
-#&gt;  -17.5663  -2.0533  -3.0612 -11.6610  -2.1712  -3.3852  17.3522  -2.8465
-#&gt;   -1.3429  -5.6497  -2.2128  -9.5784  -3.2565   4.0714   8.0523  11.1086
-#&gt;   -0.3802  17.0905 -21.2351 -12.1744 -10.9581  -0.3790 -11.9266 -10.4089
-#&gt;   16.1953 -15.8969 -15.1788   5.1035  -9.2401   0.5737   1.3524  -1.6340
-#&gt;    2.9148 -17.2764 -19.3552  -9.0602  -6.1877   1.6814   0.5460  -3.6512
-#&gt;    1.9215  -6.9023   2.1852   2.3362 -12.8070 -12.8304  -9.0968  17.9949
-#&gt;    2.0731  -7.6171  13.5091   8.4806  -1.7754  -5.2181   5.4403  -3.1792
-#&gt; 
-#&gt; Columns 49 to 54  -7.8009  -5.0412 -11.3101  -0.1455 -10.0658  -4.4934
-#&gt;   -6.7773   8.8699  -3.2803  -3.0157  -7.1167  -2.9270
-#&gt;    8.6416   3.0564   4.3969   3.7350   4.1134   2.2771
-#&gt;    5.0447   0.9355   7.4279  -0.4950  -2.0224   1.0360
-#&gt;   -4.1091   2.4202   4.7275  -8.1695  -2.3885   1.0781
-#&gt;   -3.6389   4.6187   8.4011  -4.6030  -5.2396  -3.7235
-#&gt;   -7.3576   2.7568   9.7508  -4.0862 -14.6954  -4.2711
-#&gt;   -2.8242 -13.8575   0.5523 -12.9943   0.4010  -1.4774
-#&gt;  -14.0713  -0.9314   7.9746  -3.5199  -2.2033   3.8256
-#&gt;    2.4034   1.2173  -2.1689  -5.4604   4.5954  -1.5445
-#&gt;  -16.9723  -2.6396  -9.9269   2.8011  -6.7082  -4.5779
-#&gt;   -0.5866  -5.4818   0.8310  -2.4601   1.4083   1.4234
-#&gt;   -2.9171 -14.1694 -14.8217   5.0183   0.9621  -2.3144
-#&gt;  -12.8387  -3.2549  20.1700  -1.6390   1.5057  -4.2326
-#&gt;   -3.0797   0.6930  -4.2832   6.7080   3.7459  -2.7784
-#&gt;   16.3293   5.7961  -3.1708  -5.2474  -5.3072  -1.1681
-#&gt;  -10.9985  -5.1988  -8.9764  -5.7585  -1.7706  -1.7710
-#&gt;   -3.5024  10.8504   5.0874  -5.4715   0.3771  -5.6360
-#&gt;  -10.0881  10.1969   1.0079   2.1649 -12.9852  -3.7737
-#&gt;   -8.1959   2.8340   1.9292  -2.9023   4.3714  -2.5442
-#&gt;   -9.2574   7.4570  -8.3816   4.9228  -0.3261  -2.6142
-#&gt;   -8.0731  -8.1617   0.2807   7.4379   2.8581  -3.9411
-#&gt;   -9.0361  -9.0625  -1.5548   7.5037   5.8131  -1.9534
-#&gt;   -3.9759   0.2377   2.4541  10.5715  -5.6176  -1.5697
-#&gt;    5.4110   2.7945   7.6198  -2.7881  -0.6019  -5.0293
-#&gt;    3.2161 -12.2117   0.1510  -0.4278   4.9680  -1.9242
-#&gt;   -6.1043   5.1382   5.3742   4.4795  -4.1807   1.5050
-#&gt;    5.4176   2.2282   0.9629 -10.5786  -4.6296   0.5636
-#&gt;    9.0408  12.6402  10.3185 -20.3734 -10.9886   4.2405
-#&gt;   -7.5374  14.5608   4.4367  -8.2884   2.8315   2.0902
-#&gt;    5.4943  -3.5045  -3.1632  -2.3909  12.7183   3.4878
-#&gt;   -5.6354  -5.1595 -12.5836  -0.8797  -6.5869  -2.3077
-#&gt;   -7.9854  -0.3326 -11.4084  -8.5408   4.2826  -1.0549
-#&gt; [ CPUFloatType{20,33,54} ]</div></pre>
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-    <h1>Conv_transpose2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv_transpose2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv_transpose2d</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_conv_transpose2d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
-  stride = <span class='fl'>1L</span>,
-  padding = <span class='fl'>0L</span>,
-  output_padding = <span class='fl'>0L</span>,
-  groups = <span class='fl'>1L</span>,
-  dilation = <span class='fl'>1L</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padH, out_padW)</code>.      Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 2D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution".</p>
-<p>See <code><a href='nn_conv_transpose2d.html'>nn_conv_transpose2d()</a></code> for details and output shape.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># With square kernels and equal stride</span>
-<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>5</span>))
-<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>8</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'><a href='nnf_conv_transpose2d.html'>nnf_conv_transpose2d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>weights</span>, padding=<span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,1,.,.) = 
-#&gt;  -7.5110  3.7641 -10.4372  4.2887 -1.1483
-#&gt;   2.5014  4.1213 -1.3290  2.7715 -1.2356
-#&gt;   3.9241  2.2920  3.0992 -5.0194 -3.9354
-#&gt;   1.2425  5.6448  5.1804 -11.2531  3.6939
-#&gt;   2.7946 -0.2876 -1.8022 -2.9866  1.7587
-#&gt; 
-#&gt; (1,2,.,.) = 
-#&gt;  -5.7366 -1.2901  8.7096  0.2504 -3.9261
-#&gt;   3.7174 -1.6981  8.2566  1.5653  2.0469
-#&gt;   8.4169  8.5922  4.2183  3.2537 -8.8162
-#&gt;  -0.8450 -4.8588 -14.4069 -2.3530  4.7965
-#&gt;  -2.7730 -0.7138 -3.8965  3.0225  3.0301
-#&gt; 
-#&gt; (1,3,.,.) = 
-#&gt;   1.7159  4.1838 -1.0616 -0.8207 -5.0284
-#&gt;  -6.3134  2.0706 -3.1550  7.4245 -1.5805
-#&gt;   5.3365 -5.3354 -5.2464 -8.8019  7.0449
-#&gt;   6.8231  3.0109  6.6110 -3.6602  3.4779
-#&gt;  -0.5472  2.4284 -5.0799 -4.8086  3.5136
-#&gt; 
-#&gt; (1,4,.,.) = 
-#&gt;  -1.0399  4.2911 -1.7679  4.1818 -3.6969
-#&gt;   1.6923  3.2699 -2.3755 -2.7303 -6.1423
-#&gt;  -1.7567 -0.9807  2.1321  6.2346  3.4545
-#&gt;  -2.7856  1.0777 -0.1440  3.6303 -4.2952
-#&gt;   0.8700 -4.6312  0.7110  0.2863 -0.3070
-#&gt; 
-#&gt; (1,5,.,.) = 
-#&gt;   6.3820 -3.1108 -3.1401  1.8361  1.4169
-#&gt;  -3.5173 -1.9328 -3.2543  5.3292 -0.7929
-#&gt;  -5.1491  4.1741  1.0536  7.7839 -1.0521
-#&gt;  -0.5027 -0.5993  0.8675  3.5631 -2.8527
-#&gt;  -2.5527 -6.5371  5.4238  6.4524  3.9158
-#&gt; 
-#&gt; (1,6,.,.) = 
-#&gt;   0.1192  0.7898  5.1046  0.8291  0.8075
-#&gt;  -0.1448 -0.4707  4.0276 -6.0713  0.6670
-#&gt;   3.9042 -0.1029  0.2932 -0.5353  4.6275
-#&gt;   0.2660  5.2588 -5.4768 -5.2114 -6.2913
-#&gt;   1.3878  3.3422 -7.8499  2.2837 -1.2931
-#&gt; 
-#&gt; (1,7,.,.) = 
-#&gt;   0.3207  1.6438 -0.1820  2.9592 -5.9365
-#&gt;  -1.8610 -1.4471  2.4745  4.9803 -2.3983
-#&gt;   2.1440 -2.1082 -3.0073  4.2901  8.0834
-#&gt;  -2.2489  1.8009  1.5683  8.1625  2.1253
-#&gt;  -3.1867  0.6020 -0.8497 -3.0459  3.1883
-#&gt; 
-#&gt; (1,8,.,.) = 
-#&gt;   -4.5885   1.7673 -10.5134  10.8415   1.1800
-#&gt;    3.8736   5.2990   0.8102   3.4449   3.6823
-#&gt;   -2.2823  -1.8588   6.1128   4.2038   2.2027
-#&gt;   -0.3619   4.1382   3.2630   0.0729  -8.0006
-#&gt;   -0.2060  -0.7592   4.9095  -2.3508  -7.0914
-#&gt; [ CPUFloatType{1,8,5,5} ]</div></pre>
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-    <h1>Conv_transpose3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv_transpose3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv_transpose3d</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_conv_transpose3d</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>weight</span>,
-  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
-  stride = <span class='fl'>1L</span>,
-  padding = <span class='fl'>0L</span>,
-  output_padding = <span class='fl'>0L</span>,
-  groups = <span class='fl'>1L</span>,
-  dilation = <span class='fl'>1L</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padT, padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      <code>(out_padT, out_padH, out_padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 3D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution"</p>
-<p>See <code><a href='nn_conv_transpose3d.html'>nn_conv_transpose3d()</a></code> for details and output shape.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>10</span>, <span class='fl'>20</span>))
-<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'><a href='nnf_conv_transpose3d.html'>nnf_conv_transpose3d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>weights</span>)
-}
-}
-</div></pre>
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diff --git a/static/docs/dev/reference/torch_cos.html b/static/docs/dev/reference/torch_cos.html
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-    <h1>Cos</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cos.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cos</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cos</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cos-input-out-null-gt-tensor-"><a class="anchor" href="#cos-input-out-null-gt-tensor-"></a>cos(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the cosine  of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \cos(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_cos</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.9951
-#&gt;  0.5132
-#&gt;  0.9942
-#&gt;  0.7246
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <h1>Cosh</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cosh.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cosh</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cosh</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cosh-input-out-null-gt-tensor-"><a class="anchor" href="#cosh-input-out-null-gt-tensor-"></a>cosh(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the hyperbolic cosine  of the elements of
-<code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \cosh(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_cosh</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.0467
-#&gt;  1.8141
-#&gt;  1.0000
-#&gt;  1.0457
-#&gt; [ CPUFloatType{4} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_cosine_similarity.html b/static/docs/dev/reference/torch_cosine_similarity.html
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-    <div class="page-header">
-    <h1>Cosine_similarity</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cosine_similarity.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cosine_similarity</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cosine_similarity</span>(<span class='kw'>x1</span>, <span class='kw'>x2</span>, dim = <span class='fl'>2L</span>, eps = <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x1</th>
-      <td><p>(Tensor) First input.</p></td>
-    </tr>
-    <tr>
-      <th>x2</th>
-      <td><p>(Tensor) Second input (of size matching x1).</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) Dimension of vectors. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid division by zero.        Default: 1e-8</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cosine-similarity-x-x-dim-eps-e-gt-tensor-"><a class="anchor" href="#cosine-similarity-x-x-dim-eps-e-gt-tensor-"></a>cosine_similarity(x1, x2, dim=1, eps=1e-8) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns cosine similarity between x1 and x2, computed along dim.</p>
-<p>$$
-    \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>input1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>100</span>, <span class='fl'>128</span>))
-<span class='kw'>input2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>100</span>, <span class='fl'>128</span>))
-<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>torch_cosine_similarity</span>(<span class='kw'>input1</span>, <span class='kw'>input2</span>)
-<span class='kw'>output</span>
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.0557
-#&gt; -0.0086
-#&gt; -0.0113
-#&gt; -0.0668
-#&gt; -0.0928
-#&gt;  0.0953
-#&gt; -0.0389
-#&gt;  0.1375
-#&gt;  0.0884
-#&gt;  0.0044
-#&gt; -0.0333
-#&gt; -0.1623
-#&gt; -0.1257
-#&gt; -0.0395
-#&gt; -0.0199
-#&gt;  0.0618
-#&gt;  0.1237
-#&gt; -0.1515
-#&gt; -0.0276
-#&gt; -0.0383
-#&gt;  0.0742
-#&gt; -0.0917
-#&gt; -0.0397
-#&gt;  0.1257
-#&gt; -0.1363
-#&gt;  0.0335
-#&gt; -0.0554
-#&gt; -0.1754
-#&gt;  0.0440
-#&gt;  0.0214
-#&gt; -0.1126
-#&gt;  0.1865
-#&gt;  0.0418
-#&gt;  0.1684
-#&gt; -0.0485
-#&gt;  0.0252
-#&gt; -0.0478
-#&gt; -0.0705
-#&gt; -0.1482
-#&gt; -0.0685
-#&gt;  0.1167
-#&gt;  0.1431
-#&gt; -0.2242
-#&gt; -0.0635
-#&gt; -0.0692
-#&gt; -0.1318
-#&gt; -0.1103
-#&gt;  0.0347
-#&gt;  0.0008
-#&gt; -0.0457
-#&gt; -0.0518
-#&gt; -0.0470
-#&gt;  0.0020
-#&gt;  0.0757
-#&gt;  0.1652
-#&gt; -0.0046
-#&gt; -0.1819
-#&gt;  0.0831
-#&gt;  0.1813
-#&gt;  0.0314
-#&gt; -0.0781
-#&gt; -0.0569
-#&gt;  0.0151
-#&gt; -0.0505
-#&gt;  0.0662
-#&gt;  0.0172
-#&gt;  0.0194
-#&gt;  0.1026
-#&gt;  0.0516
-#&gt;  0.0768
-#&gt; -0.0191
-#&gt; -0.0665
-#&gt; -0.1799
-#&gt;  0.0599
-#&gt; -0.0710
-#&gt; -0.0542
-#&gt; -0.0271
-#&gt;  0.1621
-#&gt;  0.0791
-#&gt; -0.0643
-#&gt; -0.0834
-#&gt;  0.1390
-#&gt; -0.2209
-#&gt;  0.0116
-#&gt;  0.0171
-#&gt; -0.0511
-#&gt;  0.1534
-#&gt;  0.0455
-#&gt; -0.1074
-#&gt;  0.0069
-#&gt; -0.1305
-#&gt; -0.0783
-#&gt; -0.0749
-#&gt;  0.0152
-#&gt;  0.0535
-#&gt; -0.0237
-#&gt;  0.0667
-#&gt;  0.1995
-#&gt;  0.0062
-#&gt; -0.0545
-#&gt; [ CPUFloatType{100} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_cross.html b/static/docs/dev/reference/torch_cross.html
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-    <h1>Cross</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cross.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cross</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cross</span>(<span class='kw'>self</span>, <span class='kw'>other</span>, dim = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to take the cross-product in.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cross-input-other-dim-out-null-gt-tensor-"><a class="anchor" href="#cross-input-other-dim-out-null-gt-tensor-"></a>cross(input, other, dim=-1, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the cross product of vectors in dimension <code>dim</code> of <code>input</code>
-and <code>other</code>.</p>
-<p><code>input</code> and <code>other</code> must have the same size, and the size of their
-<code>dim</code> dimension should be 3.</p>
-<p>If <code>dim</code> is not given, it defaults to the first dimension found with the
-size 3.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>))
-<span class='kw'>b</span>
-<span class='fu'>torch_cross</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, dim=<span class='fl'>2</span>)
-<span class='fu'>torch_cross</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.6580  0.7530 -1.6742
-#&gt; -0.4482 -1.0402 -0.7554
-#&gt;  0.0892 -0.0706 -0.0625
-#&gt;  5.6686  3.0463 -2.4732
-#&gt; [ CPUFloatType{4,3} ]</div></pre>
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-    <div class="page-header">
-    <h1>Cummax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cummax.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cummax</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cummax</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cummax-input-dim-gt-tensor-longtensor-"><a class="anchor" href="#cummax-input-dim-gt-tensor-longtensor-"></a>cummax(input, dim) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the cumulative maximum of
-elements of <code>input</code> in the dimension <code>dim</code>. And <code>indices</code> is the index
-location of each maximum value found in the dimension <code>dim</code>.</p>
-<p>$$
-    y_i = max(x_1, x_2, x_3, \dots, x_i)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_cummax</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; -0.2138
-#&gt;  0.2566
-#&gt;  0.2566
-#&gt;  0.6534
-#&gt;  1.2378
-#&gt;  1.2378
-#&gt;  1.2378
-#&gt;  1.2378
-#&gt;  1.2378
-#&gt;  1.2378
-#&gt; [ CPUFloatType{10} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  0
-#&gt;  1
-#&gt;  1
-#&gt;  3
-#&gt;  4
-#&gt;  4
-#&gt;  4
-#&gt;  4
-#&gt;  4
-#&gt;  4
-#&gt; [ CPULongType{10} ]
-#&gt; </div></pre>
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-    <h1>Cummin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cummin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cummin</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cummin</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cummin-input-dim-gt-tensor-longtensor-"><a class="anchor" href="#cummin-input-dim-gt-tensor-longtensor-"></a>cummin(input, dim) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the cumulative minimum of
-elements of <code>input</code> in the dimension <code>dim</code>. And <code>indices</code> is the index
-location of each maximum value found in the dimension <code>dim</code>.</p>
-<p>$$
-    y_i = min(x_1, x_2, x_3, \dots, x_i)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_cummin</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  0.7780
-#&gt; -0.4104
-#&gt; -0.8853
-#&gt; -0.8853
-#&gt; -1.3677
-#&gt; -1.3677
-#&gt; -1.3677
-#&gt; -1.3677
-#&gt; -3.1445
-#&gt; -3.1445
-#&gt; [ CPUFloatType{10} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  0
-#&gt;  1
-#&gt;  2
-#&gt;  2
-#&gt;  4
-#&gt;  4
-#&gt;  4
-#&gt;  4
-#&gt;  8
-#&gt;  8
-#&gt; [ CPULongType{10} ]
-#&gt; </div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-    <div class="page-header">
-    <h1>Cumprod</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cumprod.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cumprod</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cumprod</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, dtype = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cumprod-input-dim-out-null-dtype-null-gt-tensor-"><a class="anchor" href="#cumprod-input-dim-out-null-dtype-null-gt-tensor-"></a>cumprod(input, dim, out=NULL, dtype=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the cumulative product of elements of <code>input</code> in the dimension
-<code>dim</code>.</p>
-<p>For example, if <code>input</code> is a vector of size N, the result will also be
-a vector of size N, with elements.</p>
-<p>$$
-    y_i = x_1 \times x_2\times x_3\times \dots \times x_i
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_cumprod</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 0.01 *
-#&gt; -5.4885
-#&gt; -2.3863
-#&gt;  3.5483
-#&gt;  7.2007
-#&gt;  6.1150
-#&gt;  5.2664
-#&gt; -3.4552
-#&gt;  5.6304
-#&gt;  1.8453
-#&gt;  3.7509
-#&gt; [ CPUFloatType{10} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_cumsum.html b/static/docs/dev/reference/torch_cumsum.html
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-    <h1>Cumsum</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cumsum.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cumsum</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_cumsum</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, dtype = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cumsum-input-dim-out-null-dtype-null-gt-tensor-"><a class="anchor" href="#cumsum-input-dim-out-null-dtype-null-gt-tensor-"></a>cumsum(input, dim, out=NULL, dtype=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the cumulative sum of elements of <code>input</code> in the dimension
-<code>dim</code>.</p>
-<p>For example, if <code>input</code> is a vector of size N, the result will also be
-a vector of size N, with elements.</p>
-<p>$$
-    y_i = x_1 + x_2 + x_3 + \dots + x_i
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_cumsum</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.8428
-#&gt; -2.1382
-#&gt; -2.2909
-#&gt; -3.4723
-#&gt; -2.4306
-#&gt; -2.6685
-#&gt; -2.5263
-#&gt; -3.8712
-#&gt; -2.5345
-#&gt; -1.4088
-#&gt; [ CPUFloatType{10} ]</div></pre>
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-    <div class="page-header">
-    <h1>Det</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_det.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Det</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_det</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>Backward through `det` internally uses SVD results when `input` is
-not invertible. In this case, double backward through `det` will be
-unstable in when `input` doesn't have distinct singular values. See
-`~torch.svd` for details.
-</pre>
-
-    <h2 class="hasAnchor" id="det-input-gt-tensor-"><a class="anchor" href="#det-input-gt-tensor-"></a>det(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Calculates determinant of a square matrix or batches of square matrices.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_det</span>(<span class='kw'>A</span>)
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='kw'>A</span>
-<span class='kw'>A</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.0085
-#&gt;  2.4930
-#&gt;  0.9943
-#&gt; [ CPUFloatType{3} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_device.html b/static/docs/dev/reference/torch_device.html
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-is or will be allocated.</p>
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-
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-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>type</th>
-      <td><p>(character) a device type <code>"cuda"</code> or <code>"cpu"</code></p></td>
-    </tr>
-    <tr>
-      <th>index</th>
-      <td><p>(integer) optional device ordinal for the device type.  If the device ordinal
-is not present, this object will always represent the current device for the device
-type, even after <code>torch_cuda_set_device()</code> is called; e.g., a <code>torch_tensor</code> constructed
-with device <code>'cuda'</code> is equivalent to <code>'cuda:X'</code> where X is the result of
-<code>torch_cuda_current_device()</code>.</p>
-<p>A <code>torch_device</code> can be constructed via a string or via a string and device ordinal</p></td>
-    </tr>
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-
-<span class='co'># Via string</span>
-<span class='fu'>torch_device</span>(<span class='st'>"cuda:1"</span>)
-<span class='fu'>torch_device</span>(<span class='st'>"cpu"</span>)
-<span class='fu'>torch_device</span>(<span class='st'>"cuda"</span>) <span class='co'># current cuda device</span>
-
-<span class='co'># Via string and device ordinal</span>
-<span class='fu'>torch_device</span>(<span class='st'>"cuda"</span>, <span class='fl'>0</span>)
-<span class='fu'>torch_device</span>(<span class='st'>"cpu"</span>, <span class='fl'>0</span>)
-
-}
-</div><div class='output co'>#&gt; torch_device(type='cpu', index=0)</div></pre>
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-    <h1>Diag</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_diag.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Diag</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_diag</span>(<span class='kw'>self</span>, diagonal = <span class='fl'>0L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>diagonal</th>
-      <td><p>(int, optional) the diagonal to consider</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="diag-input-diagonal-out-null-gt-tensor-"><a class="anchor" href="#diag-input-diagonal-out-null-gt-tensor-"></a>diag(input, diagonal=0, out=NULL) -&gt; Tensor </h2>
-
-    
-
-<ul>
-<li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
-with the elements of <code>input</code> as the diagonal.</p></li>
-<li><p>If <code>input</code> is a matrix (2-D tensor), then returns a 1-D tensor with
-the diagonal elements of <code>input</code>.</p></li>
-</ul>
-
-<p>The argument <code>diagonal</code> controls which diagonal to consider:</p><ul>
-<li><p>If <code>diagonal</code> = 0, it is the main diagonal.</p></li>
-<li><p>If <code>diagonal</code> &gt; 0, it is above the main diagonal.</p></li>
-<li><p>If <code>diagonal</code> &lt; 0, it is below the main diagonal.</p></li>
-</ul>
-
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-    <div class="page-header">
-    <h1>Diag_embed</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_diag_embed.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Diag_embed</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_diag_embed</span>(<span class='kw'>self</span>, offset = <span class='fl'>0L</span>, dim1 = <span class='op'>-</span><span class='fl'>2L</span>, dim2 = <span class='op'>-</span><span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor. Must be at least 1-dimensional.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></td>
-    </tr>
-    <tr>
-      <th>dim1</th>
-      <td><p>(int, optional) first dimension with respect to which to        take diagonal. Default: -2.</p></td>
-    </tr>
-    <tr>
-      <th>dim2</th>
-      <td><p>(int, optional) second dimension with respect to which to        take diagonal. Default: -1.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="diag-embed-input-offset-dim-dim-gt-tensor-"><a class="anchor" href="#diag-embed-input-offset-dim-dim-gt-tensor-"></a>diag_embed(input, offset=0, dim1=-2, dim2=-1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Creates a tensor whose diagonals of certain 2D planes (specified by
-<code>dim1</code> and <code>dim2</code>) are filled by <code>input</code>.
-To facilitate creating batched diagonal matrices, the 2D planes formed by
-the last two dimensions of the returned tensor are chosen by default.</p>
-<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
-<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
-<li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
-<li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
-</ul>
-
-<p>The size of the new matrix will be calculated to make the specified diagonal
-of the size of the last input dimension.
-Note that for <code>offset</code> other than \(0\), the order of <code>dim1</code>
-and <code>dim2</code> matters. Exchanging them is equivalent to changing the
-sign of <code>offset</code>.</p>
-<p>Applying <code>torch_diagonal</code> to the output of this function with
-the same arguments yields a matrix identical to input. However,
-<code>torch_diagonal</code> has different default dimensions, so those
-need to be explicitly specified.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_diag_embed</span>(<span class='kw'>a</span>)
-<span class='fu'>torch_diag_embed</span>(<span class='kw'>a</span>, offset=<span class='fl'>1</span>, dim1=<span class='fl'>1</span>, dim2=<span class='fl'>3</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   0.0000  0.8768  0.0000  0.0000
-#&gt;   0.0000 -0.0680  0.0000  0.0000
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   0.0000  0.0000  0.4078  0.0000
-#&gt;   0.0000  0.0000 -2.2523  0.0000
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;   0.0000  0.0000  0.0000 -0.9203
-#&gt;   0.0000  0.0000  0.0000 -0.7533
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;   0  0  0  0
-#&gt;   0  0  0  0
-#&gt; [ CPUFloatType{4,2,4} ]</div></pre>
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-    <h1>Diagflat</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_diagflat.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Diagflat</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_diagflat</span>(<span class='kw'>self</span>, offset = <span class='fl'>0L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(int, optional) the diagonal to consider. Default: 0 (main        diagonal).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="diagflat-input-offset-gt-tensor-"><a class="anchor" href="#diagflat-input-offset-gt-tensor-"></a>diagflat(input, offset=0) -&gt; Tensor </h2>
-
-    
-
-<ul>
-<li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
-with the elements of <code>input</code> as the diagonal.</p></li>
-<li><p>If <code>input</code> is a tensor with more than one dimension, then returns a
-2-D tensor with diagonal elements equal to a flattened <code>input</code>.</p></li>
-</ul>
-
-<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
-<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
-<li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
-<li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_diagflat</span>(<span class='kw'>a</span>)
-<span class='fu'>torch_diagflat</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_diagflat</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.8327  0.0000  0.0000  0.0000
-#&gt;  0.0000 -1.2530  0.0000  0.0000
-#&gt;  0.0000  0.0000 -0.1377  0.0000
-#&gt;  0.0000  0.0000  0.0000 -2.3182
-#&gt; [ CPUFloatType{4,4} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_diagonal.html b/static/docs/dev/reference/torch_diagonal.html
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-    <h1>Diagonal</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_diagonal.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Diagonal</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_diagonal</span>(<span class='kw'>self</span>, <span class='kw'>outdim</span>, dim1 = <span class='fl'>1L</span>, dim2 = <span class='fl'>2L</span>, offset = <span class='fl'>0L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor. Must be at least 2-dimensional.</p></td>
-    </tr>
-    <tr>
-      <th>outdim</th>
-      <td><p>dimension name if <code>self</code> is a named tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim1</th>
-      <td><p>(int, optional) first dimension with respect to which to        take diagonal. Default: 0.</p></td>
-    </tr>
-    <tr>
-      <th>dim2</th>
-      <td><p>(int, optional) second dimension with respect to which to        take diagonal. Default: 1.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="diagonal-input-offset-dim-dim-gt-tensor-"><a class="anchor" href="#diagonal-input-offset-dim-dim-gt-tensor-"></a>diagonal(input, offset=0, dim1=0, dim2=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a partial view of <code>input</code> with the its diagonal elements
-with respect to <code>dim1</code> and <code>dim2</code> appended as a dimension
-at the end of the shape.</p>
-<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
-<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
-<li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
-<li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
-</ul>
-
-<p>Applying <code>torch_diag_embed</code> to the output of this function with
-the same arguments yields a diagonal matrix with the diagonal entries
-of the input. However, <code>torch_diag_embed</code> has different default
-dimensions, so those need to be explicitly specified.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_diagonal</span>(<span class='kw'>a</span>, offset = <span class='fl'>0</span>)
-<span class='fu'>torch_diagonal</span>(<span class='kw'>a</span>, offset = <span class='fl'>1</span>)
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>5</span>, <span class='fl'>4</span>, <span class='fl'>2</span>))
-<span class='fu'>torch_diagonal</span>(<span class='kw'>x</span>, offset=<span class='op'>-</span><span class='fl'>1</span>, dim1=<span class='fl'>1</span>, dim2=<span class='fl'>2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -1.1875
-#&gt;   0.1589
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -0.4868
-#&gt;  -0.5057
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;   0.9102
-#&gt;  -0.9336
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;   1.1664
-#&gt;   0.2391
-#&gt; [ CPUFloatType{4,2,1} ]</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_digamma.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Digamma</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_digamma</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compute the digamma function on</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="digamma-input-out-null-gt-tensor-"><a class="anchor" href="#digamma-input-out-null-gt-tensor-"></a>digamma(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the logarithmic derivative of the gamma function on <code>input</code>.</p>
-<p>$$
-    \psi(x) = \frac{d}{dx} \ln\left(\Gamma\left(x\right)\right) = \frac{\Gamma'(x)}{\Gamma(x)}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0.5</span>))
-<span class='fu'>torch_digamma</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.5772
-#&gt; -1.9635
-#&gt; [ CPUFloatType{2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    <h1>Dist</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_dist.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Dist</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_dist</span>(<span class='kw'>self</span>, <span class='kw'>other</span>, p = <span class='fl'>2L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the Right-hand-side input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(float, optional) the norm to be computed</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="dist-input-other-p-gt-tensor-"><a class="anchor" href="#dist-input-other-p-gt-tensor-"></a>dist(input, other, p=2) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the p-norm of (<code>input</code> - <code>other</code>)</p>
-<p>The shapes of <code>input</code> and <code>other</code> must be
-broadcastable .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>x</span>
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>y</span>
-<span class='fu'>torch_dist</span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='fl'>3.5</span>)
-<span class='fu'>torch_dist</span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='fl'>3</span>)
-<span class='fu'>torch_dist</span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='fl'>0</span>)
-<span class='fu'>torch_dist</span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 7.25831
-#&gt; [ CPUFloatType{} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-</div><!--/.navbar -->
-
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-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Div</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_div.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Div</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_div</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Number) the number to be divided to each element of <code>input</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="div-input-other-out-null-gt-tensor-"><a class="anchor" href="#div-input-other-out-null-gt-tensor-"></a>div(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Divides each element of the input <code>input</code> with the scalar <code>other</code> and
-returns a new resulting tensor.</p>
-
-
-<p>Each element of the tensor <code>input</code> is divided by each element of the tensor
-<code>other</code>. The resulting tensor is returned.</p>
-<p>$$
-    \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}_i}
-$$
-The shapes of <code>input</code> and <code>other</code> must be broadcastable
-. If the <code>torch_dtype</code> of <code>input</code> and
-<code>other</code> differ, the <code>torch_dtype</code> of the result tensor is determined
-following rules described in the type promotion documentation
-. If <code>out</code> is specified, the result must be
-castable  to the <code>torch_dtype</code> of the
-specified output tensor. Integral division by zero leads to undefined behavior.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Integer division using div is deprecated, and in a future release div will
-perform true division like <code><a href='torch_true_divide.html'>torch_true_divide()</a></code>.
-Use <code><a href='torch_floor_divide.html'>torch_floor_divide()</a></code> to perform integer division,
-instead.</p>
-<p>$$
-    \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}}
-$$
-If the <code>torch_dtype</code> of <code>input</code> and <code>other</code> differ, the
-<code>torch_dtype</code> of the result tensor is determined following rules
-described in the type promotion documentation . If
-<code>out</code> is specified, the result must be castable
-to the <code>torch_dtype</code> of the specified output tensor. Integral division
-by zero leads to undefined behavior.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_div</span>(<span class='kw'>a</span>, <span class='fl'>0.5</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>b</span>
-<span class='fu'>torch_div</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3514 -1.1995  1.2964  0.8069
-#&gt;  0.0516  7.5321 -0.3148 -2.8593
-#&gt;  0.0341  2.5995  7.5356 -1.2090
-#&gt;  0.4719  3.5983  2.0742  0.2464
-#&gt; [ CPUFloatType{4,4} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Dot</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_dot.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Dot</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_dot</span>(<span class='kw'>self</span>, <span class='kw'>tensor</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>tensor</th>
-      <td><p>the other input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function does not broadcast .</p>
-    <h2 class="hasAnchor" id="dot-input-tensor-gt-tensor-"><a class="anchor" href="#dot-input-tensor-gt-tensor-"></a>dot(input, tensor) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the dot product (inner product) of two tensors.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_dot</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 7
-#&gt; [ CPUFloatType{} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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diff --git a/static/docs/dev/reference/torch_dtype.html b/static/docs/dev/reference/torch_dtype.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_eig.Rd</code></div>
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-    <p>Eig</p>
-    </div>
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-    <pre class="usage"><span class='fu'>torch_eig</span>(<span class='kw'>self</span>, eigenvectors = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the square matrix of shape \((n \times n)\) for which the eigenvalues and eigenvectors        will be computed</p></td>
-    </tr>
-    <tr>
-      <th>eigenvectors</th>
-      <td><p>(bool) <code>TRUE</code> to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>Since eigenvalues and eigenvectors might be complex, backward pass is supported only
-for [`torch_symeig`]
-</pre>
-
-    <h2 class="hasAnchor" id="eig-input-eigenvectors-false-out-null-gt-tensor-tensor-"><a class="anchor" href="#eig-input-eigenvectors-false-out-null-gt-tensor-tensor-"></a>eig(input, eigenvectors=False, out=NULL) -&gt; (Tensor, Tensor) </h2>
-
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-    <div class="page-header">
-    <h1>Einsum</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_einsum.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Einsum</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_einsum</span>(<span class='kw'>equation</span>, <span class='kw'>tensors</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>equation</th>
-      <td><p>(string) The equation is given in terms of lower case letters (indices) to be associated           with each dimension of the operands and result. The left hand side lists the operands           dimensions, separated by commas. There should be one index letter per tensor dimension.           The right hand side follows after <code>-&gt;</code> and gives the indices for the output.           If the <code>-&gt;</code> and right hand side are omitted, it implicitly defined as the alphabetically           sorted list of all indices appearing exactly once in the left hand side.           The indices not apprearing in the output are summed over after multiplying the operands           entries.           If an index appears several times for the same operand, a diagonal is taken.           Ellipses <code>...</code> represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.</p></td>
-    </tr>
-    <tr>
-      <th>tensors</th>
-      <td><p>(Tensor) The operands to compute the Einstein sum of.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="einsum-equation-operands-gt-tensor-"><a class="anchor" href="#einsum-equation-operands-gt-tensor-"></a>einsum(equation, *operands) -&gt; Tensor </h2>
-
-    
-
-
-<p>This function provides a way of computing multilinear expressions (i.e. sums of products) using the
-Einstein summation convention.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='fu'>torch_einsum</span>(<span class='st'>'i,j-&gt;ij'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>))  <span class='co'># outer product</span>
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>4</span>))
-<span class='kw'>l</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>5</span>))
-<span class='kw'>r</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>4</span>))
-<span class='fu'>torch_einsum</span>(<span class='st'>'bn,anm,bm-&gt;ba'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>l</span>, <span class='kw'>A</span>, <span class='kw'>r</span>)) <span class='co'># compare torch_nn$functional$bilinear</span>
-<span class='kw'>As</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>2</span>,<span class='fl'>5</span>))
-<span class='kw'>Bs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>4</span>))
-<span class='fu'>torch_einsum</span>(<span class='st'>'bij,bjk-&gt;bik'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>As</span>, <span class='kw'>Bs</span>)) <span class='co'># batch matrix multiplication</span>
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_einsum</span>(<span class='st'>'ii-&gt;i'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>A</span>)) <span class='co'># diagonal</span>
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_einsum</span>(<span class='st'>'...ii-&gt;...i'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>A</span>)) <span class='co'># batch diagonal</span>
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='fu'>torch_einsum</span>(<span class='st'>'...ij-&gt;...ji'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>A</span>))<span class='op'>$</span><span class='kw'>shape</span> <span class='co'># batch permute</span>
-
-}
-}
-</div></pre>
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-    <h1>Empty</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_empty.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Empty</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_empty</span>(
-  <span class='kw'>...</span>,
-  names = <span class='kw'>NULL</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>a sequence of integers defining the shape of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>names</th>
-      <td><p>optional character vector naming each dimension.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-"><a class="anchor" href="#empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-"></a>empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with uninitialized data. The shape of the tensor is
-defined by the variable argument <code>size</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_empty</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2.2101e-10  4.5592e+30  4.6533e+33
-#&gt;  2.1414e-10  1.9205e+31  1.8891e+31
-#&gt; [ CPUFloatType{2,3} ]</div></pre>
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-    <div class="page-header">
-    <h1>Empty_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_empty_like.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Empty_like</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_empty_like</span>(
-  <span class='kw'>input</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>,
-  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns an uninitialized tensor with the same size as <code>input</code>.
-<code>torch_empty_like(input)</code> is equivalent to
-<code><a href='torch_empty.html'>torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.4047e+14  1.4047e+14  0.0000e+00
-#&gt;  0.0000e+00  1.2885e+10  0.0000e+00
-#&gt; [ CPULongType{2,3} ]</div></pre>
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-    <h1>Empty_strided</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_empty_strided.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Empty_strided</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_empty_strided</span>(
-  <span class='kw'>size</span>,
-  <span class='kw'>stride</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>,
-  pin_memory = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>size</th>
-      <td><p>(tuple of ints) the shape of the output tensor</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(tuple of ints) the strides of the output tensor</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>pin_memory</th>
-      <td><p>(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-"><a class="anchor" href="#empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-"></a>empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with uninitialized data. The shape and strides of the tensor is
-defined by the variable argument <code>size</code> and <code>stride</code> respectively.
-<code>torch_empty_strided(size, stride)</code> is equivalent to
-<code>torch_empty(size).as_strided(size, stride)</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>More than one element of the created tensor may refer to a single memory
-location. As a result, in-place operations (especially ones that are
-vectorized) may result in incorrect behavior. If you need to write to
-the tensors, please clone them first.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_empty_strided</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='kw'>a</span>
-<span class='kw'>a</span><span class='op'>$</span><span class='fu'>stride</span>(<span class='fl'>1</span>)
-<span class='kw'>a</span><span class='op'>$</span><span class='fu'>size</span>(<span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; [1] 2</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Eq</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_eq.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Eq</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_eq</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare
-Must be a <code>ByteTensor</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="eq-input-other-out-null-gt-tensor-"><a class="anchor" href="#eq-input-other-out-null-gt-tensor-"></a>eq(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes element-wise equality</p>
-<p>The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_eq</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>4</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  0
-#&gt;  0
-#&gt;  1
-#&gt; [ CPUBoolType{4} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_equal.Rd</code></div>
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-    <p>Equal</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_equal</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>the other input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="equal-input-other-gt-bool-"><a class="anchor" href="#equal-input-other-gt-bool-"></a>equal(input, other) -&gt; bool </h2>
-
-    
-
-
-<p><code>TRUE</code> if two tensors have the same size and elements, <code>FALSE</code> otherwise.</p>
-
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-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_equal</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)))
-}
-</div><div class='output co'>#&gt; [1] TRUE</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_erf.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Erf</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_erf</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="erf-input-out-null-gt-tensor-"><a class="anchor" href="#erf-input-out-null-gt-tensor-"></a>erf(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the error function of each element. The error function is defined as follows:</p>
-<p>$$
-    \mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_erf</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>1.</span>, <span class='fl'>10.</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000
-#&gt; -0.8427
-#&gt;  1.0000
-#&gt; [ CPUFloatType{3} ]</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_erfc.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Erfc</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_erfc</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="erfc-input-out-null-gt-tensor-"><a class="anchor" href="#erfc-input-out-null-gt-tensor-"></a>erfc(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the complementary error function of each element of <code>input</code>.
-The complementary error function is defined as follows:</p>
-<p>$$
-    \mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_erfc</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>1.</span>, <span class='fl'>10.</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.0000e+00
-#&gt;  1.8427e+00
-#&gt;  1.4013e-45
-#&gt; [ CPUFloatType{3} ]</div></pre>
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-    <h1>Erfinv</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_erfinv.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Erfinv</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_erfinv</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="erfinv-input-out-null-gt-tensor-"><a class="anchor" href="#erfinv-input-out-null-gt-tensor-"></a>erfinv(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the inverse error function of each element of <code>input</code>.
-The inverse error function is defined in the range \((-1, 1)\) as:</p>
-<p>$$
-    \mathrm{erfinv}(\mathrm{erf}(x)) = x
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_erfinv</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>0.5</span>, <span class='op'>-</span><span class='fl'>1.</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000
-#&gt;  0.4769
-#&gt;    -inf
-#&gt; [ CPUFloatType{3} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Exp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_exp.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Exp</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_exp</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="exp-input-out-null-gt-tensor-"><a class="anchor" href="#exp-input-out-null-gt-tensor-"></a>exp(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the exponential of the elements
-of the input tensor <code>input</code>.</p>
-<p>$$
-    y_{i} = e^{x_{i}}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_exp</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>(<span class='fl'>2</span>))))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt; [ CPUFloatType{2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>Expm1</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_expm1.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Expm1</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_expm1</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="expm-input-out-null-gt-tensor-"><a class="anchor" href="#expm-input-out-null-gt-tensor-"></a>expm1(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the exponential of the elements minus 1
-of <code>input</code>.</p>
-<p>$$
-    y_{i} = e^{x_{i}} - 1
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_expm1</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>(<span class='fl'>2</span>))))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  1
-#&gt; [ CPUFloatType{2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Eye</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_eye.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Eye</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_eye</span>(
-  <span class='kw'>n</span>,
-  m = <span class='kw'>n</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>n</th>
-      <td><p>(int) the number of rows</p></td>
-    </tr>
-    <tr>
-      <th>m</th>
-      <td><p>(int, optional) the number of columns with default being <code>n</code></p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_eye</span>(<span class='fl'>3</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  0  0
-#&gt;  0  1  0
-#&gt;  0  0  1
-#&gt; [ CPUFloatType{3,3} ]</div></pre>
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-    <div class="page-header">
-    <h1>Fft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_fft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fft</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_fft</span>(<span class='kw'>self</span>, <span class='kw'>signal_ndim</span>, normalized = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
-    </tr>
-    <tr>
-      <th>signal_ndim</th>
-      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-</pre>
-
-    <h2 class="hasAnchor" id="fft-input-signal-ndim-normalized-false-gt-tensor-"><a class="anchor" href="#fft-input-signal-ndim-normalized-false-gt-tensor-"></a>fft(input, signal_ndim, normalized=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Complex-to-complex Discrete Fourier Transform</p>
-<p>This method computes the complex-to-complex discrete Fourier transform.
-Ignoring the batch dimensions, it computes the following expression:</p>
-<p>$$
-    X[\omega_1, \dots, \omega_d] =
-        \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
-         e^{-j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
-$$
-where \(d\) = <code>signal_ndim</code> is number of dimensions for the
-signal, and \(N_i\) is the size of signal dimension \(i\).</p>
-<p>This method supports 1D, 2D and 3D complex-to-complex transforms, indicated
-by <code>signal_ndim</code>. <code>input</code> must be a tensor with last dimension
-of size 2, representing the real and imaginary components of complex
-numbers, and should have at least <code>signal_ndim + 1</code> dimensions with optionally
-arbitrary number of leading batch dimensions. If <code>normalized</code> is set to
-<code>TRUE</code>, this normalizes the result by dividing it with
-\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary.</p>
-<p>Returns the real and the imaginary parts together as one tensor of the same
-shape of <code>input</code>.</p>
-<p>The inverse of this function is <code><a href='torch_ifft.html'>torch_ifft</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>For CPU tensors, this method is currently only available with MKL. Use
-<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># unbatched 2D FFT</span>
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='fu'>torch_fft</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
-<span class='co'># batched 1D FFT</span>
-<span class='fu'>torch_fft</span>(<span class='kw'>x</span>, <span class='fl'>1</span>)
-<span class='co'># arbitrary number of batch dimensions, 2D FFT</span>
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>5</span>, <span class='fl'>2</span>))
-<span class='fu'>torch_fft</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,1,1,.,.) = 
-#&gt;   1.9763  7.8467
-#&gt;  -4.3943 -2.8071
-#&gt;   8.3070  4.7473
-#&gt;   0.1214  9.9011
-#&gt;  -0.4568  1.8810
-#&gt; 
-#&gt; (2,1,1,.,.) = 
-#&gt;    1.4268  -3.2046
-#&gt;    8.7205   1.2440
-#&gt;   -6.5158  -2.4400
-#&gt;    3.4382  11.7033
-#&gt;    1.6350   2.4011
-#&gt; 
-#&gt; (3,1,1,.,.) = 
-#&gt;   6.0755  3.5094
-#&gt;   4.9627  8.6584
-#&gt;   4.3683 -0.5855
-#&gt;  -1.6353 -0.9633
-#&gt;   6.7713  6.0062
-#&gt; 
-#&gt; (1,2,1,.,.) = 
-#&gt;  -7.9359 -5.3996
-#&gt;  -10.9992  1.7149
-#&gt;   4.6450  0.8580
-#&gt;  -0.9310 -0.2429
-#&gt;   3.7647  6.5126
-#&gt; 
-#&gt; (2,2,1,.,.) = 
-#&gt;  -11.2618  -2.7025
-#&gt;    7.7288   2.3066
-#&gt;    4.5014   1.8962
-#&gt;    7.0432  -8.6552
-#&gt;   -1.4062  -3.4954
-#&gt; 
-#&gt; (3,2,1,.,.) = 
-#&gt;  -2.3604 -3.6922
-#&gt;  -0.1313  2.1074
-#&gt;   3.2983  3.5613
-#&gt;  -2.5760  2.7359
-#&gt;   0.8110 -7.0844
-#&gt; 
-#&gt; (1,3,1,.,.) = 
-#&gt;  -8.4593 -0.1551
-#&gt;   0.5104 -6.3483
-#&gt;   6.8818 -11.5768
-#&gt;  -3.7438  3.1692
-#&gt;   0.4190 -5.1557
-#&gt; 
-#&gt; (2,3,1,.,.) = 
-#&gt;  -1.1458 -11.0048
-#&gt;  -0.9352  5.5999
-#&gt;   3.9602 -5.6657
-#&gt;   8.5348 -3.0511
-#&gt;   3.4337  1.3237
-#&gt; 
-#&gt; (3,3,1,.,.) = 
-#&gt;  -3.0854 -2.5195
-#&gt;  -3.3872  0.8570
-#&gt;   2.3626  3.4360
-#&gt;  -0.9504  1.7517
-#&gt;  -4.4356 -8.5422
-#&gt; 
-#&gt; (1,1,2,.,.) = 
-#&gt;  -1.6043  4.3357
-#&gt;   1.7267 -2.7912
-#&gt;  -3.2619 -0.0287
-#&gt;  -3.7177  3.7267
-#&gt;  -6.2791  4.5578
-#&gt; 
-#&gt; (2,1,2,.,.) = 
-#&gt;  -4.7756 -5.8334
-#&gt;  -8.0739 -1.0256
-#&gt;  -6.2643  4.3290
-#&gt;  -3.6430 -1.2482
-#&gt;  -6.7862  6.6639
-#&gt; 
-#&gt; (3,1,2,.,.) = 
-#&gt;  -5.2635 -2.6365
-#&gt;   0.8090 -10.0458
-#&gt;  -4.8289 -4.6526
-#&gt;  -1.3182 -3.2208
-#&gt;  -3.6673  1.9778
-#&gt; 
-#&gt; (1,2,2,.,.) = 
-#&gt;  -10.5196   7.4601
-#&gt;    3.2538   3.9547
-#&gt;   -5.7687  -3.8533
-#&gt;    1.9987  -1.9309
-#&gt;   -1.5664   0.3644
-#&gt; 
-#&gt; (2,2,2,.,.) = 
-#&gt;  -13.2150  -9.4059
-#&gt;    0.3705  -1.1361
-#&gt;    0.3318   4.9082
-#&gt;    3.4515   5.6696
-#&gt;    2.4051  -7.6619
-#&gt; 
-#&gt; (3,2,2,.,.) = 
-#&gt;  -5.0949 -0.7683
-#&gt;  -6.1067 -2.4366
-#&gt;   1.2538  0.3982
-#&gt;  -2.7778  1.5682
-#&gt;  -1.2664  2.9718
-#&gt; 
-#&gt; (1,3,2,.,.) = 
-#&gt;  -3.4493  3.2682
-#&gt;   1.0988 -3.9018
-#&gt;  -1.9195  2.6591
-#&gt;  -0.0942 -1.2211
-#&gt;  -3.2207  3.5987
-#&gt; 
-#&gt; (2,3,2,.,.) = 
-#&gt;   -2.6009  -4.5345
-#&gt;   -0.9203   6.5752
-#&gt;    2.2495  -4.3585
-#&gt;   10.6067   1.5344
-#&gt;    0.8452   0.4637
-#&gt; 
-#&gt; (3,3,2,.,.) = 
-#&gt;   4.2682 -3.6109
-#&gt;   4.0290  3.0624
-#&gt;   0.5968 -2.8749
-#&gt;  -0.9304 -0.8756
-#&gt;  -4.1537  2.7282
-#&gt; 
-#&gt; (1,1,3,.,.) = 
-#&gt;   10.4268   0.2844
-#&gt;    3.0158   5.1906
-#&gt;   -5.6678  11.2523
-#&gt;   10.2783   0.1301
-#&gt;   -3.7462  -6.1247
-#&gt; 
-#&gt; (2,1,3,.,.) = 
-#&gt;  -0.9526  1.6430
-#&gt;   0.7569  2.2576
-#&gt;  -8.5560 -9.7035
-#&gt;   4.1302 -0.5691
-#&gt;  -6.7050 -6.3667
-#&gt; 
-#&gt; (3,1,3,.,.) = 
-#&gt;   0.2567  3.3491
-#&gt;  -8.7131  4.7589
-#&gt;   1.5954 -1.4664
-#&gt;  -5.7033  2.4085
-#&gt;   0.6070 -5.4705
-#&gt; 
-#&gt; (1,2,3,.,.) = 
-#&gt;  -5.4380 -2.4121
-#&gt;  -5.0166 -2.6143
-#&gt;  -4.2476  7.5749
-#&gt;  -1.9120  1.3121
-#&gt;   3.9339  0.7290
-#&gt; 
-#&gt; (2,2,3,.,.) = 
-#&gt;    4.8919  -9.6614
-#&gt;   -1.7365  -6.4366
-#&gt;    0.4799  10.6237
-#&gt;    3.4599   0.6882
-#&gt;   -3.5629  -1.7692
-#&gt; 
-#&gt; (3,2,3,.,.) = 
-#&gt;  -1.9953  2.9489
-#&gt;   0.9656 -4.4180
-#&gt;  -5.9864  0.6757
-#&gt;   1.9730 -1.4202
-#&gt;  -3.7969  5.7649
-#&gt; 
-#&gt; (1,3,3,.,.) = 
-#&gt;  -7.5540  0.7540
-#&gt;   1.8231 -0.1723
-#&gt;   2.5023  7.4179
-#&gt;  -0.1313  2.8180
-#&gt;   0.8127 -5.8250
-#&gt; 
-#&gt; (2,3,3,.,.) = 
-#&gt;  -8.2056 -0.8482
-#&gt;   2.0683 -18.6686
-#&gt;  -2.5826  2.1679
-#&gt;  -1.5549 -12.3176
-#&gt;   6.6992  0.0094
-#&gt; 
-#&gt; (3,3,3,.,.) = 
-#&gt;  -2.8456 -1.2566
-#&gt;  -14.1733 -0.3817
-#&gt;   2.1714 -3.5512
-#&gt;   2.5057 -5.7774
-#&gt;  -1.3546 -0.9014
-#&gt; 
-#&gt; (1,1,4,.,.) = 
-#&gt;  -3.1630 -3.0368
-#&gt;  -3.0379  2.4987
-#&gt;  -9.1058 -4.3333
-#&gt;   2.5158 -2.3408
-#&gt;   3.3329  7.4307
-#&gt; 
-#&gt; (2,1,4,.,.) = 
-#&gt;  -4.2199 -8.1722
-#&gt;   3.2436  4.1109
-#&gt;  -6.8430 -0.4683
-#&gt;   4.4940  2.2769
-#&gt;   4.1490  1.4424
-#&gt; 
-#&gt; (3,1,4,.,.) = 
-#&gt;  -16.3552   2.9946
-#&gt;    3.5135 -11.1236
-#&gt;   -8.3821   1.4669
-#&gt;    4.1577  -7.4396
-#&gt;   -2.0552  -8.3233
-#&gt; 
-#&gt; (1,2,4,.,.) = 
-#&gt;   2.3009  0.8086
-#&gt;   2.9567  7.0439
-#&gt;  -5.5452  7.5589
-#&gt;   4.3938 -2.3450
-#&gt;   0.4041 -2.7073
-#&gt; 
-#&gt; (2,2,4,.,.) = 
-#&gt;  -4.7550  3.1303
-#&gt;  -7.0661  5.5174
-#&gt;  -0.7417 -3.8804
-#&gt;   0.9515  2.4527
-#&gt;   2.2833  3.4872
-#&gt; 
-#&gt; (3,2,4,.,.) = 
-#&gt;  -0.3079  0.4233
-#&gt;  -6.6129  1.8303
-#&gt;   4.4564 -10.3904
-#&gt;  -5.6722  0.7938
-#&gt;  -10.3964  3.9085
-#&gt; 
-#&gt; (1,3,4,.,.) = 
-#&gt;  -5.9335  0.5387
-#&gt;   2.9040  1.9196
-#&gt;   1.6259 -1.0794
-#&gt;  -5.7042  0.2070
-#&gt;   2.3524  8.7244
-#&gt; 
-#&gt; (2,3,4,.,.) = 
-#&gt;   1.3367  0.6971
-#&gt;  -6.6217 -2.8865
-#&gt;   6.6995  8.9511
-#&gt;   7.3736  1.8491
-#&gt;  -3.0822  2.3404
-#&gt; 
-#&gt; (3,3,4,.,.) = 
-#&gt;    1.8480   6.0885
-#&gt;   -3.5436  -3.4685
-#&gt;   -2.1250  13.5269
-#&gt;   -3.5195   2.9393
-#&gt;    3.3513   2.1208
-#&gt; 
-#&gt; (1,1,5,.,.) = 
-#&gt;   -3.5185  -5.6899
-#&gt;    1.9912  -2.4328
-#&gt;   -5.4953  -5.4816
-#&gt;   -1.9472   1.7620
-#&gt;   10.5903   1.6089
-#&gt; 
-#&gt; (2,1,5,.,.) = 
-#&gt;   5.3416 -2.4417
-#&gt;  -3.3630 -0.8902
-#&gt;   4.4654  0.5536
-#&gt;  -6.2983  4.7187
-#&gt;  -2.3659 -6.3330
-#&gt; 
-#&gt; (3,1,5,.,.) = 
-#&gt;   4.5212 -2.4675
-#&gt;  -1.9087 -6.6206
-#&gt;  -0.2901 -14.0561
-#&gt;   2.3330 -0.0615
-#&gt;   7.6664  3.0468
-#&gt; 
-#&gt; (1,2,5,.,.) = 
-#&gt;  -0.9788  1.4510
-#&gt;   2.5766 -1.1039
-#&gt;  -6.9245 -3.7907
-#&gt;  -3.4216 -2.8843
-#&gt;   0.9847  5.5752
-#&gt; 
-#&gt; (2,2,5,.,.) = 
-#&gt;  -1.5066 -1.8273
-#&gt;  -2.2659 -6.0669
-#&gt;  -7.6243  3.6512
-#&gt;   0.9468  1.5735
-#&gt;  -2.6285 -2.4254
-#&gt; 
-#&gt; (3,2,5,.,.) = 
-#&gt;   0.9890  4.6629
-#&gt;  -3.8570 -2.8574
-#&gt;   2.9421 -5.6957
-#&gt;  -5.5224 -7.0839
-#&gt;   1.6589  5.8257
-#&gt; 
-#&gt; (1,3,5,.,.) = 
-#&gt;   -4.6225  -4.3016
-#&gt;    3.8103  15.1545
-#&gt;   11.3303   8.9338
-#&gt;   -6.1341  10.5460
-#&gt;    1.0033  -7.8533
-#&gt; 
-#&gt; (2,3,5,.,.) = 
-#&gt;  -5.7470 -3.2749
-#&gt;  -7.8943  2.6412
-#&gt;  -5.5028 -1.3226
-#&gt;   5.4086  3.1606
-#&gt;   2.9214 -4.6449
-#&gt; 
-#&gt; (3,3,5,.,.) = 
-#&gt;  -2.7212 -6.4866
-#&gt;  -2.5019 -7.4684
-#&gt;   0.3702 -2.9774
-#&gt;   7.1879 -0.6440
-#&gt;  -1.9355 -2.9521
-#&gt; [ CPUFloatType{3,3,5,5,2} ]</div></pre>
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-    <h1>Floating point type info</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/type-info.R'><code>R/type-info.R</code></a></small>
-    <div class="hidden name"><code>torch_finfo.Rd</code></div>
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-    <p>A list that represents the numerical properties of a
-floating point torch.dtype</p>
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-    <pre class="usage"><span class='fu'>torch_finfo</span>(<span class='kw'>dtype</span>)</pre>
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-      <th>dtype</th>
-      <td><p>dtype to check information</p></td>
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-    <h1>Flatten</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_flatten.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Flatten</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_flatten</span>(<span class='kw'>self</span>, <span class='kw'>dims</span>, start_dim = <span class='fl'>1L</span>, end_dim = <span class='op'>-</span><span class='fl'>1L</span>, <span class='kw'>out_dim</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>if tensor is named you can pass the name of the dimensions to
-flatten</p></td>
-    </tr>
-    <tr>
-      <th>start_dim</th>
-      <td><p>(int) the first dim to flatten</p></td>
-    </tr>
-    <tr>
-      <th>end_dim</th>
-      <td><p>(int) the last dim to flatten</p></td>
-    </tr>
-    <tr>
-      <th>out_dim</th>
-      <td><p>the name of the resulting dimension if a named tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="flatten-input-start-dim-end-dim-gt-tensor-"><a class="anchor" href="#flatten-input-start-dim-end-dim-gt-tensor-"></a>flatten(input, start_dim=0, end_dim=-1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Flattens a contiguous range of dims in a tensor.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>), ncol = <span class='fl'>2</span>))
-<span class='fu'>torch_flatten</span>(<span class='kw'>t</span>)
-<span class='fu'>torch_flatten</span>(<span class='kw'>t</span>, start_dim=<span class='fl'>2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  2
-#&gt; [ CPUFloatType{1,2} ]</div></pre>
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-    <h1>Flip</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_flip.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Flip</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_flip</span>(<span class='kw'>self</span>, <span class='kw'>dims</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(a list or tuple) axis to flip on</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="flip-input-dims-gt-tensor-"><a class="anchor" href="#flip-input-dims-gt-tensor-"></a>flip(input, dims) -&gt; Tensor </h2>
-
-    
-
-
-<p>Reverse the order of a n-D tensor along given axis in dims.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>8</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='kw'>x</span>
-<span class='fu'>torch_flip</span>(<span class='kw'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   6  7
-#&gt;   4  5
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   2  3
-#&gt;   0  1
-#&gt; [ CPUFloatType{2,2,2} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_floor.html b/static/docs/dev/reference/torch_floor.html
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-      
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-<div class="row">
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-    <div class="page-header">
-    <h1>Floor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_floor.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Floor</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_floor</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="floor-input-out-null-gt-tensor-"><a class="anchor" href="#floor-input-out-null-gt-tensor-"></a>floor(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the floor of the elements of <code>input</code>,
-the largest integer less than or equal to each element.</p>
-<p>$$
-    \mbox{out}_{i} = \left\lfloor \mbox{input}_{i} \right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_floor</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1
-#&gt; -1
-#&gt;  0
-#&gt;  1
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_floor_divide.html b/static/docs/dev/reference/torch_floor_divide.html
deleted file mode 100644
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-    <h1>Floor_divide</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_floor_divide.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Floor_divide</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_floor_divide</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the numerator tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or Scalar) the denominator</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="floor-divide-input-other-out-null-gt-tensor-"><a class="anchor" href="#floor-divide-input-other-out-null-gt-tensor-"></a>floor_divide(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Return the division of the inputs rounded down to the nearest integer. See <code><a href='torch_div.html'>torch_div</a></code>
-for type promotion and broadcasting rules.</p>
-<p>$$
-    \mbox{{out}}_i = \left\lfloor \frac{{\mbox{{input}}_i}}{{\mbox{{other}}_i}} \right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4.0</span>, <span class='fl'>3.0</span>))
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2.0</span>, <span class='fl'>2.0</span>))
-<span class='fu'>torch_floor_divide</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
-<span class='fu'>torch_floor_divide</span>(<span class='kw'>a</span>, <span class='fl'>1.4</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2
-#&gt;  2
-#&gt; [ CPUFloatType{2} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Fmod</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_fmod.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fmod</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_fmod</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the dividend</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="fmod-input-other-out-null-gt-tensor-"><a class="anchor" href="#fmod-input-other-out-null-gt-tensor-"></a>fmod(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise remainder of division.</p>
-<p>The dividend and divisor may contain both for integer and floating point
-numbers. The remainder has the same sign as the dividend <code>input</code>.</p>
-<p>When <code>other</code> is a tensor, the shapes of <code>input</code> and
-<code>other</code> must be broadcastable .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_fmod</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>3.</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)), <span class='fl'>2</span>)
-<span class='fu'>torch_fmod</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>)), <span class='fl'>1.5</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.0000
-#&gt;  0.5000
-#&gt;  0.0000
-#&gt;  1.0000
-#&gt;  0.5000
-#&gt; [ CPUFloatType{5} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_frac.html b/static/docs/dev/reference/torch_frac.html
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-    <div class="page-header">
-    <h1>Frac</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_frac.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Frac</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_frac</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="frac-input-out-null-gt-tensor-"><a class="anchor" href="#frac-input-out-null-gt-tensor-"></a>frac(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the fractional portion of each element in <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \mbox{input}_{i} - \left\lfloor |\mbox{input}_{i}| \right\rfloor * \mbox{sgn}(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_frac</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2.5</span>, <span class='op'>-</span><span class='fl'>3.2</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000
-#&gt;  0.5000
-#&gt; -0.2000
-#&gt; [ CPUFloatType{3} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Full</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_full.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Full</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_full</span>(
-  <span class='kw'>size</span>,
-  <span class='kw'>fill_value</span>,
-  names = <span class='kw'>NULL</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>size</th>
-      <td><p>(int...) a list, tuple, or <code>torch_Size</code> of integers defining the        shape of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>fill_value</th>
-      <td><p>NA the number to fill the output tensor with.</p></td>
-    </tr>
-    <tr>
-      <th>names</th>
-      <td><p>optional names of the dimensions</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor of size <code>size</code> filled with <code>fill_value</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>In PyTorch 1.5 a bool or integral <code>fill_value</code> will produce a warning if
-<code>dtype</code> or <code>out</code> are not set.
-In a future PyTorch release, when <code>dtype</code> and <code>out</code> are not set
-a bool <code>fill_value</code> will return a tensor of torch.bool dtype,
-and an integral <code>fill_value</code> will return a tensor of torch.long dtype.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_full</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fl'>3.141592</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  3.1416  3.1416  3.1416
-#&gt;  3.1416  3.1416  3.1416
-#&gt; [ CPUFloatType{2,3} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_full_like.html b/static/docs/dev/reference/torch_full_like.html
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-    <h1>Full_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_full_like.Rd</code></div>
-    </div>
-
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-    <p>Full_like</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_full_like</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>fill_value</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>,
-  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>fill_value</th>
-      <td><p>the number to fill the output tensor with.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-"><a class="anchor" href="#full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-"></a>full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, </h2>
-
-    
-
-
-<p>memory_format=torch.preserve_format) -&gt; Tensor</p>
-<p>Returns a tensor with the same size as <code>input</code> filled with <code>fill_value</code>.
-<code>torch_full_like(input, fill_value)</code> is equivalent to
-<code><a href='torch_full.html'>torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
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-    <h1>Gather</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_gather.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Gather</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_gather</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, <span class='kw'>index</span>, sparse_grad = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the source tensor</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the axis along which to index</p></td>
-    </tr>
-    <tr>
-      <th>index</th>
-      <td><p>(LongTensor) the indices of elements to gather</p></td>
-    </tr>
-    <tr>
-      <th>sparse_grad</th>
-      <td><p>(bool,optional) If <code>TRUE</code>, gradient w.r.t. <code>input</code> will be a sparse tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="gather-input-dim-index-sparse-grad-false-gt-tensor-"><a class="anchor" href="#gather-input-dim-index-sparse-grad-false-gt-tensor-"></a>gather(input, dim, index, sparse_grad=FALSE) -&gt; Tensor </h2>
-
-    
-
-
-<p>Gathers values along an axis specified by <code>dim</code>.</p>
-<p>For a 3-D tensor the output is specified by::</p><pre><span class='kw'>out</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>] <span class='op'>=</span> <span class='kw'>input</span>[<span class='kw'>index</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>]][<span class='kw'>j</span>][<span class='kw'>k</span>]  <span class='co'># if dim == 0</span>
-<span class='kw'>out</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>] <span class='op'>=</span> <span class='kw'>input</span>[<span class='kw'>i</span>][<span class='kw'>index</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>]][<span class='kw'>k</span>]  <span class='co'># if dim == 1</span>
-<span class='kw'>out</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>] <span class='op'>=</span> <span class='kw'>input</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>index</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>]]  <span class='co'># if dim == 2</span>
-</pre>
-
-<p>If <code>input</code> is an n-dimensional tensor with size
-\((x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})\)
-and <code>dim = i</code>, then <code>index</code> must be an \(n\)-dimensional tensor with
-size \((x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})\) where \(y \geq 1\)
-and <code>out</code> will have the same size as <code>index</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow = <span class='fl'>TRUE</span>))
-<span class='fu'>torch_gather</span>(<span class='kw'>t</span>, <span class='fl'>2</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>1</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  1
-#&gt;  4  3
-#&gt; [ CPUFloatType{2,2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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diff --git a/static/docs/dev/reference/torch_ge.html b/static/docs/dev/reference/torch_ge.html
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-    <div class="page-header">
-    <h1>Ge</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_ge.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ge</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_ge</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ge-input-other-out-null-gt-tensor-"><a class="anchor" href="#ge-input-other-out-null-gt-tensor-"></a>ge(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes \(\mbox{input} \geq \mbox{other}\) element-wise.</p>
-<p>The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_ge</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
-         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  1
-#&gt;  0  1
-#&gt; [ CPUBoolType{2,2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_generator.html b/static/docs/dev/reference/torch_generator.html
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-In-place random sampling functions." />
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-    <div class="page-header">
-    <h1>Create a Generator object</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/generator.R'><code>R/generator.R</code></a></small>
-    <div class="hidden name"><code>torch_generator.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A <code>torch_generator</code>  is an object which manages the state of the algorithm
-that produces pseudo random numbers. Used as a keyword argument in many
-In-place random sampling functions.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_generator</span>()</pre>
-
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># Via string</span>
-<span class='kw'>generator</span> <span class='op'>&lt;-</span> <span class='fu'>torch_generator</span>()
-<span class='kw'>generator</span><span class='op'>$</span><span class='fu'>current_seed</span>()
-<span class='kw'>generator</span><span class='op'>$</span><span class='fu'>set_current_seed</span>(<span class='fl'>1234567L</span>)
-<span class='kw'>generator</span><span class='op'>$</span><span class='fu'>current_seed</span>()
-
-
-}
-</div><div class='output co'>#&gt; integer64
-#&gt; [1] 1234567</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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diff --git a/static/docs/dev/reference/torch_geqrf.html b/static/docs/dev/reference/torch_geqrf.html
deleted file mode 100644
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-    <h1>Geqrf</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_geqrf.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Geqrf</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_geqrf</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input matrix</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="geqrf-input-out-null-gt-tensor-tensor-"><a class="anchor" href="#geqrf-input-out-null-gt-tensor-tensor-"></a>geqrf(input, out=NULL) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>This is a low-level function for calling LAPACK directly. This function
-returns a namedtuple (a, tau) as defined in <code>LAPACK documentation for geqrf</code>_ .</p>
-<p>You'll generally want to use <code><a href='torch_qr.html'>torch_qr</a></code> instead.</p>
-<p>Computes a QR decomposition of <code>input</code>, but without constructing
-\(Q\) and \(R\) as explicit separate matrices.</p>
-<p>Rather, this directly calls the underlying LAPACK function <code>?geqrf</code>
-which produces a sequence of 'elementary reflectors'.</p>
-<p>See <code>LAPACK documentation for geqrf</code>_ for further details.</p>
-
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-    <h1>Ger</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_ger.Rd</code></div>
-    </div>
-
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-    <p>Ger</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_ger</span>(<span class='kw'>self</span>, <span class='kw'>vec2</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) 1-D input vector</p></td>
-    </tr>
-    <tr>
-      <th>vec2</th>
-      <td><p>(Tensor) 1-D input vector</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function does not broadcast .</p>
-    <h2 class="hasAnchor" id="ger-input-vec-out-null-gt-tensor-"><a class="anchor" href="#ger-input-vec-out-null-gt-tensor-"></a>ger(input, vec2, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Outer product of <code>input</code> and <code>vec2</code>.
-If <code>input</code> is a vector of size \(n\) and <code>vec2</code> is a vector of
-size \(m\), then <code>out</code> must be a matrix of size \((n \times m)\).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>v1</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
-<span class='kw'>v2</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>4.</span>)
-<span class='fu'>torch_ger</span>(<span class='kw'>v1</span>, <span class='kw'>v2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;   1   2   3
-#&gt;   2   4   6
-#&gt;   3   6   9
-#&gt;   4   8  12
-#&gt; [ CPUFloatType{4,3} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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diff --git a/static/docs/dev/reference/torch_gt.html b/static/docs/dev/reference/torch_gt.html
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-    <h1>Gt</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_gt.Rd</code></div>
-    </div>
-
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-    <p>Gt</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_gt</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="gt-input-other-out-null-gt-tensor-"><a class="anchor" href="#gt-input-other-out-null-gt-tensor-"></a>gt(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes \(\mbox{input} &gt; \mbox{other}\) element-wise.</p>
-<p>The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_gt</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
-         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  1
-#&gt;  0  0
-#&gt; [ CPUBoolType{2,2} ]</div></pre>
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-    <h1>Hamming_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_hamming_window.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Hamming_window</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_hamming_window</span>(
-  <span class='kw'>window_length</span>,
-  periodic = <span class='fl'>TRUE</span>,
-  alpha = <span class='fl'>0.54</span>,
-  beta = <span class='fl'>0.46</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) the size of returned window</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(float, optional) The coefficient \(\alpha\) in the equation above</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(float, optional) The coefficient \(\beta\) in the equation above</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
-</pre>
-
-<pre>This is a generalized version of `torch_hann_window`.
-</pre>
-
-    <h2 class="hasAnchor" id="hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Hamming window function.</p>
-<p>$$
-    w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right),
-$$
-where \(N\) is the full window size.</p>
-<p>The input <code>window_length</code> is a positive integer controlling the
-returned window size. <code>periodic</code> flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-<code>torch_hamming_window(L, periodic=TRUE)</code> equal to
-<code>torch_hamming_window(L + 1, periodic=False)[:-1])</code>.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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-    <div class="page-header">
-    <h1>Hann_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_hann_window.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Hann_window</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_hann_window</span>(
-  <span class='kw'>window_length</span>,
-  periodic = <span class='fl'>TRUE</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) the size of returned window</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
-</pre>
-
-    <h2 class="hasAnchor" id="hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Hann window function.</p>
-<p>$$
-    w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] =
-            \sin^2 \left( \frac{\pi n}{N - 1} \right),
-$$
-where \(N\) is the full window size.</p>
-<p>The input <code>window_length</code> is a positive integer controlling the
-returned window size. <code>periodic</code> flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-<code>torch_hann_window(L, periodic=TRUE)</code> equal to
-<code>torch_hann_window(L + 1, periodic=False)[:-1])</code>.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_histc.html b/static/docs/dev/reference/torch_histc.html
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-    <div class="page-header">
-    <h1>Histc</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_histc.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Histc</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_histc</span>(<span class='kw'>self</span>, bins = <span class='fl'>100L</span>, min = <span class='fl'>0L</span>, max = <span class='fl'>0L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>bins</th>
-      <td><p>(int) number of histogram bins</p></td>
-    </tr>
-    <tr>
-      <th>min</th>
-      <td><p>(int) lower end of the range (inclusive)</p></td>
-    </tr>
-    <tr>
-      <th>max</th>
-      <td><p>(int) upper end of the range (inclusive)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="histc-input-bins-min-max-out-null-gt-tensor-"><a class="anchor" href="#histc-input-bins-min-max-out-null-gt-tensor-"></a>histc(input, bins=100, min=0, max=0, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the histogram of a tensor.</p>
-<p>The elements are sorted into equal width bins between <code>min</code> and
-<code>max</code>. If <code>min</code> and <code>max</code> are both zero, the minimum and
-maximum values of the data are used.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_histc</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>1</span>)), bins=<span class='fl'>4</span>, min=<span class='fl'>0</span>, max=<span class='fl'>3</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  2
-#&gt;  1
-#&gt;  0
-#&gt; [ CPUFloatType{4} ]</div></pre>
-  </div>
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-    <h1>Ifft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_ifft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ifft</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_ifft</span>(<span class='kw'>self</span>, <span class='kw'>signal_ndim</span>, normalized = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
-    </tr>
-    <tr>
-      <th>signal_ndim</th>
-      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-</pre>
-
-    <h2 class="hasAnchor" id="ifft-input-signal-ndim-normalized-false-gt-tensor-"><a class="anchor" href="#ifft-input-signal-ndim-normalized-false-gt-tensor-"></a>ifft(input, signal_ndim, normalized=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Complex-to-complex Inverse Discrete Fourier Transform</p>
-<p>This method computes the complex-to-complex inverse discrete Fourier
-transform. Ignoring the batch dimensions, it computes the following
-expression:</p>
-<p>$$
-    X[\omega_1, \dots, \omega_d] =
-        \frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
-         e^{\ j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
-$$
-where \(d\) = <code>signal_ndim</code> is number of dimensions for the
-signal, and \(N_i\) is the size of signal dimension \(i\).</p>
-<p>The argument specifications are almost identical with <code><a href='torch_fft.html'>torch_fft</a></code>.
-However, if <code>normalized</code> is set to <code>TRUE</code>, this instead returns the
-results multiplied by \(\sqrt{\prod_{i=1}^d N_i}\), to become a unitary
-operator. Therefore, to invert a <code><a href='torch_fft.html'>torch_fft</a></code>, the <code>normalized</code>
-argument should be set identically for <code><a href='torch_fft.html'>torch_fft</a></code>.</p>
-<p>Returns the real and the imaginary parts together as one tensor of the same
-shape of <code>input</code>.</p>
-<p>The inverse of this function is <code><a href='torch_fft.html'>torch_fft</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>For CPU tensors, this method is currently only available with MKL. Use
-<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='kw'>x</span>
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_fft.html'>torch_fft</a></span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
-<span class='fu'>torch_ifft</span>(<span class='kw'>y</span>, <span class='fl'>2</span>)  <span class='co'># recover x</span>
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -0.3868 -0.3174
-#&gt;  -0.0008  0.3458
-#&gt;   0.1059 -0.0942
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -0.8408 -0.4176
-#&gt;  -1.7532  0.3918
-#&gt;  -0.0479  0.2487
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;   1.3879  0.3764
-#&gt;   1.3948 -0.5118
-#&gt;   0.3470  0.2948
-#&gt; [ CPUFloatType{3,3,2} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_iinfo.html b/static/docs/dev/reference/torch_iinfo.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_imag.Rd</code></div>
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-
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-    <p>Imag</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_imag</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="imag-input-gt-tensor-"><a class="anchor" href="#imag-input-gt-tensor-"></a>imag(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the imaginary part of the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Not yet implemented.</p>
-<p>$$
-    \mbox{out}_{i} = imag(\mbox{input}_{i})
-$$</p>
-
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-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_imag</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span>)))
-}
-}
-</div></pre>
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-    <div class="page-header">
-    <h1>Index_select</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_index_select.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Index_select</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_index_select</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, <span class='kw'>index</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension in which we index</p></td>
-    </tr>
-    <tr>
-      <th>index</th>
-      <td><p>(LongTensor) the 1-D tensor containing the indices to index</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>The returned tensor does <strong>not</strong> use the same storage as the original
-tensor.  If <code>out</code> has a different shape than expected, we
-silently change it to the correct shape, reallocating the underlying
-storage if necessary.</p>
-    <h2 class="hasAnchor" id="index-select-input-dim-index-out-null-gt-tensor-"><a class="anchor" href="#index-select-input-dim-index-out-null-gt-tensor-"></a>index_select(input, dim, index, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor which indexes the <code>input</code> tensor along dimension
-<code>dim</code> using the entries in <code>index</code> which is a <code>LongTensor</code>.</p>
-<p>The returned tensor has the same number of dimensions as the original tensor
-(<code>input</code>).  The <code>dim</code>\ th dimension has the same size as the length
-of <code>index</code>; other dimensions have the same size as in the original tensor.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='kw'>x</span>
-<span class='kw'>indices</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
-<span class='fu'>torch_index_select</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='kw'>indices</span>)
-<span class='fu'>torch_index_select</span>(<span class='kw'>x</span>, <span class='fl'>2</span>, <span class='kw'>indices</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.3137  0.6039
-#&gt;  0.0957  0.1238
-#&gt;  1.5348  0.8360
-#&gt; [ CPUFloatType{3,2} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_inverse.html b/static/docs/dev/reference/torch_inverse.html
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-    <div class="page-header">
-    <h1>Inverse</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_inverse.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Inverse</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_inverse</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size \((*, n, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                    batch dimensions</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>Irrespective of the original strides, the returned tensors will be
-transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`
-</pre>
-
-    <h2 class="hasAnchor" id="inverse-input-out-null-gt-tensor-"><a class="anchor" href="#inverse-input-out-null-gt-tensor-"></a>inverse(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Takes the inverse of the square matrix <code>input</code>. <code>input</code> can be batches
-of 2D square tensors, in which case this function would return a tensor composed of
-individual inverses.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_inverse</span>(<span class='kw'>x</span>)
-<span class='kw'>z</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>)
-<span class='kw'>z</span>
-<span class='fu'><a href='torch_max.html'>torch_max</a></span>(<span class='fu'><a href='torch_abs.html'>torch_abs</a></span>(<span class='kw'>z</span> <span class='op'>-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>4</span>))) <span class='co'># Max non-zero</span>
-<span class='co'># Batched inverse example</span>
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_inverse</span>(<span class='kw'>x</span>)
-<span class='kw'>z</span> <span class='op'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>)
-<span class='fu'><a href='torch_max.html'>torch_max</a></span>(<span class='fu'><a href='torch_abs.html'>torch_abs</a></span>(<span class='kw'>z</span> <span class='op'>-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>4</span>)<span class='op'>$</span><span class='fu'>expand_as</span>(<span class='kw'>x</span>))) <span class='co'># Max non-zero</span>
-}
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/dev/reference/torch_irfft.html b/static/docs/dev/reference/torch_irfft.html
deleted file mode 100644
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--- a/static/docs/dev/reference/torch_irfft.html
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-    <h1>Irfft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_irfft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Irfft</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_irfft</span>(
-  <span class='kw'>self</span>,
-  <span class='kw'>signal_ndim</span>,
-  normalized = <span class='fl'>FALSE</span>,
-  onesided = <span class='fl'>TRUE</span>,
-  signal_sizes = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>()
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
-    </tr>
-    <tr>
-      <th>signal_ndim</th>
-      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>onesided</th>
-      <td><p>(bool, optional) controls whether <code>input</code> was halfed to avoid        redundancy, e.g., by <code><a href='torch_rfft.html'>torch_rfft()</a></code>. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>signal_sizes</th>
-      <td><p>(list or <code>torch.Size</code>, optional) the size of the original        signal (without batch dimension). Default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>Due to the conjugate symmetry, `input` do not need to contain the full
-complex frequency values. Roughly half of the values will be sufficient, as
-is the case when `input` is given by [`~torch.rfft`] with
-`rfft(signal, onesided=TRUE)`. In such case, set the `onesided`
-argument of this method to `TRUE`. Moreover, the original signal shape
-information can sometimes be lost, optionally set `signal_sizes` to be
-the size of the original signal (without the batch dimensions if in batched
-mode) to recover it with correct shape.
-
-Therefore, to invert an [torch_rfft()], the `normalized` and
-`onesided` arguments should be set identically for [torch_irfft()],
-and preferably a `signal_sizes` is given to avoid size mismatch. See the
-example below for a case of size mismatch.
-
-See [torch_rfft()] for details on conjugate symmetry.
-</pre>
-
-<p>The inverse of this function is <code><a href='torch_rfft.html'>torch_rfft()</a></code>.</p>
-<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-</pre>
-
-    <h2 class="hasAnchor" id="irfft-input-signal-ndim-normalized-false-onesided-true-signal-sizes-null-gt-tensor-"><a class="anchor" href="#irfft-input-signal-ndim-normalized-false-onesided-true-signal-sizes-null-gt-tensor-"></a>irfft(input, signal_ndim, normalized=False, onesided=TRUE, signal_sizes=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Complex-to-real Inverse Discrete Fourier Transform</p>
-<p>This method computes the complex-to-real inverse discrete Fourier transform.
-It is mathematically equivalent with <code><a href='torch_ifft.html'>torch_ifft</a></code> with differences only in
-formats of the input and output.</p>
-<p>The argument specifications are almost identical with <code><a href='torch_ifft.html'>torch_ifft</a></code>.
-Similar to <code><a href='torch_ifft.html'>torch_ifft</a></code>, if <code>normalized</code> is set to <code>TRUE</code>,
-this normalizes the result by multiplying it with
-\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary, where
-\(N_i\) is the size of signal dimension \(i\).</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Generally speaking, input to this function should contain values
-following conjugate symmetry. Note that even if <code>onesided</code> is
-<code>TRUE</code>, often symmetry on some part is still needed. When this
-requirement is not satisfied, the behavior of <code>torch_irfft</code> is
-undefined. Since <code>torch_autograd.gradcheck</code> estimates numerical
-Jacobian with point perturbations, <code>torch_irfft</code> will almost
-certainly fail the check.</p>
-
-<p>For CPU tensors, this method is currently only available with MKL. Use
-<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span>(<span class='kw'>x</span>, <span class='fl'>2</span>, onesided=<span class='fl'>TRUE</span>)
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span>(<span class='kw'>x</span>, <span class='fl'>2</span>, onesided=<span class='fl'>TRUE</span>)
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span>(<span class='kw'>x</span>, <span class='fl'>2</span>, onesided=<span class='fl'>TRUE</span>)
-<span class='fu'>torch_irfft</span>(<span class='kw'>y</span>, <span class='fl'>2</span>, onesided=<span class='fl'>TRUE</span>, signal_sizes=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>5</span>))  <span class='co'># recover x</span>
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0611 -0.4968  0.5254 -0.7379  0.8372
-#&gt;  0.9517  0.3580  1.8260  1.3673  0.8510
-#&gt;  0.8225  1.5726  0.6916  0.1345 -0.1675
-#&gt; -2.1112 -1.3643 -1.4132 -0.3427  0.1951
-#&gt; [ CPUFloatType{4,5} ]</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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-    </div>
-
-    <pre class="usage"><span class='fu'>torch_is_complex</span>(<span class='kw'>self</span>)</pre>
-
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-    <table class="ref-arguments">
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-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the PyTorch tensor to test</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="is-complex-input-gt-bool-"><a class="anchor" href="#is-complex-input-gt-bool-"></a>is_complex(input) -&gt; (bool) </h2>
-
-    
-
-
-<p>Returns TRUE if the data type of <code>input</code> is a complex data type i.e.,
-one of <code>torch_complex64</code>, and <code>torch.complex128</code>.</p>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_is_floating_point.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Is_floating_point</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_is_floating_point</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the PyTorch tensor to test</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="is-floating-point-input-gt-bool-"><a class="anchor" href="#is-floating-point-input-gt-bool-"></a>is_floating_point(input) -&gt; (bool) </h2>
-
-    
-
-
-<p>Returns TRUE if the data type of <code>input</code> is a floating point data type i.e.,
-one of <code>torch_float64</code>, <code>torch.float32</code> and <code>torch.float16</code>.</p>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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-    <pre class="usage"><span class='fu'>torch_isfinite</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) A tensor to check</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Returns a new tensor with boolean elements representing if each element is <code>Finite</code> or not.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_isfinite</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>Inf</span>, <span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>Inf</span>, <span class='fl'>NaN</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  0
-#&gt;  1
-#&gt;  0
-#&gt;  0
-#&gt; [ CPUBoolType{5} ]</div></pre>
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-    <h1>Isinf</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_isinf.Rd</code></div>
-    </div>
-
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-    <p>Isinf</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_isinf</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) A tensor to check</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Returns a new tensor with boolean elements representing if each element is <code>+/-INF</code> or not.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_isinf</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>Inf</span>, <span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>Inf</span>, <span class='fl'>NaN</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  1
-#&gt;  0
-#&gt;  1
-#&gt;  0
-#&gt; [ CPUBoolType{5} ]</div></pre>
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-    <div class="page-header">
-    <h1>Isnan</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_isnan.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Isnan</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_isnan</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) A tensor to check</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Returns a new tensor with boolean elements representing if each element is <code>NaN</code> or not.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_isnan</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>NaN</span>, <span class='fl'>2</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  1
-#&gt;  0
-#&gt; [ CPUBoolType{3} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_kthvalue.html b/static/docs/dev/reference/torch_kthvalue.html
deleted file mode 100644
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-    <h1>Kthvalue</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_kthvalue.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Kthvalue</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_kthvalue</span>(<span class='kw'>self</span>, <span class='kw'>k</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>k</th>
-      <td><p>(int) k for the k-th smallest element</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to find the kth value along</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-"></a>kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the <code>k</code> th
-smallest element of each row of the <code>input</code> tensor in the given dimension
-<code>dim</code>. And <code>indices</code> is the index location of each element found.</p>
-<p>If <code>dim</code> is not given, the last dimension of the <code>input</code> is chosen.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, both the <code>values</code> and <code>indices</code> tensors
-are the same size as <code>input</code>, except in the dimension <code>dim</code> where
-they are of size 1. Otherwise, <code>dim</code> is squeezed
-(see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in both the <code>values</code> and
-<code>indices</code> tensors having 1 fewer dimension than the <code>input</code> tensor.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>)
-<span class='kw'>x</span>
-<span class='fu'>torch_kthvalue</span>(<span class='kw'>x</span>, <span class='fl'>4</span>)
-<span class='kw'>x</span><span class='op'>=</span><span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>,<span class='fl'>7.</span>)<span class='op'>$</span><span class='fu'>resize_</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>))
-<span class='kw'>x</span>
-<span class='fu'>torch_kthvalue</span>(<span class='kw'>x</span>, <span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>TRUE</span>)
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  4  5  6
-#&gt; [ CPUFloatType{1,3} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  1  1  1
-#&gt; [ CPULongType{1,3} ]
-#&gt; </div></pre>
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-    <div class="hidden name"><code>torch_layout.Rd</code></div>
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-    <p>Creates the corresponding layout</p>
-    </div>
-
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-<span class='fu'>torch_sparse_coo</span>()</pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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-    <p>Le</p>
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-    <pre class="usage"><span class='fu'>torch_le</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="le-input-other-out-null-gt-tensor-"><a class="anchor" href="#le-input-other-out-null-gt-tensor-"></a>le(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes \(\mbox{input} \leq \mbox{other}\) element-wise.</p>
-<p>The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_le</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
-         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  0
-#&gt;  1  1
-#&gt; [ CPUBoolType{2,2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Lerp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_lerp.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lerp</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_lerp</span>(<span class='kw'>self</span>, <span class='kw'>end</span>, <span class='kw'>weight</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor with the starting points</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(Tensor) the tensor with the ending points</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(float or tensor) the weight for the interpolation formula</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="lerp-input-end-weight-out-null-"><a class="anchor" href="#lerp-input-end-weight-out-null-"></a>lerp(input, end, weight, out=NULL) </h2>
-
-    
-
-
-<p>Does a linear interpolation of two tensors <code>start</code> (given by <code>input</code>) and <code>end</code> based
-on a scalar or tensor <code>weight</code> and returns the resulting <code>out</code> tensor.</p>
-<p>$$
-    \mbox{out}_i = \mbox{start}_i + \mbox{weight}_i \times (\mbox{end}_i - \mbox{start}_i)
-$$
-The shapes of <code>start</code> and <code>end</code> must be
-broadcastable . If <code>weight</code> is a tensor, then
-the shapes of <code>weight</code>, <code>start</code>, and <code>end</code> must be broadcastable .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>start</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
-<span class='kw'>end</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>4</span>)<span class='op'>$</span><span class='fu'>fill_</span>(<span class='fl'>10</span>)
-<span class='kw'>start</span>
-<span class='kw'>end</span>
-<span class='fu'>torch_lerp</span>(<span class='kw'>start</span>, <span class='kw'>end</span>, <span class='fl'>0.5</span>)
-<span class='fu'>torch_lerp</span>(<span class='kw'>start</span>, <span class='kw'>end</span>, <span class='fu'><a href='torch_full_like.html'>torch_full_like</a></span>(<span class='kw'>start</span>, <span class='fl'>0.5</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  5.5000
-#&gt;  6.0000
-#&gt;  6.5000
-#&gt;  7.0000
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <div class="page-header">
-    <h1>Lgamma</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_lgamma.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lgamma</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_lgamma</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="lgamma-input-out-null-gt-tensor-"><a class="anchor" href="#lgamma-input-out-null-gt-tensor-"></a>lgamma(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the logarithm of the gamma function on <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \log \Gamma(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0.5</span>, <span class='fl'>2</span>, <span class='fl'>0.5</span>)
-<span class='fu'>torch_lgamma</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5724
-#&gt;  0.0000
-#&gt; -0.1208
-#&gt; [ CPUFloatType{3} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_linspace.html b/static/docs/dev/reference/torch_linspace.html
deleted file mode 100644
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-    <h1>Linspace</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_linspace.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Linspace</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_linspace</span>(
-  <span class='kw'>start</span>,
-  <span class='kw'>end</span>,
-  steps = <span class='fl'>100</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(float) the starting value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(float) the ending value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>steps</th>
-      <td><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a one-dimensional tensor of <code>steps</code>
-equally spaced points between <code>start</code> and <code>end</code>.</p>
-<p>The output tensor is 1-D of size <code>steps</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_linspace</span>(<span class='fl'>3</span>, <span class='fl'>10</span>, steps=<span class='fl'>5</span>)
-<span class='fu'>torch_linspace</span>(<span class='op'>-</span><span class='fl'>10</span>, <span class='fl'>10</span>, steps=<span class='fl'>5</span>)
-<span class='fu'>torch_linspace</span>(start=<span class='op'>-</span><span class='fl'>10</span>, end=<span class='fl'>10</span>, steps=<span class='fl'>5</span>)
-<span class='fu'>torch_linspace</span>(start=<span class='op'>-</span><span class='fl'>10</span>, end=<span class='fl'>10</span>, steps=<span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -10
-#&gt; [ CPUFloatType{1} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_load.html b/static/docs/dev/reference/torch_load.html
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-<code><a href='torch_save.html'>torch_save</a>()</code></p></div>
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-    <h1>Log</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_log.Rd</code></div>
-    </div>
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-    <p>Log</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_log</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="log-input-out-null-gt-tensor-"><a class="anchor" href="#log-input-out-null-gt-tensor-"></a>log(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the natural logarithm of the elements
-of <code>input</code>.</p>
-<p>$$
-    y_{i} = \log_{e} (x_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_log</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;     nan
-#&gt;     nan
-#&gt;     nan
-#&gt;  0.5644
-#&gt;  0.4702
-#&gt; [ CPUFloatType{5} ]</div></pre>
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-    <h1>Log10</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_log10.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Log10</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_log10</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="log-input-out-null-gt-tensor-"><a class="anchor" href="#log-input-out-null-gt-tensor-"></a>log10(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the logarithm to the base 10 of the elements
-of <code>input</code>.</p>
-<p>$$
-    y_{i} = \log_{10} (x_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fl'>5</span>)
-<span class='kw'>a</span>
-<span class='fu'>torch_log10</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6771
-#&gt; -1.3978
-#&gt; -0.7399
-#&gt; -0.0503
-#&gt; -0.0152
-#&gt; [ CPUFloatType{5} ]</div></pre>
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-    <h1>Log1p</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_log1p.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Log1p</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_log1p</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function is more accurate than <code><a href='torch_log.html'>torch_log</a></code> for small
-values of <code>input</code></p>
-    <h2 class="hasAnchor" id="log-p-input-out-null-gt-tensor-"><a class="anchor" href="#log-p-input-out-null-gt-tensor-"></a>log1p(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the natural logarithm of (1 + <code>input</code>).</p>
-<p>$$
-    y_i = \log_{e} (x_i + 1)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_log1p</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.8324
-#&gt;  0.7253
-#&gt;     nan
-#&gt; -2.7310
-#&gt;  0.8903
-#&gt; [ CPUFloatType{5} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_log2.html b/static/docs/dev/reference/torch_log2.html
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-    <div class="page-header">
-    <h1>Log2</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_log2.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Log2</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_log2</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="log-input-out-null-gt-tensor-"><a class="anchor" href="#log-input-out-null-gt-tensor-"></a>log2(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the logarithm to the base 2 of the elements
-of <code>input</code>.</p>
-<p>$$
-    y_{i} = \log_{2} (x_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fl'>5</span>)
-<span class='kw'>a</span>
-<span class='fu'>torch_log2</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.8996
-#&gt; -0.3943
-#&gt; -0.4687
-#&gt; -0.7999
-#&gt; -0.2759
-#&gt; [ CPUFloatType{5} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Logdet</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logdet.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logdet</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_logdet</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>Result is `-inf` if `input` has zero log determinant, and is `NaN` if
-`input` has negative determinant.
-</pre>
-
-<pre>Backward through `logdet` internally uses SVD results when `input`
-is not invertible. In this case, double backward through `logdet` will
-be unstable in when `input` doesn't have distinct singular values. See
-`~torch.svd` for details.
-</pre>
-
-    <h2 class="hasAnchor" id="logdet-input-gt-tensor-"><a class="anchor" href="#logdet-input-gt-tensor-"></a>logdet(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Calculates log determinant of a square matrix or batches of square matrices.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'><a href='torch_det.html'>torch_det</a></span>(<span class='kw'>A</span>)
-<span class='fu'>torch_logdet</span>(<span class='kw'>A</span>)
-<span class='kw'>A</span>
-<span class='kw'>A</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()
-<span class='kw'>A</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>()
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.87411
-#&gt; [ CPUFloatType{} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Logical_and</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logical_and.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logical_and</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_logical_and</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the tensor to compute AND with</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logical-and-input-other-out-null-gt-tensor-"><a class="anchor" href="#logical-and-input-other-out-null-gt-tensor-"></a>logical_and(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise logical AND of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
-treated as <code>TRUE</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_logical_and</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>TRUE</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>FALSE</span>)))
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
-<span class='fu'>torch_logical_and</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_logical_and</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, out=<span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>4</span>, dtype=<span class='fu'><a href='torch_dtype.html'>torch_bool</a></span>()))
-}
-}
-</div></pre>
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diff --git a/static/docs/dev/reference/torch_logical_not.html b/static/docs/dev/reference/torch_logical_not.html
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-    <h1>Logical_not</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_logical_not.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logical_not</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logical-not-input-out-null-gt-tensor-"><a class="anchor" href="#logical-not-input-out-null-gt-tensor-"></a>logical_not(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool
-dtype. If the input tensor is not a bool tensor, zeros are treated as <code>FALSE</code> and non-zeros are treated as <code>TRUE</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_logical_not</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>)))
-<span class='fu'>torch_logical_not</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>10</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>()))
-<span class='fu'>torch_logical_not</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.</span>, <span class='fl'>1.5</span>, <span class='op'>-</span><span class='fl'>10.</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  0
-#&gt;  0
-#&gt; [ CPUBoolType{3} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Logical_or</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logical_or.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logical_or</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_logical_or</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the tensor to compute OR with</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logical-or-input-other-out-null-gt-tensor-"><a class="anchor" href="#logical-or-input-other-out-null-gt-tensor-"></a>logical_or(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise logical OR of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
-treated as <code>TRUE</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_logical_or</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>TRUE</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>FALSE</span>)))
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
-<span class='fu'>torch_logical_or</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_logical_or</span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/double.html'>double</a></span>(), <span class='kw'>b</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/double.html'>double</a></span>())
-<span class='fu'>torch_logical_or</span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/double.html'>double</a></span>(), <span class='kw'>b</span>)
-<span class='fu'>torch_logical_or</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, out=<span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>4</span>, dtype=<span class='fu'><a href='torch_dtype.html'>torch_bool</a></span>()))
-}
-}
-</div></pre>
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-    <h1>Logical_xor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logical_xor.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logical_xor</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_logical_xor</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the tensor to compute XOR with</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logical-xor-input-other-out-null-gt-tensor-"><a class="anchor" href="#logical-xor-input-other-out-null-gt-tensor-"></a>logical_xor(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise logical XOR of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
-treated as <code>TRUE</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_logical_xor</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>TRUE</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>FALSE</span>)))
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
-<span class='fu'>torch_logical_xor</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
-<span class='fu'>torch_logical_xor</span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'>to</span>(dtype=<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()), <span class='kw'>b</span><span class='op'>$</span><span class='fu'>to</span>(dtype=<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()))
-<span class='fu'>torch_logical_xor</span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'>to</span>(dtype=<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()), <span class='kw'>b</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  1
-#&gt;  0
-#&gt;  0
-#&gt; [ CPUBoolType{4} ]</div></pre>
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-    <div class="page-header">
-    <h1>Logspace</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_logspace.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logspace</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_logspace</span>(
-  <span class='kw'>start</span>,
-  <span class='kw'>end</span>,
-  steps = <span class='fl'>100</span>,
-  base = <span class='fl'>10</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(float) the starting value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(float) the ending value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>steps</th>
-      <td><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></td>
-    </tr>
-    <tr>
-      <th>base</th>
-      <td><p>(float) base of the logarithm function. Default: <code>10.0</code>.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a one-dimensional tensor of <code>steps</code> points
-logarithmically spaced with base <code>base</code> between
-\({\mbox{base}}^{\mbox{start}}\) and \({\mbox{base}}^{\mbox{end}}\).</p>
-<p>The output tensor is 1-D of size <code>steps</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_logspace</span>(start=<span class='op'>-</span><span class='fl'>10</span>, end=<span class='fl'>10</span>, steps=<span class='fl'>5</span>)
-<span class='fu'>torch_logspace</span>(start=<span class='fl'>0.1</span>, end=<span class='fl'>1.0</span>, steps=<span class='fl'>5</span>)
-<span class='fu'>torch_logspace</span>(start=<span class='fl'>0.1</span>, end=<span class='fl'>1.0</span>, steps=<span class='fl'>1</span>)
-<span class='fu'>torch_logspace</span>(start=<span class='fl'>2</span>, end=<span class='fl'>2</span>, steps=<span class='fl'>1</span>, base=<span class='fl'>2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  4
-#&gt; [ CPUFloatType{1} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_logsumexp.html b/static/docs/dev/reference/torch_logsumexp.html
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-
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-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logsumexp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logsumexp.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logsumexp</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_logsumexp</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logsumexp-input-dim-keepdim-false-out-null-"><a class="anchor" href="#logsumexp-input-dim-keepdim-false-out-null-"></a>logsumexp(input, dim, keepdim=False, out=NULL) </h2>
-
-    
-
-
-<p>Returns the log of summed exponentials of each row of the <code>input</code>
-tensor in the given dimension <code>dim</code>. The computation is numerically
-stabilized.</p>
-<p>For summation index \(j\) given by <code>dim</code> and other indices \(i\), the result is</p>
-<p>$$
-        \mbox{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij})
-$$</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_logsumexp</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.6121
-#&gt;  1.9978
-#&gt;  1.3602
-#&gt; [ CPUFloatType{3} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_lstsq.html b/static/docs/dev/reference/torch_lstsq.html
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-    <h1>Lstsq</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_lstsq.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lstsq</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_lstsq</span>(<span class='kw'>self</span>, <span class='kw'>A</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the matrix \(B\)</p></td>
-    </tr>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor) the \(m\) by \(n\) matrix \(A\)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>The case when \eqn{m &lt; n} is not supported on the GPU.
-</pre>
-
-    <h2 class="hasAnchor" id="lstsq-input-a-out-null-gt-tensor-"><a class="anchor" href="#lstsq-input-a-out-null-gt-tensor-"></a>lstsq(input, A, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the solution to the least squares and least norm problems for a full
-rank matrix \(A\) of size \((m \times n)\) and a matrix \(B\) of
-size \((m \times k)\).</p>
-<p>If \(m \geq n\), <code>torch_lstsq()</code> solves the least-squares problem:</p>
-<p>$$
-   \begin{array}{ll}
-   \min_X &amp; \|AX-B\|_2.
-   \end{array}
-$$
-If \(m &lt; n\), <code>torch_lstsq()</code> solves the least-norm problem:</p>
-<p>$$
-   \begin{array}{llll}
-   \min_X &amp; \|X\|_2 &amp; \mbox{subject to} &amp; AX = B.
-   \end{array}
-$$
-Returned tensor \(X\) has shape \((\mbox{max}(m, n) \times k)\). The first \(n\)
-rows of \(X\) contains the solution. If \(m \geq n\), the residual sum of squares
-for the solution in each column is given by the sum of squares of elements in the
-remaining \(m - n\) rows of that column.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>1</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>2</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>2</span>,<span class='fl'>5</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>4</span>,<span class='fl'>3</span>)
-))
-<span class='kw'>B</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>10</span>, <span class='op'>-</span><span class='fl'>3</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>12</span>, <span class='fl'>14</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>14</span>, <span class='fl'>12</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>16</span>, <span class='fl'>16</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>18</span>, <span class='fl'>16</span>)
-))
-<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_lstsq</span>(<span class='kw'>B</span>, <span class='kw'>A</span>)
-<span class='kw'>out</span>[[<span class='fl'>1</span>]]
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;   2.0000   1.0000
-#&gt;   1.0000   1.0000
-#&gt;   1.0000   2.0000
-#&gt;  10.9635   4.8501
-#&gt;   8.9332   5.2418
-#&gt; [ CPUFloatType{5,2} ]</div></pre>
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-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/dev/reference/torch_lt.html b/static/docs/dev/reference/torch_lt.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_lt.Rd</code></div>
-    </div>
-
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-    <p>Lt</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_lt</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="lt-input-other-out-null-gt-tensor-"><a class="anchor" href="#lt-input-other-out-null-gt-tensor-"></a>lt(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes \(\mbox{input} &lt; \mbox{other}\) element-wise.</p>
-<p>The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_lt</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
-         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  0
-#&gt;  1  0
-#&gt; [ CPUBoolType{2,2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_lu.html b/static/docs/dev/reference/torch_lu.html
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-<meta property="og:description" content="Computes the LU factorization of a matrix or batches of matrices A. Returns a
-tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
-is set to True." />
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-    <div class="page-header">
-    <h1>LU</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_lu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Computes the LU factorization of a matrix or batches of matrices A. Returns a
-tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
-is set to True.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_lu</span>(<span class='kw'>A</span>, pivot = <span class='fl'>TRUE</span>, get_infos = <span class='fl'>FALSE</span>, out = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor) the tensor to factor of size (<em>, m, n)(</em>,m,n)</p></td>
-    </tr>
-    <tr>
-      <th>pivot</th>
-      <td><p>(bool, optional) – controls whether pivoting is done. Default: TRUE</p></td>
-    </tr>
-    <tr>
-      <th>get_infos</th>
-      <td><p>(bool, optional) – if set to True, returns an info IntTensor. Default: FALSE</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) – optional output tuple. If get_infos is True, then the elements
-in the tuple are Tensor, IntTensor, and IntTensor. If get_infos is False, then the
-elements in the tuple are Tensor, IntTensor. Default: NULL</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_lu</span>(<span class='kw'>A</span>)
-
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -1.0153  1.4872  0.0025
-#&gt;  -0.8561  1.7371 -1.2204
-#&gt;   0.0961  0.8913  0.4519
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -0.6588  0.4889 -0.2704
-#&gt;   0.4352  0.8778  0.4110
-#&gt;   0.6140 -0.3620  0.9234
-#&gt; [ CPUFloatType{2,3,3} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  1  2  3
-#&gt;  3  2  3
-#&gt; [ CPUIntType{2,3} ]
-#&gt; </div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_lu_solve.html b/static/docs/dev/reference/torch_lu_solve.html
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+++ /dev/null
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-    <h1>Lu_solve</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_lu_solve.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lu_solve</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_lu_solve</span>(<span class='kw'>self</span>, <span class='kw'>LU_data</span>, <span class='kw'>LU_pivots</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the RHS tensor of size \((*, m, k)\), where \(*\)                is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>LU_data</th>
-      <td><p>(Tensor) the pivoted LU factorization of A from <code>torch_lu</code> of size \((*, m, m)\),                       where \(*\) is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>LU_pivots</th>
-      <td><p>(IntTensor) the pivots of the LU factorization from <code>torch_lu</code> of size \((*, m)\),                           where \(*\) is zero or more batch dimensions.                           The batch dimensions of <code>LU_pivots</code> must be equal to the batch dimensions of                           <code>LU_data</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-"><a class="anchor" href="#lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-"></a>lu_solve(input, LU_data, LU_pivots, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the LU solve of the linear system \(Ax = b\) using the partially pivoted
-LU factorization of A from <code>torch_lu</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>))
-<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'><a href='torch_lu.html'>torch_lu</a></span>(<span class='kw'>A</span>)
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'>torch_lu_solve</span>(<span class='kw'>b</span>, <span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
-<span class='fu'><a href='torch_norm.html'>torch_norm</a></span>(<span class='fu'><a href='torch_bmm.html'>torch_bmm</a></span>(<span class='kw'>A</span>, <span class='kw'>x</span>) <span class='op'>-</span> <span class='kw'>b</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 2.02674e-06
-#&gt; [ CPUFloatType{} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Sets the seed for generating random numbers.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/generator.R'><code>R/generator.R</code></a></small>
-    <div class="hidden name"><code>torch_manual_seed.Rd</code></div>
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-    <p>Sets the seed for generating random numbers.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_manual_seed</span>(<span class='kw'>seed</span>)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>seed</th>
-      <td><p>integer seed.</p></td>
-    </tr>
-    </table>
-
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diff --git a/static/docs/dev/reference/torch_masked_select.html b/static/docs/dev/reference/torch_masked_select.html
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-    <div class="page-header">
-    <h1>Masked_select</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_masked_select.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Masked_select</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_masked_select</span>(<span class='kw'>self</span>, <span class='kw'>mask</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>mask</th>
-      <td><p>(BoolTensor) the tensor containing the binary mask to index with</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>The returned tensor does <strong>not</strong> use the same storage
-as the original tensor</p>
-    <h2 class="hasAnchor" id="masked-select-input-mask-out-null-gt-tensor-"><a class="anchor" href="#masked-select-input-mask-out-null-gt-tensor-"></a>masked_select(input, mask, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new 1-D tensor which indexes the <code>input</code> tensor according to
-the boolean mask <code>mask</code> which is a <code>BoolTensor</code>.</p>
-<p>The shapes of the <code>mask</code> tensor and the <code>input</code> tensor don't need
-to match, but they must be broadcastable .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='kw'>x</span>
-<span class='kw'>mask</span> <span class='op'>=</span> <span class='kw'>x</span><span class='op'>$</span><span class='fu'>ge</span>(<span class='fl'>0.5</span>)
-<span class='kw'>mask</span>
-<span class='fu'>torch_masked_select</span>(<span class='kw'>x</span>, <span class='kw'>mask</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.7905
-#&gt;  0.8929
-#&gt;  0.5862
-#&gt;  1.1414
-#&gt;  1.1447
-#&gt;  0.8627
-#&gt;  0.5428
-#&gt; [ CPUFloatType{7} ]</div></pre>
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-    <h1>Matmul</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_matmul.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Matmul</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_matmul</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first tensor to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second tensor to be multiplied</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>The 1-dimensional dot product version of this function does not support an `out` parameter.
-</pre>
-
-    <h2 class="hasAnchor" id="matmul-input-other-out-null-gt-tensor-"><a class="anchor" href="#matmul-input-other-out-null-gt-tensor-"></a>matmul(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Matrix product of two tensors.</p>
-<p>The behavior depends on the dimensionality of the tensors as follows:</p><ul>
-<li><p>If both tensors are 1-dimensional, the dot product (scalar) is returned.</p></li>
-<li><p>If both arguments are 2-dimensional, the matrix-matrix product is returned.</p></li>
-<li><p>If the first argument is 1-dimensional and the second argument is 2-dimensional,
-a 1 is prepended to its dimension for the purpose of the matrix multiply.
-After the matrix multiply, the prepended dimension is removed.</p></li>
-<li><p>If the first argument is 2-dimensional and the second argument is 1-dimensional,
-the matrix-vector product is returned.</p></li>
-<li><p>If both arguments are at least 1-dimensional and at least one argument is
-N-dimensional (where N &gt; 2), then a batched matrix multiply is returned.  If the first
-argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the
-batched matrix multiply and removed after.  If the second argument is 1-dimensional, a
-1 is appended to its dimension for the purpose of the batched matrix multiple and removed after.
-The non-matrix (i.e. batch) dimensions are broadcasted  (and thus
-must be broadcastable).  For example, if <code>input</code> is a
-\((j \times 1 \times n \times m)\) tensor and <code>other</code> is a \((k \times m \times p)\)
-tensor, <code>out</code> will be an \((j \times k \times n \times p)\) tensor.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># vector x vector</span>
-<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
-<span class='co'># matrix x vector</span>
-<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
-<span class='co'># batched matrix x broadcasted vector</span>
-<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
-<span class='co'># batched matrix x batched matrix</span>
-<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
-<span class='co'># batched matrix x broadcasted matrix</span>
-<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -1.4046  1.0972  1.3793  0.1343 -0.9200
-#&gt;   2.5259  0.7125  0.9600  1.1083  1.9459
-#&gt;   4.9775  3.3401 -0.9064  4.8442 -0.2738
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   1.3160 -0.7682 -2.3833  2.2334 -0.9837
-#&gt;   0.7889  0.5700 -2.6043  2.6124 -2.5028
-#&gt;  -2.4431 -0.2003  2.9551 -3.5975  1.5276
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -1.4206 -2.2656  0.7709 -2.3696  1.5808
-#&gt;  -5.0873 -0.9514 -2.0588 -1.3035 -4.6549
-#&gt;  -4.6577  1.6349  1.4223 -0.9505 -3.5166
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;   3.9218 -1.6627 -2.6389  1.9259  1.6804
-#&gt;   5.1308 -0.9231 -5.4413  3.3081 -0.2097
-#&gt;  -0.4039  0.9996  1.6099  0.7055 -0.0869
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  -4.9022  2.0938  4.0360 -3.2141 -1.2777
-#&gt;  -5.1109  2.9031  2.2307 -0.5737 -4.2214
-#&gt;   2.3829  1.5180  0.7582  1.9326  0.8930
-#&gt; 
-#&gt; (6,.,.) = 
-#&gt;   0.3606  2.3438  0.5283  1.4080 -1.2408
-#&gt;   6.2079 -0.5530 -2.2712  2.5266  2.9892
-#&gt;   4.9667 -0.9046 -3.9300  3.8964  0.4490
-#&gt; 
-#&gt; (7,.,.) = 
-#&gt;   1.4630 -3.9395  0.5511 -2.2388  4.7774
-#&gt;   2.0104 -1.4449 -0.6197 -0.2728  2.2119
-#&gt;   0.4978  0.3369  1.8703  0.0544  1.4967
-#&gt; 
-#&gt; (8,.,.) = 
-#&gt;   1.3011  0.8488 -0.1540  1.2701 -0.0019
-#&gt;   1.9088 -2.3750 -0.2006 -0.5689  3.0685
-#&gt;   0.7772 -0.7786 -1.0461 -0.6452  0.6406
-#&gt; 
-#&gt; (9,.,.) = 
-#&gt;  -0.3932  3.0508  2.4239  0.3253 -0.5432
-#&gt;  -3.9197  3.8736  2.2483 -0.7831 -3.6763
-#&gt;  -2.1005 -0.6751  0.0486 -0.4329 -1.1813
-#&gt; 
-#&gt; (10,.,.) = 
-#&gt;   2.0616 -0.9761 -2.7943  2.7273 -0.7149
-#&gt;   0.2137  3.7018 -0.7915  3.1880 -3.7685
-#&gt;   1.7987 -2.5539 -0.4268 -0.8040  3.0160
-#&gt; [ CPUFloatType{10,3,5} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_matrix_power.html b/static/docs/dev/reference/torch_matrix_power.html
deleted file mode 100644
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-    <h1>Matrix_power</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_matrix_power.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Matrix_power</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_matrix_power</span>(<span class='kw'>self</span>, <span class='kw'>n</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>n</th>
-      <td><p>(int) the power to raise the matrix to</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="matrix-power-input-n-gt-tensor-"><a class="anchor" href="#matrix-power-input-n-gt-tensor-"></a>matrix_power(input, n) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the matrix raised to the power <code>n</code> for square matrices.
-For batch of matrices, each individual matrix is raised to the power <code>n</code>.</p>
-<p>If <code>n</code> is negative, then the inverse of the matrix (if invertible) is
-raised to the power <code>n</code>.  For a batch of matrices, the batched inverse
-(if invertible) is raised to the power <code>n</code>. If <code>n</code> is 0, then an identity matrix
-is returned.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_matrix_power</span>(<span class='kw'>a</span>, <span class='fl'>3</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   2.2412 -2.1626
-#&gt;  -2.1998 -0.2272
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -0.0366  1.2683
-#&gt;   0.0572 -1.8465
-#&gt; [ CPUFloatType{2,2,2} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_matrix_rank.html b/static/docs/dev/reference/torch_matrix_rank.html
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-    <h1>Matrix_rank</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_matrix_rank.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Matrix_rank</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_matrix_rank</span>(<span class='kw'>self</span>, <span class='kw'>tol</span>, symmetric = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input 2-D tensor</p></td>
-    </tr>
-    <tr>
-      <th>tol</th>
-      <td><p>(float, optional) the tolerance value. Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>symmetric</th>
-      <td><p>(bool, optional) indicates whether <code>input</code> is symmetric.                               Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="matrix-rank-input-tol-null-symmetric-false-gt-tensor-"><a class="anchor" href="#matrix-rank-input-tol-null-symmetric-false-gt-tensor-"></a>matrix_rank(input, tol=NULL, symmetric=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the numerical rank of a 2-D tensor. The method to compute the
-matrix rank is done using SVD by default. If <code>symmetric</code> is <code>TRUE</code>,
-then <code>input</code> is assumed to be symmetric, and the computation of the
-rank is done by obtaining the eigenvalues.</p>
-<p><code>tol</code> is the threshold below which the singular values (or the eigenvalues
-when <code>symmetric</code> is <code>TRUE</code>) are considered to be 0. If <code>tol</code> is not
-specified, <code>tol</code> is set to <code>S.max() * max(S.size()) * eps</code> where <code>S</code> is the
-singular values (or the eigenvalues when <code>symmetric</code> is <code>TRUE</code>), and <code>eps</code>
-is the epsilon value for the datatype of <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>10</span>)
-<span class='fu'>torch_matrix_rank</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 10
-#&gt; [ CPULongType{} ]</div></pre>
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-    <h1>Max</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_max.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Max</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the result tuple of two output tensors (max, max_indices)</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>When the shapes do not match, the shape of the returned output tensor
-follows the broadcasting rules .</p>
-    <h2 class="hasAnchor" id="max-input-gt-tensor-"><a class="anchor" href="#max-input-gt-tensor-"></a>max(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the maximum value of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>max(input, dim, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the maximum
-value of each row of the <code>input</code> tensor in the given dimension
-<code>dim</code>. And <code>indices</code> is the index location of each maximum value found
-(argmax).</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p><code>indices</code> does not necessarily contain the first occurrence of each
-maximal value found, unless it is unique.
-The exact implementation details are device-specific.
-Do not expect the same result when run on CPU and GPU in general.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size
-as <code>input</code> except in the dimension <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting
-in the output tensors having 1 fewer dimension than <code>input</code>.</p>
-    <h2 class="hasAnchor" id="max-input-other-out-null-gt-tensor-"><a class="anchor" href="#max-input-other-out-null-gt-tensor-"></a>max(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Each element of the tensor <code>input</code> is compared with the corresponding
-element of the tensor <code>other</code> and an element-wise maximum is taken.</p>
-<p>The shapes of <code>input</code> and <code>other</code> don't need to match,
-but they must be broadcastable .</p>
-<p>$$
-    \mbox{out}_i = \max(\mbox{tensor}_i, \mbox{other}_i)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_max</span>(<span class='kw'>a</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_max</span>(<span class='kw'>a</span>, dim = <span class='fl'>1</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>b</span>
-<span class='fu'>torch_max</span>(<span class='kw'>a</span>, other = <span class='kw'>b</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.6521
-#&gt; -0.2440
-#&gt;  0.4636
-#&gt;  1.7708
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <div class="page-header">
-    <h1>Mean</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_mean.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Mean</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_mean</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>, dtype = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>the resulting data type.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="mean-input-gt-tensor-"><a class="anchor" href="#mean-input-gt-tensor-"></a>mean(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the mean value of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="mean-input-dim-keepdim-false-out-null-gt-tensor-"><a class="anchor" href="#mean-input-dim-keepdim-false-out-null-gt-tensor-"></a>mean(input, dim, keepdim=False, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the mean value of each row of the <code>input</code> tensor in the given
-dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
-reduce over all of them.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_mean</span>(<span class='kw'>a</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_mean</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
-<span class='fu'>torch_mean</span>(<span class='kw'>a</span>, <span class='fl'>1</span>, <span class='fl'>TRUE</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.1299  0.5419 -0.3449 -0.1677
-#&gt; [ CPUFloatType{1,4} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_median.html b/static/docs/dev/reference/torch_median.html
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-    <div class="page-header">
-    <h1>Median</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_median.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Median</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_median</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="median-input-gt-tensor-"><a class="anchor" href="#median-input-gt-tensor-"></a>median(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the median value of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>median(input, dim=-1, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the median
-value of each row of the <code>input</code> tensor in the given dimension
-<code>dim</code>. And <code>indices</code> is the index location of each median value found.</p>
-<p>By default, <code>dim</code> is the last dimension of the <code>input</code> tensor.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size
-as <code>input</code> except in the dimension <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
-the outputs tensor having 1 fewer dimension than <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_median</span>(<span class='kw'>a</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_median</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; -0.6388
-#&gt; -1.4378
-#&gt; -0.7114
-#&gt;  0.1334
-#&gt;  0.1381
-#&gt; [ CPUFloatType{5} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  0
-#&gt;  1
-#&gt;  2
-#&gt;  0
-#&gt;  0
-#&gt; [ CPULongType{5} ]
-#&gt; </div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/memory_format.R'><code>R/memory_format.R</code></a></small>
-    <div class="hidden name"><code>torch_memory_format.Rd</code></div>
-    </div>
-
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-    <p>Returns the correspondent memory format.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_contiguous_format</span>()
-
-<span class='fu'>torch_preserve_format</span>()
-
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-    </li>
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-    </li>
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-
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Meshgrid</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_meshgrid.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Meshgrid</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_meshgrid</span>(<span class='kw'>tensors</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be
-treated (1,).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Take \(N\) tensors, each of which can be either scalar or 1-dimensional
-vector, and create \(N\) N-dimensional grids, where the \(i\) <code>th</code> grid is defined by
-expanding the \(i\) <code>th</code> input over dimensions defined by other inputs.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>))
-<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_meshgrid</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>))
-<span class='kw'>out</span>
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  1  1  1
-#&gt;  2  2  2
-#&gt;  3  3  3
-#&gt; [ CPUFloatType{3,3} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  4  5  6
-#&gt;  4  5  6
-#&gt;  4  5  6
-#&gt; [ CPUFloatType{3,3} ]
-#&gt; </div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_min.html b/static/docs/dev/reference/torch_min.html
deleted file mode 100644
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-    <div class="page-header">
-    <h1>Min</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_min.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Min</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the tuple of two output tensors (min, min_indices)</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>When the shapes do not match, the shape of the returned output tensor
-follows the broadcasting rules .</p>
-    <h2 class="hasAnchor" id="min-input-gt-tensor-"><a class="anchor" href="#min-input-gt-tensor-"></a>min(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the minimum value of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>min(input, dim, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the minimum
-value of each row of the <code>input</code> tensor in the given dimension
-<code>dim</code>. And <code>indices</code> is the index location of each minimum value found
-(argmin).</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p><code>indices</code> does not necessarily contain the first occurrence of each
-minimal value found, unless it is unique.
-The exact implementation details are device-specific.
-Do not expect the same result when run on CPU and GPU in general.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size as
-<code>input</code> except in the dimension <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
-the output tensors having 1 fewer dimension than <code>input</code>.</p>
-    <h2 class="hasAnchor" id="min-input-other-out-null-gt-tensor-"><a class="anchor" href="#min-input-other-out-null-gt-tensor-"></a>min(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Each element of the tensor <code>input</code> is compared with the corresponding
-element of the tensor <code>other</code> and an element-wise minimum is taken.
-The resulting tensor is returned.</p>
-<p>The shapes of <code>input</code> and <code>other</code> don't need to match,
-but they must be broadcastable .</p>
-<p>$$
-    \mbox{out}_i = \min(\mbox{tensor}_i, \mbox{other}_i)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_min</span>(<span class='kw'>a</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_min</span>(<span class='kw'>a</span>, dim = <span class='fl'>1</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>b</span>
-<span class='fu'>torch_min</span>(<span class='kw'>a</span>, other = <span class='kw'>b</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6181
-#&gt; -0.3543
-#&gt; -1.5836
-#&gt; -1.4919
-#&gt; [ CPUFloatType{4} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_mm.html b/static/docs/dev/reference/torch_mm.html
deleted file mode 100644
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+++ /dev/null
@@ -1,264 +0,0 @@
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_mm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Mm</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_mm</span>(<span class='kw'>self</span>, <span class='kw'>mat2</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>mat2</th>
-      <td><p>(Tensor) the second matrix to be multiplied</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function does not broadcast .
-For broadcasting matrix products, see <code><a href='torch_matmul.html'>torch_matmul</a></code>.</p>
-    <h2 class="hasAnchor" id="mm-input-mat-out-null-gt-tensor-"><a class="anchor" href="#mm-input-mat-out-null-gt-tensor-"></a>mm(input, mat2, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a matrix multiplication of the matrices <code>input</code> and <code>mat2</code>.</p>
-<p>If <code>input</code> is a \((n \times m)\) tensor, <code>mat2</code> is a
-\((m \times p)\) tensor, <code>out</code> will be a \((n \times p)\) tensor.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>mat1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='kw'>mat2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_mm</span>(<span class='kw'>mat1</span>, <span class='kw'>mat2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.1471 -0.9405 -1.3713
-#&gt; -1.1990 -1.3174 -1.6382
-#&gt; [ CPUFloatType{2,3} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_mode.html b/static/docs/dev/reference/torch_mode.html
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-    <div class="page-header">
-    <h1>Mode</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_mode.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Mode</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_mode</span>(<span class='kw'>self</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function is not defined for <code>torch_cuda.Tensor</code> yet.</p>
-    <h2 class="hasAnchor" id="mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>mode(input, dim=-1, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the mode
-value of each row of the <code>input</code> tensor in the given dimension
-<code>dim</code>, i.e. a value which appears most often
-in that row, and <code>indices</code> is the index location of each mode value found.</p>
-<p>By default, <code>dim</code> is the last dimension of the <code>input</code> tensor.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size as
-<code>input</code> except in the dimension <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting
-in the output tensors having 1 fewer dimension than <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span>(<span class='fl'>0</span>, <span class='fl'>50</span>, size = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>5</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_mode</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; 9
-#&gt; [ CPUFloatType{} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; 2
-#&gt; [ CPULongType{} ]
-#&gt; </div></pre>
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-    <div class="page-header">
-    <h1>Mul</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_mul.Rd</code></div>
-    </div>
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-    <p>Mul</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_mul</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first multiplicand tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second multiplicand tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="mul-input-other-out-null-"><a class="anchor" href="#mul-input-other-out-null-"></a>mul(input, other, out=NULL) </h2>
-
-    
-
-
-<p>Multiplies each element of the input <code>input</code> with the scalar
-<code>other</code> and returns a new resulting tensor.</p>
-<p>$$
-    \mbox{out}_i = \mbox{other} \times \mbox{input}_i
-$$
-If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>other</code>
-should be a real number, otherwise it should be an integer</p>
-
-
-<p>Each element of the tensor <code>input</code> is multiplied by the corresponding
-element of the Tensor <code>other</code>. The resulting tensor is returned.</p>
-<p>The shapes of <code>input</code> and <code>other</code> must be
-broadcastable .</p>
-<p>$$
-    \mbox{out}_i = \mbox{input}_i \times \mbox{other}_i
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_mul</span>(<span class='kw'>a</span>, <span class='fl'>100</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>1</span>))
-<span class='kw'>a</span>
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>4</span>))
-<span class='kw'>b</span>
-<span class='fu'>torch_mul</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.1523  0.0106 -0.5148 -0.4212
-#&gt;  0.5583  0.0388 -1.8876 -1.5442
-#&gt;  0.7696  0.0535 -2.6021 -2.1287
-#&gt; -0.1972 -0.0137  0.6666  0.5454
-#&gt; [ CPUFloatType{4,4} ]</div></pre>
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-    <h1>Multinomial</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_multinomial.Rd</code></div>
-    </div>
-
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-    <p>Multinomial</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_multinomial</span>(<span class='kw'>self</span>, <span class='kw'>num_samples</span>, replacement = <span class='fl'>FALSE</span>, generator = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor containing probabilities</p></td>
-    </tr>
-    <tr>
-      <th>num_samples</th>
-      <td><p>(int) number of samples to draw</p></td>
-    </tr>
-    <tr>
-      <th>replacement</th>
-      <td><p>(bool, optional) whether to draw with replacement or not</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>The rows of `input` do not need to sum to one (in which case we use
-the values as weights), but must be non-negative, finite and have
-a non-zero sum.
-</pre>
-
-<p>Indices are ordered from left to right according to when each was sampled
-(first samples are placed in first column).</p>
-<p>If <code>input</code> is a vector, <code>out</code> is a vector of size <code>num_samples</code>.</p>
-<p>If <code>input</code> is a matrix with <code>m</code> rows, <code>out</code> is an matrix of shape
-\((m \times \mbox{num\_samples})\).</p>
-<p>If replacement is <code>TRUE</code>, samples are drawn with replacement.</p>
-<p>If not, they are drawn without replacement, which means that when a
-sample index is drawn for a row, it cannot be drawn again for that row.</p>
-<pre>When drawn without replacement, `num_samples` must be lower than
-number of non-zero elements in `input` (or the min number of non-zero
-elements in each row of `input` if it is a matrix).
-</pre>
-
-    <h2 class="hasAnchor" id="multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-"><a class="anchor" href="#multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-"></a>multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns a tensor where each row contains <code>num_samples</code> indices sampled
-from the multinomial probability distribution located in the corresponding row
-of tensor <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_float</a></span>()) <span class='co'># create a tensor of weights</span>
-<span class='fu'>torch_multinomial</span>(<span class='kw'>weights</span>, <span class='fl'>2</span>)
-<span class='fu'>torch_multinomial</span>(<span class='kw'>weights</span>, <span class='fl'>4</span>, replacement=<span class='fl'>TRUE</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  1
-#&gt;  2
-#&gt;  1
-#&gt; [ CPULongType{4} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>Mv</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_mv.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Mv</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_mv</span>(<span class='kw'>self</span>, <span class='kw'>vec</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>vec</th>
-      <td><p>(Tensor) vector to be multiplied</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function does not broadcast .</p>
-    <h2 class="hasAnchor" id="mv-input-vec-out-null-gt-tensor-"><a class="anchor" href="#mv-input-vec-out-null-gt-tensor-"></a>mv(input, vec, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a matrix-vector product of the matrix <code>input</code> and the vector
-<code>vec</code>.</p>
-<p>If <code>input</code> is a \((n \times m)\) tensor, <code>vec</code> is a 1-D tensor of
-size \(m\), <code>out</code> will be 1-D of size \(n\).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>mat</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='kw'>vec</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='fu'>torch_mv</span>(<span class='kw'>mat</span>, <span class='kw'>vec</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -3.5230
-#&gt; -0.2398
-#&gt; [ CPUFloatType{2} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_mvlgamma.html b/static/docs/dev/reference/torch_mvlgamma.html
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-    <h1>Mvlgamma</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_mvlgamma.Rd</code></div>
-    </div>
-
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-    <p>Mvlgamma</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_mvlgamma</span>(<span class='kw'>self</span>, <span class='kw'>p</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compute the multivariate log-gamma function</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(int) the number of dimensions</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="mvlgamma-input-p-gt-tensor-"><a class="anchor" href="#mvlgamma-input-p-gt-tensor-"></a>mvlgamma(input, p) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the <code>multivariate log-gamma function &lt;https://en.wikipedia.org/wiki/Multivariate_gamma_function&gt;</code>_) with dimension
-\(p\) element-wise, given by</p>
-<p>$$
-    \log(\Gamma_{p}(a)) = C + \displaystyle \sum_{i=1}^{p} \log\left(\Gamma\left(a - \frac{i - 1}{2}\right)\right)
-$$
-where \(C = \log(\pi) \times \frac{p (p - 1)}{4}\) and \(\Gamma(\cdot)\) is the Gamma function.</p>
-<p>All elements must be greater than \(\frac{p - 1}{2}\), otherwise an error would be thrown.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))<span class='op'>$</span><span class='fu'>uniform_</span>(<span class='fl'>1</span>, <span class='fl'>2</span>)
-<span class='kw'>a</span>
-<span class='fu'>torch_mvlgamma</span>(<span class='kw'>a</span>, <span class='fl'>2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.4040  0.4059  0.7450
-#&gt;  0.3997  0.8720  0.4162
-#&gt; [ CPUFloatType{2,3} ]</div></pre>
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-    <h1>Narrow</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_narrow.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Narrow</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_narrow</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, <span class='kw'>start</span>, <span class='kw'>length</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to narrow</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension along which to narrow</p></td>
-    </tr>
-    <tr>
-      <th>start</th>
-      <td><p>(int) the starting dimension</p></td>
-    </tr>
-    <tr>
-      <th>length</th>
-      <td><p>(int) the distance to the ending dimension</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="narrow-input-dim-start-length-gt-tensor-"><a class="anchor" href="#narrow-input-dim-start-length-gt-tensor-"></a>narrow(input, dim, start, length) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor that is a narrowed version of <code>input</code> tensor. The
-dimension <code>dim</code> is input from <code>start</code> to <code>start + length</code>. The
-returned tensor and <code>input</code> tensor share the same underlying storage.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>9</span>), ncol = <span class='fl'>3</span>, byrow= <span class='fl'>TRUE</span>))
-<span class='fu'>torch_narrow</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>0L</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span>(dim = <span class='fl'>1</span>), <span class='fl'>2</span>)
-<span class='fu'>torch_narrow</span>(<span class='kw'>x</span>, <span class='fl'>2</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1L</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span>(dim = <span class='fl'>1</span>), <span class='fl'>2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2  3
-#&gt;  5  6
-#&gt;  8  9
-#&gt; [ CPULongType{3,2} ]</div></pre>
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-    <h1>Ne</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_ne.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ne</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_ne</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ne-input-other-out-null-gt-tensor-"><a class="anchor" href="#ne-input-other-out-null-gt-tensor-"></a>ne(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes \(input \neq other\) element-wise.</p>
-<p>The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_ne</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
-         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/rep.html'>rep</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>4</span>), each = <span class='fl'>2</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  1
-#&gt;  1  0
-#&gt; [ CPUBoolType{2,2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>Neg</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_neg.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Neg</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_neg</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="neg-input-out-null-gt-tensor-"><a class="anchor" href="#neg-input-out-null-gt-tensor-"></a>neg(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the negative of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out} = -1 \times \mbox{input}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_neg</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.2237
-#&gt; -0.2668
-#&gt; -1.1663
-#&gt;  0.0567
-#&gt; -0.2786
-#&gt; [ CPUFloatType{5} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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diff --git a/static/docs/dev/reference/torch_nonzero.html b/static/docs/dev/reference/torch_nonzero.html
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-    <div class="page-header">
-    <h1>Nonzero</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_nonzero.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Nonzero</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_nonzero</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>[`torch_nonzero(..., as_tuple=False) &lt;torch.nonzero&gt;`] (default) returns a
-2-D tensor where each row is the index for a nonzero value.
-
-[`torch_nonzero(..., as_tuple=TRUE) &lt;torch.nonzero&gt;`] returns a tuple of 1-D
-index tensors, allowing for advanced indexing, so `x[x.nonzero(as_tuple=TRUE)]`
-gives all nonzero values of tensor `x`. Of the returned tuple, each index tensor
-contains nonzero indices for a certain dimension.
-
-See below for more details on the two behaviors.
-</pre>
-
-    <h2 class="hasAnchor" id="nonzero-input-out-null-as-tuple-false-gt-longtensor-or-tuple-of-longtensors-"><a class="anchor" href="#nonzero-input-out-null-as-tuple-false-gt-longtensor-or-tuple-of-longtensors-"></a>nonzero(input, *, out=NULL, as_tuple=False) -&gt; LongTensor or tuple of LongTensors </h2>
-
-    
-
-
-<p><strong>When</strong> <code>as_tuple</code> <strong>is <code>FALSE</code> (default)</strong>:</p>
-<p>Returns a tensor containing the indices of all non-zero elements of
-<code>input</code>.  Each row in the result contains the indices of a non-zero
-element in <code>input</code>. The result is sorted lexicographically, with
-the last index changing the fastest (C-style).</p>
-<p>If <code>input</code> has \(n\) dimensions, then the resulting indices tensor
-<code>out</code> is of size \((z \times n)\), where \(z\) is the total number of
-non-zero elements in the <code>input</code> tensor.</p>
-<p><strong>When</strong> <code>as_tuple</code> <strong>is <code>TRUE</code></strong>:</p>
-<p>Returns a tuple of 1-D tensors, one for each dimension in <code>input</code>,
-each containing the indices (in that dimension) of all non-zero elements of
-<code>input</code> .</p>
-<p>If <code>input</code> has \(n\) dimensions, then the resulting tuple contains \(n\)
-tensors of size \(z\), where \(z\) is the total number of
-non-zero elements in the <code>input</code> tensor.</p>
-<p>As a special case, when <code>input</code> has zero dimensions and a nonzero scalar
-value, it is treated as a one-dimensional tensor with one element.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_nonzero</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>0</span>, <span class='fl'>1</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  1
-#&gt;  2
-#&gt;  4
-#&gt; [ CPULongType{4,1} ]</div></pre>
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-    <div class="page-header">
-    <h1>Norm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_norm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Norm</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_norm</span>(<span class='kw'>self</span>, p = <span class='fl'>2L</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>, <span class='kw'>dtype</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: <code>'fro'</code>        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        NULL   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --        Other  as vec norm when dim is NULL  sum(abs(x)<strong>ord)</strong>(1./ord)        =====  ============================  ==========================</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, 2-tuple of ints, 2-list of ints, optional) If it is an int,        vector norm will be calculated, if it is 2-tuple of ints, matrix norm        will be calculated. If the value is NULL, matrix norm will be calculated        when the input tensor only has two dimensions, vector norm will be        calculated when the input tensor only has one dimension. If the input        tensor has more than two dimensions, the vector norm will be applied to        last dimension.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool, optional) whether the output tensors have <code>dim</code>        retained or not. Ignored if <code>dim</code> = <code>NULL</code> and        <code>out</code> = <code>NULL</code>. Default: <code>FALSE</code>
-Ignored if        <code>dim</code> = <code>NULL</code> and <code>out</code> = <code>NULL</code>.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Returns the matrix norm or vector norm of a given tensor.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>9</span>, dtype = <span class='fu'><a href='torch_dtype.html'>torch_float</a></span>())
-<span class='kw'>b</span> <span class='op'>=</span> <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/reshape.html'>reshape</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_norm</span>(<span class='kw'>a</span>)
-<span class='fu'>torch_norm</span>(<span class='kw'>b</span>)
-<span class='fu'>torch_norm</span>(<span class='kw'>a</span>, <span class='fl'>Inf</span>)
-<span class='fu'>torch_norm</span>(<span class='kw'>b</span>, <span class='fl'>Inf</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 8
-#&gt; [ CPUFloatType{} ]</div></pre>
-  </div>
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-    <h1>Normal</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_normal.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Normal</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_normal</span>(<span class='kw'>mean</span>, std = <span class='fl'>1L</span>, <span class='kw'>size</span>, generator = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>mean</th>
-      <td><p>(Tensor) the tensor of per-element means</p></td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td><p>(Tensor) the tensor of per-element standard deviations</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>When the shapes do not match, the shape of <code>mean</code>
-is used as the shape for the returned output tensor</p>
-    <h2 class="hasAnchor" id="normal-mean-std-generator-null-out-null-gt-tensor-"><a class="anchor" href="#normal-mean-std-generator-null-out-null-gt-tensor-"></a>normal(mean, std, *, generator=NULL, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor of random numbers drawn from separate normal distributions
-whose mean and standard deviation are given.</p>
-<p>The <code>mean</code> is a tensor with the mean of
-each output element's normal distribution</p>
-<p>The <code>std</code> is a tensor with the standard deviation of
-each output element's normal distribution</p>
-<p>The shapes of <code>mean</code> and <code>std</code> don't need to match, but the
-total number of elements in each tensor need to be the same.</p>
-    <h2 class="hasAnchor" id="normal-mean-std-out-null-gt-tensor-"><a class="anchor" href="#normal-mean-std-out-null-gt-tensor-"></a>normal(mean=0.0, std, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Similar to the function above, but the means are shared among all drawn
-elements.</p>
-    <h2 class="hasAnchor" id="normal-mean-std-out-null-gt-tensor-"><a class="anchor" href="#normal-mean-std-out-null-gt-tensor-"></a>normal(mean, std=1.0, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Similar to the function above, but the standard-deviations are shared among
-all drawn elements.</p>
-    <h2 class="hasAnchor" id="normal-mean-std-size-out-null-gt-tensor-"><a class="anchor" href="#normal-mean-std-size-out-null-gt-tensor-"></a>normal(mean, std, size, *, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Similar to the function above, but the means and standard deviations are shared
-among all drawn elements. The resulting tensor has size given by <code>size</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_normal</span>(mean=<span class='fl'>0</span>, std=<span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1</span>, <span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>0.1</span>))
-
-
-<span class='fu'>torch_normal</span>(mean=<span class='fl'>0.5</span>, std=<span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>))
-
-
-<span class='fu'>torch_normal</span>(mean=<span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>))
-
-
-<span class='fu'>torch_normal</span>(<span class='fl'>2</span>, <span class='fl'>3</span>, size=<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>4</span>))
-}
-}
-</div></pre>
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-      </ul>
-      
-    </div><!--/.nav-collapse -->
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-</div><!--/.navbar -->
-
-      
-
-      </header>
-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Ones</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_ones.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ones</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_ones</span>(
-  <span class='kw'>...</span>,
-  names = <span class='kw'>NULL</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>names</th>
-      <td><p>optional names for the dimensions</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with the scalar value <code>1</code>, with the shape defined
-by the variable argument <code>size</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_ones</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_ones</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  1
-#&gt;  1
-#&gt;  1
-#&gt;  1
-#&gt; [ CPUFloatType{5} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_ones_like.html b/static/docs/dev/reference/torch_ones_like.html
deleted file mode 100644
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+++ /dev/null
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-    <div class="page-header">
-    <h1>Ones_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_ones_like.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ones_like</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_ones_like</span>(
-  <span class='kw'>input</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>,
-  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with the scalar value <code>1</code>, with the same size as
-<code>input</code>. <code>torch_ones_like(input)</code> is equivalent to
-<code><a href='torch_ones.html'>torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>As of 0.4, this function does not support an <code>out</code> keyword. As an alternative,
-the old <code>torch_ones_like(input, out=output)</code> is equivalent to
-<code><a href='torch_ones.html'>torch_ones(input.size(), out=output)</a></code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_ones_like</span>(<span class='kw'>input</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  1  1
-#&gt;  1  1  1
-#&gt; [ CPUFloatType{2,3} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
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-    <h1>Orgqr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_orgqr.Rd</code></div>
-    </div>
-
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-    <p>Orgqr</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_orgqr</span>(<span class='kw'>self</span>, <span class='kw'>input2</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the <code>a</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>(Tensor) the <code>tau</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="orgqr-input-input-gt-tensor-"><a class="anchor" href="#orgqr-input-input-gt-tensor-"></a>orgqr(input, input2) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the orthogonal matrix <code>Q</code> of a QR factorization, from the <code>(input, input2)</code>
-tuple returned by <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p>
-<p>This directly calls the underlying LAPACK function <code>?orgqr</code>.
-See <code>LAPACK documentation for orgqr</code>_ for further details.</p>
-
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-    <h1>Ormqr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_ormqr.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ormqr</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_ormqr</span>(<span class='kw'>self</span>, <span class='kw'>input2</span>, <span class='kw'>input3</span>, left = <span class='fl'>TRUE</span>, transpose = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the <code>a</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>(Tensor) the <code>tau</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>input3</th>
-      <td><p>(Tensor) the matrix to be multiplied.</p></td>
-    </tr>
-    <tr>
-      <th>left</th>
-      <td><p>see LAPACK documentation</p></td>
-    </tr>
-    <tr>
-      <th>transpose</th>
-      <td><p>see LAPACK documentation</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ormqr-input-input-input-left-true-transpose-false-gt-tensor-"><a class="anchor" href="#ormqr-input-input-input-left-true-transpose-false-gt-tensor-"></a>ormqr(input, input2, input3, left=TRUE, transpose=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Multiplies <code>mat</code> (given by <code>input3</code>) by the orthogonal <code>Q</code> matrix of the QR factorization
-formed by <code><a href='torch_geqrf.html'>torch_geqrf()</a></code> that is represented by <code>(a, tau)</code> (given by (<code>input</code>, <code>input2</code>)).</p>
-<p>This directly calls the underlying LAPACK function <code>?ormqr</code>.
-See <a href='https://software.intel.com/content/www/us/en/develop/documentation/mkl-developer-reference-c/top/scalapack-routines/scalapack-computational-routines/orthogonal-factorizations-scalapack-computational-routines/p-ormqr.html'>LAPACK documentation for ormqr</a> for further details.</p>
-
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diff --git a/static/docs/dev/reference/torch_pdist.html b/static/docs/dev/reference/torch_pdist.html
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-    <h1>Pdist</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_pdist.Rd</code></div>
-    </div>
-
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-    <p>Pdist</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_pdist</span>(<span class='kw'>self</span>, p = <span class='fl'>2L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>NA input tensor of shape \(N \times M\).</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="pdist-input-p-gt-tensor-"><a class="anchor" href="#pdist-input-p-gt-tensor-"></a>pdist(input, p=2) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the p-norm distance between every pair of row vectors in the input.
-This is identical to the upper triangular portion, excluding the diagonal, of
-<code>torch_norm(input[:, NULL] - input, dim=2, p=p)</code>. This function will be faster
-if the rows are contiguous.</p>
-<p>If input has shape \(N \times M\) then the output will have shape
-\(\frac{1}{2} N (N - 1)\).</p>
-<p>This function is equivalent to <code>scipy.spatial.distance.pdist(input, 'minkowski', p=p)</code> if \(p \in (0, \infty)\). When \(p = 0\) it is
-equivalent to <code>scipy.spatial.distance.pdist(input, 'hamming') * M</code>.
-When \(p = \infty\), the closest scipy function is
-<code>scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max())</code>.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>Pinverse</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_pinverse.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Pinverse</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_pinverse</span>(<span class='kw'>self</span>, rcond = <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) The input tensor of size \((*, m, n)\) where \(*\) is zero or more batch dimensions</p></td>
-    </tr>
-    <tr>
-      <th>rcond</th>
-      <td><p>(float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>This method is implemented using the Singular Value Decomposition.
-</pre>
-
-<pre>The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_.
-Therefore, derivatives are not always existent, and exist for a constant rank only `[2]`_.
-However, this method is backprop-able due to the implementation by using SVD results, and
-could be unstable. Double-backward will also be unstable due to the usage of SVD internally.
-See `~torch.svd` for more details.
-</pre>
-
-    <h2 class="hasAnchor" id="pinverse-input-rcond-e-gt-tensor-"><a class="anchor" href="#pinverse-input-rcond-e-gt-tensor-"></a>pinverse(input, rcond=1e-15) -&gt; Tensor </h2>
-
-    
-
-
-<p>Calculates the pseudo-inverse (also known as the Moore-Penrose inverse) of a 2D tensor.
-Please look at <code>Moore-Penrose inverse</code>_ for more details</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>))
-<span class='kw'>input</span>
-<span class='fu'>torch_pinverse</span>(<span class='kw'>input</span>)
-<span class='co'># Batched pinverse example</span>
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>6</span>,<span class='fl'>3</span>))
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'>torch_pinverse</span>(<span class='kw'>a</span>)
-<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>b</span>, <span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   1.0000 -0.0000  0.0000
-#&gt;  -0.0000  1.0000  0.0000
-#&gt;  -0.0000 -0.0000  1.0000
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   1.0000e+00 -1.6391e-07 -3.5763e-07
-#&gt;  -5.9605e-08  1.0000e+00 -3.2783e-07
-#&gt;  -8.1956e-08  1.6019e-07  1.0000e+00
-#&gt; [ CPUFloatType{2,3,3} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_pixel_shuffle.html b/static/docs/dev/reference/torch_pixel_shuffle.html
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-    <h1>Pixel_shuffle</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_pixel_shuffle.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Pixel_shuffle</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_pixel_shuffle</span>(<span class='kw'>self</span>, <span class='kw'>upscale_factor</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>upscale_factor</th>
-      <td><p>(int) factor to increase spatial resolution by</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rearranges-elements-in-a-tensor-of-shape-"><a class="anchor" href="#rearranges-elements-in-a-tensor-of-shape-"></a>Rearranges elements in a tensor of shape </h2>
-
-    <p>math:<code>(*, C \times r^2, H, W)</code> to a :</p>
-<p>Rearranges elements in a tensor of shape \((*, C \times r^2, H, W)\) to a
-tensor of shape \((*, C, H \times r, W \times r)\).</p>
-<p>See <code>~torch.nn.PixelShuffle</code> for details.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>9</span>, <span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'><a href='nnf_pixel_shuffle.html'>nnf_pixel_shuffle</a></span>(<span class='kw'>input</span>, <span class='fl'>3</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>())
-}
-</div><div class='output co'>#&gt; [1]  1  1 12 12</div></pre>
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diff --git a/static/docs/dev/reference/torch_poisson.html b/static/docs/dev/reference/torch_poisson.html
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-    <h1>Poisson</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_poisson.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Poisson</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_poisson</span>(<span class='kw'>self</span>, generator = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor containing the rates of the Poisson distribution</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="poisson-input-generator-null-gt-tensor-"><a class="anchor" href="#poisson-input-generator-null-gt-tensor-"></a>poisson(input *, generator=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor of the same size as <code>input</code> with each element
-sampled from a Poisson distribution with rate parameter given by the corresponding
-element in <code>input</code> i.e.,</p>
-<p>$$
-    \mbox{out}_i \sim \mbox{Poisson}(\mbox{input}_i)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>rates</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>)) <span class='op'>*</span> <span class='fl'>5</span>  <span class='co'># rate parameter between 0 and 5</span>
-<span class='fu'>torch_poisson</span>(<span class='kw'>rates</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2  0  3  4
-#&gt;  1  6  2  4
-#&gt;  0  1  1  3
-#&gt;  5  4  2  4
-#&gt; [ CPUFloatType{4,4} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_polygamma.html b/static/docs/dev/reference/torch_polygamma.html
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-    <h1>Polygamma</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_polygamma.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Polygamma</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_polygamma</span>(<span class='kw'>n</span>, <span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>n</th>
-      <td><p>(int) the order of the polygamma function</p></td>
-    </tr>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>This function is not implemented for \eqn{n \geq 2}.
-</pre>
-
-    <h2 class="hasAnchor" id="polygamma-n-input-out-null-gt-tensor-"><a class="anchor" href="#polygamma-n-input-out-null-gt-tensor-"></a>polygamma(n, input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the \(n^{th}\) derivative of the digamma function on <code>input</code>.
-\(n \geq 0\) is called the order of the polygamma function.</p>
-<p>$$
-    \psi^{(n)}(x) = \frac{d^{(n)}}{dx^{(n)}} \psi(x)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0.5</span>))
-<span class='fu'>torch_polygamma</span>(<span class='fl'>1</span>, <span class='kw'>a</span>)
-}
-}
-</div></pre>
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-    <h1>Pow</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_pow.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Pow</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_pow</span>(<span class='kw'>self</span>, <span class='kw'>exponent</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(float) the scalar base value for the power operation</p></td>
-    </tr>
-    <tr>
-      <th>exponent</th>
-      <td><p>(float or tensor) the exponent value</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="pow-input-exponent-out-null-gt-tensor-"><a class="anchor" href="#pow-input-exponent-out-null-gt-tensor-"></a>pow(input, exponent, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Takes the power of each element in <code>input</code> with <code>exponent</code> and
-returns a tensor with the result.</p>
-<p><code>exponent</code> can be either a single <code>float</code> number or a <code>Tensor</code>
-with the same number of elements as <code>input</code>.</p>
-<p>When <code>exponent</code> is a scalar value, the operation applied is:</p>
-<p>$$
-    \mbox{out}_i = x_i^{\mbox{exponent}}
-$$
-When <code>exponent</code> is a tensor, the operation applied is:</p>
-<p>$$
-    \mbox{out}_i = x_i^{\mbox{exponent}_i}
-$$
-When <code>exponent</code> is a tensor, the shapes of <code>input</code>
-and <code>exponent</code> must be broadcastable .</p>
-    <h2 class="hasAnchor" id="pow-self-exponent-out-null-gt-tensor-"><a class="anchor" href="#pow-self-exponent-out-null-gt-tensor-"></a>pow(self, exponent, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p><code>self</code> is a scalar <code>float</code> value, and <code>exponent</code> is a tensor.
-The returned tensor <code>out</code> is of the same shape as <code>exponent</code></p>
-<p>The operation applied is:</p>
-<p>$$
-    \mbox{out}_i = \mbox{self} ^ {\mbox{exponent}_i}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_pow</span>(<span class='kw'>a</span>, <span class='fl'>2</span>)
-<span class='kw'>exp</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
-<span class='kw'>a</span>
-<span class='kw'>exp</span>
-<span class='fu'>torch_pow</span>(<span class='kw'>a</span>, <span class='kw'>exp</span>)
-
-
-<span class='kw'>exp</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
-<span class='kw'>base</span> <span class='op'>=</span> <span class='fl'>2</span>
-<span class='fu'>torch_pow</span>(<span class='kw'>base</span>, <span class='kw'>exp</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;   2
-#&gt;   4
-#&gt;   8
-#&gt;  16
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <div class="page-header">
-    <h1>Prod</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_prod.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Prod</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_prod</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>, dtype = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="prod-input-dtype-null-gt-tensor-"><a class="anchor" href="#prod-input-dtype-null-gt-tensor-"></a>prod(input, dtype=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the product of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="prod-input-dim-keepdim-false-dtype-null-gt-tensor-"><a class="anchor" href="#prod-input-dim-keepdim-false-dtype-null-gt-tensor-"></a>prod(input, dim, keepdim=False, dtype=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the product of each row of the <code>input</code> tensor in the given
-dimension <code>dim</code>.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
-the output tensor having 1 fewer dimension than <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_prod</span>(<span class='kw'>a</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_prod</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 0.01 *
-#&gt;  2.8209
-#&gt;  0.5408
-#&gt; [ CPUFloatType{2} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_promote_types.html b/static/docs/dev/reference/torch_promote_types.html
deleted file mode 100644
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-    <div class="page-header">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_promote_types.Rd</code></div>
-    </div>
-
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-    <p>Promote_types</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_promote_types</span>(<span class='kw'>type1</span>, <span class='kw'>type2</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>type1</th>
-      <td><p>(<code>torch.dtype</code>)</p></td>
-    </tr>
-    <tr>
-      <th>type2</th>
-      <td><p>(<code>torch.dtype</code>)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="promote-types-type-type-gt-dtype-"><a class="anchor" href="#promote-types-type-type-gt-dtype-"></a>promote_types(type1, type2) -&gt; dtype </h2>
-
-    
-
-
-<p>Returns the <code>torch_dtype</code> with the smallest size and scalar kind that is
-not smaller nor of lower kind than either <code>type1</code> or <code>type2</code>. See type promotion
-documentation  for more information on the type
-promotion logic.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_promote_types</span>(<span class='fu'><a href='torch_dtype.html'>torch_int32</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_float32</a></span>())
-<span class='fu'>torch_promote_types</span>(<span class='fu'><a href='torch_dtype.html'>torch_uint8</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-}
-</div><div class='output co'>#&gt; torch_Long</div></pre>
-  </div>
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-    <h1>Qr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_qr.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Qr</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_qr</span>(<span class='kw'>self</span>, some = <span class='fl'>TRUE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size \((*, m, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                batch dimensions consisting of matrices of dimension \(m \times n\).</p></td>
-    </tr>
-    <tr>
-      <th>some</th>
-      <td><p>(bool, optional) Set to <code>TRUE</code> for reduced QR decomposition and <code>FALSE</code> for                complete QR decomposition.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>precision may be lost if the magnitudes of the elements of <code>input</code>
-are large</p>
-<p>While it should always give you a valid decomposition, it may not
-give you the same one across platforms - it will depend on your
-LAPACK implementation.</p>
-    <h2 class="hasAnchor" id="qr-input-some-true-out-null-gt-tensor-tensor-"><a class="anchor" href="#qr-input-some-true-out-null-gt-tensor-tensor-"></a>qr(input, some=TRUE, out=NULL) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Computes the QR decomposition of a matrix or a batch of matrices <code>input</code>,
-and returns a namedtuple (Q, R) of tensors such that \(\mbox{input} = Q R\)
-with \(Q\) being an orthogonal matrix or batch of orthogonal matrices and
-\(R\) being an upper triangular matrix or batch of upper triangular matrices.</p>
-<p>If <code>some</code> is <code>TRUE</code>, then this function returns the thin (reduced) QR factorization.
-Otherwise, if <code>some</code> is <code>FALSE</code>, this function returns the complete QR factorization.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>12.</span>, <span class='op'>-</span><span class='fl'>51</span>, <span class='fl'>4</span>, <span class='fl'>6</span>, <span class='fl'>167</span>, <span class='op'>-</span><span class='fl'>68</span>, <span class='op'>-</span><span class='fl'>4</span>, <span class='fl'>24</span>, <span class='op'>-</span><span class='fl'>41</span>), ncol = <span class='fl'>3</span>, byrow = <span class='fl'>TRUE</span>))
-<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_qr</span>(<span class='kw'>a</span>)
-<span class='kw'>q</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
-<span class='kw'>r</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
-<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>q</span>, <span class='kw'>r</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span>()
-<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>q</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>(), <span class='kw'>q</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span>()
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  0  0
-#&gt;  0  1  0
-#&gt;  0  0  1
-#&gt; [ CPUFloatType{3,3} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_qscheme.html b/static/docs/dev/reference/torch_qscheme.html
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-    <h1>Quantize_per_channel</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_quantize_per_channel.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Quantize_per_channel</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_quantize_per_channel</span>(<span class='kw'>self</span>, <span class='kw'>scales</span>, <span class='kw'>zero_points</span>, <span class='kw'>axis</span>, <span class='kw'>dtype</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) float tensor to quantize</p></td>
-    </tr>
-    <tr>
-      <th>scales</th>
-      <td><p>(Tensor) float 1D tensor of scales to use, size should match <code>input.size(axis)</code></p></td>
-    </tr>
-    <tr>
-      <th>zero_points</th>
-      <td><p>(int) integer 1D tensor of offset to use, size should match <code>input.size(axis)</code></p></td>
-    </tr>
-    <tr>
-      <th>axis</th>
-      <td><p>(int) dimension on which apply per-channel quantization</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-"><a class="anchor" href="#quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-"></a>quantize_per_channel(input, scales, zero_points, axis, dtype) -&gt; Tensor </h2>
-
-    
-
-
-<p>Converts a float tensor to per-channel quantized tensor with given scales and zero points.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span>), ncol = <span class='fl'>2</span>, byrow = <span class='fl'>TRUE</span>))
-<span class='fu'>torch_quantize_per_channel</span>(<span class='kw'>x</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.1</span>, <span class='fl'>0.01</span>)), 
-                           <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10L</span>, <span class='fl'>0L</span>)), <span class='fl'>0</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())
-<span class='fu'>torch_quantize_per_channel</span>(<span class='kw'>x</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.1</span>, <span class='fl'>0.01</span>)), 
-                           <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10L</span>, <span class='fl'>0L</span>)), <span class='fl'>0</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())<span class='op'>$</span><span class='fu'>int_repr</span>()
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;    0   10
-#&gt;  100  200
-#&gt; [ CPUByteType{2,2} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_quantize_per_tensor.html b/static/docs/dev/reference/torch_quantize_per_tensor.html
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-    <h1>Quantize_per_tensor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_quantize_per_tensor.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Quantize_per_tensor</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_quantize_per_tensor</span>(<span class='kw'>self</span>, <span class='kw'>scale</span>, <span class='kw'>zero_point</span>, <span class='kw'>dtype</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) float tensor to quantize</p></td>
-    </tr>
-    <tr>
-      <th>scale</th>
-      <td><p>(float) scale to apply in quantization formula</p></td>
-    </tr>
-    <tr>
-      <th>zero_point</th>
-      <td><p>(int) offset in integer value that maps to float zero</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-"><a class="anchor" href="#quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-"></a>quantize_per_tensor(input, scale, zero_point, dtype) -&gt; Tensor </h2>
-
-    
-
-
-<p>Converts a float tensor to quantized tensor with given scale and zero point.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='fu'>torch_quantize_per_tensor</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span>)), <span class='fl'>0.1</span>, <span class='fl'>10</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())
-<span class='fu'>torch_quantize_per_tensor</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span>)), <span class='fl'>0.1</span>, <span class='fl'>10</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())<span class='op'>$</span><span class='fu'>int_repr</span>()
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;   0
-#&gt;  10
-#&gt;  20
-#&gt;  30
-#&gt; [ CPUByteType{4} ]</div></pre>
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-    <div class="page-header">
-    <h1>Rand</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_rand.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Rand</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_rand</span>(
-  <span class='kw'>...</span>,
-  names = <span class='kw'>NULL</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>names</th>
-      <td><p>optional dimension names</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with random numbers from a uniform distribution
-on the interval \([0, 1)\)</p>
-<p>The shape of the tensor is defined by the variable argument <code>size</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_rand</span>(<span class='fl'>4</span>)
-<span class='fu'>torch_rand</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.8450  0.7202  0.6470
-#&gt;  0.1506  0.6973  0.2796
-#&gt; [ CPUFloatType{2,3} ]</div></pre>
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-    <div class="page-header">
-    <h1>Rand_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_rand_like.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Rand_like</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_rand_like</span>(
-  <span class='kw'>input</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>,
-  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor with the same size as <code>input</code> that is filled with
-random numbers from a uniform distribution on the interval \([0, 1)\).
-<code>torch_rand_like(input)</code> is equivalent to
-<code><a href='torch_rand.html'>torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
-
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diff --git a/static/docs/dev/reference/torch_randint.html b/static/docs/dev/reference/torch_randint.html
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-<div class="row">
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-    <div class="page-header">
-    <h1>Randint</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randint.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randint</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_randint</span>(
-  <span class='kw'>low</span>,
-  <span class='kw'>high</span>,
-  <span class='kw'>size</span>,
-  generator = <span class='kw'>NULL</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>,
-  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>low</th>
-      <td><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></td>
-    </tr>
-    <tr>
-      <th>high</th>
-      <td><p>(int) One above the highest integer to be drawn from the distribution.</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(tuple) a tuple defining the shape of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>memory format for the resulting tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randint-low-high-size-generator-null-out-null-"><a class="anchor" href="#randint-low-high-size-generator-null-out-null-"></a>randint(low=0, high, size, *, generator=NULL, out=NULL, \ </h2>
-
-    
-
-
-<p>dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor</p>
-<p>Returns a tensor filled with random integers generated uniformly
-between <code>low</code> (inclusive) and <code>high</code> (exclusive).</p>
-<p>The shape of the tensor is defined by the variable argument <code>size</code>.</p>
-<p>.. note:
-With the global dtype default (<code>torch_float32</code>), this function returns
-a tensor with dtype <code>torch_int64</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_randint</span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>3</span>))
-<span class='fu'>torch_randint</span>(<span class='fl'>0</span>, <span class='fl'>10</span>, size = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='fu'>torch_randint</span>(<span class='fl'>3</span>, <span class='fl'>10</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  9  7
-#&gt;  7  3
-#&gt; [ CPUFloatType{2,2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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-    <div class="page-header">
-    <h1>Randint_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randint_like.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randint_like</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_randint_like</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>low</span>,
-  <span class='kw'>high</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>low</th>
-      <td><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></td>
-    </tr>
-    <tr>
-      <th>high</th>
-      <td><p>(int) One above the highest integer to be drawn from the distribution.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-"><a class="anchor" href="#randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-"></a>randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, </h2>
-
-    
-
-
-<p>memory_format=torch.preserve_format) -&gt; Tensor</p>
-<p>Returns a tensor with the same shape as Tensor <code>input</code> filled with
-random integers generated uniformly between <code>low</code> (inclusive) and
-<code>high</code> (exclusive).</p>
-<p>.. note:
-With the global dtype default (<code>torch_float32</code>), this function returns
-a tensor with dtype <code>torch_int64</code>.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Randn</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randn.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randn</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_randn</span>(
-  <span class='kw'>...</span>,
-  names = <span class='kw'>NULL</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>names</th>
-      <td><p>optional names for the dimensions</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with random numbers from a normal distribution
-with mean <code>0</code> and variance <code>1</code> (also called the standard normal
-distribution).</p>
-<p>$$
-    \mbox{out}_{i} \sim \mathcal{N}(0, 1)
-$$
-The shape of the tensor is defined by the variable argument <code>size</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_randn</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='fu'>torch_randn</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.2183 -0.5573 -0.9843
-#&gt; -1.3348 -2.3331 -0.3796
-#&gt; [ CPUFloatType{2,3} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
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-<div class="row">
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-    <div class="page-header">
-    <h1>Randn_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randn_like.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randn_like</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_randn_like</span>(
-  <span class='kw'>input</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>,
-  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor with the same size as <code>input</code> that is filled with
-random numbers from a normal distribution with mean 0 and variance 1.
-<code>torch_randn_like(input)</code> is equivalent to
-<code><a href='torch_randn.html'>torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
-
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diff --git a/static/docs/dev/reference/torch_randperm.html b/static/docs/dev/reference/torch_randperm.html
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Randperm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randperm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randperm</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_randperm</span>(
-  <span class='kw'>n</span>,
-  dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>(),
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>n</th>
-      <td><p>(int) the upper bound (exclusive)</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: <code>torch_int64</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-"><a class="anchor" href="#randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-"></a>randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns a random permutation of integers from <code>0</code> to <code>n - 1</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_randperm</span>(<span class='fl'>4</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  2
-#&gt;  3
-#&gt;  1
-#&gt; [ CPULongType{4} ]</div></pre>
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-    <div class="page-header">
-    <h1>Range</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_range.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Range</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_range</span>(
-  <span class='kw'>start</span>,
-  <span class='kw'>end</span>,
-  step = <span class='fl'>1</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(float) the starting value for the set of points. Default: <code>0</code>.</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(float) the ending value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>step</th>
-      <td><p>(float) the gap between each pair of adjacent points. Default: <code>1</code>.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a 1-D tensor of size \(\left\lfloor \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rfloor + 1\)
-with values from <code>start</code> to <code>end</code> with step <code>step</code>. Step is
-the gap between two values in the tensor.</p>
-<p>$$
-    \mbox{out}_{i+1} = \mbox{out}_i + \mbox{step}.
-$$</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>This function is deprecated in favor of <code><a href='torch_arange.html'>torch_arange</a></code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_range</span>(<span class='fl'>1</span>, <span class='fl'>4</span>)
-<span class='fu'>torch_range</span>(<span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>0.5</span>)
-}
-</div><div class='output co'>#&gt; <span class='warning'>Warning: This function is deprecated in favor of torch_arange.</span></div><div class='output co'>#&gt; <span class='warning'>Warning: This function is deprecated in favor of torch_arange.</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.0000
-#&gt;  1.5000
-#&gt;  2.0000
-#&gt;  2.5000
-#&gt;  3.0000
-#&gt;  3.5000
-#&gt; [ CPUFloatType{6} ]</div></pre>
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-    <h1>Real</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_real.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Real</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_real</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="real-input-gt-tensor-"><a class="anchor" href="#real-input-gt-tensor-"></a>real(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the real part of the <code>input</code> tensor. If
-<code>input</code> is a real (non-complex) tensor, this function just
-returns it.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Not yet implemented for complex tensors.</p>
-<p>$$
-    \mbox{out}_{i} = real(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_real</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span>)))
-}
-}
-</div></pre>
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-    <h1>Reciprocal</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_reciprocal.Rd</code></div>
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-    <p>Reciprocal</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_reciprocal</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="reciprocal-input-out-null-gt-tensor-"><a class="anchor" href="#reciprocal-input-out-null-gt-tensor-"></a>reciprocal(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the reciprocal of the elements of <code>input</code></p>
-<p>$$
-    \mbox{out}_{i} = \frac{1}{\mbox{input}_{i}}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_reciprocal</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.8274
-#&gt; -1.0865
-#&gt; -0.9597
-#&gt;  1.0257
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/reduction.R'><code>R/reduction.R</code></a></small>
-    <div class="hidden name"><code>torch_reduction.Rd</code></div>
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-    <p>Creates the reduction objet</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_reduction_sum</span>()
-
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-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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-    </div>
-
-    <pre class="usage"><span class='fu'>torch_relu</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="relu-input-gt-tensor-"><a class="anchor" href="#relu-input-gt-tensor-"></a>relu(input) -&gt; Tensor </h2>
-
-    
-
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-<p>Computes the relu tranformation.</p>
-
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-    <pre class="usage"><span class='fu'>torch_relu_</span>(<span class='kw'>self</span>)</pre>
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="relu-input-gt-tensor-"><a class="anchor" href="#relu-input-gt-tensor-"></a>relu_(input) -&gt; Tensor </h2>
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-    <h1>Remainder</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_remainder.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Remainder</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_remainder</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the dividend</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="remainder-input-other-out-null-gt-tensor-"><a class="anchor" href="#remainder-input-other-out-null-gt-tensor-"></a>remainder(input, other, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise remainder of division.</p>
-<p>The divisor and dividend may contain both for integer and floating point
-numbers. The remainder has the same sign as the divisor.</p>
-<p>When <code>other</code> is a tensor, the shapes of <code>input</code> and
-<code>other</code> must be broadcastable .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_remainder</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>3.</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)), <span class='fl'>2</span>)
-<span class='fu'>torch_remainder</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>)), <span class='fl'>1.5</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.0000
-#&gt;  0.5000
-#&gt;  0.0000
-#&gt;  1.0000
-#&gt;  0.5000
-#&gt; [ CPUFloatType{5} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_renorm.html b/static/docs/dev/reference/torch_renorm.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_renorm.Rd</code></div>
-    </div>
-
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-    <p>Renorm</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_renorm</span>(<span class='kw'>self</span>, <span class='kw'>p</span>, <span class='kw'>dim</span>, <span class='kw'>maxnorm</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(float) the power for the norm computation</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to slice over to get the sub-tensors</p></td>
-    </tr>
-    <tr>
-      <th>maxnorm</th>
-      <td><p>(float) the maximum norm to keep each sub-tensor under</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>If the norm of a row is lower than <code>maxnorm</code>, the row is unchanged</p>
-    <h2 class="hasAnchor" id="renorm-input-p-dim-maxnorm-out-null-gt-tensor-"><a class="anchor" href="#renorm-input-p-dim-maxnorm-out-null-gt-tensor-"></a>renorm(input, p, dim, maxnorm, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor where each sub-tensor of <code>input</code> along dimension
-<code>dim</code> is normalized such that the <code>p</code>-norm of the sub-tensor is lower
-than the value <code>maxnorm</code></p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='kw'>x</span>[<span class='fl'>2</span>,]<span class='op'>$</span><span class='fu'>fill_</span>(<span class='fl'>2</span>)
-<span class='kw'>x</span>[<span class='fl'>3</span>,]<span class='op'>$</span><span class='fu'>fill_</span>(<span class='fl'>3</span>)
-<span class='kw'>x</span>
-<span class='fu'>torch_renorm</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>5</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.0000  1.0000  1.0000
-#&gt;  1.6667  1.6667  1.6667
-#&gt;  1.6667  1.6667  1.6667
-#&gt; [ CPUFloatType{3,3} ]</div></pre>
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-    <h1>Repeat_interleave</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_repeat_interleave.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Repeat_interleave</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_repeat_interleave</span>(<span class='kw'>self</span>, <span class='kw'>repeats</span>, dim = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>repeats</th>
-      <td><p>(Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) The dimension along which to repeat values.        By default, use the flattened input array, and return a flat output        array.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="repeat-interleave-input-repeats-dim-null-gt-tensor-"><a class="anchor" href="#repeat-interleave-input-repeats-dim-null-gt-tensor-"></a>repeat_interleave(input, repeats, dim=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Repeat elements of a tensor.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-<pre>This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`.
-</pre>
-
-    <h2 class="hasAnchor" id="repeat-interleave-repeats-gt-tensor-"><a class="anchor" href="#repeat-interleave-repeats-gt-tensor-"></a>repeat_interleave(repeats) -&gt; Tensor </h2>
-
-    
-
-
-<p>If the <code>repeats</code> is <code>tensor([n1, n2, n3, ...])</code>, then the output will be
-<code>tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])</code> where <code>0</code> appears <code>n1</code> times,
-<code>1</code> appears <code>n2</code> times, <code>2</code> appears <code>n3</code> times, etc.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='kw'>x</span><span class='op'>$</span><span class='fu'>repeat_interleave</span>(<span class='fl'>2</span>)
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>))
-<span class='fu'>torch_repeat_interleave</span>(<span class='kw'>y</span>, <span class='fl'>2</span>)
-<span class='fu'>torch_repeat_interleave</span>(<span class='kw'>y</span>, <span class='fl'>3</span>, dim=<span class='fl'>1</span>)
-<span class='fu'>torch_repeat_interleave</span>(<span class='kw'>y</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)), dim=<span class='fl'>1</span>)
-}
-}
-</div></pre>
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-    <h1>Reshape</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_reshape.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Reshape</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_reshape</span>(<span class='kw'>self</span>, <span class='kw'>shape</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to be reshaped</p></td>
-    </tr>
-    <tr>
-      <th>shape</th>
-      <td><p>(tuple of ints) the new shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="reshape-input-shape-gt-tensor-"><a class="anchor" href="#reshape-input-shape-gt-tensor-"></a>reshape(input, shape) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor with the same data and number of elements as <code>input</code>,
-but with the specified shape. When possible, the returned tensor will be a view
-of <code>input</code>. Otherwise, it will be a copy. Contiguous inputs and inputs
-with compatible strides can be reshaped without copying, but you should not
-depend on the copying vs. viewing behavior.</p>
-<p>See <code>torch_Tensor.view</code> on when it is possible to return a view.</p>
-<p>A single dimension may be -1, in which case it's inferred from the remaining
-dimensions and the number of elements in <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span>)
-<span class='fu'>torch_reshape</span>(<span class='kw'>a</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>))
-<span class='fu'>torch_reshape</span>(<span class='kw'>b</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='op'>-</span><span class='fl'>1</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_result_type.Rd</code></div>
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-
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-    <p>Result_type</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_result_type</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor1</th>
-      <td><p>(Tensor or Number) an input tensor or number</p></td>
-    </tr>
-    <tr>
-      <th>tensor2</th>
-      <td><p>(Tensor or Number) an input tensor or number</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="result-type-tensor-tensor-gt-dtype-"><a class="anchor" href="#result-type-tensor-tensor-gt-dtype-"></a>result_type(tensor1, tensor2) -&gt; dtype </h2>
-
-    
-
-
-<p>Returns the <code>torch_dtype</code> that would result from performing an arithmetic
-operation on the provided input tensors. See type promotion documentation
-for more information on the type promotion logic.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_result_type</span>(tensor1 = <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int</a></span>()), tensor2 = <span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_Float</div></pre>
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-    <h1>Rfft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_rfft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Rfft</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_rfft</span>(<span class='kw'>self</span>, <span class='kw'>signal_ndim</span>, normalized = <span class='fl'>FALSE</span>, onesided = <span class='fl'>TRUE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> dimensions</p></td>
-    </tr>
-    <tr>
-      <th>signal_ndim</th>
-      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>onesided</th>
-      <td><p>(bool, optional) controls whether to return half of results to        avoid redundancy. Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-</pre>
-
-    <h2 class="hasAnchor" id="rfft-input-signal-ndim-normalized-false-onesided-true-gt-tensor-"><a class="anchor" href="#rfft-input-signal-ndim-normalized-false-onesided-true-gt-tensor-"></a>rfft(input, signal_ndim, normalized=False, onesided=TRUE) -&gt; Tensor </h2>
-
-    
-
-
-<p>Real-to-complex Discrete Fourier Transform</p>
-<p>This method computes the real-to-complex discrete Fourier transform. It is
-mathematically equivalent with <code><a href='torch_fft.html'>torch_fft</a></code> with differences only in
-formats of the input and output.</p>
-<p>This method supports 1D, 2D and 3D real-to-complex transforms, indicated
-by <code>signal_ndim</code>. <code>input</code> must be a tensor with at least
-<code>signal_ndim</code> dimensions with optionally arbitrary number of leading batch
-dimensions. If <code>normalized</code> is set to <code>TRUE</code>, this normalizes the result
-by dividing it with \(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is
-unitary, where \(N_i\) is the size of signal dimension \(i\).</p>
-<p>The real-to-complex Fourier transform results follow conjugate symmetry:</p>
-<p>$$
-    X[\omega_1, \dots, \omega_d] = X^*[N_1 - \omega_1, \dots, N_d - \omega_d],
-$$
-where the index arithmetic is computed modulus the size of the corresponding
-dimension, \(\ ^*\) is the conjugate operator, and
-\(d\) = <code>signal_ndim</code>. <code>onesided</code> flag controls whether to avoid
-redundancy in the output results. If set to <code>TRUE</code> (default), the output will
-not be full complex result of shape \((*, 2)\), where \(*\) is the shape
-of <code>input</code>, but instead the last dimension will be halfed as of size
-\(\lfloor \frac{N_d}{2} \rfloor + 1\).</p>
-<p>The inverse of this function is <code><a href='torch_irfft.html'>torch_irfft</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>For CPU tensors, this method is currently only available with MKL. Use
-<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>5</span>))
-<span class='fu'>torch_rfft</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
-<span class='fu'>torch_rfft</span>(<span class='kw'>x</span>, <span class='fl'>2</span>, onesided=<span class='fl'>FALSE</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   3.5526  0.0000
-#&gt;   4.0253  3.6969
-#&gt;   1.6249  6.1226
-#&gt;   1.6249 -6.1226
-#&gt;   4.0253 -3.6969
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -1.9402 -0.2727
-#&gt;   4.0343 -3.2595
-#&gt;  -4.9039 -4.4225
-#&gt;   6.3163  4.4783
-#&gt;   3.9317  2.2115
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -0.9574 -1.9479
-#&gt;  -3.7632 -0.9633
-#&gt;  -5.9081 -0.3430
-#&gt;  -3.4389  2.0760
-#&gt;   0.6001 -0.8882
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  -0.9574  1.9479
-#&gt;   0.6001  0.8882
-#&gt;  -3.4389 -2.0760
-#&gt;  -5.9081  0.3430
-#&gt;  -3.7632  0.9633
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  -1.9402  0.2727
-#&gt;   3.9317 -2.2115
-#&gt;   6.3163 -4.4783
-#&gt;  -4.9039  4.4225
-#&gt;   4.0343  3.2595
-#&gt; [ CPUFloatType{5,5,2} ]</div></pre>
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-    <h1>Roll</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_roll.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Roll</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_roll</span>(<span class='kw'>self</span>, <span class='kw'>shifts</span>, dims = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>())</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>shifts</th>
-      <td><p>(int or tuple of ints) The number of places by which the elements        of the tensor are shifted. If shifts is a tuple, dims must be a tuple of        the same size, and each dimension will be rolled by the corresponding        value</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(int or tuple of ints) Axis along which to roll</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="roll-input-shifts-dims-null-gt-tensor-"><a class="anchor" href="#roll-input-shifts-dims-null-gt-tensor-"></a>roll(input, shifts, dims=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Roll the tensor along the given dimension(s). Elements that are shifted beyond the
-last position are re-introduced at the first position. If a dimension is not
-specified, the tensor will be flattened before rolling and then restored
-to the original shape.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>, <span class='fl'>7</span>, <span class='fl'>8</span>))<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
-<span class='kw'>x</span>
-<span class='fu'>torch_roll</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fl'>1</span>)
-<span class='fu'>torch_roll</span>(<span class='kw'>x</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span>)
-<span class='fu'>torch_roll</span>(<span class='kw'>x</span>, shifts=<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), dims=<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  6  5
-#&gt;  8  7
-#&gt;  2  1
-#&gt;  4  3
-#&gt; [ CPUFloatType{4,2} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_rot90.html b/static/docs/dev/reference/torch_rot90.html
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-    <div class="page-header">
-    <h1>Rot90</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_rot90.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Rot90</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_rot90</span>(<span class='kw'>self</span>, k = <span class='fl'>1L</span>, dims = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>))</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>k</th>
-      <td><p>(int) number of times to rotate</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(a list or tuple) axis to rotate</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rot-input-k-dims-gt-tensor-"><a class="anchor" href="#rot-input-k-dims-gt-tensor-"></a>rot90(input, k, dims) -&gt; Tensor </h2>
-
-    
-
-
-<p>Rotate a n-D tensor by 90 degrees in the plane specified by dims axis.
-Rotation direction is from the first towards the second axis if k &gt; 0, and from the second towards the first for k &lt; 0.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='kw'>x</span>
-<span class='fu'>torch_rot90</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>8</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='kw'>x</span>
-<span class='fu'>torch_rot90</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   2  3
-#&gt;   6  7
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   0  1
-#&gt;   4  5
-#&gt; [ CPUFloatType{2,2,2} ]</div></pre>
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-    <div class="page-header">
-    <h1>Round</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_round.Rd</code></div>
-    </div>
-
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-    <p>Round</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_round</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="round-input-out-null-gt-tensor-"><a class="anchor" href="#round-input-out-null-gt-tensor-"></a>round(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with each of the elements of <code>input</code> rounded
-to the closest integer.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_round</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  0
-#&gt; -1
-#&gt; -0
-#&gt; [ CPUFloatType{4} ]</div></pre>
-  </div>
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-    <h1>Rrelu_</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_rrelu_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Rrelu_</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_rrelu_</span>(
-  <span class='kw'>self</span>,
-  lower = <span class='fl'>0.125</span>,
-  upper = <span class='fl'>0.333333</span>,
-  training = <span class='fl'>FALSE</span>,
-  generator = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>lower</th>
-      <td><p>lower bound of the uniform distribution. Default: 1/8</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>upper bound of the uniform distribution. Default: 1/3</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>bool wether it's a training pass. DEfault: FALSE</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>random number generator</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rrelu-input-lower-upper-training-false-gt-tensor-"><a class="anchor" href="#rrelu-input-lower-upper-training-false-gt-tensor-"></a>rrelu_(input, lower=1./8, upper=1./3, training=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>In-place version of <code>torch_rrelu</code>.</p>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Rsqrt</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_rsqrt.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Rsqrt</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_rsqrt</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rsqrt-input-out-null-gt-tensor-"><a class="anchor" href="#rsqrt-input-out-null-gt-tensor-"></a>rsqrt(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the reciprocal of the square-root of each of
-the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \frac{1}{\sqrt{\mbox{input}_{i}}}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_rsqrt</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  3.1352
-#&gt;     nan
-#&gt;  0.6150
-#&gt;     nan
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <h1>Saves an object to a disk file.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/save.R'><code>R/save.R</code></a></small>
-    <div class="hidden name"><code>torch_save.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function is experimental, don't use for long
-term storage.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_save</span>(<span class='kw'>obj</span>, <span class='kw'>path</span>, <span class='kw'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>obj</th>
-      <td><p>the saved object</p></td>
-    </tr>
-    <tr>
-      <th>path</th>
-      <td><p>a connection or the name of the file to save.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>not currently used.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other torch_save: 
-<code><a href='torch_load.html'>torch_load</a>()</code></p></div>
-
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-    <pre class="usage"><span class='fu'>torch_selu</span>(<span class='kw'>self</span>)</pre>
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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-    <p>Selu_</p>
-    </div>
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-    <pre class="usage"><span class='fu'>torch_selu_</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="selu-input-gt-tensor-"><a class="anchor" href="#selu-input-gt-tensor-"></a>selu_(input) -&gt; Tensor </h2>
-
-    
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-<p>In-place version of <code><a href='torch_selu.html'>torch_selu()</a></code>.</p>
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-    <h1>Sigmoid</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sigmoid.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sigmoid</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_sigmoid</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sigmoid-input-out-null-gt-tensor-"><a class="anchor" href="#sigmoid-input-out-null-gt-tensor-"></a>sigmoid(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the sigmoid of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \frac{1}{1 + e^{-\mbox{input}_{i}}}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_sigmoid</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5992
-#&gt;  0.2307
-#&gt;  0.3896
-#&gt;  0.4067
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <h1>Sign</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sign.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sign</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_sign</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sign-input-out-null-gt-tensor-"><a class="anchor" href="#sign-input-out-null-gt-tensor-"></a>sign(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the signs of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \mbox{sgn}(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.7</span>, <span class='op'>-</span><span class='fl'>1.2</span>, <span class='fl'>0.</span>, <span class='fl'>2.3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_sign</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt; -1
-#&gt;  0
-#&gt;  1
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_sin.html b/static/docs/dev/reference/torch_sin.html
deleted file mode 100644
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-    <h1>Sin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sin</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_sin</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sin-input-out-null-gt-tensor-"><a class="anchor" href="#sin-input-out-null-gt-tensor-"></a>sin(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the sine of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \sin(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_sin</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.5060
-#&gt; -0.3165
-#&gt; -0.4515
-#&gt;  0.9997
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <h1>Sinh</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sinh.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sinh</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_sinh</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sinh-input-out-null-gt-tensor-"><a class="anchor" href="#sinh-input-out-null-gt-tensor-"></a>sinh(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the hyperbolic sine of the elements of
-<code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \sinh(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_sinh</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0588
-#&gt;  0.0174
-#&gt;  0.3552
-#&gt; -0.3363
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/dev/reference/torch_slogdet.html b/static/docs/dev/reference/torch_slogdet.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_slogdet.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Slogdet</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_slogdet</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>If `input` has zero determinant, this returns `(0, -inf)`.
-</pre>
-
-<pre>Backward through `slogdet` internally uses SVD results when `input`
-is not invertible. In this case, double backward through `slogdet`
-will be unstable in when `input` doesn't have distinct singular values.
-See `~torch.svd` for details.
-</pre>
-
-    <h2 class="hasAnchor" id="slogdet-input-gt-tensor-tensor-"><a class="anchor" href="#slogdet-input-gt-tensor-tensor-"></a>slogdet(input) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='kw'>A</span>
-<span class='fu'><a href='torch_det.html'>torch_det</a></span>(<span class='kw'>A</span>)
-<span class='fu'><a href='torch_logdet.html'>torch_logdet</a></span>(<span class='kw'>A</span>)
-<span class='fu'>torch_slogdet</span>(<span class='kw'>A</span>)
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; 1
-#&gt; [ CPUFloatType{} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; -0.961507
-#&gt; [ CPUFloatType{} ]
-#&gt; </div></pre>
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-    <div class="page-header">
-    <h1>Solve</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_solve.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Solve</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_solve</span>(<span class='kw'>self</span>, <span class='kw'>A</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) input matrix \(B\) of size \((*, m, k)\) , where \(*\)                is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor) input square matrix of size \((*, m, m)\), where                \(*\) is zero or more batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>Irrespective of the original strides, the returned matrices
-`solution` and `LU` will be transposed, i.e. with strides like
-`B$contiguous()$transpose(-1, -2)$stride()` and
-`A$contiguous()$transpose(-1, -2)$stride()` respectively.
-</pre>
-
-    <h2 class="hasAnchor" id="solve-input-a-gt-tensor-tensor-"><a class="anchor" href="#solve-input-a-gt-tensor-tensor-"></a>solve(input, A) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>This function returns the solution to the system of linear
-equations represented by \(AX = B\) and the LU factorization of
-A, in order as a namedtuple <code>solution, LU</code>.</p>
-<p><code>LU</code> contains <code>L</code> and <code>U</code> factors for LU factorization of <code>A</code>.</p>
-<p><code>torch_solve(B, A)</code> can take in 2D inputs <code>B, A</code> or inputs that are
-batches of 2D matrices. If the inputs are batches, then returns
-batched outputs <code>solution, LU</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>6.80</span>, <span class='op'>-</span><span class='fl'>2.11</span>,  <span class='fl'>5.66</span>,  <span class='fl'>5.97</span>,  <span class='fl'>8.23</span>),
-                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>6.05</span>, <span class='op'>-</span><span class='fl'>3.30</span>,  <span class='fl'>5.36</span>, <span class='op'>-</span><span class='fl'>4.44</span>,  <span class='fl'>1.08</span>),
-                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>0.45</span>,  <span class='fl'>2.58</span>, <span class='op'>-</span><span class='fl'>2.70</span>,  <span class='fl'>0.27</span>,  <span class='fl'>9.04</span>),
-                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>8.32</span>,  <span class='fl'>2.71</span>,  <span class='fl'>4.35</span>,  <span class='op'>-</span><span class='fl'>7.17</span>,  <span class='fl'>2.14</span>),
-                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>9.67</span>, <span class='op'>-</span><span class='fl'>5.14</span>, <span class='op'>-</span><span class='fl'>7.26</span>,  <span class='fl'>6.08</span>, <span class='op'>-</span><span class='fl'>6.87</span>)))<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()
-<span class='kw'>B</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4.02</span>,  <span class='fl'>6.19</span>, <span class='op'>-</span><span class='fl'>8.22</span>, <span class='op'>-</span><span class='fl'>7.57</span>, <span class='op'>-</span><span class='fl'>3.03</span>),
-                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1.56</span>,  <span class='fl'>4.00</span>, <span class='op'>-</span><span class='fl'>8.67</span>,  <span class='fl'>1.75</span>,  <span class='fl'>2.86</span>),
-                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>9.81</span>, <span class='op'>-</span><span class='fl'>4.09</span>, <span class='op'>-</span><span class='fl'>4.57</span>, <span class='op'>-</span><span class='fl'>8.61</span>,  <span class='fl'>8.99</span>)))<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()
-<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_solve</span>(<span class='kw'>B</span>, <span class='kw'>A</span>)
-<span class='kw'>X</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
-<span class='kw'>LU</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
-<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='kw'>B</span>, <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>A</span>, <span class='kw'>X</span>))
-<span class='co'># Batched solver example</span>
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>B</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>6</span>))
-<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_solve</span>(<span class='kw'>B</span>, <span class='kw'>A</span>)
-<span class='kw'>X</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
-<span class='kw'>LU</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
-<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='kw'>B</span>, <span class='kw'>A</span><span class='op'>$</span><span class='fu'>matmul</span>(<span class='kw'>X</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 2.74831e-06
-#&gt; [ CPUFloatType{} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_sort.html b/static/docs/dev/reference/torch_sort.html
deleted file mode 100644
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--- a/static/docs/dev/reference/torch_sort.html
+++ /dev/null
@@ -1,281 +0,0 @@
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-
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sort</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sort.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sort</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_sort</span>(<span class='kw'>self</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, descending = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to sort along</p></td>
-    </tr>
-    <tr>
-      <th>descending</th>
-      <td><p>(bool, optional) controls the sorting order (ascending or descending)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sort-input-dim-descending-false-gt-tensor-longtensor-"><a class="anchor" href="#sort-input-dim-descending-false-gt-tensor-longtensor-"></a>sort(input, dim=-1, descending=FALSE) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Sorts the elements of the <code>input</code> tensor along a given dimension
-in ascending order by value.</p>
-<p>If <code>dim</code> is not given, the last dimension of the <code>input</code> is chosen.</p>
-<p>If <code>descending</code> is <code>TRUE</code> then the elements are sorted in descending
-order by value.</p>
-<p>A namedtuple of (values, indices) is returned, where the <code>values</code> are the
-sorted values and <code>indices</code> are the indices of the elements in the original
-<code>input</code> tensor.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_sort</span>(<span class='kw'>x</span>)
-<span class='kw'>out</span>
-<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_sort</span>(<span class='kw'>x</span>, <span class='fl'>1</span>)
-<span class='kw'>out</span>
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; -2.2169 -0.6092  0.1243 -1.0307
-#&gt; -0.0321  0.3174  0.3301 -0.8698
-#&gt;  0.2442  0.9253  0.9524  0.4194
-#&gt; [ CPUFloatType{3,4} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  1  2  1  2
-#&gt;  0  1  0  1
-#&gt;  2  0  2  0
-#&gt; [ CPULongType{3,4} ]
-#&gt; </div></pre>
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diff --git a/static/docs/dev/reference/torch_sparse_coo_tensor.html b/static/docs/dev/reference/torch_sparse_coo_tensor.html
deleted file mode 100644
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-    <div class="page-header">
-    <h1>Sparse_coo_tensor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_sparse_coo_tensor.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sparse_coo_tensor</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_sparse_coo_tensor</span>(
-  <span class='kw'>indices</span>,
-  <span class='kw'>values</span>,
-  size = <span class='kw'>NULL</span>,
-  dtype = <span class='kw'>NULL</span>,
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>indices</th>
-      <td><p>(array_like) Initial data for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types. Will be cast to a <code>torch_LongTensor</code>        internally. The indices are the coordinates of the non-zero values in the matrix, and thus        should be two-dimensional where the first dimension is the number of tensor dimensions and        the second dimension is the number of non-zero values.</p></td>
-    </tr>
-    <tr>
-      <th>values</th>
-      <td><p>(array_like) Initial values for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types.</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(list, tuple, or <code>torch.Size</code>, optional) Size of the sparse tensor. If not        provided the size will be inferred as the minimum size big enough to hold all non-zero        elements.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if NULL, infers data type from <code>values</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if NULL, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-"></a>sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given <code>indices</code>
-with the given <code>values</code>. A sparse tensor can be <code>uncoalesced</code>, in that case, there are duplicate
-coordinates in the indices, and the value at that index is the sum of all duplicate value entries:
-<code>torch_sparse</code>_.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>i</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>3</span>), ncol = <span class='fl'>3</span>, byrow = <span class='fl'>TRUE</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
-<span class='kw'>v</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_float32</a></span>())
-<span class='fu'>torch_sparse_coo_tensor</span>(<span class='kw'>i</span>, <span class='kw'>v</span>)
-<span class='fu'>torch_sparse_coo_tensor</span>(<span class='kw'>i</span>, <span class='kw'>v</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>4</span>))
-
-<span class='co'># create empty sparse tensors</span>
-<span class='kw'>S</span> <span class='op'>=</span> <span class='fu'>torch_sparse_coo_tensor</span>(
-  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()), 
-  <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>numeric</a></span>(), dtype = <span class='fu'><a href='torch_dtype.html'>torch_float32</a></span>()), 
-  <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>)
-)
-<span class='kw'>S</span> <span class='op'>=</span> <span class='fu'>torch_sparse_coo_tensor</span>(
-  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()), 
-  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>2</span>)), 
-  <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)
-)
-}
-</div></pre>
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-    <div class="page-header">
-    <h1>Split</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_split.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Split</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_split</span>(<span class='kw'>self</span>, <span class='kw'>split_size</span>, dim = <span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) tensor to split.</p></td>
-    </tr>
-    <tr>
-      <th>split_size</th>
-      <td><p>(int) size of a single chunk or        list of sizes for each chunk</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension along which to split the tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Splits the tensor into chunks. Each chunk is a view of the original tensor.</p><pre>If `split_size_or_sections` is an integer type, then `tensor` will
-be split into equally sized chunks (if possible). Last chunk will be smaller if
-the tensor size along the given dimension `dim` is not divisible by
-`split_size`.
-
-If `split_size_or_sections` is a list, then `tensor` will be split
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-    <div class="page-header">
-    <h1>Sqrt</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sqrt.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sqrt</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_sqrt</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sqrt-input-out-null-gt-tensor-"><a class="anchor" href="#sqrt-input-out-null-gt-tensor-"></a>sqrt(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the square-root of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \sqrt{\mbox{input}_{i}}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_sqrt</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;     nan
-#&gt;     nan
-#&gt;  0.9580
-#&gt;     nan
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <h1>Square</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_square.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Square</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_square</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="square-input-out-null-gt-tensor-"><a class="anchor" href="#square-input-out-null-gt-tensor-"></a>square(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the square of the elements of <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_square</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.7874
-#&gt;  1.8919
-#&gt;  0.7366
-#&gt;  0.8790
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <h1>Squeeze</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_squeeze.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Squeeze</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_squeeze</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) if given, the input will be squeezed only in           this dimension</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>The returned tensor shares the storage with the input tensor,
-so changing the contents of one will change the contents of the other.</p>
-    <h2 class="hasAnchor" id="squeeze-input-dim-null-out-null-gt-tensor-"><a class="anchor" href="#squeeze-input-dim-null-out-null-gt-tensor-"></a>squeeze(input, dim=NULL, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor with all the dimensions of <code>input</code> of size <code>1</code> removed.</p>
-<p>For example, if <code>input</code> is of shape:
-\((A \times 1 \times B \times C \times 1 \times D)\) then the <code>out</code> tensor
-will be of shape: \((A \times B \times C \times D)\).</p>
-<p>When <code>dim</code> is given, a squeeze operation is done only in the given
-dimension. If <code>input</code> is of shape: \((A \times 1 \times B)\),
-<code>squeeze(input, 0)</code> leaves the tensor unchanged, but <code>squeeze(input, 1)</code>
-will squeeze the tensor to the shape \((A \times B)\).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='kw'>x</span>
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_squeeze</span>(<span class='kw'>x</span>)
-<span class='kw'>y</span>
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_squeeze</span>(<span class='kw'>x</span>, <span class='fl'>1</span>)
-<span class='kw'>y</span>
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_squeeze</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
-<span class='kw'>y</span>
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,1,.,.) = 
-#&gt;   0  0
-#&gt; 
-#&gt; (2,1,.,.) = 
-#&gt;   0  0
-#&gt; 
-#&gt; (1,2,.,.) = 
-#&gt;   0  0
-#&gt; 
-#&gt; (2,2,.,.) = 
-#&gt;   0  0
-#&gt; [ CPUFloatType{2,2,1,2} ]</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_stack.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Stack</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_stack</span>(<span class='kw'>tensors</span>, dim = <span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(sequence of Tensors) sequence of tensors to concatenate</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="stack-tensors-dim-out-null-gt-tensor-"><a class="anchor" href="#stack-tensors-dim-out-null-gt-tensor-"></a>stack(tensors, dim=0, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Concatenates sequence of tensors along a new dimension.</p>
-<p>All tensors need to be of the same size.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>Std</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_std.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Std</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_std</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, unbiased = <span class='fl'>TRUE</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>unbiased</th>
-      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="std-input-unbiased-true-gt-tensor-"><a class="anchor" href="#std-input-unbiased-true-gt-tensor-"></a>std(input, unbiased=TRUE) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the standard-deviation of all elements in the <code>input</code> tensor.</p>
-<p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.</p>
-    <h2 class="hasAnchor" id="std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-"><a class="anchor" href="#std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-"></a>std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the standard-deviation of each row of the <code>input</code> tensor in the
-dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
-reduce over all of them.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-<p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_std</span>(<span class='kw'>a</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_std</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.2041
-#&gt;  1.0409
-#&gt;  1.0089
-#&gt;  0.3630
-#&gt; [ CPUFloatType{4} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_std_mean.html b/static/docs/dev/reference/torch_std_mean.html
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Std_mean</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_std_mean.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Std_mean</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_std_mean</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, unbiased = <span class='fl'>TRUE</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>unbiased</th>
-      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="std-mean-input-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#std-mean-input-unbiased-true-gt-tensor-tensor-"></a>std_mean(input, unbiased=TRUE) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Returns the standard-deviation and mean of all elements in the <code>input</code> tensor.</p>
-<p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.</p>
-    <h2 class="hasAnchor" id="std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-"><a class="anchor" href="#std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-"></a>std_mean(input, dim, unbiased=TRUE, keepdim=False) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Returns the standard-deviation and mean of each row of the <code>input</code> tensor in the
-dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
-reduce over all of them.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-<p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_std_mean</span>(<span class='kw'>a</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_std_mean</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  0.1522
-#&gt;  0.7823
-#&gt;  0.6119
-#&gt;  0.8016
-#&gt; [ CPUFloatType{4} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; -0.1078
-#&gt;  0.7849
-#&gt;  0.0082
-#&gt;  0.7335
-#&gt; [ CPUFloatType{4} ]
-#&gt; </div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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-    <div class="page-header">
-    <h1>Stft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_stft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Stft</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_stft</span>(
-  <span class='kw'>input</span>,
-  <span class='kw'>n_fft</span>,
-  hop_length = <span class='kw'>NULL</span>,
-  win_length = <span class='kw'>NULL</span>,
-  window = <span class='kw'>NULL</span>,
-  center = <span class='fl'>TRUE</span>,
-  pad_mode = <span class='st'>"reflect"</span>,
-  normalized = <span class='fl'>FALSE</span>,
-  onesided = <span class='fl'>TRUE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>n_fft</th>
-      <td><p>(int) size of Fourier transform</p></td>
-    </tr>
-    <tr>
-      <th>hop_length</th>
-      <td><p>(int, optional) the distance between neighboring sliding window        frames. Default: <code>NULL</code> (treated as equal to <code><a href='https://rdrr.io/r/base/Round.html'>floor(n_fft / 4)</a></code>)</p></td>
-    </tr>
-    <tr>
-      <th>win_length</th>
-      <td><p>(int, optional) the size of window frame and STFT filter.        Default: <code>NULL</code>  (treated as equal to <code>n_fft</code>)</p></td>
-    </tr>
-    <tr>
-      <th>window</th>
-      <td><p>(Tensor, optional) the optional window function.        Default: <code>NULL</code> (treated as window of all \(1\) s)</p></td>
-    </tr>
-    <tr>
-      <th>center</th>
-      <td><p>(bool, optional) whether to pad <code>input</code> on both sides so        that the \(t\)-th frame is centered at time \(t \times \mbox{hop\_length}\).        Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>pad_mode</th>
-      <td><p>(string, optional) controls the padding method used when        <code>center</code> is <code>TRUE</code>. Default: <code>"reflect"</code></p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return the normalized STFT results         Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>onesided</th>
-      <td><p>(bool, optional) controls whether to return half of results to        avoid redundancy Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="short-time-fourier-transform-stft-"><a class="anchor" href="#short-time-fourier-transform-stft-"></a>Short-time Fourier transform (STFT). </h2>
-
-    
-
-
-<p>Short-time Fourier transform (STFT).</p><pre>Ignoring the optional batch dimension, this method computes the following
-expression:
-</pre>
-
-<p>$$
-        X[m, \omega] = \sum_{k = 0}^{\mbox{win\_length-1}}%
-                            \mbox{window}[k]\ \mbox{input}[m \times \mbox{hop\_length} + k]\ %
-                            \exp\left(- j \frac{2 \pi \cdot \omega k}{\mbox{win\_length}}\right),
-$$
-where \(m\) is the index of the sliding window, and \(\omega\) is
-the frequency that \(0 \leq \omega &lt; \mbox{n\_fft}\). When
-<code>onesided</code> is the default value <code>TRUE</code>,</p><pre>* `input` must be either a 1-D time sequence or a 2-D batch of time
-  sequences.
-
-* If `hop_length` is `NULL` (default), it is treated as equal to
-  `floor(n_fft / 4)`.
-
-* If `win_length` is `NULL` (default), it is treated as equal to
-  `n_fft`.
-
-* `window` can be a 1-D tensor of size `win_length`, e.g., from
-  `torch_hann_window`. If `window` is `NULL` (default), it is
-  treated as if having \eqn{1} everywhere in the window. If
-  \eqn{\mbox{win\_length} &lt; \mbox{n\_fft}}, `window` will be padded on
-  both sides to length `n_fft` before being applied.
-
-* If `center` is `TRUE` (default), `input` will be padded on
-  both sides so that the \eqn{t}-th frame is centered at time
-  \eqn{t \times \mbox{hop\_length}}. Otherwise, the \eqn{t}-th frame
-  begins at time  \eqn{t \times \mbox{hop\_length}}.
-
-* `pad_mode` determines the padding method used on `input` when
-  `center` is `TRUE`. See `torch_nn.functional.pad` for
-  all available options. Default is `"reflect"`.
-
-* If `onesided` is `TRUE` (default), only values for \eqn{\omega}
-  in \eqn{\left[0, 1, 2, \dots, \left\lfloor \frac{\mbox{n\_fft}}{2} \right\rfloor + 1\right]}
-  are returned because the real-to-complex Fourier transform satisfies the
-  conjugate symmetry, i.e., \eqn{X[m, \omega] = X[m, \mbox{n\_fft} - \omega]^*}.
-
-* If `normalized` is `TRUE` (default is `FALSE`), the function
-  returns the normalized STFT results, i.e., multiplied by \eqn{(\mbox{frame\_length})^{-0.5}}.
-
-Returns the real and the imaginary parts together as one tensor of size
-\eqn{(* \times N \times T \times 2)}, where \eqn{*} is the optional
-batch size of `input`, \eqn{N} is the number of frequencies where
-STFT is applied, \eqn{T} is the total number of frames used, and each pair
-in the last dimension represents a complex number as the real part and the
-imaginary part.
-</pre>
-
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>This function changed signature at version 0.4.1. Calling with the
-previous signature may cause error or return incorrect result.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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diff --git a/static/docs/dev/reference/torch_sum.html b/static/docs/dev/reference/torch_sum.html
deleted file mode 100644
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--- a/static/docs/dev/reference/torch_sum.html
+++ /dev/null
@@ -1,287 +0,0 @@
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-    <h1>Sum</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sum.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sum</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_sum</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>, dtype = <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sum-input-dtype-null-gt-tensor-"><a class="anchor" href="#sum-input-dtype-null-gt-tensor-"></a>sum(input, dtype=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the sum of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="sum-input-dim-keepdim-false-dtype-null-gt-tensor-"><a class="anchor" href="#sum-input-dim-keepdim-false-dtype-null-gt-tensor-"></a>sum(input, dim, keepdim=False, dtype=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the sum of each row of the <code>input</code> tensor in the given
-dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
-reduce over all of them.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_sum</span>(<span class='kw'>a</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_sum</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span> <span class='op'>*</span> <span class='fl'>5</span> <span class='op'>*</span> <span class='fl'>6</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>))
-<span class='fu'>torch_sum</span>(<span class='kw'>b</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;   435
-#&gt;  1335
-#&gt;  2235
-#&gt;  3135
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <h1>Svd</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_svd.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Svd</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_svd</span>(<span class='kw'>self</span>, some = <span class='fl'>TRUE</span>, compute_uv = <span class='fl'>TRUE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size \((*, m, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                    batch dimensions consisting of \(m \times n\) matrices.</p></td>
-    </tr>
-    <tr>
-      <th>some</th>
-      <td><p>(bool, optional) controls the shape of returned <code>U</code> and <code>V</code></p></td>
-    </tr>
-    <tr>
-      <th>compute_uv</th>
-      <td><p>(bool, optional) option whether to compute <code>U</code> and <code>V</code> or not</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>The singular values are returned in descending order. If <code>input</code> is a batch of matrices,
-then the singular values of each matrix in the batch is returned in descending order.</p>
-<p>The implementation of SVD on CPU uses the LAPACK routine <code>?gesdd</code> (a divide-and-conquer
-algorithm) instead of <code>?gesvd</code> for speed. Analogously, the SVD on GPU uses the MAGMA routine
-<code>gesdd</code> as well.</p>
-<p>Irrespective of the original strides, the returned matrix <code>U</code>
-will be transposed, i.e. with strides <code>U.contiguous().transpose(-2, -1).stride()</code></p>
-<p>Extra care needs to be taken when backward through <code>U</code> and <code>V</code>
-outputs. Such operation is really only stable when <code>input</code> is
-full rank with all distinct singular values. Otherwise, <code>NaN</code> can
-appear as the gradients are not properly defined. Also, notice that
-double backward will usually do an additional backward through <code>U</code> and
-<code>V</code> even if the original backward is only on <code>S</code>.</p>
-<p>When <code>some</code> = <code>FALSE</code>, the gradients on <code>U[..., :, min(m, n):]</code>
-and <code>V[..., :, min(m, n):]</code> will be ignored in backward as those vectors
-can be arbitrary bases of the subspaces.</p>
-<p>When <code>compute_uv</code> = <code>FALSE</code>, backward cannot be performed since <code>U</code> and <code>V</code>
-from the forward pass is required for the backward operation.</p>
-    <h2 class="hasAnchor" id="svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-"><a class="anchor" href="#svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-"></a>svd(input, some=TRUE, compute_uv=TRUE) -&gt; (Tensor, Tensor, Tensor) </h2>
-
-    
-
-
-<p>This function returns a namedtuple <code>(U, S, V)</code> which is the singular value
-decomposition of a input real matrix or batches of real matrices <code>input</code> such that
-\(input = U \times diag(S) \times V^T\).</p>
-<p>If <code>some</code> is <code>TRUE</code> (default), the method returns the reduced singular value decomposition
-i.e., if the last two dimensions of <code>input</code> are <code>m</code> and <code>n</code>, then the returned
-<code>U</code> and <code>V</code> matrices will contain only \(min(n, m)\) orthonormal columns.</p>
-<p>If <code>compute_uv</code> is <code>FALSE</code>, the returned <code>U</code> and <code>V</code> matrices will be zero matrices
-of shape \((m \times m)\) and \((n \times n)\) respectively. <code>some</code> will be ignored here.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_svd</span>(<span class='kw'>a</span>)
-<span class='kw'>u</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
-<span class='kw'>s</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
-<span class='kw'>v</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>3</span>]]
-<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='kw'>a</span>, <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>u</span>, <span class='fu'><a href='torch_diag.html'>torch_diag</a></span>(<span class='kw'>s</span>)), <span class='kw'>v</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()))
-<span class='kw'>a_big</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>7</span>, <span class='fl'>5</span>, <span class='fl'>3</span>))
-<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_svd</span>(<span class='kw'>a_big</span>)
-<span class='kw'>u</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
-<span class='kw'>s</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
-<span class='kw'>v</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>3</span>]]
-<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='kw'>a_big</span>, <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>u</span>, <span class='fu'><a href='torch_diag_embed.html'>torch_diag_embed</a></span>(<span class='kw'>s</span>)), <span class='kw'>v</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>)))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 3.14211e-06
-#&gt; [ CPUFloatType{} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>Symeig</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_symeig.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Symeig</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_symeig</span>(<span class='kw'>self</span>, eigenvectors = <span class='fl'>FALSE</span>, upper = <span class='fl'>TRUE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size \((*, n, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                    batch dimensions consisting of symmetric matrices.</p></td>
-    </tr>
-    <tr>
-      <th>eigenvectors</th>
-      <td><p>(boolean, optional) controls whether eigenvectors have to be computed</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(boolean, optional) controls whether to consider upper-triangular or lower-triangular region</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>The eigenvalues are returned in ascending order. If <code>input</code> is a batch of matrices,
-then the eigenvalues of each matrix in the batch is returned in ascending order.</p>
-<p>Irrespective of the original strides, the returned matrix <code>V</code> will
-be transposed, i.e. with strides <code>V.contiguous().transpose(-1, -2).stride()</code>.</p>
-<p>Extra care needs to be taken when backward through outputs. Such
-operation is really only stable when all eigenvalues are distinct.
-Otherwise, <code>NaN</code> can appear as the gradients are not properly defined.</p>
-    <h2 class="hasAnchor" id="symeig-input-eigenvectors-false-upper-true-gt-tensor-tensor-"><a class="anchor" href="#symeig-input-eigenvectors-false-upper-true-gt-tensor-tensor-"></a>symeig(input, eigenvectors=False, upper=TRUE) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>This function returns eigenvalues and eigenvectors
-of a real symmetric matrix <code>input</code> or a batch of real symmetric matrices,
-represented by a namedtuple (eigenvalues, eigenvectors).</p>
-<p>This function calculates all eigenvalues (and vectors) of <code>input</code>
-such that \(\mbox{input} = V \mbox{diag}(e) V^T\).</p>
-<p>The boolean argument <code>eigenvectors</code> defines computation of
-both eigenvectors and eigenvalues or eigenvalues only.</p>
-<p>If it is <code>FALSE</code>, only eigenvalues are computed. If it is <code>TRUE</code>,
-both eigenvalues and eigenvectors are computed.</p>
-<p>Since the input matrix <code>input</code> is supposed to be symmetric,
-only the upper triangular portion is used by default.</p>
-<p>If <code>upper</code> is <code>FALSE</code>, then lower triangular portion is used.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>5</span>))
-<span class='kw'>a</span> <span class='op'>=</span> <span class='kw'>a</span> <span class='op'>+</span> <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()  <span class='co'># To make a symmetric</span>
-<span class='kw'>a</span>
-<span class='kw'>o</span> <span class='op'>=</span> <span class='fu'>torch_symeig</span>(<span class='kw'>a</span>, eigenvectors=<span class='fl'>TRUE</span>)
-<span class='kw'>e</span> <span class='op'>=</span> <span class='kw'>o</span>[[<span class='fl'>1</span>]]
-<span class='kw'>v</span> <span class='op'>=</span> <span class='kw'>o</span>[[<span class='fl'>2</span>]]
-<span class='kw'>e</span>
-<span class='kw'>v</span>
-<span class='kw'>a_big</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='kw'>a_big</span> <span class='op'>=</span> <span class='kw'>a_big</span> <span class='op'>+</span> <span class='kw'>a_big</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>)  <span class='co'># To make a_big symmetric</span>
-<span class='kw'>o</span> <span class='op'>=</span> <span class='kw'>a_big</span><span class='op'>$</span><span class='fu'>symeig</span>(eigenvectors=<span class='fl'>TRUE</span>)
-<span class='kw'>e</span> <span class='op'>=</span> <span class='kw'>o</span>[[<span class='fl'>1</span>]]
-<span class='kw'>v</span> <span class='op'>=</span> <span class='kw'>o</span>[[<span class='fl'>2</span>]]
-<span class='fu'><a href='torch_allclose.html'>torch_allclose</a></span>(<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>v</span>, <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>e</span><span class='op'>$</span><span class='fu'>diag_embed</span>(), <span class='kw'>v</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>))), <span class='kw'>a_big</span>)
-}
-</div><div class='output co'>#&gt; [1] TRUE</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    <h1>T</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_t.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>T</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_t</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="t-input-gt-tensor-"><a class="anchor" href="#t-input-gt-tensor-"></a>t(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Expects <code>input</code> to be &lt;= 2-D tensor and transposes dimensions 0
-and 1.</p>
-<p>0-D and 1-D tensors are returned as is. When input is a 2-D tensor this
-is equivalent to <code>transpose(input, 0, 1)</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>))
-<span class='kw'>x</span>
-<span class='fu'>torch_t</span>(<span class='kw'>x</span>)
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='kw'>x</span>
-<span class='fu'>torch_t</span>(<span class='kw'>x</span>)
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='kw'>x</span>
-<span class='fu'>torch_t</span>(<span class='kw'>x</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.9704 -2.3415
-#&gt;  0.4480 -1.1966
-#&gt;  1.7181 -0.1641
-#&gt; [ CPUFloatType{3,2} ]</div></pre>
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-    <h1>Take</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_take.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Take</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_take</span>(<span class='kw'>self</span>, <span class='kw'>index</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>index</th>
-      <td><p>(LongTensor) the indices into tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="take-input-index-gt-tensor-"><a class="anchor" href="#take-input-index-gt-tensor-"></a>take(input, index) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the elements of <code>input</code> at the given indices.
-The input tensor is treated as if it were viewed as a 1-D tensor. The result
-takes the same shape as the indices.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>src</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>6</span>,<span class='fl'>7</span>,<span class='fl'>8</span>), ncol = <span class='fl'>3</span>, byrow = <span class='fl'>TRUE</span>))
-<span class='fu'>torch_take</span>(<span class='kw'>src</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>5</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  4
-#&gt;  3
-#&gt;  7
-#&gt; [ CPUFloatType{3} ]</div></pre>
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-    <div class="page-header">
-    <h1>Tan</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_tan.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Tan</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_tan</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="tan-input-out-null-gt-tensor-"><a class="anchor" href="#tan-input-out-null-gt-tensor-"></a>tan(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the tangent of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \tan(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_tan</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  20.7351
-#&gt;   0.6500
-#&gt;  -1.2726
-#&gt;   0.0058
-#&gt; [ CPUFloatType{4} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_tanh.html b/static/docs/dev/reference/torch_tanh.html
deleted file mode 100644
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_tanh.Rd</code></div>
-    </div>
-
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-    <p>Tanh</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_tanh</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="tanh-input-out-null-gt-tensor-"><a class="anchor" href="#tanh-input-out-null-gt-tensor-"></a>tanh(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the hyperbolic tangent of the elements
-of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \tanh(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_tanh</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.1360
-#&gt; -0.0700
-#&gt;  0.8428
-#&gt; -0.0623
-#&gt; [ CPUFloatType{4} ]</div></pre>
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-    <h1>Converts R objects to a torch tensor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/tensor.R'><code>R/tensor.R</code></a></small>
-    <div class="hidden name"><code>torch_tensor.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Converts R objects to a torch tensor</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_tensor</span>(
-  <span class='kw'>data</span>,
-  dtype = <span class='kw'>NULL</span>,
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>,
-  pin_memory = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>data</th>
-      <td><p>an R atomic vector, matrix or array</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>a <a href='torch_dtype.html'>torch_dtype</a> instance</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>a device creted with <code><a href='torch_device.html'>torch_device()</a></code></p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>if autograd should record operations on the returned tensor.</p></td>
-    </tr>
-    <tr>
-      <th>pin_memory</th>
-      <td><p>If set, returned tensor would be allocated in the pinned memory.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='fu'>torch_tensor</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>))
-<span class='fu'>torch_tensor</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt;  4
-#&gt; [ CPUIntType{4} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_tensordot.html b/static/docs/dev/reference/torch_tensordot.html
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-    <div class="page-header">
-    <h1>Tensordot</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_tensordot.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Returns a contraction of a and b over multiple dimensions.
-<code>tensordot</code> implements a generalized matrix product.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_tensordot</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, dims = <span class='fl'>2</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>a</th>
-      <td><p>(Tensor) Left tensor to contract</p></td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td><p>(Tensor) Right tensor to contract</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(int or tuple of two lists of integers) number of dimensions to     contract or explicit lists of dimensions for <code>a</code> and     <code>b</code> respectively</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(start = <span class='fl'>0</span>, end = <span class='fl'>60.</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/reshape.html'>reshape</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(start = <span class='fl'>0</span>, end = <span class='fl'>24.</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/reshape.html'>reshape</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='fu'>torch_tensordot</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, dims = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)))
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, device=<span class='st'>'cuda'</span>)
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>, device=<span class='st'>'cuda'</span>)
-<span class='kw'>c</span> <span class='op'>=</span> <span class='fu'>torch_tensordot</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, dims=<span class='fl'>2</span>)<span class='op'>$</span><span class='fu'>cpu</span>()
-}
-}
-</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Threshold_</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_threshold_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Threshold_</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_threshold_</span>(<span class='kw'>self</span>, <span class='kw'>threshold</span>, <span class='kw'>value</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>input tensor</p></td>
-    </tr>
-    <tr>
-      <th>threshold</th>
-      <td><p>The value to threshold at</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>The value to replace with</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="threshold-input-threshold-value-gt-tensor-"><a class="anchor" href="#threshold-input-threshold-value-gt-tensor-"></a>threshold_(input, threshold, value) -&gt; Tensor </h2>
-
-    
-
-
-<p>In-place version of <code>torch_threshold</code>.</p>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>Topk</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_topk.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Topk</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_topk</span>(<span class='kw'>self</span>, <span class='kw'>k</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, largest = <span class='fl'>TRUE</span>, sorted = <span class='fl'>TRUE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>k</th>
-      <td><p>(int) the k in "top-k"</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to sort along</p></td>
-    </tr>
-    <tr>
-      <th>largest</th>
-      <td><p>(bool, optional) controls whether to return largest or           smallest elements</p></td>
-    </tr>
-    <tr>
-      <th>sorted</th>
-      <td><p>(bool, optional) controls whether to return the elements           in sorted order</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-"><a class="anchor" href="#topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-"></a>topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns the <code>k</code> largest elements of the given <code>input</code> tensor along
-a given dimension.</p>
-<p>If <code>dim</code> is not given, the last dimension of the <code>input</code> is chosen.</p>
-<p>If <code>largest</code> is <code>FALSE</code> then the <code>k</code> smallest elements are returned.</p>
-<p>A namedtuple of <code>(values, indices)</code> is returned, where the <code>indices</code> are the indices
-of the elements in the original <code>input</code> tensor.</p>
-<p>The boolean option <code>sorted</code> if <code>TRUE</code>, will make sure that the returned
-<code>k</code> elements are themselves sorted</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>)
-<span class='kw'>x</span>
-<span class='fu'>torch_topk</span>(<span class='kw'>x</span>, <span class='fl'>3</span>)
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  5
-#&gt;  4
-#&gt;  3
-#&gt; [ CPUFloatType{3} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  4
-#&gt;  3
-#&gt;  2
-#&gt; [ CPULongType{3} ]
-#&gt; </div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-<div class="row">
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-    <div class="page-header">
-    <h1>Trace</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_trace.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Trace</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_trace</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="trace-input-gt-tensor-"><a class="anchor" href="#trace-input-gt-tensor-"></a>trace(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the sum of the elements of the diagonal of the input 2-D matrix.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>10.</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='kw'>x</span>
-<span class='fu'>torch_trace</span>(<span class='kw'>x</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 15
-#&gt; [ CPUFloatType{} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_transpose.html b/static/docs/dev/reference/torch_transpose.html
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-    <div class="page-header">
-    <h1>Transpose</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_transpose.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Transpose</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_transpose</span>(<span class='kw'>self</span>, <span class='kw'>dim0</span>, <span class='kw'>dim1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim0</th>
-      <td><p>(int) the first dimension to be transposed</p></td>
-    </tr>
-    <tr>
-      <th>dim1</th>
-      <td><p>(int) the second dimension to be transposed</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="transpose-input-dim-dim-gt-tensor-"><a class="anchor" href="#transpose-input-dim-dim-gt-tensor-"></a>transpose(input, dim0, dim1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor that is a transposed version of <code>input</code>.
-The given dimensions <code>dim0</code> and <code>dim1</code> are swapped.</p>
-<p>The resulting <code>out</code> tensor shares it's underlying storage with the
-<code>input</code> tensor, so changing the content of one would change the content
-of the other.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='kw'>x</span>
-<span class='fu'>torch_transpose</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fl'>2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.5885  0.2800
-#&gt; -0.5850 -0.9637
-#&gt; -0.2765  0.8361
-#&gt; [ CPUFloatType{3,2} ]</div></pre>
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-    <h1>Trapz</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_trapz.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Trapz</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_trapz</span>(<span class='kw'>y</span>, dx = <span class='fl'>1L</span>, <span class='kw'>x</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>y</th>
-      <td><p>(Tensor) The values of the function to integrate</p></td>
-    </tr>
-    <tr>
-      <th>dx</th>
-      <td><p>(float) The distance between points at which <code>y</code> is sampled.</p></td>
-    </tr>
-    <tr>
-      <th>x</th>
-      <td><p>(Tensor) The points at which the function <code>y</code> is sampled.        If <code>x</code> is not in ascending order, intervals on which it is decreasing        contribute negatively to the estimated integral (i.e., the convention        \(\int_a^b f = -\int_b^a f\) is followed).</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) The dimension along which to integrate.        By default, use the last dimension.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="trapz-y-x-dim-gt-tensor-"><a class="anchor" href="#trapz-y-x-dim-gt-tensor-"></a>trapz(y, x, *, dim=-1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Estimate \(\int y\,dx\) along <code>dim</code>, using the trapezoid rule.</p>
-    <h2 class="hasAnchor" id="trapz-y-dx-dim-gt-tensor-"><a class="anchor" href="#trapz-y-dx-dim-gt-tensor-"></a>trapz(y, *, dx=1, dim=-1) -&gt; Tensor </h2>
-
-    
-
-
-<p>As above, but the sample points are spaced uniformly at a distance of <code>dx</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='kw'>y</span>
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), ncol = <span class='fl'>3</span>, byrow=<span class='fl'>TRUE</span>))
-<span class='fu'>torch_trapz</span>(<span class='kw'>y</span>, x = <span class='kw'>x</span>)
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.3708
-#&gt;  0.7793
-#&gt; [ CPUFloatType{2} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_triangular_solve.html b/static/docs/dev/reference/torch_triangular_solve.html
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-    <h1>Triangular_solve</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_triangular_solve.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Triangular_solve</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_triangular_solve</span>(
-  <span class='kw'>self</span>,
-  <span class='kw'>A</span>,
-  upper = <span class='fl'>TRUE</span>,
-  transpose = <span class='fl'>FALSE</span>,
-  unitriangular = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) multiple right-hand sides of size \((*, m, k)\) where                \(*\) is zero of more batch dimensions (\(b\))</p></td>
-    </tr>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor) the input triangular coefficient matrix of size \((*, m, m)\)                where \(*\) is zero or more batch dimensions</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>transpose</th>
-      <td><p>(bool, optional) whether \(A\) should be transposed before        being sent into the solver. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>unitriangular</th>
-      <td><p>(bool, optional) whether \(A\) is unit triangular.        If TRUE, the diagonal elements of \(A\) are assumed to be        1 and not referenced from \(A\). Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-"><a class="anchor" href="#triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-"></a>triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Solves a system of equations with a triangular coefficient matrix \(A\)
-and multiple right-hand sides \(b\).</p>
-<p>In particular, solves \(AX = b\) and assumes \(A\) is upper-triangular
-with the default keyword arguments.</p>
-<p><code>torch_triangular_solve(b, A)</code> can take in 2D inputs <code>b, A</code> or inputs that are
-batches of 2D matrices. If the inputs are batches, then returns
-batched outputs <code>X</code></p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))<span class='op'>$</span><span class='fu'>triu</span>()
-<span class='kw'>A</span>
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='kw'>b</span>
-<span class='fu'>torch_triangular_solve</span>(<span class='kw'>b</span>, <span class='kw'>A</span>)
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  1.1289 -0.2883 -1.5927
-#&gt;  0.2137  5.7636 -3.2552
-#&gt; [ CPUFloatType{2,3} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  0.4463 -0.0738
-#&gt;  0.0000 -0.3977
-#&gt; [ CPUFloatType{2,2} ]
-#&gt; </div></pre>
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-    <h1>Tril</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_tril.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Tril</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_tril</span>(<span class='kw'>self</span>, diagonal = <span class='fl'>0L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>diagonal</th>
-      <td><p>(int, optional) the diagonal to consider</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="tril-input-diagonal-out-null-gt-tensor-"><a class="anchor" href="#tril-input-diagonal-out-null-gt-tensor-"></a>tril(input, diagonal=0, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices
-<code>input</code>, the other elements of the result tensor <code>out</code> are set to 0.</p>
-<p>The lower triangular part of the matrix is defined as the elements on and
-below the diagonal.</p>
-<p>The argument <code>diagonal</code> controls which diagonal to consider. If
-<code>diagonal</code> = 0, all elements on and below the main diagonal are
-retained. A positive value includes just as many diagonals above the main
-diagonal, and similarly a negative value excludes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
-\(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_tril</span>(<span class='kw'>a</span>)
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>6</span>))
-<span class='kw'>b</span>
-<span class='fu'>torch_tril</span>(<span class='kw'>b</span>, diagonal=<span class='fl'>1</span>)
-<span class='fu'>torch_tril</span>(<span class='kw'>b</span>, diagonal=<span class='op'>-</span><span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
-#&gt;  0.8677  0.0000  0.0000  0.0000  0.0000  0.0000
-#&gt; -1.0570  1.5700  0.0000  0.0000  0.0000  0.0000
-#&gt;  1.7705 -0.3262  1.8047  0.0000  0.0000  0.0000
-#&gt; [ CPUFloatType{4,6} ]</div></pre>
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-    <div class="page-header">
-    <h1>Tril_indices</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_tril_indices.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Tril_indices</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_tril_indices</span>(
-  <span class='kw'>row</span>,
-  <span class='kw'>col</span>,
-  offset = <span class='fl'>0</span>,
-  dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>(),
-  device = <span class='st'>"cpu"</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>()
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>row</th>
-      <td><p>(<code>int</code>) number of rows in the 2-D matrix.</p></td>
-    </tr>
-    <tr>
-      <th>col</th>
-      <td><p>(<code>int</code>) number of columns in the 2-D matrix.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, <code>torch_long</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
-prevent overflow during calculation.
-</pre>
-
-    <h2 class="hasAnchor" id="tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"><a class="anchor" href="#tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a>tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the indices of the lower triangular part of a <code>row</code>-by-
-<code>col</code> matrix in a 2-by-N Tensor, where the first row contains row
-coordinates of all indices and the second row contains column coordinates.
-Indices are ordered based on rows and then columns.</p>
-<p>The lower triangular part of the matrix is defined as the elements on and
-below the diagonal.</p>
-<p>The argument <code>offset</code> controls which diagonal to consider. If
-<code>offset</code> = 0, all elements on and below the main diagonal are
-retained. A positive value includes just as many diagonals above the main
-diagonal, and similarly a negative value excludes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
-where \(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_tril_indices</span>(<span class='fl'>3</span>, <span class='fl'>3</span>)
-<span class='kw'>a</span>
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_tril_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='op'>-</span><span class='fl'>1</span>)
-<span class='kw'>a</span>
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_tril_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>1</span>)
-<span class='kw'>a</span>
-}
-}
-</div></pre>
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Triu</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_triu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Triu</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_triu</span>(<span class='kw'>self</span>, diagonal = <span class='fl'>0L</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>diagonal</th>
-      <td><p>(int, optional) the diagonal to consider</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="triu-input-diagonal-out-null-gt-tensor-"><a class="anchor" href="#triu-input-diagonal-out-null-gt-tensor-"></a>triu(input, diagonal=0, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
-<code>input</code>, the other elements of the result tensor <code>out</code> are set to 0.</p>
-<p>The upper triangular part of the matrix is defined as the elements on and
-above the diagonal.</p>
-<p>The argument <code>diagonal</code> controls which diagonal to consider. If
-<code>diagonal</code> = 0, all elements on and above the main diagonal are
-retained. A positive value excludes just as many diagonals above the main
-diagonal, and similarly a negative value includes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
-\(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_triu</span>(<span class='kw'>a</span>)
-<span class='fu'>torch_triu</span>(<span class='kw'>a</span>, diagonal=<span class='fl'>1</span>)
-<span class='fu'>torch_triu</span>(<span class='kw'>a</span>, diagonal=<span class='op'>-</span><span class='fl'>1</span>)
-<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>6</span>))
-<span class='kw'>b</span>
-<span class='fu'>torch_triu</span>(<span class='kw'>b</span>, diagonal=<span class='fl'>1</span>)
-<span class='fu'>torch_triu</span>(<span class='kw'>b</span>, diagonal=<span class='op'>-</span><span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0372  0.7333 -1.0043 -1.8309 -0.9522  2.1571
-#&gt;  1.0737  0.7684  0.7177 -0.0517 -0.4529  0.4365
-#&gt;  0.0000 -1.1267 -1.7875 -1.6027  1.0384 -0.0726
-#&gt;  0.0000  0.0000  1.4837  1.3963  3.1746 -0.0003
-#&gt; [ CPUFloatType{4,6} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_triu_indices.html b/static/docs/dev/reference/torch_triu_indices.html
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-    <div class="page-header">
-    <h1>Triu_indices</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_triu_indices.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Triu_indices</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_triu_indices</span>(
-  <span class='kw'>row</span>,
-  <span class='kw'>col</span>,
-  offset = <span class='fl'>0</span>,
-  dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>(),
-  device = <span class='st'>"cpu"</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>()
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>row</th>
-      <td><p>(<code>int</code>) number of rows in the 2-D matrix.</p></td>
-    </tr>
-    <tr>
-      <th>col</th>
-      <td><p>(<code>int</code>) number of columns in the 2-D matrix.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, <code>torch_long</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
-prevent overflow during calculation.
-</pre>
-
-    <h2 class="hasAnchor" id="triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"><a class="anchor" href="#triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a>triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the indices of the upper triangular part of a <code>row</code> by
-<code>col</code> matrix in a 2-by-N Tensor, where the first row contains row
-coordinates of all indices and the second row contains column coordinates.
-Indices are ordered based on rows and then columns.</p>
-<p>The upper triangular part of the matrix is defined as the elements on and
-above the diagonal.</p>
-<p>The argument <code>offset</code> controls which diagonal to consider. If
-<code>offset</code> = 0, all elements on and above the main diagonal are
-retained. A positive value excludes just as many diagonals above the main
-diagonal, and similarly a negative value includes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
-where \(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_triu_indices</span>(<span class='fl'>3</span>, <span class='fl'>3</span>)
-<span class='kw'>a</span>
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_triu_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='op'>-</span><span class='fl'>1</span>)
-<span class='kw'>a</span>
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_triu_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>1</span>)
-<span class='kw'>a</span>
-}
-}
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_true_divide.html b/static/docs/dev/reference/torch_true_divide.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_true_divide.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>TRUE_divide</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_true_divide</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the dividend</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or Scalar) the divisor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="true-divide-dividend-divisor-gt-tensor-"><a class="anchor" href="#true-divide-dividend-divisor-gt-tensor-"></a>true_divide(dividend, divisor) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs "true division" that always computes the division
-in floating point. Analogous to division in Python 3 and equivalent to
-<code><a href='torch_div.html'>torch_div</a></code> except when both inputs have bool or integer scalar types,
-in which case they are cast to the default (floating) scalar type before the division.</p>
-<p>$$
-    \mbox{out}_i = \frac{\mbox{dividend}_i}{\mbox{divisor}}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>dividend</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>3</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
-<span class='kw'>divisor</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
-<span class='fu'>torch_true_divide</span>(<span class='kw'>dividend</span>, <span class='kw'>divisor</span>)
-<span class='fu'>torch_true_divide</span>(<span class='kw'>dividend</span>, <span class='fl'>2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2.5000
-#&gt;  1.5000
-#&gt; [ CPUFloatType{2} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Trunc</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_trunc.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Trunc</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_trunc</span>(<span class='kw'>self</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="trunc-input-out-null-gt-tensor-"><a class="anchor" href="#trunc-input-out-null-gt-tensor-"></a>trunc(input, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the truncated integer values of
-the elements of <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_trunc</span>(<span class='kw'>a</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0
-#&gt; -0
-#&gt;  0
-#&gt;  0
-#&gt; [ CPUFloatType{4} ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to unbind</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension to remove</p></td>
-    </tr>
-    </table>
-
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-
-    
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-<p>Removes a tensor dimension.</p>
-<p>Returns a tuple of all slices along a given dimension, already without it.</p>
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-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_unbind</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>9</span>, ncol = <span class='fl'>3</span>, byrow=<span class='fl'>TRUE</span>)))
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt; [ CPULongType{3} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  4
-#&gt;  5
-#&gt;  6
-#&gt; [ CPULongType{3} ]
-#&gt; 
-#&gt; [[3]]
-#&gt; torch_tensor 
-#&gt;  7
-#&gt;  8
-#&gt;  9
-#&gt; [ CPULongType{3} ]
-#&gt; </div></pre>
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-    <h1>Unique_consecutive</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_unique_consecutive.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Unique_consecutive</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_unique_consecutive</span>(
-  <span class='kw'>self</span>,
-  return_inverse = <span class='fl'>FALSE</span>,
-  return_counts = <span class='fl'>FALSE</span>,
-  dim = <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>return_inverse</th>
-      <td><p>(bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.</p></td>
-    </tr>
-    <tr>
-      <th>return_counts</th>
-      <td><p>(bool) Whether to also return the counts for each unique        element.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to apply unique. If <code>NULL</code>, the unique of the        flattened input is returned. default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Eliminates all but the first element from every consecutive group of equivalent elements.</p><pre>.. note:: This function is different from [`torch_unique`] in the sense that this function
-    only eliminates consecutive duplicate values. This semantics is similar to `std::unique`
-    in C++.
-</pre>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>torch_unique_consecutive</span>(<span class='kw'>x</span>)
-<span class='kw'>output</span>
-<span class='fu'>torch_unique_consecutive</span>(<span class='kw'>x</span>, return_inverse=<span class='fl'>TRUE</span>)
-<span class='fu'>torch_unique_consecutive</span>(<span class='kw'>x</span>, return_counts=<span class='fl'>TRUE</span>)
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt;  1
-#&gt;  2
-#&gt; [ CPUFloatType{5} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; [ CPULongType{0} ]
-#&gt; 
-#&gt; [[3]]
-#&gt; torch_tensor 
-#&gt;  2
-#&gt;  2
-#&gt;  1
-#&gt;  2
-#&gt;  1
-#&gt; [ CPULongType{5} ]
-#&gt; </div></pre>
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-    <h1>Unsqueeze</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_unsqueeze.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Unsqueeze</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_unsqueeze</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the index at which to insert the singleton dimension</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="unsqueeze-input-dim-gt-tensor-"><a class="anchor" href="#unsqueeze-input-dim-gt-tensor-"></a>unsqueeze(input, dim) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with a dimension of size one inserted at the
-specified position.</p>
-<p>The returned tensor shares the same underlying data with this tensor.</p>
-<p>A <code>dim</code> value within the range <code>[-input.dim() - 1, input.dim() + 1)</code>
-can be used. Negative <code>dim</code> will correspond to <code>unsqueeze</code>
-applied at <code>dim</code> = <code>dim + input.dim() + 1</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='fu'>torch_unsqueeze</span>(<span class='kw'>x</span>, <span class='fl'>1</span>)
-<span class='fu'>torch_unsqueeze</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt;  4
-#&gt; [ CPUFloatType{4,1} ]</div></pre>
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-    <h1>Var</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_var.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Var</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_var</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, unbiased = <span class='fl'>TRUE</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>unbiased</th>
-      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="var-input-unbiased-true-gt-tensor-"><a class="anchor" href="#var-input-unbiased-true-gt-tensor-"></a>var(input, unbiased=TRUE) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the variance of all elements in the <code>input</code> tensor.</p>
-<p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
-biased estimator. Otherwise, Bessel's correction will be used.</p>
-    <h2 class="hasAnchor" id="var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-"><a class="anchor" href="#var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-"></a>var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the variance of each row of the <code>input</code> tensor in the given
-dimension <code>dim</code>.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-<p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
-biased estimator. Otherwise, Bessel's correction will be used.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_var</span>(<span class='kw'>a</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_var</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0260
-#&gt;  1.8411
-#&gt;  0.8131
-#&gt;  1.0754
-#&gt; [ CPUFloatType{4} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_var_mean.html b/static/docs/dev/reference/torch_var_mean.html
deleted file mode 100644
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Var_mean</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_var_mean.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Var_mean</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_var_mean</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, unbiased = <span class='fl'>TRUE</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>unbiased</th>
-      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="var-mean-input-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#var-mean-input-unbiased-true-gt-tensor-tensor-"></a>var_mean(input, unbiased=TRUE) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Returns the variance and mean of all elements in the <code>input</code> tensor.</p>
-<p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
-biased estimator. Otherwise, Bessel's correction will be used.</p>
-    <h2 class="hasAnchor" id="var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-"></a>var_mean(input, dim, keepdim=False, unbiased=TRUE) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Returns the variance and mean of each row of the <code>input</code> tensor in the given
-dimension <code>dim</code>.</p>
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-<p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
-biased estimator. Otherwise, Bessel's correction will be used.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_var_mean</span>(<span class='kw'>a</span>)
-
-
-<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='kw'>a</span>
-<span class='fu'>torch_var_mean</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
-}
-</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  2.4021
-#&gt;  1.1235
-#&gt;  0.3647
-#&gt;  0.9379
-#&gt; [ CPUFloatType{4} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; 0.01 *
-#&gt;  1.8941
-#&gt;  8.3990
-#&gt; -73.6180
-#&gt; -19.7358
-#&gt; [ CPUFloatType{4} ]
-#&gt; </div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_where.html b/static/docs/dev/reference/torch_where.html
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-    <h1>Where</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_where.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Where</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_where</span>(<span class='kw'>condition</span>, <span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>condition</th>
-      <td><p>(BoolTensor) When TRUE (nonzero), yield x, otherwise yield y</p></td>
-    </tr>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) values selected at indices where <code>condition</code> is <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) values selected at indices where <code>condition</code> is <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>The tensors `condition`, `x`, `y` must be broadcastable .
-</pre>
-
-<p>See also <code><a href='torch_nonzero.html'>torch_nonzero()</a></code>.</p>
-    <h2 class="hasAnchor" id="where-condition-x-y-gt-tensor-"><a class="anchor" href="#where-condition-x-y-gt-tensor-"></a>where(condition, x, y) -&gt; Tensor </h2>
-
-    
-
-
-<p>Return a tensor of elements selected from either <code>x</code> or <code>y</code>, depending on <code>condition</code>.</p>
-<p>The operation is defined as:</p>
-<p>$$
-    \mbox{out}_i = \left\{ \begin{array}{ll}
-        \mbox{x}_i &amp; \mbox{if } \mbox{condition}_i \\
-        \mbox{y}_i &amp; \mbox{otherwise} \\
-    \end{array}
-    \right.
-$$</p>
-    <h2 class="hasAnchor" id="where-condition-gt-tuple-of-longtensor-"><a class="anchor" href="#where-condition-gt-tuple-of-longtensor-"></a>where(condition) -&gt; tuple of LongTensor </h2>
-
-    
-
-
-<p><code>torch_where(condition)</code> is identical to
-<code><a href='torch_nonzero.html'>torch_nonzero(condition, as_tuple=TRUE)</a></code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='kw'>x</span>
-<span class='fu'>torch_where</span>(<span class='kw'>x</span> <span class='op'>&gt;</span> <span class='fl'>0</span>, <span class='kw'>x</span>, <span class='kw'>y</span>)
-}
-
-
-
-}
-</div></pre>
-  </div>
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-    <div class="page-header">
-    <h1>Zeros</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_zeros.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Zeros</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_zeros</span>(
-  <span class='kw'>...</span>,
-  names = <span class='kw'>NULL</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>names</th>
-      <td><p>optional dimension names</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with the scalar value <code>0</code>, with the shape defined
-by the variable argument <code>size</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_zeros</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_zeros</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  0
-#&gt;  0
-#&gt;  0
-#&gt;  0
-#&gt; [ CPUFloatType{5} ]</div></pre>
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-    <div class="page-header">
-    <h1>Zeros_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_zeros_like.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Zeros_like</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_zeros_like</span>(
-  <span class='kw'>input</span>,
-  dtype = <span class='kw'>NULL</span>,
-  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
-  device = <span class='kw'>NULL</span>,
-  requires_grad = <span class='fl'>FALSE</span>,
-  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with the scalar value <code>0</code>, with the same size as
-<code>input</code>. <code>torch_zeros_like(input)</code> is equivalent to
-<code><a href='torch_zeros.html'>torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>As of 0.4, this function does not support an <code>out</code> keyword. As an alternative,
-the old <code>torch_zeros_like(input, out=output)</code> is equivalent to
-<code><a href='torch_zeros.html'>torch_zeros(input.size(), out=output)</a></code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_zeros_like</span>(<span class='kw'>input</span>)
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  0  0
-#&gt;  0  0  0
-#&gt; [ CPUFloatType{2,3} ]</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/with_enable_grad.html b/static/docs/dev/reference/with_enable_grad.html
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-    <h1>Enable grad</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>with_enable_grad.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Context-manager that enables gradient calculation.
-Enables gradient calculation, if it has been disabled via <a href='with_no_grad.html'>with_no_grad</a>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>with_enable_grad</span>(<span class='kw'>code</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>code</th>
-      <td><p>code to be executed with gradient recording.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This context manager is thread local; it will not affect computation in
-other threads.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1</span>, requires_grad=<span class='fl'>TRUE</span>)
-<span class='fu'><a href='with_no_grad.html'>with_no_grad</a></span>({
-  <span class='fu'>with_enable_grad</span>({
-    <span class='kw'>y</span> <span class='op'>=</span> <span class='kw'>x</span> <span class='op'>*</span> <span class='fl'>2</span>
-  })
-})
-<span class='kw'>y</span><span class='op'>$</span><span class='fu'>backward</span>()
-<span class='kw'>x</span><span class='op'>$</span><span class='kw'>grad</span>
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-</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2
-#&gt; [ CPUFloatType{1} ]</div></pre>
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-    <h1>Temporarily modify gradient recording.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>with_no_grad.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Temporarily modify gradient recording.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>with_no_grad</span>(<span class='kw'>code</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>code</th>
-      <td><p>code to be executed with no gradient recording.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Uniform.html'>runif</a></span>(<span class='fl'>5</span>), requires_grad = <span class='fl'>TRUE</span>)
-<span class='fu'>with_no_grad</span>({
-  <span class='kw'>x</span><span class='op'>$</span><span class='fu'>sub_</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>as.numeric</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>5</span>)))
-})
-<span class='kw'>x</span>
-<span class='kw'>x</span><span class='op'>$</span><span class='kw'>grad</span>
-
-}
-</div><div class='output co'>#&gt; torch_tensor 
-#&gt; [ Tensor (undefined) ]</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-<div class="page-header"><h1 class="hasAnchor">
-<a href="#torch-" class="anchor"></a>torch <a href="https://torch.mlverse.org"><img src="reference/figures/torch.png" align="right" height="139"></a>
-</h1></div>
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-<a href="#installation" class="anchor"></a>Installation</h2>
-<p>Run:</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a>remotes<span class="op">::</span><span class="kw">install_github</span>(<span class="st">"mlverse/torch"</span>)</span></code></pre></div>
-<p>At the first package load additional software will be installed.</p>
-</div>
-<div id="example" class="section level2">
-<h2 class="hasAnchor">
-<a href="#example" class="anchor"></a>Example</h2>
-<p>Currently this package is only a proof of concept and you can only create a Torch Tensor from an R object. And then convert back from a torch Tensor to an R object.</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="kw">library</span>(torch)</span>
-<span id="cb2-2"><a href="#cb2-2"></a>x &lt;-<span class="st"> </span><span class="kw">array</span>(<span class="kw">runif</span>(<span class="dv">8</span>), <span class="dt">dim =</span> <span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">2</span>))</span>
-<span id="cb2-3"><a href="#cb2-3"></a>y &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(x, <span class="dt">dtype =</span> <span class="kw">torch_float64</span>())</span>
-<span id="cb2-4"><a href="#cb2-4"></a>y</span>
-<span id="cb2-5"><a href="#cb2-5"></a><span class="co">#&gt; torch_tensor </span></span>
-<span id="cb2-6"><a href="#cb2-6"></a><span class="co">#&gt; (1,.,.) = </span></span>
-<span id="cb2-7"><a href="#cb2-7"></a><span class="co">#&gt;   0.5406  0.8648</span></span>
-<span id="cb2-8"><a href="#cb2-8"></a><span class="co">#&gt;   0.3097  0.9715</span></span>
-<span id="cb2-9"><a href="#cb2-9"></a><span class="co">#&gt; </span></span>
-<span id="cb2-10"><a href="#cb2-10"></a><span class="co">#&gt; (2,.,.) = </span></span>
-<span id="cb2-11"><a href="#cb2-11"></a><span class="co">#&gt;   0.1309  0.8992</span></span>
-<span id="cb2-12"><a href="#cb2-12"></a><span class="co">#&gt;   0.4849  0.1902</span></span>
-<span id="cb2-13"><a href="#cb2-13"></a><span class="co">#&gt; [ CPUDoubleType{2,2,2} ]</span></span>
-<span id="cb2-14"><a href="#cb2-14"></a><span class="kw">identical</span>(x, <span class="kw">as_array</span>(y))</span>
-<span id="cb2-15"><a href="#cb2-15"></a><span class="co">#&gt; [1] TRUE</span></span></code></pre></div>
-<div id="simple-autograd-example" class="section level3">
-<h3 class="hasAnchor">
-<a href="#simple-autograd-example" class="anchor"></a>Simple Autograd Example</h3>
-<p>In the following snippet we let torch, using the autograd feature, calculate the derivatives:</p>
-<div class="sourceCode" id="cb3"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a>x &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">1</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
-<span id="cb3-2"><a href="#cb3-2"></a>w &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">2</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
-<span id="cb3-3"><a href="#cb3-3"></a>b &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">3</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
-<span id="cb3-4"><a href="#cb3-4"></a>y &lt;-<span class="st"> </span>w <span class="op">*</span><span class="st"> </span>x <span class="op">+</span><span class="st"> </span>b</span>
-<span id="cb3-5"><a href="#cb3-5"></a>y<span class="op">$</span><span class="kw">backward</span>()</span>
-<span id="cb3-6"><a href="#cb3-6"></a>x<span class="op">$</span>grad</span>
-<span id="cb3-7"><a href="#cb3-7"></a><span class="co">#&gt; torch_tensor </span></span>
-<span id="cb3-8"><a href="#cb3-8"></a><span class="co">#&gt;  2</span></span>
-<span id="cb3-9"><a href="#cb3-9"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span>
-<span id="cb3-10"><a href="#cb3-10"></a>w<span class="op">$</span>grad</span>
-<span id="cb3-11"><a href="#cb3-11"></a><span class="co">#&gt; torch_tensor </span></span>
-<span id="cb3-12"><a href="#cb3-12"></a><span class="co">#&gt;  1</span></span>
-<span id="cb3-13"><a href="#cb3-13"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span>
-<span id="cb3-14"><a href="#cb3-14"></a>b<span class="op">$</span>grad</span>
-<span id="cb3-15"><a href="#cb3-15"></a><span class="co">#&gt; torch_tensor </span></span>
-<span id="cb3-16"><a href="#cb3-16"></a><span class="co">#&gt;  1</span></span>
-<span id="cb3-17"><a href="#cb3-17"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span></code></pre></div>
-</div>
-<div id="linear-regression" class="section level3">
-<h3 class="hasAnchor">
-<a href="#linear-regression" class="anchor"></a>Linear Regression</h3>
-<p>In the following example we are going to fit a linear regression from scratch using torch’s Autograd.</p>
-<p><strong>Note</strong> all methods that end with <code>_</code> (eg. <code>sub_</code>), will modify the tensors in place.</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>x &lt;-<span class="st"> </span><span class="kw">torch_randn</span>(<span class="dv">100</span>, <span class="dv">2</span>)</span>
-<span id="cb4-2"><a href="#cb4-2"></a>y &lt;-<span class="st"> </span><span class="fl">0.1</span> <span class="op">+</span><span class="st"> </span><span class="fl">0.5</span><span class="op">*</span>x[,<span class="dv">1</span>] <span class="op">-</span><span class="st"> </span><span class="fl">0.7</span><span class="op">*</span>x[,<span class="dv">2</span>]</span>
-<span id="cb4-3"><a href="#cb4-3"></a></span>
-<span id="cb4-4"><a href="#cb4-4"></a>w &lt;-<span class="st"> </span><span class="kw">torch_randn</span>(<span class="dv">2</span>, <span class="dv">1</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
-<span id="cb4-5"><a href="#cb4-5"></a>b &lt;-<span class="st"> </span><span class="kw">torch_zeros</span>(<span class="dv">1</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
-<span id="cb4-6"><a href="#cb4-6"></a></span>
-<span id="cb4-7"><a href="#cb4-7"></a>lr &lt;-<span class="st"> </span><span class="fl">0.5</span></span>
-<span id="cb4-8"><a href="#cb4-8"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="op">:</span><span class="dv">100</span>) {</span>
-<span id="cb4-9"><a href="#cb4-9"></a>  y_hat &lt;-<span class="st"> </span><span class="kw">torch_mm</span>(x, w) <span class="op">+</span><span class="st"> </span>b</span>
-<span id="cb4-10"><a href="#cb4-10"></a>  loss &lt;-<span class="st"> </span><span class="kw">torch_mean</span>((y <span class="op">-</span><span class="st"> </span>y_hat<span class="op">$</span><span class="kw">squeeze</span>(<span class="dv">1</span>))<span class="op">^</span><span class="dv">2</span>)</span>
-<span id="cb4-11"><a href="#cb4-11"></a>  </span>
-<span id="cb4-12"><a href="#cb4-12"></a>  loss<span class="op">$</span><span class="kw">backward</span>()</span>
-<span id="cb4-13"><a href="#cb4-13"></a>  </span>
-<span id="cb4-14"><a href="#cb4-14"></a>  <span class="kw">with_no_grad</span>({</span>
-<span id="cb4-15"><a href="#cb4-15"></a>    w<span class="op">$</span><span class="kw">sub_</span>(w<span class="op">$</span>grad<span class="op">*</span>lr)</span>
-<span id="cb4-16"><a href="#cb4-16"></a>    b<span class="op">$</span><span class="kw">sub_</span>(b<span class="op">$</span>grad<span class="op">*</span>lr)   </span>
-<span id="cb4-17"><a href="#cb4-17"></a>    </span>
-<span id="cb4-18"><a href="#cb4-18"></a>    w<span class="op">$</span>grad<span class="op">$</span><span class="kw">zero_</span>()</span>
-<span id="cb4-19"><a href="#cb4-19"></a>    b<span class="op">$</span>grad<span class="op">$</span><span class="kw">zero_</span>()</span>
-<span id="cb4-20"><a href="#cb4-20"></a>  })</span>
-<span id="cb4-21"><a href="#cb4-21"></a>}</span>
-<span id="cb4-22"><a href="#cb4-22"></a><span class="kw">print</span>(w)</span>
-<span id="cb4-23"><a href="#cb4-23"></a><span class="co">#&gt; torch_tensor </span></span>
-<span id="cb4-24"><a href="#cb4-24"></a><span class="co">#&gt; 1e-09 *</span></span>
-<span id="cb4-25"><a href="#cb4-25"></a><span class="co">#&gt;  5.2672</span></span>
-<span id="cb4-26"><a href="#cb4-26"></a><span class="co">#&gt;  -6.7969</span></span>
-<span id="cb4-27"><a href="#cb4-27"></a><span class="co">#&gt; [ CPUFloatType{2,1} ]</span></span>
-<span id="cb4-28"><a href="#cb4-28"></a><span class="kw">print</span>(b) </span>
-<span id="cb4-29"><a href="#cb4-29"></a><span class="co">#&gt; torch_tensor </span></span>
-<span id="cb4-30"><a href="#cb4-30"></a><span class="co">#&gt; 0.01 *</span></span>
-<span id="cb4-31"><a href="#cb4-31"></a><span class="co">#&gt; -9.6802</span></span>
-<span id="cb4-32"><a href="#cb4-32"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span></code></pre></div>
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-<h2 class="hasAnchor">
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-    <div class="hidden name"><code>AutogradContext.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Class representing the context.</p>
-<p>Class representing the context.</p>
-    </div>
-
-
-
-    <h2 class="hasAnchor" id="public-fields"><a class="anchor" href="#public-fields"></a>Public fields</h2>
-
-    <p><div class="r6-fields"></p><dl'>
-<dt><code>ptr</code></dt><dd><p>(Dev related) pointer to the context c++ object.</p></dd>
-
-</dl><p></div></p>
-    <h2 class="hasAnchor" id="active-bindings"><a class="anchor" href="#active-bindings"></a>Active bindings</h2>
-
-    <p><div class="r6-active-bindings"></p><dl'>
-<dt><code>needs_input_grad</code></dt><dd><p>boolean listing arguments of <code>forward</code> and whether they require_grad.</p></dd>
-
-<dt><code>saved_variables</code></dt><dd><p>list of objects that were saved for backward via <code>save_for_backward</code>.</p></dd>
-
-</dl><p></div></p>
-    <h2 class="hasAnchor" id="methods"><a class="anchor" href="#methods"></a>Methods</h2>
-
-    
-<h3>Public methods</h3>
-
-<ul>
-<li><p><a href='#method-new'><code>AutogradContext$new()</code></a></p></li>
-<li><p><a href='#method-save_for_backward'><code>AutogradContext$save_for_backward()</code></a></p></li>
-<li><p><a href='#method-mark_non_differentiable'><code>AutogradContext$mark_non_differentiable()</code></a></p></li>
-<li><p><a href='#method-mark_dirty'><code>AutogradContext$mark_dirty()</code></a></p></li>
-<li><p><a href='#method-clone'><code>AutogradContext$clone()</code></a></p></li>
-</ul>
-<p><hr>
-<a id="method-new"></a></p><h3>Method <code>new()</code></h3>
-<p>(Dev related) Initializes the context. Not user related.</p><h3>Usage</h3>
-<p><div class="r"></p><pre><span class='no'>AutogradContext</span>$<span class='fu'>new</span>(
-  <span class='no'>ptr</span>,
-  <span class='no'>env</span>,
-  <span class='kw'>argument_names</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>argument_needs_grad</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre><p></div></p>
-
-<h3>Arguments</h3>
-<p><div class="arguments"></p><dl'>
-<dt><code>ptr</code></dt><dd><p>pointer to the c++ object</p></dd>
-
-<dt><code>env</code></dt><dd><p>environment that encloses both forward and backward</p></dd>
-
-<dt><code>argument_names</code></dt><dd><p>names of forward arguments</p></dd>
-
-<dt><code>argument_needs_grad</code></dt><dd><p>whether each argument in forward needs grad.</p></dd>
-
-</dl><p></div></p>
-<p><hr>
-<a id="method-save_for_backward"></a></p><h3>Method <code>save_for_backward()</code></h3>
-<p>Saves given objects for a future call to backward().</p>
-<p>This should be called at most once, and only from inside the <code>forward()</code>
-method.</p>
-<p>Later, saved objects can be accessed through the <code>saved_variables</code> attribute.
-Before returning them to the user, a check is made to ensure they weren’t used
-in any in-place operation that modified their content.</p>
-<p>Arguments can also be any kind of R object.</p><h3>Usage</h3>
-<p><div class="r"></p><pre><span class='no'>AutogradContext</span>$<span class='fu'>save_for_backward</span>(<span class='no'>...</span>)</pre><p></div></p>
-
-<h3>Arguments</h3>
-<p><div class="arguments"></p><dl'>
-<dt><code>...</code></dt><dd><p>any kind of R object that will be saved for the backward pass.
-It's common to pass named arguments.</p></dd>
-
-</dl><p></div></p>
-<p><hr>
-<a id="method-mark_non_differentiable"></a></p><h3>Method <code>mark_non_differentiable()</code></h3>
-<p>Marks outputs as non-differentiable.</p>
-<p>This should be called at most once, only from inside the <code>forward()</code> method,
-and all arguments should be outputs.</p>
-<p>This will mark outputs as not requiring gradients, increasing the efficiency
-of backward computation. You still need to accept a gradient for each output
-in <code>backward()</code>, but it’s always going to be a zero tensor with the same
-shape as the shape of a corresponding output.</p>
-<p>This is used e.g. for indices returned from a max Function.</p><h3>Usage</h3>
-<p><div class="r"></p><pre><span class='no'>AutogradContext</span>$<span class='fu'>mark_non_differentiable</span>(<span class='no'>...</span>)</pre><p></div></p>
-
-<h3>Arguments</h3>
-<p><div class="arguments"></p><dl'>
-<dt><code>...</code></dt><dd><p>non-differentiable outputs.</p></dd>
-
-</dl><p></div></p>
-<p><hr>
-<a id="method-mark_dirty"></a></p><h3>Method <code>mark_dirty()</code></h3>
-<p>Marks given tensors as modified in an in-place operation.</p>
-<p>This should be called at most once, only from inside the <code>forward()</code> method,
-and all arguments should be inputs.</p>
-<p>Every tensor that’s been modified in-place in a call to <code>forward()</code> should
-be given to this function, to ensure correctness of our checks. It doesn’t
-matter whether the function is called before or after modification.</p><h3>Usage</h3>
-<p><div class="r"></p><pre><span class='no'>AutogradContext</span>$<span class='fu'>mark_dirty</span>(<span class='no'>...</span>)</pre><p></div></p>
-
-<h3>Arguments</h3>
-<p><div class="arguments"></p><dl'>
-<dt><code>...</code></dt><dd><p>tensors that are modified in-place.</p></dd>
-
-</dl><p></div></p>
-<p><hr>
-<a id="method-clone"></a></p><h3>Method <code>clone()</code></h3>
-<p>The objects of this class are cloneable with this method.</p><h3>Usage</h3>
-<p><div class="r"></p><pre><span class='no'>AutogradContext</span>$<span class='fu'>clone</span>(<span class='kw'>deep</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre><p></div></p>
-
-<h3>Arguments</h3>
-<p><div class="arguments"></p><dl'>
-<dt><code>deep</code></dt><dd><p>Whether to make a deep clone.</p></dd>
-
-</dl><p></div></p>
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/tensor.R'><code>R/tensor.R</code></a></small>
-    <div class="hidden name"><code>as_array.Rd</code></div>
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-    <p>Converts to array</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>as_array</span>(<span class='no'>x</span>)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>object to be converted into an array</p></td>
-    </tr>
-    </table>
-
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-    <div class="page-header">
-    <h1>Computes the sum of gradients of given tensors w.r.t. graph leaves.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>autograd_backward.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The graph is differentiated using the chain rule. If any of tensors are
-non-scalar (i.e. their data has more than one element) and require gradient,
-then the Jacobian-vector product would be computed, in this case the function
-additionally requires specifying <code>grad_tensors</code>. It should be a sequence of
-matching length, that contains the “vector” in the Jacobian-vector product,
-usually the gradient of the differentiated function w.r.t. corresponding
-tensors (None is an acceptable value for all tensors that don’t need gradient
-tensors).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>autograd_backward</span>(
-  <span class='no'>tensors</span>,
-  <span class='kw'>grad_tensors</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>retain_graph</span> <span class='kw'>=</span> <span class='no'>create_graph</span>,
-  <span class='kw'>create_graph</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(list of Tensor) – Tensors of which the derivative will
-be computed.</p></td>
-    </tr>
-    <tr>
-      <th>grad_tensors</th>
-      <td><p>(list of (Tensor or <code>NULL)) – The “vector” in the  Jacobian-vector product, usually gradients w.r.t. each element of  corresponding tensors. </code>NULL<code>values can be specified for scalar Tensors or  ones that don’t require grad. If a</code>NULL` value would be acceptable for all
-grad_tensors, then this argument is optional.</p></td>
-    </tr>
-    <tr>
-      <th>retain_graph</th>
-      <td><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute
-the grad will be freed. Note that in nearly all cases setting this option to
-<code>TRUE</code> is not needed and often can be worked around in a much more efficient
-way. Defaults to the value of <code>create_graph</code>.</p></td>
-    </tr>
-    <tr>
-      <th>create_graph</th>
-      <td><p>(bool, optional) – If <code>TRUE</code>, graph of the derivative will
-be constructed, allowing to compute higher order derivative products.
-Defaults to <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This function accumulates gradients in the leaves - you might need to zero
-them before calling it.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>x</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1</span>, <span class='kw'>requires_grad</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='no'>y</span> <span class='kw'>&lt;-</span> <span class='fl'>2</span> * <span class='no'>x</span>
-
-<span class='no'>a</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1</span>, <span class='kw'>requires_grad</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='no'>b</span> <span class='kw'>&lt;-</span> <span class='fl'>3</span> * <span class='no'>a</span>
-
-<span class='fu'>autograd_backward</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>y</span>, <span class='no'>b</span>))
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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-performs the computation, and records that it happened. The history is
-retained in the form of a DAG of functions, with edges denoting data
-dependencies (input &amp;lt;- output). Then, when backward is called, the graph is
-processed in the topological ordering, by calling backward() methods of each
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-    <div class="page-header">
-    <h1>Records operation history and defines formulas for differentiating ops.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>autograd_function.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Every operation performed on Tensor's creates a new function object, that
-performs the computation, and records that it happened. The history is
-retained in the form of a DAG of functions, with edges denoting data
-dependencies (input &lt;- output). Then, when backward is called, the graph is
-processed in the topological ordering, by calling <code>backward()</code> methods of each
-Function object, and passing returned gradients on to next Function's.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>autograd_function</span>(<span class='no'>forward</span>, <span class='no'>backward</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>forward</th>
-      <td><p>Performs the operation. It must accept a context <code>ctx</code> as the first argument,
-followed by any number of arguments (tensors or other types). The context can be
-used to store tensors that can be then retrieved during the backward pass.
-See <a href='AutogradContext.html'>AutogradContext</a> for more information about context methods.</p></td>
-    </tr>
-    <tr>
-      <th>backward</th>
-      <td><p>Defines a formula for differentiating the operation. It must accept
-a context <code>ctx</code> as the first argument, followed by as many outputs did <code>forward()</code>
-return, and it should return a named list. Each argument is the gradient w.r.t
-the given output, and each element in the returned list should be the gradient
-w.r.t. the corresponding input. The context can be used to retrieve tensors saved
-during the forward pass. It also has an attribute <code>ctx$needs_input_grad</code> as a
-named list of booleans representing whether each input needs gradient.
-E.g., <code>backward()</code> will have <code>ctx$needs_input_grad$input = TRUE</code> if the <code>input</code>
-argument to <code>forward()</code> needs gradient computated w.r.t. the output.
-See <a href='AutogradContext.html'>AutogradContext</a> for more information about context methods.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>exp2</span> <span class='kw'>&lt;-</span> <span class='fu'>autograd_function</span>(
-  <span class='kw'>forward</span> <span class='kw'>=</span> <span class='kw'>function</span>(<span class='no'>ctx</span>, <span class='no'>i</span>) {
-    <span class='no'>result</span> <span class='kw'>&lt;-</span> <span class='no'>i</span>$<span class='fu'><a href='https://rdrr.io/r/base/Log.html'>exp</a></span>()
-    <span class='no'>ctx</span>$<span class='fu'>save_for_backward</span>(<span class='kw'>result</span> <span class='kw'>=</span> <span class='no'>result</span>)
-    <span class='no'>result</span>
-  },
-  <span class='kw'>backward</span> <span class='kw'>=</span> <span class='kw'>function</span>(<span class='no'>ctx</span>, <span class='no'>grad_output</span>) {
-    <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>i</span> <span class='kw'>=</span> <span class='no'>grad_output</span> * <span class='no'>ctx</span>$<span class='no'>saved_variable</span>$<span class='no'>result</span>)
-  }
-)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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diff --git a/static/docs/reference/autograd_grad.html b/static/docs/reference/autograd_grad.html
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-    <h1>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>autograd_grad.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>grad_outputs</code> should be a list of length matching output containing the “vector”
-in Jacobian-vector product, usually the pre-computed gradients w.r.t. each of
-the outputs. If an output doesn’t require_grad, then the gradient can be None).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>autograd_grad</span>(
-  <span class='no'>outputs</span>,
-  <span class='no'>inputs</span>,
-  <span class='kw'>grad_outputs</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>retain_graph</span> <span class='kw'>=</span> <span class='no'>create_graph</span>,
-  <span class='kw'>create_graph</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>allow_unused</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>outputs</th>
-      <td><p>(sequence of Tensor) – outputs of the differentiated function.</p></td>
-    </tr>
-    <tr>
-      <th>inputs</th>
-      <td><p>(sequence of Tensor) – Inputs w.r.t. which the gradient will be
-returned (and not accumulated into .grad).</p></td>
-    </tr>
-    <tr>
-      <th>grad_outputs</th>
-      <td><p>(sequence of Tensor) – The “vector” in the Jacobian-vector
-product. Usually gradients w.r.t. each output. None values can be specified for
-scalar Tensors or ones that don’t require grad. If a None value would be acceptable
-for all <code>grad_tensors</code>, then this argument is optional. Default: None.</p></td>
-    </tr>
-    <tr>
-      <th>retain_graph</th>
-      <td><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute the
-grad will be freed. Note that in nearly all cases setting this option to <code>TRUE</code> is
-not needed and often can be worked around in a much more efficient way.
-Defaults to the value of <code>create_graph</code>.</p></td>
-    </tr>
-    <tr>
-      <th>create_graph</th>
-      <td><p>(bool, optional) – If <code>TRUE, graph of the derivative will  be constructed, allowing to compute higher order derivative products.  Default: </code>FALSE`.</p></td>
-    </tr>
-    <tr>
-      <th>allow_unused</th>
-      <td><p>(bool, optional) – If <code>FALSE</code>, specifying inputs that were
-not used when computing outputs (and therefore their grad is always zero) is an
-error. Defaults to <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>If only_inputs is <code>TRUE</code>, the function will only return a list of gradients w.r.t
-the specified inputs. If it’s <code>FALSE</code>, then gradient w.r.t. all remaining leaves
-will still be computed, and will be accumulated into their <code>.grad</code> attribute.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>w</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>0.5</span>, <span class='kw'>requires_grad</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='no'>b</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>0.9</span>, <span class='kw'>requires_grad</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='no'>x</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Uniform.html'>runif</a></span>(<span class='fl'>100</span>))
-<span class='no'>y</span> <span class='kw'>&lt;-</span> <span class='fl'>2</span> * <span class='no'>x</span> + <span class='fl'>1</span>
-<span class='no'>loss</span> <span class='kw'>&lt;-</span> (<span class='no'>y</span> - (<span class='no'>w</span>*<span class='no'>x</span> + <span class='no'>b</span>))^<span class='fl'>2</span>
-<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='no'>loss</span>$<span class='fu'><a href='https://rdrr.io/r/base/mean.html'>mean</a></span>()
-
-<span class='no'>o</span> <span class='kw'>&lt;-</span> <span class='fu'>autograd_grad</span>(<span class='no'>loss</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>w</span>, <span class='no'>b</span>))
-<span class='no'>o</span>
-
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>autograd_set_grad_mode.Rd</code></div>
-    </div>
-
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-    <p>Sets or disables gradient history.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>autograd_set_grad_mode</span>(<span class='no'>enabled</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>enabled</th>
-      <td><p>bool wether to enable or disable the gradient recording.</p></td>
-    </tr>
-    </table>
-
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-    <div class="page-header">
-    <h1>Data loader. Combines a dataset and a sampler, and provides
-single- or multi-process iterators over the dataset.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-dataloader.R'><code>R/utils-data-dataloader.R</code></a></small>
-    <div class="hidden name"><code>dataloader.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Data loader. Combines a dataset and a sampler, and provides
-single- or multi-process iterators over the dataset.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>dataloader</span>(
-  <span class='no'>dataset</span>,
-  <span class='kw'>batch_size</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>shuffle</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>sampler</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>batch_sampler</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>num_workers</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>collate_fn</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>pin_memory</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>drop_last</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>timeout</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>worker_init_fn</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dataset</th>
-      <td><p>(Dataset): dataset from which to load the data.</p></td>
-    </tr>
-    <tr>
-      <th>batch_size</th>
-      <td><p>(int, optional): how many samples per batch to load
-(default: <code>1</code>).</p></td>
-    </tr>
-    <tr>
-      <th>shuffle</th>
-      <td><p>(bool, optional): set to <code>TRUE</code> to have the data reshuffled
-at every epoch (default: <code>FALSE</code>).</p></td>
-    </tr>
-    <tr>
-      <th>sampler</th>
-      <td><p>(Sampler, optional): defines the strategy to draw samples from
-the dataset. If specified, <code>shuffle</code> must be False.</p></td>
-    </tr>
-    <tr>
-      <th>batch_sampler</th>
-      <td><p>(Sampler, optional): like sampler, but returns a batch of
-indices at a time. Mutually exclusive with <code>batch_size</code>,
-<code>shuffle</code>, <code>sampler</code>, and <code>drop_last</code>.</p></td>
-    </tr>
-    <tr>
-      <th>num_workers</th>
-      <td><p>(int, optional): how many subprocesses to use for data
-loading. 0 means that the data will be loaded in the main process.
-(default: <code>0</code>)</p></td>
-    </tr>
-    <tr>
-      <th>collate_fn</th>
-      <td><p>(callable, optional): merges a list of samples to form a mini-batch.</p></td>
-    </tr>
-    <tr>
-      <th>pin_memory</th>
-      <td><p>(bool, optional): If <code>TRUE</code>, the data loader will copy tensors
-into CUDA pinned memory before returning them.  If your data elements
-are a custom type, or your <code>collate_fn</code> returns a batch that is a custom type
-see the example below.</p></td>
-    </tr>
-    <tr>
-      <th>drop_last</th>
-      <td><p>(bool, optional): set to <code>TRUE</code> to drop the last incomplete batch,
-if the dataset size is not divisible by the batch size. If <code>FALSE</code> and
-the size of dataset is not divisible by the batch size, then the last batch
-will be smaller. (default: <code>FALSE</code>)</p></td>
-    </tr>
-    <tr>
-      <th>timeout</th>
-      <td><p>(numeric, optional): if positive, the timeout value for collecting a batch
-from workers. Should always be non-negative. (default: <code>0</code>)</p></td>
-    </tr>
-    <tr>
-      <th>worker_init_fn</th>
-      <td><p>(callable, optional): If not <code>NULL</code>, this will be called on each
-worker subprocess with the worker id (an int in <code>[0, num_workers - 1]</code>) as
-input, after seeding and before data loading. (default: <code>NULL</code>)</p></td>
-    </tr>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-it. All subclasses should overwrite <code>get_item</code>, supporting fetching a
-data sample for a given key. Subclasses could also optionally overwrite
-<code>lenght</code>, which is expected to return the size of the dataset by many
-<code>~torch.utils.data.Sampler</code> implementations and the default options
-of <code>~torch.utils.data.DataLoader</code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>dataset</span>(<span class='kw'>name</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>inherit</span> <span class='kw'>=</span> <span class='no'>Dataset</span>, <span class='no'>...</span>, <span class='kw'>parent_env</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span>())</pre>
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-      <th>name</th>
-      <td><p>a name for the dataset. It it's also used as the class
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-    <tr>
-      <th>inherit</th>
-      <td><p>you can optionally inherit from a dataset when creating a
-new dataset.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>public methods for the dataset class</p></td>
-    </tr>
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-      <td><p>An environment to use as the parent of newly-created
-objects.</p></td>
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-
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-    <div class="hidden name"><code>default_dtype.Rd</code></div>
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-    <p>Gets and sets the default floating point dtype.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_set_default_dtype</span>(<span class='no'>d</span>)
-
-<span class='fu'>torch_get_default_dtype</span>()</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>d</th>
-      <td><p>The default floating point dtype to set. Initially set to
-<code><a href='torch_dtype.html'>torch_float()</a></code>.</p></td>
-    </tr>
-    </table>
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-enum.R'><code>R/utils-data-enum.R</code></a></small>
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-    <pre class="usage"><span class='fu'>enumerate</span>(<span class='no'>x</span>, <span class='no'>...</span>)</pre>
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>the generator to enumerate.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>passed to specific methods.</p></td>
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-          <h2 id="section-all-functions" class="hasAnchor"><a href="#section-all-functions" class="anchor"></a>All functions</h2>
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-          <p><code><a href="AutogradContext.html">AutogradContext</a></code> </p>
-        </td>
-        <td><p>Class representing the context.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="as_array.html">as_array()</a></code> </p>
-        </td>
-        <td><p>Converts to array</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="autograd_backward.html">autograd_backward()</a></code> </p>
-        </td>
-        <td><p>Computes the sum of gradients of given tensors w.r.t. graph leaves.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="autograd_function.html">autograd_function()</a></code> </p>
-        </td>
-        <td><p>Records operation history and defines formulas for differentiating ops.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="autograd_grad.html">autograd_grad()</a></code> </p>
-        </td>
-        <td><p>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="autograd_set_grad_mode.html">autograd_set_grad_mode()</a></code> </p>
-        </td>
-        <td><p>Set grad mode</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="cuda_current_device.html">cuda_current_device()</a></code> </p>
-        </td>
-        <td><p>Returns the index of a currently selected device.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="cuda_device_count.html">cuda_device_count()</a></code> </p>
-        </td>
-        <td><p>Returns the number of GPUs available.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="cuda_is_available.html">cuda_is_available()</a></code> </p>
-        </td>
-        <td><p>Returns a bool indicating if CUDA is currently available.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="dataloader.html">dataloader()</a></code> </p>
-        </td>
-        <td><p>Data loader. Combines a dataset and a sampler, and provides
-single- or multi-process iterators over the dataset.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="dataloader_make_iter.html">dataloader_make_iter()</a></code> </p>
-        </td>
-        <td><p>Creates an iterator from a DataLoader</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="dataloader_next.html">dataloader_next()</a></code> </p>
-        </td>
-        <td><p>Get the next element of a dataloader iterator</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="dataset.html">dataset()</a></code> </p>
-        </td>
-        <td><p>An abstract class representing a <code>Dataset</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="default_dtype.html">torch_set_default_dtype()</a></code> <code><a href="default_dtype.html">torch_get_default_dtype()</a></code> </p>
-        </td>
-        <td><p>Gets and sets the default floating point dtype.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="enumerate.html">enumerate()</a></code> </p>
-        </td>
-        <td><p>Enumerate an iterator</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="enumerate.dataloader.html">enumerate(<i>&lt;dataloader&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Enumerate an iterator</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="install_torch.html">install_torch()</a></code> </p>
-        </td>
-        <td><p>Install Torch</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_dataloader.html">is_dataloader()</a></code> </p>
-        </td>
-        <td><p>Checks if the object is a dataloader</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_dtype.html">is_torch_dtype()</a></code> </p>
-        </td>
-        <td><p>Check if object is a torch data type</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_layout.html">is_torch_layout()</a></code> </p>
-        </td>
-        <td><p>Check if an object is a torch layout.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_memory_format.html">is_torch_memory_format()</a></code> </p>
-        </td>
-        <td><p>Check if an object is a memory format</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_qscheme.html">is_torch_qscheme()</a></code> </p>
-        </td>
-        <td><p>Checks if an object is a QScheme</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="load_state_dict.html">load_state_dict()</a></code> </p>
-        </td>
-        <td><p>Load a state dict file</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_avg_pool1d.html">nn_adaptive_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_avg_pool2d.html">nn_adaptive_avg_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_avg_pool3d.html">nn_adaptive_avg_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_log_softmax_with_loss.html">nn_adaptive_log_softmax_with_loss()</a></code> </p>
-        </td>
-        <td><p>AdaptiveLogSoftmaxWithLoss module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_max_pool1d.html">nn_adaptive_max_pool1d()</a></code> </p>
-        </td>
-        <td><p>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_max_pool2d.html">nn_adaptive_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_max_pool3d.html">nn_adaptive_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_avg_pool1d.html">nn_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Applies a 1D average pooling over an input signal composed of several
-input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_avg_pool2d.html">nn_avg_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D average pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_avg_pool3d.html">nn_avg_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D average pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_batch_norm1d.html">nn_batch_norm1d()</a></code> </p>
-        </td>
-        <td><p>BatchNorm1D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_batch_norm2d.html">nn_batch_norm2d()</a></code> </p>
-        </td>
-        <td><p>BatchNorm2D</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_bce_loss.html">nn_bce_loss()</a></code> </p>
-        </td>
-        <td><p>Binary cross entropy loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_bilinear.html">nn_bilinear()</a></code> </p>
-        </td>
-        <td><p>Bilinear module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_celu.html">nn_celu()</a></code> </p>
-        </td>
-        <td><p>CELU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv1d.html">nn_conv1d()</a></code> </p>
-        </td>
-        <td><p>Conv1D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv2d.html">nn_conv2d()</a></code> </p>
-        </td>
-        <td><p>Conv2D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv3d.html">nn_conv3d()</a></code> </p>
-        </td>
-        <td><p>Conv3D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv_transpose1d.html">nn_conv_transpose1d()</a></code> </p>
-        </td>
-        <td><p>ConvTranspose1D</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv_transpose2d.html">nn_conv_transpose2d()</a></code> </p>
-        </td>
-        <td><p>ConvTranpose2D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv_transpose3d.html">nn_conv_transpose3d()</a></code> </p>
-        </td>
-        <td><p>ConvTranpose3D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_cross_entropy_loss.html">nn_cross_entropy_loss()</a></code> </p>
-        </td>
-        <td><p>CrossEntropyLoss module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_dropout.html">nn_dropout()</a></code> </p>
-        </td>
-        <td><p>Dropout module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_dropout2d.html">nn_dropout2d()</a></code> </p>
-        </td>
-        <td><p>Dropout2D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_dropout3d.html">nn_dropout3d()</a></code> </p>
-        </td>
-        <td><p>Dropout3D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_elu.html">nn_elu()</a></code> </p>
-        </td>
-        <td><p>ELU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_embedding.html">nn_embedding()</a></code> </p>
-        </td>
-        <td><p>Embedding module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_fractional_max_pool2d.html">nn_fractional_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D fractional max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_fractional_max_pool3d.html">nn_fractional_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D fractional max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_gelu.html">nn_gelu()</a></code> </p>
-        </td>
-        <td><p>GELU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_glu.html">nn_glu()</a></code> </p>
-        </td>
-        <td><p>GLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hardshrink.html">nn_hardshrink()</a></code> </p>
-        </td>
-        <td><p>Hardshwink module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hardsigmoid.html">nn_hardsigmoid()</a></code> </p>
-        </td>
-        <td><p>Hardsigmoid module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hardswish.html">nn_hardswish()</a></code> </p>
-        </td>
-        <td><p>Hardswish module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hardtanh.html">nn_hardtanh()</a></code> </p>
-        </td>
-        <td><p>Hardtanh module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_identity.html">nn_identity()</a></code> </p>
-        </td>
-        <td><p>Identity module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_calculate_gain.html">nn_init_calculate_gain()</a></code> </p>
-        </td>
-        <td><p>Calculate gain</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_constant_.html">nn_init_constant_()</a></code> </p>
-        </td>
-        <td><p>Constant initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_dirac_.html">nn_init_dirac_()</a></code> </p>
-        </td>
-        <td><p>Dirac initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_eye_.html">nn_init_eye_()</a></code> </p>
-        </td>
-        <td><p>Eye initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_kaiming_normal_.html">nn_init_kaiming_normal_()</a></code> </p>
-        </td>
-        <td><p>Kaiming normal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_kaiming_uniform_.html">nn_init_kaiming_uniform_()</a></code> </p>
-        </td>
-        <td><p>Kaiming uniform initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_normal_.html">nn_init_normal_()</a></code> </p>
-        </td>
-        <td><p>Normal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_ones_.html">nn_init_ones_()</a></code> </p>
-        </td>
-        <td><p>Ones initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_orthogonal_.html">nn_init_orthogonal_()</a></code> </p>
-        </td>
-        <td><p>Orthogonal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_sparse_.html">nn_init_sparse_()</a></code> </p>
-        </td>
-        <td><p>Sparse initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_trunc_normal_.html">nn_init_trunc_normal_()</a></code> </p>
-        </td>
-        <td><p>Truncated normal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_uniform_.html">nn_init_uniform_()</a></code> </p>
-        </td>
-        <td><p>Uniform initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_xavier_normal_.html">nn_init_xavier_normal_()</a></code> </p>
-        </td>
-        <td><p>Xavier normal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_xavier_uniform_.html">nn_init_xavier_uniform_()</a></code> </p>
-        </td>
-        <td><p>Xavier uniform initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_zeros_.html">nn_init_zeros_()</a></code> </p>
-        </td>
-        <td><p>Zeros initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_leaky_relu.html">nn_leaky_relu()</a></code> </p>
-        </td>
-        <td><p>LeakyReLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_linear.html">nn_linear()</a></code> </p>
-        </td>
-        <td><p>Linear module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_log_sigmoid.html">nn_log_sigmoid()</a></code> </p>
-        </td>
-        <td><p>LogSigmoid module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_log_softmax.html">nn_log_softmax()</a></code> </p>
-        </td>
-        <td><p>LogSoftmax module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_lp_pool1d.html">nn_lp_pool1d()</a></code> </p>
-        </td>
-        <td><p>Applies a 1D power-average pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_lp_pool2d.html">nn_lp_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D power-average pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_pool1d.html">nn_max_pool1d()</a></code> </p>
-        </td>
-        <td><p>MaxPool1D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_pool2d.html">nn_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>MaxPool2D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_pool3d.html">nn_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D max pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code> </p>
-        </td>
-        <td><p>Computes a partial inverse of <code>MaxPool1d</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code> </p>
-        </td>
-        <td><p>Computes a partial inverse of <code>MaxPool2d</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code> </p>
-        </td>
-        <td><p>Computes a partial inverse of <code>MaxPool3d</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_module.html">nn_module()</a></code> </p>
-        </td>
-        <td><p>Base class for all neural network modules.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_module_list.html">nn_module_list()</a></code> </p>
-        </td>
-        <td><p>Holds submodules in a list.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_multihead_attention.html">nn_multihead_attention()</a></code> </p>
-        </td>
-        <td><p>MultiHead attention</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_prelu.html">nn_prelu()</a></code> </p>
-        </td>
-        <td><p>PReLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_relu.html">nn_relu()</a></code> </p>
-        </td>
-        <td><p>ReLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_relu6.html">nn_relu6()</a></code> </p>
-        </td>
-        <td><p>ReLu6 module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_rnn.html">nn_rnn()</a></code> </p>
-        </td>
-        <td><p>RNN module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_rrelu.html">nn_rrelu()</a></code> </p>
-        </td>
-        <td><p>RReLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_selu.html">nn_selu()</a></code> </p>
-        </td>
-        <td><p>SELU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_sequential.html">nn_sequential()</a></code> </p>
-        </td>
-        <td><p>A sequential container</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_sigmoid.html">nn_sigmoid()</a></code> </p>
-        </td>
-        <td><p>Sigmoid module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softmax.html">nn_softmax()</a></code> </p>
-        </td>
-        <td><p>Softmax module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softmax2d.html">nn_softmax2d()</a></code> </p>
-        </td>
-        <td><p>Softmax2d module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softmin.html">nn_softmin()</a></code> </p>
-        </td>
-        <td><p>Softmin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softplus.html">nn_softplus()</a></code> </p>
-        </td>
-        <td><p>Softplus module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softshrink.html">nn_softshrink()</a></code> </p>
-        </td>
-        <td><p>Softshrink module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softsign.html">nn_softsign()</a></code> </p>
-        </td>
-        <td><p>Softsign module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_tanh.html">nn_tanh()</a></code> </p>
-        </td>
-        <td><p>Tanh module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_tanhshrink.html">nn_tanhshrink()</a></code> </p>
-        </td>
-        <td><p>Tanhshrink module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_threshold.html">nn_threshold()</a></code> </p>
-        </td>
-        <td><p>Threshoold module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code> </p>
-        </td>
-        <td><p>Packs a Tensor containing padded sequences of variable length.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_rnn_pack_sequence.html">nn_utils_rnn_pack_sequence()</a></code> </p>
-        </td>
-        <td><p>Packs a list of variable length Tensors</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_rnn_pad_packed_sequence.html">nn_utils_rnn_pad_packed_sequence()</a></code> </p>
-        </td>
-        <td><p>Pads a packed batch of variable length sequences.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_rnn_pad_sequence.html">nn_utils_rnn_pad_sequence()</a></code> </p>
-        </td>
-        <td><p>Pad a list of variable length Tensors with <code>padding_value</code></p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_avg_pool1d.html">nnf_adaptive_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_avg_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_avg_pool2d.html">nnf_adaptive_avg_pool2d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_avg_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_avg_pool3d.html">nnf_adaptive_avg_pool3d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_avg_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_max_pool1d.html">nnf_adaptive_max_pool1d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_max_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_max_pool2d.html">nnf_adaptive_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_max_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_max_pool3d.html">nnf_adaptive_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_max_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_affine_grid.html">nnf_affine_grid()</a></code> </p>
-        </td>
-        <td><p>Affine_grid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_alpha_dropout.html">nnf_alpha_dropout()</a></code> </p>
-        </td>
-        <td><p>Alpha_dropout</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_avg_pool1d.html">nnf_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Avg_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_avg_pool2d.html">nnf_avg_pool2d()</a></code> </p>
-        </td>
-        <td><p>Avg_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_avg_pool3d.html">nnf_avg_pool3d()</a></code> </p>
-        </td>
-        <td><p>Avg_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_batch_norm.html">nnf_batch_norm()</a></code> </p>
-        </td>
-        <td><p>Batch_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_bilinear.html">nnf_bilinear()</a></code> </p>
-        </td>
-        <td><p>Bilinear</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_binary_cross_entropy.html">nnf_binary_cross_entropy()</a></code> </p>
-        </td>
-        <td><p>Binary_cross_entropy</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_binary_cross_entropy_with_logits.html">nnf_binary_cross_entropy_with_logits()</a></code> </p>
-        </td>
-        <td><p>Binary_cross_entropy_with_logits</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_celu.html">nnf_celu()</a></code> <code><a href="nnf_celu.html">nnf_celu_()</a></code> </p>
-        </td>
-        <td><p>Celu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv1d.html">nnf_conv1d()</a></code> </p>
-        </td>
-        <td><p>Conv1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv2d.html">nnf_conv2d()</a></code> </p>
-        </td>
-        <td><p>Conv2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv3d.html">nnf_conv3d()</a></code> </p>
-        </td>
-        <td><p>Conv3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv_tbc.html">nnf_conv_tbc()</a></code> </p>
-        </td>
-        <td><p>Conv_tbc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv_transpose1d.html">nnf_conv_transpose1d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv_transpose2d.html">nnf_conv_transpose2d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv_transpose3d.html">nnf_conv_transpose3d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_cosine_embedding_loss.html">nnf_cosine_embedding_loss()</a></code> </p>
-        </td>
-        <td><p>Cosine_embedding_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_cosine_similarity.html">nnf_cosine_similarity()</a></code> </p>
-        </td>
-        <td><p>Cosine_similarity</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_cross_entropy.html">nnf_cross_entropy()</a></code> </p>
-        </td>
-        <td><p>Cross_entropy</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_ctc_loss.html">nnf_ctc_loss()</a></code> </p>
-        </td>
-        <td><p>Ctc_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_dropout.html">nnf_dropout()</a></code> </p>
-        </td>
-        <td><p>Dropout</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_dropout2d.html">nnf_dropout2d()</a></code> </p>
-        </td>
-        <td><p>Dropout2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_dropout3d.html">nnf_dropout3d()</a></code> </p>
-        </td>
-        <td><p>Dropout3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_elu.html">nnf_elu()</a></code> <code><a href="nnf_elu.html">nnf_elu_()</a></code> </p>
-        </td>
-        <td><p>Elu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_embedding.html">nnf_embedding()</a></code> </p>
-        </td>
-        <td><p>Embedding</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_embedding_bag.html">nnf_embedding_bag()</a></code> </p>
-        </td>
-        <td><p>Embedding_bag</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_fold.html">nnf_fold()</a></code> </p>
-        </td>
-        <td><p>Fold</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_fractional_max_pool2d.html">nnf_fractional_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Fractional_max_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_fractional_max_pool3d.html">nnf_fractional_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Fractional_max_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_gelu.html">nnf_gelu()</a></code> </p>
-        </td>
-        <td><p>Gelu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_glu.html">nnf_glu()</a></code> </p>
-        </td>
-        <td><p>Glu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code> </p>
-        </td>
-        <td><p>Grid_sample</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_group_norm.html">nnf_group_norm()</a></code> </p>
-        </td>
-        <td><p>Group_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_gumbel_softmax.html">nnf_gumbel_softmax()</a></code> </p>
-        </td>
-        <td><p>Gumbel_softmax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hardshrink.html">nnf_hardshrink()</a></code> </p>
-        </td>
-        <td><p>Hardshrink</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hardsigmoid.html">nnf_hardsigmoid()</a></code> </p>
-        </td>
-        <td><p>Hardsigmoid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hardswish.html">nnf_hardswish()</a></code> </p>
-        </td>
-        <td><p>Hardswish</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hardtanh.html">nnf_hardtanh()</a></code> <code><a href="nnf_hardtanh.html">nnf_hardtanh_()</a></code> </p>
-        </td>
-        <td><p>Hardtanh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hinge_embedding_loss.html">nnf_hinge_embedding_loss()</a></code> </p>
-        </td>
-        <td><p>Hinge_embedding_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_instance_norm.html">nnf_instance_norm()</a></code> </p>
-        </td>
-        <td><p>Instance_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_interpolate.html">nnf_interpolate()</a></code> </p>
-        </td>
-        <td><p>Interpolate</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_kl_div.html">nnf_kl_div()</a></code> </p>
-        </td>
-        <td><p>Kl_div</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_l1_loss.html">nnf_l1_loss()</a></code> </p>
-        </td>
-        <td><p>L1_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_layer_norm.html">nnf_layer_norm()</a></code> </p>
-        </td>
-        <td><p>Layer_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_leaky_relu.html">nnf_leaky_relu()</a></code> </p>
-        </td>
-        <td><p>Leaky_relu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_linear.html">nnf_linear()</a></code> </p>
-        </td>
-        <td><p>Linear</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_local_response_norm.html">nnf_local_response_norm()</a></code> </p>
-        </td>
-        <td><p>Local_response_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_log_softmax.html">nnf_log_softmax()</a></code> </p>
-        </td>
-        <td><p>Log_softmax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_logsigmoid.html">nnf_logsigmoid()</a></code> </p>
-        </td>
-        <td><p>Logsigmoid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_lp_pool1d.html">nnf_lp_pool1d()</a></code> </p>
-        </td>
-        <td><p>Lp_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_lp_pool2d.html">nnf_lp_pool2d()</a></code> </p>
-        </td>
-        <td><p>Lp_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_margin_ranking_loss.html">nnf_margin_ranking_loss()</a></code> </p>
-        </td>
-        <td><p>Margin_ranking_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_pool1d.html">nnf_max_pool1d()</a></code> </p>
-        </td>
-        <td><p>Max_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_pool2d.html">nnf_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Max_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_pool3d.html">nnf_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Max_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_unpool1d.html">nnf_max_unpool1d()</a></code> </p>
-        </td>
-        <td><p>Max_unpool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_unpool2d.html">nnf_max_unpool2d()</a></code> </p>
-        </td>
-        <td><p>Max_unpool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_unpool3d.html">nnf_max_unpool3d()</a></code> </p>
-        </td>
-        <td><p>Max_unpool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_mse_loss.html">nnf_mse_loss()</a></code> </p>
-        </td>
-        <td><p>Mse_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_multi_head_attention_forward.html">nnf_multi_head_attention_forward()</a></code> </p>
-        </td>
-        <td><p>Multi head attention forward</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_multi_margin_loss.html">nnf_multi_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Multi_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_multilabel_margin_loss.html">nnf_multilabel_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Multilabel_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_multilabel_soft_margin_loss.html">nnf_multilabel_soft_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Multilabel_soft_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_nll_loss.html">nnf_nll_loss()</a></code> </p>
-        </td>
-        <td><p>Nll_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_normalize.html">nnf_normalize()</a></code> </p>
-        </td>
-        <td><p>Normalize</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_one_hot.html">nnf_one_hot()</a></code> </p>
-        </td>
-        <td><p>One_hot</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_pad.html">nnf_pad()</a></code> </p>
-        </td>
-        <td><p>Pad</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_pairwise_distance.html">nnf_pairwise_distance()</a></code> </p>
-        </td>
-        <td><p>Pairwise_distance</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_pdist.html">nnf_pdist()</a></code> </p>
-        </td>
-        <td><p>Pdist</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_pixel_shuffle.html">nnf_pixel_shuffle()</a></code> </p>
-        </td>
-        <td><p>Pixel_shuffle</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_poisson_nll_loss.html">nnf_poisson_nll_loss()</a></code> </p>
-        </td>
-        <td><p>Poisson_nll_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_prelu.html">nnf_prelu()</a></code> </p>
-        </td>
-        <td><p>Prelu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_relu.html">nnf_relu()</a></code> <code><a href="nnf_relu.html">nnf_relu_()</a></code> </p>
-        </td>
-        <td><p>Relu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_relu6.html">nnf_relu6()</a></code> </p>
-        </td>
-        <td><p>Relu6</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_rrelu.html">nnf_rrelu()</a></code> <code><a href="nnf_rrelu.html">nnf_rrelu_()</a></code> </p>
-        </td>
-        <td><p>Rrelu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_selu.html">nnf_selu()</a></code> <code><a href="nnf_selu.html">nnf_selu_()</a></code> </p>
-        </td>
-        <td><p>Selu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_sigmoid.html">nnf_sigmoid()</a></code> </p>
-        </td>
-        <td><p>Sigmoid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_smooth_l1_loss.html">nnf_smooth_l1_loss()</a></code> </p>
-        </td>
-        <td><p>Smooth_l1_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_soft_margin_loss.html">nnf_soft_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Soft_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softmax.html">nnf_softmax()</a></code> </p>
-        </td>
-        <td><p>Softmax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softmin.html">nnf_softmin()</a></code> </p>
-        </td>
-        <td><p>Softmin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softplus.html">nnf_softplus()</a></code> </p>
-        </td>
-        <td><p>Softplus</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softshrink.html">nnf_softshrink()</a></code> </p>
-        </td>
-        <td><p>Softshrink</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softsign.html">nnf_softsign()</a></code> </p>
-        </td>
-        <td><p>Softsign</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_tanhshrink.html">nnf_tanhshrink()</a></code> </p>
-        </td>
-        <td><p>Tanhshrink</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_threshold.html">nnf_threshold()</a></code> <code><a href="nnf_threshold.html">nnf_threshold_()</a></code> </p>
-        </td>
-        <td><p>Threshold</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_triplet_margin_loss.html">nnf_triplet_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Triplet_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_unfold.html">nnf_unfold()</a></code> </p>
-        </td>
-        <td><p>Unfold</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_adam.html">optim_adam()</a></code> </p>
-        </td>
-        <td><p>Implements Adam algorithm.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_required.html">optim_required()</a></code> </p>
-        </td>
-        <td><p>Dummy value indicating a required value.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_sgd.html">optim_sgd()</a></code> </p>
-        </td>
-        <td><p>SGD optimizer</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="tensor_dataset.html">tensor_dataset()</a></code> </p>
-        </td>
-        <td><p>Dataset wrapping tensors.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_abs.html">torch_abs</a></code> </p>
-        </td>
-        <td><p>Abs</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_acos.html">torch_acos</a></code> </p>
-        </td>
-        <td><p>Acos</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_adaptive_avg_pool1d.html">torch_adaptive_avg_pool1d</a></code> </p>
-        </td>
-        <td><p>Adaptive_avg_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_add.html">torch_add</a></code> </p>
-        </td>
-        <td><p>Add</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addbmm.html">torch_addbmm</a></code> </p>
-        </td>
-        <td><p>Addbmm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addcdiv.html">torch_addcdiv</a></code> </p>
-        </td>
-        <td><p>Addcdiv</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addcmul.html">torch_addcmul</a></code> </p>
-        </td>
-        <td><p>Addcmul</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addmm.html">torch_addmm</a></code> </p>
-        </td>
-        <td><p>Addmm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addmv.html">torch_addmv</a></code> </p>
-        </td>
-        <td><p>Addmv</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addr.html">torch_addr</a></code> </p>
-        </td>
-        <td><p>Addr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_allclose.html">torch_allclose</a></code> </p>
-        </td>
-        <td><p>Allclose</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_angle.html">torch_angle</a></code> </p>
-        </td>
-        <td><p>Angle</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_arange.html">torch_arange</a></code> </p>
-        </td>
-        <td><p>Arange</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_argmax.html">torch_argmax</a></code> </p>
-        </td>
-        <td><p>Argmax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_argmin.html">torch_argmin</a></code> </p>
-        </td>
-        <td><p>Argmin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_argsort.html">torch_argsort</a></code> </p>
-        </td>
-        <td><p>Argsort</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_as_strided.html">torch_as_strided</a></code> </p>
-        </td>
-        <td><p>As_strided</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_asin.html">torch_asin</a></code> </p>
-        </td>
-        <td><p>Asin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_atan.html">torch_atan</a></code> </p>
-        </td>
-        <td><p>Atan</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_atan2.html">torch_atan2</a></code> </p>
-        </td>
-        <td><p>Atan2</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_avg_pool1d.html">torch_avg_pool1d</a></code> </p>
-        </td>
-        <td><p>Avg_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_baddbmm.html">torch_baddbmm</a></code> </p>
-        </td>
-        <td><p>Baddbmm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bartlett_window.html">torch_bartlett_window</a></code> </p>
-        </td>
-        <td><p>Bartlett_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bernoulli.html">torch_bernoulli</a></code> </p>
-        </td>
-        <td><p>Bernoulli</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bincount.html">torch_bincount</a></code> </p>
-        </td>
-        <td><p>Bincount</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bitwise_and.html">torch_bitwise_and</a></code> </p>
-        </td>
-        <td><p>Bitwise_and</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bitwise_not.html">torch_bitwise_not</a></code> </p>
-        </td>
-        <td><p>Bitwise_not</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bitwise_or.html">torch_bitwise_or</a></code> </p>
-        </td>
-        <td><p>Bitwise_or</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bitwise_xor.html">torch_bitwise_xor</a></code> </p>
-        </td>
-        <td><p>Bitwise_xor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_blackman_window.html">torch_blackman_window</a></code> </p>
-        </td>
-        <td><p>Blackman_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bmm.html">torch_bmm</a></code> </p>
-        </td>
-        <td><p>Bmm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_broadcast_tensors.html">torch_broadcast_tensors</a></code> </p>
-        </td>
-        <td><p>Broadcast_tensors</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_can_cast.html">torch_can_cast</a></code> </p>
-        </td>
-        <td><p>Can_cast</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cartesian_prod.html">torch_cartesian_prod</a></code> </p>
-        </td>
-        <td><p>Cartesian_prod</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cat.html">torch_cat</a></code> </p>
-        </td>
-        <td><p>Cat</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cdist.html">torch_cdist</a></code> </p>
-        </td>
-        <td><p>Cdist</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ceil.html">torch_ceil</a></code> </p>
-        </td>
-        <td><p>Ceil</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_celu_.html">torch_celu_</a></code> </p>
-        </td>
-        <td><p>Celu_</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_chain_matmul.html">torch_chain_matmul</a></code> </p>
-        </td>
-        <td><p>Chain_matmul</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cholesky.html">torch_cholesky</a></code> </p>
-        </td>
-        <td><p>Cholesky</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cholesky_inverse.html">torch_cholesky_inverse</a></code> </p>
-        </td>
-        <td><p>Cholesky_inverse</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cholesky_solve.html">torch_cholesky_solve</a></code> </p>
-        </td>
-        <td><p>Cholesky_solve</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_chunk.html">torch_chunk</a></code> </p>
-        </td>
-        <td><p>Chunk</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_clamp.html">torch_clamp</a></code> </p>
-        </td>
-        <td><p>Clamp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_combinations.html">torch_combinations</a></code> </p>
-        </td>
-        <td><p>Combinations</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conj.html">torch_conj</a></code> </p>
-        </td>
-        <td><p>Conj</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv1d.html">torch_conv1d</a></code> </p>
-        </td>
-        <td><p>Conv1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv2d.html">torch_conv2d</a></code> </p>
-        </td>
-        <td><p>Conv2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv3d.html">torch_conv3d</a></code> </p>
-        </td>
-        <td><p>Conv3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv_tbc.html">torch_conv_tbc</a></code> </p>
-        </td>
-        <td><p>Conv_tbc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv_transpose1d.html">torch_conv_transpose1d</a></code> </p>
-        </td>
-        <td><p>Conv_transpose1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv_transpose2d.html">torch_conv_transpose2d</a></code> </p>
-        </td>
-        <td><p>Conv_transpose2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv_transpose3d.html">torch_conv_transpose3d</a></code> </p>
-        </td>
-        <td><p>Conv_transpose3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cos.html">torch_cos</a></code> </p>
-        </td>
-        <td><p>Cos</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cosh.html">torch_cosh</a></code> </p>
-        </td>
-        <td><p>Cosh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cosine_similarity.html">torch_cosine_similarity</a></code> </p>
-        </td>
-        <td><p>Cosine_similarity</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cross.html">torch_cross</a></code> </p>
-        </td>
-        <td><p>Cross</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cummax.html">torch_cummax</a></code> </p>
-        </td>
-        <td><p>Cummax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cummin.html">torch_cummin</a></code> </p>
-        </td>
-        <td><p>Cummin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cumprod.html">torch_cumprod</a></code> </p>
-        </td>
-        <td><p>Cumprod</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cumsum.html">torch_cumsum</a></code> </p>
-        </td>
-        <td><p>Cumsum</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_det.html">torch_det</a></code> </p>
-        </td>
-        <td><p>Det</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_device.html">torch_device()</a></code> </p>
-        </td>
-        <td><p>Create a Device object</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diag.html">torch_diag</a></code> </p>
-        </td>
-        <td><p>Diag</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diag_embed.html">torch_diag_embed</a></code> </p>
-        </td>
-        <td><p>Diag_embed</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diagflat.html">torch_diagflat</a></code> </p>
-        </td>
-        <td><p>Diagflat</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diagonal.html">torch_diagonal</a></code> </p>
-        </td>
-        <td><p>Diagonal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_digamma.html">torch_digamma</a></code> </p>
-        </td>
-        <td><p>Digamma</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_dist.html">torch_dist</a></code> </p>
-        </td>
-        <td><p>Dist</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_div.html">torch_div</a></code> </p>
-        </td>
-        <td><p>Div</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_dot.html">torch_dot</a></code> </p>
-        </td>
-        <td><p>Dot</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_dtype.html">torch_float32()</a></code> <code><a href="torch_dtype.html">torch_float()</a></code> <code><a href="torch_dtype.html">torch_float64()</a></code> <code><a href="torch_dtype.html">torch_double()</a></code> <code><a href="torch_dtype.html">torch_float16()</a></code> <code><a href="torch_dtype.html">torch_half()</a></code> <code><a href="torch_dtype.html">torch_uint8()</a></code> <code><a href="torch_dtype.html">torch_int8()</a></code> <code><a href="torch_dtype.html">torch_int16()</a></code> <code><a href="torch_dtype.html">torch_short()</a></code> <code><a href="torch_dtype.html">torch_int32()</a></code> <code><a href="torch_dtype.html">torch_int()</a></code> <code><a href="torch_dtype.html">torch_int64()</a></code> <code><a href="torch_dtype.html">torch_long()</a></code> <code><a href="torch_dtype.html">torch_bool()</a></code> <code><a href="torch_dtype.html">torch_quint8()</a></code> <code><a href="torch_dtype.html">torch_qint8()</a></code> <code><a href="torch_dtype.html">torch_qint32()</a></code> </p>
-        </td>
-        <td><p>Torch data types</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_eig.html">torch_eig</a></code> </p>
-        </td>
-        <td><p>Eig</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_einsum.html">torch_einsum</a></code> </p>
-        </td>
-        <td><p>Einsum</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_empty.html">torch_empty</a></code> </p>
-        </td>
-        <td><p>Empty</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_empty_like.html">torch_empty_like</a></code> </p>
-        </td>
-        <td><p>Empty_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_empty_strided.html">torch_empty_strided</a></code> </p>
-        </td>
-        <td><p>Empty_strided</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_eq.html">torch_eq</a></code> </p>
-        </td>
-        <td><p>Eq</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_equal.html">torch_equal</a></code> </p>
-        </td>
-        <td><p>Equal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_erf.html">torch_erf</a></code> </p>
-        </td>
-        <td><p>Erf</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_erfc.html">torch_erfc</a></code> </p>
-        </td>
-        <td><p>Erfc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_erfinv.html">torch_erfinv</a></code> </p>
-        </td>
-        <td><p>Erfinv</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_exp.html">torch_exp</a></code> </p>
-        </td>
-        <td><p>Exp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_expm1.html">torch_expm1</a></code> </p>
-        </td>
-        <td><p>Expm1</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_eye.html">torch_eye</a></code> </p>
-        </td>
-        <td><p>Eye</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_fft.html">torch_fft</a></code> </p>
-        </td>
-        <td><p>Fft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_finfo.html">torch_finfo()</a></code> </p>
-        </td>
-        <td><p>Floating point type info</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_flatten.html">torch_flatten</a></code> </p>
-        </td>
-        <td><p>Flatten</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_flip.html">torch_flip</a></code> </p>
-        </td>
-        <td><p>Flip</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_floor.html">torch_floor</a></code> </p>
-        </td>
-        <td><p>Floor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_floor_divide.html">torch_floor_divide</a></code> </p>
-        </td>
-        <td><p>Floor_divide</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_fmod.html">torch_fmod</a></code> </p>
-        </td>
-        <td><p>Fmod</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_frac.html">torch_frac</a></code> </p>
-        </td>
-        <td><p>Frac</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_full.html">torch_full</a></code> </p>
-        </td>
-        <td><p>Full</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_full_like.html">torch_full_like</a></code> </p>
-        </td>
-        <td><p>Full_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_gather.html">torch_gather</a></code> </p>
-        </td>
-        <td><p>Gather</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ge.html">torch_ge</a></code> </p>
-        </td>
-        <td><p>Ge</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_generator.html">torch_generator()</a></code> </p>
-        </td>
-        <td><p>Create a Generator object</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_geqrf.html">torch_geqrf</a></code> </p>
-        </td>
-        <td><p>Geqrf</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ger.html">torch_ger</a></code> </p>
-        </td>
-        <td><p>Ger</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_gt.html">torch_gt</a></code> </p>
-        </td>
-        <td><p>Gt</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_hamming_window.html">torch_hamming_window</a></code> </p>
-        </td>
-        <td><p>Hamming_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_hann_window.html">torch_hann_window</a></code> </p>
-        </td>
-        <td><p>Hann_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_histc.html">torch_histc</a></code> </p>
-        </td>
-        <td><p>Histc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ifft.html">torch_ifft</a></code> </p>
-        </td>
-        <td><p>Ifft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_iinfo.html">torch_iinfo()</a></code> </p>
-        </td>
-        <td><p>Integer type info</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_imag.html">torch_imag</a></code> </p>
-        </td>
-        <td><p>Imag</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_index_select.html">torch_index_select</a></code> </p>
-        </td>
-        <td><p>Index_select</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_inverse.html">torch_inverse</a></code> </p>
-        </td>
-        <td><p>Inverse</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_irfft.html">torch_irfft</a></code> </p>
-        </td>
-        <td><p>Irfft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_is_complex.html">torch_is_complex</a></code> </p>
-        </td>
-        <td><p>Is_complex</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_is_floating_point.html">torch_is_floating_point</a></code> </p>
-        </td>
-        <td><p>Is_floating_point</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_is_installed.html">torch_is_installed()</a></code> </p>
-        </td>
-        <td><p>Verifies if torch is installed</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isfinite.html">torch_isfinite</a></code> </p>
-        </td>
-        <td><p>Isfinite</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isinf.html">torch_isinf</a></code> </p>
-        </td>
-        <td><p>Isinf</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isnan.html">torch_isnan</a></code> </p>
-        </td>
-        <td><p>Isnan</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_kthvalue.html">torch_kthvalue</a></code> </p>
-        </td>
-        <td><p>Kthvalue</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_layout.html">torch_strided()</a></code> <code><a href="torch_layout.html">torch_sparse_coo()</a></code> </p>
-        </td>
-        <td><p>Creates the corresponding layout</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_le.html">torch_le</a></code> </p>
-        </td>
-        <td><p>Le</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lerp.html">torch_lerp</a></code> </p>
-        </td>
-        <td><p>Lerp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lgamma.html">torch_lgamma</a></code> </p>
-        </td>
-        <td><p>Lgamma</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_linspace.html">torch_linspace</a></code> </p>
-        </td>
-        <td><p>Linspace</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_load.html">torch_load()</a></code> </p>
-        </td>
-        <td><p>Loads a saved object</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_log.html">torch_log</a></code> </p>
-        </td>
-        <td><p>Log</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_log10.html">torch_log10</a></code> </p>
-        </td>
-        <td><p>Log10</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_log1p.html">torch_log1p</a></code> </p>
-        </td>
-        <td><p>Log1p</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_log2.html">torch_log2</a></code> </p>
-        </td>
-        <td><p>Log2</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logdet.html">torch_logdet</a></code> </p>
-        </td>
-        <td><p>Logdet</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logical_and.html">torch_logical_and</a></code> </p>
-        </td>
-        <td><p>Logical_and</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logical_not.html">torch_logical_not</a></code> </p>
-        </td>
-        <td><p>Logical_not</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logical_or.html">torch_logical_or</a></code> </p>
-        </td>
-        <td><p>Logical_or</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logical_xor.html">torch_logical_xor</a></code> </p>
-        </td>
-        <td><p>Logical_xor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logspace.html">torch_logspace</a></code> </p>
-        </td>
-        <td><p>Logspace</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logsumexp.html">torch_logsumexp</a></code> </p>
-        </td>
-        <td><p>Logsumexp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lstsq.html">torch_lstsq</a></code> </p>
-        </td>
-        <td><p>Lstsq</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lt.html">torch_lt</a></code> </p>
-        </td>
-        <td><p>Lt</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lu.html">torch_lu()</a></code> </p>
-        </td>
-        <td><p>LU</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lu_solve.html">torch_lu_solve</a></code> </p>
-        </td>
-        <td><p>Lu_solve</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_manual_seed.html">torch_manual_seed()</a></code> </p>
-        </td>
-        <td><p>Sets the seed for generating random numbers.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_masked_select.html">torch_masked_select</a></code> </p>
-        </td>
-        <td><p>Masked_select</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_matmul.html">torch_matmul</a></code> </p>
-        </td>
-        <td><p>Matmul</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_matrix_power.html">torch_matrix_power</a></code> </p>
-        </td>
-        <td><p>Matrix_power</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_matrix_rank.html">torch_matrix_rank</a></code> </p>
-        </td>
-        <td><p>Matrix_rank</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_max.html">torch_max</a></code> </p>
-        </td>
-        <td><p>Max</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mean.html">torch_mean</a></code> </p>
-        </td>
-        <td><p>Mean</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_median.html">torch_median</a></code> </p>
-        </td>
-        <td><p>Median</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_memory_format.html">torch_contiguous_format()</a></code> <code><a href="torch_memory_format.html">torch_preserve_format()</a></code> <code><a href="torch_memory_format.html">torch_channels_last_format()</a></code> </p>
-        </td>
-        <td><p>Memory format</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_meshgrid.html">torch_meshgrid</a></code> </p>
-        </td>
-        <td><p>Meshgrid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_min.html">torch_min</a></code> </p>
-        </td>
-        <td><p>Min</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mm.html">torch_mm</a></code> </p>
-        </td>
-        <td><p>Mm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mode.html">torch_mode</a></code> </p>
-        </td>
-        <td><p>Mode</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mul.html">torch_mul</a></code> </p>
-        </td>
-        <td><p>Mul</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_multinomial.html">torch_multinomial</a></code> </p>
-        </td>
-        <td><p>Multinomial</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mv.html">torch_mv</a></code> </p>
-        </td>
-        <td><p>Mv</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mvlgamma.html">torch_mvlgamma</a></code> </p>
-        </td>
-        <td><p>Mvlgamma</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_narrow.html">torch_narrow</a></code> </p>
-        </td>
-        <td><p>Narrow</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ne.html">torch_ne</a></code> </p>
-        </td>
-        <td><p>Ne</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_neg.html">torch_neg</a></code> </p>
-        </td>
-        <td><p>Neg</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_nonzero.html">torch_nonzero</a></code> </p>
-        </td>
-        <td><p>Nonzero</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_norm.html">torch_norm</a></code> </p>
-        </td>
-        <td><p>Norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_normal.html">torch_normal</a></code> </p>
-        </td>
-        <td><p>Normal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ones.html">torch_ones</a></code> </p>
-        </td>
-        <td><p>Ones</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ones_like.html">torch_ones_like</a></code> </p>
-        </td>
-        <td><p>Ones_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_orgqr.html">torch_orgqr</a></code> </p>
-        </td>
-        <td><p>Orgqr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ormqr.html">torch_ormqr</a></code> </p>
-        </td>
-        <td><p>Ormqr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_pdist.html">torch_pdist</a></code> </p>
-        </td>
-        <td><p>Pdist</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_pinverse.html">torch_pinverse</a></code> </p>
-        </td>
-        <td><p>Pinverse</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_pixel_shuffle.html">torch_pixel_shuffle</a></code> </p>
-        </td>
-        <td><p>Pixel_shuffle</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_poisson.html">torch_poisson</a></code> </p>
-        </td>
-        <td><p>Poisson</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_polygamma.html">torch_polygamma</a></code> </p>
-        </td>
-        <td><p>Polygamma</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_pow.html">torch_pow</a></code> </p>
-        </td>
-        <td><p>Pow</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_prod.html">torch_prod</a></code> </p>
-        </td>
-        <td><p>Prod</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_promote_types.html">torch_promote_types</a></code> </p>
-        </td>
-        <td><p>Promote_types</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_qr.html">torch_qr</a></code> </p>
-        </td>
-        <td><p>Qr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_qscheme.html">torch_per_channel_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_channel_symmetric()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_symmetric()</a></code> </p>
-        </td>
-        <td><p>Creates the corresponding Scheme object</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_quantize_per_channel.html">torch_quantize_per_channel</a></code> </p>
-        </td>
-        <td><p>Quantize_per_channel</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_quantize_per_tensor.html">torch_quantize_per_tensor</a></code> </p>
-        </td>
-        <td><p>Quantize_per_tensor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rand.html">torch_rand</a></code> </p>
-        </td>
-        <td><p>Rand</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rand_like.html">torch_rand_like</a></code> </p>
-        </td>
-        <td><p>Rand_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randint.html">torch_randint</a></code> </p>
-        </td>
-        <td><p>Randint</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randint_like.html">torch_randint_like</a></code> </p>
-        </td>
-        <td><p>Randint_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randn.html">torch_randn</a></code> </p>
-        </td>
-        <td><p>Randn</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randn_like.html">torch_randn_like</a></code> </p>
-        </td>
-        <td><p>Randn_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randperm.html">torch_randperm</a></code> </p>
-        </td>
-        <td><p>Randperm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_range.html">torch_range</a></code> </p>
-        </td>
-        <td><p>Range</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_real.html">torch_real</a></code> </p>
-        </td>
-        <td><p>Real</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_reciprocal.html">torch_reciprocal</a></code> </p>
-        </td>
-        <td><p>Reciprocal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_reduction.html">torch_reduction_sum()</a></code> <code><a href="torch_reduction.html">torch_reduction_mean()</a></code> <code><a href="torch_reduction.html">torch_reduction_none()</a></code> </p>
-        </td>
-        <td><p>Creates the reduction objet</p></td>
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-        
-        <td>
-          <p><code><a href="torch_relu_.html">torch_relu_</a></code> </p>
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-        <td>
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-        <td><p>Remainder</p></td>
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-        
-        <td>
-          <p><code><a href="torch_renorm.html">torch_renorm</a></code> </p>
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-        <td>
-          <p><code><a href="torch_repeat_interleave.html">torch_repeat_interleave</a></code> </p>
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-        <td><p>Repeat_interleave</p></td>
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-        <td>
-          <p><code><a href="torch_reshape.html">torch_reshape</a></code> </p>
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-        <td><p>Rfft</p></td>
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-        <td>
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-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rot90.html">torch_rot90</a></code> </p>
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-        <td>
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-        </td>
-        <td><p>Round</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rrelu_.html">torch_rrelu_</a></code> </p>
-        </td>
-        <td><p>Rrelu_</p></td>
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-          <p><code><a href="torch_rsqrt.html">torch_rsqrt</a></code> </p>
-        </td>
-        <td><p>Rsqrt</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_save.html">torch_save()</a></code> </p>
-        </td>
-        <td><p>Saves an object to a disk file.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_selu_.html">torch_selu_</a></code> </p>
-        </td>
-        <td><p>Selu_</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sigmoid.html">torch_sigmoid</a></code> </p>
-        </td>
-        <td><p>Sigmoid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sign.html">torch_sign</a></code> </p>
-        </td>
-        <td><p>Sign</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sin.html">torch_sin</a></code> </p>
-        </td>
-        <td><p>Sin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sinh.html">torch_sinh</a></code> </p>
-        </td>
-        <td><p>Sinh</p></td>
-      </tr><tr>
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-          <p><code><a href="torch_slogdet.html">torch_slogdet</a></code> </p>
-        </td>
-        <td><p>Slogdet</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_solve.html">torch_solve</a></code> </p>
-        </td>
-        <td><p>Solve</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sort.html">torch_sort</a></code> </p>
-        </td>
-        <td><p>Sort</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sparse_coo_tensor.html">torch_sparse_coo_tensor</a></code> </p>
-        </td>
-        <td><p>Sparse_coo_tensor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_split.html">torch_split</a></code> </p>
-        </td>
-        <td><p>Split</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sqrt.html">torch_sqrt</a></code> </p>
-        </td>
-        <td><p>Sqrt</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_square.html">torch_square</a></code> </p>
-        </td>
-        <td><p>Square</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_squeeze.html">torch_squeeze</a></code> </p>
-        </td>
-        <td><p>Squeeze</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_stack.html">torch_stack</a></code> </p>
-        </td>
-        <td><p>Stack</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_std.html">torch_std</a></code> </p>
-        </td>
-        <td><p>Std</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_std_mean.html">torch_std_mean</a></code> </p>
-        </td>
-        <td><p>Std_mean</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_stft.html">torch_stft</a></code> </p>
-        </td>
-        <td><p>Stft</p></td>
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-        <td>
-          <p><code><a href="torch_sum.html">torch_sum</a></code> </p>
-        </td>
-        <td><p>Sum</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_svd.html">torch_svd</a></code> </p>
-        </td>
-        <td><p>Svd</p></td>
-      </tr><tr>
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-        <td>
-          <p><code><a href="torch_symeig.html">torch_symeig</a></code> </p>
-        </td>
-        <td><p>Symeig</p></td>
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-          <p><code><a href="torch_tensor.html">torch_tensor()</a></code> </p>
-        </td>
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-          <p><code><a href="torch_tensordot.html">torch_tensordot</a></code> </p>
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-        <td><p>Tensordot</p></td>
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-        </td>
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-        <td><p>Triangular_solve</p></td>
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-          <p><code><a href="torch_tril.html">torch_tril</a></code> </p>
-        </td>
-        <td><p>Tril</p></td>
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-        
-        <td>
-          <p><code><a href="torch_tril_indices.html">torch_tril_indices</a></code> </p>
-        </td>
-        <td><p>Tril_indices</p></td>
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-        
-        <td>
-          <p><code><a href="torch_triu.html">torch_triu</a></code> </p>
-        </td>
-        <td><p>Triu</p></td>
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-        
-        <td>
-          <p><code><a href="torch_triu_indices.html">torch_triu_indices</a></code> </p>
-        </td>
-        <td><p>Triu_indices</p></td>
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-        
-        <td>
-          <p><code><a href="torch_true_divide.html">torch_true_divide</a></code> </p>
-        </td>
-        <td><p>True_divide</p></td>
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-        
-        <td>
-          <p><code><a href="torch_trunc.html">torch_trunc</a></code> </p>
-        </td>
-        <td><p>Trunc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_unbind.html">torch_unbind</a></code> </p>
-        </td>
-        <td><p>Unbind</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_unique_consecutive.html">torch_unique_consecutive</a></code> </p>
-        </td>
-        <td><p>Unique_consecutive</p></td>
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-        
-        <td>
-          <p><code><a href="torch_unsqueeze.html">torch_unsqueeze</a></code> </p>
-        </td>
-        <td><p>Unsqueeze</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_var.html">torch_var</a></code> </p>
-        </td>
-        <td><p>Var</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_var_mean.html">torch_var_mean</a></code> </p>
-        </td>
-        <td><p>Var_mean</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_where.html">torch_where</a></code> </p>
-        </td>
-        <td><p>Where</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_zeros.html">torch_zeros</a></code> </p>
-        </td>
-        <td><p>Zeros</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_zeros_like.html">torch_zeros_like</a></code> </p>
-        </td>
-        <td><p>Zeros_like</p></td>
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-        
-        <td>
-          <p><code><a href="with_enable_grad.html">with_enable_grad()</a></code> </p>
-        </td>
-        <td><p>Enable grad</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="with_no_grad.html">with_no_grad()</a></code> </p>
-        </td>
-        <td><p>Temporarily modify gradient recording.</p></td>
-      </tr>
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-
-    <pre class="usage"><span class='fu'>install_torch</span>(
-  <span class='kw'>version</span> <span class='kw'>=</span> <span class='st'>"1.5.0"</span>,
-  <span class='kw'>type</span> <span class='kw'>=</span> <span class='fu'>install_type</span>(<span class='kw'>version</span> <span class='kw'>=</span> <span class='no'>version</span>),
-  <span class='kw'>reinstall</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>path</span> <span class='kw'>=</span> <span class='fu'>install_path</span>(),
-  <span class='no'>...</span>
-)</pre>
-
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-    <table class="ref-arguments">
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-      <td><p>The Torch version to install.</p></td>
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-      <td><p>Re-install Torch even if its already installed?</p></td>
-    </tr>
-    <tr>
-      <th>path</th>
-      <td><p>Optional path to install or check for an already existing installation.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>other optional arguments (like <code>load</code> for manual installation.)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>When using <code>path</code> to install in a specific location, make sure the <code>TORCH_HOME</code> environment
-variable is set to this same path to reuse this installation. The <code>TORCH_INSTALL</code> environment
-variable can be set to <code>0</code> to prevent auto-installing torch and <code>TORCH_LOAD</code> set to <code>0</code>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-dataloader.R'><code>R/utils-data-dataloader.R</code></a></small>
-    <div class="hidden name"><code>is_dataloader.Rd</code></div>
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-
-    <pre class="usage"><span class='fu'>is_dataloader</span>(<span class='no'>x</span>)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>object to check</p></td>
-    </tr>
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-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_avg_pool1d.Rd</code></div>
-    </div>
-
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-    <p>The output size is H, for any input size.
-The number of output features is equal to the number of input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool1d</span>(<span class='no'>output_size</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size H</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># target output size of 5</span>
-<span class='no'>m</span> <span class='kw'>=</span> <span class='fu'>nn_adaptive_avg_pool1d</span>(<span class='fl'>5</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
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-    <h1>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_avg_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The output is of size H x W, for any input size.
-The number of output features is equal to the number of input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool2d</span>(<span class='no'>output_size</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form H x W.
-Can be a tuple (H, W) or a single H for a square image H x H.
-H and W can be either a <code>int</code>, or <code>NULL</code> which means the size will
-be the same as that of the input.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># target output size of 5x7</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>))
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-<span class='co'># target output size of 7x7 (square)</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool2d</span>(<span class='fl'>7</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_avg_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The output is of size D x H x W, for any input size.
-The number of output features is equal to the number of input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool3d</span>(<span class='no'>output_size</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the form D x H x W.
-Can be a tuple (D, H, W) or a single number D for a cube D x D x D.
-D, H and W can be either a <code>int</code>, or <code>None</code> which means the size will
-be the same as that of the input.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># target output size of 5x7x9</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>,<span class='fl'>9</span>))
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>, <span class='fl'>10</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-<span class='co'># target output size of 7x7x7 (cube)</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool3d</span>(<span class='fl'>7</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>, <span class='fl'>8</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>AdaptiveLogSoftmaxWithLoss module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn_adaptive.R'><code>R/nn_adaptive.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_log_softmax_with_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Efficient softmax approximation as described in
-<a href='https://arxiv.org/abs/1609.04309'>Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou</a></p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_log_softmax_with_loss</span>(
-  <span class='no'>in_features</span>,
-  <span class='no'>n_classes</span>,
-  <span class='no'>cutoffs</span>,
-  <span class='kw'>div_value</span> <span class='kw'>=</span> <span class='fl'>4</span>,
-  <span class='kw'>head_bias</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_features</th>
-      <td><p>(int): Number of features in the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>n_classes</th>
-      <td><p>(int): Number of classes in the dataset</p></td>
-    </tr>
-    <tr>
-      <th>cutoffs</th>
-      <td><p>(Sequence): Cutoffs used to assign targets to their buckets</p></td>
-    </tr>
-    <tr>
-      <th>div_value</th>
-      <td><p>(float, optional): value used as an exponent to compute sizes
-of the clusters. Default: 4.0</p></td>
-    </tr>
-    <tr>
-      <th>head_bias</th>
-      <td><p>(bool, optional): If <code>True</code>, adds a bias term to the 'head' of the
-adaptive softmax. Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p><code>NamedTuple</code> with <code>output</code> and <code>loss</code> fields:</p><ul>
-<li><p><strong>output</strong> is a Tensor of size <code>N</code> containing computed target
-log probabilities for each example</p></li>
-<li><p><strong>loss</strong> is a Scalar representing the computed negative
-log likelihood loss</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Adaptive softmax is an approximate strategy for training models with large
-output spaces. It is most effective when the label distribution is highly
-imbalanced, for example in natural language modelling, where the word
-frequency distribution approximately follows the Zipf's law.</p>
-<p>Adaptive softmax partitions the labels into several clusters, according to
-their frequency. These clusters may contain different number of targets
-each.</p>
-<p>Additionally, clusters containing less frequent labels assign lower
-dimensional embeddings to those labels, which speeds up the computation.
-For each minibatch, only clusters for which at least one target is
-present are evaluated.</p>
-<p>The idea is that the clusters which are accessed frequently
-(like the first one, containing most frequent labels), should also be cheap
-to compute -- that is, contain a small number of assigned labels.
-We highly recommend taking a look at the original paper for more details.</p><ul>
-<li><p><code>cutoffs</code> should be an ordered Sequence of integers sorted
-in the increasing order.
-It controls number of clusters and the partitioning of targets into
-clusters. For example setting <code>cutoffs = c(10, 100, 1000)</code>
-means that first <code>10</code> targets will be assigned
-to the 'head' of the adaptive softmax, targets <code>11, 12, ..., 100</code> will be
-assigned to the first cluster, and targets <code>101, 102, ..., 1000</code> will be
-assigned to the second cluster, while targets
-<code>1001, 1002, ..., n_classes - 1</code> will be assigned
-to the last, third cluster.</p></li>
-<li><p><code>div_value</code> is used to compute the size of each additional cluster,
-which is given as
-\(\left\lfloor\frac{\mbox{in\_features}}{\mbox{div\_value}^{idx}}\right\rfloor\),
-where \(idx\) is the cluster index (with clusters
-for less frequent words having larger indices,
-and indices starting from \(1\)).</p></li>
-<li><p><code>head_bias</code> if set to True, adds a bias term to the 'head' of the
-adaptive softmax. See paper for details. Set to False in the official
-implementation.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This module returns a <code>NamedTuple</code> with <code>output</code>
-and <code>loss</code> fields. See further documentation for details.</p>
-<p>To compute log-probabilities for all classes, the <code>log_prob</code>
-method can be used.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Labels passed as inputs to this module should be sorted according to
-their frequency. This means that the most frequent label should be
-represented by the index <code>0</code>, and the least frequent
-label should be represented by the index <code>n_classes - 1</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>input: \((N, \mbox{in\_features})\)</p></li>
-<li><p>target: \((N)\) where each value satisfies \(0 &lt;= \mbox{target[i]} &lt;= \mbox{n\_classes}\)</p></li>
-<li><p>output1: \((N)\)</p></li>
-<li><p>output2: <code>Scalar</code></p></li>
-</ul>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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diff --git a/static/docs/reference/nn_adaptive_max_pool1d.html b/static/docs/reference/nn_adaptive_max_pool1d.html
deleted file mode 100644
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_max_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The output size is H, for any input size.
-The number of output features is equal to the number of input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_max_pool1d</span>(<span class='no'>output_size</span>, <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size H</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool1d.html'>nn_max_unpool1d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># target output size of 5</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool1d</span>(<span class='fl'>5</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-    <div class="page-header">
-    <h1>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_max_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The output is of size H x W, for any input size.
-The number of output features is equal to the number of input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_max_pool2d</span>(<span class='no'>output_size</span>, <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form H x W.
-Can be a tuple <code>(H, W)</code> or a single H for a square image H x H.
-H and W can be either a <code>int</code>, or <code>None</code> which means the size will
-be the same as that of the input.</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># target output size of 5x7</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>))
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-<span class='co'># target output size of 7x7 (square)</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool2d</span>(<span class='fl'>7</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-    <h1>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_max_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The output is of size D x H x W, for any input size.
-The number of output features is equal to the number of input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_adaptive_max_pool3d</span>(<span class='no'>output_size</span>, <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form D x H x W.
-Can be a tuple (D, H, W) or a single D for a cube D x D x D.
-D, H and W can be either a <code>int</code>, or <code>None</code> which means the size will
-be the same as that of the input.</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool3d.html'>nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># target output size of 5x7x9</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>,<span class='fl'>9</span>))
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>, <span class='fl'>10</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-<span class='co'># target output size of 7x7x7 (cube)</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool3d</span>(<span class='fl'>7</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>, <span class='fl'>8</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_avg_pool1d.html b/static/docs/reference/nn_avg_pool1d.html
deleted file mode 100644
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-    <div class="page-header">
-    <h1>Applies a 1D average pooling over an input signal composed of several
-input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_avg_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>In the simplest case, the output value of the layer with input size \((N, C, L)\),
-output \((N, C, L_{out})\) and <code>kernel_size</code> \(k\)
-can be precisely described as:</p>
-<p>$$
-  \mbox{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1}
-\mbox{input}(N_i, C_j, \mbox{stride} \times l + m)
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_avg_pool1d</span>(
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>count_include_pad</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
-for <code>padding</code> number of points.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can each be
-an <code>int</code> or a one-element tuple.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, L_{in})\)</p></li>
-<li><p>Output: \((N, C, L_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  L_{out} = \left\lfloor \frac{L_{in} +
-      2 \times \mbox{padding} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># pool with window of size=3, stride=2</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_avg_pool1d</span>(<span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='fu'>m</span>(<span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>8</span>))
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/nn_avg_pool2d.html b/static/docs/reference/nn_avg_pool2d.html
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-planes. — nn_avg_pool2d • torch</title>
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-planes. — nn_avg_pool2d" />
-<meta property="og:description" content="In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
-output \((N, C, H_{out}, W_{out})\) and kernel_size \((kH, kW)\)
-can be precisely described as:
-$$
-  out(N_i, C_j, h, w)  = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1}
-input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)
-$$" />
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-    <div class="page-header">
-    <h1>Applies a 2D average pooling over an input signal composed of several input
-planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_avg_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
-output \((N, C, H_{out}, W_{out})\) and <code>kernel_size</code> \((kH, kW)\)
-can be precisely described as:</p>
-<p>$$
-  out(N_i, C_j, h, w)  = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1}
-input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_avg_pool2d</span>(
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>count_include_pad</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>divisor_override</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
-    </tr>
-    <tr>
-      <th>divisor_override</th>
-      <td><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
-for <code>padding</code> number of points.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
-and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] -
-      \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
-$$
-$$
-  W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \mbox{padding}[1] -
-      \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># pool of square window of size=3, stride=2</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_avg_pool2d</span>(<span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='co'># pool of non-square window</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_avg_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), <span class='kw'>stride</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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diff --git a/static/docs/reference/nn_avg_pool3d.html b/static/docs/reference/nn_avg_pool3d.html
deleted file mode 100644
index fb04216d3..000000000
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-planes. — nn_avg_pool3d" />
-<meta property="og:description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
-output \((N, C, D_{out}, H_{out}, W_{out})\) and kernel_size \((kD, kH, kW)\)
-can be precisely described as:
-$$
-\begin{array}{ll}
-\mbox{out}(N_i, C_j, d, h, w) = &amp;amp; \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
-&amp;amp; \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW}
-\end{array}
-$$" />
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-    <div class="page-header">
-    <h1>Applies a 3D average pooling over an input signal composed of several input
-planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_avg_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
-output \((N, C, D_{out}, H_{out}, W_{out})\) and <code>kernel_size</code> \((kD, kH, kW)\)
-can be precisely described as:</p>
-<p>$$
-\begin{array}{ll}
-\mbox{out}(N_i, C_j, d, h, w) = &amp; \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
-&amp; \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW}
-\end{array}
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_avg_pool3d</span>(
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>count_include_pad</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>divisor_override</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on all three sides</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
-    </tr>
-    <tr>
-      <th>divisor_override</th>
-      <td><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on all three sides
-for <code>padding</code> number of points.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
-<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
-the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] -
-      \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
-$$
-$$
-  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] -
-      \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
-$$
-$$
-  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] -
-      \mbox{kernel\_size}[2]}{\mbox{stride}[2]} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># pool of square window of size=3, stride=2</span>
-<span class='no'>m</span> <span class='kw'>=</span> <span class='fu'>nn_avg_pool3d</span>(<span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='co'># pool of non-square window</span>
-<span class='no'>m</span> <span class='kw'>=</span> <span class='fu'>nn_avg_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>), <span class='kw'>stride</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='no'>input</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>,<span class='fl'>44</span>, <span class='fl'>31</span>)
-<span class='no'>output</span> <span class='kw'>=</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" />
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-    <h1>BatchNorm1D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-batchnorm.R'><code>R/nn-batchnorm.R</code></a></small>
-    <div class="hidden name"><code>nn_batch_norm1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D
-inputs with optional additional channel dimension) as described in the paper
-<a href='https://arxiv.org/abs/1502.03167'>Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a></p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_batch_norm1d</span>(
-  <span class='no'>num_features</span>,
-  <span class='kw'>eps</span> <span class='kw'>=</span> <span class='fl'>1e-05</span>,
-  <span class='kw'>momentum</span> <span class='kw'>=</span> <span class='fl'>0.1</span>,
-  <span class='kw'>affine</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>track_running_stats</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_features</th>
-      <td><p>\(C\) from an expected input of size
-\((N, C, L)\) or \(L\) from input of size \((N, L)\)</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability.
-Default: 1e-5</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>the value used for the running_mean and running_var
-computation. Can be set to <code>NULL</code> for cumulative moving average
-(i.e. simple average). Default: 0.1</p></td>
-    </tr>
-    <tr>
-      <th>affine</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this module has
-learnable affine parameters. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>track_running_stats</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this
-module tracks the running mean and variance, and when set to <code>FALSE</code>,
-this module does not track such statistics and always uses batch
-statistics in both training and eval modes. Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
-$$</p>
-<p>The mean and standard-deviation are calculated per-dimension over
-the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors
-of size <code>C</code> (where <code>C</code> is the input size). By default, the elements of \(\gamma\)
-are set to 1 and the elements of \(\beta\) are set to 0.</p>
-<p>Also by default, during training this layer keeps running estimates of its
-computed mean and variance, which are then used for normalization during
-evaluation. The running estimates are kept with a default :attr:<code>momentum</code>
-of 0.1.
-If <code>track_running_stats</code> is set to <code>FALSE</code>, this layer then does not
-keep running estimates, and batch statistics are instead used during
-evaluation time as well.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>This <code>momentum</code> argument is different from one used in optimizer
-classes and the conventional notion of momentum. Mathematically, the
-update rule for running statistics here is
-\(\hat{x}_{\mbox{new}} = (1 - \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t\),
-where \(\hat{x}\) is the estimated statistic and \(x_t\) is the
-new observed value.</p>
-<p>Because the Batch Normalization is done over the <code>C</code> dimension, computing statistics
-on <code>(N, L)</code> slices, it's common terminology to call this Temporal Batch Normalization.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C)\) or \((N, C, L)\)</p></li>
-<li><p>Output: \((N, C)\) or \((N, C, L)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># With Learnable Parameters</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_batch_norm1d</span>(<span class='fl'>100</span>)
-<span class='co'># Without Learnable Parameters</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_batch_norm1d</span>(<span class='fl'>100</span>, <span class='kw'>affine</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>100</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-<meta property="og:description" content="Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs
-additional channel dimension) as described in the paper
-Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift." />
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-    <div class="page-header">
-    <h1>BatchNorm2D</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-batchnorm.R'><code>R/nn-batchnorm.R</code></a></small>
-    <div class="hidden name"><code>nn_batch_norm2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs
-additional channel dimension) as described in the paper
-<a href='https://arxiv.org/abs/1502.03167'>Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_batch_norm2d</span>(
-  <span class='no'>num_features</span>,
-  <span class='kw'>eps</span> <span class='kw'>=</span> <span class='fl'>1e-05</span>,
-  <span class='kw'>momentum</span> <span class='kw'>=</span> <span class='fl'>0.1</span>,
-  <span class='kw'>affine</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>track_running_stats</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_features</th>
-      <td><p>\(C\) from an expected input of size
-\((N, C, H, W)\)</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability.
-Default: 1e-5</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>the value used for the running_mean and running_var
-computation. Can be set to <code>None</code> for cumulative moving average
-(i.e. simple average). Default: 0.1</p></td>
-    </tr>
-    <tr>
-      <th>affine</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this module has
-learnable affine parameters. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>track_running_stats</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this
-module tracks the running mean and variance, and when set to <code>FALSE</code>,
-this module does not track such statistics and uses batch statistics instead
-in both training and eval modes if the running mean and variance are <code>None</code>.
-Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
-$$</p>
-<p>The mean and standard-deviation are calculated per-dimension over
-the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors
-of size <code>C</code> (where <code>C</code> is the input size). By default, the elements of \(\gamma\) are set
-to 1 and the elements of \(\beta\) are set to 0. The standard-deviation is calculated
-via the biased estimator, equivalent to <code><a href='torch_var.html'>torch_var(input, unbiased=FALSE)</a></code>.
-Also by default, during training this layer keeps running estimates of its
-computed mean and variance, which are then used for normalization during
-evaluation. The running estimates are kept with a default <code>momentum</code>
-of 0.1.</p>
-<p>If <code>track_running_stats</code> is set to <code>FALSE</code>, this layer then does not
-keep running estimates, and batch statistics are instead used during
-evaluation time as well.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This <code>momentum</code> argument is different from one used in optimizer
-classes and the conventional notion of momentum. Mathematically, the
-update rule for running statistics here is
-\(\hat{x}_{\mbox{new}} = (1 - \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t\),
-where \(\hat{x}\) is the estimated statistic and \(x_t\) is the
-new observed value.
-Because the Batch Normalization is done over the <code>C</code> dimension, computing statistics
-on <code>(N, H, W)</code> slices, it's common terminology to call this Spatial Batch Normalization.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H, W)\)</p></li>
-<li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># With Learnable Parameters</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_batch_norm2d</span>(<span class='fl'>100</span>)
-<span class='co'># Without Learnable Parameters</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_batch_norm2d</span>(<span class='fl'>100</span>, <span class='kw'>affine</span><span class='kw'>=</span><span class='fl'>FALSE</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>100</span>, <span class='fl'>35</span>, <span class='fl'>45</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_bce_loss.html b/static/docs/reference/nn_bce_loss.html
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-    <h1>Binary cross entropy loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_bce_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that measures the Binary Cross Entropy
-between the target and the output:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_bce_loss</span>(<span class='kw'>weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='st'>"mean"</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional): a manual rescaling weight given to the loss
-of each batch element. If given, has to be a Tensor of size <code>nbatch</code>.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
-<code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
-<code>'mean'</code>: the sum of the output will be divided by the number of
-elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
-and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The unreduced (i.e. with <code>reduction</code> set to <code>'none'</code>) loss can be described as:
-$$
-  \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
-l_n = - w_n \left[ y_n \cdot \log x_n + (1 - y_n) \cdot \log (1 - x_n) \right]
-$$
-where \(N\) is the batch size. If <code>reduction</code> is not <code>'none'</code>
-(default <code>'mean'</code>), then</p>
-<p>$$
-  \ell(x, y) = \left\{ \begin{array}{ll}
-\mbox{mean}(L), &amp; \mbox{if reduction} = \mbox{'mean';}\\
-\mbox{sum}(L),  &amp; \mbox{if reduction} = \mbox{'sum'.}
-\end{array}
-\right.
-$$</p>
-<p>This is used for measuring the error of a reconstruction in for example
-an auto-encoder. Note that the targets \(y\) should be numbers
-between 0 and 1.</p>
-<p>Notice that if \(x_n\) is either 0 or 1, one of the log terms would be
-mathematically undefined in the above loss equation. PyTorch chooses to set
-\(\log (0) = -\infty\), since \(\lim_{x\to 0} \log (x) = -\infty\).</p>
-<p>However, an infinite term in the loss equation is not desirable for several reasons.
-For one, if either \(y_n = 0\) or \((1 - y_n) = 0\), then we would be
-multiplying 0 with infinity. Secondly, if we have an infinite loss value, then
-we would also have an infinite term in our gradient, since
-\(\lim_{x\to 0} \frac{d}{dx} \log (x) = \infty\).</p>
-<p>This would make BCELoss's backward method nonlinear with respect to \(x_n\),
-and using it for things like linear regression would not be straight-forward.
-Our solution is that BCELoss clamps its log function outputs to be greater than
-or equal to -100. This way, we can always have a finite loss value and a linear
-backward method.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where \(*\) means, any number of additional
-dimensions</p></li>
-<li><p>Target: \((N, *)\), same shape as the input</p></li>
-<li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then \((N, *)\), same
-shape as input.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='nn_sigmoid.html'>nn_sigmoid</a></span>()
-<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_bce_loss</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>3</span>, <span class='kw'>requires_grad</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='no'>target</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fl'>3</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>loss</span>(<span class='fu'>m</span>(<span class='no'>input</span>), <span class='no'>target</span>)
-<span class='no'>output</span>$<span class='fu'>backward</span>()
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/nn_bilinear.html b/static/docs/reference/nn_bilinear.html
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-    <h1>Bilinear module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
-    <div class="hidden name"><code>nn_bilinear.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a bilinear transformation to the incoming data
-\(y = x_1^T A x_2 + b\)</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_bilinear</span>(<span class='no'>in1_features</span>, <span class='no'>in2_features</span>, <span class='no'>out_features</span>, <span class='kw'>bias</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in1_features</th>
-      <td><p>size of each first input sample</p></td>
-    </tr>
-    <tr>
-      <th>in2_features</th>
-      <td><p>size of each second input sample</p></td>
-    </tr>
-    <tr>
-      <th>out_features</th>
-      <td><p>size of each output sample</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
-Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input1: \((N, *, H_{in1})\) \(H_{in1}=\mbox{in1\_features}\) and
-\(*\) means any number of additional dimensions. All but the last
-dimension of the inputs should be the same.</p></li>
-<li><p>Input2: \((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\).</p></li>
-<li><p>Output: \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)
-and all but the last dimension are the same shape as the input.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight: the learnable weights of the module of shape
-\((\mbox{out\_features}, \mbox{in1\_features}, \mbox{in2\_features})\).
-The values are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where
-\(k = \frac{1}{\mbox{in1\_features}}\)</p></li>
-<li><p>bias: the learnable bias of the module of shape \((\mbox{out\_features})\).
-If <code>bias</code> is <code>TRUE</code>, the values are initialized from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where
-\(k = \frac{1}{\mbox{in1\_features}}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_bilinear</span>(<span class='fl'>20</span>, <span class='fl'>30</span>, <span class='fl'>50</span>)
-<span class='no'>input1</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>20</span>)
-<span class='no'>input2</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>30</span>)
-<span class='no'>output</span> <span class='kw'>=</span> <span class='fu'>m</span>(<span class='no'>input1</span>, <span class='no'>input2</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>output</span>$<span class='fu'>size</span>())
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/static/docs/reference/nn_celu.html b/static/docs/reference/nn_celu.html
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-    <h1>CELU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_celu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the element-wise function:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_celu</span>(<span class='kw'>alpha</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>alpha</th>
-      <td><p>the \(\alpha\) value for the CELU formulation. Default: 1.0</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))
-$$</p>
-<p>More details can be found in the paper
-<a href='https://arxiv.org/abs/1704.07483'>Continuously Differentiable Exponential Linear Units</a>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_celu</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-<meta property="og:description" content="Applies a 1D convolution over an input signal composed of several input
-planes.
-In the simplest case, the output value of the layer with input size
-\((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be
-precisely described as:" />
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-    <h1>Conv1D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1D convolution over an input signal composed of several input
-planes.
-In the simplest case, the output value of the layer with input size
-\((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be
-precisely described as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_conv1d</span>(
-  <span class='no'>in_channels</span>,
-  <span class='no'>out_channels</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>groups</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>padding_mode</span> <span class='kw'>=</span> <span class='st'>"zeros"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): Zero-padding added to both sides of
-the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel
-elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input
-channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
-output. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
-<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) +
-  \sum_{k = 0}^{C_{in} - 1} \mbox{weight}(C_{\mbox{out}_j}, k)
-\star \mbox{input}(N_i, k)
-$$</p>
-<p>where \(\star\) is the valid
-<a href='https://en.wikipedia.org/wiki/Cross-correlation'>cross-correlation</a> operator,
-\(N\) is a batch size, \(C\) denotes a number of channels,
-\(L\) is a length of signal sequence.</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation, a single
-number or a one-element tuple.</p></li>
-<li><p><code>padding</code> controls the amount of implicit zero-paddings on both sides
-for <code>padding</code> number of points.</p></li>
-<li><p><code>dilation</code> controls the spacing between the kernel points; also
-known as the à trous algorithm. It is harder to describe, but this
-<a href='https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md'>link</a>
-has a nice visualization of what <code>dilation</code> does.</p></li>
-<li><p><code>groups</code> controls the connections between inputs and outputs.
-<code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
-<li><p>At groups=2, the operation becomes equivalent to having two conv
-layers side by side, each seeing half the input channels,
-and producing half the output channels, and both subsequently
-concatenated.</p></li>
-<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
-its own set of filters,
-of size \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
-</ul></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>Depending of the size of your kernel, several (of the last)
-columns of the input might be lost, because it is a valid
-<code>cross-correlation</code><em>, and not a full <code>cross-correlation</code></em>.
-It is up to the user to add proper padding.</p>
-<p>When <code>groups == in_channels</code> and <code>out_channels == K * in_channels</code>,
-where <code>K</code> is a positive integer, this operation is also termed in
-literature as depthwise convolution.
-In other words, for an input of size \((N, C_{in}, L_{in})\),
-a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
-\((C_{\mbox{in}}=C_{in}, C_{\mbox{out}}=C_{in} \times K, ..., \mbox{groups}=C_{in})\).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
-<li><p>Output: \((N, C_{out}, L_{out})\) where</p></li>
-</ul>
-
-<p>$$
-  L_{out} = \left\lfloor\frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation}
-    \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor
-$$</p>
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
-\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}, \mbox{kernel\_size})\).
-The values of these weights are sampled from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)</p></li>
-<li><p>bias (Tensor): the learnable bias of the module of shape
-(out_channels). If <code>bias</code> is <code>TRUE</code>, then the values of these weights are
-sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_conv1d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    <div class="page-header">
-    <h1>Conv2D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D convolution over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_conv2d</span>(
-  <span class='no'>in_channels</span>,
-  <span class='no'>out_channels</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>groups</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>padding_mode</span> <span class='kw'>=</span> <span class='st'>"zeros"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): Zero-padding added to both sides of
-the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input
-channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
-output. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
-<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>In the simplest case, the output value of the layer with input size
-\((N, C_{\mbox{in}}, H, W)\) and output \((N, C_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}})\)
-can be precisely described as:</p>
-<p>$$
-\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) +
-  \sum_{k = 0}^{C_{\mbox{in}} - 1} \mbox{weight}(C_{\mbox{out}_j}, k) \star \mbox{input}(N_i, k)
-$$</p>
-<p>where \(\star\) is the valid 2D cross-correlation operator,
-\(N\) is a batch size, \(C\) denotes a number of channels,
-\(H\) is a height of input planes in pixels, and \(W\) is
-width in pixels.</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation, a single
-number or a tuple.</p></li>
-<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
-sides for <code>padding</code> number of points for each dimension.</p></li>
-<li><p><code>dilation</code> controls the spacing between the kernel points; also
-known as the à trous algorithm. It is harder to describe, but this <code>link</code>_
-has a nice visualization of what <code>dilation</code> does.</p></li>
-<li><p><code>groups</code> controls the connections between inputs and outputs.
-<code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
-<li><p>At groups=2, the operation becomes equivalent to having two conv
-layers side by side, each seeing half the input channels,
-and producing half the output channels, and both subsequently
-concatenated.</p></li>
-<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
-its own set of filters, of size:
-\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
-</ul></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and
-width dimension</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
-and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>Depending of the size of your kernel, several (of the last)
-columns of the input might be lost, because it is a valid cross-correlation,
-and not a full cross-correlation.
-It is up to the user to add proper padding.</p>
-<p>When <code>groups == in_channels</code> and <code>out_channels == K * in_channels</code>,
-where <code>K</code> is a positive integer, this operation is also termed in
-literature as depthwise convolution.
-In other words, for an input of size :math:<code>(N, C_{in}, H_{in}, W_{in})</code>,
-a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
-\((in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})\).</p>
-<p>In some circumstances when using the CUDA backend with CuDNN, this operator
-may select a nondeterministic algorithm to increase performance. If this is
-undesirable, you can try to make the operation deterministic (potentially at
-a performance cost) by setting <code>backends_cudnn_deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C_{out}, H_{out}, W_{out})\) where
-$$
-  H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] - \mbox{dilation}[0]
-    \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
-$$
-$$
-  W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \mbox{padding}[1] - \mbox{dilation}[1]
-    \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
-$$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
-\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}\),
-\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\).
-The values of these weights are sampled from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
-<li><p>bias (Tensor): the learnable bias of the module of shape
-(out_channels). If <code>bias</code> is <code>TRUE</code>,
-then the values of these weights are
-sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># With square kernels and equal stride</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_conv2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>2</span>)
-<span class='co'># non-square kernels and unequal stride and with padding</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_conv2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>), <span class='kw'>stride</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), <span class='kw'>padding</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
-<span class='co'># non-square kernels and unequal stride and with padding and dilation</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_conv2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>), <span class='kw'>stride</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), <span class='kw'>padding</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>), <span class='kw'>dilation</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\)
-and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:" />
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-    <h1>Conv3D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 3D convolution over an input signal composed of several input
-planes.
-In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\)
-and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_conv3d</span>(
-  <span class='no'>in_channels</span>,
-  <span class='no'>out_channels</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>groups</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>padding_mode</span> <span class='kw'>=</span> <span class='st'>"zeros"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  out(N_i, C_{out_j}) = bias(C_{out_j}) +
-  \sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i, k)
-$$</p>
-<p>where \(\star\) is the valid 3D <code>cross-correlation</code> operator</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
-<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
-sides for <code>padding</code> number of points for each dimension.</p></li>
-<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
-It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
-<li><p><code>groups</code> controls the connections between inputs and outputs.
-<code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p></li>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
-<li><p>At groups=2, the operation becomes equivalent to having two conv
-layers side by side, each seeing half the input channels,
-and producing half the output channels, and both subsequently
-concatenated.</p></li>
-<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
-its own set of filters, of size
-\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
-<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
-the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>Depending of the size of your kernel, several (of the last)
-columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
-and not a full <code>cross-correlation</code></em>.
-It is up to the user to add proper padding.</p>
-<p>When <code>groups == in_channels</code> and <code>out_channels == K * in_channels</code>,
-where <code>K</code> is a positive integer, this operation is also termed in
-literature as depthwise convolution.
-In other words, for an input of size \((N, C_{in}, D_{in}, H_{in}, W_{in})\),
-a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
-\((in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})\).</p>
-<p>In some circumstances when using the CUDA backend with CuDNN, this operator
-may select a nondeterministic algorithm to increase performance. If this is
-undesirable, you can try to make the operation deterministic (potentially at
-a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.
-Please see the notes on :doc:<code>/notes/randomness</code> for background.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where
-$$
-  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0]
-    \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
- $$
-$$
-  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1]
-    \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
- $$
-$$
-  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2]
-    \times (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor
- $$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
-\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}},\)
-\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\).
-The values of these weights are sampled from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
-<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels). If <code>bias</code> is <code>True</code>,
-then the values of these weights are
-sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># With square kernels and equal stride</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_conv3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='co'># non-square kernels and unequal stride and with padding</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_conv3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>2</span>), <span class='kw'>stride</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>1</span>), <span class='kw'>padding</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>, <span class='fl'>0</span>))
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>10</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/static/docs/reference/nn_conv_transpose1d.html b/static/docs/reference/nn_conv_transpose1d.html
deleted file mode 100644
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+++ /dev/null
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-    <h1>ConvTranspose1D</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv_transpose1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1D transposed convolution operator over an input image
-composed of several input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_conv_transpose1d</span>(
-  <span class='no'>in_channels</span>,
-  <span class='no'>out_channels</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>output_padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>groups</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding_mode</span> <span class='kw'>=</span> <span class='st'>"zeros"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
-will be added to both sides of the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>(int or tuple, optional): Additional size added to one side
-of the output shape. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
-<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This module can be seen as the gradient of Conv1d with respect to its input.
-It is also known as a fractionally-strided convolution or
-a deconvolution (although it is not an actual deconvolution operation).</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
-<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
-sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
-below for details.</p></li>
-<li><p><code>output_padding</code> controls the additional size added to one side
-of the output shape. See note below for details.</p></li>
-<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the
-à trous algorithm. It is harder to describe, but this <a href='https://github.com/vdumoulin/conv_arithmetic'>link</a>
-has a nice visualization of what <code>dilation</code> does.</p></li>
-<li><p><code>groups</code> controls the connections between inputs and outputs.
-<code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
-<li><p>At groups=2, the operation becomes equivalent to having two conv
-layers side by side, each seeing half the input channels,
-and producing half the output channels, and both subsequently
-concatenated.</p></li>
-<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
-its own set of filters (of size
-\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
-</ul></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>Depending of the size of your kernel, several (of the last)
-columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
-and not a full <code>cross-correlation</code></em>.
-It is up to the user to add proper padding.</p>
-<p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
-amount of zero padding to both sizes of the input. This is set so that
-when a <code>~torch.nn.Conv1d</code> and a <code>~torch.nn.ConvTranspose1d</code>
-are initialized with same parameters, they are inverses of each other in
-regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
-<code>~torch.nn.Conv1d</code> maps multiple input shapes to the same output
-shape. <code>output_padding</code> is provided to resolve this ambiguity by
-effectively increasing the calculated output shape on one side. Note
-that <code>output_padding</code> is only used to find output shape, but does
-not actually add zero-padding to output.</p>
-<p>In some circumstances when using the CUDA backend with CuDNN, this operator
-may select a nondeterministic algorithm to increase performance. If this is
-undesirable, you can try to make the operation deterministic (potentially at
-a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
-<li><p>Output: \((N, C_{out}, L_{out})\) where
-$$
-  L_{out} = (L_{in} - 1) \times \mbox{stride} - 2 \times \mbox{padding} + \mbox{dilation}
-\times (\mbox{kernel\_size} - 1) + \mbox{output\_padding} + 1
-$$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
-\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
-\(\mbox{kernel\_size})\).
-The values of these weights are sampled from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)</p></li>
-<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels).
-If <code>bias</code> is <code>TRUE</code>, then the values of these weights are
-sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_conv_transpose1d</span>(<span class='fl'>32</span>, <span class='fl'>16</span>, <span class='fl'>2</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>10</span>, <span class='fl'>32</span>, <span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-    <h1>ConvTranpose2D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv_transpose2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D transposed convolution operator over an input image
-composed of several input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_conv_transpose2d</span>(
-  <span class='no'>in_channels</span>,
-  <span class='no'>out_channels</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>output_padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>groups</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding_mode</span> <span class='kw'>=</span> <span class='st'>"zeros"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
-will be added to both sides of each dimension in the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>(int or tuple, optional): Additional size added to one side
-of each dimension in the output shape. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
-<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This module can be seen as the gradient of Conv2d with respect to its input.
-It is also known as a fractionally-strided convolution or
-a deconvolution (although it is not an actual deconvolution operation).</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
-<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
-sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
-below for details.</p></li>
-<li><p><code>output_padding</code> controls the additional size added to one side
-of the output shape. See note below for details.</p></li>
-<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
-It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
-<li><p><code>groups</code> controls the connections between inputs and outputs.
-<code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
-<li><p>At groups=2, the operation becomes equivalent to having two conv
-layers side by side, each seeing half the input channels,
-and producing half the output channels, and both subsequently
-concatenated.</p></li>
-<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
-its own set of filters (of size
-\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
-</ul></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
-can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimensions</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
-and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>Depending of the size of your kernel, several (of the last)
-columns of the input might be lost, because it is a valid <code>cross-correlation</code>_,
-and not a full <code>cross-correlation</code>. It is up to the user to add proper padding.</p>
-<p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
-amount of zero padding to both sizes of the input. This is set so that
-when a <a href='nn_conv2d.html'>nn_conv2d</a> and a nn_conv_transpose2d are initialized with same
-parameters, they are inverses of each other in
-regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
-<a href='nn_conv2d.html'>nn_conv2d</a> maps multiple input shapes to the same output
-shape. <code>output_padding</code> is provided to resolve this ambiguity by
-effectively increasing the calculated output shape on one side. Note
-that <code>output_padding</code> is only used to find output shape, but does
-not actually add zero-padding to output.</p>
-<p>In some circumstances when using the CUDA backend with CuDNN, this operator
-may select a nondeterministic algorithm to increase performance. If this is
-undesirable, you can try to make the operation deterministic (potentially at
-a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C_{out}, H_{out}, W_{out})\) where
-$$
-  H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0]
-\times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1
-$$
-$$
-  W_{out} = (W_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1]
-\times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1
-$$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
-\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
-\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\).
-The values of these weights are sampled from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
-<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels)
-If <code>bias</code> is <code>True</code>, then the values of these weights are
-sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># With square kernels and equal stride</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='co'># non-square kernels and unequal stride and with padding</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>), <span class='kw'>stride</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), <span class='kw'>padding</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-<span class='co'># exact output size can be also specified as an argument</span>
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>16</span>, <span class='fl'>12</span>, <span class='fl'>12</span>)
-<span class='no'>downsample</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>16</span>, <span class='fl'>16</span>, <span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>, <span class='kw'>padding</span><span class='kw'>=</span><span class='fl'>1</span>)
-<span class='no'>upsample</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span>(<span class='fl'>16</span>, <span class='fl'>16</span>, <span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>, <span class='kw'>padding</span><span class='kw'>=</span><span class='fl'>1</span>)
-<span class='no'>h</span> <span class='kw'>&lt;-</span> <span class='fu'>downsample</span>(<span class='no'>input</span>)
-<span class='no'>h</span>$<span class='fu'>size</span>()
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>upsample</span>(<span class='no'>h</span>, <span class='kw'>output_size</span><span class='kw'>=</span><span class='no'>input</span>$<span class='fu'>size</span>())
-<span class='no'>output</span>$<span class='fu'>size</span>()
-
-}</div></pre>
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-     
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-</li>
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-
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>ConvTranpose3D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv_transpose3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 3D transposed convolution operator over an input image composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_conv_transpose3d</span>(
-  <span class='no'>in_channels</span>,
-  <span class='no'>out_channels</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>output_padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>groups</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding_mode</span> <span class='kw'>=</span> <span class='st'>"zeros"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
-will be added to both sides of each dimension in the input. Default: 0
-output_padding (int or tuple, optional): Additional size added to one side
-of each dimension in the output shape. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>(int or tuple, optional): Additional size added to one side
-of each dimension in the output shape. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The transposed convolution operator multiplies each input value element-wise by a learnable kernel,
-and sums over the outputs from all input feature planes.</p>
-<p>This module can be seen as the gradient of Conv3d with respect to its input.
-It is also known as a fractionally-strided convolution or
-a deconvolution (although it is not an actual deconvolution operation).</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
-<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
-sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
-below for details.</p></li>
-<li><p><code>output_padding</code> controls the additional size added to one side
-of the output shape. See note below for details.</p></li>
-<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
-It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
-<li><p><code>groups</code> controls the connections between inputs and outputs.
-<code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
-<li><p>At groups=2, the operation becomes equivalent to having two conv
-layers side by side, each seeing half the input channels,
-and producing half the output channels, and both subsequently
-concatenated.</p></li>
-<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
-its own set of filters (of size
-\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
-</ul></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
-can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimensions</p></li>
-<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
-the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>Depending of the size of your kernel, several (of the last)
-columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
-and not a full <code>cross-correlation</code></em>.
-It is up to the user to add proper padding.</p>
-<p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
-amount of zero padding to both sizes of the input. This is set so that
-when a <code>~torch.nn.Conv3d</code> and a <code>~torch.nn.ConvTranspose3d</code>
-are initialized with same parameters, they are inverses of each other in
-regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
-<code>~torch.nn.Conv3d</code> maps multiple input shapes to the same output
-shape. <code>output_padding</code> is provided to resolve this ambiguity by
-effectively increasing the calculated output shape on one side. Note
-that <code>output_padding</code> is only used to find output shape, but does
-not actually add zero-padding to output.</p>
-<p>In some circumstances when using the CUDA backend with CuDNN, this operator
-may select a nondeterministic algorithm to increase performance. If this is
-undesirable, you can try to make the operation deterministic (potentially at
-a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where
-$$
-  D_{out} = (D_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0]
-\times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1
-$$
-$$
-  H_{out} = (H_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1]
-\times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1
-$$
-$$
-  W_{out} = (W_{in} - 1) \times \mbox{stride}[2] - 2 \times \mbox{padding}[2] + \mbox{dilation}[2]
-\times (\mbox{kernel\_size}[2] - 1) + \mbox{output\_padding}[2] + 1
-$$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
-\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
-\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\).
-The values of these weights are sampled from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
-<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels)
-If <code>bias</code> is <code>True</code>, then the values of these weights are
-sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='co'># With square kernels and equal stride</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_conv_transpose3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='co'># non-square kernels and unequal stride and with padding</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_conv_transpose3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>2</span>), <span class='kw'>stride</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>1</span>), <span class='kw'>padding</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span>, <span class='fl'>2</span>))
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>10</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-}
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    <h1>CrossEntropyLoss module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_cross_entropy_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This criterion combines <code><a href='nn_log_softmax.html'>nn_log_softmax()</a></code> and <code>nn_nll_loss()</code> in one single class.
-It is useful when training a classification problem with <code>C</code> classes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_cross_entropy_loss</span>(<span class='kw'>weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>ignore_index</span> <span class='kw'>=</span> -<span class='fl'>100</span>, <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='st'>"mean"</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional): a manual rescaling weight given to each class.
-If given, has to be a Tensor of size <code>C</code></p></td>
-    </tr>
-    <tr>
-      <th>ignore_index</th>
-      <td><p>(int, optional): Specifies a target value that is ignored
-and does not contribute to the input gradient. When <code>size_average</code> is
-<code>TRUE</code>, the loss is averaged over non-ignored targets.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
-<code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
-<code>'mean'</code>: the sum of the output will be divided by the number of
-elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
-and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>If provided, the optional argument <code>weight</code> should be a 1D <code>Tensor</code>
-assigning weight to each of the classes.</p>
-<p>This is particularly useful when you have an unbalanced training set.
-The <code>input</code> is expected to contain raw, unnormalized scores for each class.
-<code>input</code> has to be a Tensor of size either \((minibatch, C)\) or
-\((minibatch, C, d_1, d_2, ..., d_K)\)
-with \(K \geq 1\) for the <code>K</code>-dimensional case (described later).</p>
-<p>This criterion expects a class index in the range \([0, C-1]\) as the
-<code>target</code> for each value of a 1D tensor of size <code>minibatch</code>; if <code>ignore_index</code>
-is specified, this criterion also accepts this class index (this index may not
-necessarily be in the class range).</p>
-<p>The loss can be described as:
-$$
-  \mbox{loss}(x, class) = -\log\left(\frac{\exp(x[class])}{\sum_j \exp(x[j])}\right)
-= -x[class] + \log\left(\sum_j \exp(x[j])\right)
-$$
-or in the case of the <code>weight</code> argument being specified:
-$$
-  \mbox{loss}(x, class) = weight[class] \left(-x[class] + \log\left(\sum_j \exp(x[j])\right)\right)
-$$</p>
-<p>The losses are averaged across observations for each minibatch.
-Can also be used for higher dimension inputs, such as 2D images, by providing
-an input of size \((minibatch, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\),
-where \(K\) is the number of dimensions, and a target of appropriate shape
-(see below).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C)\) where <code>C = number of classes</code>, or
-\((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\)
-in the case of <code>K</code>-dimensional loss.</p></li>
-<li><p>Target: \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\), or
-\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case of
-K-dimensional loss.</p></li>
-<li><p>Output: scalar.
-If <code>reduction</code> is <code>'none'</code>, then the same size as the target:
-\((N)\), or
-\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case
-of K-dimensional loss.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_cross_entropy_loss</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='kw'>requires_grad</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='no'>target</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span>(<span class='kw'>low</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>high</span> <span class='kw'>=</span> <span class='fl'>5</span>, <span class='kw'>size</span> <span class='kw'>=</span> <span class='fl'>3</span>, <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>loss</span>(<span class='no'>input</span>, <span class='no'>target</span>)
-<span class='no'>output</span>$<span class='fu'>backward</span>()
-
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Dropout module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
-    <div class="hidden name"><code>nn_dropout.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>During training, randomly zeroes some of the elements of the input
-tensor with probability <code>p</code> using samples from a Bernoulli
-distribution. Each channel will be zeroed out independently on every forward
-call.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_dropout</span>(<span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>p</th>
-      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This has proven to be an effective technique for regularization and
-preventing the co-adaptation of neurons as described in the paper
-<a href='https://arxiv.org/abs/1207.0580'>Improving neural networks by preventing co-adaptation of feature detectors</a>.</p>
-<p>Furthermore, the outputs are scaled by a factor of :math:<code>\frac{1}{1-p}</code> during
-training. This means that during evaluation the module simply computes an
-identity function.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((*)\). Input can be of any shape</p></li>
-<li><p>Output: \((*)\). Output is of the same shape as input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_dropout</span>(<span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>0.2</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/nn_dropout2d.html b/static/docs/reference/nn_dropout2d.html
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-    <h1>Dropout2D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
-    <div class="hidden name"><code>nn_dropout2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randomly zero out entire channels (a channel is a 2D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 2D tensor \(\mbox{input}[i, j]\)).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_dropout2d</span>(<span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>p</th>
-      <td><p>(float, optional): probability of an element to be zero-ed.</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
-in-place</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Each channel will be zeroed out independently on every forward call with
-probability <code>p</code> using samples from a Bernoulli distribution.
-Usually the input comes from <a href='nn_conv2d.html'>nn_conv2d</a> modules.</p>
-<p>As described in the paper
-<a href='https://arxiv.org/abs/1411.4280'>Efficient Object Localization Using Convolutional Networks</a> ,
-if adjacent pixels within feature maps are strongly correlated
-(as is normally the case in early convolution layers) then i.i.d. dropout
-will not regularize the activations and will otherwise just result
-in an effective learning rate decrease.
-In this case, nn_dropout2d will help promote independence between
-feature maps and should be used instead.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H, W)\)</p></li>
-<li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_dropout2d</span>(<span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>0.2</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>32</span>, <span class='fl'>32</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-batched input is a 3D tensor \(\mbox{input}[i, j]\))." />
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-    <h1>Dropout3D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
-    <div class="hidden name"><code>nn_dropout3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randomly zero out entire channels (a channel is a 3D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 3D tensor \(\mbox{input}[i, j]\)).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_dropout3d</span>(<span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>p</th>
-      <td><p>(float, optional): probability of an element to be zeroed.</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
-in-place</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Each channel will be zeroed out independently on every forward call with
-probability <code>p</code> using samples from a Bernoulli distribution.
-Usually the input comes from <a href='nn_conv2d.html'>nn_conv2d</a> modules.</p>
-<p>As described in the paper
-<a href='https://arxiv.org/abs/1411.4280'>Efficient Object Localization Using Convolutional Networks</a> ,
-if adjacent pixels within feature maps are strongly correlated
-(as is normally the case in early convolution layers) then i.i.d. dropout
-will not regularize the activations and will otherwise just result
-in an effective learning rate decrease.</p>
-<p>In this case, nn_dropout3d will help promote independence between
-feature maps and should be used instead.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, D, H, W)\)</p></li>
-<li><p>Output: \((N, C, D, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_dropout3d</span>(<span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>0.2</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>4</span>, <span class='fl'>32</span>, <span class='fl'>32</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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-    <p>Applies the element-wise function:</p>
-    </div>
-
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-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>alpha</th>
-      <td><p>the \(\alpha\) value for the ELU formulation. Default: 1.0</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{ELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1))
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
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-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_elu</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span>  <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-This module is often used to store word embeddings and retrieve them using indices.
-The input to the module is a list of indices, and the output is the corresponding
-word embeddings." />
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-    <h1>Embedding module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-sparse.R'><code>R/nn-sparse.R</code></a></small>
-    <div class="hidden name"><code>nn_embedding.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A simple lookup table that stores embeddings of a fixed dictionary and size.
-This module is often used to store word embeddings and retrieve them using indices.
-The input to the module is a list of indices, and the output is the corresponding
-word embeddings.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_embedding</span>(
-  <span class='no'>num_embeddings</span>,
-  <span class='no'>embedding_dim</span>,
-  <span class='kw'>padding_idx</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>max_norm</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>norm_type</span> <span class='kw'>=</span> <span class='fl'>2</span>,
-  <span class='kw'>scale_grad_by_freq</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>sparse</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>.weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_embeddings</th>
-      <td><p>(int): size of the dictionary of embeddings</p></td>
-    </tr>
-    <tr>
-      <th>embedding_dim</th>
-      <td><p>(int): the size of each embedding vector</p></td>
-    </tr>
-    <tr>
-      <th>padding_idx</th>
-      <td><p>(int, optional): If given, pads the output with the embedding vector at <code>padding_idx</code>
-(initialized to zeros) whenever it encounters the index.</p></td>
-    </tr>
-    <tr>
-      <th>max_norm</th>
-      <td><p>(float, optional): If given, each embedding vector with norm larger than <code>max_norm</code>
-is renormalized to have norm <code>max_norm</code>.</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>(float, optional): The p of the p-norm to compute for the <code>max_norm</code> option. Default <code>2</code>.</p></td>
-    </tr>
-    <tr>
-      <th>scale_grad_by_freq</th>
-      <td><p>(boolean, optional): If given, this will scale gradients by the inverse of frequency of
-the words in the mini-batch. Default <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>sparse</th>
-      <td><p>(bool, optional): If <code>True</code>, gradient w.r.t. <code>weight</code> matrix will be a sparse tensor.</p></td>
-    </tr>
-    <tr>
-      <th>.weight</th>
-      <td><p>(Tensor) embeddings weights (in case you want to set it manually)</p>
-<p>See Notes for more details regarding sparse gradients.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>Keep in mind that only a limited number of optimizers support
-sparse gradients: currently it's <code>optim.SGD</code> (<code>CUDA</code> and <code>CPU</code>),
-<code>optim.SparseAdam</code> (<code>CUDA</code> and <code>CPU</code>) and <code>optim.Adagrad</code> (<code>CPU</code>)</p>
-<p>With <code>padding_idx</code> set, the embedding vector at
-<code>padding_idx</code> is initialized to all zeros. However, note that this
-vector can be modified afterwards, e.g., using a customized
-initialization method, and thus changing the vector used to pad the
-output. The gradient for this vector from nn_embedding
-is always zero.</p>
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
-initialized from \(\mathcal{N}(0, 1)\)</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((*)\), LongTensor of arbitrary shape containing the indices to extract</p></li>
-<li><p>Output: \((*, H)\), where <code>*</code> is the input shape and \(H=\mbox{embedding\_dim}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># an Embedding module containing 10 tensors of size 3</span>
-<span class='no'>embedding</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_embedding</span>(<span class='fl'>10</span>, <span class='fl'>3</span>)
-<span class='co'># a batch of 2 samples of 4 indices each</span>
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>4</span>,<span class='fl'>5</span>),<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>2</span>,<span class='fl'>9</span>)), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-<span class='fu'>embedding</span>(<span class='no'>input</span>)
-<span class='co'># example with padding_idx</span>
-<span class='no'>embedding</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_embedding</span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='kw'>padding_idx</span><span class='kw'>=</span><span class='fl'>1</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>3</span>,<span class='fl'>1</span>,<span class='fl'>6</span>), <span class='kw'>nrow</span> <span class='kw'>=</span> <span class='fl'>1</span>), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-<span class='fu'>embedding</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-    <h1>Applies a 2D fractional max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_fractional_max_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fractional MaxPooling is described in detail in the paper
-<a href='https://arxiv.org/abs/1412.6071'>Fractional MaxPooling</a> by Ben Graham</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_fractional_max_pool2d</span>(
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>output_size</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>output_ratio</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over.
-Can be a single number k (for a square kernel of k x k) or a tuple <code>(kh, kw)</code></p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form <code>oH x oW</code>.
-Can be a tuple <code>(oH, oW)</code> or a single number oH for a square image <code>oH x oH</code></p></td>
-    </tr>
-    <tr>
-      <th>output_ratio</th>
-      <td><p>If one wants to have an output size as a ratio of the input size, this option can be given.
-This has to be a number or tuple in the range (0, 1)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The max-pooling operation is applied in \(kH \times kW\) regions by a stochastic
-step size determined by the target output size.
-The number of output features is equal to the number of input planes.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># pool of square window of size=3, and target output size 13x12</span>
-<span class='no'>m</span> <span class='kw'>=</span> <span class='fu'>nn_fractional_max_pool2d</span>(<span class='fl'>3</span>, <span class='kw'>output_size</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>13</span>, <span class='fl'>12</span>))
-<span class='co'># pool of square window and target output size being half of input image size</span>
-<span class='no'>m</span> <span class='kw'>=</span> <span class='fu'>nn_fractional_max_pool2d</span>(<span class='fl'>3</span>, <span class='kw'>output_ratio</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.5</span>, <span class='fl'>0.5</span>))
-<span class='no'>input</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
-<span class='no'>output</span> <span class='kw'>=</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-    <h1>Applies a 3D fractional max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_fractional_max_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fractional MaxPooling is described in detail in the paper
-<a href='https://arxiv.org/abs/1412.6071'>Fractional MaxPooling</a> by Ben Graham</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_fractional_max_pool3d</span>(
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>output_size</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>output_ratio</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over.
-Can be a single number k (for a square kernel of k x k x k) or a tuple <code>(kt x kh x kw)</code></p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form <code>oT x oH x oW</code>.
-Can be a tuple <code>(oT, oH, oW)</code> or a single number oH for a square image <code>oH x oH x oH</code></p></td>
-    </tr>
-    <tr>
-      <th>output_ratio</th>
-      <td><p>If one wants to have an output size as a ratio of the input size, this option can be given.
-This has to be a number or tuple in the range (0, 1)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool3d.html'>nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The max-pooling operation is applied in \(kTxkHxkW\) regions by a stochastic
-step size determined by the target output size.
-The number of output features is equal to the number of input planes.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># pool of cubic window of size=3, and target output size 13x12x11</span>
-<span class='no'>m</span> <span class='kw'>=</span> <span class='fu'>nn_fractional_max_pool3d</span>(<span class='fl'>3</span>, <span class='kw'>output_size</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>13</span>, <span class='fl'>12</span>, <span class='fl'>11</span>))
-<span class='co'># pool of cubic window and target output size being half of input size</span>
-<span class='no'>m</span> <span class='kw'>=</span> <span class='fu'>nn_fractional_max_pool3d</span>(<span class='fl'>3</span>, <span class='kw'>output_ratio</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.5</span>, <span class='fl'>0.5</span>, <span class='fl'>0.5</span>))
-<span class='no'>input</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>, <span class='fl'>16</span>)
-<span class='no'>output</span> <span class='kw'>=</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>GELU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_gelu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the Gaussian Error Linear Units function:
-$$\mbox{GELU}(x) = x * \Phi(x)$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_gelu</span>()</pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>where \(\Phi(x)\) is the Cumulative Distribution Function for Gaussian Distribution.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>=</span> <span class='fu'>nn_gelu</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="hidden name"><code>nn_glu.Rd</code></div>
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-
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-    <p>Applies the gated linear unit function
-\({GLU}(a, b)= a \otimes \sigma(b)\) where \(a\) is the first half
-of the input matrices and \(b\) is the second half.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_glu</span>(<span class='kw'>dim</span> <span class='kw'>=</span> -<span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>(int): the dimension on which to split the input. Default: -1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((\ast_1, N, \ast_2)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((\ast_1, M, \ast_2)\) where \(M=N/2\)</p></li>
-</ul>
-
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-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_glu</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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diff --git a/static/docs/reference/nn_hardshrink.html b/static/docs/reference/nn_hardshrink.html
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-    <h1>Hardshwink module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_hardshrink.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the hard shrinkage function element-wise:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_hardshrink</span>(<span class='kw'>lambd</span> <span class='kw'>=</span> <span class='fl'>0.5</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>lambd</th>
-      <td><p>the \(\lambda\) value for the Hardshrink formulation. Default: 0.5</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{HardShrink}(x) =
-  \left\{ \begin{array}{ll}
-x, &amp; \mbox{ if } x &gt; \lambda \\
-x, &amp; \mbox{ if } x &lt; -\lambda \\
-0, &amp; \mbox{ otherwise }
-\end{array}
-\right.
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_hardshrink</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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diff --git a/static/docs/reference/nn_hardsigmoid.html b/static/docs/reference/nn_hardsigmoid.html
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-
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-    <div class="page-header">
-    <h1>Hardsigmoid module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_hardsigmoid.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the element-wise function:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_hardsigmoid</span>()</pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-\mbox{Hardsigmoid}(x) = \left\{ \begin{array}{ll}
-  0 &amp; \mbox{if~} x \le -3, \\
-  1 &amp; \mbox{if~} x \ge +3, \\
-  x / 6 + 1 / 2 &amp; \mbox{otherwise}
-\end{array}
-\right.
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_hardsigmoid</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
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diff --git a/static/docs/reference/nn_hardswish.html b/static/docs/reference/nn_hardswish.html
deleted file mode 100644
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-    <h1>Hardswish module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_hardswish.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the hardswish function, element-wise, as described in the paper:
-<a href='https://arxiv.org/abs/1905.02244'>Searching for MobileNetV3</a></p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_hardswish</span>()</pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$ \mbox{Hardswish}(x) = \left\{
-  \begin{array}{ll}
-  0 &amp; \mbox{if } x \le -3, \\
-  x &amp; \mbox{if } x \ge +3, \\
-  x \cdot (x + 3)/6 &amp; \mbox{otherwise}
-  \end{array}
-  \right. $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_hardswish</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-}
-
-}</div></pre>
-  </div>
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diff --git a/static/docs/reference/nn_hardtanh.html b/static/docs/reference/nn_hardtanh.html
deleted file mode 100644
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-
-    <div class="ref-description">
-    <p>Applies the HardTanh function element-wise
-HardTanh is defined as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_hardtanh</span>(<span class='kw'>min_val</span> <span class='kw'>=</span> -<span class='fl'>1</span>, <span class='kw'>max_val</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>min_val</th>
-      <td><p>minimum value of the linear region range. Default: -1</p></td>
-    </tr>
-    <tr>
-      <th>max_val</th>
-      <td><p>maximum value of the linear region range. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-\mbox{HardTanh}(x) = \left\{ \begin{array}{ll}
-  1 &amp; \mbox{ if } x &gt; 1 \\
-  -1 &amp; \mbox{ if } x &lt; -1 \\
-  x &amp; \mbox{ otherwise } \\
-\end{array}
-\right.
-$$</p>
-<p>The range of the linear region :math:<code>[-1, 1]</code> can be adjusted using
-<code>min_val</code> and <code>max_val</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_hardtanh</span>(-<span class='fl'>2</span>, <span class='fl'>2</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-    <h1>Identity module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
-    <div class="hidden name"><code>nn_identity.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A placeholder identity operator that is argument-insensitive.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_identity</span>(<span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>any arguments (unused)</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_identity</span>(<span class='fl'>54</span>, <span class='kw'>unused_argument1</span> <span class='kw'>=</span> <span class='fl'>0.1</span>, <span class='kw'>unused_argument2</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>20</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>output</span>$<span class='fu'>size</span>())
-
-}</div></pre>
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diff --git a/static/docs/reference/nn_init_calculate_gain.html b/static/docs/reference/nn_init_calculate_gain.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
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-      <td><p>an n-dimensional <code>Tensor</code></p></td>
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-<span class='fu'>nn_init_constant_</span>(<span class='no'>w</span>, <span class='fl'>0.3</span>)
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
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-    <p>Fills the 3, 4, 5-dimensional input <code>Tensor</code> with the Dirac
-delta function. Preserves the identity of the inputs in <code>Convolutional</code>
-layers, where as many input channels are preserved as possible. In case
-of groups&gt;1, each group of channels preserves identity.</p>
-    </div>
-
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-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>a 3, 4, 5-dimensional <code>torch.Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(optional) number of groups in the conv layer (default: 1)</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_eye_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the 2-dimensional input <code>Tensor</code> with the identity matrix.
-Preserves the identity of the inputs in <code>Linear</code> layers, where as
-many inputs are preserved as possible.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_eye_</span>(<span class='no'>tensor</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>a 2-dimensional torch tensor.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>w</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_eye_</span>(<span class='no'>w</span>)
-
-}</div></pre>
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diff --git a/static/docs/reference/nn_init_kaiming_normal_.html b/static/docs/reference/nn_init_kaiming_normal_.html
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-    <h1>Kaiming normal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_kaiming_normal_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the input <code>Tensor</code> with values according to the method
-described in <code>Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification</code> - He, K. et al. (2015), using a
-normal distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_kaiming_normal_</span>(
-  <span class='no'>tensor</span>,
-  <span class='kw'>a</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>mode</span> <span class='kw'>=</span> <span class='st'>"fan_in"</span>,
-  <span class='kw'>nonlinearity</span> <span class='kw'>=</span> <span class='st'>"leaky_relu"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>torch.Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td><p>the negative slope of the rectifier used after this layer (only used
-with <code>'leaky_relu'</code>)</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
-the magnitude of the variance of the weights in the forward pass. Choosing
-'fan_out' preserves the magnitudes in the backwards pass.</p></td>
-    </tr>
-    <tr>
-      <th>nonlinearity</th>
-      <td><p>the non-linear function. recommended to use only with 'relu'
-or 'leaky_relu' (default).</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>w</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_kaiming_normal_</span>(<span class='no'>w</span>, <span class='kw'>mode</span> <span class='kw'>=</span> <span class='st'>"fan_in"</span>, <span class='kw'>nonlinearity</span> <span class='kw'>=</span> <span class='st'>"leaky_relu"</span>)
-
-}</div></pre>
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-    <h1>Kaiming uniform initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_kaiming_uniform_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the input <code>Tensor</code> with values according to the method
-described in <code>Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification</code> - He, K. et al. (2015), using a
-uniform distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_kaiming_uniform_</span>(
-  <span class='no'>tensor</span>,
-  <span class='kw'>a</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>mode</span> <span class='kw'>=</span> <span class='st'>"fan_in"</span>,
-  <span class='kw'>nonlinearity</span> <span class='kw'>=</span> <span class='st'>"leaky_relu"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>torch.Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td><p>the negative slope of the rectifier used after this layer (only used
-with <code>'leaky_relu'</code>)</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
-the magnitude of the variance of the weights in the forward pass. Choosing
-'fan_out' preserves the magnitudes in the backwards pass.</p></td>
-    </tr>
-    <tr>
-      <th>nonlinearity</th>
-      <td><p>the non-linear function. recommended to use only with 'relu'
-or 'leaky_relu' (default).</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>w</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_kaiming_uniform_</span>(<span class='no'>w</span>, <span class='kw'>mode</span> <span class='kw'>=</span> <span class='st'>"fan_in"</span>, <span class='kw'>nonlinearity</span> <span class='kw'>=</span> <span class='st'>"leaky_relu"</span>)
-
-}</div></pre>
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diff --git a/static/docs/reference/nn_init_normal_.html b/static/docs/reference/nn_init_normal_.html
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-    <h1>Normal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_normal_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the input Tensor with values drawn from the normal distribution</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_normal_</span>(<span class='no'>tensor</span>, <span class='kw'>mean</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>std</span> <span class='kw'>=</span> <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional Tensor</p></td>
-    </tr>
-    <tr>
-      <th>mean</th>
-      <td><p>the mean of the normal distribution</p></td>
-    </tr>
-    <tr>
-      <th>std</th>
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-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_orthogonal_.Rd</code></div>
-    </div>
-
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-    <p>Fills the input <code>Tensor</code> with a (semi) orthogonal matrix, as
-described in <code>Exact solutions to the nonlinear dynamics of learning in deep linear neural networks</code> - Saxe, A. et al. (2013). The input tensor must have
-at least 2 dimensions, and for tensors with more than 2 dimensions the
-trailing dimensions are flattened.</p>
-    </div>
-
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-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>gain</th>
-      <td><p>optional scaling factor</p></td>
-    </tr>
-    </table>
-
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-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
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-<span class='fu'>nn_init_orthogonal_</span>(<span class='no'>w</span>)
-
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-    <h1>Sparse initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_sparse_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the 2D input <code>Tensor</code> as a sparse matrix, where the
-non-zero elements will be drawn from the normal distribution
-as described in <code>Deep learning via Hessian-free optimization</code> - Martens, J. (2010).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_sparse_</span>(<span class='no'>tensor</span>, <span class='no'>sparsity</span>, <span class='kw'>std</span> <span class='kw'>=</span> <span class='fl'>0.01</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>sparsity</th>
-      <td><p>The fraction of elements in each column to be set to zero</p></td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td><p>the standard deviation of the normal distribution used to generate
-the non-zero values</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>w</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_sparse_</span>(<span class='no'>w</span>, <span class='kw'>sparsity</span> <span class='kw'>=</span> <span class='fl'>0.1</span>)
-}
-}</div></pre>
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diff --git a/static/docs/reference/nn_init_trunc_normal_.html b/static/docs/reference/nn_init_trunc_normal_.html
deleted file mode 100644
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-    <h1>Truncated normal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_trunc_normal_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the input Tensor with values drawn from a truncated
-normal distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_trunc_normal_</span>(<span class='no'>tensor</span>, <span class='kw'>mean</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>std</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>a</span> <span class='kw'>=</span> -<span class='fl'>2</span>, <span class='kw'>b</span> <span class='kw'>=</span> -<span class='fl'>2</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional Tensor</p></td>
-    </tr>
-    <tr>
-      <th>mean</th>
-      <td><p>the mean of the normal distribution</p></td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td><p>the standard deviation of the normal distribution</p></td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td><p>the minimum cutoff value</p></td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td><p>the maximum cutoff value</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>w</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_trunc_normal_</span>(<span class='no'>w</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_init_uniform_.html b/static/docs/reference/nn_init_uniform_.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_uniform_.Rd</code></div>
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-    <p>Fills the input Tensor with values drawn from the uniform distribution</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_uniform_</span>(<span class='no'>tensor</span>, <span class='kw'>a</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>b</span> <span class='kw'>=</span> <span class='fl'>1</span>)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional Tensor</p></td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td><p>the lower bound of the uniform distribution</p></td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td><p>the upper bound of the uniform distribution</p></td>
-    </tr>
-    </table>
-
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-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>w</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_uniform_</span>(<span class='no'>w</span>)
-
-}</div></pre>
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diff --git a/static/docs/reference/nn_init_xavier_normal_.html b/static/docs/reference/nn_init_xavier_normal_.html
deleted file mode 100644
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-    <h1>Xavier normal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_xavier_normal_.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fills the input <code>Tensor</code> with values according to the method
-described in <code>Understanding the difficulty of training deep feedforward neural networks</code> - Glorot, X. &amp; Bengio, Y. (2010), using a normal
-distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_init_xavier_normal_</span>(<span class='no'>tensor</span>, <span class='kw'>gain</span> <span class='kw'>=</span> <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>gain</th>
-      <td><p>an optional scaling factor</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>w</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_xavier_normal_</span>(<span class='no'>w</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_init_xavier_uniform_.html b/static/docs/reference/nn_init_xavier_uniform_.html
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-distribution.</p>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_zeros_.Rd</code></div>
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-    <p>Fills the input Tensor with the scalar value <code>0</code></p>
-    </div>
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional tensor</p></td>
-    </tr>
-    </table>
-
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-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>w</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
-<span class='fu'>nn_init_zeros_</span>(<span class='no'>w</span>)
-
-}</div></pre>
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-    <div class="hidden name"><code>nn_leaky_relu.Rd</code></div>
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-    <p>Applies the element-wise function:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_leaky_relu</span>(<span class='kw'>negative_slope</span> <span class='kw'>=</span> <span class='fl'>0.01</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>negative_slope</th>
-      <td><p>Controls the angle of the negative slope. Default: 1e-2</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{LeakyReLU}(x) = \max(0, x) + \mbox{negative\_slope} * \min(0, x)
-$$
-or</p>
-<p>$$
-  \mbox{LeakyRELU}(x) =
-  \left\{ \begin{array}{ll}
-x, &amp; \mbox{ if } x \geq 0 \\
-\mbox{negative\_slope} \times x, &amp; \mbox{ otherwise }
-\end{array}
-\right.
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_leaky_relu</span>(<span class='fl'>0.1</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-    <h1>Linear module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
-    <div class="hidden name"><code>nn_linear.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a linear transformation to the incoming data: <code>y = xA^T + b</code></p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_linear</span>(<span class='no'>in_features</span>, <span class='no'>out_features</span>, <span class='kw'>bias</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_features</th>
-      <td><p>size of each input sample</p></td>
-    </tr>
-    <tr>
-      <th>out_features</th>
-      <td><p>size of each output sample</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
-Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: <code>(N, *, H_in)</code> where <code>*</code> means any number of
-additional dimensions and <code>H_in = in_features</code>.</p></li>
-<li><p>Output: <code>(N, *, H_out)</code> where all but the last dimension
-are the same shape as the input and :math:<code>H_out = out_features</code>.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight: the learnable weights of the module of shape
-<code>(out_features, in_features)</code>. The values are
-initialized from \(U(-\sqrt{k}, \sqrt{k})\)s, where
-\(k = \frac{1}{\mbox{in\_features}}\)</p></li>
-<li><p>bias: the learnable bias of the module of shape \((\mbox{out\_features})\).
-If <code>bias</code> is <code>TRUE</code>, the values are initialized from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{1}{\mbox{in\_features}}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_linear</span>(<span class='fl'>20</span>, <span class='fl'>30</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>20</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>output</span>$<span class='fu'>size</span>())
-
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <p>Applies the element-wise function:
-$$
-  \mbox{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
- $$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_log_sigmoid</span>()</pre>
-
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
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-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
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-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_log_sigmoid</span>()
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-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
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-    <h1>LogSoftmax module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_log_softmax.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the \(\log(\mbox{Softmax}(x))\) function to an n-dimensional
-input Tensor. The LogSoftmax formulation can be simplified as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_log_softmax</span>(<span class='no'>dim</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>(int): A dimension along which LogSoftmax will be computed.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>a Tensor of the same dimension and shape as the input with
-values in the range [-inf, 0)</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{LogSoftmax}(x_{i}) = \log\left(\frac{\exp(x_i) }{ \sum_j \exp(x_j)} \right)
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((*)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((*)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_log_softmax</span>(<span class='fl'>1</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_lp_pool1d.html b/static/docs/reference/nn_lp_pool1d.html
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-    <h1>Applies a 1D power-average pooling over an input signal composed of several input
-planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_lp_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>On each window, the function computed is:</p>
-<p>$$
-  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_lp_pool1d</span>(<span class='no'>norm_type</span>, <span class='no'>kernel_size</span>, <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>norm_type</th>
-      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>a single int, the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>a single int, the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    
-<ul>
-<li><p>At p = \(\infty\), one gets Max Pooling</p></li>
-<li><p>At p = 1, one gets Sum Pooling (which is proportional to Average Pooling)</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>If the sum to the power of <code>p</code> is zero, the gradient of this function is
-not defined. This implementation will set the gradient to zero in this case.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, L_{in})\)</p></li>
-<li><p>Output: \((N, C, L_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  L_{out} = \left\lfloor\frac{L_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># power-2 pool of window of length 3, with stride 2.</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_lp_pool1d</span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
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-    <div class="page-header">
-    <h1>Applies a 2D power-average pooling over an input signal composed of several input
-planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_lp_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>On each window, the function computed is:</p>
-<p>$$
-  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_lp_pool2d</span>(<span class='no'>norm_type</span>, <span class='no'>kernel_size</span>, <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>norm_type</th>
-      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    
-<ul>
-<li><p>At p = \(\infty\), one gets Max Pooling</p></li>
-<li><p>At p = 1, one gets Sum Pooling (which is proportional to average pooling)</p></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
-and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>If the sum to the power of <code>p</code> is zero, the gradient of this function is
-not defined. This implementation will set the gradient to zero in this case.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  H_{out} = \left\lfloor\frac{H_{in} - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
-$$
-$$
-  W_{out} = \left\lfloor\frac{W_{in} - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># power-2 pool of square window of size=3, stride=2</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_lp_pool2d</span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='co'># pool of non-square window of power 1.2</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_lp_pool2d</span>(<span class='fl'>1.2</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), <span class='kw'>stride</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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-    <h1>MaxPool1D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1D max pooling over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_max_pool1d</span>(
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements in the window</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
-Useful for  <code><a href='nn_max_unpool1d.html'>nn_max_unpool1d()</a></code> later.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>In the simplest case, the output value of the layer with input size \((N, C, L)\)
-and output \((N, C, L_{out})\) can be precisely described as:</p>
-<p>$$
-  out(N_i, C_j, k) = \max_{m=0, \ldots, \mbox{kernel\_size} - 1}
-input(N_i, C_j, stride \times k + m)
-$$</p>
-<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
-for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
-It is harder to describe, but this <a href='https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md'>link</a>
-has a nice visualization of what <code>dilation</code> does.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, L_{in})\)</p></li>
-<li><p>Output: \((N, C, L_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  L_{out} = \left\lfloor \frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation}
-    \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># pool of size=3, stride=2</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_max_pool1d</span>(<span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/nn_max_pool2d.html b/static/docs/reference/nn_max_pool2d.html
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-    <div class="page-header">
-    <h1>MaxPool2D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D max pooling over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_max_pool2d</span>(
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements in the window</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
-Useful for <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code> later.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
-output \((N, C, H_{out}, W_{out})\) and <code>kernel_size</code> \((kH, kW)\)
-can be precisely described as:</p>
-<p>$$
-\begin{array}{ll}
-out(N_i, C_j, h, w) ={} &amp; \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
-&amp; \mbox{input}(N_i, C_j, \mbox{stride[0]} \times h + m,
-               \mbox{stride[1]} \times w + n)
-\end{array}
-$$</p>
-<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
-for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
-It is harder to describe, but this <code>link</code> has a nice visualization of what <code>dilation</code> does.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
-and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  H_{out} = \left\lfloor\frac{H_{in} + 2 * \mbox{padding[0]} - \mbox{dilation[0]}
-    \times (\mbox{kernel\_size[0]} - 1) - 1}{\mbox{stride[0]}} + 1\right\rfloor
-$$</p>
-<p>$$
-  W_{out} = \left\lfloor\frac{W_{in} + 2 * \mbox{padding[1]} - \mbox{dilation[1]}
-    \times (\mbox{kernel\_size[1]} - 1) - 1}{\mbox{stride[1]}} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># pool of square window of size=3, stride=2</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_max_pool2d</span>(<span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='co'># pool of non-square window</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_max_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), <span class='kw'>stride</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>Applies a 3D max pooling over an input signal composed of several input
-planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
-output \((N, C, D_{out}, H_{out}, W_{out})\) and <code>kernel_size</code> \((kD, kH, kW)\)
-can be precisely described as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_max_pool3d</span>(
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on all three sides</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements in the window</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
-Useful for <code>torch_nn.MaxUnpool3d</code> later</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-\begin{array}{ll}
-\mbox{out}(N_i, C_j, d, h, w) = &amp; \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
- &amp; \mbox{input}(N_i, C_j, \mbox{stride[0]} \times d + k, \mbox{stride[1]} \times h + m, \mbox{stride[2]} \times w + n)
-\end{array}
-$$</p>
-<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
-for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
-It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.
-The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
-<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
-the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where
-$$
-  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0] \times
-    (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
-$$</p></li>
-</ul>
-
-<p>$$
-  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1] \times
-    (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
-$$</p>
-<p>$$
-  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2] \times
-    (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='co'># pool of square window of size=3, stride=2</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_max_pool3d</span>(<span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='co'># pool of non-square window</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_max_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>), <span class='kw'>stride</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>,<span class='fl'>44</span>, <span class='fl'>31</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-    <h1>Computes a partial inverse of <code>MaxPool1d</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_unpool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>MaxPool1d</code> is not fully invertible, since the non-maximal values are lost.
-<code>MaxUnpool1d</code> takes in as input the output of <code>MaxPool1d</code>
-including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_max_unpool1d</span>(<span class='no'>kernel_size</span>, <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple): Stride of the max pooling window.
-It is set to <code>kernel_size</code> by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple): Padding that was added to the input</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p><code>MaxPool1d</code> can map several input sizes to the same output
-sizes. Hence, the inversion process can get ambiguous.
-To accommodate this, you can provide the needed output size
-as an additional argument <code>output_size</code> in the forward call.
-See the Inputs and Example below.</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
-    
-
-<ul>
-<li><p><code>input</code>: the input Tensor to invert</p></li>
-<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool1d.html'>nn_max_pool1d()</a></code></p></li>
-<li><p><code>output_size</code> (optional): the targeted output size</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H_{in})\)</p></li>
-<li><p>Output: \((N, C, H_{out})\), where
-$$
-  H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{kernel\_size}[0]
-$$
-or as given by <code>output_size</code> in the call operator</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>pool</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='nn_max_pool1d.html'>nn_max_pool1d</a></span>(<span class='fl'>2</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>, <span class='kw'>return_indices</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='no'>unpool</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_max_unpool1d</span>(<span class='fl'>2</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/array.html'>array</a></span>(<span class='fl'>1</span>:<span class='fl'>8</span>/<span class='fl'>1</span>, <span class='kw'>dim</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>8</span>)))
-<span class='no'>out</span> <span class='kw'>&lt;-</span> <span class='fu'>pool</span>(<span class='no'>input</span>)
-<span class='fu'>unpool</span>(<span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]], <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]])
-
-<span class='co'># Example showcasing the use of output_size</span>
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/array.html'>array</a></span>(<span class='fl'>1</span>:<span class='fl'>8</span>/<span class='fl'>1</span>, <span class='kw'>dim</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>8</span>)))
-<span class='no'>out</span> <span class='kw'>&lt;-</span> <span class='fu'>pool</span>(<span class='no'>input</span>)
-<span class='fu'>unpool</span>(<span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]], <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]], <span class='kw'>output_size</span><span class='kw'>=</span><span class='no'>input</span>$<span class='fu'>size</span>())
-<span class='fu'>unpool</span>(<span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]], <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]])
-
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_max_unpool2d.html b/static/docs/reference/nn_max_unpool2d.html
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-    <div class="page-header">
-    <h1>Computes a partial inverse of <code>MaxPool2d</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_unpool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>MaxPool2d</code> is not fully invertible, since the non-maximal values are lost.
-<code>MaxUnpool2d</code> takes in as input the output of <code>MaxPool2d</code>
-including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_max_unpool2d</span>(<span class='no'>kernel_size</span>, <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple): Stride of the max pooling window.
-It is set to <code>kernel_size</code> by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple): Padding that was added to the input</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p><code>MaxPool2d</code> can map several input sizes to the same output
-sizes. Hence, the inversion process can get ambiguous.
-To accommodate this, you can provide the needed output size
-as an additional argument <code>output_size</code> in the forward call.
-See the Inputs and Example below.</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
-    
-
-<ul>
-<li><p><code>input</code>: the input Tensor to invert</p></li>
-<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool2d.html'>nn_max_pool2d()</a></code></p></li>
-<li><p><code>output_size</code> (optional): the targeted output size</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, H_{out}, W_{out})\), where
-$$
-  H_{out} = (H_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
-$$
-$$
-  W_{out} = (W_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
-$$
-or as given by <code>output_size</code> in the call operator</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>pool</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='nn_max_pool2d.html'>nn_max_pool2d</a></span>(<span class='fl'>2</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>, <span class='kw'>return_indices</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='no'>unpool</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_max_unpool2d</span>(<span class='fl'>2</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>)
-<span class='no'>out</span> <span class='kw'>&lt;-</span> <span class='fu'>pool</span>(<span class='no'>input</span>)
-<span class='fu'>unpool</span>(<span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]], <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]])
-
-<span class='co'># specify a different output size than input size</span>
-<span class='fu'>unpool</span>(<span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]], <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]], <span class='kw'>output_size</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>5</span>, <span class='fl'>5</span>))
-
-}</div></pre>
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-MaxUnpool3d takes in as input the output of MaxPool3d
-including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero." />
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-    <div class="page-header">
-    <h1>Computes a partial inverse of <code>MaxPool3d</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_unpool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>MaxPool3d</code> is not fully invertible, since the non-maximal values are lost.
-<code>MaxUnpool3d</code> takes in as input the output of <code>MaxPool3d</code>
-including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_max_unpool3d</span>(<span class='no'>kernel_size</span>, <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple): Stride of the max pooling window.
-It is set to <code>kernel_size</code> by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple): Padding that was added to the input</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p><code>MaxPool3d</code> can map several input sizes to the same output
-sizes. Hence, the inversion process can get ambiguous.
-To accommodate this, you can provide the needed output size
-as an additional argument <code>output_size</code> in the forward call.
-See the Inputs section below.</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
-    
-
-<ul>
-<li><p><code>input</code>: the input Tensor to invert</p></li>
-<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool3d.html'>nn_max_pool3d()</a></code></p></li>
-<li><p><code>output_size</code> (optional): the targeted output size</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
-<li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
-  D_{out} = (D_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
-$$
-$$
-  H_{out} = (H_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
-$$
-$$
-  W_{out} = (W_{in} - 1) \times \mbox{stride[2]} - 2 \times \mbox{padding[2]} + \mbox{kernel\_size[2]}
-$$</p>
-<p>or as given by <code>output_size</code> in the call operator</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># pool of square window of size=3, stride=2</span>
-<span class='no'>pool</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='nn_max_pool3d.html'>nn_max_pool3d</a></span>(<span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>, <span class='kw'>return_indices</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='no'>unpool</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_max_unpool3d</span>(<span class='fl'>3</span>, <span class='kw'>stride</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='no'>out</span> <span class='kw'>&lt;-</span> <span class='fu'>pool</span>(<span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>51</span>, <span class='fl'>33</span>, <span class='fl'>15</span>))
-<span class='no'>unpooled_output</span> <span class='kw'>&lt;-</span> <span class='fu'>unpool</span>(<span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]], <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]])
-<span class='no'>unpooled_output</span>$<span class='fu'>size</span>()
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-    <h1>Base class for all neural network modules.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
-    <div class="hidden name"><code>nn_module.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Your models should also subclass this class.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_module</span>(
-  <span class='kw'>classname</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>inherit</span> <span class='kw'>=</span> <span class='no'>nn_Module</span>,
-  <span class='no'>...</span>,
-  <span class='kw'>parent_env</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span>()
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>classname</th>
-      <td><p>an optional name for the module</p></td>
-    </tr>
-    <tr>
-      <th>inherit</th>
-      <td><p>an optional module to inherit from</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>methods implementation</p></td>
-    </tr>
-    <tr>
-      <th>parent_env</th>
-      <td><p>passed to <code><a href='https://rdrr.io/pkg/R6/man/R6Class.html'>R6::R6Class()</a></code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Modules can also contain other Modules, allowing to nest them in a tree
-structure. You can assign the submodules as regular attributes.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>model</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_module</span>(
- <span class='kw'>initialize</span> <span class='kw'>=</span> <span class='kw'>function</span>() {
-   <span class='no'>self</span>$<span class='no'>conv1</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>1</span>, <span class='fl'>20</span>, <span class='fl'>5</span>)
-   <span class='no'>self</span>$<span class='no'>conv2</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>20</span>, <span class='fl'>20</span>, <span class='fl'>5</span>)
- },
- <span class='kw'>forward</span> <span class='kw'>=</span> <span class='kw'>function</span>(<span class='no'>input</span>) {
-   <span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='no'>self</span>$<span class='fu'>conv1</span>(<span class='no'>input</span>)
-   <span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='nnf_relu.html'>nnf_relu</a></span>(<span class='no'>input</span>)
-   <span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='no'>self</span>$<span class='fu'>conv2</span>(<span class='no'>input</span>)
-   <span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='nnf_relu.html'>nnf_relu</a></span>(<span class='no'>input</span>)
-   <span class='no'>input</span>
- }
-)
-
-}</div></pre>
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diff --git a/static/docs/reference/nn_module_list.html b/static/docs/reference/nn_module_list.html
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-</div><!--/.navbar -->
-
-      
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Holds submodules in a list.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
-    <div class="hidden name"><code>nn_module_list.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>nn_module_list can be indexed like a regular R list, but
-modules it contains are properly registered, and will be visible by all
-<code>nn_module</code> methods.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_module_list</span>(<span class='kw'>modules</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>())</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>modules</th>
-      <td><p>a list of modules to add</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>my_module</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='nn_module.html'>nn_module</a></span>(
- <span class='kw'>initialize</span> <span class='kw'>=</span> <span class='kw'>function</span>() {
-   <span class='no'>self</span>$<span class='no'>linears</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_module_list</span>(<span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span>(<span class='fl'>1</span>:<span class='fl'>10</span>, <span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'><a href='nn_linear.html'>nn_linear</a></span>(<span class='fl'>10</span>, <span class='fl'>10</span>)))
- },
- <span class='kw'>forward</span> <span class='kw'>=</span> <span class='kw'>function</span>(<span class='no'>x</span>) {
-  <span class='kw'>for</span> (<span class='no'>i</span> <span class='kw'>in</span> <span class='fl'>1</span>:<span class='fu'><a href='https://rdrr.io/r/base/length.html'>length</a></span>(<span class='no'>self</span>$<span class='no'>linears</span>))
-    <span class='no'>x</span> <span class='kw'>&lt;-</span> <span class='no'>self</span>$<span class='no'>linears</span><span class='kw'>[[</span><span class='no'>i</span>]](<span class='no'>x</span>)
-  <span class='no'>x</span>
- }
-)
-
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-See reference: Attention Is All You Need" />
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-    <h1>MultiHead attention</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_multihead_attention.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Allows the model to jointly attend to information
-from different representation subspaces.
-See reference: Attention Is All You Need</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_multihead_attention</span>(
-  <span class='no'>embed_dim</span>,
-  <span class='no'>num_heads</span>,
-  <span class='kw'>dropout</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>add_bias_kv</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>add_zero_attn</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>kdim</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>vdim</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>embed_dim</th>
-      <td><p>total dimension of the model.</p></td>
-    </tr>
-    <tr>
-      <th>num_heads</th>
-      <td><p>parallel attention heads.</p></td>
-    </tr>
-    <tr>
-      <th>dropout</th>
-      <td><p>a Dropout layer on attn_output_weights. Default: 0.0.</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>add bias as module parameter. Default: True.</p></td>
-    </tr>
-    <tr>
-      <th>add_bias_kv</th>
-      <td><p>add bias to the key and value sequences at dim=0.</p></td>
-    </tr>
-    <tr>
-      <th>add_zero_attn</th>
-      <td><p>add a new batch of zeros to the key and
-value sequences at dim=1.</p></td>
-    </tr>
-    <tr>
-      <th>kdim</th>
-      <td><p>total number of features in key. Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>vdim</th>
-      <td><p>total number of features in value. Default: <code>NULL</code>.
-Note: if kdim and vdim are <code>NULL</code>, they will be set to embed_dim such that
-query, key, and value have the same number of features.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{MultiHead}(Q, K, V) = \mbox{Concat}(head_1,\dots,head_h)W^O
-\mbox{where} head_i = \mbox{Attention}(QW_i^Q, KW_i^K, VW_i^V)
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-
-<p>Inputs:</p><ul>
-<li><p>query: \((L, N, E)\) where L is the target sequence length, N is the batch size, E is
-the embedding dimension.</p></li>
-<li><p>key: \((S, N, E)\), where S is the source sequence length, N is the batch size, E is
-the embedding dimension.</p></li>
-<li><p>value: \((S, N, E)\) where S is the source sequence length, N is the batch size, E is
-the embedding dimension.</p></li>
-<li><p>key_padding_mask: \((N, S)\) where N is the batch size, S is the source sequence length.
-If a ByteTensor is provided, the non-zero positions will be ignored while the position
-with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
-value of <code>True</code> will be ignored while the position with the value of <code>False</code> will be unchanged.</p></li>
-<li><p>attn_mask: 2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
-3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
-S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
-positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
-while the zero positions will be unchanged. If a BoolTensor is provided, positions with <code>True</code>
-is not allowed to attend while <code>False</code> values will be unchanged. If a FloatTensor
-is provided, it will be added to the attention weight.</p></li>
-</ul>
-
-<p>Outputs:</p><ul>
-<li><p>attn_output: \((L, N, E)\) where L is the target sequence length, N is the batch size,
-E is the embedding dimension.</p></li>
-<li><p>attn_output_weights: \((N, L, S)\) where N is the batch size,
-L is the target sequence length, S is the source sequence length.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>multihead_attn</span> <span class='kw'>=</span> <span class='fu'>nn_multihead_attention</span>(<span class='no'>embed_dim</span>, <span class='no'>num_heads</span>)
-<span class='no'>out</span> <span class='kw'>&lt;-</span> <span class='fu'>multihead_attn</span>(<span class='no'>query</span>, <span class='no'>key</span>, <span class='no'>value</span>)
-<span class='no'>attn_output</span> <span class='kw'>&lt;-</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]]
-<span class='no'>attn_output_weights</span> <span class='kw'>&lt;-</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]]
-}
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_prelu.html b/static/docs/reference/nn_prelu.html
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-$$
-  \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
-$$
-or
-$$
-  \mbox{PReLU}(x) =
-  \left\{ \begin{array}{ll}
-x, &amp;amp; \mbox{ if } x \geq 0 \\
-ax, &amp;amp; \mbox{ otherwise }
-\end{array}
-\right.
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-    <h1>PReLU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_prelu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the element-wise function:
-$$
-  \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
-$$
-or
-$$
-  \mbox{PReLU}(x) =
-  \left\{ \begin{array}{ll}
-x, &amp; \mbox{ if } x \geq 0 \\
-ax, &amp; \mbox{ otherwise }
-\end{array}
-\right.
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_prelu</span>(<span class='kw'>num_parameters</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>init</span> <span class='kw'>=</span> <span class='fl'>0.25</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_parameters</th>
-      <td><p>(int): number of \(a\) to learn.
-Although it takes an int as input, there is only two values are legitimate:
-1, or the number of channels at input. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>init</th>
-      <td><p>(float): the initial value of \(a\). Default: 0.25</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Here \(a\) is a learnable parameter. When called without arguments, <code>nn.prelu()</code> uses a single
-parameter \(a\) across all input channels. If called with <code>nn_prelu(nChannels)</code>,
-a separate \(a\) is used for each input channel.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>weight decay should not be used when learning \(a\) for good performance.</p>
-<p>Channel dim is the 2nd dim of input. When input has dims &lt; 2, then there is
-no channel dim and the number of channels = 1.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p>weight (Tensor): the learnable weights of shape (<code>num_parameters</code>).</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_prelu</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-    <h1>ReLU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_relu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the rectified linear unit function element-wise
-$$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_relu</span>(<span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_relu</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_relu6.html b/static/docs/reference/nn_relu6.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
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-
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-    </div>
-
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-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{ReLU6}(x) = \min(\max(0,x), 6)
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
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-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_relu6</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-    <h1>RNN module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-rnn.R'><code>R/nn-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_rnn.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a multi-layer Elman RNN with \(\tanh\) or \(\mbox{ReLU}\) non-linearity
-to an input sequence.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_rnn</span>(
-  <span class='no'>input_size</span>,
-  <span class='no'>hidden_size</span>,
-  <span class='kw'>num_layers</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>nonlinearity</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>batch_first</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>dropout</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>bidirectional</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='no'>...</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input_size</th>
-      <td><p>The number of expected features in the input <code>x</code></p></td>
-    </tr>
-    <tr>
-      <th>hidden_size</th>
-      <td><p>The number of features in the hidden state <code>h</code></p></td>
-    </tr>
-    <tr>
-      <th>num_layers</th>
-      <td><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
-would mean stacking two RNNs together to form a <code>stacked RNN</code>,
-with the second RNN taking in outputs of the first RNN and
-computing the final results. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>nonlinearity</th>
-      <td><p>The non-linearity to use. Can be either <code>'tanh'</code> or
-<code>'relu'</code>. Default: <code>'tanh'</code></p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and
-<code>b_hh</code>. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>If <code>TRUE</code>, then the input and output tensors are provided
-as <code>(batch, seq, feature)</code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>dropout</th>
-      <td><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
-RNN layer except the last layer, with dropout probability equal to
-<code>dropout</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>bidirectional</th>
-      <td><p>If <code>TRUE</code>, becomes a bidirectional RNN. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>other arguments that can be passed to the super class.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>For each element in the input sequence, each layer computes the following
-function:</p>
-<p>$$
-h_t = \tanh(W_{ih} x_t + b_{ih} + W_{hh} h_{(t-1)} + b_{hh})
-$$</p>
-<p>where \(h_t\) is the hidden state at time <code>t</code>, \(x_t\) is
-the input at time <code>t</code>, and \(h_{(t-1)}\) is the hidden state of the
-previous layer at time <code>t-1</code> or the initial hidden state at time <code>0</code>.
-If <code>nonlinearity</code> is <code>'relu'</code>, then \(\mbox{ReLU}\) is used instead of
-\(\tanh\).</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
-    
-
-<ul>
-<li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
-of the input sequence. The input can also be a packed variable length
-sequence.</p></li>
-<li><p><strong>h_0</strong> of shape <code>(num_layers * num_directions, batch, hidden_size)</code>: tensor
-containing the initial hidden state for each element in the batch.
-Defaults to zero if not provided. If the RNN is bidirectional,
-num_directions should be 2, else it should be 1.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="outputs"><a class="anchor" href="#outputs"></a>Outputs</h2>
-
-    
-
-<ul>
-<li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
-containing the output features (<code>h_t</code>) from the last layer of the RNN,
-for each <code>t</code>.  If a :class:<code>nn_packed_sequence</code> has
-been given as the input, the output will also be a packed sequence.
-For the unpacked case, the directions can be separated
-using <code>output$view(seq_len, batch, num_directions, hidden_size)</code>,
-with forward and backward being direction <code>0</code> and <code>1</code> respectively.
-Similarly, the directions can be separated in the packed case.</p></li>
-<li><p><strong>h_n</strong> of shape <code>(num_layers * num_directions, batch, hidden_size)</code>: tensor
-containing the hidden state for <code>t = seq_len</code>.
-Like <em>output</em>, the layers can be separated using
-<code>h_n$view(num_layers, num_directions, batch, hidden_size)</code>.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input1: \((L, N, H_{in})\) tensor containing input features where
-\(H_{in}=\mbox{input\_size}\) and <code>L</code> represents a sequence length.</p></li>
-<li><p>Input2: \((S, N, H_{out})\) tensor
-containing the initial hidden state for each element in the batch.
-\(H_{out}=\mbox{hidden\_size}\)
-Defaults to zero if not provided. where \(S=\mbox{num\_layers} * \mbox{num\_directions}\)
-If the RNN is bidirectional, num_directions should be 2, else it should be 1.</p></li>
-<li><p>Output1: \((L, N, H_{all})\) where \(H_{all}=\mbox{num\_directions} * \mbox{hidden\_size}\)</p></li>
-<li><p>Output2: \((S, N, H_{out})\) tensor containing the next hidden state
-for each element in the batch</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
-    
-
-<ul>
-<li><p><code>weight_ih_l[k]</code>: the learnable input-hidden weights of the k-th layer,
-of shape <code>(hidden_size, input_size)</code> for <code>k = 0</code>. Otherwise, the shape is
-<code>(hidden_size, num_directions * hidden_size)</code></p></li>
-<li><p><code>weight_hh_l[k]</code>: the learnable hidden-hidden weights of the k-th layer,
-of shape <code>(hidden_size, hidden_size)</code></p></li>
-<li><p><code>bias_ih_l[k]</code>: the learnable input-hidden bias of the k-th layer,
-of shape <code>(hidden_size)</code></p></li>
-<li><p><code>bias_hh_l[k]</code>: the learnable hidden-hidden bias of the k-th layer,
-of shape <code>(hidden_size)</code></p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\)
-where \(k = \frac{1}{\mbox{hidden\_size}}\)</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>rnn</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_rnn</span>(<span class='fl'>10</span>, <span class='fl'>20</span>, <span class='fl'>2</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>5</span>, <span class='fl'>3</span>, <span class='fl'>10</span>)
-<span class='no'>h0</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>20</span>)
-<span class='fu'>rnn</span>(<span class='no'>input</span>, <span class='no'>h0</span>)
-
-}</div></pre>
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-    <h1>RReLU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_rrelu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the randomized leaky rectified liner unit function, element-wise,
-as described in the paper:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_rrelu</span>(<span class='kw'>lower</span> <span class='kw'>=</span> <span class='fl'>1</span>/<span class='fl'>8</span>, <span class='kw'>upper</span> <span class='kw'>=</span> <span class='fl'>1</span>/<span class='fl'>3</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>lower</th>
-      <td><p>lower bound of the uniform distribution. Default: \(\frac{1}{8}\)</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>upper bound of the uniform distribution. Default: \(\frac{1}{3}\)</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p><code>Empirical Evaluation of Rectified Activations in Convolutional Network</code>.</p>
-<p>The function is defined as:</p>
-<p>$$
-\mbox{RReLU}(x) =
-\left\{ \begin{array}{ll}
-x &amp; \mbox{if } x \geq 0 \\
-ax &amp; \mbox{ otherwise }
-\end{array}
-\right.
-$$</p>
-<p>where \(a\) is randomly sampled from uniform distribution
-\(\mathcal{U}(\mbox{lower}, \mbox{upper})\).
-See: https://arxiv.org/pdf/1505.00853.pdf</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_rrelu</span>(<span class='fl'>0.1</span>, <span class='fl'>0.3</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nn_selu.html b/static/docs/reference/nn_selu.html
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-    <h1>SELU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_selu.Rd</code></div>
-    </div>
-
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-    <p>Applied element-wise, as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_selu</span>(<span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>inplace</th>
-      <td><p>(bool, optional): can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{SELU}(x) = \mbox{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))
-$$</p>
-<p>with \(\alpha = 1.6732632423543772848170429916717\) and
-\(\mbox{scale} = 1.0507009873554804934193349852946\).</p>
-<p>More details can be found in the paper
-<a href='https://arxiv.org/abs/1706.02515'>Self-Normalizing Neural Networks</a>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_selu</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
-    <div class="hidden name"><code>nn_sequential.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A sequential container.
-Modules will be added to it in the order they are passed in the constructor.
-See examples.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_sequential</span>(<span class='no'>...</span>, <span class='kw'>name</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>sequence of modules to be added</p></td>
-    </tr>
-    <tr>
-      <th>name</th>
-      <td><p>optional name for the generated module.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>model</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_sequential</span>(
-  <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>1</span>, <span class='fl'>20</span>, <span class='fl'>5</span>),
-  <span class='fu'><a href='nn_relu.html'>nn_relu</a></span>(),
-  <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>20</span>, <span class='fl'>64</span>, <span class='fl'>5</span>),
-  <span class='fu'><a href='nn_relu.html'>nn_relu</a></span>()
-)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>32</span>, <span class='fl'>1</span>, <span class='fl'>28</span>, <span class='fl'>28</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>model</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_sigmoid.html b/static/docs/reference/nn_sigmoid.html
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-    <h1>Sigmoid module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_sigmoid.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the element-wise function:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_sigmoid</span>()</pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)}
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_sigmoid</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-    <h1>Softmax module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softmax.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the Softmax function to an n-dimensional input Tensor
-rescaling them so that the elements of the n-dimensional output Tensor
-lie in the range <code>[0,1]</code> and sum to 1.
-Softmax is defined as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_softmax</span>(<span class='no'>dim</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>(int): A dimension along which Softmax will be computed (so every slice
-along dim will sum to 1).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>:
-a Tensor of the same dimension and shape as the input with
-values in the range <code>[0, 1]</code></p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}
-$$</p>
-<p>When the input Tensor is a sparse tensor then the unspecifed
-values are treated as <code>-Inf</code>.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This module doesn't work directly with NLLLoss,
-which expects the Log to be computed between the Softmax and itself.
-Use <code>LogSoftmax</code> instead (it's faster and has better numerical properties).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((*)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((*)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_softmax</span>(<span class='fl'>1</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Softmax2d module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softmax2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies SoftMax over features to each spatial location.
-When given an image of <code>Channels x Height x Width</code>, it will
-apply <code>Softmax</code> to each location \((Channels, h_i, w_j)\)</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_softmax2d</span>()</pre>
-
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>a Tensor of the same dimension and shape as the input with
-values in the range <code>[0, 1]</code></p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, C, H, W)\)</p></li>
-<li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_softmax2d</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>12</span>, <span class='fl'>13</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nn_softmin.html b/static/docs/reference/nn_softmin.html
deleted file mode 100644
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-<meta property="og:description" content="Applies the Softmin function to an n-dimensional input Tensor
-rescaling them so that the elements of the n-dimensional output Tensor
-lie in the range [0, 1] and sum to 1.
-Softmin is defined as:" />
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-    <h1>Softmin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softmin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the Softmin function to an n-dimensional input Tensor
-rescaling them so that the elements of the n-dimensional output Tensor
-lie in the range <code>[0, 1]</code> and sum to 1.
-Softmin is defined as:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_softmin</span>(<span class='no'>dim</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>(int): A dimension along which Softmin will be computed (so every slice
-along dim will sum to 1).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>a Tensor of the same dimension and shape as the input, with
-values in the range <code>[0, 1]</code>.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)}
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((*)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((*)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_softmin</span>(<span class='kw'>dim</span> <span class='kw'>=</span> <span class='fl'>1</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_softplus.html b/static/docs/reference/nn_softplus.html
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-$$" />
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-    <div class="page-header">
-    <h1>Softplus module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softplus.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the element-wise function:
-$$
-  \mbox{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
-$$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_softplus</span>(<span class='kw'>beta</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>threshold</span> <span class='kw'>=</span> <span class='fl'>20</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>beta</th>
-      <td><p>the \(\beta\) value for the Softplus formulation. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>threshold</th>
-      <td><p>values above this revert to a linear function. Default: 20</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>SoftPlus is a smooth approximation to the ReLU function and can be used
-to constrain the output of a machine to always be positive.
-For numerical stability the implementation reverts to the linear function
-when \(input \times \beta &gt; threshold\).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_softplus</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Softshrink module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softshrink.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the soft shrinkage function elementwise:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_softshrink</span>(<span class='kw'>lambd</span> <span class='kw'>=</span> <span class='fl'>0.5</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>lambd</th>
-      <td><p>the \(\lambda\) (must be no less than zero) value for the Softshrink formulation. Default: 0.5</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{SoftShrinkage}(x) =
-  \left\{ \begin{array}{ll}
-x - \lambda, &amp; \mbox{ if } x &gt; \lambda \\
-x + \lambda, &amp; \mbox{ if } x &lt; -\lambda \\
-0, &amp; \mbox{ otherwise }
-\end{array}
-\right.
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_softshrink</span>()
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_softsign.html b/static/docs/reference/nn_softsign.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
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-$$
-  \mbox{SoftSign}(x) = \frac{x}{ 1 + |x|}
-$$</p>
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-dimensions</p></li>
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-</ul>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_tanh.Rd</code></div>
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-
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-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-  \mbox{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)} {\exp(x) + \exp(-x)}
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
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-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_tanh</span>()
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diff --git a/static/docs/reference/nn_tanhshrink.html b/static/docs/reference/nn_tanhshrink.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_tanhshrink.Rd</code></div>
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-
-    <p>$$
-  \mbox{Tanhshrink}(x) = x - \tanh(x)
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
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-    <h1>Threshoold module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_threshold.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Thresholds each element of the input Tensor.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_threshold</span>(<span class='no'>threshold</span>, <span class='no'>value</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>threshold</th>
-      <td><p>The value to threshold at</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>The value to replace with</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Threshold is defined as:
-$$
-  y = 
-  \left\{ \begin{array}{ll}
-  x, &amp;\mbox{ if } x &gt; \mbox{threshold} \\
-  \mbox{value}, &amp;\mbox{ otherwise }
-  \end{array}
-  \right.
-$$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
-    
-
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
-dimensions</p></li>
-<li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_threshold</span>(<span class='fl'>0.1</span>, <span class='fl'>20</span>)
-<span class='no'>input</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
-<span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>m</span>(<span class='no'>input</span>)
-
-}</div></pre>
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-    <h1>Packs a Tensor containing padded sequences of variable length.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_rnn_pack_padded_sequence.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>input</code> can be of size <code>T x B x *</code> where <code>T</code> is the length of the
-longest sequence (equal to <code>lengths[1]</code>), <code>B</code> is the batch size, and
-<code>*</code> is any number of dimensions (including 0). If <code>batch_first</code> is
-<code>TRUE</code>, <code>B x T x *</code> <code>input</code> is expected.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_utils_rnn_pack_padded_sequence</span>(
-  <span class='no'>input</span>,
-  <span class='no'>lengths</span>,
-  <span class='kw'>batch_first</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>enforce_sorted</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor): padded batch of variable length sequences.</p></td>
-    </tr>
-    <tr>
-      <th>lengths</th>
-      <td><p>(Tensor): list of sequences lengths of each batch element.</p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>(bool, optional): if <code>TRUE</code>, the input is expected in <code>B x T x *</code>
-format.</p></td>
-    </tr>
-    <tr>
-      <th>enforce_sorted</th>
-      <td><p>(bool, optional): if <code>TRUE</code>, the input is expected to
-contain sequences sorted by length in a decreasing order. If
-<code>FALSE</code>, the input will get sorted unconditionally. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>a <code>PackedSequence</code> object</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>For unsorted sequences, use <code>enforce_sorted = FALSE</code>. If <code>enforce_sorted</code> is
-<code>TRUE</code>, the sequences should be sorted by length in a decreasing order, i.e.
-<code>input[,1]</code> should be the longest sequence, and <code>input[,B]</code> the shortest
-one. <code>enforce_sorted = TRUE</code> is only necessary for ONNX export.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function accepts any input that has at least two dimensions. You
-can apply it to pack the labels, and use the output of the RNN with
-them to compute the loss directly. A Tensor can be retrieved from
-a <code>PackedSequence</code> object by accessing its <code>.data</code> attribute.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_utils_rnn_pack_sequence.html b/static/docs/reference/nn_utils_rnn_pack_sequence.html
deleted file mode 100644
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-    <h1>Packs a list of variable length Tensors</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_rnn_pack_sequence.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>sequences</code> should be a list of Tensors of size <code>L x *</code>, where <code>L</code> is
-the length of a sequence and <code>*</code> is any number of trailing dimensions,
-including zero.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_utils_rnn_pack_sequence</span>(<span class='no'>sequences</span>, <span class='kw'>enforce_sorted</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>sequences</th>
-      <td><p><code>(list[Tensor])</code>: A list of sequences of decreasing length.</p></td>
-    </tr>
-    <tr>
-      <th>enforce_sorted</th>
-      <td><p>(bool, optional): if <code>TRUE</code>, checks that the input
-contains sequences sorted by length in a decreasing order. If
-<code>FALSE</code>, this condition is not checked. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>a <code>PackedSequence</code> object</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>For unsorted sequences, use <code>enforce_sorted = FALSE</code>. If <code>enforce_sorted</code>
-is <code>TRUE</code>, the sequences should be sorted in the order of decreasing length.
-<code>enforce_sorted = TRUE</code> is only necessary for ONNX export.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>x</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-<span class='no'>y</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-<span class='no'>z</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>6</span>), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-
-<span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_utils_rnn_pack_sequence</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>x</span>, <span class='no'>y</span>, <span class='no'>z</span>))
-
-}</div></pre>
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-    <h1>Pads a packed batch of variable length sequences.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_rnn_pad_packed_sequence.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>It is an inverse operation to <code><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence()</a></code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_utils_rnn_pad_packed_sequence</span>(
-  <span class='no'>sequence</span>,
-  <span class='kw'>batch_first</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>padding_value</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>total_length</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>sequence</th>
-      <td><p>(PackedSequence): batch to pad</p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>(bool, optional): if <code>True</code>, the output will be in ``B x T x *`
-format.</p></td>
-    </tr>
-    <tr>
-      <th>padding_value</th>
-      <td><p>(float, optional): values for padded elements.</p></td>
-    </tr>
-    <tr>
-      <th>total_length</th>
-      <td><p>(int, optional): if not <code>NULL</code>, the output will be padded to
-have length <code>total_length</code>. This method will throw <code>ValueError</code>
-if <code>total_length</code> is less than the max sequence length in
-<code>sequence</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>Tuple of Tensor containing the padded sequence, and a Tensor
-containing the list of lengths of each sequence in the batch.
-Batch elements will be re-ordered as they were ordered originally when
-the batch was passed to <code><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence()</a></code> or
-<code><a href='nn_utils_rnn_pack_sequence.html'>nn_utils_rnn_pack_sequence()</a></code>.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The returned Tensor's data will be of size <code>T x B x *</code>, where <code>T</code> is the length
-of the longest sequence and <code>B</code> is the batch size. If <code>batch_first</code> is <code>TRUE</code>,
-the data will be transposed into <code>B x T x *</code> format.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p><code>total_length</code> is useful to implement the
-<code>pack sequence -&gt; recurrent network -&gt; unpack sequence</code> pattern in a
-<code>nn_module</code> wrapped in <code>~torch.nn.DataParallel</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>seq</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>0</span>,<span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>6</span>)))
-<span class='no'>lens</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>1</span>,<span class='fl'>3</span>)
-<span class='no'>packed</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence</a></span>(<span class='no'>seq</span>, <span class='no'>lens</span>, <span class='kw'>batch_first</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-                                            <span class='kw'>enforce_sorted</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
-<span class='no'>packed</span>
-<span class='fu'>nn_utils_rnn_pad_packed_sequence</span>(<span class='no'>packed</span>, <span class='kw'>batch_first</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_utils_rnn_pad_sequence.html b/static/docs/reference/nn_utils_rnn_pad_sequence.html
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-and pads them to equal length. For example, if the input is list of
-sequences with size L x * and if batch_first is False, and T x B x *
-otherwise." />
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-    <h1>Pad a list of variable length Tensors with <code>padding_value</code></h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_rnn_pad_sequence.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>pad_sequence</code> stacks a list of Tensors along a new dimension,
-and pads them to equal length. For example, if the input is list of
-sequences with size <code>L x *</code> and if batch_first is False, and <code>T x B x *</code>
-otherwise.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_utils_rnn_pad_sequence</span>(<span class='no'>sequences</span>, <span class='kw'>batch_first</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>padding_value</span> <span class='kw'>=</span> <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>sequences</th>
-      <td><p><code>(list[Tensor])</code>: list of variable length sequences.</p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>(bool, optional): output will be in <code>B x T x *</code> if <code>TRUE</code>,
-or in <code>T x B x *</code> otherwise</p></td>
-    </tr>
-    <tr>
-      <th>padding_value</th>
-      <td><p>(float, optional): value for padded elements. Default: 0.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>Tensor of size <code>T x B x *</code> if <code>batch_first</code> is <code>FALSE</code>.
-Tensor of size <code>B x T x *</code> otherwise</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p><code>B</code> is batch size. It is equal to the number of elements in <code>sequences</code>.
-<code>T</code> is length of the longest sequence.
-<code>L</code> is length of the sequence.
-<code>*</code> is any number of trailing dimensions, including none.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function returns a Tensor of size <code>T x B x *</code> or <code>B x T x *</code>
-where <code>T</code> is the length of the longest sequence. This function assumes
-trailing dimensions and type of all the Tensors in sequences are same.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>a</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fl'>25</span>, <span class='fl'>300</span>)
-<span class='no'>b</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fl'>22</span>, <span class='fl'>300</span>)
-<span class='no'>c</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fl'>15</span>, <span class='fl'>300</span>)
-<span class='fu'>nn_utils_rnn_pad_sequence</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>a</span>, <span class='no'>b</span>, <span class='no'>c</span>))$<span class='fu'>size</span>()
-
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_adaptive_avg_pool1d.html b/static/docs/reference/nnf_adaptive_avg_pool1d.html
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-      <th>output_size</th>
-      <td><p>the target output size (single integer)</p></td>
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-several input planes.</p>
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-      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
-    </tr>
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-      <th>output_size</th>
-      <td><p>the target output size (single integer or triple-integer tuple)</p></td>
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-      <td><p>whether to return pooling indices. Default:<code>FALSE</code></p></td>
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Affine_grid</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-vision.R'><code>R/nnf-vision.R</code></a></small>
-    <div class="hidden name"><code>nnf_affine_grid.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Generates a 2D or 3D flow field (sampling grid), given a batch of
-affine matrices <code>theta</code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_affine_grid</span>(<span class='no'>theta</span>, <span class='no'>size</span>, <span class='kw'>align_corners</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>theta</th>
-      <td><p>(Tensor) input batch of affine matrices with shape
-(\(N \times 2 \times 3\)) for 2D or  (\(N \times 3 \times 4\)) for 3D</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(torch.Size) the target output image size. (\(N \times C \times H \times W\)
-for 2D or \(N \times C \times D \times H \times W\) for 3D)
-Example: torch.Size((32, 3, 24, 24))</p></td>
-    </tr>
-    <tr>
-      <th>align_corners</th>
-      <td><p>(bool, optional) if <code>True</code>, consider <code>-1</code> and <code>1</code>
-to refer to the centers of the corner pixels rather than the image corners.
-Refer to <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code> for a more complete description. A grid generated by
-<code>nnf_affine_grid()</code> should be passed to <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code>  with the same setting for
-this option. Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>This function is often used in conjunction with <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code>
-to build <code>Spatial Transformer Networks</code>_ .</p>
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
-    <div class="hidden name"><code>nnf_alpha_dropout.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies alpha dropout to the input.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_alpha_dropout</span>(<span class='no'>input</span>, <span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>training</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
-Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
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diff --git a/static/docs/reference/nnf_avg_pool1d.html b/static/docs/reference/nnf_avg_pool1d.html
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-    <h1>Avg_pool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_avg_pool1d.Rd</code></div>
-    </div>
-
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-    <p>Applies a 1D average pooling over an input signal composed of several
-input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_avg_pool1d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>count_include_pad</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window. Can be a single number or a
-tuple <code>(kW,)</code>.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Can be a single number or a tuple
-<code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
-output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when True, will include the zero-padding in the
-averaging calculation. Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>Avg_pool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_avg_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies 2D average-pooling operation in \(kH * kW\) regions by step size
-\(sH * sW\) steps. The number of output features is equal to the number of
-input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_avg_pool2d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>count_include_pad</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>divisor_override</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>size of the pooling region. Can be a single number or a
-tuple <code>(kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>stride of the pooling operation. Can be a single number or a
-tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
-to compute the output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when True, will include the zero-padding in the
-averaging calculation. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>divisor_override</th>
-      <td><p>if specified, it will be used as divisor, otherwise
-size of the pooling region will be used. Default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-
-      
-
-      </header>
-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Avg_pool3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_avg_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies 3D average-pooling operation in \(kT * kH * kW\) regions by step
-size \(sT * sH * sW\) steps. The number of output features is equal to
-\(\lfloor \frac{ \mbox{input planes} }{sT} \rfloor\).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_avg_pool3d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>count_include_pad</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>divisor_override</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>size of the pooling region. Can be a single number or a
-tuple <code>(kT, kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>stride of the pooling operation. Can be a single number or a
-tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
-to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when True, will include the zero-padding in the
-averaging calculation</p></td>
-    </tr>
-    <tr>
-      <th>divisor_override</th>
-      <td><p>NA if specified, it will be used as divisor, otherwise
-size of the pooling region will be used. Default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/static/docs/reference/nnf_batch_norm.html b/static/docs/reference/nnf_batch_norm.html
deleted file mode 100644
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-    <h1>Batch_norm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-batchnorm.R'><code>R/nnf-batchnorm.R</code></a></small>
-    <div class="hidden name"><code>nnf_batch_norm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies Batch Normalization for each channel across a batch of data.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_batch_norm</span>(
-  <span class='no'>input</span>,
-  <span class='no'>running_mean</span>,
-  <span class='no'>running_var</span>,
-  <span class='kw'>weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>training</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>momentum</span> <span class='kw'>=</span> <span class='fl'>0.1</span>,
-  <span class='kw'>eps</span> <span class='kw'>=</span> <span class='fl'>1e-05</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor</p></td>
-    </tr>
-    <tr>
-      <th>running_mean</th>
-      <td><p>the running_mean tensor</p></td>
-    </tr>
-    <tr>
-      <th>running_var</th>
-      <td><p>the running_var tensor</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>the weight tensor</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>the bias tensor</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>bool wether it's training. Default: FALSE</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>the value used for the <code>running_mean</code> and <code>running_var</code> computation.
-Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
-    </tr>
-    </table>
-
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-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_bilinear.html b/static/docs/reference/nnf_bilinear.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-linear.R'><code>R/nnf-linear.R</code></a></small>
-    <div class="hidden name"><code>nnf_bilinear.Rd</code></div>
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-\(y = x_1 A x_2 + b\)</p>
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-
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-
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-    <tr>
-      <th>input1</th>
-      <td><p>\((N, *, H_{in1})\) where \(H_{in1}=\mbox{in1\_features}\)
-and \(*\) means any number of additional dimensions.
-All but the last dimension of the inputs should be the same.</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>\((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>\((\mbox{out\_features}, \mbox{in1\_features},
-\mbox{in2\_features})\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>\((\mbox{out\_features})\)</p></td>
-    </tr>
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-
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-
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-and all but the last dimension are the same shape as the input.</p>
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-    <h1>Binary_cross_entropy</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_binary_cross_entropy.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Function that measures the Binary Cross Entropy
-between the target and the output.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_binary_cross_entropy</span>(
-  <span class='no'>input</span>,
-  <span class='no'>target</span>,
-  <span class='kw'>weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>)
-)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(tensor) weight for each value.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-    <h1>Binary_cross_entropy_with_logits</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_binary_cross_entropy_with_logits.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Function that measures Binary Cross Entropy between target and output
-logits.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_binary_cross_entropy_with_logits</span>(
-  <span class='no'>input</span>,
-  <span class='no'>target</span>,
-  <span class='kw'>weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>),
-  <span class='kw'>pos_weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>Tensor of arbitrary shape</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>Tensor of the same shape as input</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional) a manual rescaling weight if provided it's
-repeated to match input tensor shape.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    <tr>
-      <th>pos_weight</th>
-      <td><p>(Tensor, optional) a weight of positive examples.
-Must be a vector with length equal to the number of classes.</p></td>
-    </tr>
-    </table>
-
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diff --git a/static/docs/reference/nnf_celu.html b/static/docs/reference/nnf_celu.html
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-    <h1>Celu</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_celu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies element-wise, \(CELU(x) = max(0,x) + min(0, \alpha * (exp(x \alpha) - 1))\).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_celu</span>(<span class='no'>input</span>, <span class='kw'>alpha</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
-
-<span class='fu'>nnf_celu_</span>(<span class='no'>input</span>, <span class='kw'>alpha</span> <span class='kw'>=</span> <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
-
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diff --git a/static/docs/reference/nnf_conv1d.html b/static/docs/reference/nnf_conv1d.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
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-    <p>Applies a 1D convolution over an input signal composed of several input
-planes.</p>
-    </div>
-
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-  <span class='no'>input</span>,
-  <span class='no'>weight</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>groups</span> <span class='kw'>=</span> <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels, in_channels/groups , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or
-a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by
-the number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
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-    <div class="page-header">
-    <h1>Conv2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D convolution over an input image composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv2d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>weight</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>groups</span> <span class='kw'>=</span> <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a
-tuple <code>(sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a tuple <code>(dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by the
-number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_conv3d.html b/static/docs/reference/nnf_conv3d.html
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-    <h1>Conv3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 3D convolution over an input image composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv3d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>weight</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>groups</span> <span class='kw'>=</span> <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a
-tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by
-the number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Conv_tbc</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv_tbc.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1-dimensional sequence convolution over an input sequence.
-Input and output dimensions are (Time, Batch, Channels) - hence TBC.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv_tbc</span>(<span class='no'>input</span>, <span class='no'>weight</span>, <span class='no'>bias</span>, <span class='kw'>pad</span> <span class='kw'>=</span> <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{sequence length} \times 
-batch \times \mbox{in\_channels})\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} 
-\times \mbox{out\_channels}\))</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>bias of shape (\(\mbox{out\_channels}\))</p></td>
-    </tr>
-    <tr>
-      <th>pad</th>
-      <td><p>number of timesteps to pad. Default: 0</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_conv_transpose1d.html b/static/docs/reference/nnf_conv_transpose1d.html
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-
-    <div class="ref-description">
-    <p>Applies a 1D transposed convolution operator over an input signal
-composed of several input planes, sometimes also called "deconvolution".</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv_transpose1d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>weight</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>output_padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>groups</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels, in_channels/groups , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or
-a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>padding applied to the output</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by
-the number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-    <h1>Conv_transpose2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv_transpose2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution".</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv_transpose2d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>weight</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>output_padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>groups</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a
-tuple <code>(sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>padding applied to the output</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by the
-number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a tuple <code>(dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_conv_transpose3d.html b/static/docs/reference/nnf_conv_transpose3d.html
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-
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-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv_transpose3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv_transpose3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 3D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution"</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_conv_transpose3d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>weight</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>output_padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>groups</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a
-tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>padding applied to the output</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by
-the number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Cosine_embedding_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_cosine_embedding_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that measures the loss given input tensors x_1, x_2 and a
-Tensor label y with values 1 or -1. This is used for measuring whether two inputs
-are similar or dissimilar, using the cosine distance, and is typically used
-for learning nonlinear embeddings or semi-supervised learning.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_cosine_embedding_loss</span>(
-  <span class='no'>input1</span>,
-  <span class='no'>input2</span>,
-  <span class='no'>target</span>,
-  <span class='kw'>margin</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>)
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input1</th>
-      <td><p>the input x_1 tensor</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>the input x_2 tensor</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>the target tensor</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Should be a number from -1 to 1 , 0 to 0.5 is suggested. If margin
-is missing, the default value is 0.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_cosine_similarity.html b/static/docs/reference/nnf_cosine_similarity.html
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-    <h1>Cosine_similarity</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-distance.R'><code>R/nnf-distance.R</code></a></small>
-    <div class="hidden name"><code>nnf_cosine_similarity.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Returns cosine similarity between x1 and x2, computed along dim.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_cosine_similarity</span>(<span class='no'>x1</span>, <span class='no'>x2</span>, <span class='kw'>dim</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>eps</span> <span class='kw'>=</span> <span class='fl'>1e-08</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x1</th>
-      <td><p>(Tensor) First input.</p></td>
-    </tr>
-    <tr>
-      <th>x2</th>
-      <td><p>(Tensor) Second input (of size matching x1).</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) Dimension of vectors. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid division by zero.
-Default: 1e-8</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$
-    \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
-$$</p>
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_cross_entropy.Rd</code></div>
-    </div>
-
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-    <p>This criterion combines <code>log_softmax</code> and <code>nll_loss</code> in a single
-function.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_cross_entropy</span>(
-  <span class='no'>input</span>,
-  <span class='no'>target</span>,
-  <span class='kw'>weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>ignore_index</span> <span class='kw'>=</span> -<span class='fl'>100</span>,
-  <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>)
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) \((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\)
-in case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\)
-in the case of K-dimensional loss.</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>(Tensor) \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
-or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional) a manual rescaling weight given to each class. If
-given, has to be a Tensor of size <code>C</code></p></td>
-    </tr>
-    <tr>
-      <th>ignore_index</th>
-      <td><p>(int, optional) Specifies a target value that is ignored
-and does not contribute to the input gradient.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Ctc_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_ctc_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The Connectionist Temporal Classification loss.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_ctc_loss</span>(
-  <span class='no'>log_probs</span>,
-  <span class='no'>targets</span>,
-  <span class='no'>input_lengths</span>,
-  <span class='no'>target_lengths</span>,
-  <span class='kw'>blank</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>),
-  <span class='kw'>zero_infinity</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>log_probs</th>
-      <td><p>\((T, N, C)\) where C = number of characters in alphabet including blank,
-T = input length, and N = batch size. The logarithmized probabilities of
-the outputs (e.g. obtained with <a href='nnf_log_softmax.html'>nnf_log_softmax</a>).</p></td>
-    </tr>
-    <tr>
-      <th>targets</th>
-      <td><p>\((N, S)\) or <code>(sum(target_lengths))</code>. Targets cannot be blank.
-In the second form, the targets are assumed to be concatenated.</p></td>
-    </tr>
-    <tr>
-      <th>input_lengths</th>
-      <td><p>\((N)\). Lengths of the inputs (must each be \(\leq T\))</p></td>
-    </tr>
-    <tr>
-      <th>target_lengths</th>
-      <td><p>\((N)\). Lengths of the targets</p></td>
-    </tr>
-    <tr>
-      <th>blank</th>
-      <td><p>(int, optional) Blank label. Default \(0\).</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    <tr>
-      <th>zero_infinity</th>
-      <td><p>(bool, optional) Whether to zero infinite losses and the
-associated gradients. Default: <code>FALSE</code> Infinite losses mainly occur when the
-inputs are too short to be aligned to the targets.</p></td>
-    </tr>
-    </table>
-
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-    <h1>Dropout</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
-    <div class="hidden name"><code>nnf_dropout.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>During training, randomly zeroes some of the elements of the input
-tensor with probability <code>p</code> using samples from a Bernoulli
-distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_dropout</span>(<span class='no'>input</span>, <span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>training</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
-Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_dropout2d.html b/static/docs/reference/nnf_dropout2d.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
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-    </div>
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-    <p>Randomly zero out entire channels (a channel is a 2D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 2D tensor \(input[i, j]\)) of the input tensor).
-Each channel will be zeroed out independently on every forward call with
-probability <code>p</code> using samples from a Bernoulli distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_dropout2d</span>(<span class='no'>input</span>, <span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>training</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
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-    <table class="ref-arguments">
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-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>probability of a channel to be zeroed. Default: 0.5</p></td>
-    </tr>
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-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
-Default: <code>FALSE</code></p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
-    <div class="hidden name"><code>nnf_dropout3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randomly zero out entire channels (a channel is a 3D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 3D tensor \(input[i, j]\)) of the input tensor).
-Each channel will be zeroed out independently on every forward call with
-probability <code>p</code> using samples from a Bernoulli distribution.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_dropout3d</span>(<span class='no'>input</span>, <span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>training</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>probability of a channel to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
-Default: <code>FALSE</code></p></td>
-    </tr>
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-    <h1>Elu</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_elu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies element-wise,
-$$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_elu</span>(<span class='no'>input</span>, <span class='kw'>alpha</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
-
-<span class='fu'>nnf_elu_</span>(<span class='no'>input</span>, <span class='kw'>alpha</span> <span class='kw'>=</span> <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>the alpha value for the ELU formulation. Default: 1.0</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>x</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>)
-<span class='no'>y</span> <span class='kw'>&lt;-</span> <span class='fu'>nnf_elu</span>(<span class='no'>x</span>, <span class='kw'>alpha</span> <span class='kw'>=</span> <span class='fl'>1</span>)
-<span class='fu'>nnf_elu_</span>(<span class='no'>x</span>, <span class='kw'>alpha</span> <span class='kw'>=</span> <span class='fl'>1</span>)
-<span class='fu'><a href='torch_equal.html'>torch_equal</a></span>(<span class='no'>x</span>, <span class='no'>y</span>)
-
-}</div></pre>
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diff --git a/static/docs/reference/nnf_embedding.html b/static/docs/reference/nnf_embedding.html
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-    <h1>Embedding</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
-    <div class="hidden name"><code>nnf_embedding.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A simple lookup table that looks up embeddings in a fixed dictionary and size.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_embedding</span>(
-  <span class='no'>input</span>,
-  <span class='no'>weight</span>,
-  <span class='kw'>padding_idx</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>max_norm</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>norm_type</span> <span class='kw'>=</span> <span class='fl'>2</span>,
-  <span class='kw'>scale_grad_by_freq</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>sparse</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(LongTensor) Tensor containing indices into the embedding matrix</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor) The embedding matrix with number of rows equal to the
-maximum possible index + 1, and number of columns equal to the embedding size</p></td>
-    </tr>
-    <tr>
-      <th>padding_idx</th>
-      <td><p>(int, optional) If given, pads the output with the embedding
-vector at <code>padding_idx</code> (initialized to zeros) whenever it encounters the index.</p></td>
-    </tr>
-    <tr>
-      <th>max_norm</th>
-      <td><p>(float, optional) If given, each embedding vector with norm larger
-than <code>max_norm</code> is renormalized to have norm <code>max_norm</code>. Note: this will modify
-<code>weight</code> in-place.</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>(float, optional) The p of the p-norm to compute for the <code>max_norm</code>
-option. Default <code>2</code>.</p></td>
-    </tr>
-    <tr>
-      <th>scale_grad_by_freq</th>
-      <td><p>(boolean, optional) If given, this will scale gradients
-by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>sparse</th>
-      <td><p>(bool, optional) If <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
-sparse tensor. See Notes under <code>nn_embedding</code> for more details regarding
-sparse gradients.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This module is often used to retrieve word embeddings using indices.
-The input to the module is a list of indices, and the embedding matrix,
-and the output is the corresponding word embeddings.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_embedding_bag.html b/static/docs/reference/nnf_embedding_bag.html
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-    <div class="page-header">
-    <h1>Embedding_bag</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
-    <div class="hidden name"><code>nnf_embedding_bag.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Computes sums, means or maxes of <code>bags</code> of embeddings, without instantiating the
-intermediate embeddings.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_embedding_bag</span>(
-  <span class='no'>input</span>,
-  <span class='no'>weight</span>,
-  <span class='kw'>offsets</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>max_norm</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>norm_type</span> <span class='kw'>=</span> <span class='fl'>2</span>,
-  <span class='kw'>scale_grad_by_freq</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>mode</span> <span class='kw'>=</span> <span class='st'>"mean"</span>,
-  <span class='kw'>sparse</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>per_sample_weights</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>include_last_offset</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(LongTensor) Tensor containing bags of indices into the embedding matrix</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor) The embedding matrix with number of rows equal to the
-maximum possible index + 1, and number of columns equal to the embedding size</p></td>
-    </tr>
-    <tr>
-      <th>offsets</th>
-      <td><p>(LongTensor, optional) Only used when <code>input</code> is 1D. <code>offsets</code>
-determines the starting index position of each bag (sequence) in <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>max_norm</th>
-      <td><p>(float, optional) If given, each embedding vector with norm
-larger than <code>max_norm</code> is renormalized to have norm <code>max_norm</code>.
-Note: this will modify <code>weight</code> in-place.</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>(float, optional) The <code>p</code> in the <code>p</code>-norm to compute for the
-<code>max_norm</code> option. Default <code>2</code>.</p></td>
-    </tr>
-    <tr>
-      <th>scale_grad_by_freq</th>
-      <td><p>(boolean, optional) if given, this will scale gradients
-by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.                                            Note: this option is not supported when <code>mode="max"</code>.</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>(string, optional) <code>"sum"</code>, <code>"mean"</code> or <code>"max"</code>. Specifies
-the way to reduce the bag. Default: 'mean'</p></td>
-    </tr>
-    <tr>
-      <th>sparse</th>
-      <td><p>(bool, optional) if <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
-sparse tensor. See Notes under <code>nn_embedding</code> for more details regarding
-sparse gradients. Note: this option is not supported when <code>mode="max"</code>.</p></td>
-    </tr>
-    <tr>
-      <th>per_sample_weights</th>
-      <td><p>(Tensor, optional) a tensor of float / double weights,
-or NULL to indicate all weights should be taken to be 1. If specified,
-<code>per_sample_weights</code> must have exactly the same shape as input and is treated
-as having the same <code>offsets</code>, if those are not <code>NULL</code>.</p></td>
-    </tr>
-    <tr>
-      <th>include_last_offset</th>
-      <td><p>(bool, optional) if <code>TRUE</code>, the size of offsets is
-equal to the number of bags + 1.</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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diff --git a/static/docs/reference/nnf_fold.html b/static/docs/reference/nnf_fold.html
deleted file mode 100644
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-    <div class="page-header">
-    <h1>Fold</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-fold.R'><code>R/nnf-fold.R</code></a></small>
-    <div class="hidden name"><code>nnf_fold.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Combines an array of sliding local blocks into a large containing
-tensor.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_fold</span>(
-  <span class='no'>input</span>,
-  <span class='no'>output_size</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the shape of the spatial dimensions of the output (i.e.,
-<code>output$sizes()[-c(1,2)]</code>)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the sliding blocks</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements within the
-neighborhood. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides of input.
-Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the sliding blocks in the input spatial dimensions.
-Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-
-<p>Currently, only 4-D output tensors (batched image-like tensors) are
-supported.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_fractional_max_pool2d.html b/static/docs/reference/nnf_fractional_max_pool2d.html
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-    <div class="page-header">
-    <h1>Fractional_max_pool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_fractional_max_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies 2D fractional max pooling over an input signal composed of several input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_fractional_max_pool2d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>output_size</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>output_ratio</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>random_samples</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over. Can be a
-single number \(k\) (for a square kernel of \(k * k\)) or
-a tuple <code>(kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form \(oH * oW\).
-Can be a tuple <code>(oH, oW)</code> or a single number \(oH\) for a square image \(oH * oH\)</p></td>
-    </tr>
-    <tr>
-      <th>output_ratio</th>
-      <td><p>If one wants to have an output size as a ratio of the input size,
-this option can be given. This has to be a number or tuple in the range (0, 1)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>True</code>, will return the indices along with the outputs.</p></td>
-    </tr>
-    <tr>
-      <th>random_samples</th>
-      <td><p>optional random samples.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Fractional MaxPooling is described in detail in the paper <code>Fractional MaxPooling</code>_ by Ben Graham</p>
-<p>The max-pooling operation is applied in \(kH * kW\) regions by a stochastic
-step size determined by the target output size.
-The number of output features is equal to the number of input planes.</p>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Fractional_max_pool3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_fractional_max_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies 3D fractional max pooling over an input signal composed of several input planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_fractional_max_pool3d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>output_size</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>output_ratio</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>random_samples</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over. Can be a single number \(k\)
-(for a square kernel of \(k * k * k\)) or a tuple <code>(kT, kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the form \(oT * oH * oW\).
-Can be a tuple <code>(oT, oH, oW)</code> or a single number \(oH\) for a cubic output
-\(oH * oH * oH\)</p></td>
-    </tr>
-    <tr>
-      <th>output_ratio</th>
-      <td><p>If one wants to have an output size as a ratio of the
-input size, this option can be given. This has to be a number or tuple in the
-range (0, 1)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>True</code>, will return the indices along with the outputs.</p></td>
-    </tr>
-    <tr>
-      <th>random_samples</th>
-      <td><p>undocumented argument.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Fractional MaxPooling is described in detail in the paper <code>Fractional MaxPooling</code>_ by Ben Graham</p>
-<p>The max-pooling operation is applied in \(kT * kH * kW\) regions by a stochastic
-step size determined by the target output size.
-The number of output features is equal to the number of input planes.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_gelu.html b/static/docs/reference/nnf_gelu.html
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-      <td><p>(N,*) tensor, where * means, any number of additional
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-
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-
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-<p>Applies element-wise the function
-\(GELU(x) = x * \Phi(x)\)</p>
-<p>where \(\Phi(x)\) is the Cumulative Distribution Function for
-Gaussian Distribution.</p>
-<p>See <a href='https://arxiv.org/abs/1606.08415'>Gaussian Error Linear Units (GELUs)</a>.</p>
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-    <p>The gated linear unit. Computes:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_glu</span>(<span class='no'>input</span>, <span class='kw'>dim</span> <span class='kw'>=</span> -<span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input tensor</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension on which to split the input. Default: -1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$GLU(a, b) = a \otimes \sigma(b)$$</p>
-<p>where <code>input</code> is split in half along <code>dim</code> to form <code>a</code> and <code>b</code>, \(\sigma\)
-is the sigmoid function and \(\otimes\) is the element-wise product
-between matrices.</p>
-<p>See <a href='https://arxiv.org/abs/1612.08083'>Language Modeling with Gated Convolutional Networks</a>.</p>
-
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-    <div class="page-header">
-    <h1>Grid_sample</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-vision.R'><code>R/nnf-vision.R</code></a></small>
-    <div class="hidden name"><code>nnf_grid_sample.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Given an <code>input</code> and a flow-field <code>grid</code>, computes the
-<code>output</code> using <code>input</code> values and pixel locations from <code>grid</code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_grid_sample</span>(
-  <span class='no'>input</span>,
-  <span class='no'>grid</span>,
-  <span class='kw'>mode</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"bilinear"</span>, <span class='st'>"nearest"</span>),
-  <span class='kw'>padding_mode</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"zeros"</span>, <span class='st'>"border"</span>, <span class='st'>"reflection"</span>),
-  <span class='kw'>align_corners</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input of shape \((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) (4-D case)                    or \((N, C, D_{\mbox{in}}, H_{\mbox{in}}, W_{\mbox{in}})\) (5-D case)</p></td>
-    </tr>
-    <tr>
-      <th>grid</th>
-      <td><p>(Tensor) flow-field of shape \((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\) (4-D case)                   or \((N, D_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}}, 3)\) (5-D case)</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>(str) interpolation mode to calculate output values <code>'bilinear'</code> | <code>'nearest'</code>.
-Default: <code>'bilinear'</code></p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(str) padding mode for outside grid values <code>'zeros'</code> | <code>'border'</code>
-| <code>'reflection'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    <tr>
-      <th>align_corners</th>
-      <td><p>(bool, optional) Geometrically, we consider the pixels of the
-input  as squares rather than points. If set to <code>True</code>, the extrema (<code>-1</code> and
-<code>1</code>) are considered as referring to the center points of the input's corner pixels.
-If set to <code>False</code>, they are instead considered as referring to the corner
-points of the input's corner pixels, making the sampling more resolution
-agnostic. This option parallels the <code>align_corners</code> option in  <code><a href='nnf_interpolate.html'>nnf_interpolate()</a></code>, and
-so whichever option is used here should also be used there to resize the input
-image before grid sampling. Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Currently, only spatial (4-D) and volumetric (5-D) <code>input</code> are
-supported.</p>
-<p>In the spatial (4-D) case, for <code>input</code> with shape
-\((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) and <code>grid</code> with shape
-\((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\), the output will have shape
-\((N, C, H_{\mbox{out}}, W_{\mbox{out}})\).</p>
-<p>For each output location <code>output[n, :, h, w]</code>, the size-2 vector
-<code>grid[n, h, w]</code> specifies <code>input</code> pixel locations <code>x</code> and <code>y</code>,
-which are used to interpolate the output value <code>output[n, :, h, w]</code>.
-In the case of 5D inputs, <code>grid[n, d, h, w]</code> specifies the
-<code>x</code>, <code>y</code>, <code>z</code> pixel locations for interpolating
-<code>output[n, :, d, h, w]</code>. <code>mode</code> argument specifies <code>nearest</code> or
-<code>bilinear</code> interpolation method to sample the input pixels.</p>
-<p><code>grid</code> specifies the sampling pixel locations normalized by the
-<code>input</code> spatial dimensions. Therefore, it should have most values in
-the range of <code>[-1, 1]</code>. For example, values <code>x = -1, y = -1</code> is the
-left-top pixel of <code>input</code>, and values  <code>x = 1, y = 1</code> is the
-right-bottom pixel of <code>input</code>.</p>
-<p>If <code>grid</code> has values outside the range of <code>[-1, 1]</code>, the corresponding
-outputs are handled as defined by <code>padding_mode</code>. Options are</p><ul>
-<li><p><code>padding_mode="zeros"</code>: use <code>0</code> for out-of-bound grid locations,</p></li>
-<li><p><code>padding_mode="border"</code>: use border values for out-of-bound grid locations,</p></li>
-<li><p><code>padding_mode="reflection"</code>: use values at locations reflected by
-the border for out-of-bound grid locations. For location far away
-from the border, it will keep being reflected until becoming in bound,
-e.g., (normalized) pixel location <code>x = -3.5</code> reflects by border <code>-1</code>
-and becomes <code>x' = 1.5</code>, then reflects by border <code>1</code> and becomes
-<code>x'' = -0.5</code>.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>This function is often used in conjunction with <code><a href='nnf_affine_grid.html'>nnf_affine_grid()</a></code>
-to build <code>Spatial Transformer Networks</code>_ .</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_group_norm.html b/static/docs/reference/nnf_group_norm.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
-    <div class="hidden name"><code>nnf_group_norm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies Group Normalization for last certain number of dimensions.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_group_norm</span>(<span class='no'>input</span>, <span class='no'>num_groups</span>, <span class='kw'>weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>bias</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>eps</span> <span class='kw'>=</span> <span class='fl'>1e-05</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>num_groups</th>
-      <td><p>number of groups to separate the channels into</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>the weight tensor</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>the bias tensor</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_gumbel_softmax.html b/static/docs/reference/nnf_gumbel_softmax.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
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-    <p>Applies the hard shrinkage function element-wise</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_hardshrink</span>(<span class='no'>input</span>, <span class='kw'>lambd</span> <span class='kw'>=</span> <span class='fl'>0.5</span>)</pre>
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-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>lambd</th>
-      <td><p>the lambda value for the Hardshrink formulation. Default: 0.5</p></td>
-    </tr>
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-    <h1>Hardswish</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_hardswish.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the hardswish function, element-wise, as described in the paper:
-Searching for MobileNetV3.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_hardswish</span>(<span class='no'>input</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>$$ \mbox{Hardswish}(x) = \left\{
-  \begin{array}{ll}
-  0 &amp; \mbox{if } x \le -3, \\
-  x &amp; \mbox{if } x \ge +3, \\
-  x \cdot (x + 3)/6 &amp; \mbox{otherwise}
-  \end{array}
-  \right. $$</p>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_hinge_embedding_loss.Rd</code></div>
-    </div>
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-    <div class="ref-description">
-    <p>Measures the loss given an input tensor xx and a labels tensor yy (containing 1 or -1).
-This is usually used for measuring whether two inputs are similar or dissimilar, e.g.
-using the L1 pairwise distance as xx , and is typically used for learning nonlinear
-embeddings or semi-supervised learning.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_hinge_embedding_loss</span>(<span class='no'>input</span>, <span class='no'>target</span>, <span class='kw'>margin</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='st'>"mean"</span>)</pre>
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-    <table class="ref-arguments">
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-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Has a default value of 1.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-    <div class="page-header">
-    <h1>Instance_norm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-instancenorm.R'><code>R/nnf-instancenorm.R</code></a></small>
-    <div class="hidden name"><code>nnf_instance_norm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies Instance Normalization for each channel in each data sample in a
-batch.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_instance_norm</span>(
-  <span class='no'>input</span>,
-  <span class='kw'>running_mean</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>running_var</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>use_input_stats</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>momentum</span> <span class='kw'>=</span> <span class='fl'>0.1</span>,
-  <span class='kw'>eps</span> <span class='kw'>=</span> <span class='fl'>1e-05</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>running_mean</th>
-      <td><p>the running_mean tensor</p></td>
-    </tr>
-    <tr>
-      <th>running_var</th>
-      <td><p>the running var tensor</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>the weight tensor</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>the bias tensor</p></td>
-    </tr>
-    <tr>
-      <th>use_input_stats</th>
-      <td><p>whether to use input stats</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>a double for the momentum</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>an eps double for numerical stability</p></td>
-    </tr>
-    </table>
-
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-    <h1>Interpolate</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-upsampling.R'><code>R/nnf-upsampling.R</code></a></small>
-    <div class="hidden name"><code>nnf_interpolate.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Down/up samples the input to either the given <code>size</code> or the given
-<code>scale_factor</code></p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_interpolate</span>(
-  <span class='no'>input</span>,
-  <span class='kw'>size</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>scale_factor</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>mode</span> <span class='kw'>=</span> <span class='st'>"nearest"</span>,
-  <span class='kw'>align_corners</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>recompute_scale_factor</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(int or <code>Tuple[int]</code> or <code>Tuple[int, int]</code> or <code>Tuple[int, int, int]</code>)
-output spatial size.</p></td>
-    </tr>
-    <tr>
-      <th>scale_factor</th>
-      <td><p>(float or <code>Tuple[float]</code>) multiplier for spatial size.
-Has to match input size if it is a tuple.</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>(str) algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear'
-| 'bicubic' | 'trilinear' | 'area' Default: 'nearest'</p></td>
-    </tr>
-    <tr>
-      <th>align_corners</th>
-      <td><p>(bool, optional) Geometrically, we consider the pixels
-of the input and output as squares rather than points. If set to TRUE,
-the input and output tensors are aligned by the center points of their corner
-pixels, preserving the values at the corner pixels. If set to False, the
-input and output tensors are aligned by the corner points of their corner pixels,
-and the interpolation uses edge value padding for out-of-boundary values,
-making this operation <em>independent</em> of input size when <code>scale_factor</code> is kept
-the same. This only has an effect when <code>mode</code>  is <code>'linear'</code>, <code>'bilinear'</code>,
-<code>'bicubic'</code> or <code>'trilinear'</code>.  Default: <code>False</code></p></td>
-    </tr>
-    <tr>
-      <th>recompute_scale_factor</th>
-      <td><p>(bool, optional) recompute the scale_factor
-for use in the interpolation calculation.  When <code>scale_factor</code> is passed
-as a parameter, it is used to compute the <code>output_size</code>.  If <code>recompute_scale_factor</code>
-is ```True`` or not specified, a new <code>scale_factor</code> will be computed based on
-the output and input sizes for use in the interpolation computation (i.e. the
-computation will be identical to if the computed `output_size` were passed-in
-explicitly).  Otherwise, the passed-in `scale_factor` will be used in the
-interpolation computation.  Note that when `scale_factor` is floating-point,
-the recomputed scale_factor may differ from the one passed in due to rounding
-and precision issues.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The algorithm used for interpolation is determined by <code>mode</code>.</p>
-<p>Currently temporal, spatial and volumetric sampling are supported, i.e.
-expected inputs are 3-D, 4-D or 5-D in shape.</p>
-<p>The input dimensions are interpreted in the form:
-<code>mini-batch x channels x [optional depth] x [optional height] x width</code>.</p>
-<p>The modes available for resizing are: <code>nearest</code>, <code>linear</code> (3D-only),
-<code>bilinear</code>, <code>bicubic</code> (4D-only), <code>trilinear</code> (5D-only), <code>area</code></p>
-
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-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
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-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
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-
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-  <span class='no'>input</span>,
-  <span class='no'>normalized_shape</span>,
-  <span class='kw'>weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>bias</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>eps</span> <span class='kw'>=</span> <span class='fl'>1e-05</span>
-)</pre>
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-      <td><p>the input tensor</p></td>
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-    <tr>
-      <th>normalized_shape</th>
-      <td><p>input shape from an expected input of size. If a single
-integer is used, it is treated as a singleton list, and this module will normalize
-over the last dimension which is expected to be of that specific size.</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>the weight tensor</p></td>
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-      <td><p>the bias tensor</p></td>
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-\(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\)</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_leaky_relu</span>(<span class='no'>input</span>, <span class='kw'>negative_slope</span> <span class='kw'>=</span> <span class='fl'>0.01</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
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-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>negative_slope</th>
-      <td><p>Controls the angle of the negative slope. Default: 1e-2</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
-    <div class="hidden name"><code>nnf_local_response_norm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies local response normalization over an input signal composed of
-several input planes, where channels occupy the second dimension.
-Applies normalization across channels.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_local_response_norm</span>(<span class='no'>input</span>, <span class='no'>size</span>, <span class='kw'>alpha</span> <span class='kw'>=</span> <span class='fl'>1e-04</span>, <span class='kw'>beta</span> <span class='kw'>=</span> <span class='fl'>0.75</span>, <span class='kw'>k</span> <span class='kw'>=</span> <span class='fl'>1</span>)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>amount of neighbouring channels used for normalization</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>multiplicative factor. Default: 0.0001</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>exponent. Default: 0.75</p></td>
-    </tr>
-    <tr>
-      <th>k</th>
-      <td><p>additive factor. Default: 1</p></td>
-    </tr>
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-    <h1>Log_softmax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_log_softmax.Rd</code></div>
-    </div>
-
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-    <p>Applies a softmax followed by a logarithm.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_log_softmax</span>(<span class='no'>input</span>, <span class='kw'>dim</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) A dimension along which log_softmax will be computed.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.
-If specified, the input tensor is casted to <code>dtype</code> before the operation
-is performed. This is useful for preventing data type overflows.
-Default: <code>NULL</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>While mathematically equivalent to log(softmax(x)), doing these two
-operations separately is slower, and numerically unstable. This function
-uses an alternative formulation to compute the output and gradient correctly.</p>
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_lp_pool1d.Rd</code></div>
-    </div>
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-    <p>Applies a 1D power-average pooling over an input signal composed of
-several input planes. If the sum of all inputs to the power of <code>p</code> is
-zero, the gradient is set to zero as well.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_lp_pool1d</span>(<span class='no'>input</span>, <span class='no'>norm_type</span>, <span class='no'>kernel_size</span>, <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>a single int, the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>a single int, the stride of the window. Default value is kernel_size</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use ceil instead of floor to compute the output shape</p></td>
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-several input planes. If the sum of all inputs to the power of <code>p</code> is
-zero, the gradient is set to zero as well.</p>
-    </div>
-
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-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>a single int, the size of the window</p></td>
-    </tr>
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-      <th>stride</th>
-      <td><p>a single int, the stride of the window. Default value is kernel_size</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use ceil instead of floor to compute the output shape</p></td>
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-    <h1>Margin_ranking_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_margin_ranking_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that measures the loss given inputs x1 , x2 , two 1D
-mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_margin_ranking_loss</span>(<span class='no'>input1</span>, <span class='no'>input2</span>, <span class='no'>target</span>, <span class='kw'>margin</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='st'>"mean"</span>)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input1</th>
-      <td><p>the first tensor</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>the second input tensor</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>the target tensor</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Has a default value of 00 .</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
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diff --git a/static/docs/reference/nnf_max_pool1d.html b/static/docs/reference/nnf_max_pool1d.html
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-    <h1>Max_pool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 1D max pooling over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_max_pool1d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window. Can be a single number or a
-tuple <code>(kW,)</code>.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Can be a single number or a tuple
-<code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>controls the spacing between the kernel points; also known as
-the à trous algorithm.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
-output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return the indices where the max occurs.</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_max_pool2d.html b/static/docs/reference/nnf_max_pool2d.html
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-    <h1>Max_pool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_pool2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 2D max pooling over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_max_pool2d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='no'>kernel_size</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>size of the pooling region. Can be a single number or a
-tuple <code>(kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>stride of the pooling operation. Can be a single number or a
-tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>controls the spacing between the kernel points; also known as
-the à trous algorithm.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
-to compute the output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return the indices where the max occurs.</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/nnf_max_pool3d.html b/static/docs/reference/nnf_max_pool3d.html
deleted file mode 100644
index 188fdff04..000000000
--- a/static/docs/reference/nnf_max_pool3d.html
+++ /dev/null
@@ -1,273 +0,0 @@
-<!-- Generated by pkgdown: do not edit by hand -->
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-    <h1>Max_pool3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_pool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a 3D max pooling over an input signal composed of several input
-planes.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_max_pool3d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>ceil_mode</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>return_indices</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>size of the pooling region. Can be a single number or a
-tuple <code>(kT, kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>stride of the pooling operation. Can be a single number or a
-tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>controls the spacing between the kernel points; also known as
-the à trous algorithm.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
-to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return the indices where the max occurs.</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_max_unpool1d.html b/static/docs/reference/nnf_max_unpool1d.html
deleted file mode 100644
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-    <h1>Max_unpool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_unpool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Computes a partial inverse of <code>MaxPool1d</code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_max_unpool1d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>indices</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>output_size</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input Tensor to invert</p></td>
-    </tr>
-    <tr>
-      <th>indices</th>
-      <td><p>the indices given out by max pool</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>Padding that was added to the input</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the targeted output size</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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diff --git a/static/docs/reference/nnf_max_unpool2d.html b/static/docs/reference/nnf_max_unpool2d.html
deleted file mode 100644
index 2cb8f0d28..000000000
--- a/static/docs/reference/nnf_max_unpool2d.html
+++ /dev/null
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-<!-- Generated by pkgdown: do not edit by hand -->
-<!DOCTYPE html>
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-    <h1>Max_unpool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_unpool2d.Rd</code></div>
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-    <p>Computes a partial inverse of <code>MaxPool2d</code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_max_unpool2d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>indices</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>output_size</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input Tensor to invert</p></td>
-    </tr>
-    <tr>
-      <th>indices</th>
-      <td><p>the indices given out by max pool</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>Padding that was added to the input</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the targeted output size</p></td>
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-    <div class="page-header">
-    <h1>Max_unpool3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_unpool3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Computes a partial inverse of <code>MaxPool3d</code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_max_unpool3d</span>(
-  <span class='no'>input</span>,
-  <span class='no'>indices</span>,
-  <span class='no'>kernel_size</span>,
-  <span class='kw'>stride</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>output_size</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input Tensor to invert</p></td>
-    </tr>
-    <tr>
-      <th>indices</th>
-      <td><p>the indices given out by max pool</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>Padding that was added to the input</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the targeted output size</p></td>
-    </tr>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_mse_loss.html b/static/docs/reference/nnf_mse_loss.html
deleted file mode 100644
index 3cd5dfe6d..000000000
--- a/static/docs/reference/nnf_mse_loss.html
+++ /dev/null
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-      <td><p>tensor (N,*) , same shape as the input</p></td>
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-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-subspaces. See reference: Attention Is All You Need" />
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-    <div class="page-header">
-    <h1>Multi head attention forward</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_multi_head_attention_forward.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Allows the model to jointly attend to information from different representation
-subspaces. See reference: Attention Is All You Need</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_multi_head_attention_forward</span>(
-  <span class='no'>query</span>,
-  <span class='no'>key</span>,
-  <span class='no'>value</span>,
-  <span class='no'>embed_dim_to_check</span>,
-  <span class='no'>num_heads</span>,
-  <span class='no'>in_proj_weight</span>,
-  <span class='no'>in_proj_bias</span>,
-  <span class='no'>bias_k</span>,
-  <span class='no'>bias_v</span>,
-  <span class='no'>add_zero_attn</span>,
-  <span class='no'>dropout_p</span>,
-  <span class='no'>out_proj_weight</span>,
-  <span class='no'>out_proj_bias</span>,
-  <span class='kw'>training</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>key_padding_mask</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>need_weights</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>attn_mask</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>use_separate_proj_weight</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>q_proj_weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>k_proj_weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>v_proj_weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>static_k</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>static_v</span> <span class='kw'>=</span> <span class='kw'>NULL</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>query</th>
-      <td><p>\((L, N, E)\) where L is the target sequence length, N is the batch size, E is
-the embedding dimension.</p></td>
-    </tr>
-    <tr>
-      <th>key</th>
-      <td><p>\((S, N, E)\), where S is the source sequence length, N is the batch size, E is
-the embedding dimension.</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>\((S, N, E)\) where S is the source sequence length, N is the batch size, E is
-the embedding dimension.</p></td>
-    </tr>
-    <tr>
-      <th>embed_dim_to_check</th>
-      <td><p>total dimension of the model.</p></td>
-    </tr>
-    <tr>
-      <th>num_heads</th>
-      <td><p>parallel attention heads.</p></td>
-    </tr>
-    <tr>
-      <th>in_proj_weight</th>
-      <td><p>input projection weight and bias.</p></td>
-    </tr>
-    <tr>
-      <th>in_proj_bias</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>bias_k</th>
-      <td><p>bias of the key and value sequences to be added at dim=0.</p></td>
-    </tr>
-    <tr>
-      <th>bias_v</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>add_zero_attn</th>
-      <td><p>add a new batch of zeros to the key and
-value sequences at dim=1.</p></td>
-    </tr>
-    <tr>
-      <th>dropout_p</th>
-      <td><p>probability of an element to be zeroed.</p></td>
-    </tr>
-    <tr>
-      <th>out_proj_weight</th>
-      <td><p>the output projection weight and bias.</p></td>
-    </tr>
-    <tr>
-      <th>out_proj_bias</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>key_padding_mask</th>
-      <td><p>\((N, S)\) where N is the batch size, S is the source sequence length.
-If a ByteTensor is provided, the non-zero positions will be ignored while the position
-with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
-value of <code>True</code> will be ignored while the position with the value of <code>False</code> will be unchanged.</p></td>
-    </tr>
-    <tr>
-      <th>need_weights</th>
-      <td><p>output attn_output_weights.</p></td>
-    </tr>
-    <tr>
-      <th>attn_mask</th>
-      <td><p>2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
-3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
-S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
-positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
-while the zero positions will be unchanged. If a BoolTensor is provided, positions with <code>True</code>
-is not allowed to attend while <code>False</code> values will be unchanged. If a FloatTensor
-is provided, it will be added to the attention weight.</p></td>
-    </tr>
-    <tr>
-      <th>use_separate_proj_weight</th>
-      <td><p>the function accept the proj. weights for
-query, key, and value in different forms. If false, in_proj_weight will be used,
-which is a combination of q_proj_weight, k_proj_weight, v_proj_weight.</p></td>
-    </tr>
-    <tr>
-      <th>q_proj_weight</th>
-      <td><p>input projection weight and bias.</p></td>
-    </tr>
-    <tr>
-      <th>k_proj_weight</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>v_proj_weight</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>static_k</th>
-      <td><p>static key and value used for attention operators.</p></td>
-    </tr>
-    <tr>
-      <th>static_v</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
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-
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-    <div class="page-header">
-    <h1>Multi_margin_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_multi_margin_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that optimizes a multi-class classification hinge loss
-(margin-based loss) between input x (a 2D mini-batch Tensor) and output y
-(which is a 1D tensor of target class indices, <code>0 &lt;= y &lt;= x$size(2) - 1</code> ).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_multi_margin_loss</span>(
-  <span class='no'>input</span>,
-  <span class='no'>target</span>,
-  <span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>margin</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='st'>"mean"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>Has a default value of 1. 1 and 2 are the only supported values.</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Has a default value of 1.</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>a manual rescaling weight given to each class. If given, it has to
-be a Tensor of size C. Otherwise, it is treated as if having all ones.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="hidden name"><code>nnf_multilabel_margin_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that optimizes a multi-class multi-classification hinge loss
-(margin-based loss) between input x (a 2D mini-batch Tensor) and output y (which
-is a 2D Tensor of target class indices).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_multilabel_margin_loss</span>(<span class='no'>input</span>, <span class='no'>target</span>, <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='st'>"mean"</span>)</pre>
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-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
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-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_multilabel_soft_margin_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that optimizes a multi-label one-versus-all loss based on
-max-entropy, between input x and target y of size (N, C).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_multilabel_soft_margin_loss</span>(<span class='no'>input</span>, <span class='no'>target</span>, <span class='no'>weight</span>, <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='st'>"mean"</span>)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>weight tensor to apply on the loss.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_nll_loss.Rd</code></div>
-    </div>
-
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-    <p>The negative log likelihood loss.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_nll_loss</span>(
-  <span class='no'>input</span>,
-  <span class='no'>target</span>,
-  <span class='kw'>weight</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>ignore_index</span> <span class='kw'>=</span> -<span class='fl'>100</span>,
-  <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='st'>"mean"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>\((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\) in
-case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\) in
-the case of K-dimensional loss.</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>\((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
-or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional) a manual rescaling weight given to each class.
-If given, has to be a Tensor of size <code>C</code></p></td>
-    </tr>
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-does not contribute to the input gradient.</p></td>
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-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
-    <div class="hidden name"><code>nnf_normalize.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Performs \(L_p\) normalization of inputs over specified dimension.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_normalize</span>(<span class='no'>input</span>, <span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>dim</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>eps</span> <span class='kw'>=</span> <span class='fl'>1e-12</span>, <span class='kw'>out</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of any shape</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(float) the exponent value in the norm formulation. Default: 2</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float) small value to avoid division by zero. Default: 1e-12</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor. If <code>out</code> is used, this                            operation won't be differentiable.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>For a tensor <code>input</code> of sizes \((n_0, ..., n_{dim}, ..., n_k)\), each
-\(n_{dim}\) -element vector \(v\) along dimension <code>dim</code> is transformed as</p>
-<p>$$
-        v = \frac{v}{\max(\Vert v \Vert_p, \epsilon)}.
-$$</p>
-<p>With the default arguments it uses the Euclidean norm over vectors along
-dimension \(1\) for normalization.</p>
-
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-    <h1>One_hot</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
-    <div class="hidden name"><code>nnf_one_hot.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Takes LongTensor with index values of shape <code>(*)</code> and returns a tensor
-of shape <code>(*, num_classes)</code> that have zeros everywhere except where the
-index of last dimension matches the corresponding value of the input tensor,
-in which case it will be 1.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_one_hot</span>(<span class='no'>tensor</span>, <span class='kw'>num_classes</span> <span class='kw'>=</span> -<span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>(LongTensor) class values of any shape.</p></td>
-    </tr>
-    <tr>
-      <th>num_classes</th>
-      <td><p>(int) Total number of classes. If set to -1, the number
-of classes will be inferred as one greater than the largest class value in
-the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>One-hot on Wikipedia: https://en.wikipedia.org/wiki/One-hot</p>
-
-  </div>
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-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_pad</span>(<span class='no'>input</span>, <span class='no'>pad</span>, <span class='kw'>mode</span> <span class='kw'>=</span> <span class='st'>"constant"</span>, <span class='kw'>value</span> <span class='kw'>=</span> <span class='fl'>0</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) N-dimensional tensor</p></td>
-    </tr>
-    <tr>
-      <th>pad</th>
-      <td><p>(tuple) m-elements tuple, where \(\frac{m}{2} \leq\) input dimensions
-and \(m\) is even.</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>fill value for 'constant' padding. Default: 0.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="padding-size"><a class="anchor" href="#padding-size"></a>Padding size</h2>
-
-    
-
-
-<p>The padding size by which to pad some dimensions of <code>input</code>
-are described starting from the last dimension and moving forward.
-\(\left\lfloor\frac{\mbox{len(pad)}}{2}\right\rfloor\) dimensions
-of <code>input</code> will be padded.
-For example, to pad only the last dimension of the input tensor, then
-<code>pad</code> has the form
-\((\mbox{padding\_left}, \mbox{padding\_right})\);
-to pad the last 2 dimensions of the input tensor, then use
-\((\mbox{padding\_left}, \mbox{padding\_right},\)
-\(\mbox{padding\_top}, \mbox{padding\_bottom})\);
-to pad the last 3 dimensions, use
-\((\mbox{padding\_left}, \mbox{padding\_right},\)
-\(\mbox{padding\_top}, \mbox{padding\_bottom}\)
-\(\mbox{padding\_front}, \mbox{padding\_back})\).</p>
-    <h2 class="hasAnchor" id="padding-mode"><a class="anchor" href="#padding-mode"></a>Padding mode</h2>
-
-    
-
-
-<p>See <code>nn_constant_pad_2d</code>, <code>nn_reflection_pad_2d</code>, and
-<code>nn_replication_pad_2d</code> for concrete examples on how each of the
-padding modes works. Constant padding is implemented for arbitrary dimensions.
-tensor, or the last 2 dimensions of 4D input tensor, or the last dimension of
-3D input tensor. Reflect padding is only implemented for padding the last 2
-dimensions of 4D input tensor, or the last dimension of 3D input tensor.</p>
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-    <h1>Pairwise_distance</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-distance.R'><code>R/nnf-distance.R</code></a></small>
-    <div class="hidden name"><code>nnf_pairwise_distance.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Computes the batchwise pairwise distance between vectors using the p-norm.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_pairwise_distance</span>(<span class='no'>x1</span>, <span class='no'>x2</span>, <span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>eps</span> <span class='kw'>=</span> <span class='fl'>1e-06</span>, <span class='kw'>keepdim</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x1</th>
-      <td><p>(Tensor) First input.</p></td>
-    </tr>
-    <tr>
-      <th>x2</th>
-      <td><p>(Tensor) Second input (of size matching x1).</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>the norm degree. Default: 2</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid division by zero.
-Default: 1e-8</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>Determines whether or not to keep the vector dimension. Default: False</p></td>
-    </tr>
-    </table>
-
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-      <td><p>input tensor of shape \(N \times M\).</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>p value for the p-norm distance to calculate between each vector pair
-\(\in [0, \infty]\).</p></td>
-    </tr>
-    </table>
-
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-
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-\(\frac{1}{2} N (N - 1)\).</p>
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-tensor of shape \((*, C, H \times r, W \times r)\).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_pixel_shuffle</span>(<span class='no'>input</span>, <span class='no'>upscale_factor</span>)</pre>
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-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>upscale_factor</th>
-      <td><p>(int) factor to increase spatial resolution by</p></td>
-    </tr>
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-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_poisson_nll_loss</span>(
-  <span class='no'>input</span>,
-  <span class='no'>target</span>,
-  <span class='kw'>log_input</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>full</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>eps</span> <span class='kw'>=</span> <span class='fl'>1e-08</span>,
-  <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='st'>"mean"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>log_input</th>
-      <td><p>if <code>TRUE</code> the loss is computed as \(\exp(\mbox{input}) - \mbox{target} * \mbox{input}\),
-if <code>FALSE</code> then loss is \(\mbox{input} - \mbox{target} * \log(\mbox{input}+\mbox{eps})\).
-Default: <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>full</th>
-      <td><p>whether to compute full loss, i. e. to add the Stirling approximation
-term. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid evaluation of \(\log(0)\) when
-<code>log_input</code>=<code>FALSE</code>. Default: 1e-8</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-    <h1>Prelu</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_prelu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies element-wise the function
-\(PReLU(x) = max(0,x) + weight * min(0,x)\)
-where weight is a learnable parameter.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_prelu</span>(<span class='no'>input</span>, <span class='no'>weight</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor) the learnable weights</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
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-
-    <pre class="usage"><span class='fu'>nnf_rrelu</span>(<span class='no'>input</span>, <span class='kw'>lower</span> <span class='kw'>=</span> <span class='fl'>1</span>/<span class='fl'>8</span>, <span class='kw'>upper</span> <span class='kw'>=</span> <span class='fl'>1</span>/<span class='fl'>3</span>, <span class='kw'>training</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
-
-<span class='fu'>nnf_rrelu_</span>(<span class='no'>input</span>, <span class='kw'>lower</span> <span class='kw'>=</span> <span class='fl'>1</span>/<span class='fl'>8</span>, <span class='kw'>upper</span> <span class='kw'>=</span> <span class='fl'>1</span>/<span class='fl'>3</span>, <span class='kw'>training</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
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-dimensions</p></td>
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-      <td><p>lower bound of the uniform distribution. Default: 1/8</p></td>
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-      <td><p>upper bound of the uniform distribution. Default: 1/3</p></td>
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-      <td><p>bool wether it's a training pass. DEfault: FALSE</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_selu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies element-wise,
-$$SELU(x) = scale * (max(0,x) + min(0, \alpha * (exp(x) - 1)))$$,
-with \(\alpha=1.6732632423543772848170429916717\) and
-\(scale=1.0507009873554804934193349852946\).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_selu</span>(<span class='no'>input</span>, <span class='kw'>inplace</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
-
-<span class='fu'>nnf_selu_</span>(<span class='no'>input</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>x</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>)
-<span class='no'>y</span> <span class='kw'>&lt;-</span> <span class='fu'>nnf_selu</span>(<span class='no'>x</span>)
-<span class='fu'>nnf_selu_</span>(<span class='no'>x</span>)
-<span class='fu'><a href='torch_equal.html'>torch_equal</a></span>(<span class='no'>x</span>, <span class='no'>y</span>)
-
-}</div></pre>
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diff --git a/static/docs/reference/nnf_sigmoid.html b/static/docs/reference/nnf_sigmoid.html
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-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
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-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_softmax.Rd</code></div>
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-
-    <pre class="usage"><span class='fu'>nnf_softmax</span>(<span class='no'>input</span>, <span class='no'>dim</span>, <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) A dimension along which softmax will be computed.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed. This is useful for preventing data type overflows.
-Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Softmax is defined as:</p>
-<p>$$Softmax(x_{i}) = exp(x_i)/\sum_j exp(x_j)$$</p>
-<p>It is applied to all slices along dim, and will re-scale them so that the elements
-lie in the range <code>[0, 1]</code> and sum to 1.</p>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_softmin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies a softmin function.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_softmin</span>(<span class='no'>input</span>, <span class='no'>dim</span>, <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) A dimension along which softmin will be computed
-(so every slice        along dim will sum to 1).</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed.
-This is useful for preventing data type overflows. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Note that</p>
-<p>$$Softmin(x) = Softmax(-x)$$.</p>
-<p>See <a href='nnf_softmax.html'>nnf_softmax</a> definition for mathematical formula.</p>
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_softplus.Rd</code></div>
-    </div>
-
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-    <p>Applies element-wise, the function \(Softplus(x) = 1/\beta * log(1 + exp(\beta * x))\).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_softplus</span>(<span class='no'>input</span>, <span class='kw'>beta</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>threshold</span> <span class='kw'>=</span> <span class='fl'>20</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>the beta value for the Softplus formulation. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>threshold</th>
-      <td><p>values above this revert to a linear function. Default: 20</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>For numerical stability the implementation reverts to the linear function
-when \(input * \beta &gt; threshold\).</p>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <p>Applies the soft shrinkage function elementwise</p>
-    </div>
-
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-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>lambd</th>
-      <td><p>the lambda (must be no less than zero) value for the Softshrink
-formulation. Default: 0.5</p></td>
-    </tr>
-    </table>
-
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-
-<span class='fu'>nnf_threshold_</span>(<span class='no'>input</span>, <span class='no'>threshold</span>, <span class='no'>value</span>)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>threshold</th>
-      <td><p>The value to threshold at</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>The value to replace with</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
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-x2 , x3 and a margin with a value greater than 0 . This is used for measuring
-a relative similarity between samples. A triplet is composed by a, p and n (i.e.,
-anchor, positive examples and negative examples respectively). The shapes of all
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-    <h1>Triplet_margin_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_triplet_margin_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that measures the triplet loss given an input tensors x1 ,
-x2 , x3 and a margin with a value greater than 0 . This is used for measuring
-a relative similarity between samples. A triplet is composed by a, p and n (i.e.,
-anchor, positive examples and negative examples respectively). The shapes of all
-input tensors should be (N, D).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_triplet_margin_loss</span>(
-  <span class='no'>anchor</span>,
-  <span class='no'>positive</span>,
-  <span class='no'>negative</span>,
-  <span class='kw'>margin</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>p</span> <span class='kw'>=</span> <span class='fl'>2</span>,
-  <span class='kw'>eps</span> <span class='kw'>=</span> <span class='fl'>1e-06</span>,
-  <span class='kw'>swap</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>reduction</span> <span class='kw'>=</span> <span class='st'>"mean"</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>anchor</th>
-      <td><p>the anchor input tensor</p></td>
-    </tr>
-    <tr>
-      <th>positive</th>
-      <td><p>the positive input tensor</p></td>
-    </tr>
-    <tr>
-      <th>negative</th>
-      <td><p>the negative input tensor</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Default: 1.</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>The norm degree for pairwise distance. Default: 2.</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid division by zero.</p></td>
-    </tr>
-    <tr>
-      <th>swap</th>
-      <td><p>The distance swap is described in detail in the paper Learning shallow
-convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
-Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-fold.R'><code>R/nnf-fold.R</code></a></small>
-    <div class="hidden name"><code>nnf_unfold.Rd</code></div>
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-
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-    <p>Extracts sliding local blocks from an batched input tensor.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_unfold</span>(<span class='no'>input</span>, <span class='no'>kernel_size</span>, <span class='kw'>dilation</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>stride</span> <span class='kw'>=</span> <span class='fl'>1</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the sliding blocks</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements within the
-neighborhood. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides of input.
-Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the sliding blocks in the input spatial dimensions.
-Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-
-<p>Currently, only 4-D input tensors (batched image-like tensors) are
-supported.</p>
-
-
-<p>More than one element of the unfolded tensor may refer to a single
-memory location. As a result, in-place operations (especially ones that
-are vectorized) may result in incorrect behavior. If you need to write
-to the tensor, please clone it first.</p>
-
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diff --git a/static/docs/reference/optim_adam.html b/static/docs/reference/optim_adam.html
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-    <h1>Implements Adam algorithm.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-adam.R'><code>R/optim-adam.R</code></a></small>
-    <div class="hidden name"><code>optim_adam.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>It has been proposed in <a href='https://arxiv.org/abs/1412.6980'>Adam: A Method for Stochastic Optimization</a>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>optim_adam</span>(
-  <span class='no'>params</span>,
-  <span class='kw'>lr</span> <span class='kw'>=</span> <span class='fl'>0.001</span>,
-  <span class='kw'>betas</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.9</span>, <span class='fl'>0.999</span>),
-  <span class='kw'>eps</span> <span class='kw'>=</span> <span class='fl'>1e-08</span>,
-  <span class='kw'>weight_decay</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>amsgrad</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>params</th>
-      <td><p>(iterable): iterable of parameters to optimize or dicts defining
-parameter groups</p></td>
-    </tr>
-    <tr>
-      <th>lr</th>
-      <td><p>(float, optional): learning rate (default: 1e-3)</p></td>
-    </tr>
-    <tr>
-      <th>betas</th>
-      <td><p>(<code>Tuple[float, float]</code>, optional): coefficients used for computing
-running averages of gradient and its square (default: (0.9, 0.999))</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional): term added to the denominator to improve
-numerical stability (default: 1e-8)</p></td>
-    </tr>
-    <tr>
-      <th>weight_decay</th>
-      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>amsgrad</th>
-      <td><p>(boolean, optional): whether to use the AMSGrad variant of this
-algorithm from the paper <a href='https://openreview.net/forum?id=ryQu7f-RZ'>On the Convergence of Adam and Beyond</a>
-(default: FALSE)</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>optimizer</span> <span class='kw'>&lt;-</span> <span class='fu'>optim_adam</span>(<span class='no'>model</span>$<span class='fu'>parameters</span>(), <span class='kw'>lr</span><span class='kw'>=</span><span class='fl'>0.1</span>)
-<span class='no'>optimizer</span>$<span class='fu'>zero_grad</span>()
-<span class='fu'>loss_fn</span>(<span class='fu'>model</span>(<span class='no'>input</span>), <span class='no'>target</span>)$<span class='fu'>backward</span>()
-<span class='no'>optimizer</span>$<span class='fu'><a href='https://rdrr.io/r/stats/step.html'>step</a></span>()
-}
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/optim_required.html b/static/docs/reference/optim_required.html
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-    <h1>SGD optimizer</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-sgd.R'><code>R/optim-sgd.R</code></a></small>
-    <div class="hidden name"><code>optim_sgd.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Implements stochastic gradient descent (optionally with momentum).
-Nesterov momentum is based on the formula from
-On the importance of initialization and momentum in deep learning.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>optim_sgd</span>(
-  <span class='no'>params</span>,
-  <span class='kw'>lr</span> <span class='kw'>=</span> <span class='fu'><a href='optim_required.html'>optim_required</a></span>(),
-  <span class='kw'>momentum</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>dampening</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>weight_decay</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>nesterov</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>params</th>
-      <td><p>(iterable): iterable of parameters to optimize or dicts defining
-parameter groups</p></td>
-    </tr>
-    <tr>
-      <th>lr</th>
-      <td><p>(float): learning rate</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>(float, optional): momentum factor (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>dampening</th>
-      <td><p>(float, optional): dampening for momentum (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>weight_decay</th>
-      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>nesterov</th>
-      <td><p>(bool, optional): enables Nesterov momentum (default: FALSE)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-
-
-<p>The implementation of SGD with Momentum-Nesterov subtly differs from
-Sutskever et. al. and implementations in some other frameworks.</p>
-<p>Considering the specific case of Momentum, the update can be written as
-$$
-  \begin{array}{ll}
-v_{t+1} &amp; = \mu * v_{t} + g_{t+1}, \\
-p_{t+1} &amp; = p_{t} - \mbox{lr} * v_{t+1},
-\end{array}
-$$</p>
-<p>where \(p\), \(g\), \(v\) and \(\mu\) denote the
-parameters, gradient, velocity, and momentum respectively.</p>
-<p>This is in contrast to Sutskever et. al. and
-other frameworks which employ an update of the form</p>
-<p>$$
-  \begin{array}{ll}
-v_{t+1} &amp; = \mu * v_{t} + \mbox{lr} * g_{t+1}, \\
-p_{t+1} &amp; = p_{t} - v_{t+1}.
-\end{array}
-$$
-The Nesterov version is analogously modified.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>optimizer</span> <span class='kw'>&lt;-</span> <span class='fu'>optim_sgd</span>(<span class='no'>model</span>$<span class='fu'>parameters</span>(), <span class='kw'>lr</span><span class='kw'>=</span><span class='fl'>0.1</span>, <span class='kw'>momentum</span><span class='kw'>=</span><span class='fl'>0.9</span>)
-<span class='no'>optimizer</span>$<span class='fu'>zero_grad</span>()
-<span class='fu'>loss_fn</span>(<span class='fu'>model</span>(<span class='no'>input</span>), <span class='no'>target</span>)$<span class='fu'>backward</span>()
-<span class='no'>optimizer</span>$<span class='fu'><a href='https://rdrr.io/r/stats/step.html'>step</a></span>()
-}
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_abs.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Abs</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="abs-input-out-none-gt-tensor-"><a class="anchor" href="#abs-input-out-none-gt-tensor-"></a>abs(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise absolute value of the given <code>input</code> tensor.</p>
-<p>$$
-    \mbox{out}_{i} = |\mbox{input}_{i}|
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_abs</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>1</span>, -<span class='fl'>2</span>, <span class='fl'>3</span>)))
-}</div></pre>
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-    <h2 class="hasAnchor" id="adaptive-avg-pool-d-input-output-size-gt-tensor-"><a class="anchor" href="#adaptive-avg-pool-d-input-output-size-gt-tensor-"></a>adaptive_avg_pool1d(input, output_size) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 1D adaptive average pooling over an input signal composed of
-several input planes.</p>
-<p>See <code>~torch.nn.AdaptiveAvgPool1d</code> for details and output shape.</p>
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-</div><!--/.navbar -->
-
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-
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-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Add</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_add.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Add</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>(Number) the number to be added to each element of <code>input</code></p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number) the scalar multiplier for <code>other</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="add-input-other-out-none-"><a class="anchor" href="#add-input-other-out-none-"></a>add(input, other, out=None) </h2>
-
-    
-
-
-<p>Adds the scalar <code>other</code> to each element of the input <code>input</code>
-and returns a new resulting tensor.</p>
-<p>$$
-    \mbox{out} = \mbox{input} + \mbox{other}
-$$
-If <code>input</code> is of type FloatTensor or DoubleTensor, <code>other</code> must be
-a real number, otherwise it should be an integer.</p>
-    <h2 class="hasAnchor" id="add-input-other-alpha-out-none-"><a class="anchor" href="#add-input-other-alpha-out-none-"></a>add(input, other, *, alpha=1, out=None) </h2>
-
-    
-
-
-<p>Each element of the tensor <code>other</code> is multiplied by the scalar
-<code>alpha</code> and added to each element of the tensor <code>input</code>.
-The resulting tensor is returned.</p>
-<p>The shapes of <code>input</code> and <code>other</code> must be
-broadcastable .</p>
-<p>$$
-    \mbox{out} = \mbox{input} + \mbox{alpha} \times \mbox{other}
-$$
-If <code>other</code> is of type FloatTensor or DoubleTensor, <code>alpha</code> must be
-a real number, otherwise it should be an integer.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_add</span>(<span class='no'>a</span>, <span class='fl'>20</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>1</span>))
-<span class='no'>b</span>
-<span class='fu'>torch_add</span>(<span class='no'>a</span>, <span class='no'>b</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Addbmm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_addbmm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Addbmm</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>batch1</th>
-      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>batch2</th>
-      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) matrix to be added</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for <code>batch1 @ batch2</code> (\(\alpha\))</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addbmm-input-batch-batch-beta-alpha-out-none-gt-tensor-"><a class="anchor" href="#addbmm-input-batch-batch-beta-alpha-out-none-gt-tensor-"></a>addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a batch matrix-matrix product of matrices stored
-in <code>batch1</code> and <code>batch2</code>,
-with a reduced add step (all matrix multiplications get accumulated
-along the first dimension).
-<code>input</code> is added to the final result.</p>
-<p><code>batch1</code> and <code>batch2</code> must be 3-D tensors each containing the
-same number of matrices.</p>
-<p>If <code>batch1</code> is a \((b \times n \times m)\) tensor, <code>batch2</code> is a
-\((b \times m \times p)\) tensor, <code>input</code> must be
-broadcastable  with a \((n \times p)\) tensor
-and <code>out</code> will be a \((n \times p)\) tensor.</p>
-<p>$$
-    out = \beta\ \mbox{input} + \alpha\ (\sum_{i=0}^{b-1} \mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
-$$
-For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and <code>alpha</code>
-must be real numbers, otherwise they should be integers.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>M</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>))
-<span class='no'>batch1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='no'>batch2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='fu'>torch_addbmm</span>(<span class='no'>M</span>, <span class='no'>batch1</span>, <span class='no'>batch2</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/torch_addcdiv.html b/static/docs/reference/torch_addcdiv.html
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-    <h1>Addcdiv</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_addcdiv.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Addcdiv</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to be added</p></td>
-    </tr>
-    <tr>
-      <th>tensor1</th>
-      <td><p>(Tensor) the numerator tensor</p></td>
-    </tr>
-    <tr>
-      <th>tensor2</th>
-      <td><p>(Tensor) the denominator tensor</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>(Number, optional) multiplier for \(\mbox{tensor1} / \mbox{tensor2}\)</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addcdiv-input-tensor-tensor-value-out-none-gt-tensor-"><a class="anchor" href="#addcdiv-input-tensor-tensor-value-out-none-gt-tensor-"></a>addcdiv(input, tensor1, tensor2, *, value=1, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs the element-wise division of <code>tensor1</code> by <code>tensor2</code>,
-multiply the result by the scalar <code>value</code> and add it to <code>input</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Integer division with addcdiv is deprecated, and in a future release
-addcdiv will perform a true division of <code>tensor1</code> and <code>tensor2</code>.
-The current addcdiv behavior can be replicated using <code><a href='torch_floor_divide.html'>torch_floor_divide()</a></code>
-for integral inputs
-(<code>input</code> + <code>value</code> * <code>tensor1</code> // <code>tensor2</code>)
-and <code><a href='torch_div.html'>torch_div()</a></code> for float inputs
-(<code>input</code> + <code>value</code> * <code>tensor1</code> / <code>tensor2</code>).
-The new addcdiv behavior can be implemented with <code><a href='torch_true_divide.html'>torch_true_divide()</a></code>
-(<code>input</code> + <code>value</code> * torch.true_divide(<code>tensor1</code>,
-<code>tensor2</code>).</p>
-<p>$$
-    \mbox{out}_i = \mbox{input}_i + \mbox{value} \times \frac{\mbox{tensor1}_i}{\mbox{tensor2}_i}
-$$</p>
-<p>The shapes of <code>input</code>, <code>tensor1</code>, and <code>tensor2</code> must be
-broadcastable .</p>
-<p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code> must be
-a real number, otherwise an integer.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>t</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>t1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))
-<span class='no'>t2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_addcdiv</span>(<span class='no'>t</span>, <span class='no'>t1</span>, <span class='no'>t2</span>, <span class='fl'>0.1</span>)
-}</div></pre>
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-    <h1>Addcmul</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_addcmul.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Addcmul</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to be added</p></td>
-    </tr>
-    <tr>
-      <th>tensor1</th>
-      <td><p>(Tensor) the tensor to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>tensor2</th>
-      <td><p>(Tensor) the tensor to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>(Number, optional) multiplier for \(tensor1 .* tensor2\)</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addcmul-input-tensor-tensor-value-out-none-gt-tensor-"><a class="anchor" href="#addcmul-input-tensor-tensor-value-out-none-gt-tensor-"></a>addcmul(input, tensor1, tensor2, *, value=1, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs the element-wise multiplication of <code>tensor1</code>
-by <code>tensor2</code>, multiply the result by the scalar <code>value</code>
-and add it to <code>input</code>.</p>
-<p>$$
-    \mbox{out}_i = \mbox{input}_i + \mbox{value} \times \mbox{tensor1}_i \times \mbox{tensor2}_i
-$$
-The shapes of <code>tensor</code>, <code>tensor1</code>, and <code>tensor2</code> must be
-broadcastable .</p>
-<p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code> must be
-a real number, otherwise an integer.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>t</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>t1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))
-<span class='no'>t2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_addcmul</span>(<span class='no'>t</span>, <span class='no'>t1</span>, <span class='no'>t2</span>, <span class='fl'>0.1</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Addmm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_addmm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Addmm</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) matrix to be added</p></td>
-    </tr>
-    <tr>
-      <th>mat1</th>
-      <td><p>(Tensor) the first matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>mat2</th>
-      <td><p>(Tensor) the second matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for \(mat1 @ mat2\) (\(\alpha\))</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addmm-input-mat-mat-beta-alpha-out-none-gt-tensor-"><a class="anchor" href="#addmm-input-mat-mat-beta-alpha-out-none-gt-tensor-"></a>addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a matrix multiplication of the matrices <code>mat1</code> and <code>mat2</code>.
-The matrix <code>input</code> is added to the final result.</p>
-<p>If <code>mat1</code> is a \((n \times m)\) tensor, <code>mat2</code> is a
-\((m \times p)\) tensor, then <code>input</code> must be
-broadcastable  with a \((n \times p)\) tensor
-and <code>out</code> will be a \((n \times p)\) tensor.</p>
-<p><code>alpha</code> and <code>beta</code> are scaling factors on matrix-vector product between
-<code>mat1</code> and <code>mat2</code> and the added matrix <code>input</code> respectively.</p>
-<p>$$
-    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{mat1}_i \mathbin{@} \mbox{mat2}_i)
-$$
-For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
-<code>alpha</code> must be real numbers, otherwise they should be integers.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>M</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='no'>mat1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='no'>mat2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_addmm</span>(<span class='no'>M</span>, <span class='no'>mat1</span>, <span class='no'>mat2</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_addmv.html b/static/docs/reference/torch_addmv.html
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-      <a href="../articles/extending-autograd.html">Extending autograd</a>
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-</li>
-<li class="dropdown-header">Examples</li>
-<li>
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-    <div class="page-header">
-    <h1>Addmv</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_addmv.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Addmv</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) vector to be added</p></td>
-    </tr>
-    <tr>
-      <th>mat</th>
-      <td><p>(Tensor) matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>vec</th>
-      <td><p>(Tensor) vector to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for \(mat @ vec\) (\(\alpha\))</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addmv-input-mat-vec-beta-alpha-out-none-gt-tensor-"><a class="anchor" href="#addmv-input-mat-vec-beta-alpha-out-none-gt-tensor-"></a>addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a matrix-vector product of the matrix <code>mat</code> and
-the vector <code>vec</code>.
-The vector <code>input</code> is added to the final result.</p>
-<p>If <code>mat</code> is a \((n \times m)\) tensor, <code>vec</code> is a 1-D tensor of
-size <code>m</code>, then <code>input</code> must be
-broadcastable  with a 1-D tensor of size <code>n</code> and
-<code>out</code> will be 1-D tensor of size <code>n</code>.</p>
-<p><code>alpha</code> and <code>beta</code> are scaling factors on matrix-vector product between
-<code>mat</code> and <code>vec</code> and the added tensor <code>input</code> respectively.</p>
-<p>$$
-    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{mat} \mathbin{@} \mbox{vec})
-$$
-For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
-<code>alpha</code> must be real numbers, otherwise they should be integers</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>M</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>))
-<span class='no'>mat</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='no'>vec</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='fu'>torch_addmv</span>(<span class='no'>M</span>, <span class='no'>mat</span>, <span class='no'>vec</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-</div>
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-</div>
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diff --git a/static/docs/reference/torch_addr.html b/static/docs/reference/torch_addr.html
deleted file mode 100644
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-    <div class="page-header">
-    <h1>Addr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_addr.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Addr</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) matrix to be added</p></td>
-    </tr>
-    <tr>
-      <th>vec1</th>
-      <td><p>(Tensor) the first vector of the outer product</p></td>
-    </tr>
-    <tr>
-      <th>vec2</th>
-      <td><p>(Tensor) the second vector of the outer product</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for \(\mbox{vec1} \otimes \mbox{vec2}\) (\(\alpha\))</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addr-input-vec-vec-beta-alpha-out-none-gt-tensor-"><a class="anchor" href="#addr-input-vec-vec-beta-alpha-out-none-gt-tensor-"></a>addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs the outer-product of vectors <code>vec1</code> and <code>vec2</code>
-and adds it to the matrix <code>input</code>.</p>
-<p>Optional values <code>beta</code> and <code>alpha</code> are scaling factors on the
-outer product between <code>vec1</code> and <code>vec2</code> and the added matrix
-<code>input</code> respectively.</p>
-<p>$$
-    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{vec1} \otimes \mbox{vec2})
-$$
-If <code>vec1</code> is a vector of size <code>n</code> and <code>vec2</code> is a vector
-of size <code>m</code>, then <code>input</code> must be
-broadcastable  with a matrix of size
-\((n \times m)\) and <code>out</code> will be a matrix of size
-\((n \times m)\).</p>
-<p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
-<code>alpha</code> must be real numbers, otherwise they should be integers</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>vec1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>4.</span>)
-<span class='no'>vec2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>3.</span>)
-<span class='no'>M</span> <span class='kw'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='fu'>torch_addr</span>(<span class='no'>M</span>, <span class='no'>vec1</span>, <span class='no'>vec2</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-</div>
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diff --git a/static/docs/reference/torch_allclose.html b/static/docs/reference/torch_allclose.html
deleted file mode 100644
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-    <div class="page-header">
-    <h1>Allclose</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_allclose.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Allclose</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) first tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) second tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>atol</th>
-      <td><p>(float, optional) absolute tolerance. Default: 1e-08</p></td>
-    </tr>
-    <tr>
-      <th>rtol</th>
-      <td><p>(float, optional) relative tolerance. Default: 1e-05</p></td>
-    </tr>
-    <tr>
-      <th>equal_nan</th>
-      <td><p>(bool, optional) if <code>True</code>, then two <code>NaN</code> s will be compared as equal. Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-"><a class="anchor" href="#allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-"></a>allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -&gt; bool </h2>
-
-    
-
-
-<p>This function checks if all <code>input</code> and <code>other</code> satisfy the condition:</p>
-<p>$$
-    \vert \mbox{input} - \mbox{other} \vert \leq \mbox{atol} + \mbox{rtol} \times \vert \mbox{other} \vert
-$$
-elementwise, for all elements of <code>input</code> and <code>other</code>. The behaviour of this function is analogous to
-<code>numpy.allclose &lt;https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html&gt;</code>_</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.</span>, <span class='fl'>1e-07</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.1</span>, <span class='fl'>1e-08</span>)))
-<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.</span>, <span class='fl'>1e-08</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.1</span>, <span class='fl'>1e-09</span>)))
-<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)))
-<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)), <span class='kw'>equal_nan</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_angle.html b/static/docs/reference/torch_angle.html
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-    <div class="page-header">
-    <h1>Angle</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_angle.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Angle</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="angle-input-out-none-gt-tensor-"><a class="anchor" href="#angle-input-out-none-gt-tensor-"></a>angle(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise angle (in radians) of the given <code>input</code> tensor.</p>
-<p>$$
-    \mbox{out}_{i} = angle(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_angle</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>1</span> + <span class='fl'>1i</span>, -<span class='fl'>2</span> + <span class='fl'>2i</span>, <span class='fl'>3</span> - <span class='fl'>3i</span>)))*<span class='fl'>180</span>/<span class='fl'>3.14159</span>
-}
-
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_arange.html b/static/docs/reference/torch_arange.html
deleted file mode 100644
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-    <h1>Arange</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_arange.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Arange</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(Number) the starting value for the set of points. Default: <code>0</code>.</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(Number) the ending value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>step</th>
-      <td><p>(Number) the gap between each pair of adjacent points. Default: <code>1</code>.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="arange-start-end-step-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#arange-start-end-step-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a 1-D tensor of size \(\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil\)
-with values from the interval <code>[start, end)</code> taken with common difference
-<code>step</code> beginning from <code>start</code>.</p>
-<p>Note that non-integer <code>step</code> is subject to floating point rounding errors when
-comparing against <code>end</code>; to avoid inconsistency, we advise adding a small epsilon to <code>end</code>
-in such cases.</p>
-<p>$$
-    \mbox{out}_{{i+1}} = \mbox{out}_{i} + \mbox{step}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_arange</span>(<span class='kw'>start</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>end</span> <span class='kw'>=</span> <span class='fl'>5</span>)
-<span class='fu'>torch_arange</span>(<span class='fl'>1</span>, <span class='fl'>4</span>)
-<span class='fu'>torch_arange</span>(<span class='fl'>1</span>, <span class='fl'>2.5</span>, <span class='fl'>0.5</span>)
-}</div></pre>
-  </div>
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-    <h1>Argmax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_argmax.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Argmax</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce. If <code>None</code>, the argmax of the flattened input is returned.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=None</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="argmax-input-gt-longtensor-"><a class="anchor" href="#argmax-input-gt-longtensor-"></a>argmax(input) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns the indices of the maximum value of all elements in the <code>input</code> tensor.</p>
-<p>This is the second value returned by <code>torch_max</code>. See its
-documentation for the exact semantics of this method.</p>
-    <h2 class="hasAnchor" id="argmax-input-dim-keepdim-false-gt-longtensor-"><a class="anchor" href="#argmax-input-dim-keepdim-false-gt-longtensor-"></a>argmax(input, dim, keepdim=False) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns the indices of the maximum values of a tensor across a dimension.</p>
-<p>This is the second value returned by <code>torch_max</code>. See its
-documentation for the exact semantics of this method.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_argmax</span>(<span class='no'>a</span>)
-}
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_argmax</span>(<span class='no'>a</span>, <span class='kw'>dim</span><span class='kw'>=</span><span class='fl'>1</span>)
-}</div></pre>
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-    <p>Argmin</p>
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-
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce. If <code>None</code>, the argmin of the flattened input is returned.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=None</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="argmin-input-gt-longtensor-"><a class="anchor" href="#argmin-input-gt-longtensor-"></a>argmin(input) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns the indices of the minimum value of all elements in the <code>input</code> tensor.</p>
-<p>This is the second value returned by <code>torch_min</code>. See its
-documentation for the exact semantics of this method.</p>
-    <h2 class="hasAnchor" id="argmin-input-dim-keepdim-false-out-none-gt-longtensor-"><a class="anchor" href="#argmin-input-dim-keepdim-false-out-none-gt-longtensor-"></a>argmin(input, dim, keepdim=False, out=None) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns the indices of the minimum values of a tensor across a dimension.</p>
-<p>This is the second value returned by <code>torch_min</code>. See its
-documentation for the exact semantics of this method.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
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-<span class='no'>a</span>
-<span class='fu'>torch_argmin</span>(<span class='no'>a</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_argmin</span>(<span class='no'>a</span>, <span class='kw'>dim</span><span class='kw'>=</span><span class='fl'>1</span>)
-}</div></pre>
-  </div>
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diff --git a/static/docs/reference/torch_argsort.html b/static/docs/reference/torch_argsort.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_argsort.Rd</code></div>
-    </div>
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-    <p>Argsort</p>
-    </div>
-
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to sort along</p></td>
-    </tr>
-    <tr>
-      <th>descending</th>
-      <td><p>(bool, optional) controls the sorting order (ascending or descending)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="argsort-input-dim-descending-false-gt-longtensor-"><a class="anchor" href="#argsort-input-dim-descending-false-gt-longtensor-"></a>argsort(input, dim=-1, descending=False) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns the indices that sort a tensor along a given dimension in ascending
-order by value.</p>
-<p>This is the second value returned by <code>torch_sort</code>.  See its documentation
-for the exact semantics of this method.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_argsort</span>(<span class='no'>a</span>, <span class='kw'>dim</span><span class='kw'>=</span><span class='fl'>1</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>As_strided</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_as_strided.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>As_strided</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(tuple or ints) the shape of the output tensor</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(tuple or ints) the stride of the output tensor</p></td>
-    </tr>
-    <tr>
-      <th>storage_offset</th>
-      <td><p>(int, optional) the offset in the underlying storage of the output tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="as-strided-input-size-stride-storage-offset-gt-tensor-"><a class="anchor" href="#as-strided-input-size-stride-storage-offset-gt-tensor-"></a>as_strided(input, size, stride, storage_offset=0) -&gt; Tensor </h2>
-
-    
-
-
-<p>Create a view of an existing <code>torch_Tensor</code> <code>input</code> with specified
-<code>size</code>, <code>stride</code> and <code>storage_offset</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>More than one element of a created tensor may refer to a single memory
-location. As a result, in-place operations (especially ones that are
-vectorized) may result in incorrect behavior. If you need to write to
-the tensors, please clone them first.</p><pre>Many PyTorch functions, which return a view of a tensor, are internally
-implemented with this function. Those functions, like
-`torch_Tensor.expand`, are easier to read and are therefore more
-advisable to use.
-</pre>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='no'>x</span>
-<span class='no'>t</span> <span class='kw'>=</span> <span class='fu'>torch_as_strided</span>(<span class='no'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>), <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='no'>t</span>
-<span class='no'>t</span> <span class='kw'>=</span> <span class='fu'>torch_as_strided</span>(<span class='no'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>), <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>), <span class='fl'>1</span>)
-<span class='no'>t</span>
-}</div></pre>
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-    <div class="page-header">
-    <h1>Asin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_asin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Asin</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="asin-input-out-none-gt-tensor-"><a class="anchor" href="#asin-input-out-none-gt-tensor-"></a>asin(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the arcsine  of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \sin^{-1}(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
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-<span class='no'>a</span>
-<span class='fu'>torch_asin</span>(<span class='no'>a</span>)
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_atan.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Atan</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="atan-input-out-none-gt-tensor-"><a class="anchor" href="#atan-input-out-none-gt-tensor-"></a>atan(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the arctangent  of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \tan^{-1}(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_atan</span>(<span class='no'>a</span>)
-}</div></pre>
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-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the first input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="atan-input-other-out-none-gt-tensor-"><a class="anchor" href="#atan-input-other-out-none-gt-tensor-"></a>atan2(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Element-wise arctangent of \(\mbox{input}_{i} / \mbox{other}_{i}\)
-with consideration of the quadrant. Returns a new tensor with the signed angles
-in radians between vector \((\mbox{other}_{i}, \mbox{input}_{i})\)
-and vector \((1, 0)\). (Note that \(\mbox{other}_{i}\), the second
-parameter, is the x-coordinate, while \(\mbox{input}_{i}\), the first
-parameter, is the y-coordinate.)</p>
-<p>The shapes of <code>input</code> and <code>other</code> must be
-broadcastable .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_atan2</span>(<span class='no'>a</span>, <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>)))
-}</div></pre>
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-    <h1>Avg_pool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_avg_pool1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Avg_pool1d</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>NA the size of the window. Can be a single number or a      tuple <code>(kW,)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>NA the stride of the window. Can be a single number or a tuple      <code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>NA implicit zero paddings on both sides of the input. Can be a      single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>NA when True, will use <code>ceil</code> instead of <code>floor</code> to compute the        output shape. Default: <code>False</code></p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>NA when True, will include the zero-padding in the        averaging calculation. Default: <code>True</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="avg-pool-d-input-kernel-size-stride-none-padding-ceil-mode-false-count-include-pad-true-gt-tensor-"><a class="anchor" href="#avg-pool-d-input-kernel-size-stride-none-padding-ceil-mode-false-count-include-pad-true-gt-tensor-"></a>avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 1D average pooling over an input signal composed of several
-input planes.</p>
-<p>See <code>~torch.nn.AvgPool1d</code> for details and output shape.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_baddbmm.html b/static/docs/reference/torch_baddbmm.html
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-    <div class="page-header">
-    <h1>Baddbmm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_baddbmm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Baddbmm</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to be added</p></td>
-    </tr>
-    <tr>
-      <th>batch1</th>
-      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>batch2</th>
-      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for \(\mbox{batch1} \mathbin{@} \mbox{batch2}\) (\(\alpha\))</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="baddbmm-input-batch-batch-beta-alpha-out-none-gt-tensor-"><a class="anchor" href="#baddbmm-input-batch-batch-beta-alpha-out-none-gt-tensor-"></a>baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a batch matrix-matrix product of matrices in <code>batch1</code>
-and <code>batch2</code>.
-<code>input</code> is added to the final result.</p>
-<p><code>batch1</code> and <code>batch2</code> must be 3-D tensors each containing the same
-number of matrices.</p>
-<p>If <code>batch1</code> is a \((b \times n \times m)\) tensor, <code>batch2</code> is a
-\((b \times m \times p)\) tensor, then <code>input</code> must be
-broadcastable  with a
-\((b \times n \times p)\) tensor and <code>out</code> will be a
-\((b \times n \times p)\) tensor. Both <code>alpha</code> and <code>beta</code> mean the
-same as the scaling factors used in <code>torch_addbmm</code>.</p>
-<p>$$
-    \mbox{out}_i = \beta\ \mbox{input}_i + \alpha\ (\mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
-$$
-For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
-<code>alpha</code> must be real numbers, otherwise they should be integers.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>M</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>5</span>))
-<span class='no'>batch1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='no'>batch2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='fu'>torch_baddbmm</span>(<span class='no'>M</span>, <span class='no'>batch1</span>, <span class='no'>batch2</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-    <h1>Bartlett_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_bartlett_window.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Bartlett_window</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) the size of returned window</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
-</pre>
-
-    <h2 class="hasAnchor" id="bartlett-window-window-length-periodic-true-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#bartlett-window-window-length-periodic-true-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>bartlett_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Bartlett window function.</p>
-<p>$$
-    w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \left\{ \begin{array}{ll}
-        \frac{2n}{N - 1} &amp; \mbox{if } 0 \leq n \leq \frac{N - 1}{2} \\
-        2 - \frac{2n}{N - 1} &amp; \mbox{if } \frac{N - 1}{2} &lt; n &lt; N \\
-    \end{array}
-    \right. ,
-$$
-where \(N\) is the full window size.</p>
-<p>The input <code>window_length</code> is a positive integer controlling the
-returned window size. <code>periodic</code> flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-<code>torch_bartlett_window(L, periodic=True)</code> equal to
-<code>torch_bartlett_window(L + 1, periodic=False)[:-1])</code>.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_bernoulli.html b/static/docs/reference/torch_bernoulli.html
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-    <h1>Bernoulli</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_bernoulli.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Bernoulli</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor of probability values for the Bernoulli distribution</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bernoulli-input-generator-none-out-none-gt-tensor-"><a class="anchor" href="#bernoulli-input-generator-none-out-none-gt-tensor-"></a>bernoulli(input, *, generator=None, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Draws binary random numbers (0 or 1) from a Bernoulli distribution.</p>
-<p>The <code>input</code> tensor should be a tensor containing probabilities
-to be used for drawing the binary random number.
-Hence, all values in <code>input</code> have to be in the range:
-\(0 \leq \mbox{input}_i \leq 1\).</p>
-<p>The \(\mbox{i}^{th}\) element of the output tensor will draw a
-value \(1\) according to the \(\mbox{i}^{th}\) probability value given
-in <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} \sim \mathrm{Bernoulli}(p = \mbox{input}_{i})
-$$
-The returned <code>out</code> tensor only has values 0 or 1 and is of the same
-shape as <code>input</code>.</p>
-<p><code>out</code> can have integral <code>dtype</code>, but <code>input</code> must have floating
-point <code>dtype</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))$<span class='fu'>uniform_</span>(<span class='fl'>0</span>, <span class='fl'>1</span>)  <span class='co'># generate a uniform random matrix with range c(0, 1)</span>
-<span class='no'>a</span>
-<span class='fu'>torch_bernoulli</span>(<span class='no'>a</span>)
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>)) <span class='co'># probability of drawing "1" is 1</span>
-<span class='fu'>torch_bernoulli</span>(<span class='no'>a</span>)
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>)) <span class='co'># probability of drawing "1" is 0</span>
-<span class='fu'>torch_bernoulli</span>(<span class='no'>a</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_bincount.html b/static/docs/reference/torch_bincount.html
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-    <h1>Bincount</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_bincount.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Bincount</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) 1-d int tensor</p></td>
-    </tr>
-    <tr>
-      <th>weights</th>
-      <td><p>(Tensor) optional, weight for each value in the input tensor.        Should be of same size as input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>minlength</th>
-      <td><p>(int) optional, minimum number of bins. Should be non-negative.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bincount-input-weights-none-minlength-gt-tensor-"><a class="anchor" href="#bincount-input-weights-none-minlength-gt-tensor-"></a>bincount(input, weights=None, minlength=0) -&gt; Tensor </h2>
-
-    
-
-
-<p>Count the frequency of each value in an array of non-negative ints.</p>
-<p>The number of bins (size 1) is one larger than the largest value in
-<code>input</code> unless <code>input</code> is empty, in which case the result is a
-tensor of size 0. If <code>minlength</code> is specified, the number of bins is at least
-<code>minlength</code> and if <code>input</code> is empty, then the result is tensor of size
-<code>minlength</code> filled with zeros. If <code>n</code> is the value at position <code>i</code>,
-<code>out[n] += weights[i]</code> if <code>weights</code> is specified else
-<code>out[n] += 1</code>.</p>
-<p>.. include:: cuda_deterministic.rst</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>input</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span>(<span class='fl'>0</span>, <span class='fl'>8</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>5</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
-<span class='no'>weights</span> <span class='kw'>=</span> <span class='fu'><a href='torch_linspace.html'>torch_linspace</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='kw'>steps</span><span class='kw'>=</span><span class='fl'>5</span>)
-<span class='no'>input</span>
-<span class='no'>weights</span>
-<span class='fu'>torch_bincount</span>(<span class='no'>input</span>, <span class='no'>weights</span>)
-<span class='no'>input</span>$<span class='fu'>bincount</span>(<span class='no'>weights</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_bitwise_and.html b/static/docs/reference/torch_bitwise_and.html
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-      <th>input</th>
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-      <td><p>NA the second input tensor</p></td>
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-      <td><p>(Tensor, optional) the output tensor.</p></td>
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
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-    <h2 class="hasAnchor" id="bitwise-not-input-out-none-gt-tensor-"><a class="anchor" href="#bitwise-not-input-out-none-gt-tensor-"></a>bitwise_not(input, out=None) -&gt; Tensor </h2>
-
-    
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-<p>Computes the bitwise NOT of the given input tensor. The input tensor must be of
-integral or Boolean types. For bool tensors, it computes the logical NOT.</p>
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-      <td><p>(Tensor, optional) the output tensor.</p></td>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>NA the first input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>NA the second input tensor</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bitwise-xor-input-other-out-none-gt-tensor-"><a class="anchor" href="#bitwise-xor-input-other-out-none-gt-tensor-"></a>bitwise_xor(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
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-<p>Computes the bitwise XOR of <code>input</code> and <code>other</code>. The input tensor must be of
-integral or Boolean types. For bool tensors, it computes the logical XOR.</p>
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-    <div class="page-header">
-    <h1>Blackman_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_blackman_window.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Blackman_window</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) the size of returned window</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
-</pre>
-
-    <h2 class="hasAnchor" id="blackman-window-window-length-periodic-true-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#blackman-window-window-length-periodic-true-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>blackman_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Blackman window function.</p>
-<p>$$
-    w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right)
-$$
-where \(N\) is the full window size.</p>
-<p>The input <code>window_length</code> is a positive integer controlling the
-returned window size. <code>periodic</code> flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-<code>torch_blackman_window(L, periodic=True)</code> equal to
-<code>torch_blackman_window(L + 1, periodic=False)[:-1])</code>.</p>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Bmm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_bmm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Bmm</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>mat2</th>
-      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function does not broadcast .
-For broadcasting matrix products, see <code><a href='torch_matmul.html'>torch_matmul</a></code>.</p>
-    <h2 class="hasAnchor" id="bmm-input-mat-out-none-gt-tensor-"><a class="anchor" href="#bmm-input-mat-out-none-gt-tensor-"></a>bmm(input, mat2, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a batch matrix-matrix product of matrices stored in <code>input</code>
-and <code>mat2</code>.</p>
-<p><code>input</code> and <code>mat2</code> must be 3-D tensors each containing
-the same number of matrices.</p>
-<p>If <code>input</code> is a \((b \times n \times m)\) tensor, <code>mat2</code> is a
-\((b \times m \times p)\) tensor, <code>out</code> will be a
-\((b \times n \times p)\) tensor.</p>
-<p>$$
-    \mbox{out}_i = \mbox{input}_i \mathbin{@} \mbox{mat2}_i
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>input</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='no'>mat2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='no'>res</span> <span class='kw'>=</span> <span class='fu'>torch_bmm</span>(<span class='no'>input</span>, <span class='no'>mat2</span>)
-<span class='no'>res</span>
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>*tensors</th>
-      <td><p>NA any number of tensors of the same type</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="broadcast-tensors-tensors-gt-list-of-tensors-"><a class="anchor" href="#broadcast-tensors-tensors-gt-list-of-tensors-"></a>broadcast_tensors(*tensors) -&gt; List of Tensors </h2>
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-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>2</span>)$<span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
-<span class='no'>out</span> <span class='kw'>=</span> <span class='fu'>torch_broadcast_tensors</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>x</span>, <span class='no'>y</span>))
-<span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]]
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_can_cast.Rd</code></div>
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-    <p>Can_cast</p>
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>from</th>
-      <td><p>(dtype) The original <code>torch_dtype</code>.</p></td>
-    </tr>
-    <tr>
-      <th>to</th>
-      <td><p>(dtype) The target <code>torch_dtype</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="can-cast-from-to-gt-bool-"><a class="anchor" href="#can-cast-from-to-gt-bool-"></a>can_cast(from, to) -&gt; bool </h2>
-
-    
-
-
-<p>Determines if a type conversion is allowed under PyTorch casting rules
-described in the type promotion documentation .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_can_cast</span>(<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_float</a></span>())
-<span class='fu'>torch_can_cast</span>(<span class='fu'><a href='torch_dtype.html'>torch_float</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
-}</div></pre>
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diff --git a/static/docs/reference/torch_cartesian_prod.html b/static/docs/reference/torch_cartesian_prod.html
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-    <h1>Cartesian_prod</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cartesian_prod.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cartesian_prod</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>*tensors</th>
-      <td><p>NA any number of 1 dimensional tensors.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Do cartesian product of the given sequence of tensors. The behavior is similar to
-python's <code>itertools.product</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>)
-<span class='no'>tensor_a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='no'>a</span>)
-<span class='no'>tensor_b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='no'>b</span>)
-<span class='fu'>torch_cartesian_prod</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>tensor_a</span>, <span class='no'>tensor_b</span>))
-}</div></pre>
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diff --git a/static/docs/reference/torch_cat.html b/static/docs/reference/torch_cat.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cat.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cat</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(sequence of Tensors) any python sequence of tensors of the same type.        Non-empty tensors provided must have the same shape, except in the        cat dimension.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension over which the tensors are concatenated</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cat-tensors-dim-out-none-gt-tensor-"><a class="anchor" href="#cat-tensors-dim-out-none-gt-tensor-"></a>cat(tensors, dim=0, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Concatenates the given sequence of <code>seq</code> tensors in the given dimension.
-All tensors must either have the same shape (except in the concatenating
-dimension) or be empty.</p>
-<p><code>torch_cat</code> can be seen as an inverse operation for <code><a href='torch_split.html'>torch_split()</a></code>
-and <code><a href='torch_chunk.html'>torch_chunk</a></code>.</p>
-<p><code>torch_cat</code> can be best understood via examples.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='no'>x</span>
-<span class='fu'>torch_cat</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>x</span>, <span class='no'>x</span>, <span class='no'>x</span>), <span class='fl'>1</span>)
-<span class='fu'>torch_cat</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>x</span>, <span class='no'>x</span>, <span class='no'>x</span>), <span class='fl'>2</span>)
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_cdist.html b/static/docs/reference/torch_cdist.html
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-    <tr>
-      <th>x1</th>
-      <td><p>(Tensor) input tensor of shape \(B \times P \times M\).</p></td>
-    </tr>
-    <tr>
-      <th>x2</th>
-      <td><p>(Tensor) input tensor of shape \(B \times R \times M\).</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></td>
-    </tr>
-    <tr>
-      <th>compute_mode</th>
-      <td><p>NA 'use_mm_for_euclid_dist_if_necessary' - will use matrix multiplication approach to calculate        euclidean distance (p = 2) if P &gt; 25 or R &gt; 25        'use_mm_for_euclid_dist' - will always use matrix multiplication approach to calculate        euclidean distance (p = 2)        'donot_use_mm_for_euclid_dist' - will never use matrix multiplication approach to calculate        euclidean distance (p = 2)        Default: use_mm_for_euclid_dist_if_necessary.</p></td>
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-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
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-    <h1>Ceil</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_ceil.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ceil</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ceil-input-out-none-gt-tensor-"><a class="anchor" href="#ceil-input-out-none-gt-tensor-"></a>ceil(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the ceil of the elements of <code>input</code>,
-the smallest integer greater than or equal to each element.</p>
-<p>$$
-    \mbox{out}_{i} = \left\lceil \mbox{input}_{i} \right\rceil = \left\lfloor \mbox{input}_{i} \right\rfloor + 1
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_ceil</span>(<span class='no'>a</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Chain_matmul</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_chain_matmul.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Chain_matmul</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>matrices</th>
-      <td><p>(Tensors...) a sequence of 2 or more 2-D tensors whose product is to be determined.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Returns the matrix product of the \(N\) 2-D tensors. This product is efficiently computed
-using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
-of arithmetic operations (<code>[CLRS]</code>_). Note that since this is a function to compute the product, \(N\)
-needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
-If \(N\) is 1, then this is a no-op - the original matrix is returned as is.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='no'>c</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>6</span>))
-<span class='no'>d</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>6</span>, <span class='fl'>7</span>))
-<span class='fu'>torch_chain_matmul</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>a</span>, <span class='no'>b</span>, <span class='no'>c</span>, <span class='no'>d</span>))
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_cholesky.html b/static/docs/reference/torch_cholesky.html
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-    <div class="page-header">
-    <h1>Cholesky</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cholesky.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cholesky</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor \(A\) of size \((*, n, n)\) where <code>*</code> is zero or more                batch dimensions consisting of symmetric positive-definite matrices.</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(bool, optional) flag that indicates whether to return a                            upper or lower triangular matrix. Default: <code>False</code></p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output matrix</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cholesky-input-upper-false-out-none-gt-tensor-"><a class="anchor" href="#cholesky-input-upper-false-out-none-gt-tensor-"></a>cholesky(input, upper=False, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the Cholesky decomposition of a symmetric positive-definite
-matrix \(A\) or for batches of symmetric positive-definite matrices.</p>
-<p>If <code>upper</code> is <code>True</code>, the returned matrix <code>U</code> is upper-triangular, and
-the decomposition has the form:</p>
-<p>$$
-  A = U^TU
-$$
-If <code>upper</code> is <code>False</code>, the returned matrix <code>L</code> is lower-triangular, and
-the decomposition has the form:</p>
-<p>$$
-    A = LL^T
-$$
-If <code>upper</code> is <code>True</code>, and \(A\) is a batch of symmetric positive-definite
-matrices, then the returned tensor will be composed of upper-triangular Cholesky factors
-of each of the individual matrices. Similarly, when <code>upper</code> is <code>False</code>, the returned
-tensor will be composed of lower-triangular Cholesky factors of each of the individual
-matrices.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='no'>a</span>, <span class='no'>a</span>$<span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) <span class='co'># make symmetric positive-definite</span>
-<span class='no'>l</span> <span class='kw'>=</span> <span class='fu'>torch_cholesky</span>(<span class='no'>a</span>)
-<span class='no'>a</span>
-<span class='no'>l</span>
-<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='no'>l</span>, <span class='no'>l</span>$<span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>())
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='no'>a</span>, <span class='no'>a</span>$<span class='fu'>transpose</span>(-<span class='fl'>1</span>, -<span class='fl'>2</span>)) + <span class='fl'>1e-03</span> <span class='co'># make symmetric positive-definite</span>
-<span class='no'>l</span> <span class='kw'>=</span> <span class='fu'>torch_cholesky</span>(<span class='no'>a</span>)
-<span class='no'>z</span> <span class='kw'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='no'>l</span>, <span class='no'>l</span>$<span class='fu'>transpose</span>(-<span class='fl'>1</span>, -<span class='fl'>2</span>))
-<span class='fu'><a href='torch_max.html'>torch_max</a></span>(<span class='fu'><a href='torch_abs.html'>torch_abs</a></span>(<span class='no'>z</span> - <span class='no'>a</span>)) <span class='co'># Max non-zero</span>
-}
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_cholesky_inverse.html b/static/docs/reference/torch_cholesky_inverse.html
deleted file mode 100644
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+++ /dev/null
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-    <div class="page-header">
-    <h1>Cholesky_inverse</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cholesky_inverse.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cholesky_inverse</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input 2-D tensor \(u\), a upper or lower triangular           Cholesky factor</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(bool, optional) whether to return a lower (default) or upper triangular matrix</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor for <code>inv</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cholesky-inverse-input-upper-false-out-none-gt-tensor-"><a class="anchor" href="#cholesky-inverse-input-upper-false-out-none-gt-tensor-"></a>cholesky_inverse(input, upper=False, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the inverse of a symmetric positive-definite matrix \(A\) using its
-Cholesky factor \(u\): returns matrix <code>inv</code>. The inverse is computed using
-LAPACK routines <code>dpotri</code> and <code>spotri</code> (and the corresponding MAGMA routines).</p>
-<p>If <code>upper</code> is <code>False</code>, \(u\) is lower triangular
-such that the returned tensor is</p>
-<p>$$
-    inv = (uu^{{T}})^{{-1}}
-$$
-If <code>upper</code> is <code>True</code> or not provided, \(u\) is upper
-triangular such that the returned tensor is</p>
-<p>$$
-    inv = (u^T u)^{{-1}}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='no'>a</span>, <span class='no'>a</span>$<span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) + <span class='fl'>1e-05</span> * <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>3</span>) <span class='co'># make symmetric positive definite</span>
-<span class='no'>u</span> <span class='kw'>=</span> <span class='fu'><a href='torch_cholesky.html'>torch_cholesky</a></span>(<span class='no'>a</span>)
-<span class='no'>a</span>
-<span class='fu'>torch_cholesky_inverse</span>(<span class='no'>u</span>)
-<span class='no'>a</span>$<span class='fu'>inverse</span>()
-}
-}</div></pre>
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diff --git a/static/docs/reference/torch_cholesky_solve.html b/static/docs/reference/torch_cholesky_solve.html
deleted file mode 100644
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-    <h1>Cholesky_solve</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cholesky_solve.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cholesky_solve</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input matrix \(b\) of size \((*, m, k)\),                where \(*\) is zero or more batch dimensions</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>(Tensor) input matrix \(u\) of size \((*, m, m)\),                where \(*\) is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor for <code>c</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cholesky-solve-input-input-upper-false-out-none-gt-tensor-"><a class="anchor" href="#cholesky-solve-input-input-upper-false-out-none-gt-tensor-"></a>cholesky_solve(input, input2, upper=False, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Solves a linear system of equations with a positive semidefinite
-matrix to be inverted given its Cholesky factor matrix \(u\).</p>
-<p>If <code>upper</code> is <code>False</code>, \(u\) is and lower triangular and <code>c</code> is
-returned such that:</p>
-<p>$$
-    c = (u u^T)^{{-1}} b
-$$
-If <code>upper</code> is <code>True</code> or not provided, \(u\) is upper triangular
-and <code>c</code> is returned such that:</p>
-<p>$$
-    c = (u^T u)^{{-1}} b
-$$
-<code>torch_cholesky_solve(b, u)</code> can take in 2D inputs <code>b, u</code> or inputs that are
-batches of 2D matrices. If the inputs are batches, then returns
-batched outputs <code>c</code></p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='no'>a</span>, <span class='no'>a</span>$<span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) <span class='co'># make symmetric positive definite</span>
-<span class='no'>u</span> <span class='kw'>=</span> <span class='fu'><a href='torch_cholesky.html'>torch_cholesky</a></span>(<span class='no'>a</span>)
-<span class='no'>a</span>
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='no'>b</span>
-<span class='fu'>torch_cholesky_solve</span>(<span class='no'>b</span>, <span class='no'>u</span>)
-<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='no'>a</span>$<span class='fu'>inverse</span>(), <span class='no'>b</span>)
-}</div></pre>
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-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to split</p></td>
-    </tr>
-    <tr>
-      <th>chunks</th>
-      <td><p>(int) number of chunks to return</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension along which to split the tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="chunk-input-chunks-dim-gt-list-of-tensors-"><a class="anchor" href="#chunk-input-chunks-dim-gt-list-of-tensors-"></a>chunk(input, chunks, dim=0) -&gt; List of Tensors </h2>
-
-    
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-
-<p>Splits a tensor into a specific number of chunks. Each chunk is a view of
-the input tensor.</p>
-<p>Last chunk will be smaller if the tensor size along the given dimension
-<code>dim</code> is not divisible by <code>chunks</code>.</p>
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-    <h1>Clamp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_clamp.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Clamp</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>min</th>
-      <td><p>(Number) lower-bound of the range to be clamped to</p></td>
-    </tr>
-    <tr>
-      <th>max</th>
-      <td><p>(Number) upper-bound of the range to be clamped to</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>(Number) minimal value of each element in the output</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="clamp-input-min-max-out-none-gt-tensor-"><a class="anchor" href="#clamp-input-min-max-out-none-gt-tensor-"></a>clamp(input, min, max, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Clamp all elements in <code>input</code> into the range <code>[</code> <code>min</code>, <code>max</code> <code>]</code> and return
-a resulting tensor:</p>
-<p>$$
-    y_i = \left\{ \begin{array}{ll}
-        \mbox{min} &amp; \mbox{if } x_i &lt; \mbox{min} \\
-        x_i &amp; \mbox{if } \mbox{min} \leq x_i \leq \mbox{max} \\
-        \mbox{max} &amp; \mbox{if } x_i &gt; \mbox{max}
-    \end{array}
-    \right.
-$$
-If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, args <code>min</code>
-and <code>max</code> must be real numbers, otherwise they should be integers.</p>
-    <h2 class="hasAnchor" id="clamp-input-min-out-none-gt-tensor-"><a class="anchor" href="#clamp-input-min-out-none-gt-tensor-"></a>clamp(input, *, min, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Clamps all elements in <code>input</code> to be larger or equal <code>min</code>.</p>
-<p>If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code>
-should be a real number, otherwise it should be an integer.</p>
-    <h2 class="hasAnchor" id="clamp-input-max-out-none-gt-tensor-"><a class="anchor" href="#clamp-input-max-out-none-gt-tensor-"></a>clamp(input, *, max, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Clamps all elements in <code>input</code> to be smaller or equal <code>max</code>.</p>
-<p>If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code>
-should be a real number, otherwise it should be an integer.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_clamp</span>(<span class='no'>a</span>, <span class='kw'>min</span><span class='kw'>=</span>-<span class='fl'>0.5</span>, <span class='kw'>max</span><span class='kw'>=</span><span class='fl'>0.5</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_clamp</span>(<span class='no'>a</span>, <span class='kw'>min</span><span class='kw'>=</span><span class='fl'>0.5</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_clamp</span>(<span class='no'>a</span>, <span class='kw'>max</span><span class='kw'>=</span><span class='fl'>0.5</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_combinations.html b/static/docs/reference/torch_combinations.html
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-    <h1>Combinations</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_combinations.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Combinations</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) 1D vector.</p></td>
-    </tr>
-    <tr>
-      <th>r</th>
-      <td><p>(int, optional) number of elements to combine</p></td>
-    </tr>
-    <tr>
-      <th>with_replacement</th>
-      <td><p>(boolean, optional) whether to allow duplication in combination</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="combinations-input-r-with-replacement-false-gt-seq-"><a class="anchor" href="#combinations-input-r-with-replacement-false-gt-seq-"></a>combinations(input, r=2, with_replacement=False) -&gt; seq </h2>
-
-    
-
-
-<p>Compute combinations of length \(r\) of the given tensor. The behavior is similar to
-python's <code>itertools.combinations</code> when <code>with_replacement</code> is set to <code>False</code>, and
-<code>itertools.combinations_with_replacement</code> when <code>with_replacement</code> is set to <code>True</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)
-<span class='no'>tensor_a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='no'>a</span>)
-<span class='fu'>torch_combinations</span>(<span class='no'>tensor_a</span>)
-<span class='fu'>torch_combinations</span>(<span class='no'>tensor_a</span>, <span class='kw'>r</span><span class='kw'>=</span><span class='fl'>3</span>)
-<span class='fu'>torch_combinations</span>(<span class='no'>tensor_a</span>, <span class='kw'>with_replacement</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-}</div></pre>
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-    <div class="page-header">
-    <h1>Conj</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_conj.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conj</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conj-input-out-none-gt-tensor-"><a class="anchor" href="#conj-input-out-none-gt-tensor-"></a>conj(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise conjugate of the given <code>input</code> tensor.</p>
-<p>$$
-    \mbox{out}_{i} = conj(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_conj</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>1</span> + <span class='fl'>1i</span>, -<span class='fl'>2</span> + <span class='fl'>2i</span>, <span class='fl'>3</span> - <span class='fl'>3i</span>)))
-}
-}</div></pre>
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_conv1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv1d</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>NA filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>NA optional bias of shape \((\mbox{out\_channels})\). Default: <code>None</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>NA the stride of the convolving kernel. Can be a single number or      a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>NA implicit paddings on both sides of the input. Can be a      single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>NA the spacing between kernel elements. Can be a single number or      a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>NA split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-d-input-weight-bias-none-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-none-stride-padding-dilation-groups-gt-tensor-"></a>conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 1D convolution over an input signal composed of several input
-planes.</p>
-<p>See <code>~torch.nn.Conv1d</code> for details and output shape.</p>
-<p>.. include:: cudnn_deterministic.rst</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>filters</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>33</span>, <span class='fl'>16</span>, <span class='fl'>3</span>))
-<span class='no'>inputs</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>))
-<span class='fu'><a href='nnf_conv1d.html'>nnf_conv1d</a></span>(<span class='no'>inputs</span>, <span class='no'>filters</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_conv2d.html b/static/docs/reference/torch_conv2d.html
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-    <h1>Conv2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_conv2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv2d</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>NA filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>NA optional bias tensor of shape \((\mbox{out\_channels})\). Default: <code>None</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>NA the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>NA implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>NA the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>NA split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-d-input-weight-bias-none-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-none-stride-padding-dilation-groups-gt-tensor-"></a>conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 2D convolution over an input image composed of several input
-planes.</p>
-<p>See <code>~torch.nn.Conv2d</code> for details and output shape.</p>
-<p>.. include:: cudnn_deterministic.rst</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># With square kernels and equal stride</span>
-<span class='no'>filters</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>8</span>,<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>3</span>))
-<span class='no'>inputs</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>5</span>))
-<span class='fu'><a href='nnf_conv2d.html'>nnf_conv2d</a></span>(<span class='no'>inputs</span>, <span class='no'>filters</span>, <span class='kw'>padding</span><span class='kw'>=</span><span class='fl'>1</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_conv3d.html b/static/docs/reference/torch_conv3d.html
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-    <div class="hidden name"><code>torch_conv3d.Rd</code></div>
-    </div>
-
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-    <p>Conv3d</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>NA filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>NA optional bias tensor of shape \((\mbox{out\_channels})\). Default: None</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>NA the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>NA implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>NA the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>NA split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-d-input-weight-bias-none-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-none-stride-padding-dilation-groups-gt-tensor-"></a>conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 3D convolution over an input image composed of several input
-planes.</p>
-<p>See <code>~torch.nn.Conv3d</code> for details and output shape.</p>
-<p>.. include:: cudnn_deterministic.rst</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># filters = torch_randn(c(33, 16, 3, 3, 3))</span>
-<span class='co'># inputs = torch_randn(c(20, 16, 50, 10, 20))</span>
-<span class='co'># nnf_conv3d(inputs, filters)</span>
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_conv_tbc.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv_tbc</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>NA input tensor of shape \((\mbox{sequence length} \times batch \times \mbox{in\_channels})\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>NA filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} \times \mbox{out\_channels}\))</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>NA bias of shape (\(\mbox{out\_channels}\))</p></td>
-    </tr>
-    <tr>
-      <th>pad</th>
-      <td><p>NA number of timesteps to pad. Default: 0</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Applies a 1-dimensional sequence convolution over an input sequence.
-Input and output dimensions are (Time, Batch, Channels) - hence TBC.</p>
-
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-    <h1>Conv_transpose1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_conv_transpose1d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv_transpose1d</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>NA filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>NA optional bias of shape \((\mbox{out\_channels})\). Default: None</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>NA the stride of the convolving kernel. Can be a single number or a      tuple <code>(sW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>NA <code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>NA split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>NA the spacing between kernel elements. Can be a single number or      a tuple <code>(dW,)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-none-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-none-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 1D transposed convolution operator over an input signal
-composed of several input planes, sometimes also called "deconvolution".</p>
-<p>See <code>~torch.nn.ConvTranspose1d</code> for details and output shape.</p>
-<p>.. include:: cudnn_deterministic.rst</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>inputs</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>))
-<span class='no'>weights</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>5</span>))
-<span class='fu'><a href='nnf_conv_transpose1d.html'>nnf_conv_transpose1d</a></span>(<span class='no'>inputs</span>, <span class='no'>weights</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Conv_transpose2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_conv_transpose2d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv_transpose2d</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>NA filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>NA optional bias of shape \((\mbox{out\_channels})\). Default: None</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>NA the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>NA <code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padH, out_padW)</code>.      Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>NA split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>NA the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-none-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-none-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 2D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution".</p>
-<p>See <code>~torch.nn.ConvTranspose2d</code> for details and output shape.</p>
-<p>.. include:: cudnn_deterministic.rst</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># With square kernels and equal stride</span>
-<span class='no'>inputs</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>5</span>))
-<span class='no'>weights</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>8</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'><a href='nnf_conv_transpose2d.html'>nnf_conv_transpose2d</a></span>(<span class='no'>inputs</span>, <span class='no'>weights</span>, <span class='kw'>padding</span><span class='kw'>=</span><span class='fl'>1</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_conv_transpose3d.html b/static/docs/reference/torch_conv_transpose3d.html
deleted file mode 100644
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--- a/static/docs/reference/torch_conv_transpose3d.html
+++ /dev/null
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-    <h1>Conv_transpose3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_conv_transpose3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Conv_transpose3d</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>NA filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>NA optional bias of shape \((\mbox{out\_channels})\). Default: None</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>NA the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>NA <code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padT, padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      <code>(out_padT, out_padH, out_padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>NA split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>NA the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-none-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-none-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Applies a 3D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution"</p>
-<p>See <code>~torch.nn.ConvTranspose3d</code> for details and output shape.</p>
-<p>.. include:: cudnn_deterministic.rst</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>inputs</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>10</span>, <span class='fl'>20</span>))
-<span class='no'>weights</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'><a href='nnf_conv_transpose3d.html'>nnf_conv_transpose3d</a></span>(<span class='no'>inputs</span>, <span class='no'>weights</span>)
-}
-}</div></pre>
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diff --git a/static/docs/reference/torch_cos.html b/static/docs/reference/torch_cos.html
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-    <h1>Cos</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cos.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cos</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cos-input-out-none-gt-tensor-"><a class="anchor" href="#cos-input-out-none-gt-tensor-"></a>cos(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the cosine  of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \cos(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_cos</span>(<span class='no'>a</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cosh.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cosh</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cosh-input-out-none-gt-tensor-"><a class="anchor" href="#cosh-input-out-none-gt-tensor-"></a>cosh(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the hyperbolic cosine  of the elements of
-<code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \cosh(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_cosh</span>(<span class='no'>a</span>)
-}</div></pre>
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-    <h1>Cosine_similarity</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cosine_similarity.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cosine_similarity</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x1</th>
-      <td><p>(Tensor) First input.</p></td>
-    </tr>
-    <tr>
-      <th>x2</th>
-      <td><p>(Tensor) Second input (of size matching x1).</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) Dimension of vectors. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid division by zero.        Default: 1e-8</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cosine-similarity-x-x-dim-eps-e-gt-tensor-"><a class="anchor" href="#cosine-similarity-x-x-dim-eps-e-gt-tensor-"></a>cosine_similarity(x1, x2, dim=1, eps=1e-8) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns cosine similarity between x1 and x2, computed along dim.</p>
-<p>$$
-    \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>input1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>100</span>, <span class='fl'>128</span>))
-<span class='no'>input2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>100</span>, <span class='fl'>128</span>))
-<span class='no'>output</span> <span class='kw'>=</span> <span class='fu'>torch_cosine_similarity</span>(<span class='no'>input1</span>, <span class='no'>input2</span>)
-<span class='no'>output</span>
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cross.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cross</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to take the cross-product in.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cross-input-other-dim-out-none-gt-tensor-"><a class="anchor" href="#cross-input-other-dim-out-none-gt-tensor-"></a>cross(input, other, dim=-1, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the cross product of vectors in dimension <code>dim</code> of <code>input</code>
-and <code>other</code>.</p>
-<p><code>input</code> and <code>other</code> must have the same size, and the size of their
-<code>dim</code> dimension should be 3.</p>
-<p>If <code>dim</code> is not given, it defaults to the first dimension found with the
-size 3.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>))
-<span class='no'>b</span>
-<span class='fu'>torch_cross</span>(<span class='no'>a</span>, <span class='no'>b</span>, <span class='kw'>dim</span><span class='kw'>=</span><span class='fl'>2</span>)
-<span class='fu'>torch_cross</span>(<span class='no'>a</span>, <span class='no'>b</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_cummax.html b/static/docs/reference/torch_cummax.html
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-    <h1>Cummax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cummax.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cummax</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the result tuple of two output tensors (values, indices)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cummax-input-dim-out-none-gt-tensor-longtensor-"><a class="anchor" href="#cummax-input-dim-out-none-gt-tensor-longtensor-"></a>cummax(input, dim, out=None) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the cumulative maximum of
-elements of <code>input</code> in the dimension <code>dim</code>. And <code>indices</code> is the index
-location of each maximum value found in the dimension <code>dim</code>.</p>
-<p>$$
-    y_i = max(x_1, x_2, x_3, \dots, x_i)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_cummax</span>(<span class='no'>a</span>, <span class='kw'>dim</span><span class='kw'>=</span><span class='fl'>1</span>)
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cummin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cummin</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the result tuple of two output tensors (values, indices)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cummin-input-dim-out-none-gt-tensor-longtensor-"><a class="anchor" href="#cummin-input-dim-out-none-gt-tensor-longtensor-"></a>cummin(input, dim, out=None) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the cumulative minimum of
-elements of <code>input</code> in the dimension <code>dim</code>. And <code>indices</code> is the index
-location of each maximum value found in the dimension <code>dim</code>.</p>
-<p>$$
-    y_i = min(x_1, x_2, x_3, \dots, x_i)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_cummin</span>(<span class='no'>a</span>, <span class='kw'>dim</span><span class='kw'>=</span><span class='fl'>1</span>)
-}</div></pre>
-  </div>
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diff --git a/static/docs/reference/torch_cumprod.html b/static/docs/reference/torch_cumprod.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cumprod.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cumprod</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: None.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cumprod-input-dim-out-none-dtype-none-gt-tensor-"><a class="anchor" href="#cumprod-input-dim-out-none-dtype-none-gt-tensor-"></a>cumprod(input, dim, out=None, dtype=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the cumulative product of elements of <code>input</code> in the dimension
-<code>dim</code>.</p>
-<p>For example, if <code>input</code> is a vector of size N, the result will also be
-a vector of size N, with elements.</p>
-<p>$$
-    y_i = x_1 \times x_2\times x_3\times \dots \times x_i
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_cumprod</span>(<span class='no'>a</span>, <span class='kw'>dim</span><span class='kw'>=</span><span class='fl'>1</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Cumsum</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_cumsum.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Cumsum</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: None.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cumsum-input-dim-out-none-dtype-none-gt-tensor-"><a class="anchor" href="#cumsum-input-dim-out-none-dtype-none-gt-tensor-"></a>cumsum(input, dim, out=None, dtype=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the cumulative sum of elements of <code>input</code> in the dimension
-<code>dim</code>.</p>
-<p>For example, if <code>input</code> is a vector of size N, the result will also be
-a vector of size N, with elements.</p>
-<p>$$
-    y_i = x_1 + x_2 + x_3 + \dots + x_i
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_cumsum</span>(<span class='no'>a</span>, <span class='kw'>dim</span><span class='kw'>=</span><span class='fl'>1</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_det.html b/static/docs/reference/torch_det.html
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-    <h1>Det</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_det.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Det</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code>*</code> is zero or more                batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>Backward through `det` internally uses SVD results when `input` is
-not invertible. In this case, double backward through `det` will be
-unstable in when `input` doesn't have distinct singular values. See
-`~torch.svd` for details.
-</pre>
-
-    <h2 class="hasAnchor" id="det-input-gt-tensor-"><a class="anchor" href="#det-input-gt-tensor-"></a>det(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Calculates determinant of a square matrix or batches of square matrices.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_det</span>(<span class='no'>A</span>)
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='no'>A</span>
-<span class='no'>A</span>$<span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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deleted file mode 100644
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-    <h1>Create a Device object</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/device.R'><code>R/device.R</code></a></small>
-    <div class="hidden name"><code>torch_device.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A <code>torch_device</code>  is an object representing the device on which a <code>torch_tensor</code>
-is or will be allocated.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_device</span>(<span class='no'>type</span>, <span class='kw'>index</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>type</th>
-      <td><p>(character) a device type <code>"cuda"</code> or <code>"cpu"</code></p></td>
-    </tr>
-    <tr>
-      <th>index</th>
-      <td><p>(integer) optional device ordinal for the device type.  If the device ordinal
-is not present, this object will always represent the current device for the device
-type, even after <code>torch_cuda_set_device()</code> is called; e.g., a <code>torch_tensor</code> constructed
-with device <code>'cuda'</code> is equivalent to <code>'cuda:X'</code> where X is the result of
-<code>torch_cuda_current_device()</code>.</p>
-<p>A <code>torch_device</code> can be constructed via a string or via a string and device ordinal</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># Via string</span>
-<span class='fu'>torch_device</span>(<span class='st'>"cuda:1"</span>)
-<span class='fu'>torch_device</span>(<span class='st'>"cpu"</span>)
-<span class='fu'>torch_device</span>(<span class='st'>"cuda"</span>) <span class='co'># current cuda device</span>
-
-<span class='co'># Via string and device ordinal</span>
-<span class='fu'>torch_device</span>(<span class='st'>"cuda"</span>, <span class='fl'>0</span>)
-<span class='fu'>torch_device</span>(<span class='st'>"cpu"</span>, <span class='fl'>0</span>)
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_diag.html b/static/docs/reference/torch_diag.html
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-    </div>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>diagonal</th>
-      <td><p>(int, optional) the diagonal to consider</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="diag-input-diagonal-out-none-gt-tensor-"><a class="anchor" href="#diag-input-diagonal-out-none-gt-tensor-"></a>diag(input, diagonal=0, out=None) -&gt; Tensor </h2>
-
-    
-
-<ul>
-<li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
-with the elements of <code>input</code> as the diagonal.</p></li>
-<li><p>If <code>input</code> is a matrix (2-D tensor), then returns a 1-D tensor with
-the diagonal elements of <code>input</code>.</p></li>
-</ul>
-
-<p>The argument <code>diagonal</code> controls which diagonal to consider:</p><ul>
-<li><p>If <code>diagonal</code> = 0, it is the main diagonal.</p></li>
-<li><p>If <code>diagonal</code> &gt; 0, it is above the main diagonal.</p></li>
-<li><p>If <code>diagonal</code> &lt; 0, it is below the main diagonal.</p></li>
-</ul>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    </nav>
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-    <h1>Diag_embed</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_diag_embed.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Diag_embed</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor. Must be at least 1-dimensional.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></td>
-    </tr>
-    <tr>
-      <th>dim1</th>
-      <td><p>(int, optional) first dimension with respect to which to        take diagonal. Default: -2.</p></td>
-    </tr>
-    <tr>
-      <th>dim2</th>
-      <td><p>(int, optional) second dimension with respect to which to        take diagonal. Default: -1.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="diag-embed-input-offset-dim-dim-gt-tensor-"><a class="anchor" href="#diag-embed-input-offset-dim-dim-gt-tensor-"></a>diag_embed(input, offset=0, dim1=-2, dim2=-1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Creates a tensor whose diagonals of certain 2D planes (specified by
-<code>dim1</code> and <code>dim2</code>) are filled by <code>input</code>.
-To facilitate creating batched diagonal matrices, the 2D planes formed by
-the last two dimensions of the returned tensor are chosen by default.</p>
-<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
-<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
-<li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
-<li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
-</ul>
-
-<p>The size of the new matrix will be calculated to make the specified diagonal
-of the size of the last input dimension.
-Note that for <code>offset</code> other than \(0\), the order of <code>dim1</code>
-and <code>dim2</code> matters. Exchanging them is equivalent to changing the
-sign of <code>offset</code>.</p>
-<p>Applying <code>torch_diagonal</code> to the output of this function with
-the same arguments yields a matrix identical to input. However,
-<code>torch_diagonal</code> has different default dimensions, so those
-need to be explicitly specified.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_diag_embed</span>(<span class='no'>a</span>)
-<span class='fu'>torch_diag_embed</span>(<span class='no'>a</span>, <span class='kw'>offset</span><span class='kw'>=</span><span class='fl'>1</span>, <span class='kw'>dim1</span><span class='kw'>=</span><span class='fl'>1</span>, <span class='kw'>dim2</span><span class='kw'>=</span><span class='fl'>3</span>)
-}</div></pre>
-  </div>
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Diagflat</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_diagflat.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Diagflat</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(int, optional) the diagonal to consider. Default: 0 (main        diagonal).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="diagflat-input-offset-gt-tensor-"><a class="anchor" href="#diagflat-input-offset-gt-tensor-"></a>diagflat(input, offset=0) -&gt; Tensor </h2>
-
-    
-
-<ul>
-<li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
-with the elements of <code>input</code> as the diagonal.</p></li>
-<li><p>If <code>input</code> is a tensor with more than one dimension, then returns a
-2-D tensor with diagonal elements equal to a flattened <code>input</code>.</p></li>
-</ul>
-
-<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
-<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
-<li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
-<li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_diagflat</span>(<span class='no'>a</span>)
-<span class='fu'>torch_diagflat</span>(<span class='no'>a</span>, <span class='fl'>1</span>)
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_diagflat</span>(<span class='no'>a</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-    <div class="page-header">
-    <h1>Diagonal</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_diagonal.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Diagonal</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor. Must be at least 2-dimensional.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></td>
-    </tr>
-    <tr>
-      <th>dim1</th>
-      <td><p>(int, optional) first dimension with respect to which to        take diagonal. Default: 0.</p></td>
-    </tr>
-    <tr>
-      <th>dim2</th>
-      <td><p>(int, optional) second dimension with respect to which to        take diagonal. Default: 1.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="diagonal-input-offset-dim-dim-gt-tensor-"><a class="anchor" href="#diagonal-input-offset-dim-dim-gt-tensor-"></a>diagonal(input, offset=0, dim1=0, dim2=1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a partial view of <code>input</code> with the its diagonal elements
-with respect to <code>dim1</code> and <code>dim2</code> appended as a dimension
-at the end of the shape.</p>
-<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
-<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
-<li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
-<li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
-</ul>
-
-<p>Applying <code>torch_diag_embed</code> to the output of this function with
-the same arguments yields a diagonal matrix with the diagonal entries
-of the input. However, <code>torch_diag_embed</code> has different default
-dimensions, so those need to be explicitly specified.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_diagonal</span>(<span class='no'>a</span>, <span class='kw'>offset</span> <span class='kw'>=</span> <span class='fl'>0</span>)
-<span class='fu'>torch_diagonal</span>(<span class='no'>a</span>, <span class='kw'>offset</span> <span class='kw'>=</span> <span class='fl'>1</span>)
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>5</span>, <span class='fl'>4</span>, <span class='fl'>2</span>))
-<span class='fu'>torch_diagonal</span>(<span class='no'>x</span>, <span class='kw'>offset</span><span class='kw'>=</span>-<span class='fl'>1</span>, <span class='kw'>dim1</span><span class='kw'>=</span><span class='fl'>1</span>, <span class='kw'>dim2</span><span class='kw'>=</span><span class='fl'>2</span>)
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_digamma.html b/static/docs/reference/torch_digamma.html
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-    <h1>Digamma</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_digamma.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Digamma</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to compute the digamma function on</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="digamma-input-out-none-gt-tensor-"><a class="anchor" href="#digamma-input-out-none-gt-tensor-"></a>digamma(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the logarithmic derivative of the gamma function on <code>input</code>.</p>
-<p>$$
-    \psi(x) = \frac{d}{dx} \ln\left(\Gamma\left(x\right)\right) = \frac{\Gamma'(x)}{\Gamma(x)}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0.5</span>))
-<span class='fu'>torch_digamma</span>(<span class='no'>a</span>)
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_dist.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Dist</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the Right-hand-side input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(float, optional) the norm to be computed</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="dist-input-other-p-gt-tensor-"><a class="anchor" href="#dist-input-other-p-gt-tensor-"></a>dist(input, other, p=2) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the p-norm of (<code>input</code> - <code>other</code>)</p>
-<p>The shapes of <code>input</code> and <code>other</code> must be
-broadcastable .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>x</span>
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>y</span>
-<span class='fu'>torch_dist</span>(<span class='no'>x</span>, <span class='no'>y</span>, <span class='fl'>3.5</span>)
-<span class='fu'>torch_dist</span>(<span class='no'>x</span>, <span class='no'>y</span>, <span class='fl'>3</span>)
-<span class='fu'>torch_dist</span>(<span class='no'>x</span>, <span class='no'>y</span>, <span class='fl'>0</span>)
-<span class='fu'>torch_dist</span>(<span class='no'>x</span>, <span class='no'>y</span>, <span class='fl'>1</span>)
-}</div></pre>
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-    <div class="page-header">
-    <h1>Div</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_div.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Div</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Number) the number to be divided to each element of <code>input</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="div-input-other-out-none-gt-tensor-"><a class="anchor" href="#div-input-other-out-none-gt-tensor-"></a>div(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Divides each element of the input <code>input</code> with the scalar <code>other</code> and
-returns a new resulting tensor.</p>
-
-
-<p>Each element of the tensor <code>input</code> is divided by each element of the tensor
-<code>other</code>. The resulting tensor is returned.</p>
-<p>$$
-    \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}_i}
-$$
-The shapes of <code>input</code> and <code>other</code> must be broadcastable
-. If the <code>torch_dtype</code> of <code>input</code> and
-<code>other</code> differ, the <code>torch_dtype</code> of the result tensor is determined
-following rules described in the type promotion documentation
-. If <code>out</code> is specified, the result must be
-castable  to the <code>torch_dtype</code> of the
-specified output tensor. Integral division by zero leads to undefined behavior.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Integer division using div is deprecated, and in a future release div will
-perform true division like <code><a href='torch_true_divide.html'>torch_true_divide</a></code>.
-Use <code><a href='torch_floor_divide.html'>torch_floor_divide</a></code> (// in Python) to perform integer division,
-instead.</p>
-<p>$$
-    \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}}
-$$
-If the <code>torch_dtype</code> of <code>input</code> and <code>other</code> differ, the
-<code>torch_dtype</code> of the result tensor is determined following rules
-described in the type promotion documentation . If
-<code>out</code> is specified, the result must be castable
-to the <code>torch_dtype</code> of the specified output tensor. Integral division
-by zero leads to undefined behavior.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_div</span>(<span class='no'>a</span>, <span class='fl'>0.5</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>b</span>
-<span class='fu'>torch_div</span>(<span class='no'>a</span>, <span class='no'>b</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>Dot</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_dot.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Dot</p>
-    </div>
-
-
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function does not broadcast .</p>
-    <h2 class="hasAnchor" id="dot-input-tensor-gt-tensor-"><a class="anchor" href="#dot-input-tensor-gt-tensor-"></a>dot(input, tensor) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the dot product (inner product) of two tensors.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_dot</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>)))
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_dtype.html b/static/docs/reference/torch_dtype.html
deleted file mode 100644
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-    <p>Eig</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the square matrix of shape \((n \times n)\) for which the eigenvalues and eigenvectors        will be computed</p></td>
-    </tr>
-    <tr>
-      <th>eigenvectors</th>
-      <td><p>(bool) <code>True</code> to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the output tensors</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>Since eigenvalues and eigenvectors might be complex, backward pass is supported only
-for [`torch_symeig`]
-</pre>
-
-    <h2 class="hasAnchor" id="eig-input-eigenvectors-false-out-none-gt-tensor-tensor-"><a class="anchor" href="#eig-input-eigenvectors-false-out-none-gt-tensor-tensor-"></a>eig(input, eigenvectors=False, out=None) -&gt; (Tensor, Tensor) </h2>
-
-    
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-<p>Computes the eigenvalues and eigenvectors of a real square matrix.</p>
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-    <h1>Einsum</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_einsum.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Einsum</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>equation</th>
-      <td><p>(string) The equation is given in terms of lower case letters (indices) to be associated           with each dimension of the operands and result. The left hand side lists the operands           dimensions, separated by commas. There should be one index letter per tensor dimension.           The right hand side follows after <code>-&gt;</code> and gives the indices for the output.           If the <code>-&gt;</code> and right hand side are omitted, it implicitly defined as the alphabetically           sorted list of all indices appearing exactly once in the left hand side.           The indices not apprearing in the output are summed over after multiplying the operands           entries.           If an index appears several times for the same operand, a diagonal is taken.           Ellipses <code>...</code> represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.</p></td>
-    </tr>
-    <tr>
-      <th>operands</th>
-      <td><p>(Tensor) The operands to compute the Einstein sum of.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="einsum-equation-operands-gt-tensor-"><a class="anchor" href="#einsum-equation-operands-gt-tensor-"></a>einsum(equation, *operands) -&gt; Tensor </h2>
-
-    
-
-
-<p>This function provides a way of computing multilinear expressions (i.e. sums of products) using the
-Einstein summation convention.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='fu'>torch_einsum</span>(<span class='st'>'i,j-&gt;ij'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>x</span>, <span class='no'>y</span>))  <span class='co'># outer product</span>
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>4</span>))
-<span class='no'>l</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>5</span>))
-<span class='no'>r</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>4</span>))
-<span class='fu'>torch_einsum</span>(<span class='st'>'bn,anm,bm-&gt;ba'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>l</span>, <span class='no'>A</span>, <span class='no'>r</span>)) <span class='co'># compare torch_nn$functional$bilinear</span>
-<span class='no'>As</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>2</span>,<span class='fl'>5</span>))
-<span class='no'>Bs</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>4</span>))
-<span class='fu'>torch_einsum</span>(<span class='st'>'bij,bjk-&gt;bik'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>As</span>, <span class='no'>Bs</span>)) <span class='co'># batch matrix multiplication</span>
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_einsum</span>(<span class='st'>'ii-&gt;i'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>A</span>)) <span class='co'># diagonal</span>
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_einsum</span>(<span class='st'>'...ii-&gt;...i'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>A</span>)) <span class='co'># batch diagonal</span>
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='fu'>torch_einsum</span>(<span class='st'>'...ij-&gt;...ji'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>A</span>))$<span class='no'>shape</span> <span class='co'># batch permute</span>
-
-}
-}</div></pre>
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diff --git a/static/docs/reference/torch_empty.html b/static/docs/reference/torch_empty.html
deleted file mode 100644
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+++ /dev/null
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-    <h1>Empty</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_empty.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Empty</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>size</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>pin_memory</th>
-      <td><p>(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_contiguous_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="empty-size-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-pin-memory-false-gt-tensor-"><a class="anchor" href="#empty-size-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-pin-memory-false-gt-tensor-"></a>empty(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with uninitialized data. The shape of the tensor is
-defined by the variable argument <code>size</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_empty</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-}</div></pre>
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diff --git a/static/docs/reference/torch_empty_like.html b/static/docs/reference/torch_empty_like.html
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-    <h1>Empty_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_empty_like.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Empty_like</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>None</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>None</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="empty-like-input-dtype-none-layout-none-device-none-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#empty-like-input-dtype-none-layout-none-device-none-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>empty_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns an uninitialized tensor with the same size as <code>input</code>.
-<code>torch_empty_like(input)</code> is equivalent to
-<code><a href='torch_empty.html'>torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
-}</div></pre>
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-    <h1>Empty_strided</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_empty_strided.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Empty_strided</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>size</th>
-      <td><p>(tuple of ints) the shape of the output tensor</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(tuple of ints) the strides of the output tensor</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>pin_memory</th>
-      <td><p>(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="empty-strided-size-stride-dtype-none-layout-none-device-none-requires-grad-false-pin-memory-false-gt-tensor-"><a class="anchor" href="#empty-strided-size-stride-dtype-none-layout-none-device-none-requires-grad-false-pin-memory-false-gt-tensor-"></a>empty_strided(size, stride, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with uninitialized data. The shape and strides of the tensor is
-defined by the variable argument <code>size</code> and <code>stride</code> respectively.
-<code>torch_empty_strided(size, stride)</code> is equivalent to
-<code>torch_empty(size).as_strided(size, stride)</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>More than one element of the created tensor may refer to a single memory
-location. As a result, in-place operations (especially ones that are
-vectorized) may result in incorrect behavior. If you need to write to
-the tensors, please clone them first.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'>torch_empty_strided</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='no'>a</span>
-<span class='no'>a</span>$<span class='fu'>stride</span>(<span class='fl'>1</span>)
-<span class='no'>a</span>$<span class='fu'>size</span>(<span class='fl'>1</span>)
-}</div></pre>
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-    <h1>Eq</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_eq.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Eq</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor. Must be a <code>ByteTensor</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="eq-input-other-out-none-gt-tensor-"><a class="anchor" href="#eq-input-other-out-none-gt-tensor-"></a>eq(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes element-wise equality</p>
-<p>The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_eq</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>4</span>)))
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/torch_equal.html b/static/docs/reference/torch_equal.html
deleted file mode 100644
index 035d08fbf..000000000
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+++ /dev/null
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_equal.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Equal</p>
-    </div>
-
-
-
-    <h2 class="hasAnchor" id="equal-input-other-gt-bool-"><a class="anchor" href="#equal-input-other-gt-bool-"></a>equal(input, other) -&gt; bool </h2>
-
-    
-
-
-<p><code>True</code> if two tensors have the same size and elements, <code>False</code> otherwise.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_erf.Rd</code></div>
-    </div>
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-    <div class="ref-description">
-    <p>Erf</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="erf-input-out-none-gt-tensor-"><a class="anchor" href="#erf-input-out-none-gt-tensor-"></a>erf(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the error function of each element. The error function is defined as follows:</p>
-<p>$$
-    \mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_erf</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, -<span class='fl'>1.</span>, <span class='fl'>10.</span>)))
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_erfc.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Erfc</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="erfc-input-out-none-gt-tensor-"><a class="anchor" href="#erfc-input-out-none-gt-tensor-"></a>erfc(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the complementary error function of each element of <code>input</code>.
-The complementary error function is defined as follows:</p>
-<p>$$
-    \mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_erfc</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, -<span class='fl'>1.</span>, <span class='fl'>10.</span>)))
-}</div></pre>
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-    <h1>Erfinv</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_erfinv.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Erfinv</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="erfinv-input-out-none-gt-tensor-"><a class="anchor" href="#erfinv-input-out-none-gt-tensor-"></a>erfinv(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the inverse error function of each element of <code>input</code>.
-The inverse error function is defined in the range \((-1, 1)\) as:</p>
-<p>$$
-    \mathrm{erfinv}(\mathrm{erf}(x)) = x
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_erfinv</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>0.5</span>, -<span class='fl'>1.</span>)))
-}</div></pre>
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-    <h1>Exp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_exp.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Exp</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="exp-input-out-none-gt-tensor-"><a class="anchor" href="#exp-input-out-none-gt-tensor-"></a>exp(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the exponential of the elements
-of the input tensor <code>input</code>.</p>
-<p>$$
-    y_{i} = e^{x_{i}}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_exp</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>(<span class='fl'>2</span>))))
-}</div></pre>
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diff --git a/static/docs/reference/torch_expm1.html b/static/docs/reference/torch_expm1.html
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-    <h1>Expm1</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_expm1.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Expm1</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="expm-input-out-none-gt-tensor-"><a class="anchor" href="#expm-input-out-none-gt-tensor-"></a>expm1(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the exponential of the elements minus 1
-of <code>input</code>.</p>
-<p>$$
-    y_{i} = e^{x_{i}} - 1
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_expm1</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>(<span class='fl'>2</span>))))
-}</div></pre>
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diff --git a/static/docs/reference/torch_eye.html b/static/docs/reference/torch_eye.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_eye.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Eye</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>n</th>
-      <td><p>(int) the number of rows</p></td>
-    </tr>
-    <tr>
-      <th>m</th>
-      <td><p>(int, optional) the number of columns with default being <code>n</code></p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="eye-n-m-none-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#eye-n-m-none-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>eye(n, m=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_eye</span>(<span class='fl'>3</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_fft.html b/static/docs/reference/torch_fft.html
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-    <h1>Fft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_fft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fft</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
-    </tr>
-    <tr>
-      <th>signal_ndim</th>
-      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-</pre>
-
-    <h2 class="hasAnchor" id="fft-input-signal-ndim-normalized-false-gt-tensor-"><a class="anchor" href="#fft-input-signal-ndim-normalized-false-gt-tensor-"></a>fft(input, signal_ndim, normalized=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Complex-to-complex Discrete Fourier Transform</p>
-<p>This method computes the complex-to-complex discrete Fourier transform.
-Ignoring the batch dimensions, it computes the following expression:</p>
-<p>$$
-    X[\omega_1, \dots, \omega_d] =
-        \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
-         e^{-j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
-$$
-where \(d\) = <code>signal_ndim</code> is number of dimensions for the
-signal, and \(N_i\) is the size of signal dimension \(i\).</p>
-<p>This method supports 1D, 2D and 3D complex-to-complex transforms, indicated
-by <code>signal_ndim</code>. <code>input</code> must be a tensor with last dimension
-of size 2, representing the real and imaginary components of complex
-numbers, and should have at least <code>signal_ndim + 1</code> dimensions with optionally
-arbitrary number of leading batch dimensions. If <code>normalized</code> is set to
-<code>True</code>, this normalizes the result by dividing it with
-\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary.</p>
-<p>Returns the real and the imaginary parts together as one tensor of the same
-shape of <code>input</code>.</p>
-<p>The inverse of this function is <code><a href='torch_ifft.html'>torch_ifft</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>For CPU tensors, this method is currently only available with MKL. Use
-<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># unbatched 2D FFT</span>
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='fu'>torch_fft</span>(<span class='no'>x</span>, <span class='fl'>2</span>)
-<span class='co'># batched 1D FFT</span>
-<span class='fu'>torch_fft</span>(<span class='no'>x</span>, <span class='fl'>1</span>)
-<span class='co'># arbitrary number of batch dimensions, 2D FFT</span>
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>5</span>, <span class='fl'>2</span>))
-<span class='fu'>torch_fft</span>(<span class='no'>x</span>, <span class='fl'>2</span>)
-
-}</div></pre>
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diff --git a/static/docs/reference/torch_finfo.html b/static/docs/reference/torch_finfo.html
deleted file mode 100644
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+++ /dev/null
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-    <h1>Floating point type info</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/type-info.R'><code>R/type-info.R</code></a></small>
-    <div class="hidden name"><code>torch_finfo.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A list that represents the numerical properties of a
-floating point torch.dtype</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_finfo</span>(<span class='no'>dtype</span>)</pre>
-
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-    <table class="ref-arguments">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_flatten.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Flatten</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>start_dim</th>
-      <td><p>(int) the first dim to flatten</p></td>
-    </tr>
-    <tr>
-      <th>end_dim</th>
-      <td><p>(int) the last dim to flatten</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="flatten-input-start-dim-end-dim-gt-tensor-"><a class="anchor" href="#flatten-input-start-dim-end-dim-gt-tensor-"></a>flatten(input, start_dim=0, end_dim=-1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Flattens a contiguous range of dims in a tensor.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>t</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>))
-<span class='fu'>torch_flatten</span>(<span class='no'>t</span>)
-<span class='fu'>torch_flatten</span>(<span class='no'>t</span>, <span class='kw'>start_dim</span><span class='kw'>=</span><span class='fl'>2</span>)
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_flip.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Flip</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(a list or tuple) axis to flip on</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="flip-input-dims-gt-tensor-"><a class="anchor" href="#flip-input-dims-gt-tensor-"></a>flip(input, dims) -&gt; Tensor </h2>
-
-    
-
-
-<p>Reverse the order of a n-D tensor along given axis in dims.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>8</span>)$<span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='no'>x</span>
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-}</div></pre>
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-    <h1>Floor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_floor.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Floor</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="floor-input-out-none-gt-tensor-"><a class="anchor" href="#floor-input-out-none-gt-tensor-"></a>floor(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the floor of the elements of <code>input</code>,
-the largest integer less than or equal to each element.</p>
-<p>$$
-    \mbox{out}_{i} = \left\lfloor \mbox{input}_{i} \right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_floor</span>(<span class='no'>a</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_floor_divide.html b/static/docs/reference/torch_floor_divide.html
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-    <h1>Floor_divide</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_floor_divide.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Floor_divide</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the numerator tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or Scalar) the denominator</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="floor-divide-input-other-out-none-gt-tensor-"><a class="anchor" href="#floor-divide-input-other-out-none-gt-tensor-"></a>floor_divide(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Return the division of the inputs rounded down to the nearest integer. See <code><a href='torch_div.html'>torch_div</a></code>
-for type promotion and broadcasting rules.</p>
-<p>$$
-    \mbox{{out}}_i = \left\lfloor \frac{{\mbox{{input}}_i}}{{\mbox{{other}}_i}} \right\rfloor
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4.0</span>, <span class='fl'>3.0</span>))
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2.0</span>, <span class='fl'>2.0</span>))
-<span class='fu'>torch_floor_divide</span>(<span class='no'>a</span>, <span class='no'>b</span>)
-<span class='fu'>torch_floor_divide</span>(<span class='no'>a</span>, <span class='fl'>1.4</span>)
-}</div></pre>
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-    <h1>Fmod</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_fmod.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fmod</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the dividend</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="fmod-input-other-out-none-gt-tensor-"><a class="anchor" href="#fmod-input-other-out-none-gt-tensor-"></a>fmod(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise remainder of division.</p>
-<p>The dividend and divisor may contain both for integer and floating point
-numbers. The remainder has the same sign as the dividend <code>input</code>.</p>
-<p>When <code>other</code> is a tensor, the shapes of <code>input</code> and
-<code>other</code> must be broadcastable .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_fmod</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>3.</span>, -<span class='fl'>2</span>, -<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)), <span class='fl'>2</span>)
-<span class='fu'>torch_fmod</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>)), <span class='fl'>1.5</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_frac.html b/static/docs/reference/torch_frac.html
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-    <h1>Frac</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_frac.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Frac</p>
-    </div>
-
-
-
-    <h2 class="hasAnchor" id="frac-input-out-none-gt-tensor-"><a class="anchor" href="#frac-input-out-none-gt-tensor-"></a>frac(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the fractional portion of each element in <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \mbox{input}_{i} - \left\lfloor |\mbox{input}_{i}| \right\rfloor * \mbox{sgn}(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
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-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_full.Rd</code></div>
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-    <div class="ref-description">
-    <p>Full</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>size</th>
-      <td><p>(int...) a list, tuple, or <code>torch_Size</code> of integers defining the        shape of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>fill_value</th>
-      <td><p>NA the number to fill the output tensor with.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="full-size-fill-value-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#full-size-fill-value-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>full(size, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor of size <code>size</code> filled with <code>fill_value</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>In PyTorch 1.5 a bool or integral <code>fill_value</code> will produce a warning if
-<code>dtype</code> or <code>out</code> are not set.
-In a future PyTorch release, when <code>dtype</code> and <code>out</code> are not set
-a bool <code>fill_value</code> will return a tensor of torch.bool dtype,
-and an integral <code>fill_value</code> will return a tensor of torch.long dtype.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_full</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fl'>3.141592</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_full_like.html b/static/docs/reference/torch_full_like.html
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>fill_value</th>
-      <td><p>NA the number to fill the output tensor with.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>None</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>None</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="full-like-input-fill-value-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-"><a class="anchor" href="#full-like-input-fill-value-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-"></a>full_like(input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, </h2>
-
-    
-
-
-<p>memory_format=torch.preserve_format) -&gt; Tensor</p>
-<p>Returns a tensor with the same size as <code>input</code> filled with <code>fill_value</code>.
-<code>torch_full_like(input, fill_value)</code> is equivalent to
-<code><a href='torch_full.html'>torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
-
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diff --git a/static/docs/reference/torch_gather.html b/static/docs/reference/torch_gather.html
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-    <h1>Gather</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_gather.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Gather</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the source tensor</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the axis along which to index</p></td>
-    </tr>
-    <tr>
-      <th>index</th>
-      <td><p>(LongTensor) the indices of elements to gather</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the destination tensor</p></td>
-    </tr>
-    <tr>
-      <th>sparse_grad</th>
-      <td><p>(bool,optional) If <code>True</code>, gradient w.r.t. <code>input</code> will be a sparse tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="gather-input-dim-index-out-none-sparse-grad-false-gt-tensor-"><a class="anchor" href="#gather-input-dim-index-out-none-sparse-grad-false-gt-tensor-"></a>gather(input, dim, index, out=None, sparse_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Gathers values along an axis specified by <code>dim</code>.</p>
-<p>For a 3-D tensor the output is specified by::</p><pre><span class='no'>out</span>[<span class='no'>i</span>][<span class='no'>j</span>][<span class='no'>k</span>] <span class='kw'>=</span> <span class='no'>input</span>[<span class='no'>index</span>[<span class='no'>i</span>][<span class='no'>j</span>][<span class='no'>k</span>]][<span class='no'>j</span>][<span class='no'>k</span>]  <span class='co'># if dim == 0</span>
-<span class='no'>out</span>[<span class='no'>i</span>][<span class='no'>j</span>][<span class='no'>k</span>] <span class='kw'>=</span> <span class='no'>input</span>[<span class='no'>i</span>][<span class='no'>index</span>[<span class='no'>i</span>][<span class='no'>j</span>][<span class='no'>k</span>]][<span class='no'>k</span>]  <span class='co'># if dim == 1</span>
-<span class='no'>out</span>[<span class='no'>i</span>][<span class='no'>j</span>][<span class='no'>k</span>] <span class='kw'>=</span> <span class='no'>input</span>[<span class='no'>i</span>][<span class='no'>j</span>][<span class='no'>index</span>[<span class='no'>i</span>][<span class='no'>j</span>][<span class='no'>k</span>]]  <span class='co'># if dim == 2</span></pre>
-
-<p>If <code>input</code> is an n-dimensional tensor with size
-\((x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})\)
-and <code>dim = i</code>, then <code>index</code> must be an \(n\)-dimensional tensor with
-size \((x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})\) where \(y \geq 1\)
-and <code>out</code> will have the same size as <code>index</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>t</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))
-<span class='fu'>torch_gather</span>(<span class='no'>t</span>, <span class='fl'>2</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>1</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()))
-}</div></pre>
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-    <h1>Ge</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_ge.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ge</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor that must be a <code>BoolTensor</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ge-input-other-out-none-gt-tensor-"><a class="anchor" href="#ge-input-other-out-none-gt-tensor-"></a>ge(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes \(\mbox{input} \geq \mbox{other}\) element-wise.</p>
-<p>The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_ge</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span>:<span class='fl'>4</span>, <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>)),
-         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>)))
-}</div></pre>
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diff --git a/static/docs/reference/torch_generator.html b/static/docs/reference/torch_generator.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/generator.R'><code>R/generator.R</code></a></small>
-    <div class="hidden name"><code>torch_generator.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A <code>torch_generator</code>  is an object which manages the state of the algorithm
-that produces pseudo random numbers. Used as a keyword argument in many
-In-place random sampling functions.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_generator</span>()</pre>
-
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># Via string</span>
-<span class='no'>generator</span> <span class='kw'>&lt;-</span> <span class='fu'>torch_generator</span>()
-<span class='no'>generator</span>$<span class='fu'>current_seed</span>()
-<span class='no'>generator</span>$<span class='fu'>set_current_seed</span>(<span class='fl'>1234567L</span>)
-<span class='no'>generator</span>$<span class='fu'>current_seed</span>()
-
-
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_geqrf.html b/static/docs/reference/torch_geqrf.html
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-
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-    <div class="page-header">
-    <h1>Geqrf</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_geqrf.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Geqrf</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input matrix</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the output tuple of (Tensor, Tensor)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="geqrf-input-out-none-gt-tensor-tensor-"><a class="anchor" href="#geqrf-input-out-none-gt-tensor-tensor-"></a>geqrf(input, out=None) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>This is a low-level function for calling LAPACK directly. This function
-returns a namedtuple (a, tau) as defined in <code>LAPACK documentation for geqrf</code>_ .</p>
-<p>You'll generally want to use <code><a href='torch_qr.html'>torch_qr</a></code> instead.</p>
-<p>Computes a QR decomposition of <code>input</code>, but without constructing
-\(Q\) and \(R\) as explicit separate matrices.</p>
-<p>Rather, this directly calls the underlying LAPACK function <code>?geqrf</code>
-which produces a sequence of 'elementary reflectors'.</p>
-<p>See <code>LAPACK documentation for geqrf</code>_ for further details.</p>
-
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diff --git a/static/docs/reference/torch_ger.html b/static/docs/reference/torch_ger.html
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-    <h1>Ger</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_ger.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ger</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) 1-D input vector</p></td>
-    </tr>
-    <tr>
-      <th>vec2</th>
-      <td><p>(Tensor) 1-D input vector</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) optional output matrix</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function does not broadcast .</p>
-    <h2 class="hasAnchor" id="ger-input-vec-out-none-gt-tensor-"><a class="anchor" href="#ger-input-vec-out-none-gt-tensor-"></a>ger(input, vec2, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Outer product of <code>input</code> and <code>vec2</code>.
-If <code>input</code> is a vector of size \(n\) and <code>vec2</code> is a vector of
-size \(m\), then <code>out</code> must be a matrix of size \((n \times m)\).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>v1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
-<span class='no'>v2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>4.</span>)
-<span class='fu'>torch_ger</span>(<span class='no'>v1</span>, <span class='no'>v2</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/static/docs/reference/torch_gt.html b/static/docs/reference/torch_gt.html
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-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor that must be a <code>BoolTensor</code></p></td>
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-         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>)))
-}</div></pre>
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-    <h1>Hamming_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_hamming_window.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Hamming_window</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) the size of returned window</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(float, optional) The coefficient \(\alpha\) in the equation above</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(float, optional) The coefficient \(\beta\) in the equation above</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
-</pre>
-
-<pre>This is a generalized version of `torch_hann_window`.
-</pre>
-
-    <h2 class="hasAnchor" id="hamming-window-window-length-periodic-true-alpha-beta-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#hamming-window-window-length-periodic-true-alpha-beta-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Hamming window function.</p>
-<p>$$
-    w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right),
-$$
-where \(N\) is the full window size.</p>
-<p>The input <code>window_length</code> is a positive integer controlling the
-returned window size. <code>periodic</code> flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-<code>torch_hamming_window(L, periodic=True)</code> equal to
-<code>torch_hamming_window(L + 1, periodic=False)[:-1])</code>.</p>
-
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-    <h1>Hann_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_hann_window.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Hann_window</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) the size of returned window</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
-</pre>
-
-    <h2 class="hasAnchor" id="hann-window-window-length-periodic-true-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#hann-window-window-length-periodic-true-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>hann_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Hann window function.</p>
-<p>$$
-    w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] =
-            \sin^2 \left( \frac{\pi n}{N - 1} \right),
-$$
-where \(N\) is the full window size.</p>
-<p>The input <code>window_length</code> is a positive integer controlling the
-returned window size. <code>periodic</code> flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-<code>torch_hann_window(L, periodic=True)</code> equal to
-<code>torch_hann_window(L + 1, periodic=False)[:-1])</code>.</p>
-
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-    <h1>Histc</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_histc.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Histc</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>bins</th>
-      <td><p>(int) number of histogram bins</p></td>
-    </tr>
-    <tr>
-      <th>min</th>
-      <td><p>(int) lower end of the range (inclusive)</p></td>
-    </tr>
-    <tr>
-      <th>max</th>
-      <td><p>(int) upper end of the range (inclusive)</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="histc-input-bins-min-max-out-none-gt-tensor-"><a class="anchor" href="#histc-input-bins-min-max-out-none-gt-tensor-"></a>histc(input, bins=100, min=0, max=0, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the histogram of a tensor.</p>
-<p>The elements are sorted into equal width bins between <code>min</code> and
-<code>max</code>. If <code>min</code> and <code>max</code> are both zero, the minimum and
-maximum values of the data are used.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_histc</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>1</span>)), <span class='kw'>bins</span><span class='kw'>=</span><span class='fl'>4</span>, <span class='kw'>min</span><span class='kw'>=</span><span class='fl'>0</span>, <span class='kw'>max</span><span class='kw'>=</span><span class='fl'>3</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Ifft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_ifft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ifft</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
-    </tr>
-    <tr>
-      <th>signal_ndim</th>
-      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-</pre>
-
-    <h2 class="hasAnchor" id="ifft-input-signal-ndim-normalized-false-gt-tensor-"><a class="anchor" href="#ifft-input-signal-ndim-normalized-false-gt-tensor-"></a>ifft(input, signal_ndim, normalized=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Complex-to-complex Inverse Discrete Fourier Transform</p>
-<p>This method computes the complex-to-complex inverse discrete Fourier
-transform. Ignoring the batch dimensions, it computes the following
-expression:</p>
-<p>$$
-    X[\omega_1, \dots, \omega_d] =
-        \frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
-         e^{\ j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
-$$
-where \(d\) = <code>signal_ndim</code> is number of dimensions for the
-signal, and \(N_i\) is the size of signal dimension \(i\).</p>
-<p>The argument specifications are almost identical with <code><a href='torch_fft.html'>torch_fft</a></code>.
-However, if <code>normalized</code> is set to <code>True</code>, this instead returns the
-results multiplied by \(\sqrt{\prod_{i=1}^d N_i}\), to become a unitary
-operator. Therefore, to invert a <code><a href='torch_fft.html'>torch_fft</a></code>, the <code>normalized</code>
-argument should be set identically for <code><a href='torch_fft.html'>torch_fft</a></code>.</p>
-<p>Returns the real and the imaginary parts together as one tensor of the same
-shape of <code>input</code>.</p>
-<p>The inverse of this function is <code><a href='torch_fft.html'>torch_fft</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>For CPU tensors, this method is currently only available with MKL. Use
-<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='no'>x</span>
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'><a href='torch_fft.html'>torch_fft</a></span>(<span class='no'>x</span>, <span class='fl'>2</span>)
-<span class='fu'>torch_ifft</span>(<span class='no'>y</span>, <span class='fl'>2</span>)  <span class='co'># recover x</span>
-}</div></pre>
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diff --git a/static/docs/reference/torch_iinfo.html b/static/docs/reference/torch_iinfo.html
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-    <h1>Integer type info</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/type-info.R'><code>R/type-info.R</code></a></small>
-    <div class="hidden name"><code>torch_iinfo.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A list that represents the numerical properties of a integer
-type.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_iinfo</span>(<span class='no'>dtype</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dtype</th>
-      <td><p>dtype to get information from.</p></td>
-    </tr>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_imag.html b/static/docs/reference/torch_imag.html
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-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
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-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
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-
-    <h2 class="hasAnchor" id="imag-input-out-none-gt-tensor-"><a class="anchor" href="#imag-input-out-none-gt-tensor-"></a>imag(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the imaginary part of the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Not yet implemented.</p>
-<p>$$
-    \mbox{out}_{i} = imag(\mbox{input}_{i})
-$$</p>
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-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_imag</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>1</span> + <span class='fl'>1i</span>, -<span class='fl'>2</span> + <span class='fl'>2i</span>, <span class='fl'>3</span> - <span class='fl'>3i</span>)))
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diff --git a/static/docs/reference/torch_index_select.html b/static/docs/reference/torch_index_select.html
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-    <h1>Index_select</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_index_select.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Index_select</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension in which we index</p></td>
-    </tr>
-    <tr>
-      <th>index</th>
-      <td><p>(LongTensor) the 1-D tensor containing the indices to index</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>The returned tensor does <strong>not</strong> use the same storage as the original
-tensor.  If <code>out</code> has a different shape than expected, we
-silently change it to the correct shape, reallocating the underlying
-storage if necessary.</p>
-    <h2 class="hasAnchor" id="index-select-input-dim-index-out-none-gt-tensor-"><a class="anchor" href="#index-select-input-dim-index-out-none-gt-tensor-"></a>index_select(input, dim, index, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor which indexes the <code>input</code> tensor along dimension
-<code>dim</code> using the entries in <code>index</code> which is a <code>LongTensor</code>.</p>
-<p>The returned tensor has the same number of dimensions as the original tensor
-(<code>input</code>).  The <code>dim</code>\ th dimension has the same size as the length
-of <code>index</code>; other dimensions have the same size as in the original tensor.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='no'>x</span>
-<span class='no'>indices</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
-<span class='fu'>torch_index_select</span>(<span class='no'>x</span>, <span class='fl'>1</span>, <span class='no'>indices</span>)
-<span class='fu'>torch_index_select</span>(<span class='no'>x</span>, <span class='fl'>2</span>, <span class='no'>indices</span>)
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_inverse.html b/static/docs/reference/torch_inverse.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_inverse.Rd</code></div>
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-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor of size \((*, n, n)\) where <code>*</code> is zero or more                    batch dimensions</p></td>
-    </tr>
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-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
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-
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-
-    
-<pre>Irrespective of the original strides, the returned tensors will be
-transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`
-</pre>
-
-    <h2 class="hasAnchor" id="inverse-input-out-none-gt-tensor-"><a class="anchor" href="#inverse-input-out-none-gt-tensor-"></a>inverse(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Takes the inverse of the square matrix <code>input</code>. <code>input</code> can be batches
-of 2D square tensors, in which case this function would return a tensor composed of
-individual inverses.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'>torch_inverse</span>(<span class='no'>x</span>)
-<span class='no'>z</span> <span class='kw'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='no'>x</span>, <span class='no'>y</span>)
-<span class='no'>z</span>
-<span class='fu'><a href='torch_max.html'>torch_max</a></span>(<span class='fu'><a href='torch_abs.html'>torch_abs</a></span>(<span class='no'>z</span> - <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>4</span>))) <span class='co'># Max non-zero</span>
-<span class='co'># Batched inverse example</span>
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'>torch_inverse</span>(<span class='no'>x</span>)
-<span class='no'>z</span> <span class='kw'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='no'>x</span>, <span class='no'>y</span>)
-<span class='fu'><a href='torch_max.html'>torch_max</a></span>(<span class='fu'><a href='torch_abs.html'>torch_abs</a></span>(<span class='no'>z</span> - <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>4</span>)$<span class='fu'>expand_as</span>(<span class='no'>x</span>))) <span class='co'># Max non-zero</span>
-}
-}</div></pre>
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diff --git a/static/docs/reference/torch_irfft.html b/static/docs/reference/torch_irfft.html
deleted file mode 100644
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-    <h1>Irfft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_irfft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Irfft</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
-    </tr>
-    <tr>
-      <th>signal_ndim</th>
-      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>False</code></p></td>
-    </tr>
-    <tr>
-      <th>onesided</th>
-      <td><p>(bool, optional) controls whether <code>input</code> was halfed to avoid        redundancy, e.g., by <code><a href='torch_rfft.html'>torch_rfft()</a></code>. Default: <code>True</code></p></td>
-    </tr>
-    <tr>
-      <th>signal_sizes</th>
-      <td><p>(list or <code>torch.Size</code>, optional) the size of the original        signal (without batch dimension). Default: <code>None</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>Due to the conjugate symmetry, `input` do not need to contain the full
-complex frequency values. Roughly half of the values will be sufficient, as
-is the case when `input` is given by [`~torch.rfft`] with
-``rfft(signal, onesided=True)``. In such case, set the `onesided`
-argument of this method to ``True``. Moreover, the original signal shape
-information can sometimes be lost, optionally set `signal_sizes` to be
-the size of the original signal (without the batch dimensions if in batched
-mode) to recover it with correct shape.
-
-Therefore, to invert an [torch_rfft()], the `normalized` and
-`onesided` arguments should be set identically for [torch_irfft()],
-and preferably a `signal_sizes` is given to avoid size mismatch. See the
-example below for a case of size mismatch.
-
-See [torch_rfft()] for details on conjugate symmetry.
-</pre>
-
-<p>The inverse of this function is <code><a href='torch_rfft.html'>torch_rfft()</a></code>.</p>
-<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-</pre>
-
-    <h2 class="hasAnchor" id="irfft-input-signal-ndim-normalized-false-onesided-true-signal-sizes-none-gt-tensor-"><a class="anchor" href="#irfft-input-signal-ndim-normalized-false-onesided-true-signal-sizes-none-gt-tensor-"></a>irfft(input, signal_ndim, normalized=False, onesided=True, signal_sizes=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Complex-to-real Inverse Discrete Fourier Transform</p>
-<p>This method computes the complex-to-real inverse discrete Fourier transform.
-It is mathematically equivalent with <code><a href='torch_ifft.html'>torch_ifft</a></code> with differences only in
-formats of the input and output.</p>
-<p>The argument specifications are almost identical with <code><a href='torch_ifft.html'>torch_ifft</a></code>.
-Similar to <code><a href='torch_ifft.html'>torch_ifft</a></code>, if <code>normalized</code> is set to <code>True</code>,
-this normalizes the result by multiplying it with
-\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary, where
-\(N_i\) is the size of signal dimension \(i\).</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Generally speaking, input to this function should contain values
-following conjugate symmetry. Note that even if <code>onesided</code> is
-<code>True</code>, often symmetry on some part is still needed. When this
-requirement is not satisfied, the behavior of <code>torch_irfft</code> is
-undefined. Since <code>torch_autograd.gradcheck</code> estimates numerical
-Jacobian with point perturbations, <code>torch_irfft</code> will almost
-certainly fail the check.</p>
-
-<p>For CPU tensors, this method is currently only available with MKL. Use
-<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span>(<span class='no'>x</span>, <span class='fl'>2</span>, <span class='kw'>onesided</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span>(<span class='no'>x</span>, <span class='fl'>2</span>, <span class='kw'>onesided</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span>(<span class='no'>x</span>, <span class='fl'>2</span>, <span class='kw'>onesided</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='fu'>torch_irfft</span>(<span class='no'>y</span>, <span class='fl'>2</span>, <span class='kw'>onesided</span><span class='kw'>=</span><span class='fl'>TRUE</span>, <span class='kw'>signal_sizes</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>5</span>))  <span class='co'># recover x</span>
-}</div></pre>
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-    <div class="page-header">
-    <h1>Is_complex</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_is_complex.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Is_complex</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the PyTorch tensor to test</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="is-complex-input-gt-bool-"><a class="anchor" href="#is-complex-input-gt-bool-"></a>is_complex(input) -&gt; (bool) </h2>
-
-    
-
-
-<p>Returns True if the data type of <code>input</code> is a complex data type i.e.,
-one of <code>torch_complex64</code>, and <code>torch.complex128</code>.</p>
-
-  </div>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
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-    <table class="ref-arguments">
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-    <tr>
-      <th>tensor</th>
-      <td><p>(Tensor) A tensor to check</p></td>
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-
-<span class='fu'>torch_isfinite</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>Inf</span>, <span class='fl'>2</span>, -<span class='fl'>Inf</span>, <span class='fl'>NaN</span>)))
-}</div></pre>
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-    <h1>Kthvalue</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_kthvalue.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Kthvalue</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>k</th>
-      <td><p>(int) k for the k-th smallest element</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to find the kth value along</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the output tuple of (Tensor, LongTensor)                           can be optionally given to be used as output buffers</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="kthvalue-input-k-dim-none-keepdim-false-out-none-gt-tensor-longtensor-"><a class="anchor" href="#kthvalue-input-k-dim-none-keepdim-false-out-none-gt-tensor-longtensor-"></a>kthvalue(input, k, dim=None, keepdim=False, out=None) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the <code>k</code> th
-smallest element of each row of the <code>input</code> tensor in the given dimension
-<code>dim</code>. And <code>indices</code> is the index location of each element found.</p>
-<p>If <code>dim</code> is not given, the last dimension of the <code>input</code> is chosen.</p>
-<p>If <code>keepdim</code> is <code>True</code>, both the <code>values</code> and <code>indices</code> tensors
-are the same size as <code>input</code>, except in the dimension <code>dim</code> where
-they are of size 1. Otherwise, <code>dim</code> is squeezed
-(see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in both the <code>values</code> and
-<code>indices</code> tensors having 1 fewer dimension than the <code>input</code> tensor.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>)
-<span class='no'>x</span>
-<span class='fu'>torch_kthvalue</span>(<span class='no'>x</span>, <span class='fl'>4</span>)
-<span class='no'>x</span><span class='kw'>=</span><span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>,<span class='fl'>7.</span>)$<span class='fu'>resize_</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>))
-<span class='no'>x</span>
-<span class='fu'>torch_kthvalue</span>(<span class='no'>x</span>, <span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>TRUE</span>)
-}</div></pre>
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-
-<span class='fu'>torch_sparse_coo</span>()</pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_le.Rd</code></div>
-    </div>
-
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-    <p>Le</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor that must be a <code>BoolTensor</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="le-input-other-out-none-gt-tensor-"><a class="anchor" href="#le-input-other-out-none-gt-tensor-"></a>le(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes \(\mbox{input} \leq \mbox{other}\) element-wise.</p>
-<p>The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_le</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span>:<span class='fl'>4</span>, <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>)),
-         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>)))
-}</div></pre>
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diff --git a/static/docs/reference/torch_lerp.html b/static/docs/reference/torch_lerp.html
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-    <h1>Lerp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_lerp.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lerp</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor with the starting points</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(Tensor) the tensor with the ending points</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(float or tensor) the weight for the interpolation formula</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="lerp-input-end-weight-out-none-"><a class="anchor" href="#lerp-input-end-weight-out-none-"></a>lerp(input, end, weight, out=None) </h2>
-
-    
-
-
-<p>Does a linear interpolation of two tensors <code>start</code> (given by <code>input</code>) and <code>end</code> based
-on a scalar or tensor <code>weight</code> and returns the resulting <code>out</code> tensor.</p>
-<p>$$
-    \mbox{out}_i = \mbox{start}_i + \mbox{weight}_i \times (\mbox{end}_i - \mbox{start}_i)
-$$
-The shapes of <code>start</code> and <code>end</code> must be
-broadcastable . If <code>weight</code> is a tensor, then
-the shapes of <code>weight</code>, <code>start</code>, and <code>end</code> must be broadcastable .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>start</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
-<span class='no'>end</span> <span class='kw'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>4</span>)$<span class='fu'>fill_</span>(<span class='fl'>10</span>)
-<span class='no'>start</span>
-<span class='no'>end</span>
-<span class='fu'>torch_lerp</span>(<span class='no'>start</span>, <span class='no'>end</span>, <span class='fl'>0.5</span>)
-<span class='fu'>torch_lerp</span>(<span class='no'>start</span>, <span class='no'>end</span>, <span class='fu'><a href='torch_full_like.html'>torch_full_like</a></span>(<span class='no'>start</span>, <span class='fl'>0.5</span>))
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_lgamma.html b/static/docs/reference/torch_lgamma.html
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-    <h1>Lgamma</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_lgamma.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lgamma</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="lgamma-input-out-none-gt-tensor-"><a class="anchor" href="#lgamma-input-out-none-gt-tensor-"></a>lgamma(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the logarithm of the gamma function on <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \log \Gamma(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0.5</span>, <span class='fl'>2</span>, <span class='fl'>0.5</span>)
-<span class='fu'>torch_lgamma</span>(<span class='no'>a</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_linspace.html b/static/docs/reference/torch_linspace.html
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-    <h1>Linspace</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_linspace.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Linspace</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(float) the starting value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(float) the ending value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>steps</th>
-      <td><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="linspace-start-end-steps-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#linspace-start-end-steps-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>linspace(start, end, steps=100, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a one-dimensional tensor of <code>steps</code>
-equally spaced points between <code>start</code> and <code>end</code>.</p>
-<p>The output tensor is 1-D of size <code>steps</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_linspace</span>(<span class='fl'>3</span>, <span class='fl'>10</span>, <span class='kw'>steps</span><span class='kw'>=</span><span class='fl'>5</span>)
-<span class='fu'>torch_linspace</span>(-<span class='fl'>10</span>, <span class='fl'>10</span>, <span class='kw'>steps</span><span class='kw'>=</span><span class='fl'>5</span>)
-<span class='fu'>torch_linspace</span>(<span class='kw'>start</span><span class='kw'>=</span>-<span class='fl'>10</span>, <span class='kw'>end</span><span class='kw'>=</span><span class='fl'>10</span>, <span class='kw'>steps</span><span class='kw'>=</span><span class='fl'>5</span>)
-<span class='fu'>torch_linspace</span>(<span class='kw'>start</span><span class='kw'>=</span>-<span class='fl'>10</span>, <span class='kw'>end</span><span class='kw'>=</span><span class='fl'>10</span>, <span class='kw'>steps</span><span class='kw'>=</span><span class='fl'>1</span>)
-}</div></pre>
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-    <h1>Loads a saved object</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/save.R'><code>R/save.R</code></a></small>
-    <div class="hidden name"><code>torch_load.Rd</code></div>
-    </div>
-
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-    <p>Loads a saved object</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_load</span>(<span class='no'>path</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>path</th>
-      <td><p>a path to the saved object</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other torch_save: 
-<code><a href='torch_save.html'>torch_save</a>()</code></p></div>
-
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diff --git a/static/docs/reference/torch_log.html b/static/docs/reference/torch_log.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_log.Rd</code></div>
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-    <p>Log</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="log-input-out-none-gt-tensor-"><a class="anchor" href="#log-input-out-none-gt-tensor-"></a>log(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the natural logarithm of the elements
-of <code>input</code>.</p>
-<p>$$
-    y_{i} = \log_{e} (x_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_log</span>(<span class='no'>a</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Log10</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_log10.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Log10</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="log-input-out-none-gt-tensor-"><a class="anchor" href="#log-input-out-none-gt-tensor-"></a>log10(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the logarithm to the base 10 of the elements
-of <code>input</code>.</p>
-<p>$$
-    y_{i} = \log_{10} (x_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fl'>5</span>)
-<span class='no'>a</span>
-<span class='fu'>torch_log10</span>(<span class='no'>a</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_log1p.html b/static/docs/reference/torch_log1p.html
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-    <h1>Log1p</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_log1p.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Log1p</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function is more accurate than <code><a href='torch_log.html'>torch_log</a></code> for small
-values of <code>input</code></p>
-    <h2 class="hasAnchor" id="log-p-input-out-none-gt-tensor-"><a class="anchor" href="#log-p-input-out-none-gt-tensor-"></a>log1p(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the natural logarithm of (1 + <code>input</code>).</p>
-<p>$$
-    y_i = \log_{e} (x_i + 1)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_log1p</span>(<span class='no'>a</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_log2.html b/static/docs/reference/torch_log2.html
deleted file mode 100644
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+++ /dev/null
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_log2.Rd</code></div>
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-    <p>Log2</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="log-input-out-none-gt-tensor-"><a class="anchor" href="#log-input-out-none-gt-tensor-"></a>log2(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the logarithm to the base 2 of the elements
-of <code>input</code>.</p>
-<p>$$
-    y_{i} = \log_{2} (x_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
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-<span class='no'>a</span>
-<span class='fu'>torch_log2</span>(<span class='no'>a</span>)
-}</div></pre>
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-    <h1>Logdet</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_logdet.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logdet</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code>*</code> is zero or more                batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>Result is ``-inf`` if `input` has zero log determinant, and is ``nan`` if
-`input` has negative determinant.
-</pre>
-
-<pre>Backward through `logdet` internally uses SVD results when `input`
-is not invertible. In this case, double backward through `logdet` will
-be unstable in when `input` doesn't have distinct singular values. See
-`~torch.svd` for details.
-</pre>
-
-    <h2 class="hasAnchor" id="logdet-input-gt-tensor-"><a class="anchor" href="#logdet-input-gt-tensor-"></a>logdet(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Calculates log determinant of a square matrix or batches of square matrices.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'><a href='torch_det.html'>torch_det</a></span>(<span class='no'>A</span>)
-<span class='fu'>torch_logdet</span>(<span class='no'>A</span>)
-<span class='no'>A</span>
-<span class='no'>A</span>$<span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()
-<span class='no'>A</span>$<span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()$<span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>()
-}</div></pre>
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diff --git a/static/docs/reference/torch_logical_and.html b/static/docs/reference/torch_logical_and.html
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-    <h1>Logical_and</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_logical_and.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logical_and</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the tensor to compute AND with</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logical-and-input-other-out-none-gt-tensor-"><a class="anchor" href="#logical-and-input-other-out-none-gt-tensor-"></a>logical_and(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise logical AND of the given input tensors. Zeros are treated as <code>False</code> and nonzeros are
-treated as <code>True</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_logical_and</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>TRUE</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>FALSE</span>)))
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
-<span class='fu'>torch_logical_and</span>(<span class='no'>a</span>, <span class='no'>b</span>)
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_logical_and</span>(<span class='no'>a</span>, <span class='no'>b</span>, <span class='kw'>out</span><span class='kw'>=</span><span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>4</span>, <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_bool</a></span>()))
-}
-}</div></pre>
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-    <h1>Logical_not</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_logical_not.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logical_not</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logical-not-input-out-none-gt-tensor-"><a class="anchor" href="#logical-not-input-out-none-gt-tensor-"></a>logical_not(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool
-dtype. If the input tensor is not a bool tensor, zeros are treated as <code>False</code> and non-zeros are treated as <code>True</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_logical_not</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>)))
-<span class='fu'>torch_logical_not</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, -<span class='fl'>10</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>()))
-<span class='fu'>torch_logical_not</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.</span>, <span class='fl'>1.5</span>, -<span class='fl'>10.</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()))
-}</div></pre>
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-    <h1>Logical_or</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_logical_or.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logical_or</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the tensor to compute OR with</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logical-or-input-other-out-none-gt-tensor-"><a class="anchor" href="#logical-or-input-other-out-none-gt-tensor-"></a>logical_or(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise logical OR of the given input tensors. Zeros are treated as <code>False</code> and nonzeros are
-treated as <code>True</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_logical_or</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>TRUE</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>FALSE</span>)))
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
-<span class='fu'>torch_logical_or</span>(<span class='no'>a</span>, <span class='no'>b</span>)
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_logical_or</span>(<span class='no'>a</span>$<span class='fu'><a href='https://rdrr.io/r/base/double.html'>double</a></span>(), <span class='no'>b</span>$<span class='fu'><a href='https://rdrr.io/r/base/double.html'>double</a></span>())
-<span class='fu'>torch_logical_or</span>(<span class='no'>a</span>$<span class='fu'><a href='https://rdrr.io/r/base/double.html'>double</a></span>(), <span class='no'>b</span>)
-<span class='fu'>torch_logical_or</span>(<span class='no'>a</span>, <span class='no'>b</span>, <span class='kw'>out</span><span class='kw'>=</span><span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>4</span>, <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_bool</a></span>()))
-}
-}</div></pre>
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diff --git a/static/docs/reference/torch_logical_xor.html b/static/docs/reference/torch_logical_xor.html
deleted file mode 100644
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-    <div class="page-header">
-    <h1>Logical_xor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_logical_xor.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logical_xor</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the tensor to compute XOR with</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logical-xor-input-other-out-none-gt-tensor-"><a class="anchor" href="#logical-xor-input-other-out-none-gt-tensor-"></a>logical_xor(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise logical XOR of the given input tensors. Zeros are treated as <code>False</code> and nonzeros are
-treated as <code>True</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_logical_xor</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>TRUE</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>FALSE</span>)))
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
-<span class='fu'>torch_logical_xor</span>(<span class='no'>a</span>, <span class='no'>b</span>)
-<span class='fu'>torch_logical_xor</span>(<span class='no'>a</span>$<span class='fu'>to</span>(<span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()), <span class='no'>b</span>$<span class='fu'>to</span>(<span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()))
-<span class='fu'>torch_logical_xor</span>(<span class='no'>a</span>$<span class='fu'>to</span>(<span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()), <span class='no'>b</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_logspace.html b/static/docs/reference/torch_logspace.html
deleted file mode 100644
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-    <div class="page-header">
-    <h1>Logspace</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_logspace.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logspace</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(float) the starting value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(float) the ending value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>steps</th>
-      <td><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></td>
-    </tr>
-    <tr>
-      <th>base</th>
-      <td><p>(float) base of the logarithm function. Default: <code>10.0</code>.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logspace-start-end-steps-base-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#logspace-start-end-steps-base-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>logspace(start, end, steps=100, base=10.0, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a one-dimensional tensor of <code>steps</code> points
-logarithmically spaced with base <code>base</code> between
-\({\mbox{base}}^{\mbox{start}}\) and \({\mbox{base}}^{\mbox{end}}\).</p>
-<p>The output tensor is 1-D of size <code>steps</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_logspace</span>(<span class='kw'>start</span><span class='kw'>=</span>-<span class='fl'>10</span>, <span class='kw'>end</span><span class='kw'>=</span><span class='fl'>10</span>, <span class='kw'>steps</span><span class='kw'>=</span><span class='fl'>5</span>)
-<span class='fu'>torch_logspace</span>(<span class='kw'>start</span><span class='kw'>=</span><span class='fl'>0.1</span>, <span class='kw'>end</span><span class='kw'>=</span><span class='fl'>1.0</span>, <span class='kw'>steps</span><span class='kw'>=</span><span class='fl'>5</span>)
-<span class='fu'>torch_logspace</span>(<span class='kw'>start</span><span class='kw'>=</span><span class='fl'>0.1</span>, <span class='kw'>end</span><span class='kw'>=</span><span class='fl'>1.0</span>, <span class='kw'>steps</span><span class='kw'>=</span><span class='fl'>1</span>)
-<span class='fu'>torch_logspace</span>(<span class='kw'>start</span><span class='kw'>=</span><span class='fl'>2</span>, <span class='kw'>end</span><span class='kw'>=</span><span class='fl'>2</span>, <span class='kw'>steps</span><span class='kw'>=</span><span class='fl'>1</span>, <span class='kw'>base</span><span class='kw'>=</span><span class='fl'>2</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/torch_logsumexp.html b/static/docs/reference/torch_logsumexp.html
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-    <h1>Logsumexp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_logsumexp.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Logsumexp</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logsumexp-input-dim-keepdim-false-out-none-"><a class="anchor" href="#logsumexp-input-dim-keepdim-false-out-none-"></a>logsumexp(input, dim, keepdim=False, out=None) </h2>
-
-    
-
-
-<p>Returns the log of summed exponentials of each row of the <code>input</code>
-tensor in the given dimension <code>dim</code>. The computation is numerically
-stabilized.</p>
-<p>For summation index \(j\) given by <code>dim</code> and other indices \(i\), the result is</p>
-<p>$$
-        \mbox{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij})
-$$</p>
-<p>If <code>keepdim</code> is <code>True</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_logsumexp</span>(<span class='no'>a</span>, <span class='fl'>1</span>)
-}</div></pre>
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-    <h1>Lstsq</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_lstsq.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lstsq</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the matrix \(B\)</p></td>
-    </tr>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor) the \(m\) by \(n\) matrix \(A\)</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the optional destination tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>The case when \eqn{m &amp;lt; n} is not supported on the GPU.
-</pre>
-
-    <h2 class="hasAnchor" id="lstsq-input-a-out-none-gt-tensor-"><a class="anchor" href="#lstsq-input-a-out-none-gt-tensor-"></a>lstsq(input, A, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the solution to the least squares and least norm problems for a full
-rank matrix \(A\) of size \((m \times n)\) and a matrix \(B\) of
-size \((m \times k)\).</p>
-<p>If \(m \geq n\), <code>torch_lstsq()</code> solves the least-squares problem:</p>
-<p>$$
-   \begin{array}{ll}
-   \min_X &amp; \|AX-B\|_2.
-   \end{array}
-$$
-If \(m &lt; n\), <code>torch_lstsq()</code> solves the least-norm problem:</p>
-<p>$$
-   \begin{array}{llll}
-   \min_X &amp; \|X\|_2 &amp; \mbox{subject to} &amp; AX = B.
-   \end{array}
-$$
-Returned tensor \(X\) has shape \((\mbox{max}(m, n) \times k)\). The first \(n\)
-rows of \(X\) contains the solution. If \(m \geq n\), the residual sum of squares
-for the solution in each column is given by the sum of squares of elements in the
-remaining \(m - n\) rows of that column.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>1</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>2</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>2</span>,<span class='fl'>5</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>4</span>,<span class='fl'>3</span>)
-))
-<span class='no'>B</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>10</span>, -<span class='fl'>3</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>12</span>, <span class='fl'>14</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>14</span>, <span class='fl'>12</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>16</span>, <span class='fl'>16</span>),
- <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>18</span>, <span class='fl'>16</span>)
-))
-<span class='no'>out</span> <span class='kw'>=</span> <span class='fu'>torch_lstsq</span>(<span class='no'>B</span>, <span class='no'>A</span>)
-<span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]]
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_lt.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lt</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor that must be a <code>BoolTensor</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="lt-input-other-out-none-gt-tensor-"><a class="anchor" href="#lt-input-other-out-none-gt-tensor-"></a>lt(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes \(\mbox{input} &lt; \mbox{other}\) element-wise.</p>
-<p>The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_lt</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span>:<span class='fl'>4</span>, <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>)),
-         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>)))
-}</div></pre>
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diff --git a/static/docs/reference/torch_lu.html b/static/docs/reference/torch_lu.html
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-<meta property="og:description" content="Computes the LU factorization of a matrix or batches of matrices A. Returns a
-tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
-is set to True." />
-
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-<!-- mathjax -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
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-    <div class="page-header">
-    <h1>LU</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_lu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Computes the LU factorization of a matrix or batches of matrices A. Returns a
-tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
-is set to True.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_lu</span>(<span class='no'>A</span>, <span class='kw'>pivot</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>get_infos</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>out</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor) the tensor to factor of size (<em>, m, n)(</em>,m,n)</p></td>
-    </tr>
-    <tr>
-      <th>pivot</th>
-      <td><p>(bool, optional) – controls whether pivoting is done. Default: TRUE</p></td>
-    </tr>
-    <tr>
-      <th>get_infos</th>
-      <td><p>(bool, optional) – if set to True, returns an info IntTensor. Default: FALSE</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) – optional output tuple. If get_infos is True, then the elements
-in the tuple are Tensor, IntTensor, and IntTensor. If get_infos is False, then the
-elements in the tuple are Tensor, IntTensor. Default: NULL</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_lu</span>(<span class='no'>A</span>)
-
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_lu_solve.Rd</code></div>
-    </div>
-
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-    <p>Lu_solve</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>b</th>
-      <td><p>(Tensor) the RHS tensor of size \((*, m, k)\), where \(*\)                is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>LU_data</th>
-      <td><p>(Tensor) the pivoted LU factorization of A from <code>torch_lu</code> of size \((*, m, m)\),                       where \(*\) is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>LU_pivots</th>
-      <td><p>(IntTensor) the pivots of the LU factorization from <code>torch_lu</code> of size \((*, m)\),                           where \(*\) is zero or more batch dimensions.                           The batch dimensions of <code>LU_pivots</code> must be equal to the batch dimensions of                           <code>LU_data</code>.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="lu-solve-input-lu-data-lu-pivots-out-none-gt-tensor-"><a class="anchor" href="#lu-solve-input-lu-data-lu-pivots-out-none-gt-tensor-"></a>lu_solve(input, LU_data, LU_pivots, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the LU solve of the linear system \(Ax = b\) using the partially pivoted
-LU factorization of A from <code>torch_lu</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>))
-<span class='no'>out</span> <span class='kw'>=</span> <span class='fu'><a href='torch_lu.html'>torch_lu</a></span>(<span class='no'>A</span>)
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'>torch_lu_solve</span>(<span class='no'>b</span>, <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]], <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]])
-<span class='fu'><a href='torch_norm.html'>torch_norm</a></span>(<span class='fu'><a href='torch_bmm.html'>torch_bmm</a></span>(<span class='no'>A</span>, <span class='no'>x</span>) - <span class='no'>b</span>)
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/generator.R'><code>R/generator.R</code></a></small>
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>seed</th>
-      <td><p>integer seed.</p></td>
-    </tr>
-    </table>
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-    <h1>Masked_select</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_masked_select.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Masked_select</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>mask</th>
-      <td><p>(BoolTensor) the tensor containing the binary mask to index with</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>The returned tensor does <strong>not</strong> use the same storage
-as the original tensor</p>
-    <h2 class="hasAnchor" id="masked-select-input-mask-out-none-gt-tensor-"><a class="anchor" href="#masked-select-input-mask-out-none-gt-tensor-"></a>masked_select(input, mask, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new 1-D tensor which indexes the <code>input</code> tensor according to
-the boolean mask <code>mask</code> which is a <code>BoolTensor</code>.</p>
-<p>The shapes of the <code>mask</code> tensor and the <code>input</code> tensor don't need
-to match, but they must be broadcastable .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='no'>x</span>
-<span class='no'>mask</span> <span class='kw'>=</span> <span class='no'>x</span>$<span class='fu'>ge</span>(<span class='fl'>0.5</span>)
-<span class='no'>mask</span>
-<span class='fu'>torch_masked_select</span>(<span class='no'>x</span>, <span class='no'>mask</span>)
-}</div></pre>
-  </div>
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-    <h1>Matmul</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_matmul.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Matmul</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the first tensor to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second tensor to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>The 1-dimensional dot product version of this function does not support an `out` parameter.
-</pre>
-
-    <h2 class="hasAnchor" id="matmul-input-other-out-none-gt-tensor-"><a class="anchor" href="#matmul-input-other-out-none-gt-tensor-"></a>matmul(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Matrix product of two tensors.</p>
-<p>The behavior depends on the dimensionality of the tensors as follows:</p><ul>
-<li><p>If both tensors are 1-dimensional, the dot product (scalar) is returned.</p></li>
-<li><p>If both arguments are 2-dimensional, the matrix-matrix product is returned.</p></li>
-<li><p>If the first argument is 1-dimensional and the second argument is 2-dimensional,
-a 1 is prepended to its dimension for the purpose of the matrix multiply.
-After the matrix multiply, the prepended dimension is removed.</p></li>
-<li><p>If the first argument is 2-dimensional and the second argument is 1-dimensional,
-the matrix-vector product is returned.</p></li>
-<li><p>If both arguments are at least 1-dimensional and at least one argument is
-N-dimensional (where N &gt; 2), then a batched matrix multiply is returned.  If the first
-argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the
-batched matrix multiply and removed after.  If the second argument is 1-dimensional, a
-1 is appended to its dimension for the purpose of the batched matrix multiple and removed after.
-The non-matrix (i.e. batch) dimensions are broadcasted  (and thus
-must be broadcastable).  For example, if <code>input</code> is a
-\((j \times 1 \times n \times m)\) tensor and <code>other</code> is a \((k \times m \times p)\)
-tensor, <code>out</code> will be an \((j \times k \times n \times p)\) tensor.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='co'># vector x vector</span>
-<span class='no'>tensor1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='no'>tensor2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='fu'>torch_matmul</span>(<span class='no'>tensor1</span>, <span class='no'>tensor2</span>)
-<span class='co'># matrix x vector</span>
-<span class='no'>tensor1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='no'>tensor2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='fu'>torch_matmul</span>(<span class='no'>tensor1</span>, <span class='no'>tensor2</span>)
-<span class='co'># batched matrix x broadcasted vector</span>
-<span class='no'>tensor1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='no'>tensor2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='fu'>torch_matmul</span>(<span class='no'>tensor1</span>, <span class='no'>tensor2</span>)
-<span class='co'># batched matrix x batched matrix</span>
-<span class='no'>tensor1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='no'>tensor2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='fu'>torch_matmul</span>(<span class='no'>tensor1</span>, <span class='no'>tensor2</span>)
-<span class='co'># batched matrix x broadcasted matrix</span>
-<span class='no'>tensor1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='no'>tensor2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='fu'>torch_matmul</span>(<span class='no'>tensor1</span>, <span class='no'>tensor2</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_matrix_power.html b/static/docs/reference/torch_matrix_power.html
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-    <h1>Matrix_power</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_matrix_power.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Matrix_power</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>n</th>
-      <td><p>(int) the power to raise the matrix to</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="matrix-power-input-n-gt-tensor-"><a class="anchor" href="#matrix-power-input-n-gt-tensor-"></a>matrix_power(input, n) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the matrix raised to the power <code>n</code> for square matrices.
-For batch of matrices, each individual matrix is raised to the power <code>n</code>.</p>
-<p>If <code>n</code> is negative, then the inverse of the matrix (if invertible) is
-raised to the power <code>n</code>.  For a batch of matrices, the batched inverse
-(if invertible) is raised to the power <code>n</code>. If <code>n</code> is 0, then an identity matrix
-is returned.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_matrix_power</span>(<span class='no'>a</span>, <span class='fl'>3</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_matrix_rank.html b/static/docs/reference/torch_matrix_rank.html
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-    <div class="page-header">
-    <h1>Matrix_rank</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_matrix_rank.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Matrix_rank</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input 2-D tensor</p></td>
-    </tr>
-    <tr>
-      <th>tol</th>
-      <td><p>(float, optional) the tolerance value. Default: <code>None</code></p></td>
-    </tr>
-    <tr>
-      <th>symmetric</th>
-      <td><p>(bool, optional) indicates whether <code>input</code> is symmetric.                               Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="matrix-rank-input-tol-none-symmetric-false-gt-tensor-"><a class="anchor" href="#matrix-rank-input-tol-none-symmetric-false-gt-tensor-"></a>matrix_rank(input, tol=None, symmetric=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the numerical rank of a 2-D tensor. The method to compute the
-matrix rank is done using SVD by default. If <code>symmetric</code> is <code>True</code>,
-then <code>input</code> is assumed to be symmetric, and the computation of the
-rank is done by obtaining the eigenvalues.</p>
-<p><code>tol</code> is the threshold below which the singular values (or the eigenvalues
-when <code>symmetric</code> is <code>True</code>) are considered to be 0. If <code>tol</code> is not
-specified, <code>tol</code> is set to <code>S.max() * max(S.size()) * eps</code> where <code>S</code> is the
-singular values (or the eigenvalues when <code>symmetric</code> is <code>True</code>), and <code>eps</code>
-is the epsilon value for the datatype of <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>10</span>)
-<span class='fu'>torch_matrix_rank</span>(<span class='no'>a</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_max.html b/static/docs/reference/torch_max.html
deleted file mode 100644
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-    <h1>Max</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_max.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Max</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the result tuple of two output tensors (max, max_indices)</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>When the shapes do not match, the shape of the returned output tensor
-follows the broadcasting rules .</p>
-    <h2 class="hasAnchor" id="max-input-gt-tensor-"><a class="anchor" href="#max-input-gt-tensor-"></a>max(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the maximum value of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="max-input-dim-keepdim-false-out-none-gt-tensor-longtensor-"><a class="anchor" href="#max-input-dim-keepdim-false-out-none-gt-tensor-longtensor-"></a>max(input, dim, keepdim=False, out=None) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the maximum
-value of each row of the <code>input</code> tensor in the given dimension
-<code>dim</code>. And <code>indices</code> is the index location of each maximum value found
-(argmax).</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p><code>indices</code> does not necessarily contain the first occurrence of each
-maximal value found, unless it is unique.
-The exact implementation details are device-specific.
-Do not expect the same result when run on CPU and GPU in general.</p>
-<p>If <code>keepdim</code> is <code>True</code>, the output tensors are of the same size
-as <code>input</code> except in the dimension <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting
-in the output tensors having 1 fewer dimension than <code>input</code>.</p>
-    <h2 class="hasAnchor" id="max-input-other-out-none-gt-tensor-"><a class="anchor" href="#max-input-other-out-none-gt-tensor-"></a>max(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Each element of the tensor <code>input</code> is compared with the corresponding
-element of the tensor <code>other</code> and an element-wise maximum is taken.</p>
-<p>The shapes of <code>input</code> and <code>other</code> don't need to match,
-but they must be broadcastable .</p>
-<p>$$
-    \mbox{out}_i = \max(\mbox{tensor}_i, \mbox{other}_i)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_max</span>(<span class='no'>a</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_max</span>(<span class='no'>a</span>, <span class='kw'>dim</span> <span class='kw'>=</span> <span class='fl'>1</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>b</span>
-<span class='fu'>torch_max</span>(<span class='no'>a</span>, <span class='kw'>other</span> <span class='kw'>=</span> <span class='no'>b</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/torch_mean.html b/static/docs/reference/torch_mean.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_mean.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Mean</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="mean-input-gt-tensor-"><a class="anchor" href="#mean-input-gt-tensor-"></a>mean(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the mean value of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="mean-input-dim-keepdim-false-out-none-gt-tensor-"><a class="anchor" href="#mean-input-dim-keepdim-false-out-none-gt-tensor-"></a>mean(input, dim, keepdim=False, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the mean value of each row of the <code>input</code> tensor in the given
-dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
-reduce over all of them.</p>
-<p>If <code>keepdim</code> is <code>True</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_mean</span>(<span class='no'>a</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_mean</span>(<span class='no'>a</span>, <span class='fl'>1</span>)
-<span class='fu'>torch_mean</span>(<span class='no'>a</span>, <span class='fl'>1</span>, <span class='fl'>TRUE</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_median.html b/static/docs/reference/torch_median.html
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-    <div class="page-header">
-    <h1>Median</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_median.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Median</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the result tuple of two output tensors (max, max_indices)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="median-input-gt-tensor-"><a class="anchor" href="#median-input-gt-tensor-"></a>median(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the median value of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="median-input-dim-keepdim-false-out-none-gt-tensor-longtensor-"><a class="anchor" href="#median-input-dim-keepdim-false-out-none-gt-tensor-longtensor-"></a>median(input, dim=-1, keepdim=False, out=None) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the median
-value of each row of the <code>input</code> tensor in the given dimension
-<code>dim</code>. And <code>indices</code> is the index location of each median value found.</p>
-<p>By default, <code>dim</code> is the last dimension of the <code>input</code> tensor.</p>
-<p>If <code>keepdim</code> is <code>True</code>, the output tensors are of the same size
-as <code>input</code> except in the dimension <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
-the outputs tensor having 1 fewer dimension than <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_median</span>(<span class='no'>a</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_median</span>(<span class='no'>a</span>, <span class='fl'>1</span>)
-}</div></pre>
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-      <th>tensors</th>
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-    <tr>
-      <th>treated</th>
-      <td><p>(1,)</p></td>
-    </tr>
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-
-    
-
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-<p>Take \(N\) tensors, each of which can be either scalar or 1-dimensional
-vector, and create \(N\) N-dimensional grids, where the \(i\) <code>th</code> grid is defined by
-expanding the \(i\) <code>th</code> input over dimensions defined by other inputs.</p>
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-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>))
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-<span class='no'>out</span> <span class='kw'>=</span> <span class='fu'>torch_meshgrid</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='no'>x</span>, <span class='no'>y</span>))
-<span class='no'>out</span>
-}</div></pre>
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-    <h1>Min</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_min.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Min</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the tuple of two output tensors (min, min_indices)</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>When the shapes do not match, the shape of the returned output tensor
-follows the broadcasting rules .</p>
-    <h2 class="hasAnchor" id="min-input-gt-tensor-"><a class="anchor" href="#min-input-gt-tensor-"></a>min(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the minimum value of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="min-input-dim-keepdim-false-out-none-gt-tensor-longtensor-"><a class="anchor" href="#min-input-dim-keepdim-false-out-none-gt-tensor-longtensor-"></a>min(input, dim, keepdim=False, out=None) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the minimum
-value of each row of the <code>input</code> tensor in the given dimension
-<code>dim</code>. And <code>indices</code> is the index location of each minimum value found
-(argmin).</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p><code>indices</code> does not necessarily contain the first occurrence of each
-minimal value found, unless it is unique.
-The exact implementation details are device-specific.
-Do not expect the same result when run on CPU and GPU in general.</p>
-<p>If <code>keepdim</code> is <code>True</code>, the output tensors are of the same size as
-<code>input</code> except in the dimension <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
-the output tensors having 1 fewer dimension than <code>input</code>.</p>
-    <h2 class="hasAnchor" id="min-input-other-out-none-gt-tensor-"><a class="anchor" href="#min-input-other-out-none-gt-tensor-"></a>min(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Each element of the tensor <code>input</code> is compared with the corresponding
-element of the tensor <code>other</code> and an element-wise minimum is taken.
-The resulting tensor is returned.</p>
-<p>The shapes of <code>input</code> and <code>other</code> don't need to match,
-but they must be broadcastable .</p>
-<p>$$
-    \mbox{out}_i = \min(\mbox{tensor}_i, \mbox{other}_i)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_min</span>(<span class='no'>a</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_min</span>(<span class='no'>a</span>, <span class='kw'>dim</span> <span class='kw'>=</span> <span class='fl'>1</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>b</span>
-<span class='fu'>torch_min</span>(<span class='no'>a</span>, <span class='kw'>other</span> <span class='kw'>=</span> <span class='no'>b</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_mm.html b/static/docs/reference/torch_mm.html
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-    <h1>Mm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_mm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Mm</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the first matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>mat2</th>
-      <td><p>(Tensor) the second matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function does not broadcast .
-For broadcasting matrix products, see <code><a href='torch_matmul.html'>torch_matmul</a></code>.</p>
-    <h2 class="hasAnchor" id="mm-input-mat-out-none-gt-tensor-"><a class="anchor" href="#mm-input-mat-out-none-gt-tensor-"></a>mm(input, mat2, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a matrix multiplication of the matrices <code>input</code> and <code>mat2</code>.</p>
-<p>If <code>input</code> is a \((n \times m)\) tensor, <code>mat2</code> is a
-\((m \times p)\) tensor, <code>out</code> will be a \((n \times p)\) tensor.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>mat1</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='no'>mat2</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_mm</span>(<span class='no'>mat1</span>, <span class='no'>mat2</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_mode.html b/static/docs/reference/torch_mode.html
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-    <div class="page-header">
-    <h1>Mode</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_mode.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Mode</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the result tuple of two output tensors (values, indices)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function is not defined for <code>torch_cuda.Tensor</code> yet.</p>
-    <h2 class="hasAnchor" id="mode-input-dim-keepdim-false-out-none-gt-tensor-longtensor-"><a class="anchor" href="#mode-input-dim-keepdim-false-out-none-gt-tensor-longtensor-"></a>mode(input, dim=-1, keepdim=False, out=None) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the mode
-value of each row of the <code>input</code> tensor in the given dimension
-<code>dim</code>, i.e. a value which appears most often
-in that row, and <code>indices</code> is the index location of each mode value found.</p>
-<p>By default, <code>dim</code> is the last dimension of the <code>input</code> tensor.</p>
-<p>If <code>keepdim</code> is <code>True</code>, the output tensors are of the same size as
-<code>input</code> except in the dimension <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting
-in the output tensors having 1 fewer dimension than <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span>(<span class='fl'>0</span>, <span class='fl'>50</span>, <span class='kw'>size</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>5</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_mode</span>(<span class='no'>a</span>, <span class='fl'>1</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <h1>Mul</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_mul.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Mul</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>NA</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>(Number) the number to be multiplied to each element of <code>input</code></p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>NA</p></td>
-    </tr>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the first multiplicand tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second multiplicand tensor</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="mul-input-other-out-none-"><a class="anchor" href="#mul-input-other-out-none-"></a>mul(input, other, out=None) </h2>
-
-    
-
-
-<p>Multiplies each element of the input <code>input</code> with the scalar
-<code>other</code> and returns a new resulting tensor.</p>
-<p>$$
-    \mbox{out}_i = \mbox{other} \times \mbox{input}_i
-$$
-If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>other</code>
-should be a real number, otherwise it should be an integer</p>
-
-
-<p>Each element of the tensor <code>input</code> is multiplied by the corresponding
-element of the Tensor <code>other</code>. The resulting tensor is returned.</p>
-<p>The shapes of <code>input</code> and <code>other</code> must be
-broadcastable .</p>
-<p>$$
-    \mbox{out}_i = \mbox{input}_i \times \mbox{other}_i
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_mul</span>(<span class='no'>a</span>, <span class='fl'>100</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>1</span>))
-<span class='no'>a</span>
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>4</span>))
-<span class='no'>b</span>
-<span class='fu'>torch_mul</span>(<span class='no'>a</span>, <span class='no'>b</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-    <h1>Multinomial</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_multinomial.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Multinomial</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor containing probabilities</p></td>
-    </tr>
-    <tr>
-      <th>num_samples</th>
-      <td><p>(int) number of samples to draw</p></td>
-    </tr>
-    <tr>
-      <th>replacement</th>
-      <td><p>(bool, optional) whether to draw with replacement or not</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>The rows of `input` do not need to sum to one (in which case we use
-the values as weights), but must be non-negative, finite and have
-a non-zero sum.
-</pre>
-
-<p>Indices are ordered from left to right according to when each was sampled
-(first samples are placed in first column).</p>
-<p>If <code>input</code> is a vector, <code>out</code> is a vector of size <code>num_samples</code>.</p>
-<p>If <code>input</code> is a matrix with <code>m</code> rows, <code>out</code> is an matrix of shape
-\((m \times \mbox{num\_samples})\).</p>
-<p>If replacement is <code>True</code>, samples are drawn with replacement.</p>
-<p>If not, they are drawn without replacement, which means that when a
-sample index is drawn for a row, it cannot be drawn again for that row.</p>
-<pre>When drawn without replacement, `num_samples` must be lower than
-number of non-zero elements in `input` (or the min number of non-zero
-elements in each row of `input` if it is a matrix).
-</pre>
-
-    <h2 class="hasAnchor" id="multinomial-input-num-samples-replacement-false-generator-none-out-none-gt-longtensor-"><a class="anchor" href="#multinomial-input-num-samples-replacement-false-generator-none-out-none-gt-longtensor-"></a>multinomial(input, num_samples, replacement=False, *, generator=None, out=None) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns a tensor where each row contains <code>num_samples</code> indices sampled
-from the multinomial probability distribution located in the corresponding row
-of tensor <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>weights</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>0</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float</a></span>()) <span class='co'># create a tensor of weights</span>
-<span class='fu'>torch_multinomial</span>(<span class='no'>weights</span>, <span class='fl'>2</span>)
-<span class='fu'>torch_multinomial</span>(<span class='no'>weights</span>, <span class='fl'>4</span>, <span class='kw'>replacement</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_mv.html b/static/docs/reference/torch_mv.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_mv.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Mv</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>vec</th>
-      <td><p>(Tensor) vector to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function does not broadcast .</p>
-    <h2 class="hasAnchor" id="mv-input-vec-out-none-gt-tensor-"><a class="anchor" href="#mv-input-vec-out-none-gt-tensor-"></a>mv(input, vec, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs a matrix-vector product of the matrix <code>input</code> and the vector
-<code>vec</code>.</p>
-<p>If <code>input</code> is a \((n \times m)\) tensor, <code>vec</code> is a 1-D tensor of
-size \(m\), <code>out</code> will be 1-D of size \(n\).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>mat</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='no'>vec</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='fu'>torch_mv</span>(<span class='no'>mat</span>, <span class='no'>vec</span>)
-}</div></pre>
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-    <h1>Mvlgamma</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_mvlgamma.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Mvlgamma</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to compute the multivariate log-gamma function</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(int) the number of dimensions</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="mvlgamma-input-p-gt-tensor-"><a class="anchor" href="#mvlgamma-input-p-gt-tensor-"></a>mvlgamma(input, p) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the <code>multivariate log-gamma function &lt;https://en.wikipedia.org/wiki/Multivariate_gamma_function&gt;</code>_) with dimension
-\(p\) element-wise, given by</p>
-<p>$$
-    \log(\Gamma_{p}(a)) = C + \displaystyle \sum_{i=1}^{p} \log\left(\Gamma\left(a - \frac{i - 1}{2}\right)\right)
-$$
-where \(C = \log(\pi) \times \frac{p (p - 1)}{4}\) and \(\Gamma(\cdot)\) is the Gamma function.</p>
-<p>All elements must be greater than \(\frac{p - 1}{2}\), otherwise an error would be thrown.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))$<span class='fu'>uniform_</span>(<span class='fl'>1</span>, <span class='fl'>2</span>)
-<span class='no'>a</span>
-<span class='fu'>torch_mvlgamma</span>(<span class='no'>a</span>, <span class='fl'>2</span>)
-}</div></pre>
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-    <h1>Narrow</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_narrow.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Narrow</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to narrow</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension along which to narrow</p></td>
-    </tr>
-    <tr>
-      <th>start</th>
-      <td><p>(int) the starting dimension</p></td>
-    </tr>
-    <tr>
-      <th>length</th>
-      <td><p>(int) the distance to the ending dimension</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="narrow-input-dim-start-length-gt-tensor-"><a class="anchor" href="#narrow-input-dim-start-length-gt-tensor-"></a>narrow(input, dim, start, length) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor that is a narrowed version of <code>input</code> tensor. The
-dimension <code>dim</code> is input from <code>start</code> to <code>start + length</code>. The
-returned tensor and <code>input</code> tensor share the same underlying storage.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>:<span class='fl'>9</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>3</span>, <span class='kw'>byrow</span><span class='kw'>=</span> <span class='fl'>TRUE</span>))
-<span class='fu'>torch_narrow</span>(<span class='no'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>0L</span>)$<span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span>(<span class='kw'>dim</span> <span class='kw'>=</span> <span class='fl'>1</span>), <span class='fl'>2</span>)
-<span class='fu'>torch_narrow</span>(<span class='no'>x</span>, <span class='fl'>2</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1L</span>)$<span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span>(<span class='kw'>dim</span> <span class='kw'>=</span> <span class='fl'>1</span>), <span class='fl'>2</span>)
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_ne.html b/static/docs/reference/torch_ne.html
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-    <h1>Ne</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_ne.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ne</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor that must be a <code>BoolTensor</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ne-input-other-out-none-gt-tensor-"><a class="anchor" href="#ne-input-other-out-none-gt-tensor-"></a>ne(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes \(input \neq other\) element-wise.</p>
-<p>The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_ne</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span>:<span class='fl'>4</span>, <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>)),
-         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/rep.html'>rep</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>4</span>), <span class='kw'>each</span> <span class='kw'>=</span> <span class='fl'>2</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>)))
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_neg.html b/static/docs/reference/torch_neg.html
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-    <h1>Neg</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_neg.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Neg</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="neg-input-out-none-gt-tensor-"><a class="anchor" href="#neg-input-out-none-gt-tensor-"></a>neg(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the negative of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out} = -1 \times \mbox{input}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_neg</span>(<span class='no'>a</span>)
-}</div></pre>
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-    <h1>Nonzero</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_nonzero.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Nonzero</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(LongTensor, optional) the output tensor containing indices</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>[`torch_nonzero(..., as_tuple=False) &amp;lt;torch.nonzero&amp;gt;`] (default) returns a
-2-D tensor where each row is the index for a nonzero value.
-
-[`torch_nonzero(..., as_tuple=True) &amp;lt;torch.nonzero&amp;gt;`] returns a tuple of 1-D
-index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]``
-gives all nonzero values of tensor ``x``. Of the returned tuple, each index tensor
-contains nonzero indices for a certain dimension.
-
-See below for more details on the two behaviors.
-</pre>
-
-    <h2 class="hasAnchor" id="nonzero-input-out-none-as-tuple-false-gt-longtensor-or-tuple-of-longtensors-"><a class="anchor" href="#nonzero-input-out-none-as-tuple-false-gt-longtensor-or-tuple-of-longtensors-"></a>nonzero(input, *, out=None, as_tuple=False) -&gt; LongTensor or tuple of LongTensors </h2>
-
-    
-
-
-<p><strong>When</strong> <code>as_tuple</code> <strong>is <code>False</code> (default)</strong>:</p>
-<p>Returns a tensor containing the indices of all non-zero elements of
-<code>input</code>.  Each row in the result contains the indices of a non-zero
-element in <code>input</code>. The result is sorted lexicographically, with
-the last index changing the fastest (C-style).</p>
-<p>If <code>input</code> has \(n\) dimensions, then the resulting indices tensor
-<code>out</code> is of size \((z \times n)\), where \(z\) is the total number of
-non-zero elements in the <code>input</code> tensor.</p>
-<p><strong>When</strong> <code>as_tuple</code> <strong>is <code>True</code></strong>:</p>
-<p>Returns a tuple of 1-D tensors, one for each dimension in <code>input</code>,
-each containing the indices (in that dimension) of all non-zero elements of
-<code>input</code> .</p>
-<p>If <code>input</code> has \(n\) dimensions, then the resulting tuple contains \(n\)
-tensors of size \(z\), where \(z\) is the total number of
-non-zero elements in the <code>input</code> tensor.</p>
-<p>As a special case, when <code>input</code> has zero dimensions and a nonzero scalar
-value, it is treated as a one-dimensional tensor with one element.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_nonzero</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>0</span>, <span class='fl'>1</span>)))
-}</div></pre>
-  </div>
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-    <h1>Norm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_norm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Norm</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: <code>'fro'</code>        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        None   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --        Other  as vec norm when dim is None  sum(abs(x)<strong>ord)</strong>(1./ord)        =====  ============================  ==========================</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, 2-tuple of ints, 2-list of ints, optional) If it is an int,        vector norm will be calculated, if it is 2-tuple of ints, matrix norm        will be calculated. If the value is None, matrix norm will be calculated        when the input tensor only has two dimensions, vector norm will be        calculated when the input tensor only has one dimension. If the input        tensor has more than two dimensions, the vector norm will be applied to        last dimension.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool, optional) whether the output tensors have <code>dim</code>        retained or not. Ignored if <code>dim</code> = <code>None</code> and        <code>out</code> = <code>None</code>. Default: <code>False</code></p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor. Ignored if        <code>dim</code> = <code>None</code> and <code>out</code> = <code>None</code>.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: None.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Returns the matrix norm or vector norm of a given tensor.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>9</span>, <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_float</a></span>())
-<span class='no'>b</span> <span class='kw'>=</span> <span class='no'>a</span>$<span class='fu'><a href='https://rdrr.io/r/stats/reshape.html'>reshape</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_norm</span>(<span class='no'>a</span>)
-<span class='fu'>torch_norm</span>(<span class='no'>b</span>)
-<span class='fu'>torch_norm</span>(<span class='no'>a</span>, <span class='fl'>Inf</span>)
-<span class='fu'>torch_norm</span>(<span class='no'>b</span>, <span class='fl'>Inf</span>)
-
-}</div></pre>
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-    <div class="page-header">
-    <h1>Normal</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_normal.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Normal</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>mean</th>
-      <td><p>(Tensor) the tensor of per-element means</p></td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td><p>(Tensor) the tensor of per-element standard deviations</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>When the shapes do not match, the shape of <code>mean</code>
-is used as the shape for the returned output tensor</p>
-    <h2 class="hasAnchor" id="normal-mean-std-generator-none-out-none-gt-tensor-"><a class="anchor" href="#normal-mean-std-generator-none-out-none-gt-tensor-"></a>normal(mean, std, *, generator=None, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor of random numbers drawn from separate normal distributions
-whose mean and standard deviation are given.</p>
-<p>The <code>mean</code> is a tensor with the mean of
-each output element's normal distribution</p>
-<p>The <code>std</code> is a tensor with the standard deviation of
-each output element's normal distribution</p>
-<p>The shapes of <code>mean</code> and <code>std</code> don't need to match, but the
-total number of elements in each tensor need to be the same.</p>
-    <h2 class="hasAnchor" id="normal-mean-std-out-none-gt-tensor-"><a class="anchor" href="#normal-mean-std-out-none-gt-tensor-"></a>normal(mean=0.0, std, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Similar to the function above, but the means are shared among all drawn
-elements.</p>
-    <h2 class="hasAnchor" id="normal-mean-std-out-none-gt-tensor-"><a class="anchor" href="#normal-mean-std-out-none-gt-tensor-"></a>normal(mean, std=1.0, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Similar to the function above, but the standard-deviations are shared among
-all drawn elements.</p>
-    <h2 class="hasAnchor" id="normal-mean-std-size-out-none-gt-tensor-"><a class="anchor" href="#normal-mean-std-size-out-none-gt-tensor-"></a>normal(mean, std, size, *, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Similar to the function above, but the means and standard deviations are shared
-among all drawn elements. The resulting tensor has size given by <code>size</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='fu'>torch_normal</span>(<span class='kw'>mean</span><span class='kw'>=</span><span class='fl'>0</span>, <span class='kw'>std</span><span class='kw'>=</span><span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1</span>, <span class='fl'>0</span>, -<span class='fl'>0.1</span>))
-
-
-<span class='fu'>torch_normal</span>(<span class='kw'>mean</span><span class='kw'>=</span><span class='fl'>0.5</span>, <span class='kw'>std</span><span class='kw'>=</span><span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>))
-
-
-<span class='fu'>torch_normal</span>(<span class='kw'>mean</span><span class='kw'>=</span><span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>))
-
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-<span class='fu'>torch_normal</span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='kw'>size</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>4</span>))
-}
-}</div></pre>
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-    <h1>Ones</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_ones.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ones</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>size</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ones-size-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#ones-size-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with the scalar value <code>1</code>, with the shape defined
-by the variable argument <code>size</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_ones</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_ones</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-}</div></pre>
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diff --git a/static/docs/reference/torch_ones_like.html b/static/docs/reference/torch_ones_like.html
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-    <h1>Ones_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_ones_like.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ones_like</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>None</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>None</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ones-like-input-dtype-none-layout-none-device-none-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#ones-like-input-dtype-none-layout-none-device-none-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>ones_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with the scalar value <code>1</code>, with the same size as
-<code>input</code>. <code>torch_ones_like(input)</code> is equivalent to
-<code><a href='torch_ones.html'>torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>As of 0.4, this function does not support an <code>out</code> keyword. As an alternative,
-the old <code>torch_ones_like(input, out=output)</code> is equivalent to
-<code><a href='torch_ones.html'>torch_ones(input.size(), out=output)</a></code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>input</span> <span class='kw'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_ones_like</span>(<span class='no'>input</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_orgqr.html b/static/docs/reference/torch_orgqr.html
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-    <h1>Orgqr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_orgqr.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Orgqr</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the <code>a</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>(Tensor) the <code>tau</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="orgqr-input-input-gt-tensor-"><a class="anchor" href="#orgqr-input-input-gt-tensor-"></a>orgqr(input, input2) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the orthogonal matrix <code>Q</code> of a QR factorization, from the <code>(input, input2)</code>
-tuple returned by <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p>
-<p>This directly calls the underlying LAPACK function <code>?orgqr</code>.
-See <code>LAPACK documentation for orgqr</code>_ for further details.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_ormqr.html b/static/docs/reference/torch_ormqr.html
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-    <div class="page-header">
-    <h1>Ormqr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_ormqr.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ormqr</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the <code>a</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>(Tensor) the <code>tau</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>input3</th>
-      <td><p>(Tensor) the matrix to be multiplied.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ormqr-input-input-input-left-true-transpose-false-gt-tensor-"><a class="anchor" href="#ormqr-input-input-input-left-true-transpose-false-gt-tensor-"></a>ormqr(input, input2, input3, left=True, transpose=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Multiplies <code>mat</code> (given by <code>input3</code>) by the orthogonal <code>Q</code> matrix of the QR factorization
-formed by <code><a href='torch_geqrf.html'>torch_geqrf</a></code> that is represented by <code>(a, tau)</code> (given by (<code>input</code>, <code>input2</code>)).</p>
-<p>This directly calls the underlying LAPACK function <code>?ormqr</code>.
-See <code>LAPACK documentation for ormqr</code>_ for further details.</p>
-
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-    <p>Pdist</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>NA input tensor of shape \(N \times M\).</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="pdist-input-p-gt-tensor-"><a class="anchor" href="#pdist-input-p-gt-tensor-"></a>pdist(input, p=2) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the p-norm distance between every pair of row vectors in the input.
-This is identical to the upper triangular portion, excluding the diagonal, of
-<code>torch_norm(input[:, None] - input, dim=2, p=p)</code>. This function will be faster
-if the rows are contiguous.</p>
-<p>If input has shape \(N \times M\) then the output will have shape
-\(\frac{1}{2} N (N - 1)\).</p>
-<p>This function is equivalent to <code>scipy.spatial.distance.pdist(input, 'minkowski', p=p)</code> if \(p \in (0, \infty)\). When \(p = 0\) it is
-equivalent to <code>scipy.spatial.distance.pdist(input, 'hamming') * M</code>.
-When \(p = \infty\), the closest scipy function is
-<code>scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max())</code>.</p>
-
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_pinverse.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Pinverse</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) The input tensor of size \((*, m, n)\) where \(*\) is zero or more batch dimensions</p></td>
-    </tr>
-    <tr>
-      <th>rcond</th>
-      <td><p>(float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>This method is implemented using the Singular Value Decomposition.
-</pre>
-
-<pre>The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_.
-Therefore, derivatives are not always existent, and exist for a constant rank only `[2]`_.
-However, this method is backprop-able due to the implementation by using SVD results, and
-could be unstable. Double-backward will also be unstable due to the usage of SVD internally.
-See `~torch.svd` for more details.
-</pre>
-
-    <h2 class="hasAnchor" id="pinverse-input-rcond-e-gt-tensor-"><a class="anchor" href="#pinverse-input-rcond-e-gt-tensor-"></a>pinverse(input, rcond=1e-15) -&gt; Tensor </h2>
-
-    
-
-
-<p>Calculates the pseudo-inverse (also known as the Moore-Penrose inverse) of a 2D tensor.
-Please look at <code>Moore-Penrose inverse</code>_ for more details</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>input</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>))
-<span class='no'>input</span>
-<span class='fu'>torch_pinverse</span>(<span class='no'>input</span>)
-<span class='co'># Batched pinverse example</span>
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>6</span>,<span class='fl'>3</span>))
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'>torch_pinverse</span>(<span class='no'>a</span>)
-<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='no'>b</span>, <span class='no'>a</span>)
-}</div></pre>
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-    <h1>Pixel_shuffle</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_pixel_shuffle.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Pixel_shuffle</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>upscale_factor</th>
-      <td><p>(int) factor to increase spatial resolution by</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rearranges-elements-in-a-tensor-of-shape-"><a class="anchor" href="#rearranges-elements-in-a-tensor-of-shape-"></a>Rearranges elements in a tensor of shape </h2>
-
-    <p>math:<code>(*, C \times r^2, H, W)</code> to a :</p>
-<p>Rearranges elements in a tensor of shape \((*, C \times r^2, H, W)\) to a
-tensor of shape \((*, C, H \times r, W \times r)\).</p>
-<p>See <code>~torch.nn.PixelShuffle</code> for details.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>input</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>9</span>, <span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>output</span> <span class='kw'>=</span> <span class='fu'><a href='nnf_pixel_shuffle.html'>nnf_pixel_shuffle</a></span>(<span class='no'>input</span>, <span class='fl'>3</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>output</span>$<span class='fu'>size</span>())
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_poisson.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Poisson</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor containing the rates of the Poisson distribution</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="poisson-input-generator-none-gt-tensor-"><a class="anchor" href="#poisson-input-generator-none-gt-tensor-"></a>poisson(input *, generator=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor of the same size as <code>input</code> with each element
-sampled from a Poisson distribution with rate parameter given by the corresponding
-element in <code>input</code> i.e.,</p>
-<p>$$
-    \mbox{out}_i \sim \mbox{Poisson}(\mbox{input}_i)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>rates</span> <span class='kw'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>)) * <span class='fl'>5</span>  <span class='co'># rate parameter between 0 and 5</span>
-<span class='fu'>torch_poisson</span>(<span class='no'>rates</span>)
-}</div></pre>
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-    <h1>Polygamma</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_polygamma.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Polygamma</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>n</th>
-      <td><p>(int) the order of the polygamma function</p></td>
-    </tr>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>This function is not implemented for \eqn{n \geq 2}.
-</pre>
-
-    <h2 class="hasAnchor" id="polygamma-n-input-out-none-gt-tensor-"><a class="anchor" href="#polygamma-n-input-out-none-gt-tensor-"></a>polygamma(n, input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the \(n^{th}\) derivative of the digamma function on <code>input</code>.
-\(n \geq 0\) is called the order of the polygamma function.</p>
-<p>$$
-    \psi^{(n)}(x) = \frac{d^{(n)}}{dx^{(n)}} \psi(x)
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0.5</span>))
-<span class='fu'>torch_polygamma</span>(<span class='fl'>1</span>, <span class='no'>a</span>)
-}
-}</div></pre>
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-    <h1>Pow</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_pow.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Pow</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>exponent</th>
-      <td><p>(float or tensor) the exponent value</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>self</th>
-      <td><p>(float) the scalar base value for the power operation</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="pow-input-exponent-out-none-gt-tensor-"><a class="anchor" href="#pow-input-exponent-out-none-gt-tensor-"></a>pow(input, exponent, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Takes the power of each element in <code>input</code> with <code>exponent</code> and
-returns a tensor with the result.</p>
-<p><code>exponent</code> can be either a single <code>float</code> number or a <code>Tensor</code>
-with the same number of elements as <code>input</code>.</p>
-<p>When <code>exponent</code> is a scalar value, the operation applied is:</p>
-<p>$$
-    \mbox{out}_i = x_i^{\mbox{exponent}}
-$$
-When <code>exponent</code> is a tensor, the operation applied is:</p>
-<p>$$
-    \mbox{out}_i = x_i^{\mbox{exponent}_i}
-$$
-When <code>exponent</code> is a tensor, the shapes of <code>input</code>
-and <code>exponent</code> must be broadcastable .</p>
-    <h2 class="hasAnchor" id="pow-self-exponent-out-none-gt-tensor-"><a class="anchor" href="#pow-self-exponent-out-none-gt-tensor-"></a>pow(self, exponent, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p><code>self</code> is a scalar <code>float</code> value, and <code>exponent</code> is a tensor.
-The returned tensor <code>out</code> is of the same shape as <code>exponent</code></p>
-<p>The operation applied is:</p>
-<p>$$
-    \mbox{out}_i = \mbox{self} ^ {\mbox{exponent}_i}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_pow</span>(<span class='no'>a</span>, <span class='fl'>2</span>)
-<span class='no'>exp</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
-<span class='no'>a</span>
-<span class='no'>exp</span>
-<span class='fu'>torch_pow</span>(<span class='no'>a</span>, <span class='no'>exp</span>)
-
-
-<span class='no'>exp</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
-<span class='no'>base</span> <span class='kw'>=</span> <span class='fl'>2</span>
-<span class='fu'>torch_pow</span>(<span class='no'>base</span>, <span class='no'>exp</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_prod.html b/static/docs/reference/torch_prod.html
deleted file mode 100644
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-    <div class="page-header">
-    <h1>Prod</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_prod.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Prod</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: None.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="prod-input-dtype-none-gt-tensor-"><a class="anchor" href="#prod-input-dtype-none-gt-tensor-"></a>prod(input, dtype=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the product of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="prod-input-dim-keepdim-false-dtype-none-gt-tensor-"><a class="anchor" href="#prod-input-dim-keepdim-false-dtype-none-gt-tensor-"></a>prod(input, dim, keepdim=False, dtype=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the product of each row of the <code>input</code> tensor in the given
-dimension <code>dim</code>.</p>
-<p>If <code>keepdim</code> is <code>True</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
-the output tensor having 1 fewer dimension than <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_prod</span>(<span class='no'>a</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_prod</span>(<span class='no'>a</span>, <span class='fl'>1</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_promote_types.html b/static/docs/reference/torch_promote_types.html
deleted file mode 100644
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_promote_types.Rd</code></div>
-    </div>
-
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-    <p>Promote_types</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>type1</th>
-      <td><p>(<code>torch.dtype</code>)</p></td>
-    </tr>
-    <tr>
-      <th>type2</th>
-      <td><p>(<code>torch.dtype</code>)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="promote-types-type-type-gt-dtype-"><a class="anchor" href="#promote-types-type-type-gt-dtype-"></a>promote_types(type1, type2) -&gt; dtype </h2>
-
-    
-
-
-<p>Returns the <code>torch_dtype</code> with the smallest size and scalar kind that is
-not smaller nor of lower kind than either <code>type1</code> or <code>type2</code>. See type promotion
-documentation  for more information on the type
-promotion logic.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_promote_types</span>(<span class='fu'><a href='torch_dtype.html'>torch_int32</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_float32</a></span>())
-<span class='fu'>torch_promote_types</span>(<span class='fu'><a href='torch_dtype.html'>torch_uint8</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
-}</div></pre>
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-    <h1>Qr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_qr.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Qr</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor of size \((*, m, n)\) where <code>*</code> is zero or more                batch dimensions consisting of matrices of dimension \(m \times n\).</p></td>
-    </tr>
-    <tr>
-      <th>some</th>
-      <td><p>(bool, optional) Set to <code>True</code> for reduced QR decomposition and <code>False</code> for                complete QR decomposition.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) tuple of <code>Q</code> and <code>R</code> tensors                satisfying <code>input = torch.matmul(Q, R)</code>.                The dimensions of <code>Q</code> and <code>R</code> are \((*, m, k)\) and \((*, k, n)\)                respectively, where \(k = \min(m, n)\) if <code>some:</code> is <code>True</code> and                \(k = m\) otherwise.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>precision may be lost if the magnitudes of the elements of <code>input</code>
-are large</p>
-<p>While it should always give you a valid decomposition, it may not
-give you the same one across platforms - it will depend on your
-LAPACK implementation.</p>
-    <h2 class="hasAnchor" id="qr-input-some-true-out-none-gt-tensor-tensor-"><a class="anchor" href="#qr-input-some-true-out-none-gt-tensor-tensor-"></a>qr(input, some=True, out=None) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Computes the QR decomposition of a matrix or a batch of matrices <code>input</code>,
-and returns a namedtuple (Q, R) of tensors such that \(\mbox{input} = Q R\)
-with \(Q\) being an orthogonal matrix or batch of orthogonal matrices and
-\(R\) being an upper triangular matrix or batch of upper triangular matrices.</p>
-<p>If <code>some</code> is <code>True</code>, then this function returns the thin (reduced) QR factorization.
-Otherwise, if <code>some</code> is <code>False</code>, this function returns the complete QR factorization.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>12.</span>, -<span class='fl'>51</span>, <span class='fl'>4</span>, <span class='fl'>6</span>, <span class='fl'>167</span>, -<span class='fl'>68</span>, -<span class='fl'>4</span>, <span class='fl'>24</span>, -<span class='fl'>41</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>3</span>, <span class='kw'>byrow</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))
-<span class='no'>out</span> <span class='kw'>=</span> <span class='fu'>torch_qr</span>(<span class='no'>a</span>)
-<span class='no'>q</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]]
-<span class='no'>r</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]]
-<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='no'>q</span>, <span class='no'>r</span>)$<span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span>()
-<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='no'>q</span>$<span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>(), <span class='no'>q</span>)$<span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span>()
-}</div></pre>
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diff --git a/static/docs/reference/torch_qscheme.html b/static/docs/reference/torch_qscheme.html
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-<span class='fu'>torch_per_tensor_affine</span>()
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-
-<span class='fu'>torch_per_tensor_symmetric</span>()</pre>
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diff --git a/static/docs/reference/torch_quantize_per_channel.html b/static/docs/reference/torch_quantize_per_channel.html
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-    <h1>Quantize_per_channel</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_quantize_per_channel.Rd</code></div>
-    </div>
-
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-    <p>Quantize_per_channel</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) float tensor to quantize</p></td>
-    </tr>
-    <tr>
-      <th>scales</th>
-      <td><p>(Tensor) float 1D tensor of scales to use, size should match <code>input.size(axis)</code></p></td>
-    </tr>
-    <tr>
-      <th>zero_points</th>
-      <td><p>(int) integer 1D tensor of offset to use, size should match <code>input.size(axis)</code></p></td>
-    </tr>
-    <tr>
-      <th>axis</th>
-      <td><p>(int) dimension on which apply per-channel quantization</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-"><a class="anchor" href="#quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-"></a>quantize_per_channel(input, scales, zero_points, axis, dtype) -&gt; Tensor </h2>
-
-    
-
-
-<p>Converts a float tensor to per-channel quantized tensor with given scales and zero points.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))
-<span class='fu'>torch_quantize_per_channel</span>(<span class='no'>x</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.1</span>, <span class='fl'>0.01</span>)),
-                           <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10L</span>, <span class='fl'>0L</span>)), <span class='fl'>0</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())
-<span class='fu'>torch_quantize_per_channel</span>(<span class='no'>x</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.1</span>, <span class='fl'>0.01</span>)),
-                           <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10L</span>, <span class='fl'>0L</span>)), <span class='fl'>0</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())$<span class='fu'>int_repr</span>()
-}</div></pre>
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diff --git a/static/docs/reference/torch_quantize_per_tensor.html b/static/docs/reference/torch_quantize_per_tensor.html
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-    <h1>Quantize_per_tensor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_quantize_per_tensor.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Quantize_per_tensor</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) float tensor to quantize</p></td>
-    </tr>
-    <tr>
-      <th>scale</th>
-      <td><p>(float) scale to apply in quantization formula</p></td>
-    </tr>
-    <tr>
-      <th>zero_point</th>
-      <td><p>(int) offset in integer value that maps to float zero</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-"><a class="anchor" href="#quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-"></a>quantize_per_tensor(input, scale, zero_point, dtype) -&gt; Tensor </h2>
-
-    
-
-
-<p>Converts a float tensor to quantized tensor with given scale and zero point.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='fu'>torch_quantize_per_tensor</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span>)), <span class='fl'>0.1</span>, <span class='fl'>10</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())
-<span class='fu'>torch_quantize_per_tensor</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span>)), <span class='fl'>0.1</span>, <span class='fl'>10</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())$<span class='fu'>int_repr</span>()
-}</div></pre>
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-    <h1>Rand</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_rand.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Rand</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>size</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rand-size-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#rand-size-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>rand(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with random numbers from a uniform distribution
-on the interval \([0, 1)\)</p>
-<p>The shape of the tensor is defined by the variable argument <code>size</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_rand</span>(<span class='fl'>4</span>)
-<span class='fu'>torch_rand</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-}</div></pre>
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-    <h1>Rand_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_rand_like.Rd</code></div>
-    </div>
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-    <p>Rand_like</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>None</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>None</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
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-    <h2 class="hasAnchor" id="rand-like-input-dtype-none-layout-none-device-none-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#rand-like-input-dtype-none-layout-none-device-none-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>rand_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor with the same size as <code>input</code> that is filled with
-random numbers from a uniform distribution on the interval \([0, 1)\).
-<code>torch_rand_like(input)</code> is equivalent to
-<code><a href='torch_rand.html'>torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
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-    <h1>Randint</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randint.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randint</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>low</th>
-      <td><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></td>
-    </tr>
-    <tr>
-      <th>high</th>
-      <td><p>(int) One above the highest integer to be drawn from the distribution.</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(tuple) a tuple defining the shape of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randint-low-high-size-generator-none-out-none-"><a class="anchor" href="#randint-low-high-size-generator-none-out-none-"></a>randint(low=0, high, size, *, generator=None, out=None, \ </h2>
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-
-
-<p>dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor</p>
-<p>Returns a tensor filled with random integers generated uniformly
-between <code>low</code> (inclusive) and <code>high</code> (exclusive).</p>
-<p>The shape of the tensor is defined by the variable argument <code>size</code>.</p>
-<p>.. note:
-With the global dtype default (<code>torch_float32</code>), this function returns
-a tensor with dtype <code>torch_int64</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_randint</span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>3</span>))
-<span class='fu'>torch_randint</span>(<span class='fl'>0</span>, <span class='fl'>10</span>, <span class='kw'>size</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='fu'>torch_randint</span>(<span class='fl'>3</span>, <span class='fl'>10</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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-    <h1>Randint_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randint_like.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randint_like</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>low</th>
-      <td><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></td>
-    </tr>
-    <tr>
-      <th>high</th>
-      <td><p>(int) One above the highest integer to be drawn from the distribution.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>None</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>None</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randint-like-input-low-high-dtype-none-layout-torch-strided-device-none-requires-grad-false-"><a class="anchor" href="#randint-like-input-low-high-dtype-none-layout-torch-strided-device-none-requires-grad-false-"></a>randint_like(input, low=0, high, dtype=None, layout=torch.strided, device=None, requires_grad=False, </h2>
-
-    
-
-
-<p>memory_format=torch.preserve_format) -&gt; Tensor</p>
-<p>Returns a tensor with the same shape as Tensor <code>input</code> filled with
-random integers generated uniformly between <code>low</code> (inclusive) and
-<code>high</code> (exclusive).</p>
-<p>.. note:
-With the global dtype default (<code>torch_float32</code>), this function returns
-a tensor with dtype <code>torch_int64</code>.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    <div class="page-header">
-    <h1>Randn</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randn.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randn</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>size</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randn-size-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#randn-size-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>randn(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with random numbers from a normal distribution
-with mean <code>0</code> and variance <code>1</code> (also called the standard normal
-distribution).</p>
-<p>$$
-    \mbox{out}_{i} \sim \mathcal{N}(0, 1)
-$$
-The shape of the tensor is defined by the variable argument <code>size</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_randn</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='fu'>torch_randn</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-    <div class="page-header">
-    <h1>Randn_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randn_like.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Randn_like</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>None</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>None</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randn-like-input-dtype-none-layout-none-device-none-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#randn-like-input-dtype-none-layout-none-device-none-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>randn_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor with the same size as <code>input</code> that is filled with
-random numbers from a normal distribution with mean 0 and variance 1.
-<code>torch_randn_like(input)</code> is equivalent to
-<code><a href='torch_randn.html'>torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
-
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diff --git a/static/docs/reference/torch_randperm.html b/static/docs/reference/torch_randperm.html
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-    <h1>Randperm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randperm.Rd</code></div>
-    </div>
-
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-    <p>Randperm</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>n</th>
-      <td><p>(int) the upper bound (exclusive)</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: <code>torch_int64</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randperm-n-out-none-dtype-torch-int-layout-torch-strided-device-none-requires-grad-false-gt-longtensor-"><a class="anchor" href="#randperm-n-out-none-dtype-torch-int-layout-torch-strided-device-none-requires-grad-false-gt-longtensor-"></a>randperm(n, out=None, dtype=torch.int64, layout=torch.strided, device=None, requires_grad=False) -&gt; LongTensor </h2>
-
-    
-
-
-<p>Returns a random permutation of integers from <code>0</code> to <code>n - 1</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_randperm</span>(<span class='fl'>4</span>)
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_range.html b/static/docs/reference/torch_range.html
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-    <h1>Range</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_range.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Range</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(float) the starting value for the set of points. Default: <code>0</code>.</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(float) the ending value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>step</th>
-      <td><p>(float) the gap between each pair of adjacent points. Default: <code>1</code>.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="range-start-end-step-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#range-start-end-step-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>range(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a 1-D tensor of size \(\left\lfloor \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rfloor + 1\)
-with values from <code>start</code> to <code>end</code> with step <code>step</code>. Step is
-the gap between two values in the tensor.</p>
-<p>$$
-    \mbox{out}_{i+1} = \mbox{out}_i + \mbox{step}.
-$$</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>This function is deprecated in favor of <code><a href='torch_arange.html'>torch_arange</a></code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_range</span>(<span class='fl'>1</span>, <span class='fl'>4</span>)
-<span class='fu'>torch_range</span>(<span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>0.5</span>)
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_real.html b/static/docs/reference/torch_real.html
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="real-input-out-none-gt-tensor-"><a class="anchor" href="#real-input-out-none-gt-tensor-"></a>real(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the real part of the <code>input</code> tensor. If
-<code>input</code> is a real (non-complex) tensor, this function just
-returns it.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Not yet implemented for complex tensors.</p>
-<p>$$
-    \mbox{out}_{i} = real(\mbox{input}_{i})
-$$</p>
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-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
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-    <div class="hidden name"><code>torch_reciprocal.Rd</code></div>
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-    </div>
-
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="reciprocal-input-out-none-gt-tensor-"><a class="anchor" href="#reciprocal-input-out-none-gt-tensor-"></a>reciprocal(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the reciprocal of the elements of <code>input</code></p>
-<p>$$
-    \mbox{out}_{i} = \frac{1}{\mbox{input}_{i}}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_reciprocal</span>(<span class='no'>a</span>)
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_remainder.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Remainder</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the dividend</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="remainder-input-other-out-none-gt-tensor-"><a class="anchor" href="#remainder-input-other-out-none-gt-tensor-"></a>remainder(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Computes the element-wise remainder of division.</p>
-<p>The divisor and dividend may contain both for integer and floating point
-numbers. The remainder has the same sign as the divisor.</p>
-<p>When <code>other</code> is a tensor, the shapes of <code>input</code> and
-<code>other</code> must be broadcastable .</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_remainder</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>3.</span>, -<span class='fl'>2</span>, -<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)), <span class='fl'>2</span>)
-<span class='fu'>torch_remainder</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>)), <span class='fl'>1.5</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Renorm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_renorm.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Renorm</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(float) the power for the norm computation</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to slice over to get the sub-tensors</p></td>
-    </tr>
-    <tr>
-      <th>maxnorm</th>
-      <td><p>(float) the maximum norm to keep each sub-tensor under</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>If the norm of a row is lower than <code>maxnorm</code>, the row is unchanged</p>
-    <h2 class="hasAnchor" id="renorm-input-p-dim-maxnorm-out-none-gt-tensor-"><a class="anchor" href="#renorm-input-p-dim-maxnorm-out-none-gt-tensor-"></a>renorm(input, p, dim, maxnorm, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor where each sub-tensor of <code>input</code> along dimension
-<code>dim</code> is normalized such that the <code>p</code>-norm of the sub-tensor is lower
-than the value <code>maxnorm</code></p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='no'>x</span>[<span class='fl'>2</span>,]$<span class='fu'>fill_</span>(<span class='fl'>2</span>)
-<span class='no'>x</span>[<span class='fl'>3</span>,]$<span class='fu'>fill_</span>(<span class='fl'>3</span>)
-<span class='no'>x</span>
-<span class='fu'>torch_renorm</span>(<span class='no'>x</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>5</span>)
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_repeat_interleave.html b/static/docs/reference/torch_repeat_interleave.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_repeat_interleave.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Repeat_interleave</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>repeats</th>
-      <td><p>(Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) The dimension along which to repeat values.        By default, use the flattened input array, and return a flat output        array.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="repeat-interleave-input-repeats-dim-none-gt-tensor-"><a class="anchor" href="#repeat-interleave-input-repeats-dim-none-gt-tensor-"></a>repeat_interleave(input, repeats, dim=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Repeat elements of a tensor.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-<pre>This is different from `torch_Tensor.repeat` but similar to ``numpy.repeat``.
-</pre>
-
-    <h2 class="hasAnchor" id="repeat-interleave-repeats-gt-tensor-"><a class="anchor" href="#repeat-interleave-repeats-gt-tensor-"></a>repeat_interleave(repeats) -&gt; Tensor </h2>
-
-    
-
-
-<p>If the <code>repeats</code> is <code>tensor([n1, n2, n3, ...])</code>, then the output will be
-<code>tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])</code> where <code>0</code> appears <code>n1</code> times,
-<code>1</code> appears <code>n2</code> times, <code>2</code> appears <code>n3</code> times, etc.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='no'>x</span>$<span class='fu'>repeat_interleave</span>(<span class='fl'>2</span>)
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>))
-<span class='fu'>torch_repeat_interleave</span>(<span class='no'>y</span>, <span class='fl'>2</span>)
-<span class='fu'>torch_repeat_interleave</span>(<span class='no'>y</span>, <span class='fl'>3</span>, <span class='kw'>dim</span><span class='kw'>=</span><span class='fl'>1</span>)
-<span class='fu'>torch_repeat_interleave</span>(<span class='no'>y</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)), <span class='kw'>dim</span><span class='kw'>=</span><span class='fl'>1</span>)
-}
-}</div></pre>
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diff --git a/static/docs/reference/torch_reshape.html b/static/docs/reference/torch_reshape.html
deleted file mode 100644
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-    <h1>Reshape</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_reshape.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Reshape</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to be reshaped</p></td>
-    </tr>
-    <tr>
-      <th>shape</th>
-      <td><p>(tuple of ints) the new shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="reshape-input-shape-gt-tensor-"><a class="anchor" href="#reshape-input-shape-gt-tensor-"></a>reshape(input, shape) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor with the same data and number of elements as <code>input</code>,
-but with the specified shape. When possible, the returned tensor will be a view
-of <code>input</code>. Otherwise, it will be a copy. Contiguous inputs and inputs
-with compatible strides can be reshaped without copying, but you should not
-depend on the copying vs. viewing behavior.</p>
-<p>See <code>torch_Tensor.view</code> on when it is possible to return a view.</p>
-<p>A single dimension may be -1, in which case it's inferred from the remaining
-dimensions and the number of elements in <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span>)
-<span class='fu'>torch_reshape</span>(<span class='no'>a</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>))
-<span class='fu'>torch_reshape</span>(<span class='no'>b</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(-<span class='fl'>1</span>))
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_result_type.html b/static/docs/reference/torch_result_type.html
deleted file mode 100644
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-    <div class="hidden name"><code>torch_result_type.Rd</code></div>
-    </div>
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-    <p>Result_type</p>
-    </div>
-
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor1</th>
-      <td><p>(Tensor or Number) an input tensor or number</p></td>
-    </tr>
-    <tr>
-      <th>tensor2</th>
-      <td><p>(Tensor or Number) an input tensor or number</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="result-type-tensor-tensor-gt-dtype-"><a class="anchor" href="#result-type-tensor-tensor-gt-dtype-"></a>result_type(tensor1, tensor2) -&gt; dtype </h2>
-
-    
-
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-<p>Returns the <code>torch_dtype</code> that would result from performing an arithmetic
-operation on the provided input tensors. See type promotion documentation
-for more information on the type promotion logic.</p>
-
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-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
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-}</div></pre>
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-    <h1>Rfft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_rfft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Rfft</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> dimensions</p></td>
-    </tr>
-    <tr>
-      <th>signal_ndim</th>
-      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>False</code></p></td>
-    </tr>
-    <tr>
-      <th>onesided</th>
-      <td><p>(bool, optional) controls whether to return half of results to        avoid redundancy. Default: <code>True</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-</pre>
-
-    <h2 class="hasAnchor" id="rfft-input-signal-ndim-normalized-false-onesided-true-gt-tensor-"><a class="anchor" href="#rfft-input-signal-ndim-normalized-false-onesided-true-gt-tensor-"></a>rfft(input, signal_ndim, normalized=False, onesided=True) -&gt; Tensor </h2>
-
-    
-
-
-<p>Real-to-complex Discrete Fourier Transform</p>
-<p>This method computes the real-to-complex discrete Fourier transform. It is
-mathematically equivalent with <code><a href='torch_fft.html'>torch_fft</a></code> with differences only in
-formats of the input and output.</p>
-<p>This method supports 1D, 2D and 3D real-to-complex transforms, indicated
-by <code>signal_ndim</code>. <code>input</code> must be a tensor with at least
-<code>signal_ndim</code> dimensions with optionally arbitrary number of leading batch
-dimensions. If <code>normalized</code> is set to <code>True</code>, this normalizes the result
-by dividing it with \(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is
-unitary, where \(N_i\) is the size of signal dimension \(i\).</p>
-<p>The real-to-complex Fourier transform results follow conjugate symmetry:</p>
-<p>$$
-    X[\omega_1, \dots, \omega_d] = X^*[N_1 - \omega_1, \dots, N_d - \omega_d],
-$$
-where the index arithmetic is computed modulus the size of the corresponding
-dimension, \(\ ^*\) is the conjugate operator, and
-\(d\) = <code>signal_ndim</code>. <code>onesided</code> flag controls whether to avoid
-redundancy in the output results. If set to <code>True</code> (default), the output will
-not be full complex result of shape \((*, 2)\), where \(*\) is the shape
-of <code>input</code>, but instead the last dimension will be halfed as of size
-\(\lfloor \frac{N_d}{2} \rfloor + 1\).</p>
-<p>The inverse of this function is <code><a href='torch_irfft.html'>torch_irfft</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>For CPU tensors, this method is currently only available with MKL. Use
-<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>5</span>))
-<span class='fu'>torch_rfft</span>(<span class='no'>x</span>, <span class='fl'>2</span>)
-<span class='fu'>torch_rfft</span>(<span class='no'>x</span>, <span class='fl'>2</span>, <span class='kw'>onesided</span><span class='kw'>=</span><span class='fl'>FALSE</span>)
-}</div></pre>
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diff --git a/static/docs/reference/torch_roll.html b/static/docs/reference/torch_roll.html
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-    <h1>Roll</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_roll.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Roll</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>shifts</th>
-      <td><p>(int or tuple of ints) The number of places by which the elements        of the tensor are shifted. If shifts is a tuple, dims must be a tuple of        the same size, and each dimension will be rolled by the corresponding        value</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(int or tuple of ints) Axis along which to roll</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="roll-input-shifts-dims-none-gt-tensor-"><a class="anchor" href="#roll-input-shifts-dims-none-gt-tensor-"></a>roll(input, shifts, dims=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Roll the tensor along the given dimension(s). Elements that are shifted beyond the
-last position are re-introduced at the first position. If a dimension is not
-specified, the tensor will be flattened before rolling and then restored
-to the original shape.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>, <span class='fl'>7</span>, <span class='fl'>8</span>))$<span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
-<span class='no'>x</span>
-<span class='fu'>torch_roll</span>(<span class='no'>x</span>, <span class='fl'>1</span>, <span class='fl'>1</span>)
-<span class='fu'>torch_roll</span>(<span class='no'>x</span>, -<span class='fl'>1</span>, <span class='fl'>1</span>)
-<span class='fu'>torch_roll</span>(<span class='no'>x</span>, <span class='kw'>shifts</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), <span class='kw'>dims</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
-}</div></pre>
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diff --git a/static/docs/reference/torch_rot90.html b/static/docs/reference/torch_rot90.html
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-    <div class="page-header">
-    <h1>Rot90</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_rot90.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Rot90</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>k</th>
-      <td><p>(int) number of times to rotate</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(a list or tuple) axis to rotate</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rot-input-k-dims-gt-tensor-"><a class="anchor" href="#rot-input-k-dims-gt-tensor-"></a>rot90(input, k, dims) -&gt; Tensor </h2>
-
-    
-
-
-<p>Rotate a n-D tensor by 90 degrees in the plane specified by dims axis.
-Rotation direction is from the first towards the second axis if k &gt; 0, and from the second towards the first for k &lt; 0.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span>)$<span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='no'>x</span>
-<span class='fu'>torch_rot90</span>(<span class='no'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>8</span>)$<span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='no'>x</span>
-<span class='fu'>torch_rot90</span>(<span class='no'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-</div>
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diff --git a/static/docs/reference/torch_round.html b/static/docs/reference/torch_round.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_rsqrt.Rd</code></div>
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-    <p>Rsqrt</p>
-    </div>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rsqrt-input-out-none-gt-tensor-"><a class="anchor" href="#rsqrt-input-out-none-gt-tensor-"></a>rsqrt(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the reciprocal of the square-root of each of
-the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \frac{1}{\sqrt{\mbox{input}_{i}}}
-$$</p>
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-<span class='no'>a</span>
-<span class='fu'>torch_rsqrt</span>(<span class='no'>a</span>)
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/save.R'><code>R/save.R</code></a></small>
-    <div class="hidden name"><code>torch_save.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function is experimental, don't use for long
-term storage.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_save</span>(<span class='no'>obj</span>, <span class='no'>path</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>obj</th>
-      <td><p>the saved object</p></td>
-    </tr>
-    <tr>
-      <th>path</th>
-      <td><p>a connection or the name of the file to save.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>not currently used.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other torch_save: 
-<code><a href='torch_load.html'>torch_load</a>()</code></p></div>
-
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diff --git a/static/docs/reference/torch_selu_.html b/static/docs/reference/torch_selu_.html
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-    <div class="hidden name"><code>torch_sigmoid.Rd</code></div>
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-    <p>Sigmoid</p>
-    </div>
-
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sigmoid-input-out-none-gt-tensor-"><a class="anchor" href="#sigmoid-input-out-none-gt-tensor-"></a>sigmoid(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the sigmoid of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \frac{1}{1 + e^{-\mbox{input}_{i}}}
-$$</p>
-
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-<span class='no'>a</span>
-<span class='fu'>torch_sigmoid</span>(<span class='no'>a</span>)
-}</div></pre>
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-      <th>input</th>
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-<p>$$
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_sin.Rd</code></div>
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-    <div class="ref-description">
-    <p>Sin</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sin-input-out-none-gt-tensor-"><a class="anchor" href="#sin-input-out-none-gt-tensor-"></a>sin(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the sine of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \sin(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_sin</span>(<span class='no'>a</span>)
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_sinh.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sinh</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sinh-input-out-none-gt-tensor-"><a class="anchor" href="#sinh-input-out-none-gt-tensor-"></a>sinh(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the hyperbolic sine of the elements of
-<code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \sinh(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
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-<span class='no'>a</span>
-<span class='fu'>torch_sinh</span>(<span class='no'>a</span>)
-}</div></pre>
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-    <h1>Slogdet</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_slogdet.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Slogdet</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code>*</code> is zero or more                batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>If ``input`` has zero determinant, this returns ``(0, -inf)``.
-</pre>
-
-<pre>Backward through `slogdet` internally uses SVD results when `input`
-is not invertible. In this case, double backward through `slogdet`
-will be unstable in when `input` doesn't have distinct singular values.
-See `~torch.svd` for details.
-</pre>
-
-    <h2 class="hasAnchor" id="slogdet-input-gt-tensor-tensor-"><a class="anchor" href="#slogdet-input-gt-tensor-tensor-"></a>slogdet(input) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='no'>A</span>
-<span class='fu'><a href='torch_det.html'>torch_det</a></span>(<span class='no'>A</span>)
-<span class='fu'><a href='torch_logdet.html'>torch_logdet</a></span>(<span class='no'>A</span>)
-<span class='fu'>torch_slogdet</span>(<span class='no'>A</span>)
-}</div></pre>
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-    <h1>Solve</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_solve.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Solve</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input matrix \(B\) of size \((*, m, k)\) , where \(*\)                is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor) input square matrix of size \((*, m, m)\), where                \(*\) is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>((Tensor, Tensor) optional output tuple.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>Irrespective of the original strides, the returned matrices
-`solution` and `LU` will be transposed, i.e. with strides like
-`B.contiguous().transpose(-1, -2).stride()` and
-`A.contiguous().transpose(-1, -2).stride()` respectively.
-</pre>
-
-    <h2 class="hasAnchor" id="torch-solve-input-a-out-none-gt-tensor-tensor-"><a class="anchor" href="#torch-solve-input-a-out-none-gt-tensor-tensor-"></a>torch.solve(input, A, out=None) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>This function returns the solution to the system of linear
-equations represented by \(AX = B\) and the LU factorization of
-A, in order as a namedtuple <code>solution, LU</code>.</p>
-<p><code>LU</code> contains <code>L</code> and <code>U</code> factors for LU factorization of <code>A</code>.</p>
-<p><code>torch_solve(B, A)</code> can take in 2D inputs <code>B, A</code> or inputs that are
-batches of 2D matrices. If the inputs are batches, then returns
-batched outputs <code>solution, LU</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>6.80</span>, -<span class='fl'>2.11</span>,  <span class='fl'>5.66</span>,  <span class='fl'>5.97</span>,  <span class='fl'>8.23</span>),
-                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>6.05</span>, -<span class='fl'>3.30</span>,  <span class='fl'>5.36</span>, -<span class='fl'>4.44</span>,  <span class='fl'>1.08</span>),
-                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>0.45</span>,  <span class='fl'>2.58</span>, -<span class='fl'>2.70</span>,  <span class='fl'>0.27</span>,  <span class='fl'>9.04</span>),
-                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>8.32</span>,  <span class='fl'>2.71</span>,  <span class='fl'>4.35</span>,  -<span class='fl'>7.17</span>,  <span class='fl'>2.14</span>),
-                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>9.67</span>, -<span class='fl'>5.14</span>, -<span class='fl'>7.26</span>,  <span class='fl'>6.08</span>, -<span class='fl'>6.87</span>)))$<span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()
-<span class='no'>B</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4.02</span>,  <span class='fl'>6.19</span>, -<span class='fl'>8.22</span>, -<span class='fl'>7.57</span>, -<span class='fl'>3.03</span>),
-                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(-<span class='fl'>1.56</span>,  <span class='fl'>4.00</span>, -<span class='fl'>8.67</span>,  <span class='fl'>1.75</span>,  <span class='fl'>2.86</span>),
-                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>9.81</span>, -<span class='fl'>4.09</span>, -<span class='fl'>4.57</span>, -<span class='fl'>8.61</span>,  <span class='fl'>8.99</span>)))$<span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()
-<span class='no'>out</span> <span class='kw'>=</span> <span class='fu'>torch_solve</span>(<span class='no'>B</span>, <span class='no'>A</span>)
-<span class='no'>X</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]]
-<span class='no'>LU</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]]
-<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='no'>B</span>, <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='no'>A</span>, <span class='no'>X</span>))
-<span class='co'># Batched solver example</span>
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>B</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>6</span>))
-<span class='no'>out</span> <span class='kw'>=</span> <span class='fu'>torch_solve</span>(<span class='no'>B</span>, <span class='no'>A</span>)
-<span class='no'>X</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]]
-<span class='no'>LU</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]]
-<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='no'>B</span>, <span class='no'>A</span>$<span class='fu'>matmul</span>(<span class='no'>X</span>))
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_sort.html b/static/docs/reference/torch_sort.html
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sort</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_sort.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sort</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to sort along</p></td>
-    </tr>
-    <tr>
-      <th>descending</th>
-      <td><p>(bool, optional) controls the sorting order (ascending or descending)</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the output tuple of (<code>Tensor</code>, <code>LongTensor</code>) that can        be optionally given to be used as output buffers</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sort-input-dim-descending-false-out-none-gt-tensor-longtensor-"><a class="anchor" href="#sort-input-dim-descending-false-out-none-gt-tensor-longtensor-"></a>sort(input, dim=-1, descending=False, out=None) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Sorts the elements of the <code>input</code> tensor along a given dimension
-in ascending order by value.</p>
-<p>If <code>dim</code> is not given, the last dimension of the <code>input</code> is chosen.</p>
-<p>If <code>descending</code> is <code>True</code> then the elements are sorted in descending
-order by value.</p>
-<p>A namedtuple of (values, indices) is returned, where the <code>values</code> are the
-sorted values and <code>indices</code> are the indices of the elements in the original
-<code>input</code> tensor.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='no'>out</span> <span class='kw'>=</span> <span class='fu'>torch_sort</span>(<span class='no'>x</span>)
-<span class='no'>out</span>
-<span class='no'>out</span> <span class='kw'>=</span> <span class='fu'>torch_sort</span>(<span class='no'>x</span>, <span class='fl'>1</span>)
-<span class='no'>out</span>
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_sparse_coo_tensor.html b/static/docs/reference/torch_sparse_coo_tensor.html
deleted file mode 100644
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-    <h1>Sparse_coo_tensor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_sparse_coo_tensor.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sparse_coo_tensor</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>indices</th>
-      <td><p>(array_like) Initial data for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types. Will be cast to a <code>torch_LongTensor</code>        internally. The indices are the coordinates of the non-zero values in the matrix, and thus        should be two-dimensional where the first dimension is the number of tensor dimensions and        the second dimension is the number of non-zero values.</p></td>
-    </tr>
-    <tr>
-      <th>values</th>
-      <td><p>(array_like) Initial values for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types.</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(list, tuple, or <code>torch.Size</code>, optional) Size of the sparse tensor. If not        provided the size will be inferred as the minimum size big enough to hold all non-zero        elements.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if None, infers data type from <code>values</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sparse-coo-tensor-indices-values-size-none-dtype-none-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#sparse-coo-tensor-indices-values-size-none-dtype-none-device-none-requires-grad-false-gt-tensor-"></a>sparse_coo_tensor(indices, values, size=None, dtype=None, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given <code>indices</code>
-with the given <code>values</code>. A sparse tensor can be <code>uncoalesced</code>, in that case, there are duplicate
-coordinates in the indices, and the value at that index is the sum of all duplicate value entries:
-<code>torch_sparse</code>_.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>i</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>3</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>3</span>, <span class='kw'>byrow</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
-<span class='no'>v</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float32</a></span>())
-<span class='fu'>torch_sparse_coo_tensor</span>(<span class='no'>i</span>, <span class='no'>v</span>)
-<span class='fu'>torch_sparse_coo_tensor</span>(<span class='no'>i</span>, <span class='no'>v</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>4</span>))
-
-<span class='co'># create empty sparse tensors</span>
-<span class='no'>S</span> <span class='kw'>=</span> <span class='fu'>torch_sparse_coo_tensor</span>(
-  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0</span>), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()),
-  <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>numeric</a></span>(), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_float32</a></span>()),
-  <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>)
-)
-<span class='no'>S</span> <span class='kw'>=</span> <span class='fu'>torch_sparse_coo_tensor</span>(
-  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0</span>), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()),
-  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>2</span>)),
-  <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)
-)
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_split.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Split</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>(Tensor) tensor to split.</p></td>
-    </tr>
-    <tr>
-      <th>split_size_or_sections</th>
-      <td><p>(int) size of a single chunk or        list of sizes for each chunk</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension along which to split the tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Splits the tensor into chunks. Each chunk is a view of the original tensor.</p><pre>If `split_size_or_sections` is an integer type, then `tensor` will
-be split into equally sized chunks (if possible). Last chunk will be smaller if
-the tensor size along the given dimension `dim` is not divisible by
-`split_size`.
-
-If `split_size_or_sections` is a list, then `tensor` will be split
-into ``len(split_size_or_sections)`` chunks with sizes in `dim` according
-to `split_size_or_sections`.
-</pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_sqrt.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sqrt</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sqrt-input-out-none-gt-tensor-"><a class="anchor" href="#sqrt-input-out-none-gt-tensor-"></a>sqrt(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the square-root of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \sqrt{\mbox{input}_{i}}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_sqrt</span>(<span class='no'>a</span>)
-}</div></pre>
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-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="square-input-out-none-gt-tensor-"><a class="anchor" href="#square-input-out-none-gt-tensor-"></a>square(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the square of the elements of <code>input</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_square</span>(<span class='no'>a</span>)
-}</div></pre>
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-    <h1>Squeeze</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_squeeze.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Squeeze</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) if given, the input will be squeezed only in           this dimension</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>The returned tensor shares the storage with the input tensor,
-so changing the contents of one will change the contents of the other.</p>
-    <h2 class="hasAnchor" id="squeeze-input-dim-none-out-none-gt-tensor-"><a class="anchor" href="#squeeze-input-dim-none-out-none-gt-tensor-"></a>squeeze(input, dim=None, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor with all the dimensions of <code>input</code> of size <code>1</code> removed.</p>
-<p>For example, if <code>input</code> is of shape:
-\((A \times 1 \times B \times C \times 1 \times D)\) then the <code>out</code> tensor
-will be of shape: \((A \times B \times C \times D)\).</p>
-<p>When <code>dim</code> is given, a squeeze operation is done only in the given
-dimension. If <code>input</code> is of shape: \((A \times 1 \times B)\),
-<code>squeeze(input, 0)</code> leaves the tensor unchanged, but <code>squeeze(input, 1)</code>
-will squeeze the tensor to the shape \((A \times B)\).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='no'>x</span>
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'>torch_squeeze</span>(<span class='no'>x</span>)
-<span class='no'>y</span>
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'>torch_squeeze</span>(<span class='no'>x</span>, <span class='fl'>1</span>)
-<span class='no'>y</span>
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'>torch_squeeze</span>(<span class='no'>x</span>, <span class='fl'>2</span>)
-<span class='no'>y</span>
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_stack.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Stack</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(sequence of Tensors) sequence of tensors to concatenate</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="stack-tensors-dim-out-none-gt-tensor-"><a class="anchor" href="#stack-tensors-dim-out-none-gt-tensor-"></a>stack(tensors, dim=0, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Concatenates sequence of tensors along a new dimension.</p>
-<p>All tensors need to be of the same size.</p>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Std</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_std.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Std</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>unbiased</th>
-      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="std-input-unbiased-true-gt-tensor-"><a class="anchor" href="#std-input-unbiased-true-gt-tensor-"></a>std(input, unbiased=True) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the standard-deviation of all elements in the <code>input</code> tensor.</p>
-<p>If <code>unbiased</code> is <code>False</code>, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.</p>
-    <h2 class="hasAnchor" id="std-input-dim-unbiased-true-keepdim-false-out-none-gt-tensor-"><a class="anchor" href="#std-input-dim-unbiased-true-keepdim-false-out-none-gt-tensor-"></a>std(input, dim, unbiased=True, keepdim=False, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the standard-deviation of each row of the <code>input</code> tensor in the
-dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
-reduce over all of them.</p>
-<p>If <code>keepdim</code> is <code>True</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-<p>If <code>unbiased</code> is <code>False</code>, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_std</span>(<span class='no'>a</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_std</span>(<span class='no'>a</span>, <span class='kw'>dim</span><span class='kw'>=</span><span class='fl'>1</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_std_mean.html b/static/docs/reference/torch_std_mean.html
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-    <div class="page-header">
-    <h1>Std_mean</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_std_mean.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Std_mean</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>unbiased</th>
-      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="std-mean-input-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#std-mean-input-unbiased-true-gt-tensor-tensor-"></a>std_mean(input, unbiased=True) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Returns the standard-deviation and mean of all elements in the <code>input</code> tensor.</p>
-<p>If <code>unbiased</code> is <code>False</code>, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.</p>
-    <h2 class="hasAnchor" id="std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-"><a class="anchor" href="#std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-"></a>std_mean(input, dim, unbiased=True, keepdim=False) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Returns the standard-deviation and mean of each row of the <code>input</code> tensor in the
-dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
-reduce over all of them.</p>
-<p>If <code>keepdim</code> is <code>True</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-<p>If <code>unbiased</code> is <code>False</code>, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_std_mean</span>(<span class='no'>a</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_std_mean</span>(<span class='no'>a</span>, <span class='fl'>1</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    <div class="page-header">
-    <h1>Stft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_stft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Stft</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>n_fft</th>
-      <td><p>(int) size of Fourier transform</p></td>
-    </tr>
-    <tr>
-      <th>hop_length</th>
-      <td><p>(int, optional) the distance between neighboring sliding window        frames. Default: <code>None</code> (treated as equal to <code><a href='https://rdrr.io/r/base/Round.html'>floor(n_fft / 4)</a></code>)</p></td>
-    </tr>
-    <tr>
-      <th>win_length</th>
-      <td><p>(int, optional) the size of window frame and STFT filter.        Default: <code>None</code>  (treated as equal to <code>n_fft</code>)</p></td>
-    </tr>
-    <tr>
-      <th>window</th>
-      <td><p>(Tensor, optional) the optional window function.        Default: <code>None</code> (treated as window of all \(1\) s)</p></td>
-    </tr>
-    <tr>
-      <th>center</th>
-      <td><p>(bool, optional) whether to pad <code>input</code> on both sides so        that the \(t\)-th frame is centered at time \(t \times \mbox{hop\_length}\).        Default: <code>True</code></p></td>
-    </tr>
-    <tr>
-      <th>pad_mode</th>
-      <td><p>(string, optional) controls the padding method used when        <code>center</code> is <code>True</code>. Default: <code>"reflect"</code></p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return the normalized STFT results         Default: <code>False</code></p></td>
-    </tr>
-    <tr>
-      <th>onesided</th>
-      <td><p>(bool, optional) controls whether to return half of results to        avoid redundancy Default: <code>True</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="short-time-fourier-transform-stft-"><a class="anchor" href="#short-time-fourier-transform-stft-"></a>Short-time Fourier transform (STFT). </h2>
-
-    
-
-
-<p>Short-time Fourier transform (STFT).</p><pre>Ignoring the optional batch dimension, this method computes the following
-expression:
-</pre>
-
-<p>$$
-        X[m, \omega] = \sum_{k = 0}^{\mbox{win\_length-1}}%
-                            \mbox{window}[k]\ \mbox{input}[m \times \mbox{hop\_length} + k]\ %
-                            \exp\left(- j \frac{2 \pi \cdot \omega k}{\mbox{win\_length}}\right),
-$$
-where \(m\) is the index of the sliding window, and \(\omega\) is
-the frequency that \(0 \leq \omega &lt; \mbox{n\_fft}\). When
-<code>onesided</code> is the default value <code>True</code>,</p><pre>* `input` must be either a 1-D time sequence or a 2-D batch of time
-  sequences.
-
-* If `hop_length` is ``None`` (default), it is treated as equal to
-  ``floor(n_fft / 4)``.
-
-* If `win_length` is ``None`` (default), it is treated as equal to
-  `n_fft`.
-
-* `window` can be a 1-D tensor of size `win_length`, e.g., from
-  `torch_hann_window`. If `window` is ``None`` (default), it is
-  treated as if having \eqn{1} everywhere in the window. If
-  \eqn{\mbox{win\_length} &amp;lt; \mbox{n\_fft}}, `window` will be padded on
-  both sides to length `n_fft` before being applied.
-
-* If `center` is ``True`` (default), `input` will be padded on
-  both sides so that the \eqn{t}-th frame is centered at time
-  \eqn{t \times \mbox{hop\_length}}. Otherwise, the \eqn{t}-th frame
-  begins at time  \eqn{t \times \mbox{hop\_length}}.
-
-* `pad_mode` determines the padding method used on `input` when
-  `center` is ``True``. See `torch_nn.functional.pad` for
-  all available options. Default is ``"reflect"``.
-
-* If `onesided` is ``True`` (default), only values for \eqn{\omega}
-  in \eqn{\left[0, 1, 2, \dots, \left\lfloor \frac{\mbox{n\_fft}}{2} \right\rfloor + 1\right]}
-  are returned because the real-to-complex Fourier transform satisfies the
-  conjugate symmetry, i.e., \eqn{X[m, \omega] = X[m, \mbox{n\_fft} - \omega]^*}.
-
-* If `normalized` is ``True`` (default is ``False``), the function
-  returns the normalized STFT results, i.e., multiplied by \eqn{(\mbox{frame\_length})^{-0.5}}.
-
-Returns the real and the imaginary parts together as one tensor of size
-\eqn{(* \times N \times T \times 2)}, where \eqn{*} is the optional
-batch size of `input`, \eqn{N} is the number of frequencies where
-STFT is applied, \eqn{T} is the total number of frames used, and each pair
-in the last dimension represents a complex number as the real part and the
-imaginary part.
-
-.. warning::
-  This function changed signature at version 0.4.1. Calling with the
-  previous signature may cause error or return incorrect result.
-</pre>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_sum.html b/static/docs/reference/torch_sum.html
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-    <h1>Sum</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_sum.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sum</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: None.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sum-input-dtype-none-gt-tensor-"><a class="anchor" href="#sum-input-dtype-none-gt-tensor-"></a>sum(input, dtype=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the sum of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="sum-input-dim-keepdim-false-dtype-none-gt-tensor-"><a class="anchor" href="#sum-input-dim-keepdim-false-dtype-none-gt-tensor-"></a>sum(input, dim, keepdim=False, dtype=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the sum of each row of the <code>input</code> tensor in the given
-dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
-reduce over all of them.</p>
-<p>If <code>keepdim</code> is <code>True</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_sum</span>(<span class='no'>a</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_sum</span>(<span class='no'>a</span>, <span class='fl'>1</span>)
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span> * <span class='fl'>5</span> * <span class='fl'>6</span>)$<span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>))
-<span class='fu'>torch_sum</span>(<span class='no'>b</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
-}</div></pre>
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-    <div class="page-header">
-    <h1>Svd</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_svd.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Svd</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor of size \((*, m, n)\) where <code>*</code> is zero or more                    batch dimensions consisting of \(m \times n\) matrices.</p></td>
-    </tr>
-    <tr>
-      <th>some</th>
-      <td><p>(bool, optional) controls the shape of returned <code>U</code> and <code>V</code></p></td>
-    </tr>
-    <tr>
-      <th>compute_uv</th>
-      <td><p>(bool, optional) option whether to compute <code>U</code> and <code>V</code> or not</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the output tuple of tensors</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>The singular values are returned in descending order. If <code>input</code> is a batch of matrices,
-then the singular values of each matrix in the batch is returned in descending order.</p>
-<p>The implementation of SVD on CPU uses the LAPACK routine <code>?gesdd</code> (a divide-and-conquer
-algorithm) instead of <code>?gesvd</code> for speed. Analogously, the SVD on GPU uses the MAGMA routine
-<code>gesdd</code> as well.</p>
-<p>Irrespective of the original strides, the returned matrix <code>U</code>
-will be transposed, i.e. with strides <code>U.contiguous().transpose(-2, -1).stride()</code></p>
-<p>Extra care needs to be taken when backward through <code>U</code> and <code>V</code>
-outputs. Such operation is really only stable when <code>input</code> is
-full rank with all distinct singular values. Otherwise, <code>NaN</code> can
-appear as the gradients are not properly defined. Also, notice that
-double backward will usually do an additional backward through <code>U</code> and
-<code>V</code> even if the original backward is only on <code>S</code>.</p>
-<p>When <code>some</code> = <code>False</code>, the gradients on <code>U[..., :, min(m, n):]</code>
-and <code>V[..., :, min(m, n):]</code> will be ignored in backward as those vectors
-can be arbitrary bases of the subspaces.</p>
-<p>When <code>compute_uv</code> = <code>False</code>, backward cannot be performed since <code>U</code> and <code>V</code>
-from the forward pass is required for the backward operation.</p>
-    <h2 class="hasAnchor" id="svd-input-some-true-compute-uv-true-out-none-gt-tensor-tensor-tensor-"><a class="anchor" href="#svd-input-some-true-compute-uv-true-out-none-gt-tensor-tensor-tensor-"></a>svd(input, some=True, compute_uv=True, out=None) -&gt; (Tensor, Tensor, Tensor) </h2>
-
-    
-
-
-<p>This function returns a namedtuple <code>(U, S, V)</code> which is the singular value
-decomposition of a input real matrix or batches of real matrices <code>input</code> such that
-\(input = U \times diag(S) \times V^T\).</p>
-<p>If <code>some</code> is <code>True</code> (default), the method returns the reduced singular value decomposition
-i.e., if the last two dimensions of <code>input</code> are <code>m</code> and <code>n</code>, then the returned
-<code>U</code> and <code>V</code> matrices will contain only \(min(n, m)\) orthonormal columns.</p>
-<p>If <code>compute_uv</code> is <code>False</code>, the returned <code>U</code> and <code>V</code> matrices will be zero matrices
-of shape \((m \times m)\) and \((n \times n)\) respectively. <code>some</code> will be ignored here.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='no'>out</span> <span class='kw'>=</span> <span class='fu'>torch_svd</span>(<span class='no'>a</span>)
-<span class='no'>u</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]]
-<span class='no'>s</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]]
-<span class='no'>v</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>3</span>]]
-<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='no'>a</span>, <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='no'>u</span>, <span class='fu'><a href='torch_diag.html'>torch_diag</a></span>(<span class='no'>s</span>)), <span class='no'>v</span>$<span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()))
-<span class='no'>a_big</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>7</span>, <span class='fl'>5</span>, <span class='fl'>3</span>))
-<span class='no'>out</span> <span class='kw'>=</span> <span class='fu'>torch_svd</span>(<span class='no'>a_big</span>)
-<span class='no'>u</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]]
-<span class='no'>s</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]]
-<span class='no'>v</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>3</span>]]
-<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='no'>a_big</span>, <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='no'>u</span>, <span class='fu'><a href='torch_diag_embed.html'>torch_diag_embed</a></span>(<span class='no'>s</span>)), <span class='no'>v</span>$<span class='fu'>transpose</span>(-<span class='fl'>2</span>, -<span class='fl'>1</span>)))
-}</div></pre>
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diff --git a/static/docs/reference/torch_symeig.html b/static/docs/reference/torch_symeig.html
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-    <h1>Symeig</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_symeig.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Symeig</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor of size \((*, n, n)\) where <code>*</code> is zero or more                    batch dimensions consisting of symmetric matrices.</p></td>
-    </tr>
-    <tr>
-      <th>eigenvectors</th>
-      <td><p>(boolean, optional) controls whether eigenvectors have to be computed</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(boolean, optional) controls whether to consider upper-triangular or lower-triangular region</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the output tuple of (Tensor, Tensor)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>The eigenvalues are returned in ascending order. If <code>input</code> is a batch of matrices,
-then the eigenvalues of each matrix in the batch is returned in ascending order.</p>
-<p>Irrespective of the original strides, the returned matrix <code>V</code> will
-be transposed, i.e. with strides <code>V.contiguous().transpose(-1, -2).stride()</code>.</p>
-<p>Extra care needs to be taken when backward through outputs. Such
-operation is really only stable when all eigenvalues are distinct.
-Otherwise, <code>NaN</code> can appear as the gradients are not properly defined.</p>
-    <h2 class="hasAnchor" id="symeig-input-eigenvectors-false-upper-true-out-none-gt-tensor-tensor-"><a class="anchor" href="#symeig-input-eigenvectors-false-upper-true-out-none-gt-tensor-tensor-"></a>symeig(input, eigenvectors=False, upper=True, out=None) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>This function returns eigenvalues and eigenvectors
-of a real symmetric matrix <code>input</code> or a batch of real symmetric matrices,
-represented by a namedtuple (eigenvalues, eigenvectors).</p>
-<p>This function calculates all eigenvalues (and vectors) of <code>input</code>
-such that \(\mbox{input} = V \mbox{diag}(e) V^T\).</p>
-<p>The boolean argument <code>eigenvectors</code> defines computation of
-both eigenvectors and eigenvalues or eigenvalues only.</p>
-<p>If it is <code>False</code>, only eigenvalues are computed. If it is <code>True</code>,
-both eigenvalues and eigenvectors are computed.</p>
-<p>Since the input matrix <code>input</code> is supposed to be symmetric,
-only the upper triangular portion is used by default.</p>
-<p>If <code>upper</code> is <code>False</code>, then lower triangular portion is used.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>5</span>))
-<span class='no'>a</span> <span class='kw'>=</span> <span class='no'>a</span> + <span class='no'>a</span>$<span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()  <span class='co'># To make a symmetric</span>
-<span class='no'>a</span>
-<span class='no'>o</span> <span class='kw'>=</span> <span class='fu'>torch_symeig</span>(<span class='no'>a</span>, <span class='kw'>eigenvectors</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='no'>e</span> <span class='kw'>=</span> <span class='no'>o</span><span class='kw'>[[</span><span class='fl'>1</span>]]
-<span class='no'>v</span> <span class='kw'>=</span> <span class='no'>o</span><span class='kw'>[[</span><span class='fl'>2</span>]]
-<span class='no'>e</span>
-<span class='no'>v</span>
-<span class='no'>a_big</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='no'>a_big</span> <span class='kw'>=</span> <span class='no'>a_big</span> + <span class='no'>a_big</span>$<span class='fu'>transpose</span>(-<span class='fl'>2</span>, -<span class='fl'>1</span>)  <span class='co'># To make a_big symmetric</span>
-<span class='no'>o</span> <span class='kw'>=</span> <span class='no'>a_big</span>$<span class='fu'>symeig</span>(<span class='kw'>eigenvectors</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='no'>e</span> <span class='kw'>=</span> <span class='no'>o</span><span class='kw'>[[</span><span class='fl'>1</span>]]
-<span class='no'>v</span> <span class='kw'>=</span> <span class='no'>o</span><span class='kw'>[[</span><span class='fl'>2</span>]]
-<span class='fu'><a href='torch_allclose.html'>torch_allclose</a></span>(<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='no'>v</span>, <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='no'>e</span>$<span class='fu'>diag_embed</span>(), <span class='no'>v</span>$<span class='fu'>transpose</span>(-<span class='fl'>2</span>, -<span class='fl'>1</span>))), <span class='no'>a_big</span>)
-}</div></pre>
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-    <h1>T</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_t.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>T</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="t-input-gt-tensor-"><a class="anchor" href="#t-input-gt-tensor-"></a>t(input) -&gt; Tensor </h2>
-
-    
-
-
-<p>Expects <code>input</code> to be &lt;= 2-D tensor and transposes dimensions 0
-and 1.</p>
-<p>0-D and 1-D tensors are returned as is. When input is a 2-D tensor this
-is equivalent to <code>transpose(input, 0, 1)</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>))
-<span class='no'>x</span>
-<span class='fu'>torch_t</span>(<span class='no'>x</span>)
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
-<span class='no'>x</span>
-<span class='fu'>torch_t</span>(<span class='no'>x</span>)
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='no'>x</span>
-<span class='fu'>torch_t</span>(<span class='no'>x</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_take.html b/static/docs/reference/torch_take.html
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-    <div class="page-header">
-    <h1>Take</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_take.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Take</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>indices</th>
-      <td><p>(LongTensor) the indices into tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="take-input-index-gt-tensor-"><a class="anchor" href="#take-input-index-gt-tensor-"></a>take(input, index) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the elements of <code>input</code> at the given indices.
-The input tensor is treated as if it were viewed as a 1-D tensor. The result
-takes the same shape as the indices.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>src</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>6</span>,<span class='fl'>7</span>,<span class='fl'>8</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>3</span>, <span class='kw'>byrow</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))
-<span class='fu'>torch_take</span>(<span class='no'>src</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>5</span>), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()))
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/torch_tan.html b/static/docs/reference/torch_tan.html
deleted file mode 100644
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_tan.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Tan</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="tan-input-out-none-gt-tensor-"><a class="anchor" href="#tan-input-out-none-gt-tensor-"></a>tan(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the tangent of the elements of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \tan(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_tan</span>(<span class='no'>a</span>)
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_tanh.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Tanh</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="tanh-input-out-none-gt-tensor-"><a class="anchor" href="#tanh-input-out-none-gt-tensor-"></a>tanh(input, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with the hyperbolic tangent of the elements
-of <code>input</code>.</p>
-<p>$$
-    \mbox{out}_{i} = \tanh(\mbox{input}_{i})
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_tanh</span>(<span class='no'>a</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-  <span class='no'>data</span>,
-  <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>device</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>requires_grad</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>pin_memory</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>
-)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>data</th>
-      <td><p>an R atomic vector, matrix or array</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>a <a href='torch_dtype.html'>torch_dtype</a> instance</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>a device creted with <code><a href='torch_device.html'>torch_device()</a></code></p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>if autograd should record operations on the returned tensor.</p></td>
-    </tr>
-    <tr>
-      <th>pin_memory</th>
-      <td><p>If set, returned tensor would be allocated in the pinned memory.</p></td>
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-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='fu'>torch_tensor</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>))
-<span class='fu'>torch_tensor</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>), <span class='kw'>dtype</span> <span class='kw'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
-
-}</div></pre>
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diff --git a/static/docs/reference/torch_tensordot.html b/static/docs/reference/torch_tensordot.html
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-    <h1>Tensordot</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_tensordot.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Tensordot</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>a</th>
-      <td><p>(Tensor) Left tensor to contract</p></td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td><p>(Tensor) Right tensor to contract</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(int or tuple of two lists of integers) number of dimensions to     contract or explicit lists of dimensions for <code>a</code> and     <code>b</code> respectively</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Returns a contraction of a and b over multiple dimensions.</p><pre>`tensordot` implements a generalized matrix product.
-</pre>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='kw'>start</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>end</span> <span class='kw'>=</span> <span class='fl'>60.</span>)$<span class='fu'><a href='https://rdrr.io/r/stats/reshape.html'>reshape</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='kw'>start</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>end</span> <span class='kw'>=</span> <span class='fl'>24.</span>)$<span class='fu'><a href='https://rdrr.io/r/stats/reshape.html'>reshape</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='fu'>torch_tensordot</span>(<span class='no'>a</span>, <span class='no'>b</span>, <span class='kw'>dims_self</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), <span class='kw'>dims_other</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, <span class='kw'>device</span><span class='kw'>=</span><span class='st'>'cuda'</span>)
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>, <span class='kw'>device</span><span class='kw'>=</span><span class='st'>'cuda'</span>)
-<span class='no'>c</span> <span class='kw'>=</span> <span class='fu'>torch_tensordot</span>(<span class='no'>a</span>, <span class='no'>b</span>, <span class='kw'>dims</span><span class='kw'>=</span><span class='fl'>2</span>)$<span class='fu'>cpu</span>()
-}
-}</div></pre>
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diff --git a/static/docs/reference/torch_threshold_.html b/static/docs/reference/torch_threshold_.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_topk.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Topk</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>k</th>
-      <td><p>(int) the k in "top-k"</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to sort along</p></td>
-    </tr>
-    <tr>
-      <th>largest</th>
-      <td><p>(bool, optional) controls whether to return largest or           smallest elements</p></td>
-    </tr>
-    <tr>
-      <th>sorted</th>
-      <td><p>(bool, optional) controls whether to return the elements           in sorted order</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the output tuple of (Tensor, LongTensor) that can be        optionally given to be used as output buffers</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="topk-input-k-dim-none-largest-true-sorted-true-out-none-gt-tensor-longtensor-"><a class="anchor" href="#topk-input-k-dim-none-largest-true-sorted-true-out-none-gt-tensor-longtensor-"></a>topk(input, k, dim=None, largest=True, sorted=True, out=None) -&gt; (Tensor, LongTensor) </h2>
-
-    
-
-
-<p>Returns the <code>k</code> largest elements of the given <code>input</code> tensor along
-a given dimension.</p>
-<p>If <code>dim</code> is not given, the last dimension of the <code>input</code> is chosen.</p>
-<p>If <code>largest</code> is <code>False</code> then the <code>k</code> smallest elements are returned.</p>
-<p>A namedtuple of <code>(values, indices)</code> is returned, where the <code>indices</code> are the indices
-of the elements in the original <code>input</code> tensor.</p>
-<p>The boolean option <code>sorted</code> if <code>True</code>, will make sure that the returned
-<code>k</code> elements are themselves sorted</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>)
-<span class='no'>x</span>
-<span class='fu'>torch_topk</span>(<span class='no'>x</span>, <span class='fl'>3</span>)
-}</div></pre>
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim0</th>
-      <td><p>(int) the first dimension to be transposed</p></td>
-    </tr>
-    <tr>
-      <th>dim1</th>
-      <td><p>(int) the second dimension to be transposed</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="transpose-input-dim-dim-gt-tensor-"><a class="anchor" href="#transpose-input-dim-dim-gt-tensor-"></a>transpose(input, dim0, dim1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor that is a transposed version of <code>input</code>.
-The given dimensions <code>dim0</code> and <code>dim1</code> are swapped.</p>
-<p>The resulting <code>out</code> tensor shares it's underlying storage with the
-<code>input</code> tensor, so changing the content of one would change the content
-of the other.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
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-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='no'>x</span>
-<span class='fu'>torch_transpose</span>(<span class='no'>x</span>, <span class='fl'>1</span>, <span class='fl'>2</span>)
-}</div></pre>
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-    <div class="hidden name"><code>torch_trapz.Rd</code></div>
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-    <p>Trapz</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>y</th>
-      <td><p>(Tensor) The values of the function to integrate</p></td>
-    </tr>
-    <tr>
-      <th>x</th>
-      <td><p>(Tensor) The points at which the function <code>y</code> is sampled.        If <code>x</code> is not in ascending order, intervals on which it is decreasing        contribute negatively to the estimated integral (i.e., the convention        \(\int_a^b f = -\int_b^a f\) is followed).</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) The dimension along which to integrate.        By default, use the last dimension.</p></td>
-    </tr>
-    <tr>
-      <th>dx</th>
-      <td><p>(float) The distance between points at which <code>y</code> is sampled.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="trapz-y-x-dim-gt-tensor-"><a class="anchor" href="#trapz-y-x-dim-gt-tensor-"></a>trapz(y, x, *, dim=-1) -&gt; Tensor </h2>
-
-    
-
-
-<p>Estimate \(\int y\,dx\) along <code>dim</code>, using the trapezoid rule.</p>
-    <h2 class="hasAnchor" id="trapz-y-dx-dim-gt-tensor-"><a class="anchor" href="#trapz-y-dx-dim-gt-tensor-"></a>trapz(y, *, dx=1, dim=-1) -&gt; Tensor </h2>
-
-    
-
-
-<p>As above, but the sample points are spaced uniformly at a distance of <code>dx</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='no'>y</span>
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>3</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>))
-<span class='fu'>torch_trapz</span>(<span class='no'>y</span>, <span class='kw'>x</span> <span class='kw'>=</span> <span class='no'>x</span>)
-
-}</div></pre>
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-    <h1>Triangular_solve</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_triangular_solve.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Triangular_solve</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) multiple right-hand sides of size \((*, m, k)\) where                \(*\) is zero of more batch dimensions (\(b\))</p></td>
-    </tr>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor) the input triangular coefficient matrix of size \((*, m, m)\)                where \(*\) is zero or more batch dimensions</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: <code>True</code>.</p></td>
-    </tr>
-    <tr>
-      <th>transpose</th>
-      <td><p>(bool, optional) whether \(A\) should be transposed before        being sent into the solver. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>unitriangular</th>
-      <td><p>(bool, optional) whether \(A\) is unit triangular.        If True, the diagonal elements of \(A\) are assumed to be        1 and not referenced from \(A\). Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-"><a class="anchor" href="#triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-"></a>triangular_solve(input, A, upper=True, transpose=False, unitriangular=False) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Solves a system of equations with a triangular coefficient matrix \(A\)
-and multiple right-hand sides \(b\).</p>
-<p>In particular, solves \(AX = b\) and assumes \(A\) is upper-triangular
-with the default keyword arguments.</p>
-<p><code>torch_triangular_solve(b, A)</code> can take in 2D inputs <code>b, A</code> or inputs that are
-batches of 2D matrices. If the inputs are batches, then returns
-batched outputs <code>X</code></p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>A</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))$<span class='fu'>triu</span>()
-<span class='no'>A</span>
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='no'>b</span>
-<span class='fu'>torch_triangular_solve</span>(<span class='no'>b</span>, <span class='no'>A</span>)
-}</div></pre>
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-    <h1>Tril</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_tril.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Tril</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>diagonal</th>
-      <td><p>(int, optional) the diagonal to consider</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="tril-input-diagonal-out-none-gt-tensor-"><a class="anchor" href="#tril-input-diagonal-out-none-gt-tensor-"></a>tril(input, diagonal=0, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices
-<code>input</code>, the other elements of the result tensor <code>out</code> are set to 0.</p>
-<p>The lower triangular part of the matrix is defined as the elements on and
-below the diagonal.</p>
-<p>The argument <code>diagonal</code> controls which diagonal to consider. If
-<code>diagonal</code> = 0, all elements on and below the main diagonal are
-retained. A positive value includes just as many diagonals above the main
-diagonal, and similarly a negative value excludes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
-\(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_tril</span>(<span class='no'>a</span>)
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>6</span>))
-<span class='no'>b</span>
-<span class='fu'>torch_tril</span>(<span class='no'>b</span>, <span class='kw'>diagonal</span><span class='kw'>=</span><span class='fl'>1</span>)
-<span class='fu'>torch_tril</span>(<span class='no'>b</span>, <span class='kw'>diagonal</span><span class='kw'>=</span>-<span class='fl'>1</span>)
-}</div></pre>
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-    <div class="page-header">
-    <h1>Tril_indices</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_tril_indices.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Tril_indices</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>row</th>
-      <td><p>(<code>int</code>) number of rows in the 2-D matrix.</p></td>
-    </tr>
-    <tr>
-      <th>col</th>
-      <td><p>(<code>int</code>) number of columns in the 2-D matrix.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, <code>torch_long</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to
-prevent overflow during calculation.
-</pre>
-
-    <h2 class="hasAnchor" id="tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"><a class="anchor" href="#tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a>tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the indices of the lower triangular part of a <code>row</code>-by-
-<code>col</code> matrix in a 2-by-N Tensor, where the first row contains row
-coordinates of all indices and the second row contains column coordinates.
-Indices are ordered based on rows and then columns.</p>
-<p>The lower triangular part of the matrix is defined as the elements on and
-below the diagonal.</p>
-<p>The argument <code>offset</code> controls which diagonal to consider. If
-<code>offset</code> = 0, all elements on and below the main diagonal are
-retained. A positive value includes just as many diagonals above the main
-diagonal, and similarly a negative value excludes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
-where \(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'>torch_tril_indices</span>(<span class='fl'>3</span>, <span class='fl'>3</span>)
-<span class='no'>a</span>
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'>torch_tril_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, -<span class='fl'>1</span>)
-<span class='no'>a</span>
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'>torch_tril_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>1</span>)
-<span class='no'>a</span>
-}
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <h1>Triu</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_triu.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Triu</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>diagonal</th>
-      <td><p>(int, optional) the diagonal to consider</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="triu-input-diagonal-out-none-gt-tensor-"><a class="anchor" href="#triu-input-diagonal-out-none-gt-tensor-"></a>triu(input, diagonal=0, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
-<code>input</code>, the other elements of the result tensor <code>out</code> are set to 0.</p>
-<p>The upper triangular part of the matrix is defined as the elements on and
-above the diagonal.</p>
-<p>The argument <code>diagonal</code> controls which diagonal to consider. If
-<code>diagonal</code> = 0, all elements on and above the main diagonal are
-retained. A positive value excludes just as many diagonals above the main
-diagonal, and similarly a negative value includes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
-\(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_triu</span>(<span class='no'>a</span>)
-<span class='fu'>torch_triu</span>(<span class='no'>a</span>, <span class='kw'>diagonal</span><span class='kw'>=</span><span class='fl'>1</span>)
-<span class='fu'>torch_triu</span>(<span class='no'>a</span>, <span class='kw'>diagonal</span><span class='kw'>=</span>-<span class='fl'>1</span>)
-<span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>6</span>))
-<span class='no'>b</span>
-<span class='fu'>torch_triu</span>(<span class='no'>b</span>, <span class='kw'>diagonal</span><span class='kw'>=</span><span class='fl'>1</span>)
-<span class='fu'>torch_triu</span>(<span class='no'>b</span>, <span class='kw'>diagonal</span><span class='kw'>=</span>-<span class='fl'>1</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_triu_indices.html b/static/docs/reference/torch_triu_indices.html
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-    <h1>Triu_indices</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_triu_indices.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Triu_indices</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>row</th>
-      <td><p>(<code>int</code>) number of rows in the 2-D matrix.</p></td>
-    </tr>
-    <tr>
-      <th>col</th>
-      <td><p>(<code>int</code>) number of columns in the 2-D matrix.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, <code>torch_long</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to
-prevent overflow during calculation.
-</pre>
-
-    <h2 class="hasAnchor" id="triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"><a class="anchor" href="#triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a>triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the indices of the upper triangular part of a <code>row</code> by
-<code>col</code> matrix in a 2-by-N Tensor, where the first row contains row
-coordinates of all indices and the second row contains column coordinates.
-Indices are ordered based on rows and then columns.</p>
-<p>The upper triangular part of the matrix is defined as the elements on and
-above the diagonal.</p>
-<p>The argument <code>offset</code> controls which diagonal to consider. If
-<code>offset</code> = 0, all elements on and above the main diagonal are
-retained. A positive value excludes just as many diagonals above the main
-diagonal, and similarly a negative value includes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
-where \(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'>torch_triu_indices</span>(<span class='fl'>3</span>, <span class='fl'>3</span>)
-<span class='no'>a</span>
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'>torch_triu_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, -<span class='fl'>1</span>)
-<span class='no'>a</span>
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'>torch_triu_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>1</span>)
-<span class='no'>a</span>
-}
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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diff --git a/static/docs/reference/torch_true_divide.html b/static/docs/reference/torch_true_divide.html
deleted file mode 100644
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-    <h1>True_divide</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_true_divide.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>True_divide</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dividend</th>
-      <td><p>(Tensor) the dividend</p></td>
-    </tr>
-    <tr>
-      <th>divisor</th>
-      <td><p>(Tensor or Scalar) the divisor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="true-divide-dividend-divisor-gt-tensor-"><a class="anchor" href="#true-divide-dividend-divisor-gt-tensor-"></a>true_divide(dividend, divisor) -&gt; Tensor </h2>
-
-    
-
-
-<p>Performs "true division" that always computes the division
-in floating point. Analogous to division in Python 3 and equivalent to
-<code><a href='torch_div.html'>torch_div</a></code> except when both inputs have bool or integer scalar types,
-in which case they are cast to the default (floating) scalar type before the division.</p>
-<p>$$
-    \mbox{out}_i = \frac{\mbox{dividend}_i}{\mbox{divisor}}
-$$</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>dividend</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>3</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
-<span class='no'>divisor</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), <span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
-<span class='fu'>torch_true_divide</span>(<span class='no'>dividend</span>, <span class='no'>divisor</span>)
-<span class='fu'>torch_true_divide</span>(<span class='no'>dividend</span>, <span class='fl'>2</span>)
-}</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_trunc.html b/static/docs/reference/torch_trunc.html
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-    <div class="hidden name"><code>torch_unbind.Rd</code></div>
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
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-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to unbind</p></td>
-    </tr>
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-      <th>dim</th>
-      <td><p>(int) dimension to remove</p></td>
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-    </table>
-
-    <h2 class="hasAnchor" id="unbind-input-dim-gt-seq-"><a class="anchor" href="#unbind-input-dim-gt-seq-"></a>unbind(input, dim=0) -&gt; seq </h2>
-
-    
-
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-<p>Removes a tensor dimension.</p>
-<p>Returns a tuple of all slices along a given dimension, already without it.</p>
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-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_unbind</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span>:<span class='fl'>9</span>, <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>3</span>, <span class='kw'>byrow</span><span class='kw'>=</span><span class='fl'>TRUE</span>)))
-}</div></pre>
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-    <h1>Unique_consecutive</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_unique_consecutive.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Unique_consecutive</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>return_inverse</th>
-      <td><p>(bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.</p></td>
-    </tr>
-    <tr>
-      <th>return_counts</th>
-      <td><p>(bool) Whether to also return the counts for each unique        element.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to apply unique. If <code>None</code>, the unique of the        flattened input is returned. default: <code>None</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
-
-    
-
-
-<p>Eliminates all but the first element from every consecutive group of equivalent elements.</p><pre>.. note:: This function is different from [`torch_unique`] in the sense that this function
-    only eliminates consecutive duplicate values. This semantics is similar to `std::unique`
-    in C++.
-</pre>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
-<span class='no'>output</span> <span class='kw'>=</span> <span class='fu'>torch_unique_consecutive</span>(<span class='no'>x</span>)
-<span class='no'>output</span>
-<span class='fu'>torch_unique_consecutive</span>(<span class='no'>x</span>, <span class='kw'>return_inverse</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='fu'>torch_unique_consecutive</span>(<span class='no'>x</span>, <span class='kw'>return_counts</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-}</div></pre>
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-    <h1>Unsqueeze</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_unsqueeze.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Unsqueeze</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the index at which to insert the singleton dimension</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="unsqueeze-input-dim-gt-tensor-"><a class="anchor" href="#unsqueeze-input-dim-gt-tensor-"></a>unsqueeze(input, dim) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a new tensor with a dimension of size one inserted at the
-specified position.</p>
-<p>The returned tensor shares the same underlying data with this tensor.</p>
-<p>A <code>dim</code> value within the range <code>[-input.dim() - 1, input.dim() + 1)</code>
-can be used. Negative <code>dim</code> will correspond to <code>unsqueeze</code>
-applied at <code>dim</code> = <code>dim + input.dim() + 1</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
-<span class='fu'>torch_unsqueeze</span>(<span class='no'>x</span>, <span class='fl'>1</span>)
-<span class='fu'>torch_unsqueeze</span>(<span class='no'>x</span>, <span class='fl'>2</span>)
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_var.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Var</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>unbiased</th>
-      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="var-input-unbiased-true-gt-tensor-"><a class="anchor" href="#var-input-unbiased-true-gt-tensor-"></a>var(input, unbiased=True) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the variance of all elements in the <code>input</code> tensor.</p>
-<p>If <code>unbiased</code> is <code>False</code>, then the variance will be calculated via the
-biased estimator. Otherwise, Bessel's correction will be used.</p>
-    <h2 class="hasAnchor" id="var-input-dim-keepdim-false-unbiased-true-out-none-gt-tensor-"><a class="anchor" href="#var-input-dim-keepdim-false-unbiased-true-out-none-gt-tensor-"></a>var(input, dim, keepdim=False, unbiased=True, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns the variance of each row of the <code>input</code> tensor in the given
-dimension <code>dim</code>.</p>
-<p>If <code>keepdim</code> is <code>True</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-<p>If <code>unbiased</code> is <code>False</code>, then the variance will be calculated via the
-biased estimator. Otherwise, Bessel's correction will be used.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_var</span>(<span class='no'>a</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_var</span>(<span class='no'>a</span>, <span class='fl'>1</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_var_mean.html b/static/docs/reference/torch_var_mean.html
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-    <div class="page-header">
-    <h1>Var_mean</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_var_mean.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Var_mean</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>unbiased</th>
-      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="var-mean-input-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#var-mean-input-unbiased-true-gt-tensor-tensor-"></a>var_mean(input, unbiased=True) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Returns the variance and mean of all elements in the <code>input</code> tensor.</p>
-<p>If <code>unbiased</code> is <code>False</code>, then the variance will be calculated via the
-biased estimator. Otherwise, Bessel's correction will be used.</p>
-    <h2 class="hasAnchor" id="var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-"></a>var_mean(input, dim, keepdim=False, unbiased=True) -&gt; (Tensor, Tensor) </h2>
-
-    
-
-
-<p>Returns the variance and mean of each row of the <code>input</code> tensor in the given
-dimension <code>dim</code>.</p>
-<p>If <code>keepdim</code> is <code>True</code>, the output tensor is of the same size
-as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
-output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
-<p>If <code>unbiased</code> is <code>False</code>, then the variance will be calculated via the
-biased estimator. Otherwise, Bessel's correction will be used.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_var_mean</span>(<span class='no'>a</span>)
-
-
-<span class='no'>a</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
-<span class='no'>a</span>
-<span class='fu'>torch_var_mean</span>(<span class='no'>a</span>, <span class='fl'>1</span>)
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_where.html b/static/docs/reference/torch_where.html
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-    <h1>Where</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_where.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Where</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>condition</th>
-      <td><p>(BoolTensor) When True (nonzero), yield x, otherwise yield y</p></td>
-    </tr>
-    <tr>
-      <th>x</th>
-      <td><p>(Tensor) values selected at indices where <code>condition</code> is <code>True</code></p></td>
-    </tr>
-    <tr>
-      <th>y</th>
-      <td><p>(Tensor) values selected at indices where <code>condition</code> is <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>The tensors `condition`, `x`, `y` must be broadcastable .
-</pre>
-
-<pre>See also [`torch_nonzero`].
-</pre>
-
-    <h2 class="hasAnchor" id="where-condition-x-y-gt-tensor-"><a class="anchor" href="#where-condition-x-y-gt-tensor-"></a>where(condition, x, y) -&gt; Tensor </h2>
-
-    
-
-
-<p>Return a tensor of elements selected from either <code>x</code> or <code>y</code>, depending on <code>condition</code>.</p>
-<p>The operation is defined as:</p>
-<p>$$
-    \mbox{out}_i = \left\{ \begin{array}{ll}
-        \mbox{x}_i &amp; \mbox{if } \mbox{condition}_i \\
-        \mbox{y}_i &amp; \mbox{otherwise} \\
-    \end{array}
-    \right.
-$$</p>
-    <h2 class="hasAnchor" id="where-condition-gt-tuple-of-longtensor-"><a class="anchor" href="#where-condition-gt-tuple-of-longtensor-"></a>where(condition) -&gt; tuple of LongTensor </h2>
-
-    
-
-
-<p><code>torch_where(condition)</code> is identical to
-<code><a href='torch_nonzero.html'>torch_nonzero(condition, as_tuple=True)</a></code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>x</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='no'>x</span>
-<span class='fu'>torch_where</span>(<span class='no'>x</span> <span class='kw'>&gt;</span> <span class='fl'>0</span>, <span class='no'>x</span>, <span class='no'>y</span>)
-}
-
-
-
-}</div></pre>
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diff --git a/static/docs/reference/torch_zeros.html b/static/docs/reference/torch_zeros.html
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-    <div class="page-header">
-    <h1>Zeros</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_zeros.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Zeros</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>size</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>None</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="zeros-size-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#zeros-size-out-none-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with the scalar value <code>0</code>, with the shape defined
-by the variable argument <code>size</code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='fu'>torch_zeros</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
-<span class='fu'>torch_zeros</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
-}</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_zeros_like.html b/static/docs/reference/torch_zeros_like.html
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-    <h1>Zeros_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_zeros_like.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Zeros_like</p>
-    </div>
-
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>None</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>None</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>None</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="zeros-like-input-dtype-none-layout-none-device-none-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#zeros-like-input-dtype-none-layout-none-device-none-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
-
-    
-
-
-<p>Returns a tensor filled with the scalar value <code>0</code>, with the same size as
-<code>input</code>. <code>torch_zeros_like(input)</code> is equivalent to
-<code><a href='torch_zeros.html'>torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>As of 0.4, this function does not support an <code>out</code> keyword. As an alternative,
-the old <code>torch_zeros_like(input, out=output)</code> is equivalent to
-<code><a href='torch_zeros.html'>torch_zeros(input.size(), out=output)</a></code>.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
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-<span class='fu'>torch_zeros_like</span>(<span class='no'>input</span>)
-}</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>with_enable_grad.Rd</code></div>
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-Enables gradient calculation, if it has been disabled via <a href='with_no_grad.html'>with_no_grad</a>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>with_enable_grad</span>(<span class='no'>code</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>code</th>
-      <td><p>code to be executed with gradient recording.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This context manager is thread local; it will not affect computation in
-other threads.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-
-<span class='no'>x</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1</span>, <span class='kw'>requires_grad</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='fu'><a href='with_no_grad.html'>with_no_grad</a></span>({
-  <span class='fu'>with_enable_grad</span>({
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-    <h1>Temporarily modify gradient recording.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>with_no_grad.Rd</code></div>
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-    <p>Temporarily modify gradient recording.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>with_no_grad</span>(<span class='no'>code</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>code</th>
-      <td><p>code to be executed with no gradient recording.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
-<span class='no'>x</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Uniform.html'>runif</a></span>(<span class='fl'>5</span>), <span class='kw'>requires_grad</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>with_no_grad</span>({
-  <span class='no'>x</span>$<span class='fu'>sub_</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>as.numeric</a></span>(<span class='fl'>1</span>:<span class='fl'>5</span>)))
-})
-<span class='no'>x</span>
-<span class='no'>x</span>$<span class='no'>grad</span>
-
-}</div></pre>
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