From fb9dc08b729bbf05a86944e9c8813f4479ae299b Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 27 Jul 2020 19:52:47 -0300
Subject: [PATCH 001/157] address inheritance of datasets in different packages

---
 R/utils-data.R | 10 ++++++++--
 man/dataset.Rd |  5 ++++-
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/R/utils-data.R b/R/utils-data.R
index abf76458d..e12ecf02f 100644
--- a/R/utils-data.R
+++ b/R/utils-data.R
@@ -43,9 +43,11 @@ get_init <- function(x) {
 #' @param inherit you can optionally inherit from a dataset when creating a 
 #'   new dataset.
 #' @param ... public methods for the dataset class
+#' @param parent_env An environment to use as the parent of newly-created 
+#'   objects.
 #' 
 #' @export
-dataset <- function(name = NULL, inherit = Dataset, ...) {
+dataset <- function(name = NULL, inherit = Dataset, ..., parent_env = parent.frame()) {
   
   args <- list(...)
   
@@ -55,13 +57,17 @@ dataset <- function(name = NULL, inherit = Dataset, ...) {
   
   if (!is.null(attr(inherit, "Dataset")))
     inherit <- attr(inherit, "Dataset")
+  
+  e <- new.env(parent = parent_env)
+  e$inherit <- inherit
     
   d <- R6::R6Class(
     classname = name,
     lock_objects = FALSE,
     inherit = inherit,
     public = args,
-    active = active
+    active = active,
+    parent_env = e
   )
   
   init <- get_init(d)
diff --git a/man/dataset.Rd b/man/dataset.Rd
index 75430d621..99e8010dd 100644
--- a/man/dataset.Rd
+++ b/man/dataset.Rd
@@ -4,7 +4,7 @@
 \alias{dataset}
 \title{An abstract class representing a \code{Dataset}.}
 \usage{
-dataset(name = NULL, inherit = Dataset, ...)
+dataset(name = NULL, inherit = Dataset, ..., parent_env = parent.frame())
 }
 \arguments{
 \item{name}{a name for the dataset. It it's also used as the class
@@ -14,6 +14,9 @@ for it.}
 new dataset.}
 
 \item{...}{public methods for the dataset class}
+
+\item{parent_env}{An environment to use as the parent of newly-created
+objects.}
 }
 \description{
 All datasets that represent a map from keys to data samples should subclass
-- 
GitLab


From de086a46b4e7ee0d4ea9f0d77af022a03c6bd0e2 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 28 Jul 2020 17:24:20 -0300
Subject: [PATCH 002/157] must return nullopt not NULL

---
 lantern/src/utils.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/lantern/src/utils.cpp b/lantern/src/utils.cpp
index d2351dabd..8a8fac420 100644
--- a/lantern/src/utils.cpp
+++ b/lantern/src/utils.cpp
@@ -50,7 +50,7 @@ void *_lantern_optional_double(double x, bool is_null)
   LANTERN_FUNCTION_START
   c10::optional<double> out;
   if (is_null)
-    out = NULL;
+    out = c10::nullopt;
   else
     out = x;
 
-- 
GitLab


From 08e0b507524c9a1e733737c7997abca34f797585 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 28 Jul 2020 17:24:38 -0300
Subject: [PATCH 003/157] fixes for interpolate

---
 R/nnf-upsampling.R | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/R/nnf-upsampling.R b/R/nnf-upsampling.R
index 28ce4830b..791566c73 100644
--- a/R/nnf-upsampling.R
+++ b/R/nnf-upsampling.R
@@ -107,12 +107,20 @@ nnf_interpolate <- function(input, size = NULL, scale_factor = NULL,
                             recompute_scale_factor = NULL) {
   
   scale_factor_len <- input$dim() - 2
-  if (length(scale_factor) == 1)
-    scale_factor_repeated <- rep(scale_factor, scale_factor_len)
-  else
-    scale_factor_repeated <- scale_factor
+  scale_factor_list <- scale_factor
   
-  sfl <- scale_factor_repeated
+  if (!is.null(scale_factor) && (
+    is.null(recompute_scale_factor) || !recompute_scale_factor)) {
+    
+    if (length(scale_factor) == 1)
+      scale_factor_repeated <- rep(scale_factor, scale_factor_len)
+    else
+      scale_factor_repeated <- scale_factor
+    
+    scale_factor_list <- scale_factor_repeated
+  }
+    
+  sfl <- scale_factor_list
   sze <- interp_output_size(input, size = size, scale_factor = scale_factor, 
                             recompute_scale_factor = recompute_scale_factor)
   
-- 
GitLab


From dad67bb301484cc03ca14cd065c920504724f252 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 28 Jul 2020 17:24:45 -0300
Subject: [PATCH 004/157] add interpolate tests

---
 tests/testthat/test-nnf-upsampling.R | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 tests/testthat/test-nnf-upsampling.R

diff --git a/tests/testthat/test-nnf-upsampling.R b/tests/testthat/test-nnf-upsampling.R
new file mode 100644
index 000000000..2f982292f
--- /dev/null
+++ b/tests/testthat/test-nnf-upsampling.R
@@ -0,0 +1,12 @@
+test_that("interpolate", {
+  
+  img <- torch_rand(3, 32, 32)
+  
+  expect_tensor_shape(nnf_interpolate(img, size = 40), c(3, 32, 40))
+  
+  img <- torch_rand(1, 3, 32, 32)
+  o <- nnf_interpolate(img, size = c(40, 40), mode = "bilinear")
+  expect_tensor_shape(o, c(1, 3, 40, 40))
+  expect_true(!as_array(torch_any(torch_isnan(o)))) # no nans
+  
+})
-- 
GitLab


From d478071f3cf13079418d557d28a80e1840c1fa11 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 28 Jul 2020 19:01:45 -0300
Subject: [PATCH 005/157] also dont ignore the deps folder (so it works when
 building lantern in the CI)

---
 .Rbuildignore | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.Rbuildignore b/.Rbuildignore
index 162d5905c..122d7ce66 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -1,6 +1,5 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
-^deps.*
 ^.github.*
 ^.vscode.*
 tools/torchgen/.Rbuildignore
@@ -20,3 +19,4 @@ tools/torchgen/.Rbuildignore
 ^.*torchpkg.dll$
 # uncomment below for CRAN submission
 # ^inst/deps/.*
+# ^deps.*
-- 
GitLab


From a1e211de00412749e0fd9f4f6963f8f605a42ad4 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 28 Jul 2020 21:24:20 -0300
Subject: [PATCH 006/157] try removing this line from build ignore

---
 .Rbuildignore | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.Rbuildignore b/.Rbuildignore
index 122d7ce66..68cf93235 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -11,7 +11,6 @@ tools/torchgen/.Rbuildignore
 ^lantern$
 .*\.tmp
 ^vignettes/examples/.*
-^deps/.*
 ^bench/.*
 ^docker/.*
 ^test.html
-- 
GitLab


From 6118b05a9b46eda542223206e66f7029fa7f8df9 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 28 Jul 2020 23:20:38 -0300
Subject: [PATCH 007/157] try writing test like this

---
 tests/testthat/test-nnf-upsampling.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/testthat/test-nnf-upsampling.R b/tests/testthat/test-nnf-upsampling.R
index 2f982292f..5ea906b11 100644
--- a/tests/testthat/test-nnf-upsampling.R
+++ b/tests/testthat/test-nnf-upsampling.R
@@ -7,6 +7,6 @@ test_that("interpolate", {
   img <- torch_rand(1, 3, 32, 32)
   o <- nnf_interpolate(img, size = c(40, 40), mode = "bilinear")
   expect_tensor_shape(o, c(1, 3, 40, 40))
-  expect_true(!as_array(torch_any(torch_isnan(o)))) # no nans
+  expect_true(!any(as_array(torch_isnan(o)))) # no nans
   
 })
-- 
GitLab


From 0af68fc9b762c5eda27742e56e768fc50e7f0ebc Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 29 Jul 2020 00:13:57 -0300
Subject: [PATCH 008/157] make the test deterministic

---
 tests/testthat/test-nnf-upsampling.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/testthat/test-nnf-upsampling.R b/tests/testthat/test-nnf-upsampling.R
index 5ea906b11..7240a4064 100644
--- a/tests/testthat/test-nnf-upsampling.R
+++ b/tests/testthat/test-nnf-upsampling.R
@@ -1,10 +1,10 @@
 test_that("interpolate", {
   
-  img <- torch_rand(3, 32, 32)
+  img <- torch_ones(3, 32, 32)
   
   expect_tensor_shape(nnf_interpolate(img, size = 40), c(3, 32, 40))
   
-  img <- torch_rand(1, 3, 32, 32)
+  img <- torch_ones(1, 3, 32, 32)
   o <- nnf_interpolate(img, size = c(40, 40), mode = "bilinear")
   expect_tensor_shape(o, c(1, 3, 40, 40))
   expect_true(!any(as_array(torch_isnan(o)))) # no nans
-- 
GitLab


From 117a6f1e674de300fbeb9102c86fe60923eb0db7 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 29 Jul 2020 00:15:41 -0300
Subject: [PATCH 009/157] ignore the deps folder again

---
 .Rbuildignore | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.Rbuildignore b/.Rbuildignore
index 68cf93235..11acc5d37 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -16,6 +16,6 @@ tools/torchgen/.Rbuildignore
 ^test.html
 ^LICENSE\.md$
 ^.*torchpkg.dll$
+^deps.*
 # uncomment below for CRAN submission
 # ^inst/deps/.*
-# ^deps.*
-- 
GitLab


From 931944c770b843013c32a336840f32dce4524fd8 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 29 Jul 2020 15:44:46 -0300
Subject: [PATCH 010/157] dont create the optional value as we aready convert
 it before passing.

---
 lantern/headers/src/main.cpp |  6 +++-
 lantern/src/lantern.cpp      | 56 ++++++++++++++++++------------------
 2 files changed, 33 insertions(+), 29 deletions(-)

diff --git a/lantern/headers/src/main.cpp b/lantern/headers/src/main.cpp
index 4a00dcb7b..20a372a88 100644
--- a/lantern/headers/src/main.cpp
+++ b/lantern/headers/src/main.cpp
@@ -142,7 +142,11 @@ std::string buildCalls(std::string name, YAML::Node node, size_t start)
         std::string call = node[idx]["name"].as<std::string>();
         if ((dtype.find("c10::optional") != std::string::npos) & (type != "std::vector<torch::Dimname>"))
         {
-            call = "optional<" + addNamespace(type) + ">(" + call + ")";
+            if (type != "double")
+            {
+                call = "optional<" + addNamespace(type) + ">(" + call + ")";
+            }
+            
             type = "c10::optional<" + type + ">";
         }
 
diff --git a/lantern/src/lantern.cpp b/lantern/src/lantern.cpp
index fae964ee8..1c07e44ab 100644
--- a/lantern/src/lantern.cpp
+++ b/lantern/src/lantern.cpp
@@ -12891,7 +12891,7 @@ void* _lantern_upsample_linear1d_out_tensor_tensor_intarrayref_bool_double(void*
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_linear1d_out(
-        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales))->get()));
+        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12899,7 +12899,7 @@ void* _lantern_upsample_linear1d_tensor_intarrayref_bool_double(void* self, void
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_linear1d(
-        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales))->get()));
+        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12907,7 +12907,7 @@ void* _lantern_upsample_linear1d_backward_out_tensor_tensor_intarrayref_intarray
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_linear1d_backward_out(
-        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales))->get()));
+        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12915,7 +12915,7 @@ void* _lantern_upsample_linear1d_backward_tensor_intarrayref_intarrayref_bool_do
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_linear1d_backward(
-        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales))->get()));
+        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12923,7 +12923,7 @@ void* _lantern_upsample_bilinear2d_out_tensor_tensor_intarrayref_bool_double_dou
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_bilinear2d_out(
-        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12931,7 +12931,7 @@ void* _lantern_upsample_bilinear2d_tensor_intarrayref_bool_double_double(void* s
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_bilinear2d(
-        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12939,7 +12939,7 @@ void* _lantern_upsample_bilinear2d_backward_out_tensor_tensor_intarrayref_intarr
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_bilinear2d_backward_out(
-        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12947,7 +12947,7 @@ void* _lantern_upsample_bilinear2d_backward_tensor_intarrayref_intarrayref_bool_
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_bilinear2d_backward(
-        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12955,7 +12955,7 @@ void* _lantern_upsample_bicubic2d_out_tensor_tensor_intarrayref_bool_double_doub
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_bicubic2d_out(
-        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12963,7 +12963,7 @@ void* _lantern_upsample_bicubic2d_tensor_intarrayref_bool_double_double(void* se
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_bicubic2d(
-        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12971,7 +12971,7 @@ void* _lantern_upsample_bicubic2d_backward_out_tensor_tensor_intarrayref_intarra
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_bicubic2d_backward_out(
-        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12979,7 +12979,7 @@ void* _lantern_upsample_bicubic2d_backward_tensor_intarrayref_intarrayref_bool_d
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_bicubic2d_backward(
-        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12987,7 +12987,7 @@ void* _lantern_upsample_trilinear3d_out_tensor_tensor_intarrayref_bool_double_do
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_trilinear3d_out(
-        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_d))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales_d)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12995,7 +12995,7 @@ void* _lantern_upsample_trilinear3d_tensor_intarrayref_bool_double_double_double
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_trilinear3d(
-        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_d))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales_d)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13003,7 +13003,7 @@ void* _lantern_upsample_trilinear3d_backward_out_tensor_tensor_intarrayref_intar
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_trilinear3d_backward_out(
-        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_d))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales_d)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13011,7 +13011,7 @@ void* _lantern_upsample_trilinear3d_backward_tensor_intarrayref_intarrayref_bool
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_trilinear3d_backward(
-        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_d))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<bool>*)align_corners)->get(), ((LanternObject<c10::optional<double>>*)scales_d)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13019,7 +13019,7 @@ void* _lantern_upsample_nearest1d_out_tensor_tensor_intarrayref_double(void* out
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_nearest1d_out(
-        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales))->get()));
+        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<c10::optional<double>>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13027,7 +13027,7 @@ void* _lantern_upsample_nearest1d_tensor_intarrayref_double(void* self, void* ou
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_nearest1d(
-        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales))->get()));
+        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<c10::optional<double>>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13035,7 +13035,7 @@ void* _lantern_upsample_nearest1d_backward_out_tensor_tensor_intarrayref_intarra
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_nearest1d_backward_out(
-        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales))->get()));
+        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<c10::optional<double>>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13043,7 +13043,7 @@ void* _lantern_upsample_nearest1d_backward_tensor_intarrayref_intarrayref_double
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_nearest1d_backward(
-        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales))->get()));
+        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<c10::optional<double>>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13051,7 +13051,7 @@ void* _lantern_upsample_nearest2d_out_tensor_tensor_intarrayref_double_double(vo
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_nearest2d_out(
-        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13059,7 +13059,7 @@ void* _lantern_upsample_nearest2d_tensor_intarrayref_double_double(void* self, v
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_nearest2d(
-        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13067,7 +13067,7 @@ void* _lantern_upsample_nearest2d_backward_out_tensor_tensor_intarrayref_intarra
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_nearest2d_backward_out(
-        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13075,7 +13075,7 @@ void* _lantern_upsample_nearest2d_backward_tensor_intarrayref_intarrayref_double
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_nearest2d_backward(
-        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13083,7 +13083,7 @@ void* _lantern_upsample_nearest3d_out_tensor_tensor_intarrayref_double_double_do
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_nearest3d_out(
-        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_d))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)out)->get(), ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<c10::optional<double>>*)scales_d)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13091,7 +13091,7 @@ void* _lantern_upsample_nearest3d_tensor_intarrayref_double_double_double(void*
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_nearest3d(
-        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_d))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)self)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<c10::optional<double>>*)scales_d)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13099,7 +13099,7 @@ void* _lantern_upsample_nearest3d_backward_out_tensor_tensor_intarrayref_intarra
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_nearest3d_backward_out(
-        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_d))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)grad_input)->get(), ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<c10::optional<double>>*)scales_d)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13107,7 +13107,7 @@ void* _lantern_upsample_nearest3d_backward_tensor_intarrayref_intarrayref_double
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject<torch::Tensor>(torch::upsample_nearest3d_backward(
-        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_d))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_h))->get(), ((LanternObject<c10::optional<double>>*)optional<double>(scales_w))->get()));
+        ((LanternObject<torch::Tensor>*)grad_output)->get(), ((LanternObject<std::vector<int64_t>>*)output_size)->get(), ((LanternObject<std::vector<int64_t>>*)input_size)->get(), ((LanternObject<c10::optional<double>>*)scales_d)->get(), ((LanternObject<c10::optional<double>>*)scales_h)->get(), ((LanternObject<c10::optional<double>>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
-- 
GitLab


From b18c60c31d40b46b63c206ac1e6cd52e7ade8f90 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 29 Jul 2020 15:45:07 -0300
Subject: [PATCH 011/157] remove commented code in the example

---
 R/gen-namespace-examples.R | 1 -
 1 file changed, 1 deletion(-)

diff --git a/R/gen-namespace-examples.R b/R/gen-namespace-examples.R
index 5d400681e..27e65b5db 100644
--- a/R/gen-namespace-examples.R
+++ b/R/gen-namespace-examples.R
@@ -2091,7 +2091,6 @@ NULL
 #' @examples
 #'
 #' torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_int()), 1.0)
-#' # torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_uint8()), torch_tensor(1))
 NULL
 # -> result_type <-
 
-- 
GitLab


From 3da861bd3ebe9b2d9bafb2123beeb5f186857bdb Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 29 Jul 2020 17:14:25 -0300
Subject: [PATCH 012/157] increase timeout + allow warnings

---
 .github/workflows/main.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml
index 4fec726d9..6cf1dfa19 100644
--- a/.github/workflows/main.yaml
+++ b/.github/workflows/main.yaml
@@ -25,7 +25,7 @@ jobs:
             install: 1
     runs-on: ${{ matrix.os }}
     name: ${{ matrix.os }}
-    timeout-minutes: 30
+    timeout-minutes: 45
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
       INSTALL_TORCH: ${{ matrix.install }}
@@ -54,7 +54,7 @@ jobs:
           Rscript tools/buildlantern.R
       - name: Check
         run:  |
-          rcmdcheck::rcmdcheck(args = c("--no-multiarch", "--no-manual"), error_on = "warning", check_dir = "check")
+          rcmdcheck::rcmdcheck(args = c("--no-multiarch", "--no-manual"), error_on = "error", check_dir = "check")
         shell: Rscript {0}
       - name: Install
         run: |
-- 
GitLab


From 5689315336f976171eba306fc2b08ef318dcce8e Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 29 Jul 2020 17:56:45 -0300
Subject: [PATCH 013/157] cite pytorch paper in the description

---
 DESCRIPTION | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index dfc925790..b123c08d6 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -3,14 +3,16 @@ Type: Package
 Title: Tensors and Neural Networks with 'GPU' Acceleration
 Version: 0.0.1
 Authors@R: c(
-    person("Daniel", "Falbel", email = "daniel@rstudio.com", role = c("aut", "cre")),
-    person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut")),
+    person("Daniel", "Falbel", email = "daniel@rstudio.com", role = c("aut", "cre", "cph")),
+    person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cph")),
     person("Dmitriy", "Selivanov", role = c("ctb")),
     person("Athos", "Damiani", role = c("ctb")),
     person(family = "RStudio", role = c("cph"))
     )
-Description: Provides functionality similar to 'PyTorch' but written entirely in R and
-    the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.
+Description: Provides functionality to define and train neural networks similar to 
+    'PyTorch' by Paszke et al (2019) <arXiv:1912.01703> but written entirely in R 
+    using the 'libtorch' library. Also supports low-level tensor operations and 
+    'GPU' acceleration.
 License: MIT + file LICENSE
 URL: http://mlverse.github.io/torch, https://github.com/mlverse/torch
 BugReports: https://github.com/mlverse/torch/issues
-- 
GitLab


From 0e0e5630d533d5b9331c1019f55b333ef199461f Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 30 Jul 2020 16:47:38 -0300
Subject: [PATCH 014/157] add if torch_installed to examples

---
 tools/document.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/document.R b/tools/document.R
index 8bfbec696..9acd96451 100644
--- a/tools/document.R
+++ b/tools/document.R
@@ -11,7 +11,7 @@ library(roxygen2)
 .S3method("format", "rd_section_examples", function (x, ...) {
   value <- paste0(x$value, collapse = "\n")
   roxygen2:::rd_macro("examples",
-    roxygen2:::rd_macro("dontrun", value, space = TRUE),
+    c("if (torch_is_installed()) {", value, "}"),
     space = TRUE
   )
 })
-- 
GitLab


From 5f3d42cc0296bb959d028e264db5d3011839d13c Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 30 Jul 2020 16:48:43 -0300
Subject: [PATCH 015/157] export a function that checks if torch is installed

---
 R/install.R | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/R/install.R b/R/install.R
index cb5217ff7..a0d1a105e 100644
--- a/R/install.R
+++ b/R/install.R
@@ -88,6 +88,13 @@ install_exists <- function() {
   dir.exists(install_path())
 }
 
+#' Verifies if torch is installed
+#'
+#' @export
+torch_is_installed <- function() {
+  install_exists()
+}
+
 lib_installed <- function(library_name, install_path) {
   x <- list.files(install_path)
   
@@ -216,6 +223,7 @@ install_type <- function(version) {
 #' @export
 install_torch <- function(version = "1.5.0", type = install_type(version = version), reinstall = FALSE,
                           path = install_path(), ...) {
+  
   if (reinstall) {
     unlink(path, recursive = TRUE)
   }
-- 
GitLab


From f3835737333d6a1f4a1c0cfa88c61030e6117dfb Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 30 Jul 2020 16:49:08 -0300
Subject: [PATCH 016/157] re-document

---
 NAMESPACE                               |  1 +
 man/autograd_backward.Rd                |  2 +-
 man/autograd_function.Rd                |  2 +-
 man/autograd_grad.Rd                    |  2 +-
 man/install_torch.Rd                    |  2 +-
 man/nn_batch_norm1d.Rd                  |  2 +-
 man/nn_batch_norm2d.Rd                  |  2 +-
 man/nn_bce_loss.Rd                      |  2 +-
 man/nn_bilinear.Rd                      |  2 +-
 man/nn_celu.Rd                          |  2 +-
 man/nn_conv1d.Rd                        |  2 +-
 man/nn_conv2d.Rd                        |  2 +-
 man/nn_conv3d.Rd                        |  2 +-
 man/nn_conv_transpose1d.Rd              |  2 +-
 man/nn_conv_transpose2d.Rd              |  2 +-
 man/nn_conv_transpose3d.Rd              |  2 +-
 man/nn_cross_entropy_loss.Rd            |  2 +-
 man/nn_dropout.Rd                       |  2 +-
 man/nn_dropout2d.Rd                     |  2 +-
 man/nn_dropout3d.Rd                     |  2 +-
 man/nn_elu.Rd                           |  2 +-
 man/nn_embedding.Rd                     |  2 +-
 man/nn_gelu.Rd                          |  2 +-
 man/nn_glu.Rd                           |  2 +-
 man/nn_hardshrink.Rd                    |  2 +-
 man/nn_hardsigmoid.Rd                   |  2 +-
 man/nn_hardswish.Rd                     |  2 +-
 man/nn_hardtanh.Rd                      |  2 +-
 man/nn_identity.Rd                      |  2 +-
 man/nn_init_constant_.Rd                |  2 +-
 man/nn_init_dirac_.Rd                   |  2 +-
 man/nn_init_eye_.Rd                     |  2 +-
 man/nn_init_kaiming_normal_.Rd          |  2 +-
 man/nn_init_kaiming_uniform_.Rd         |  2 +-
 man/nn_init_normal_.Rd                  |  2 +-
 man/nn_init_ones_.Rd                    |  2 +-
 man/nn_init_orthogonal_.Rd              |  2 +-
 man/nn_init_sparse_.Rd                  |  2 +-
 man/nn_init_trunc_normal_.Rd            |  2 +-
 man/nn_init_uniform_.Rd                 |  2 +-
 man/nn_init_xavier_normal_.Rd           |  2 +-
 man/nn_init_xavier_uniform_.Rd          |  2 +-
 man/nn_init_zeros_.Rd                   |  2 +-
 man/nn_leaky_relu.Rd                    |  2 +-
 man/nn_linear.Rd                        |  2 +-
 man/nn_log_sigmoid.Rd                   |  2 +-
 man/nn_log_softmax.Rd                   |  2 +-
 man/nn_max_pool1d.Rd                    |  2 +-
 man/nn_max_pool2d.Rd                    |  2 +-
 man/nn_module.Rd                        |  2 +-
 man/nn_module_list.Rd                   |  2 +-
 man/nn_multihead_attention.Rd           |  2 +-
 man/nn_prelu.Rd                         |  2 +-
 man/nn_relu.Rd                          |  2 +-
 man/nn_relu6.Rd                         |  2 +-
 man/nn_rnn.Rd                           |  2 +-
 man/nn_rrelu.Rd                         |  2 +-
 man/nn_selu.Rd                          |  2 +-
 man/nn_sequential.Rd                    |  2 +-
 man/nn_sigmoid.Rd                       |  2 +-
 man/nn_softmax.Rd                       |  2 +-
 man/nn_softmax2d.Rd                     |  2 +-
 man/nn_softmin.Rd                       |  2 +-
 man/nn_softplus.Rd                      |  2 +-
 man/nn_softshrink.Rd                    |  2 +-
 man/nn_softsign.Rd                      |  2 +-
 man/nn_tanh.Rd                          |  2 +-
 man/nn_tanhshrink.Rd                    |  2 +-
 man/nn_threshold.Rd                     |  2 +-
 man/nn_utils_rnn_pack_sequence.Rd       |  2 +-
 man/nn_utils_rnn_pad_packed_sequence.Rd |  2 +-
 man/nn_utils_rnn_pad_sequence.Rd        |  2 +-
 man/nnf_elu.Rd                          |  2 +-
 man/nnf_selu.Rd                         |  2 +-
 man/optim_adam.Rd                       |  2 +-
 man/optim_sgd.Rd                        |  2 +-
 man/torch_abs.Rd                        |  2 +-
 man/torch_acos.Rd                       |  2 +-
 man/torch_add.Rd                        |  2 +-
 man/torch_addbmm.Rd                     |  2 +-
 man/torch_addcdiv.Rd                    |  2 +-
 man/torch_addcmul.Rd                    |  2 +-
 man/torch_addmm.Rd                      |  2 +-
 man/torch_addmv.Rd                      |  2 +-
 man/torch_addr.Rd                       |  2 +-
 man/torch_allclose.Rd                   |  2 +-
 man/torch_angle.Rd                      |  2 +-
 man/torch_arange.Rd                     |  2 +-
 man/torch_argmax.Rd                     |  2 +-
 man/torch_argmin.Rd                     |  2 +-
 man/torch_argsort.Rd                    |  2 +-
 man/torch_as_strided.Rd                 |  2 +-
 man/torch_asin.Rd                       |  2 +-
 man/torch_atan.Rd                       |  2 +-
 man/torch_atan2.Rd                      |  2 +-
 man/torch_baddbmm.Rd                    |  2 +-
 man/torch_bernoulli.Rd                  |  2 +-
 man/torch_bincount.Rd                   |  2 +-
 man/torch_bmm.Rd                        |  2 +-
 man/torch_broadcast_tensors.Rd          |  2 +-
 man/torch_can_cast.Rd                   |  2 +-
 man/torch_cartesian_prod.Rd             |  2 +-
 man/torch_cat.Rd                        |  2 +-
 man/torch_ceil.Rd                       |  2 +-
 man/torch_chain_matmul.Rd               |  2 +-
 man/torch_cholesky.Rd                   |  2 +-
 man/torch_cholesky_inverse.Rd           |  2 +-
 man/torch_cholesky_solve.Rd             |  2 +-
 man/torch_clamp.Rd                      |  2 +-
 man/torch_combinations.Rd               |  2 +-
 man/torch_conj.Rd                       |  2 +-
 man/torch_conv1d.Rd                     |  2 +-
 man/torch_conv2d.Rd                     |  2 +-
 man/torch_conv3d.Rd                     |  2 +-
 man/torch_conv_transpose1d.Rd           |  2 +-
 man/torch_conv_transpose2d.Rd           |  2 +-
 man/torch_conv_transpose3d.Rd           |  2 +-
 man/torch_cos.Rd                        |  2 +-
 man/torch_cosh.Rd                       |  2 +-
 man/torch_cosine_similarity.Rd          |  2 +-
 man/torch_cross.Rd                      |  2 +-
 man/torch_cummax.Rd                     |  2 +-
 man/torch_cummin.Rd                     |  2 +-
 man/torch_cumprod.Rd                    |  2 +-
 man/torch_cumsum.Rd                     |  2 +-
 man/torch_det.Rd                        |  2 +-
 man/torch_device.Rd                     |  2 +-
 man/torch_diag_embed.Rd                 |  2 +-
 man/torch_diagflat.Rd                   |  2 +-
 man/torch_diagonal.Rd                   |  2 +-
 man/torch_digamma.Rd                    |  2 +-
 man/torch_dist.Rd                       |  2 +-
 man/torch_div.Rd                        |  2 +-
 man/torch_dot.Rd                        |  2 +-
 man/torch_einsum.Rd                     |  2 +-
 man/torch_empty.Rd                      |  2 +-
 man/torch_empty_like.Rd                 |  2 +-
 man/torch_empty_strided.Rd              |  2 +-
 man/torch_eq.Rd                         |  2 +-
 man/torch_equal.Rd                      |  2 +-
 man/torch_erf.Rd                        |  2 +-
 man/torch_erfc.Rd                       |  2 +-
 man/torch_erfinv.Rd                     |  2 +-
 man/torch_exp.Rd                        |  2 +-
 man/torch_expm1.Rd                      |  2 +-
 man/torch_eye.Rd                        |  2 +-
 man/torch_fft.Rd                        |  2 +-
 man/torch_flatten.Rd                    |  2 +-
 man/torch_flip.Rd                       |  2 +-
 man/torch_floor.Rd                      |  2 +-
 man/torch_floor_divide.Rd               |  2 +-
 man/torch_fmod.Rd                       |  2 +-
 man/torch_frac.Rd                       |  2 +-
 man/torch_full.Rd                       |  2 +-
 man/torch_gather.Rd                     |  2 +-
 man/torch_ge.Rd                         |  2 +-
 man/torch_generator.Rd                  |  2 +-
 man/torch_ger.Rd                        |  2 +-
 man/torch_gt.Rd                         |  2 +-
 man/torch_histc.Rd                      |  2 +-
 man/torch_ifft.Rd                       |  2 +-
 man/torch_imag.Rd                       |  2 +-
 man/torch_index_select.Rd               |  2 +-
 man/torch_inverse.Rd                    |  2 +-
 man/torch_irfft.Rd                      |  2 +-
 man/torch_is_installed.Rd               | 11 +++++++++++
 man/torch_isfinite.Rd                   |  2 +-
 man/torch_isinf.Rd                      |  2 +-
 man/torch_isnan.Rd                      |  2 +-
 man/torch_kthvalue.Rd                   |  2 +-
 man/torch_le.Rd                         |  2 +-
 man/torch_lerp.Rd                       |  2 +-
 man/torch_lgamma.Rd                     |  2 +-
 man/torch_linspace.Rd                   |  2 +-
 man/torch_log.Rd                        |  2 +-
 man/torch_log10.Rd                      |  2 +-
 man/torch_log1p.Rd                      |  2 +-
 man/torch_log2.Rd                       |  2 +-
 man/torch_logdet.Rd                     |  2 +-
 man/torch_logical_and.Rd                |  2 +-
 man/torch_logical_not.Rd                |  2 +-
 man/torch_logical_or.Rd                 |  2 +-
 man/torch_logical_xor.Rd                |  2 +-
 man/torch_logspace.Rd                   |  2 +-
 man/torch_logsumexp.Rd                  |  2 +-
 man/torch_lstsq.Rd                      |  2 +-
 man/torch_lt.Rd                         |  2 +-
 man/torch_lu.Rd                         |  2 +-
 man/torch_lu_solve.Rd                   |  2 +-
 man/torch_masked_select.Rd              |  2 +-
 man/torch_matmul.Rd                     |  2 +-
 man/torch_matrix_power.Rd               |  2 +-
 man/torch_matrix_rank.Rd                |  2 +-
 man/torch_max.Rd                        |  2 +-
 man/torch_mean.Rd                       |  2 +-
 man/torch_median.Rd                     |  2 +-
 man/torch_meshgrid.Rd                   |  2 +-
 man/torch_min.Rd                        |  2 +-
 man/torch_mm.Rd                         |  2 +-
 man/torch_mode.Rd                       |  2 +-
 man/torch_mul.Rd                        |  2 +-
 man/torch_multinomial.Rd                |  2 +-
 man/torch_mv.Rd                         |  2 +-
 man/torch_mvlgamma.Rd                   |  2 +-
 man/torch_narrow.Rd                     |  2 +-
 man/torch_ne.Rd                         |  2 +-
 man/torch_neg.Rd                        |  2 +-
 man/torch_nonzero.Rd                    |  2 +-
 man/torch_norm.Rd                       |  2 +-
 man/torch_normal.Rd                     |  2 +-
 man/torch_ones.Rd                       |  2 +-
 man/torch_ones_like.Rd                  |  2 +-
 man/torch_pinverse.Rd                   |  2 +-
 man/torch_pixel_shuffle.Rd              |  2 +-
 man/torch_poisson.Rd                    |  2 +-
 man/torch_polygamma.Rd                  |  2 +-
 man/torch_pow.Rd                        |  2 +-
 man/torch_prod.Rd                       |  2 +-
 man/torch_promote_types.Rd              |  2 +-
 man/torch_qr.Rd                         |  2 +-
 man/torch_quantize_per_channel.Rd       |  2 +-
 man/torch_quantize_per_tensor.Rd        |  2 +-
 man/torch_rand.Rd                       |  2 +-
 man/torch_randint.Rd                    |  2 +-
 man/torch_randn.Rd                      |  2 +-
 man/torch_randperm.Rd                   |  2 +-
 man/torch_range.Rd                      |  2 +-
 man/torch_real.Rd                       |  2 +-
 man/torch_reciprocal.Rd                 |  2 +-
 man/torch_remainder.Rd                  |  2 +-
 man/torch_renorm.Rd                     |  2 +-
 man/torch_repeat_interleave.Rd          |  2 +-
 man/torch_reshape.Rd                    |  2 +-
 man/torch_result_type.Rd                |  3 +--
 man/torch_rfft.Rd                       |  2 +-
 man/torch_roll.Rd                       |  2 +-
 man/torch_rot90.Rd                      |  2 +-
 man/torch_round.Rd                      |  2 +-
 man/torch_rsqrt.Rd                      |  2 +-
 man/torch_sigmoid.Rd                    |  2 +-
 man/torch_sign.Rd                       |  2 +-
 man/torch_sin.Rd                        |  2 +-
 man/torch_sinh.Rd                       |  2 +-
 man/torch_slogdet.Rd                    |  2 +-
 man/torch_solve.Rd                      |  2 +-
 man/torch_sort.Rd                       |  2 +-
 man/torch_sparse_coo_tensor.Rd          |  2 +-
 man/torch_sqrt.Rd                       |  2 +-
 man/torch_square.Rd                     |  2 +-
 man/torch_squeeze.Rd                    |  2 +-
 man/torch_std.Rd                        |  2 +-
 man/torch_std_mean.Rd                   |  2 +-
 man/torch_sum.Rd                        |  2 +-
 man/torch_svd.Rd                        |  2 +-
 man/torch_symeig.Rd                     |  2 +-
 man/torch_t.Rd                          |  2 +-
 man/torch_take.Rd                       |  2 +-
 man/torch_tan.Rd                        |  2 +-
 man/torch_tanh.Rd                       |  2 +-
 man/torch_tensor.Rd                     |  2 +-
 man/torch_tensordot.Rd                  |  2 +-
 man/torch_topk.Rd                       |  2 +-
 man/torch_trace.Rd                      |  2 +-
 man/torch_transpose.Rd                  |  2 +-
 man/torch_trapz.Rd                      |  2 +-
 man/torch_triangular_solve.Rd           |  2 +-
 man/torch_tril.Rd                       |  2 +-
 man/torch_tril_indices.Rd               |  2 +-
 man/torch_triu.Rd                       |  2 +-
 man/torch_triu_indices.Rd               |  2 +-
 man/torch_true_divide.Rd                |  2 +-
 man/torch_trunc.Rd                      |  2 +-
 man/torch_unbind.Rd                     |  2 +-
 man/torch_unique_consecutive.Rd         |  2 +-
 man/torch_unsqueeze.Rd                  |  2 +-
 man/torch_var.Rd                        |  2 +-
 man/torch_var_mean.Rd                   |  2 +-
 man/torch_where.Rd                      |  2 +-
 man/torch_zeros.Rd                      |  2 +-
 man/torch_zeros_like.Rd                 |  2 +-
 man/with_enable_grad.Rd                 |  2 +-
 man/with_no_grad.Rd                     |  2 +-
 282 files changed, 292 insertions(+), 281 deletions(-)
 create mode 100644 man/torch_is_installed.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 2b70b1ba1..1a028c2c6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -397,6 +397,7 @@ export(torch_inverse)
 export(torch_irfft)
 export(torch_is_complex)
 export(torch_is_floating_point)
+export(torch_is_installed)
 export(torch_isfinite)
 export(torch_isinf)
 export(torch_isnan)
diff --git a/man/autograd_backward.Rd b/man/autograd_backward.Rd
index c2950f180..0146e3ac7 100644
--- a/man/autograd_backward.Rd
+++ b/man/autograd_backward.Rd
@@ -42,7 +42,7 @@ This function accumulates gradients in the leaves - you might need to zero
 them before calling it.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x <- torch_tensor(1, requires_grad = TRUE)
 y <- 2 * x
 
diff --git a/man/autograd_function.Rd b/man/autograd_function.Rd
index 35c74b5b3..951cbd7f7 100644
--- a/man/autograd_function.Rd
+++ b/man/autograd_function.Rd
@@ -32,7 +32,7 @@ processed in the topological ordering, by calling \code{backward()} methods of e
 Function object, and passing returned gradients on to next Function's.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 exp2 <- autograd_function(
   forward = function(ctx, i) {
diff --git a/man/autograd_grad.Rd b/man/autograd_grad.Rd
index 6ae0e294b..87f3f6c2f 100644
--- a/man/autograd_grad.Rd
+++ b/man/autograd_grad.Rd
@@ -46,7 +46,7 @@ the specified inputs. If it’s \code{FALSE}, then gradient w.r.t. all remaining
 will still be computed, and will be accumulated into their \code{.grad} attribute.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_tensor(0.5, requires_grad = TRUE)
 b <- torch_tensor(0.9, requires_grad = TRUE)
 x <- torch_tensor(runif(100))
diff --git a/man/install_torch.Rd b/man/install_torch.Rd
index b411a38e6..5be327262 100644
--- a/man/install_torch.Rd
+++ b/man/install_torch.Rd
@@ -8,7 +8,7 @@ install_torch(
   version = "1.5.0",
   type = install_type(version = version),
   reinstall = FALSE,
-  path = install_path(),
+  path = NULL,
   ...
 )
 }
diff --git a/man/nn_batch_norm1d.Rd b/man/nn_batch_norm1d.Rd
index 89afd7a95..a9fc56560 100644
--- a/man/nn_batch_norm1d.Rd
+++ b/man/nn_batch_norm1d.Rd
@@ -77,7 +77,7 @@ on \verb{(N, L)} slices, it's common terminology to call this Temporal Batch Nor
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # With Learnable Parameters
 m <- nn_batch_norm1d(100)
 # Without Learnable Parameters
diff --git a/man/nn_batch_norm2d.Rd b/man/nn_batch_norm2d.Rd
index 09dfcd263..a307791f7 100644
--- a/man/nn_batch_norm2d.Rd
+++ b/man/nn_batch_norm2d.Rd
@@ -75,7 +75,7 @@ on \verb{(N, H, W)} slices, it's common terminology to call this Spatial Batch N
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # With Learnable Parameters
 m <- nn_batch_norm2d(100)
 # Without Learnable Parameters
diff --git a/man/nn_bce_loss.Rd b/man/nn_bce_loss.Rd
index 11a5ab1e3..836c025b9 100644
--- a/man/nn_bce_loss.Rd
+++ b/man/nn_bce_loss.Rd
@@ -70,7 +70,7 @@ shape as input.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_sigmoid()
 loss <- nn_bce_loss()
 input <- torch_randn(3, requires_grad=TRUE)
diff --git a/man/nn_bilinear.Rd b/man/nn_bilinear.Rd
index 319e724a8..38cd14c88 100644
--- a/man/nn_bilinear.Rd
+++ b/man/nn_bilinear.Rd
@@ -47,7 +47,7 @@ If \code{bias} is \code{TRUE}, the values are initialized from
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_bilinear(20, 30, 50)
 input1 <- torch_randn(128, 20)
 input2 <- torch_randn(128, 30)
diff --git a/man/nn_celu.Rd b/man/nn_celu.Rd
index f73023dc0..1610da86e 100644
--- a/man/nn_celu.Rd
+++ b/man/nn_celu.Rd
@@ -32,7 +32,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_celu()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_conv1d.Rd b/man/nn_conv1d.Rd
index afdaf9599..c5da12ae3 100644
--- a/man/nn_conv1d.Rd
+++ b/man/nn_conv1d.Rd
@@ -127,7 +127,7 @@ sampled from \eqn{\mathcal{U}(-\sqrt{k}, \sqrt{k})} where
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_conv1d(16, 33, 3, stride=2)
 input <- torch_randn(20, 16, 50)
 output <- m(input)
diff --git a/man/nn_conv2d.Rd b/man/nn_conv2d.Rd
index c547406d7..ba8e5f5d9 100644
--- a/man/nn_conv2d.Rd
+++ b/man/nn_conv2d.Rd
@@ -143,7 +143,7 @@ sampled from \eqn{\mathcal{U}(-\sqrt{k}, \sqrt{k})} where
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # With square kernels and equal stride
 m <- nn_conv2d(16, 33, 3, stride = 2)
diff --git a/man/nn_conv3d.Rd b/man/nn_conv3d.Rd
index da36ea3f0..61f8a0a6b 100644
--- a/man/nn_conv3d.Rd
+++ b/man/nn_conv3d.Rd
@@ -130,7 +130,7 @@ sampled from \eqn{\mathcal{U}(-\sqrt{k}, \sqrt{k})} where
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # With square kernels and equal stride
 m <- nn_conv3d(16, 33, 3, stride=2)
 # non-square kernels and unequal stride and with padding
diff --git a/man/nn_conv_transpose1d.Rd b/man/nn_conv_transpose1d.Rd
index 779b68658..9ec9486eb 100644
--- a/man/nn_conv_transpose1d.Rd
+++ b/man/nn_conv_transpose1d.Rd
@@ -125,7 +125,7 @@ sampled from \eqn{\mathcal{U}(-\sqrt{k}, \sqrt{k})} where
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_conv_transpose1d(32, 16, 2)
 input <- torch_randn(10, 32, 2)
 output <- m(input)
diff --git a/man/nn_conv_transpose2d.Rd b/man/nn_conv_transpose2d.Rd
index e09b88c26..6f0921c99 100644
--- a/man/nn_conv_transpose2d.Rd
+++ b/man/nn_conv_transpose2d.Rd
@@ -135,7 +135,7 @@ sampled from \eqn{\mathcal{U}(-\sqrt{k}, \sqrt{k})} where
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # With square kernels and equal stride
 m <- nn_conv_transpose2d(16, 33, 3, stride=2)
 # non-square kernels and unequal stride and with padding
diff --git a/man/nn_conv_transpose3d.Rd b/man/nn_conv_transpose3d.Rd
index 1122159d8..b00b749d4 100644
--- a/man/nn_conv_transpose3d.Rd
+++ b/man/nn_conv_transpose3d.Rd
@@ -144,7 +144,7 @@ sampled from \eqn{\mathcal{U}(-\sqrt{k}, \sqrt{k})} where
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 # With square kernels and equal stride
 m <- nn_conv_transpose3d(16, 33, 3, stride=2)
diff --git a/man/nn_cross_entropy_loss.Rd b/man/nn_cross_entropy_loss.Rd
index 15bff8071..3c16d3345 100644
--- a/man/nn_cross_entropy_loss.Rd
+++ b/man/nn_cross_entropy_loss.Rd
@@ -74,7 +74,7 @@ of K-dimensional loss.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 loss <- nn_cross_entropy_loss()
 input <- torch_randn(3, 5, requires_grad=TRUE)
 target <- torch_randint(low = 1, high = 5, size = 3, dtype = torch_long())
diff --git a/man/nn_dropout.Rd b/man/nn_dropout.Rd
index e56c82b19..2d6da3498 100644
--- a/man/nn_dropout.Rd
+++ b/man/nn_dropout.Rd
@@ -35,7 +35,7 @@ identity function.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_dropout(p = 0.2)
 input <- torch_randn(20, 16)
 output <- m(input)
diff --git a/man/nn_dropout2d.Rd b/man/nn_dropout2d.Rd
index 500a0479f..b7ab40124 100644
--- a/man/nn_dropout2d.Rd
+++ b/man/nn_dropout2d.Rd
@@ -40,7 +40,7 @@ feature maps and should be used instead.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_dropout2d(p = 0.2)
 input <- torch_randn(20, 16, 32, 32)
 output <- m(input)
diff --git a/man/nn_dropout3d.Rd b/man/nn_dropout3d.Rd
index 87f6dd168..e51ced1c9 100644
--- a/man/nn_dropout3d.Rd
+++ b/man/nn_dropout3d.Rd
@@ -41,7 +41,7 @@ feature maps and should be used instead.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_dropout3d(p = 0.2)
 input <- torch_randn(20, 16, 4, 32, 32)
 output <- m(input)
diff --git a/man/nn_elu.Rd b/man/nn_elu.Rd
index 4804599a2..b7052433d 100644
--- a/man/nn_elu.Rd
+++ b/man/nn_elu.Rd
@@ -29,7 +29,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_elu()
 input <- torch_randn(2)
 output <-  m(input)
diff --git a/man/nn_embedding.Rd b/man/nn_embedding.Rd
index e12961531..42c28dff0 100644
--- a/man/nn_embedding.Rd
+++ b/man/nn_embedding.Rd
@@ -72,7 +72,7 @@ initialized from \eqn{\mathcal{N}(0, 1)}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # an Embedding module containing 10 tensors of size 3
 embedding <- nn_embedding(10, 3)
 # a batch of 2 samples of 4 indices each
diff --git a/man/nn_gelu.Rd b/man/nn_gelu.Rd
index 704760939..470be9acb 100644
--- a/man/nn_gelu.Rd
+++ b/man/nn_gelu.Rd
@@ -23,7 +23,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m = nn_gelu()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_glu.Rd b/man/nn_glu.Rd
index 5e3d3bce5..4c778c50a 100644
--- a/man/nn_glu.Rd
+++ b/man/nn_glu.Rd
@@ -24,7 +24,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_glu()
 input <- torch_randn(4, 2)
 output <- m(input)
diff --git a/man/nn_hardshrink.Rd b/man/nn_hardshrink.Rd
index 3eb111e2e..6e690070b 100644
--- a/man/nn_hardshrink.Rd
+++ b/man/nn_hardshrink.Rd
@@ -33,7 +33,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_hardshrink()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_hardsigmoid.Rd b/man/nn_hardsigmoid.Rd
index 6ad2cb532..d96648106 100644
--- a/man/nn_hardsigmoid.Rd
+++ b/man/nn_hardsigmoid.Rd
@@ -29,7 +29,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_hardsigmoid()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_hardswish.Rd b/man/nn_hardswish.Rd
index 33ba738ee..e1dc183f2 100644
--- a/man/nn_hardswish.Rd
+++ b/man/nn_hardswish.Rd
@@ -29,7 +29,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 m <- nn_hardswish()
 input <- torch_randn(2)
diff --git a/man/nn_hardtanh.Rd b/man/nn_hardtanh.Rd
index 0af0b740f..0e3ed49b2 100644
--- a/man/nn_hardtanh.Rd
+++ b/man/nn_hardtanh.Rd
@@ -40,7 +40,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_hardtanh(-2, 2)
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_identity.Rd b/man/nn_identity.Rd
index 3c830f2c3..7ab40e318 100644
--- a/man/nn_identity.Rd
+++ b/man/nn_identity.Rd
@@ -13,7 +13,7 @@ nn_identity(...)
 A placeholder identity operator that is argument-insensitive.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_identity(54, unused_argument1 = 0.1, unused_argument2 = FALSE)
 input <- torch_randn(128, 20)
 output <- m(input)
diff --git a/man/nn_init_constant_.Rd b/man/nn_init_constant_.Rd
index 3892155b8..85705b26f 100644
--- a/man/nn_init_constant_.Rd
+++ b/man/nn_init_constant_.Rd
@@ -15,7 +15,7 @@ nn_init_constant_(tensor, val)
 Fills the input Tensor with the value \code{val}.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_constant_(w, 0.3)
 
diff --git a/man/nn_init_dirac_.Rd b/man/nn_init_dirac_.Rd
index 7e0a6715c..14f70c443 100644
--- a/man/nn_init_dirac_.Rd
+++ b/man/nn_init_dirac_.Rd
@@ -18,7 +18,7 @@ layers, where as many input channels are preserved as possible. In case
 of groups>1, each group of channels preserves identity.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 w <- torch_empty(3, 16, 5, 5)
 nn_init_dirac_(w)
diff --git a/man/nn_init_eye_.Rd b/man/nn_init_eye_.Rd
index af1ea83b6..61c129055 100644
--- a/man/nn_init_eye_.Rd
+++ b/man/nn_init_eye_.Rd
@@ -15,7 +15,7 @@ Preserves the identity of the inputs in \code{Linear} layers, where as
 many inputs are preserved as possible.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_eye_(w)
 
diff --git a/man/nn_init_kaiming_normal_.Rd b/man/nn_init_kaiming_normal_.Rd
index 10aa3a5a4..ed62a0c9d 100644
--- a/man/nn_init_kaiming_normal_.Rd
+++ b/man/nn_init_kaiming_normal_.Rd
@@ -30,7 +30,7 @@ described in \verb{Delving deep into rectifiers: Surpassing human-level performa
 normal distribution.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_kaiming_normal_(w, mode = "fan_in", nonlinearity = "leaky_relu")
 
diff --git a/man/nn_init_kaiming_uniform_.Rd b/man/nn_init_kaiming_uniform_.Rd
index f3290d526..c46b03987 100644
--- a/man/nn_init_kaiming_uniform_.Rd
+++ b/man/nn_init_kaiming_uniform_.Rd
@@ -30,7 +30,7 @@ described in \verb{Delving deep into rectifiers: Surpassing human-level performa
 uniform distribution.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_kaiming_uniform_(w, mode = "fan_in", nonlinearity = "leaky_relu")
 
diff --git a/man/nn_init_normal_.Rd b/man/nn_init_normal_.Rd
index a7d8bafc0..d9628c3f6 100644
--- a/man/nn_init_normal_.Rd
+++ b/man/nn_init_normal_.Rd
@@ -17,7 +17,7 @@ nn_init_normal_(tensor, mean = 0, std = 1)
 Fills the input Tensor with values drawn from the normal distribution
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_normal_(w)
 
diff --git a/man/nn_init_ones_.Rd b/man/nn_init_ones_.Rd
index b28e681e6..0015a321b 100644
--- a/man/nn_init_ones_.Rd
+++ b/man/nn_init_ones_.Rd
@@ -13,7 +13,7 @@ nn_init_ones_(tensor)
 Fills the input Tensor with the scalar value \code{1}
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_ones_(w)
 
diff --git a/man/nn_init_orthogonal_.Rd b/man/nn_init_orthogonal_.Rd
index f400418a8..629eb414f 100644
--- a/man/nn_init_orthogonal_.Rd
+++ b/man/nn_init_orthogonal_.Rd
@@ -18,7 +18,7 @@ at least 2 dimensions, and for tensors with more than 2 dimensions the
 trailing dimensions are flattened.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3,5)
 nn_init_orthogonal_(w)
 
diff --git a/man/nn_init_sparse_.Rd b/man/nn_init_sparse_.Rd
index 143ac5817..6cae14856 100644
--- a/man/nn_init_sparse_.Rd
+++ b/man/nn_init_sparse_.Rd
@@ -20,7 +20,7 @@ non-zero elements will be drawn from the normal distribution
 as described in \verb{Deep learning via Hessian-free optimization} - Martens, J. (2010).
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 w <- torch_empty(3, 5)
 nn_init_sparse_(w, sparsity = 0.1)
diff --git a/man/nn_init_trunc_normal_.Rd b/man/nn_init_trunc_normal_.Rd
index 0d88e1f67..2d59c6d46 100644
--- a/man/nn_init_trunc_normal_.Rd
+++ b/man/nn_init_trunc_normal_.Rd
@@ -22,7 +22,7 @@ Fills the input Tensor with values drawn from a truncated
 normal distribution.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_trunc_normal_(w)
 
diff --git a/man/nn_init_uniform_.Rd b/man/nn_init_uniform_.Rd
index 4bf1cd2ba..557665fd5 100644
--- a/man/nn_init_uniform_.Rd
+++ b/man/nn_init_uniform_.Rd
@@ -17,7 +17,7 @@ nn_init_uniform_(tensor, a = 0, b = 1)
 Fills the input Tensor with values drawn from the uniform distribution
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_uniform_(w)
 
diff --git a/man/nn_init_xavier_normal_.Rd b/man/nn_init_xavier_normal_.Rd
index 93c2336da..d6a282ae7 100644
--- a/man/nn_init_xavier_normal_.Rd
+++ b/man/nn_init_xavier_normal_.Rd
@@ -17,7 +17,7 @@ described in \verb{Understanding the difficulty of training deep feedforward neu
 distribution.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_xavier_normal_(w)
 
diff --git a/man/nn_init_xavier_uniform_.Rd b/man/nn_init_xavier_uniform_.Rd
index 522cfc337..d0904cba0 100644
--- a/man/nn_init_xavier_uniform_.Rd
+++ b/man/nn_init_xavier_uniform_.Rd
@@ -17,7 +17,7 @@ described in \verb{Understanding the difficulty of training deep feedforward neu
 distribution.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_xavier_uniform_(w)
 
diff --git a/man/nn_init_zeros_.Rd b/man/nn_init_zeros_.Rd
index 96ba10e4e..39b48a4fc 100644
--- a/man/nn_init_zeros_.Rd
+++ b/man/nn_init_zeros_.Rd
@@ -13,7 +13,7 @@ nn_init_zeros_(tensor)
 Fills the input Tensor with the scalar value \code{0}
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_zeros_(w)
 
diff --git a/man/nn_leaky_relu.Rd b/man/nn_leaky_relu.Rd
index 2fe9923b0..d2cb6874e 100644
--- a/man/nn_leaky_relu.Rd
+++ b/man/nn_leaky_relu.Rd
@@ -39,7 +39,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_leaky_relu(0.1)
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_linear.Rd b/man/nn_linear.Rd
index 9230a0bd2..182ea9f67 100644
--- a/man/nn_linear.Rd
+++ b/man/nn_linear.Rd
@@ -42,7 +42,7 @@ If \code{bias} is \code{TRUE}, the values are initialized from
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_linear(20, 30)
 input <- torch_randn(128, 20)
 output <- m(input)
diff --git a/man/nn_log_sigmoid.Rd b/man/nn_log_sigmoid.Rd
index 2aee1877a..5f7284b1c 100644
--- a/man/nn_log_sigmoid.Rd
+++ b/man/nn_log_sigmoid.Rd
@@ -22,7 +22,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_log_sigmoid()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_log_softmax.Rd b/man/nn_log_softmax.Rd
index 59b96ab80..7f9a35a4f 100644
--- a/man/nn_log_softmax.Rd
+++ b/man/nn_log_softmax.Rd
@@ -32,7 +32,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_log_softmax(1)
 input <- torch_randn(2, 3)
 output <- m(input)
diff --git a/man/nn_max_pool1d.Rd b/man/nn_max_pool1d.Rd
index d597f6c1f..3a50f9d28 100644
--- a/man/nn_max_pool1d.Rd
+++ b/man/nn_max_pool1d.Rd
@@ -59,7 +59,7 @@ has a nice visualization of what \code{dilation} does.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # pool of size=3, stride=2
 m <- nn_max_pool1d(3, stride=2)
 input <- torch_randn(20, 16, 50)
diff --git a/man/nn_max_pool2d.Rd b/man/nn_max_pool2d.Rd
index 10acccfd1..260d1de22 100644
--- a/man/nn_max_pool2d.Rd
+++ b/man/nn_max_pool2d.Rd
@@ -74,7 +74,7 @@ and the second \code{int} for the width dimension
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # pool of square window of size=3, stride=2
 m <- nn_max_pool2d(3, stride=2)
 # pool of non-square window
diff --git a/man/nn_module.Rd b/man/nn_module.Rd
index 3f7ac0fea..ecceaf7cb 100644
--- a/man/nn_module.Rd
+++ b/man/nn_module.Rd
@@ -21,7 +21,7 @@ Modules can also contain other Modules, allowing to nest them in a tree
 structure. You can assign the submodules as regular attributes.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 model <- nn_module(
  initialize = function() {
    self$conv1 <- nn_conv2d(1, 20, 5)
diff --git a/man/nn_module_list.Rd b/man/nn_module_list.Rd
index beaea6f09..54fbdc1e3 100644
--- a/man/nn_module_list.Rd
+++ b/man/nn_module_list.Rd
@@ -15,7 +15,7 @@ modules it contains are properly registered, and will be visible by all
 \code{nn_module} methods.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 my_module <- nn_module(
  initialize = function() {
diff --git a/man/nn_multihead_attention.Rd b/man/nn_multihead_attention.Rd
index bb4fb9364..ba8441bc7 100644
--- a/man/nn_multihead_attention.Rd
+++ b/man/nn_multihead_attention.Rd
@@ -80,7 +80,7 @@ L is the target sequence length, S is the source sequence length.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 multihead_attn = nn_multihead_attention(embed_dim, num_heads)
 out <- multihead_attn(query, key, value)
diff --git a/man/nn_prelu.Rd b/man/nn_prelu.Rd
index ec64cb390..10d883643 100644
--- a/man/nn_prelu.Rd
+++ b/man/nn_prelu.Rd
@@ -56,7 +56,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_prelu()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_relu.Rd b/man/nn_relu.Rd
index 33776705b..348d09aa7 100644
--- a/man/nn_relu.Rd
+++ b/man/nn_relu.Rd
@@ -23,7 +23,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_relu()
 input <- torch_randn(2)
 m(input)
diff --git a/man/nn_relu6.Rd b/man/nn_relu6.Rd
index 82baa25c8..27c4dd41c 100644
--- a/man/nn_relu6.Rd
+++ b/man/nn_relu6.Rd
@@ -27,7 +27,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_relu6()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_rnn.Rd b/man/nn_rnn.Rd
index d4f40de2e..f60d8f043 100644
--- a/man/nn_rnn.Rd
+++ b/man/nn_rnn.Rd
@@ -131,7 +131,7 @@ where \eqn{k = \frac{1}{\mbox{hidden\_size}}}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 rnn <- nn_rnn(10, 20, 2)
 input <- torch_randn(5, 3, 10)
 h0 <- torch_randn(2, 3, 20)
diff --git a/man/nn_rrelu.Rd b/man/nn_rrelu.Rd
index a2c61f439..4d3efd174 100644
--- a/man/nn_rrelu.Rd
+++ b/man/nn_rrelu.Rd
@@ -45,7 +45,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_rrelu(0.1, 0.3)
 input <- torch_randn(2)
 m(input)
diff --git a/man/nn_selu.Rd b/man/nn_selu.Rd
index 4446c0229..c368e2d36 100644
--- a/man/nn_selu.Rd
+++ b/man/nn_selu.Rd
@@ -33,7 +33,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_selu()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_sequential.Rd b/man/nn_sequential.Rd
index 26cec8adf..bbc44c25a 100644
--- a/man/nn_sequential.Rd
+++ b/man/nn_sequential.Rd
@@ -17,7 +17,7 @@ Modules will be added to it in the order they are passed in the constructor.
 See examples.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 model <- nn_sequential(
   nn_conv2d(1, 20, 5),
diff --git a/man/nn_sigmoid.Rd b/man/nn_sigmoid.Rd
index 680d5078a..fa351fc1a 100644
--- a/man/nn_sigmoid.Rd
+++ b/man/nn_sigmoid.Rd
@@ -24,7 +24,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_sigmoid()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_softmax.Rd b/man/nn_softmax.Rd
index 6d76fa43f..87dae5141 100644
--- a/man/nn_softmax.Rd
+++ b/man/nn_softmax.Rd
@@ -44,7 +44,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_softmax(1)
 input <- torch_randn(2, 3)
 output <- m(input)
diff --git a/man/nn_softmax2d.Rd b/man/nn_softmax2d.Rd
index 3d1b44d96..87fa085fc 100644
--- a/man/nn_softmax2d.Rd
+++ b/man/nn_softmax2d.Rd
@@ -24,7 +24,7 @@ apply \code{Softmax} to each location \eqn{(Channels, h_i, w_j)}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_softmax2d()
 input <- torch_randn(2, 3, 12, 13)
 output <- m(input)
diff --git a/man/nn_softmin.Rd b/man/nn_softmin.Rd
index f92c29183..368596455 100644
--- a/man/nn_softmin.Rd
+++ b/man/nn_softmin.Rd
@@ -35,7 +35,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_softmin(dim = 1)
 input <- torch_randn(2, 2)
 output <- m(input)
diff --git a/man/nn_softplus.Rd b/man/nn_softplus.Rd
index 581943c25..5d7aca475 100644
--- a/man/nn_softplus.Rd
+++ b/man/nn_softplus.Rd
@@ -33,7 +33,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_softplus()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_softshrink.Rd b/man/nn_softshrink.Rd
index 833dcb05e..0d0a70eeb 100644
--- a/man/nn_softshrink.Rd
+++ b/man/nn_softshrink.Rd
@@ -33,7 +33,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_softshrink()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_softsign.Rd b/man/nn_softsign.Rd
index 263d37f10..d72dfbc99 100644
--- a/man/nn_softsign.Rd
+++ b/man/nn_softsign.Rd
@@ -22,7 +22,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_softsign()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_tanh.Rd b/man/nn_tanh.Rd
index 9536cd0a4..e27331f1d 100644
--- a/man/nn_tanh.Rd
+++ b/man/nn_tanh.Rd
@@ -24,7 +24,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_tanh()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_tanhshrink.Rd b/man/nn_tanhshrink.Rd
index d7e7b7d5b..28e5c1de6 100644
--- a/man/nn_tanhshrink.Rd
+++ b/man/nn_tanhshrink.Rd
@@ -24,7 +24,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_tanhshrink()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_threshold.Rd b/man/nn_threshold.Rd
index 74a425925..dfc746d1a 100644
--- a/man/nn_threshold.Rd
+++ b/man/nn_threshold.Rd
@@ -37,7 +37,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_threshold(0.1, 20)
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_utils_rnn_pack_sequence.Rd b/man/nn_utils_rnn_pack_sequence.Rd
index 1ac69baa4..7eb3bd027 100644
--- a/man/nn_utils_rnn_pack_sequence.Rd
+++ b/man/nn_utils_rnn_pack_sequence.Rd
@@ -27,7 +27,7 @@ is \code{TRUE}, the sequences should be sorted in the order of decreasing length
 \code{enforce_sorted = TRUE} is only necessary for ONNX export.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x <- torch_tensor(c(1,2,3), dtype = torch_long())
 y <- torch_tensor(c(4, 5), dtype = torch_long())
 z <- torch_tensor(c(6), dtype = torch_long())
diff --git a/man/nn_utils_rnn_pad_packed_sequence.Rd b/man/nn_utils_rnn_pad_packed_sequence.Rd
index 2da99d8e7..89004084e 100644
--- a/man/nn_utils_rnn_pad_packed_sequence.Rd
+++ b/man/nn_utils_rnn_pad_packed_sequence.Rd
@@ -45,7 +45,7 @@ the data will be transposed into \verb{B x T x *} format.
 \code{nn_module} wrapped in \code{~torch.nn.DataParallel}.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 seq <- torch_tensor(rbind(c(1,2,0), c(3,0,0), c(4,5,6)))        
 lens <- c(2,1,3)
 packed <- nn_utils_rnn_pack_padded_sequence(seq, lens, batch_first = TRUE,
diff --git a/man/nn_utils_rnn_pad_sequence.Rd b/man/nn_utils_rnn_pad_sequence.Rd
index f82849b48..45a8f703e 100644
--- a/man/nn_utils_rnn_pad_sequence.Rd
+++ b/man/nn_utils_rnn_pad_sequence.Rd
@@ -36,7 +36,7 @@ where \code{T} is the length of the longest sequence. This function assumes
 trailing dimensions and type of all the Tensors in sequences are same.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 a <- torch_ones(25, 300)
 b <- torch_ones(22, 300)
 c <- torch_ones(15, 300)
diff --git a/man/nnf_elu.Rd b/man/nnf_elu.Rd
index 770156a1c..fb3b47474 100644
--- a/man/nnf_elu.Rd
+++ b/man/nnf_elu.Rd
@@ -22,7 +22,7 @@ Applies element-wise,
 \deqn{ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))}.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x <- torch_randn(2, 2)
 y <- nnf_elu(x, alpha = 1)
 nnf_elu_(x, alpha = 1)
diff --git a/man/nnf_selu.Rd b/man/nnf_selu.Rd
index 0e344eccc..89f5993bb 100644
--- a/man/nnf_selu.Rd
+++ b/man/nnf_selu.Rd
@@ -22,7 +22,7 @@ with \eqn{\alpha=1.6732632423543772848170429916717} and
 \eqn{scale=1.0507009873554804934193349852946}.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x <- torch_randn(2, 2)
 y <- nnf_selu(x)
 nnf_selu_(x)
diff --git a/man/optim_adam.Rd b/man/optim_adam.Rd
index 6948c81aa..6dac2bf34 100644
--- a/man/optim_adam.Rd
+++ b/man/optim_adam.Rd
@@ -35,7 +35,7 @@ algorithm from the paper \href{https://openreview.net/forum?id=ryQu7f-RZ}{On the
 It has been proposed in \href{https://arxiv.org/abs/1412.6980}{Adam: A Method for Stochastic Optimization}.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 optimizer <- optim_adam(model$parameters(), lr=0.1)
 optimizer$zero_grad()
diff --git a/man/optim_sgd.Rd b/man/optim_sgd.Rd
index 5bde16fac..71adb63e0 100644
--- a/man/optim_sgd.Rd
+++ b/man/optim_sgd.Rd
@@ -62,7 +62,7 @@ The Nesterov version is analogously modified.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 optimizer <- optim_sgd(model$parameters(), lr=0.1, momentum=0.9)
 optimizer$zero_grad()
diff --git a/man/torch_abs.Rd b/man/torch_abs.Rd
index 97eb63d8b..576f39c6a 100644
--- a/man/torch_abs.Rd
+++ b/man/torch_abs.Rd
@@ -23,7 +23,7 @@ Computes the element-wise absolute value of the given \code{input} tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_abs(torch_tensor(c(-1, -2, 3)))
 }
diff --git a/man/torch_acos.Rd b/man/torch_acos.Rd
index aeea12bb6..a35172d9d 100644
--- a/man/torch_acos.Rd
+++ b/man/torch_acos.Rd
@@ -23,7 +23,7 @@ Returns a new tensor with the arccosine  of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_add.Rd b/man/torch_add.Rd
index 00b0b5748..e28ed4ebe 100644
--- a/man/torch_add.Rd
+++ b/man/torch_add.Rd
@@ -47,7 +47,7 @@ a real number, otherwise it should be an integer.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_addbmm.Rd b/man/torch_addbmm.Rd
index 904909b62..8c6ec8fdf 100644
--- a/man/torch_addbmm.Rd
+++ b/man/torch_addbmm.Rd
@@ -45,7 +45,7 @@ must be real numbers, otherwise they should be integers.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 M = torch_randn(c(3, 5))
 batch1 = torch_randn(c(10, 3, 4))
diff --git a/man/torch_addcdiv.Rd b/man/torch_addcdiv.Rd
index 0db51953a..b8cc67385 100644
--- a/man/torch_addcdiv.Rd
+++ b/man/torch_addcdiv.Rd
@@ -50,7 +50,7 @@ a real number, otherwise an integer.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 t = torch_randn(c(1, 3))
 t1 = torch_randn(c(3, 1))
diff --git a/man/torch_addcmul.Rd b/man/torch_addcmul.Rd
index 490b56da9..55571d3d1 100644
--- a/man/torch_addcmul.Rd
+++ b/man/torch_addcmul.Rd
@@ -36,7 +36,7 @@ a real number, otherwise an integer.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 t = torch_randn(c(1, 3))
 t1 = torch_randn(c(3, 1))
diff --git a/man/torch_addmm.Rd b/man/torch_addmm.Rd
index b7b791b14..ea9b40ff1 100644
--- a/man/torch_addmm.Rd
+++ b/man/torch_addmm.Rd
@@ -42,7 +42,7 @@ For inputs of type \code{FloatTensor} or \code{DoubleTensor}, arguments \code{be
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 M = torch_randn(c(2, 3))
 mat1 = torch_randn(c(2, 3))
diff --git a/man/torch_addmv.Rd b/man/torch_addmv.Rd
index eaebe9e06..5b32d1806 100644
--- a/man/torch_addmv.Rd
+++ b/man/torch_addmv.Rd
@@ -43,7 +43,7 @@ For inputs of type \code{FloatTensor} or \code{DoubleTensor}, arguments \code{be
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 M = torch_randn(c(2))
 mat = torch_randn(c(2, 3))
diff --git a/man/torch_addr.Rd b/man/torch_addr.Rd
index 056a92cc6..ffb890bbd 100644
--- a/man/torch_addr.Rd
+++ b/man/torch_addr.Rd
@@ -44,7 +44,7 @@ For inputs of type \code{FloatTensor} or \code{DoubleTensor}, arguments \code{be
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 vec1 = torch_arange(1., 4.)
 vec2 = torch_arange(1., 3.)
diff --git a/man/torch_allclose.Rd b/man/torch_allclose.Rd
index af72fe558..66c9ba584 100644
--- a/man/torch_allclose.Rd
+++ b/man/torch_allclose.Rd
@@ -31,7 +31,7 @@ elementwise, for all elements of \code{input} and \code{other}. The behaviour of
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_allclose(torch_tensor(c(10000., 1e-07)), torch_tensor(c(10000.1, 1e-08)))
 torch_allclose(torch_tensor(c(10000., 1e-08)), torch_tensor(c(10000.1, 1e-09)))
diff --git a/man/torch_angle.Rd b/man/torch_angle.Rd
index b49ab8e3f..f64198baf 100644
--- a/man/torch_angle.Rd
+++ b/man/torch_angle.Rd
@@ -23,7 +23,7 @@ Computes the element-wise angle (in radians) of the given \code{input} tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 torch_angle(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))*180/3.14159
 }
diff --git a/man/torch_arange.Rd b/man/torch_arange.Rd
index 1599a99b0..12d1200c6 100644
--- a/man/torch_arange.Rd
+++ b/man/torch_arange.Rd
@@ -41,7 +41,7 @@ in such cases.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_arange(start = 0, end = 5)
 torch_arange(1, 4)
diff --git a/man/torch_argmax.Rd b/man/torch_argmax.Rd
index d033d7308..b40d5ceb5 100644
--- a/man/torch_argmax.Rd
+++ b/man/torch_argmax.Rd
@@ -33,7 +33,7 @@ documentation for the exact semantics of this method.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 \dontrun{
 a = torch_randn(c(4, 4))
diff --git a/man/torch_argmin.Rd b/man/torch_argmin.Rd
index 66c956b6c..d028f7db4 100644
--- a/man/torch_argmin.Rd
+++ b/man/torch_argmin.Rd
@@ -33,7 +33,7 @@ documentation for the exact semantics of this method.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4, 4))
 a
diff --git a/man/torch_argsort.Rd b/man/torch_argsort.Rd
index 494bfc513..706e61687 100644
--- a/man/torch_argsort.Rd
+++ b/man/torch_argsort.Rd
@@ -25,7 +25,7 @@ for the exact semantics of this method.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4, 4))
 a
diff --git a/man/torch_as_strided.Rd b/man/torch_as_strided.Rd
index 91ec954a0..a07d9ae48 100644
--- a/man/torch_as_strided.Rd
+++ b/man/torch_as_strided.Rd
@@ -36,7 +36,7 @@ advisable to use.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(3, 3))
 x
diff --git a/man/torch_asin.Rd b/man/torch_asin.Rd
index d7f9d9440..844eb97c9 100644
--- a/man/torch_asin.Rd
+++ b/man/torch_asin.Rd
@@ -23,7 +23,7 @@ Returns a new tensor with the arcsine  of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_atan.Rd b/man/torch_atan.Rd
index f803b4e94..fd065feca 100644
--- a/man/torch_atan.Rd
+++ b/man/torch_atan.Rd
@@ -23,7 +23,7 @@ Returns a new tensor with the arctangent  of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_atan2.Rd b/man/torch_atan2.Rd
index c64301f30..8cb88e954 100644
--- a/man/torch_atan2.Rd
+++ b/man/torch_atan2.Rd
@@ -29,7 +29,7 @@ broadcastable .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_baddbmm.Rd b/man/torch_baddbmm.Rd
index 9af53cd6f..170192019 100644
--- a/man/torch_baddbmm.Rd
+++ b/man/torch_baddbmm.Rd
@@ -45,7 +45,7 @@ For inputs of type \code{FloatTensor} or \code{DoubleTensor}, arguments \code{be
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 M = torch_randn(c(10, 3, 5))
 batch1 = torch_randn(c(10, 3, 4))
diff --git a/man/torch_bernoulli.Rd b/man/torch_bernoulli.Rd
index 27d2bd185..236cef0ec 100644
--- a/man/torch_bernoulli.Rd
+++ b/man/torch_bernoulli.Rd
@@ -39,7 +39,7 @@ point \code{dtype}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_empty(c(3, 3))$uniform_(0, 1)  # generate a uniform random matrix with range c(0, 1)
 a
diff --git a/man/torch_bincount.Rd b/man/torch_bincount.Rd
index 61577dd0b..d0c7d7002 100644
--- a/man/torch_bincount.Rd
+++ b/man/torch_bincount.Rd
@@ -31,7 +31,7 @@ tensor of size 0. If \code{minlength} is specified, the number of bins is at lea
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input = torch_randint(0, 8, list(5), dtype=torch_int64())
 weights = torch_linspace(0, 1, steps=5)
diff --git a/man/torch_bmm.Rd b/man/torch_bmm.Rd
index ea324ab85..2818210ad 100644
--- a/man/torch_bmm.Rd
+++ b/man/torch_bmm.Rd
@@ -37,7 +37,7 @@ If \code{input} is a \eqn{(b \times n \times m)} tensor, \code{mat2} is a
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input = torch_randn(c(10, 3, 4))
 mat2 = torch_randn(c(10, 4, 5))
diff --git a/man/torch_broadcast_tensors.Rd b/man/torch_broadcast_tensors.Rd
index 33c2f9be9..1b2184e7a 100644
--- a/man/torch_broadcast_tensors.Rd
+++ b/man/torch_broadcast_tensors.Rd
@@ -17,7 +17,7 @@ Broadcasts the given tensors according to broadcasting-semantics.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_arange(0, 3)$view(c(1, 3))
 y = torch_arange(0, 2)$view(c(2, 1))
diff --git a/man/torch_can_cast.Rd b/man/torch_can_cast.Rd
index 940b8a881..b6918f152 100644
--- a/man/torch_can_cast.Rd
+++ b/man/torch_can_cast.Rd
@@ -20,7 +20,7 @@ described in the type promotion documentation .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_can_cast(torch_double(), torch_float())
 torch_can_cast(torch_float(), torch_int())
diff --git a/man/torch_cartesian_prod.Rd b/man/torch_cartesian_prod.Rd
index 6b22cabc1..0a8f7278a 100644
--- a/man/torch_cartesian_prod.Rd
+++ b/man/torch_cartesian_prod.Rd
@@ -18,7 +18,7 @@ python's \code{itertools.product}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = c(1, 2, 3)
 b = c(4, 5)
diff --git a/man/torch_cat.Rd b/man/torch_cat.Rd
index 0b1d5eed8..aa135d3cf 100644
--- a/man/torch_cat.Rd
+++ b/man/torch_cat.Rd
@@ -28,7 +28,7 @@ and \code{\link{torch_chunk}}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(2, 3))
 x
diff --git a/man/torch_ceil.Rd b/man/torch_ceil.Rd
index 5199c98a1..76b664b69 100644
--- a/man/torch_ceil.Rd
+++ b/man/torch_ceil.Rd
@@ -24,7 +24,7 @@ the smallest integer greater than or equal to each element.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_chain_matmul.Rd b/man/torch_chain_matmul.Rd
index 5c644f5e6..408cd87fb 100644
--- a/man/torch_chain_matmul.Rd
+++ b/man/torch_chain_matmul.Rd
@@ -21,7 +21,7 @@ If \eqn{N} is 1, then this is a no-op - the original matrix is returned as is.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 4))
 b = torch_randn(c(4, 5))
diff --git a/man/torch_cholesky.Rd b/man/torch_cholesky.Rd
index 1d89e5c50..6e7246df4 100644
--- a/man/torch_cholesky.Rd
+++ b/man/torch_cholesky.Rd
@@ -40,7 +40,7 @@ matrices.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 3))
 a = torch_mm(a, a$t()) # make symmetric positive-definite
diff --git a/man/torch_cholesky_inverse.Rd b/man/torch_cholesky_inverse.Rd
index 5bd3748fe..4c62050bc 100644
--- a/man/torch_cholesky_inverse.Rd
+++ b/man/torch_cholesky_inverse.Rd
@@ -36,7 +36,7 @@ triangular such that the returned tensor is
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 \dontrun{
 a = torch_randn(c(3, 3))
diff --git a/man/torch_cholesky_solve.Rd b/man/torch_cholesky_solve.Rd
index 266b66247..71c4ee6e9 100644
--- a/man/torch_cholesky_solve.Rd
+++ b/man/torch_cholesky_solve.Rd
@@ -40,7 +40,7 @@ batched outputs \code{c}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 3))
 a = torch_mm(a, a$t()) # make symmetric positive definite
diff --git a/man/torch_clamp.Rd b/man/torch_clamp.Rd
index d28c41bc2..52796d108 100644
--- a/man/torch_clamp.Rd
+++ b/man/torch_clamp.Rd
@@ -55,7 +55,7 @@ should be a real number, otherwise it should be an integer.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_combinations.Rd b/man/torch_combinations.Rd
index ed1431d31..da508224f 100644
--- a/man/torch_combinations.Rd
+++ b/man/torch_combinations.Rd
@@ -23,7 +23,7 @@ python's \code{itertools.combinations} when \code{with_replacement} is set to \c
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = c(1, 2, 3)
 tensor_a = torch_tensor(a)
diff --git a/man/torch_conj.Rd b/man/torch_conj.Rd
index 66329edc6..f385d54bb 100644
--- a/man/torch_conj.Rd
+++ b/man/torch_conj.Rd
@@ -23,7 +23,7 @@ Computes the element-wise conjugate of the given \code{input} tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 torch_conj(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))
 }
diff --git a/man/torch_conv1d.Rd b/man/torch_conv1d.Rd
index 47624ae4e..cdfa36eb3 100644
--- a/man/torch_conv1d.Rd
+++ b/man/torch_conv1d.Rd
@@ -34,7 +34,7 @@ See \code{~torch.nn.Conv1d} for details and output shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 filters = torch_randn(c(33, 16, 3))
 inputs = torch_randn(c(20, 16, 50))
diff --git a/man/torch_conv2d.Rd b/man/torch_conv2d.Rd
index f2f10add1..185226a64 100644
--- a/man/torch_conv2d.Rd
+++ b/man/torch_conv2d.Rd
@@ -34,7 +34,7 @@ See \code{~torch.nn.Conv2d} for details and output shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # With square kernels and equal stride
 filters = torch_randn(c(8,4,3,3))
diff --git a/man/torch_conv3d.Rd b/man/torch_conv3d.Rd
index a85f09949..5b544a164 100644
--- a/man/torch_conv3d.Rd
+++ b/man/torch_conv3d.Rd
@@ -34,7 +34,7 @@ See \code{~torch.nn.Conv3d} for details and output shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # filters = torch_randn(c(33, 16, 3, 3, 3))
 # inputs = torch_randn(c(20, 16, 50, 10, 20))
diff --git a/man/torch_conv_transpose1d.Rd b/man/torch_conv_transpose1d.Rd
index 85c97bef8..9b7ff3cf2 100644
--- a/man/torch_conv_transpose1d.Rd
+++ b/man/torch_conv_transpose1d.Rd
@@ -36,7 +36,7 @@ See \code{~torch.nn.ConvTranspose1d} for details and output shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 inputs = torch_randn(c(20, 16, 50))
 weights = torch_randn(c(16, 33, 5))
diff --git a/man/torch_conv_transpose2d.Rd b/man/torch_conv_transpose2d.Rd
index c12b79377..8a91d86ce 100644
--- a/man/torch_conv_transpose2d.Rd
+++ b/man/torch_conv_transpose2d.Rd
@@ -36,7 +36,7 @@ See \code{~torch.nn.ConvTranspose2d} for details and output shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # With square kernels and equal stride
 inputs = torch_randn(c(1, 4, 5, 5))
diff --git a/man/torch_conv_transpose3d.Rd b/man/torch_conv_transpose3d.Rd
index e7d5682af..c5c2ad239 100644
--- a/man/torch_conv_transpose3d.Rd
+++ b/man/torch_conv_transpose3d.Rd
@@ -36,7 +36,7 @@ See \code{~torch.nn.ConvTranspose3d} for details and output shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 inputs = torch_randn(c(20, 16, 50, 10, 20))
 weights = torch_randn(c(16, 33, 3, 3, 3))
diff --git a/man/torch_cos.Rd b/man/torch_cos.Rd
index bed5b8224..2381bcff7 100644
--- a/man/torch_cos.Rd
+++ b/man/torch_cos.Rd
@@ -23,7 +23,7 @@ Returns a new tensor with the cosine  of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_cosh.Rd b/man/torch_cosh.Rd
index 2c21e2d0c..285862ba3 100644
--- a/man/torch_cosh.Rd
+++ b/man/torch_cosh.Rd
@@ -24,7 +24,7 @@ Returns a new tensor with the hyperbolic cosine  of the elements of
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_cosine_similarity.Rd b/man/torch_cosine_similarity.Rd
index 11fd2d096..b88054d82 100644
--- a/man/torch_cosine_similarity.Rd
+++ b/man/torch_cosine_similarity.Rd
@@ -27,7 +27,7 @@ Returns cosine similarity between x1 and x2, computed along dim.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input1 = torch_randn(c(100, 128))
 input2 = torch_randn(c(100, 128))
diff --git a/man/torch_cross.Rd b/man/torch_cross.Rd
index e0db09612..b860e4bf9 100644
--- a/man/torch_cross.Rd
+++ b/man/torch_cross.Rd
@@ -30,7 +30,7 @@ size 3.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4, 3))
 a
diff --git a/man/torch_cummax.Rd b/man/torch_cummax.Rd
index d509af4af..a8a8e3f7f 100644
--- a/man/torch_cummax.Rd
+++ b/man/torch_cummax.Rd
@@ -27,7 +27,7 @@ location of each maximum value found in the dimension \code{dim}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(10))
 a
diff --git a/man/torch_cummin.Rd b/man/torch_cummin.Rd
index 76b7ce05b..81679be46 100644
--- a/man/torch_cummin.Rd
+++ b/man/torch_cummin.Rd
@@ -27,7 +27,7 @@ location of each maximum value found in the dimension \code{dim}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(10))
 a
diff --git a/man/torch_cumprod.Rd b/man/torch_cumprod.Rd
index 1fbedbccf..7b414808b 100644
--- a/man/torch_cumprod.Rd
+++ b/man/torch_cumprod.Rd
@@ -31,7 +31,7 @@ a vector of size N, with elements.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(10))
 a
diff --git a/man/torch_cumsum.Rd b/man/torch_cumsum.Rd
index 87e3c1e9a..8ea1d01a1 100644
--- a/man/torch_cumsum.Rd
+++ b/man/torch_cumsum.Rd
@@ -31,7 +31,7 @@ a vector of size N, with elements.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(10))
 a
diff --git a/man/torch_det.Rd b/man/torch_det.Rd
index 8bdd81a65..4030c7fd9 100644
--- a/man/torch_det.Rd
+++ b/man/torch_det.Rd
@@ -24,7 +24,7 @@ Calculates determinant of a square matrix or batches of square matrices.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_randn(c(3, 3))
 torch_det(A)
diff --git a/man/torch_device.Rd b/man/torch_device.Rd
index d94312547..8df2e3b91 100644
--- a/man/torch_device.Rd
+++ b/man/torch_device.Rd
@@ -22,7 +22,7 @@ A \code{torch_device}  is an object representing the device on which a \code{tor
 is or will be allocated.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # Via string
 torch_device("cuda:1")
diff --git a/man/torch_diag_embed.Rd b/man/torch_diag_embed.Rd
index 9378b6eb6..00fbde12e 100644
--- a/man/torch_diag_embed.Rd
+++ b/man/torch_diag_embed.Rd
@@ -44,7 +44,7 @@ need to be explicitly specified.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(2, 3))
 torch_diag_embed(a)
diff --git a/man/torch_diagflat.Rd b/man/torch_diagflat.Rd
index 5a4ef9f12..c4d1b7079 100644
--- a/man/torch_diagflat.Rd
+++ b/man/torch_diagflat.Rd
@@ -30,7 +30,7 @@ The argument \code{offset} controls which diagonal to consider:
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3))
 a
diff --git a/man/torch_diagonal.Rd b/man/torch_diagonal.Rd
index cb0fc2212..480ca6dda 100644
--- a/man/torch_diagonal.Rd
+++ b/man/torch_diagonal.Rd
@@ -37,7 +37,7 @@ dimensions, so those need to be explicitly specified.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 3))
 a
diff --git a/man/torch_digamma.Rd b/man/torch_digamma.Rd
index eec6b8305..315c1916c 100644
--- a/man/torch_digamma.Rd
+++ b/man/torch_digamma.Rd
@@ -21,7 +21,7 @@ Computes the logarithmic derivative of the gamma function on \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_tensor(c(1, 0.5))
 torch_digamma(a)
diff --git a/man/torch_dist.Rd b/man/torch_dist.Rd
index f36a9a373..457463436 100644
--- a/man/torch_dist.Rd
+++ b/man/torch_dist.Rd
@@ -24,7 +24,7 @@ broadcastable .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(4))
 x
diff --git a/man/torch_div.Rd b/man/torch_div.Rd
index 7ce99611a..c0d3e8f03 100644
--- a/man/torch_div.Rd
+++ b/man/torch_div.Rd
@@ -54,7 +54,7 @@ by zero leads to undefined behavior.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(5))
 a
diff --git a/man/torch_dot.Rd b/man/torch_dot.Rd
index 398efde86..26435dd78 100644
--- a/man/torch_dot.Rd
+++ b/man/torch_dot.Rd
@@ -17,7 +17,7 @@ Computes the dot product (inner product) of two tensors.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_dot(torch_tensor(c(2, 3)), torch_tensor(c(2, 1)))
 }
diff --git a/man/torch_einsum.Rd b/man/torch_einsum.Rd
index bdb195f35..1f1902fb2 100644
--- a/man/torch_einsum.Rd
+++ b/man/torch_einsum.Rd
@@ -20,7 +20,7 @@ Einstein summation convention.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(5))
 y = torch_randn(c(4))
diff --git a/man/torch_empty.Rd b/man/torch_empty.Rd
index 8412dbd92..2f59b2426 100644
--- a/man/torch_empty.Rd
+++ b/man/torch_empty.Rd
@@ -32,7 +32,7 @@ defined by the variable argument \code{size}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_empty(c(2, 3))
 }
diff --git a/man/torch_empty_like.Rd b/man/torch_empty_like.Rd
index 80e8bb107..d6d6aefce 100644
--- a/man/torch_empty_like.Rd
+++ b/man/torch_empty_like.Rd
@@ -29,7 +29,7 @@ Returns an uninitialized tensor with the same size as \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_empty(list(2,3), dtype = torch_int64())
 }
diff --git a/man/torch_empty_strided.Rd b/man/torch_empty_strided.Rd
index 983d79fb2..67240ac86 100644
--- a/man/torch_empty_strided.Rd
+++ b/man/torch_empty_strided.Rd
@@ -40,7 +40,7 @@ the tensors, please clone them first.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_empty_strided(list(2, 3), list(1, 2))
 a
diff --git a/man/torch_eq.Rd b/man/torch_eq.Rd
index d187438c3..e13863200 100644
--- a/man/torch_eq.Rd
+++ b/man/torch_eq.Rd
@@ -24,7 +24,7 @@ broadcastable  with the first argument.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_eq(torch_tensor(c(1,2,3,4)), torch_tensor(c(1, 3, 2, 4)))
 }
diff --git a/man/torch_equal.Rd b/man/torch_equal.Rd
index c740641d4..6830abb8b 100644
--- a/man/torch_equal.Rd
+++ b/man/torch_equal.Rd
@@ -14,7 +14,7 @@ Equal
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_equal(torch_tensor(c(1, 2)), torch_tensor(c(1, 2)))
 }
diff --git a/man/torch_erf.Rd b/man/torch_erf.Rd
index b7d286630..d18ec53a5 100644
--- a/man/torch_erf.Rd
+++ b/man/torch_erf.Rd
@@ -23,7 +23,7 @@ Computes the error function of each element. The error function is defined as fo
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_erf(torch_tensor(c(0, -1., 10.)))
 }
diff --git a/man/torch_erfc.Rd b/man/torch_erfc.Rd
index 59108819d..f58465cac 100644
--- a/man/torch_erfc.Rd
+++ b/man/torch_erfc.Rd
@@ -24,7 +24,7 @@ The complementary error function is defined as follows:
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_erfc(torch_tensor(c(0, -1., 10.)))
 }
diff --git a/man/torch_erfinv.Rd b/man/torch_erfinv.Rd
index 13aefc489..1e36f2799 100644
--- a/man/torch_erfinv.Rd
+++ b/man/torch_erfinv.Rd
@@ -24,7 +24,7 @@ The inverse error function is defined in the range \eqn{(-1, 1)} as:
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_erfinv(torch_tensor(c(0, 0.5, -1.)))
 }
diff --git a/man/torch_exp.Rd b/man/torch_exp.Rd
index bd6da89d4..5942c916c 100644
--- a/man/torch_exp.Rd
+++ b/man/torch_exp.Rd
@@ -24,7 +24,7 @@ of the input tensor \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_exp(torch_tensor(c(0, log(2))))
 }
diff --git a/man/torch_expm1.Rd b/man/torch_expm1.Rd
index ee95ba071..cb06a0ac0 100644
--- a/man/torch_expm1.Rd
+++ b/man/torch_expm1.Rd
@@ -24,7 +24,7 @@ of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_expm1(torch_tensor(c(0, log(2))))
 }
diff --git a/man/torch_eye.Rd b/man/torch_eye.Rd
index 74e7595fc..0533d3e40 100644
--- a/man/torch_eye.Rd
+++ b/man/torch_eye.Rd
@@ -29,7 +29,7 @@ Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_eye(3)
 }
diff --git a/man/torch_fft.Rd b/man/torch_fft.Rd
index 507083d1e..a94c0dbb5 100644
--- a/man/torch_fft.Rd
+++ b/man/torch_fft.Rd
@@ -58,7 +58,7 @@ For CPU tensors, this method is currently only available with MKL. Use
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # unbatched 2D FFT
 x = torch_randn(c(4, 3, 2))
diff --git a/man/torch_flatten.Rd b/man/torch_flatten.Rd
index 1573d8552..fc2b8fee4 100644
--- a/man/torch_flatten.Rd
+++ b/man/torch_flatten.Rd
@@ -21,7 +21,7 @@ Flattens a contiguous range of dims in a tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 t = torch_tensor(matrix(c(1, 2), ncol = 2))
 torch_flatten(t)
diff --git a/man/torch_flip.Rd b/man/torch_flip.Rd
index 3121f3b7a..d9d104c7b 100644
--- a/man/torch_flip.Rd
+++ b/man/torch_flip.Rd
@@ -19,7 +19,7 @@ Reverse the order of a n-D tensor along given axis in dims.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_arange(0, 8)$view(c(2, 2, 2))
 x
diff --git a/man/torch_floor.Rd b/man/torch_floor.Rd
index 1100decd9..fc743a901 100644
--- a/man/torch_floor.Rd
+++ b/man/torch_floor.Rd
@@ -24,7 +24,7 @@ the largest integer less than or equal to each element.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_floor_divide.Rd b/man/torch_floor_divide.Rd
index efba31aae..9d4cae433 100644
--- a/man/torch_floor_divide.Rd
+++ b/man/torch_floor_divide.Rd
@@ -24,7 +24,7 @@ for type promotion and broadcasting rules.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_tensor(c(4.0, 3.0))
 b = torch_tensor(c(2.0, 2.0))
diff --git a/man/torch_fmod.Rd b/man/torch_fmod.Rd
index a53db0ec7..337e60708 100644
--- a/man/torch_fmod.Rd
+++ b/man/torch_fmod.Rd
@@ -27,7 +27,7 @@ When \code{other} is a tensor, the shapes of \code{input} and
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_fmod(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2)
 torch_fmod(torch_tensor(c(1., 2, 3, 4, 5)), 1.5)
diff --git a/man/torch_frac.Rd b/man/torch_frac.Rd
index 182e09bda..b7a468f39 100644
--- a/man/torch_frac.Rd
+++ b/man/torch_frac.Rd
@@ -18,7 +18,7 @@ Computes the fractional portion of each element in \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_frac(torch_tensor(c(1, 2.5, -3.2)))
 }
diff --git a/man/torch_full.Rd b/man/torch_full.Rd
index 4687d1eab..6e9ff662c 100644
--- a/man/torch_full.Rd
+++ b/man/torch_full.Rd
@@ -38,7 +38,7 @@ and an integral \code{fill_value} will return a tensor of torch.long dtype.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_full(list(2, 3), 3.141592)
 }
diff --git a/man/torch_gather.Rd b/man/torch_gather.Rd
index c5379030f..2e6c7ed5b 100644
--- a/man/torch_gather.Rd
+++ b/man/torch_gather.Rd
@@ -36,7 +36,7 @@ and \code{out} will have the same size as \code{index}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 t = torch_tensor(matrix(c(1,2,3,4), ncol = 2, byrow = TRUE))
 torch_gather(t, 2, torch_tensor(matrix(c(1,1,2,1), ncol = 2, byrow=TRUE), dtype = torch_int64()))
diff --git a/man/torch_ge.Rd b/man/torch_ge.Rd
index 479b5196c..f4c332397 100644
--- a/man/torch_ge.Rd
+++ b/man/torch_ge.Rd
@@ -24,7 +24,7 @@ broadcastable  with the first argument.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_ge(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
          torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
diff --git a/man/torch_generator.Rd b/man/torch_generator.Rd
index 6d042a5a5..64667c8ed 100644
--- a/man/torch_generator.Rd
+++ b/man/torch_generator.Rd
@@ -12,7 +12,7 @@ that produces pseudo random numbers. Used as a keyword argument in many
 In-place random sampling functions.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # Via string
 generator <- torch_generator()
diff --git a/man/torch_ger.Rd b/man/torch_ger.Rd
index 0d03fc5da..8640dbe20 100644
--- a/man/torch_ger.Rd
+++ b/man/torch_ger.Rd
@@ -26,7 +26,7 @@ size \eqn{m}, then \code{out} must be a matrix of size \eqn{(n \times m)}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 v1 = torch_arange(1., 5.)
 v2 = torch_arange(1., 4.)
diff --git a/man/torch_gt.Rd b/man/torch_gt.Rd
index eed4e66ee..9cc4dad89 100644
--- a/man/torch_gt.Rd
+++ b/man/torch_gt.Rd
@@ -24,7 +24,7 @@ broadcastable  with the first argument.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_gt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
          torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
diff --git a/man/torch_histc.Rd b/man/torch_histc.Rd
index 8d6a29736..9e1ab1830 100644
--- a/man/torch_histc.Rd
+++ b/man/torch_histc.Rd
@@ -29,7 +29,7 @@ maximum values of the data are used.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_histc(torch_tensor(c(1., 2, 1)), bins=4, min=0, max=3)
 }
diff --git a/man/torch_ifft.Rd b/man/torch_ifft.Rd
index 1de2da601..e157efab1 100644
--- a/man/torch_ifft.Rd
+++ b/man/torch_ifft.Rd
@@ -57,7 +57,7 @@ For CPU tensors, this method is currently only available with MKL. Use
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(3, 3, 2))
 x
diff --git a/man/torch_imag.Rd b/man/torch_imag.Rd
index 2a1b1aeba..39a396f71 100644
--- a/man/torch_imag.Rd
+++ b/man/torch_imag.Rd
@@ -28,7 +28,7 @@ Not yet implemented.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 torch_imag(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))
 }
diff --git a/man/torch_index_select.Rd b/man/torch_index_select.Rd
index c3bb4d519..942c3bd15 100644
--- a/man/torch_index_select.Rd
+++ b/man/torch_index_select.Rd
@@ -34,7 +34,7 @@ of \code{index}; other dimensions have the same size as in the original tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(3, 4))
 x
diff --git a/man/torch_inverse.Rd b/man/torch_inverse.Rd
index f14f5ef2b..19fac6cb3 100644
--- a/man/torch_inverse.Rd
+++ b/man/torch_inverse.Rd
@@ -26,7 +26,7 @@ individual inverses.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 x = torch_rand(c(4, 4))
 y = torch_inverse(x)
diff --git a/man/torch_irfft.Rd b/man/torch_irfft.Rd
index 4a90548f6..83473fb0b 100644
--- a/man/torch_irfft.Rd
+++ b/man/torch_irfft.Rd
@@ -76,7 +76,7 @@ For CPU tensors, this method is currently only available with MKL. Use
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(4, 4))
 torch_rfft(x, 2, onesided=TRUE)
diff --git a/man/torch_is_installed.Rd b/man/torch_is_installed.Rd
new file mode 100644
index 000000000..46acb15f3
--- /dev/null
+++ b/man/torch_is_installed.Rd
@@ -0,0 +1,11 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/install.R
+\name{torch_is_installed}
+\alias{torch_is_installed}
+\title{Verifies if torch is installed}
+\usage{
+torch_is_installed()
+}
+\description{
+Verifies if torch is installed
+}
diff --git a/man/torch_isfinite.Rd b/man/torch_isfinite.Rd
index b08b8e501..cf7f41e87 100644
--- a/man/torch_isfinite.Rd
+++ b/man/torch_isfinite.Rd
@@ -17,7 +17,7 @@ Returns a new tensor with boolean elements representing if each element is \code
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_isfinite(torch_tensor(c(1, Inf, 2, -Inf, NaN)))
 }
diff --git a/man/torch_isinf.Rd b/man/torch_isinf.Rd
index 08adc3579..19c01319d 100644
--- a/man/torch_isinf.Rd
+++ b/man/torch_isinf.Rd
@@ -17,7 +17,7 @@ Returns a new tensor with boolean elements representing if each element is \verb
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_isinf(torch_tensor(c(1, Inf, 2, -Inf, NaN)))
 }
diff --git a/man/torch_isnan.Rd b/man/torch_isnan.Rd
index 742cebad1..764fde453 100644
--- a/man/torch_isnan.Rd
+++ b/man/torch_isnan.Rd
@@ -17,7 +17,7 @@ Returns a new tensor with boolean elements representing if each element is \code
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_isnan(torch_tensor(c(1, NaN, 2)))
 }
diff --git a/man/torch_kthvalue.Rd b/man/torch_kthvalue.Rd
index ee744246d..3616916cb 100644
--- a/man/torch_kthvalue.Rd
+++ b/man/torch_kthvalue.Rd
@@ -35,7 +35,7 @@ they are of size 1. Otherwise, \code{dim} is squeezed
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_arange(1., 6.)
 x
diff --git a/man/torch_le.Rd b/man/torch_le.Rd
index 69a6bd6d9..85e497f05 100644
--- a/man/torch_le.Rd
+++ b/man/torch_le.Rd
@@ -24,7 +24,7 @@ broadcastable  with the first argument.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_le(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
          torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
diff --git a/man/torch_lerp.Rd b/man/torch_lerp.Rd
index 64a468bef..66be2f225 100644
--- a/man/torch_lerp.Rd
+++ b/man/torch_lerp.Rd
@@ -31,7 +31,7 @@ the shapes of \code{weight}, \code{start}, and \code{end} must be broadcastable
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 start = torch_arange(1., 5.)
 end = torch_empty(4)$fill_(10)
diff --git a/man/torch_lgamma.Rd b/man/torch_lgamma.Rd
index 02fd194fb..6ae297ded 100644
--- a/man/torch_lgamma.Rd
+++ b/man/torch_lgamma.Rd
@@ -23,7 +23,7 @@ Computes the logarithm of the gamma function on \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_arange(0.5, 2, 0.5)
 torch_lgamma(a)
diff --git a/man/torch_linspace.Rd b/man/torch_linspace.Rd
index 678c62247..8c57a248f 100644
--- a/man/torch_linspace.Rd
+++ b/man/torch_linspace.Rd
@@ -34,7 +34,7 @@ The output tensor is 1-D of size \code{steps}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_linspace(3, 10, steps=5)
 torch_linspace(-10, 10, steps=5)
diff --git a/man/torch_log.Rd b/man/torch_log.Rd
index c3daf84b8..95e5c80ec 100644
--- a/man/torch_log.Rd
+++ b/man/torch_log.Rd
@@ -24,7 +24,7 @@ of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(5))
 a
diff --git a/man/torch_log10.Rd b/man/torch_log10.Rd
index 7e09dabe0..c087b1e93 100644
--- a/man/torch_log10.Rd
+++ b/man/torch_log10.Rd
@@ -24,7 +24,7 @@ of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_rand(5)
 a
diff --git a/man/torch_log1p.Rd b/man/torch_log1p.Rd
index 72aae87db..075bf23d4 100644
--- a/man/torch_log1p.Rd
+++ b/man/torch_log1p.Rd
@@ -27,7 +27,7 @@ Returns a new tensor with the natural logarithm of (1 + \code{input}).
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(5))
 a
diff --git a/man/torch_log2.Rd b/man/torch_log2.Rd
index f23ba906a..a7175d328 100644
--- a/man/torch_log2.Rd
+++ b/man/torch_log2.Rd
@@ -24,7 +24,7 @@ of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_rand(5)
 a
diff --git a/man/torch_logdet.Rd b/man/torch_logdet.Rd
index f9367f6cd..cb922c129 100644
--- a/man/torch_logdet.Rd
+++ b/man/torch_logdet.Rd
@@ -28,7 +28,7 @@ Calculates log determinant of a square matrix or batches of square matrices.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_randn(c(3, 3))
 torch_det(A)
diff --git a/man/torch_logical_and.Rd b/man/torch_logical_and.Rd
index 2575f2813..7179072a6 100644
--- a/man/torch_logical_and.Rd
+++ b/man/torch_logical_and.Rd
@@ -22,7 +22,7 @@ treated as \code{True}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_logical_and(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
 a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
diff --git a/man/torch_logical_not.Rd b/man/torch_logical_not.Rd
index 21d5e7145..b9345cfdb 100644
--- a/man/torch_logical_not.Rd
+++ b/man/torch_logical_not.Rd
@@ -20,7 +20,7 @@ dtype. If the input tensor is not a bool tensor, zeros are treated as \code{Fals
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_logical_not(torch_tensor(c(TRUE, FALSE)))
 torch_logical_not(torch_tensor(c(0, 1, -10), dtype=torch_int8()))
diff --git a/man/torch_logical_or.Rd b/man/torch_logical_or.Rd
index d79615a82..b4b66d9cb 100644
--- a/man/torch_logical_or.Rd
+++ b/man/torch_logical_or.Rd
@@ -22,7 +22,7 @@ treated as \code{True}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_logical_or(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
 a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
diff --git a/man/torch_logical_xor.Rd b/man/torch_logical_xor.Rd
index d3730c904..4c32ced8f 100644
--- a/man/torch_logical_xor.Rd
+++ b/man/torch_logical_xor.Rd
@@ -22,7 +22,7 @@ treated as \code{True}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_logical_xor(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
 a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
diff --git a/man/torch_logspace.Rd b/man/torch_logspace.Rd
index 0b15c0881..25d6ac11c 100644
--- a/man/torch_logspace.Rd
+++ b/man/torch_logspace.Rd
@@ -37,7 +37,7 @@ The output tensor is 1-D of size \code{steps}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_logspace(start=-10, end=10, steps=5)
 torch_logspace(start=0.1, end=1.0, steps=5)
diff --git a/man/torch_logsumexp.Rd b/man/torch_logsumexp.Rd
index b737d58f0..398f92ccd 100644
--- a/man/torch_logsumexp.Rd
+++ b/man/torch_logsumexp.Rd
@@ -36,7 +36,7 @@ output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 3))
 torch_logsumexp(a, 1)
diff --git a/man/torch_lstsq.Rd b/man/torch_lstsq.Rd
index f357b66a0..4778caffa 100644
--- a/man/torch_lstsq.Rd
+++ b/man/torch_lstsq.Rd
@@ -46,7 +46,7 @@ remaining \eqn{m - n} rows of that column.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_tensor(rbind(
  c(1,1,1),
diff --git a/man/torch_lt.Rd b/man/torch_lt.Rd
index e56577eae..567fd30df 100644
--- a/man/torch_lt.Rd
+++ b/man/torch_lt.Rd
@@ -24,7 +24,7 @@ broadcastable  with the first argument.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_lt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
          torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
diff --git a/man/torch_lu.Rd b/man/torch_lu.Rd
index 904b7d5e9..039e1ba1a 100644
--- a/man/torch_lu.Rd
+++ b/man/torch_lu.Rd
@@ -23,7 +23,7 @@ tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
 is set to True.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_randn(c(2, 3, 3))
 torch_lu(A)
diff --git a/man/torch_lu_solve.Rd b/man/torch_lu_solve.Rd
index 66c05b134..7fbbfe45c 100644
--- a/man/torch_lu_solve.Rd
+++ b/man/torch_lu_solve.Rd
@@ -24,7 +24,7 @@ LU factorization of A from \code{torch_lu}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 A = torch_randn(c(2, 3, 3))
 b = torch_randn(c(2, 3, 1))
 out = torch_lu(A)
diff --git a/man/torch_masked_select.Rd b/man/torch_masked_select.Rd
index 29492eb81..065c0b7be 100644
--- a/man/torch_masked_select.Rd
+++ b/man/torch_masked_select.Rd
@@ -29,7 +29,7 @@ to match, but they must be broadcastable .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(3, 4))
 x
diff --git a/man/torch_matmul.Rd b/man/torch_matmul.Rd
index 82fbb2dcc..f11474fe7 100644
--- a/man/torch_matmul.Rd
+++ b/man/torch_matmul.Rd
@@ -45,7 +45,7 @@ tensor, \code{out} will be an \eqn{(j \times k \times n \times p)} tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # vector x vector
 tensor1 = torch_randn(c(3))
diff --git a/man/torch_matrix_power.Rd b/man/torch_matrix_power.Rd
index 46f8d9175..5202822e1 100644
--- a/man/torch_matrix_power.Rd
+++ b/man/torch_matrix_power.Rd
@@ -25,7 +25,7 @@ is returned.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(2, 2, 2))
 a
diff --git a/man/torch_matrix_rank.Rd b/man/torch_matrix_rank.Rd
index 6443ffc8b..811d3efc3 100644
--- a/man/torch_matrix_rank.Rd
+++ b/man/torch_matrix_rank.Rd
@@ -30,7 +30,7 @@ is the epsilon value for the datatype of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_eye(10)
 torch_matrix_rank(a)
diff --git a/man/torch_max.Rd b/man/torch_max.Rd
index 1de50eee2..2b4e7c154 100644
--- a/man/torch_max.Rd
+++ b/man/torch_max.Rd
@@ -65,7 +65,7 @@ but they must be broadcastable .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_mean.Rd b/man/torch_mean.Rd
index 52c6a45c1..5d597f841 100644
--- a/man/torch_mean.Rd
+++ b/man/torch_mean.Rd
@@ -36,7 +36,7 @@ output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_median.Rd b/man/torch_median.Rd
index 44525ab7d..49a5ce580 100644
--- a/man/torch_median.Rd
+++ b/man/torch_median.Rd
@@ -38,7 +38,7 @@ the outputs tensor having 1 fewer dimension than \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_meshgrid.Rd b/man/torch_meshgrid.Rd
index c4b4a12f2..fd681386c 100644
--- a/man/torch_meshgrid.Rd
+++ b/man/torch_meshgrid.Rd
@@ -21,7 +21,7 @@ expanding the \eqn{i} \code{th} input over dimensions defined by other inputs.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_tensor(c(1, 2, 3))
 y = torch_tensor(c(4, 5, 6))
diff --git a/man/torch_min.Rd b/man/torch_min.Rd
index 3f5cb9f76..4dbc40bd3 100644
--- a/man/torch_min.Rd
+++ b/man/torch_min.Rd
@@ -66,7 +66,7 @@ but they must be broadcastable .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_mm.Rd b/man/torch_mm.Rd
index 898ae45d1..d7a95a389 100644
--- a/man/torch_mm.Rd
+++ b/man/torch_mm.Rd
@@ -28,7 +28,7 @@ If \code{input} is a \eqn{(n \times m)} tensor, \code{mat2} is a
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 mat1 = torch_randn(c(2, 3))
 mat2 = torch_randn(c(3, 3))
diff --git a/man/torch_mode.Rd b/man/torch_mode.Rd
index 571689bad..77110e48a 100644
--- a/man/torch_mode.Rd
+++ b/man/torch_mode.Rd
@@ -36,7 +36,7 @@ in the output tensors having 1 fewer dimension than \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randint(0, 50, size = list(5))
 a
diff --git a/man/torch_mul.Rd b/man/torch_mul.Rd
index e31f6f8a3..dac0045bf 100644
--- a/man/torch_mul.Rd
+++ b/man/torch_mul.Rd
@@ -46,7 +46,7 @@ broadcastable .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3))
 a
diff --git a/man/torch_multinomial.Rd b/man/torch_multinomial.Rd
index a12552845..8726beaf2 100644
--- a/man/torch_multinomial.Rd
+++ b/man/torch_multinomial.Rd
@@ -51,7 +51,7 @@ of tensor \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 weights = torch_tensor(c(0, 10, 3, 0), dtype=torch_float()) # create a tensor of weights
 torch_multinomial(weights, 2)
diff --git a/man/torch_mv.Rd b/man/torch_mv.Rd
index 3b6a76a41..e0e0a8611 100644
--- a/man/torch_mv.Rd
+++ b/man/torch_mv.Rd
@@ -28,7 +28,7 @@ size \eqn{m}, \code{out} will be 1-D of size \eqn{n}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 mat = torch_randn(c(2, 3))
 vec = torch_randn(c(3))
diff --git a/man/torch_mvlgamma.Rd b/man/torch_mvlgamma.Rd
index 5c9d1d911..58bb475f4 100644
--- a/man/torch_mvlgamma.Rd
+++ b/man/torch_mvlgamma.Rd
@@ -27,7 +27,7 @@ All elements must be greater than \eqn{\frac{p - 1}{2}}, otherwise an error woul
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_empty(c(2, 3))$uniform_(1, 2)
 a
diff --git a/man/torch_narrow.Rd b/man/torch_narrow.Rd
index afad9d4df..1ca8bb3e7 100644
--- a/man/torch_narrow.Rd
+++ b/man/torch_narrow.Rd
@@ -25,7 +25,7 @@ returned tensor and \code{input} tensor share the same underlying storage.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_tensor(matrix(c(1:9), ncol = 3, byrow= TRUE))
 torch_narrow(x, 1, torch_tensor(0L)$sum(dim = 1), 2)
diff --git a/man/torch_ne.Rd b/man/torch_ne.Rd
index 366a50104..fc31508f9 100644
--- a/man/torch_ne.Rd
+++ b/man/torch_ne.Rd
@@ -24,7 +24,7 @@ broadcastable  with the first argument.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_ne(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
          torch_tensor(matrix(rep(c(1,4), each = 2), ncol = 2, byrow=TRUE)))
diff --git a/man/torch_neg.Rd b/man/torch_neg.Rd
index 677b43d7a..de4a5133f 100644
--- a/man/torch_neg.Rd
+++ b/man/torch_neg.Rd
@@ -23,7 +23,7 @@ Returns a new tensor with the negative of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(5))
 a
diff --git a/man/torch_nonzero.Rd b/man/torch_nonzero.Rd
index 5a4165420..85eedee9d 100644
--- a/man/torch_nonzero.Rd
+++ b/man/torch_nonzero.Rd
@@ -53,7 +53,7 @@ value, it is treated as a one-dimensional tensor with one element.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_nonzero(torch_tensor(c(1, 1, 1, 0, 1)))
 }
diff --git a/man/torch_norm.Rd b/man/torch_norm.Rd
index 6873517e5..7600be494 100644
--- a/man/torch_norm.Rd
+++ b/man/torch_norm.Rd
@@ -27,7 +27,7 @@ Returns the matrix norm or vector norm of a given tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_arange(0, 9, dtype = torch_float())
 b = a$reshape(list(3, 3))
diff --git a/man/torch_normal.Rd b/man/torch_normal.Rd
index ec43d79b2..18601ca32 100644
--- a/man/torch_normal.Rd
+++ b/man/torch_normal.Rd
@@ -60,7 +60,7 @@ among all drawn elements. The resulting tensor has size given by \code{size}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 \dontrun{
 torch_normal(mean=0, std=torch_arange(1, 0, -0.1))
diff --git a/man/torch_ones.Rd b/man/torch_ones.Rd
index 98be2a116..d2bbc86e9 100644
--- a/man/torch_ones.Rd
+++ b/man/torch_ones.Rd
@@ -28,7 +28,7 @@ by the variable argument \code{size}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_ones(c(2, 3))
 torch_ones(c(5))
diff --git a/man/torch_ones_like.Rd b/man/torch_ones_like.Rd
index 072991da8..be0df6d18 100644
--- a/man/torch_ones_like.Rd
+++ b/man/torch_ones_like.Rd
@@ -36,7 +36,7 @@ the old \code{torch_ones_like(input, out=output)} is equivalent to
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input = torch_empty(c(2, 3))
 torch_ones_like(input)
diff --git a/man/torch_pinverse.Rd b/man/torch_pinverse.Rd
index 360fc8227..131b5149f 100644
--- a/man/torch_pinverse.Rd
+++ b/man/torch_pinverse.Rd
@@ -31,7 +31,7 @@ Please look at \verb{Moore-Penrose inverse}_ for more details
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input = torch_randn(c(3, 5))
 input
diff --git a/man/torch_pixel_shuffle.Rd b/man/torch_pixel_shuffle.Rd
index 584b9f454..1df105203 100644
--- a/man/torch_pixel_shuffle.Rd
+++ b/man/torch_pixel_shuffle.Rd
@@ -22,7 +22,7 @@ See \code{~torch.nn.PixelShuffle} for details.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input = torch_randn(c(1, 9, 4, 4))
 output = nnf_pixel_shuffle(input, 3)
diff --git a/man/torch_poisson.Rd b/man/torch_poisson.Rd
index 50e8b2c73..6bb3122c8 100644
--- a/man/torch_poisson.Rd
+++ b/man/torch_poisson.Rd
@@ -25,7 +25,7 @@ element in \code{input} i.e.,
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 rates = torch_rand(c(4, 4)) * 5  # rate parameter between 0 and 5
 torch_poisson(rates)
diff --git a/man/torch_polygamma.Rd b/man/torch_polygamma.Rd
index 360b0f6e9..4ab7249f8 100644
--- a/man/torch_polygamma.Rd
+++ b/man/torch_polygamma.Rd
@@ -30,7 +30,7 @@ Computes the \eqn{n^{th}} derivative of the digamma function on \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 a = torch_tensor(c(1, 0.5))
 torch_polygamma(1, a)
diff --git a/man/torch_pow.Rd b/man/torch_pow.Rd
index 567dc9039..b11720826 100644
--- a/man/torch_pow.Rd
+++ b/man/torch_pow.Rd
@@ -53,7 +53,7 @@ The operation applied is:
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_prod.Rd b/man/torch_prod.Rd
index cc0a9aa13..b2fc17d06 100644
--- a/man/torch_prod.Rd
+++ b/man/torch_prod.Rd
@@ -35,7 +35,7 @@ the output tensor having 1 fewer dimension than \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_promote_types.Rd b/man/torch_promote_types.Rd
index 9d93b511e..a6e05dcb3 100644
--- a/man/torch_promote_types.Rd
+++ b/man/torch_promote_types.Rd
@@ -22,7 +22,7 @@ promotion logic.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_promote_types(torch_int32(), torch_float32())
 torch_promote_types(torch_uint8(), torch_long())
diff --git a/man/torch_qr.Rd b/man/torch_qr.Rd
index 2c6cf71e3..6463d155a 100644
--- a/man/torch_qr.Rd
+++ b/man/torch_qr.Rd
@@ -35,7 +35,7 @@ Otherwise, if \code{some} is \code{False}, this function returns the complete QR
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_tensor(matrix(c(12., -51, 4, 6, 167, -68, -4, 24, -41), ncol = 3, byrow = TRUE))
 out = torch_qr(a)
diff --git a/man/torch_quantize_per_channel.Rd b/man/torch_quantize_per_channel.Rd
index f6027a8dd..683a9576a 100644
--- a/man/torch_quantize_per_channel.Rd
+++ b/man/torch_quantize_per_channel.Rd
@@ -25,7 +25,7 @@ Converts a float tensor to per-channel quantized tensor with given scales and ze
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x = torch_tensor(matrix(c(-1.0, 0.0, 1.0, 2.0), ncol = 2, byrow = TRUE))
 torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)), 
                            torch_tensor(c(10L, 0L)), 0, torch_quint8())
diff --git a/man/torch_quantize_per_tensor.Rd b/man/torch_quantize_per_tensor.Rd
index e0e9db15c..962a816aa 100644
--- a/man/torch_quantize_per_tensor.Rd
+++ b/man/torch_quantize_per_tensor.Rd
@@ -23,7 +23,7 @@ Converts a float tensor to quantized tensor with given scale and zero point.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())
 torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())$int_repr()
 }
diff --git a/man/torch_rand.Rd b/man/torch_rand.Rd
index 8bf27242b..49e2fbb0e 100644
--- a/man/torch_rand.Rd
+++ b/man/torch_rand.Rd
@@ -30,7 +30,7 @@ The shape of the tensor is defined by the variable argument \code{size}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_rand(4)
 torch_rand(c(2, 3))
diff --git a/man/torch_randint.Rd b/man/torch_randint.Rd
index 397bf569b..f806ea80f 100644
--- a/man/torch_randint.Rd
+++ b/man/torch_randint.Rd
@@ -42,7 +42,7 @@ a tensor with dtype \code{torch_int64}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_randint(3, 5, list(3))
 torch_randint(0, 10, size = list(2, 2))
diff --git a/man/torch_randn.Rd b/man/torch_randn.Rd
index 25b695f01..2e2e77387 100644
--- a/man/torch_randn.Rd
+++ b/man/torch_randn.Rd
@@ -34,7 +34,7 @@ The shape of the tensor is defined by the variable argument \code{size}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_randn(c(4))
 torch_randn(c(2, 3))
diff --git a/man/torch_randperm.Rd b/man/torch_randperm.Rd
index 93d832ece..b1e9fea4a 100644
--- a/man/torch_randperm.Rd
+++ b/man/torch_randperm.Rd
@@ -27,7 +27,7 @@ Returns a random permutation of integers from \code{0} to \code{n - 1}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_randperm(4)
 }
diff --git a/man/torch_range.Rd b/man/torch_range.Rd
index 352f3b520..306c49123 100644
--- a/man/torch_range.Rd
+++ b/man/torch_range.Rd
@@ -42,7 +42,7 @@ This function is deprecated in favor of \code{\link{torch_arange}}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_range(1, 4)
 torch_range(1, 4, 0.5)
diff --git a/man/torch_real.Rd b/man/torch_real.Rd
index 92b995600..a9b86c77e 100644
--- a/man/torch_real.Rd
+++ b/man/torch_real.Rd
@@ -30,7 +30,7 @@ Not yet implemented for complex tensors.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 torch_real(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))
 }
diff --git a/man/torch_reciprocal.Rd b/man/torch_reciprocal.Rd
index cb24bfbda..6009938b7 100644
--- a/man/torch_reciprocal.Rd
+++ b/man/torch_reciprocal.Rd
@@ -23,7 +23,7 @@ Returns a new tensor with the reciprocal of the elements of \code{input}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_remainder.Rd b/man/torch_remainder.Rd
index 0d1e071ef..10fd367f8 100644
--- a/man/torch_remainder.Rd
+++ b/man/torch_remainder.Rd
@@ -27,7 +27,7 @@ When \code{other} is a tensor, the shapes of \code{input} and
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_remainder(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2)
 torch_remainder(torch_tensor(c(1., 2, 3, 4, 5)), 1.5)
diff --git a/man/torch_renorm.Rd b/man/torch_renorm.Rd
index 6e5b1de2b..84755448c 100644
--- a/man/torch_renorm.Rd
+++ b/man/torch_renorm.Rd
@@ -30,7 +30,7 @@ than the value \code{maxnorm}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x = torch_ones(c(3, 3))
 x[2,]$fill_(2)
 x[3,]$fill_(3)
diff --git a/man/torch_repeat_interleave.Rd b/man/torch_repeat_interleave.Rd
index 4c2393a45..aa40042bd 100644
--- a/man/torch_repeat_interleave.Rd
+++ b/man/torch_repeat_interleave.Rd
@@ -34,7 +34,7 @@ If the \code{repeats} is \verb{tensor([n1, n2, n3, ...])}, then the output will
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 x = torch_tensor(c(1, 2, 3))
 x$repeat_interleave(2)
diff --git a/man/torch_reshape.Rd b/man/torch_reshape.Rd
index 01105c5f3..dd06c69eb 100644
--- a/man/torch_reshape.Rd
+++ b/man/torch_reshape.Rd
@@ -28,7 +28,7 @@ dimensions and the number of elements in \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_arange(0, 4)
 torch_reshape(a, list(2, 2))
diff --git a/man/torch_result_type.Rd b/man/torch_result_type.Rd
index 42eaa4c3e..6519045eb 100644
--- a/man/torch_result_type.Rd
+++ b/man/torch_result_type.Rd
@@ -21,9 +21,8 @@ for more information on the type promotion logic.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_int()), 1.0)
-# torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_uint8()), torch_tensor(1))
 }
 }
diff --git a/man/torch_rfft.Rd b/man/torch_rfft.Rd
index 99d095f5c..b94c2515f 100644
--- a/man/torch_rfft.Rd
+++ b/man/torch_rfft.Rd
@@ -62,7 +62,7 @@ For CPU tensors, this method is currently only available with MKL. Use
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(5, 5))
 torch_rfft(x, 2)
diff --git a/man/torch_roll.Rd b/man/torch_roll.Rd
index d2e1e2218..5d237d9f7 100644
--- a/man/torch_roll.Rd
+++ b/man/torch_roll.Rd
@@ -24,7 +24,7 @@ to the original shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_tensor(c(1, 2, 3, 4, 5, 6, 7, 8))$view(c(4, 2))
 x
diff --git a/man/torch_rot90.Rd b/man/torch_rot90.Rd
index 4ddc51f2b..980f1f98c 100644
--- a/man/torch_rot90.Rd
+++ b/man/torch_rot90.Rd
@@ -22,7 +22,7 @@ Rotation direction is from the first towards the second axis if k > 0, and from
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_arange(0, 4)$view(c(2, 2))
 x
diff --git a/man/torch_round.Rd b/man/torch_round.Rd
index faac60d6b..26d7258df 100644
--- a/man/torch_round.Rd
+++ b/man/torch_round.Rd
@@ -20,7 +20,7 @@ to the closest integer.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_rsqrt.Rd b/man/torch_rsqrt.Rd
index d37448a8b..bc8a54ba6 100644
--- a/man/torch_rsqrt.Rd
+++ b/man/torch_rsqrt.Rd
@@ -24,7 +24,7 @@ the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_sigmoid.Rd b/man/torch_sigmoid.Rd
index e67da88c5..bd3459c60 100644
--- a/man/torch_sigmoid.Rd
+++ b/man/torch_sigmoid.Rd
@@ -23,7 +23,7 @@ Returns a new tensor with the sigmoid of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_sign.Rd b/man/torch_sign.Rd
index 7c90ef229..81b8eb32c 100644
--- a/man/torch_sign.Rd
+++ b/man/torch_sign.Rd
@@ -23,7 +23,7 @@ Returns a new tensor with the signs of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_tensor(c(0.7, -1.2, 0., 2.3))
 a
diff --git a/man/torch_sin.Rd b/man/torch_sin.Rd
index 2f6739f40..3d61e9032 100644
--- a/man/torch_sin.Rd
+++ b/man/torch_sin.Rd
@@ -23,7 +23,7 @@ Returns a new tensor with the sine of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_sinh.Rd b/man/torch_sinh.Rd
index 857b3625e..b164ae5d5 100644
--- a/man/torch_sinh.Rd
+++ b/man/torch_sinh.Rd
@@ -24,7 +24,7 @@ Returns a new tensor with the hyperbolic sine of the elements of
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_slogdet.Rd b/man/torch_slogdet.Rd
index 0a9aaa01d..ec5cb000f 100644
--- a/man/torch_slogdet.Rd
+++ b/man/torch_slogdet.Rd
@@ -27,7 +27,7 @@ Calculates the sign and log absolute value of the determinant(s) of a square mat
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_randn(c(3, 3))
 A
diff --git a/man/torch_solve.Rd b/man/torch_solve.Rd
index 9050afcb6..d5332079f 100644
--- a/man/torch_solve.Rd
+++ b/man/torch_solve.Rd
@@ -36,7 +36,7 @@ batched outputs \verb{solution, LU}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_tensor(rbind(c(6.80, -2.11,  5.66,  5.97,  8.23),
                       c(-6.05, -3.30,  5.36, -4.44,  1.08),
diff --git a/man/torch_sort.Rd b/man/torch_sort.Rd
index 2238df664..d74974a10 100644
--- a/man/torch_sort.Rd
+++ b/man/torch_sort.Rd
@@ -33,7 +33,7 @@ sorted values and \code{indices} are the indices of the elements in the original
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(3, 4))
 out = torch_sort(x)
diff --git a/man/torch_sparse_coo_tensor.Rd b/man/torch_sparse_coo_tensor.Rd
index f9778a0d2..d0814f66b 100644
--- a/man/torch_sparse_coo_tensor.Rd
+++ b/man/torch_sparse_coo_tensor.Rd
@@ -30,7 +30,7 @@ coordinates in the indices, and the value at that index is the sum of all duplic
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 i = torch_tensor(matrix(c(1, 2, 2, 3, 1, 3), ncol = 3, byrow = TRUE), dtype=torch_int64())
 v = torch_tensor(c(3, 4, 5), dtype=torch_float32())
diff --git a/man/torch_sqrt.Rd b/man/torch_sqrt.Rd
index 53a7d94db..3757f1820 100644
--- a/man/torch_sqrt.Rd
+++ b/man/torch_sqrt.Rd
@@ -23,7 +23,7 @@ Returns a new tensor with the square-root of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_square.Rd b/man/torch_square.Rd
index 64c3bad93..428cfb2d2 100644
--- a/man/torch_square.Rd
+++ b/man/torch_square.Rd
@@ -19,7 +19,7 @@ Returns a new tensor with the square of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_squeeze.Rd b/man/torch_squeeze.Rd
index 923276bbd..a35aa7e53 100644
--- a/man/torch_squeeze.Rd
+++ b/man/torch_squeeze.Rd
@@ -34,7 +34,7 @@ will squeeze the tensor to the shape \eqn{(A \times B)}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_zeros(c(2, 1, 2, 1, 2))
 x
diff --git a/man/torch_std.Rd b/man/torch_std.Rd
index d77a2b69c..8b01e57c1 100644
--- a/man/torch_std.Rd
+++ b/man/torch_std.Rd
@@ -44,7 +44,7 @@ via the biased estimator. Otherwise, Bessel's correction will be used.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_std_mean.Rd b/man/torch_std_mean.Rd
index 7e6e68bf6..c763aff18 100644
--- a/man/torch_std_mean.Rd
+++ b/man/torch_std_mean.Rd
@@ -42,7 +42,7 @@ via the biased estimator. Otherwise, Bessel's correction will be used.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_sum.Rd b/man/torch_sum.Rd
index 990de622f..1a1c56e81 100644
--- a/man/torch_sum.Rd
+++ b/man/torch_sum.Rd
@@ -36,7 +36,7 @@ output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_svd.Rd b/man/torch_svd.Rd
index 1ae3bdca9..a47d7621b 100644
--- a/man/torch_svd.Rd
+++ b/man/torch_svd.Rd
@@ -57,7 +57,7 @@ of shape \eqn{(m \times m)} and \eqn{(n \times n)} respectively. \code{some} wil
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(5, 3))
 a
diff --git a/man/torch_symeig.Rd b/man/torch_symeig.Rd
index dd6821118..85871a34f 100644
--- a/man/torch_symeig.Rd
+++ b/man/torch_symeig.Rd
@@ -50,7 +50,7 @@ If \code{upper} is \code{False}, then lower triangular portion is used.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(5, 5))
 a = a + a$t()  # To make a symmetric
diff --git a/man/torch_t.Rd b/man/torch_t.Rd
index e09c651f6..5f74e590f 100644
--- a/man/torch_t.Rd
+++ b/man/torch_t.Rd
@@ -21,7 +21,7 @@ is equivalent to \code{transpose(input, 0, 1)}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(2,3))
 x
diff --git a/man/torch_take.Rd b/man/torch_take.Rd
index 3c5ae3c83..cea95bd46 100644
--- a/man/torch_take.Rd
+++ b/man/torch_take.Rd
@@ -21,7 +21,7 @@ takes the same shape as the indices.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 src = torch_tensor(matrix(c(4,3,5,6,7,8), ncol = 3, byrow = TRUE))
 torch_take(src, torch_tensor(c(0, 2, 5), dtype = torch_int64()))
diff --git a/man/torch_tan.Rd b/man/torch_tan.Rd
index 6a0a52ed6..6b75f0017 100644
--- a/man/torch_tan.Rd
+++ b/man/torch_tan.Rd
@@ -23,7 +23,7 @@ Returns a new tensor with the tangent of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_tanh.Rd b/man/torch_tanh.Rd
index 3093397bb..36e79c323 100644
--- a/man/torch_tanh.Rd
+++ b/man/torch_tanh.Rd
@@ -24,7 +24,7 @@ of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_tensor.Rd b/man/torch_tensor.Rd
index e68c9d0a4..ae1312ce5 100644
--- a/man/torch_tensor.Rd
+++ b/man/torch_tensor.Rd
@@ -27,7 +27,7 @@ torch_tensor(
 Converts R objects to a torch tensor
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 torch_tensor(c(1,2,3,4))
 torch_tensor(c(1,2,3,4), dtype = torch_int())
 
diff --git a/man/torch_tensordot.Rd b/man/torch_tensordot.Rd
index 3eb123238..10386f3c0 100644
--- a/man/torch_tensordot.Rd
+++ b/man/torch_tensordot.Rd
@@ -22,7 +22,7 @@ Returns a contraction of a and b over multiple dimensions.\preformatted{`tensord
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_arange(start = 0, end = 60.)$reshape(c(3, 4, 5))
 b = torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2))
diff --git a/man/torch_topk.Rd b/man/torch_topk.Rd
index c3aa8d7e5..a37b2d99b 100644
--- a/man/torch_topk.Rd
+++ b/man/torch_topk.Rd
@@ -38,7 +38,7 @@ The boolean option \code{sorted} if \code{True}, will make sure that the returne
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_arange(1., 6.)
 x
diff --git a/man/torch_trace.Rd b/man/torch_trace.Rd
index b35f4a219..5961dd02a 100644
--- a/man/torch_trace.Rd
+++ b/man/torch_trace.Rd
@@ -14,7 +14,7 @@ Returns the sum of the elements of the diagonal of the input 2-D matrix.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_arange(1., 10.)$view(c(3, 3))
 x
diff --git a/man/torch_transpose.Rd b/man/torch_transpose.Rd
index b7e2e5d7f..937c04347 100644
--- a/man/torch_transpose.Rd
+++ b/man/torch_transpose.Rd
@@ -26,7 +26,7 @@ of the other.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(2, 3))
 x
diff --git a/man/torch_trapz.Rd b/man/torch_trapz.Rd
index 824a276e6..81042d8d4 100644
--- a/man/torch_trapz.Rd
+++ b/man/torch_trapz.Rd
@@ -29,7 +29,7 @@ As above, but the sample points are spaced uniformly at a distance of \code{dx}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 y = torch_randn(list(2, 3))
 y
diff --git a/man/torch_triangular_solve.Rd b/man/torch_triangular_solve.Rd
index c5c7bf0d4..c01554484 100644
--- a/man/torch_triangular_solve.Rd
+++ b/man/torch_triangular_solve.Rd
@@ -33,7 +33,7 @@ batched outputs \code{X}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_randn(c(2, 2))$triu()
 A
diff --git a/man/torch_tril.Rd b/man/torch_tril.Rd
index fa1ff34e8..e09c3c4a4 100644
--- a/man/torch_tril.Rd
+++ b/man/torch_tril.Rd
@@ -33,7 +33,7 @@ the main diagonal. The main diagonal are the set of indices
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 3))
 a
diff --git a/man/torch_tril_indices.Rd b/man/torch_tril_indices.Rd
index 92982a1be..36bac6c9d 100644
--- a/man/torch_tril_indices.Rd
+++ b/man/torch_tril_indices.Rd
@@ -46,7 +46,7 @@ where \eqn{d_{1}, d_{2}} are the dimensions of the matrix.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 a = torch_tril_indices(3, 3)
 a
diff --git a/man/torch_triu.Rd b/man/torch_triu.Rd
index b2579cf1a..57ff765f6 100644
--- a/man/torch_triu.Rd
+++ b/man/torch_triu.Rd
@@ -33,7 +33,7 @@ the main diagonal. The main diagonal are the set of indices
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 3))
 a
diff --git a/man/torch_triu_indices.Rd b/man/torch_triu_indices.Rd
index 60438b283..7a1f3cf06 100644
--- a/man/torch_triu_indices.Rd
+++ b/man/torch_triu_indices.Rd
@@ -46,7 +46,7 @@ where \eqn{d_{1}, d_{2}} are the dimensions of the matrix.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 a = torch_triu_indices(3, 3)
 a
diff --git a/man/torch_true_divide.Rd b/man/torch_true_divide.Rd
index 7e5a27e5a..493543389 100644
--- a/man/torch_true_divide.Rd
+++ b/man/torch_true_divide.Rd
@@ -26,7 +26,7 @@ in which case they are cast to the default (floating) scalar type before the div
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 dividend = torch_tensor(c(5, 3), dtype=torch_int())
 divisor = torch_tensor(c(3, 2), dtype=torch_int())
diff --git a/man/torch_trunc.Rd b/man/torch_trunc.Rd
index 27ec1c2fd..a0599bf98 100644
--- a/man/torch_trunc.Rd
+++ b/man/torch_trunc.Rd
@@ -20,7 +20,7 @@ the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_unbind.Rd b/man/torch_unbind.Rd
index da95839a4..2f37ed2c1 100644
--- a/man/torch_unbind.Rd
+++ b/man/torch_unbind.Rd
@@ -21,7 +21,7 @@ Returns a tuple of all slices along a given dimension, already without it.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_unbind(torch_tensor(matrix(1:9, ncol = 3, byrow=TRUE)))
 }
diff --git a/man/torch_unique_consecutive.Rd b/man/torch_unique_consecutive.Rd
index 3c55f9964..a362da738 100644
--- a/man/torch_unique_consecutive.Rd
+++ b/man/torch_unique_consecutive.Rd
@@ -26,7 +26,7 @@ Eliminates all but the first element from every consecutive group of equivalent
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x = torch_tensor(c(1, 1, 2, 2, 3, 1, 1, 2))
 output = torch_unique_consecutive(x)
 output
diff --git a/man/torch_unsqueeze.Rd b/man/torch_unsqueeze.Rd
index 8f082b84d..3d9b23a12 100644
--- a/man/torch_unsqueeze.Rd
+++ b/man/torch_unsqueeze.Rd
@@ -26,7 +26,7 @@ applied at \code{dim} = \code{dim + input.dim() + 1}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_tensor(c(1, 2, 3, 4))
 torch_unsqueeze(x, 1)
diff --git a/man/torch_var.Rd b/man/torch_var.Rd
index c3a8fbc62..91d5092d9 100644
--- a/man/torch_var.Rd
+++ b/man/torch_var.Rd
@@ -43,7 +43,7 @@ biased estimator. Otherwise, Bessel's correction will be used.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_var_mean.Rd b/man/torch_var_mean.Rd
index 9e959a0f7..95b42720c 100644
--- a/man/torch_var_mean.Rd
+++ b/man/torch_var_mean.Rd
@@ -41,7 +41,7 @@ biased estimator. Otherwise, Bessel's correction will be used.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_where.Rd b/man/torch_where.Rd
index f368ad083..2e119d46e 100644
--- a/man/torch_where.Rd
+++ b/man/torch_where.Rd
@@ -45,7 +45,7 @@ The operation is defined as:
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 \dontrun{
 x = torch_randn(c(3, 2))
diff --git a/man/torch_zeros.Rd b/man/torch_zeros.Rd
index 46b4ea41e..e060eac58 100644
--- a/man/torch_zeros.Rd
+++ b/man/torch_zeros.Rd
@@ -28,7 +28,7 @@ by the variable argument \code{size}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_zeros(c(2, 3))
 torch_zeros(c(5))
diff --git a/man/torch_zeros_like.Rd b/man/torch_zeros_like.Rd
index cc5915865..3c82caf7e 100644
--- a/man/torch_zeros_like.Rd
+++ b/man/torch_zeros_like.Rd
@@ -36,7 +36,7 @@ the old \code{torch_zeros_like(input, out=output)} is equivalent to
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input = torch_empty(c(2, 3))
 torch_zeros_like(input)
diff --git a/man/with_enable_grad.Rd b/man/with_enable_grad.Rd
index 11fd30cb6..3b93fd560 100644
--- a/man/with_enable_grad.Rd
+++ b/man/with_enable_grad.Rd
@@ -18,7 +18,7 @@ This context manager is thread local; it will not affect computation in
 other threads.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x <- torch_tensor(1, requires_grad=TRUE)
 with_no_grad({
diff --git a/man/with_no_grad.Rd b/man/with_no_grad.Rd
index 251abf77a..7565b5ebd 100644
--- a/man/with_no_grad.Rd
+++ b/man/with_no_grad.Rd
@@ -13,7 +13,7 @@ with_no_grad(code)
 Temporarily modify gradient recording.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x <- torch_tensor(runif(5), requires_grad = TRUE)
 with_no_grad({
   x$sub_(torch_tensor(as.numeric(1:5)))
-- 
GitLab


From ac8047a3d0e843b45e2f6532b487db167ec3acf1 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 30 Jul 2020 17:29:08 -0300
Subject: [PATCH 017/157] re-document and fix example

---
 R/gen-namespace-examples.R | 2 +-
 man/install_torch.Rd       | 2 +-
 man/torch_tensordot.Rd     | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/gen-namespace-examples.R b/R/gen-namespace-examples.R
index 27e65b5db..6839fdf70 100644
--- a/R/gen-namespace-examples.R
+++ b/R/gen-namespace-examples.R
@@ -1739,7 +1739,7 @@ NULL
 #'
 #' a = torch_arange(start = 0, end = 60.)$reshape(c(3, 4, 5))
 #' b = torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2))
-#' torch_tensordot(a, b, dims_self=c(1, 0), dims_other = c(0, 1))
+#' torch_tensordot(a, b, dims_self=c(2, 1), dims_other = c(1, 2))
 #' \dontrun{
 #' a = torch_randn(3, 4, 5, device='cuda')
 #' b = torch_randn(4, 5, 6, device='cuda')
diff --git a/man/install_torch.Rd b/man/install_torch.Rd
index 5be327262..b411a38e6 100644
--- a/man/install_torch.Rd
+++ b/man/install_torch.Rd
@@ -8,7 +8,7 @@ install_torch(
   version = "1.5.0",
   type = install_type(version = version),
   reinstall = FALSE,
-  path = NULL,
+  path = install_path(),
   ...
 )
 }
diff --git a/man/torch_tensordot.Rd b/man/torch_tensordot.Rd
index 10386f3c0..fc8bae339 100644
--- a/man/torch_tensordot.Rd
+++ b/man/torch_tensordot.Rd
@@ -26,7 +26,7 @@ if (torch_is_installed()) {
 
 a = torch_arange(start = 0, end = 60.)$reshape(c(3, 4, 5))
 b = torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2))
-torch_tensordot(a, b, dims_self=c(1, 0), dims_other = c(0, 1))
+torch_tensordot(a, b, dims_self=c(2, 1), dims_other = c(1, 2))
 \dontrun{
 a = torch_randn(3, 4, 5, device='cuda')
 b = torch_randn(4, 5, 6, device='cuda')
-- 
GitLab


From 100caf6d17cf7546bd26971c0fd3b5240e830fbd Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 3 Aug 2020 16:37:09 -0300
Subject: [PATCH 018/157] fix for new bit64 version. better to use strings to
 pass the seeds for now

---
 R/generator.R       |  5 ++---
 src/RcppExports.cpp |  6 +++---
 src/generator.cpp   | 17 ++++++++++-------
 3 files changed, 15 insertions(+), 13 deletions(-)

diff --git a/R/generator.R b/R/generator.R
index 0734f5efe..1daf22fe5 100644
--- a/R/generator.R
+++ b/R/generator.R
@@ -19,15 +19,14 @@ Generator <- R6::R6Class(
       if (!requireNamespace("bit64"))
         warning("bit64 is required to correctly show the seed.")
       
-      cpp_generator_current_seed(self$ptr)
+      bit64::as.integer64(cpp_generator_current_seed(self$ptr))
     },
     set_current_seed = function(seed) {
      
       if ((!is.integer(seed)) && (!inherits(seed, "integer64")))
         stop("Seed must an integer or integer64.")
       
-      if (is.integer(seed))
-        seed <- as.numeric(seed)
+      seed <- as.character(seed)
       
       cpp_generator_set_current_seed(self$ptr, seed)
     }
diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
index c28c9836d..3e5ef12ba 100644
--- a/src/RcppExports.cpp
+++ b/src/RcppExports.cpp
@@ -22980,7 +22980,7 @@ BEGIN_RCPP
 END_RCPP
 }
 // cpp_generator_current_seed
-Rcpp::NumericVector cpp_generator_current_seed(Rcpp::XPtr<XPtrTorchGenerator> generator);
+std::string cpp_generator_current_seed(Rcpp::XPtr<XPtrTorchGenerator> generator);
 RcppExport SEXP _torch_cpp_generator_current_seed(SEXP generatorSEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
@@ -22991,12 +22991,12 @@ BEGIN_RCPP
 END_RCPP
 }
 // cpp_generator_set_current_seed
-void cpp_generator_set_current_seed(Rcpp::XPtr<XPtrTorchGenerator> generator, std::uint64_t seed);
+void cpp_generator_set_current_seed(Rcpp::XPtr<XPtrTorchGenerator> generator, std::string seed);
 RcppExport SEXP _torch_cpp_generator_set_current_seed(SEXP generatorSEXP, SEXP seedSEXP) {
 BEGIN_RCPP
     Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::XPtr<XPtrTorchGenerator> >::type generator(generatorSEXP);
-    Rcpp::traits::input_parameter< std::uint64_t >::type seed(seedSEXP);
+    Rcpp::traits::input_parameter< std::string >::type seed(seedSEXP);
     cpp_generator_set_current_seed(generator, seed);
     return R_NilValue;
 END_RCPP
diff --git a/src/generator.cpp b/src/generator.cpp
index 4af28882f..86750acc3 100644
--- a/src/generator.cpp
+++ b/src/generator.cpp
@@ -11,15 +11,18 @@ Rcpp::XPtr<XPtrTorchGenerator> cpp_torch_generator () {
 }
 
 // [[Rcpp::export]]
-Rcpp::NumericVector cpp_generator_current_seed (Rcpp::XPtr<XPtrTorchGenerator> generator) {
-  Rcpp::NumericVector out(1);
+std::string cpp_generator_current_seed (Rcpp::XPtr<XPtrTorchGenerator> generator) {
   uint64_t seed = lantern_Generator_current_seed(generator->get());
-  std::memcpy(&(out[0]), &(seed), sizeof(double));
-  out.attr("class") = "integer64";
-  return out;
+  auto seed_str = std::to_string(seed);
+  return seed_str;
 }
 
 // [[Rcpp::export]]
-void cpp_generator_set_current_seed (Rcpp::XPtr<XPtrTorchGenerator> generator, std::uint64_t seed) {
-  lantern_Generator_set_current_seed(generator->get(), seed);
+void cpp_generator_set_current_seed (Rcpp::XPtr<XPtrTorchGenerator> generator, std::string seed) {
+  
+  uint64_t value;
+  std::istringstream iss(seed);
+  iss >> value;
+  
+  lantern_Generator_set_current_seed(generator->get(), value);
 }
-- 
GitLab


From 123037f1fd78e54854e9eda5759293493642281d Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 4 Aug 2020 14:26:48 -0300
Subject: [PATCH 019/157] dont run einsum example on windows

---
 R/gen-namespace-examples.R | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/R/gen-namespace-examples.R b/R/gen-namespace-examples.R
index 6839fdf70..adad21063 100644
--- a/R/gen-namespace-examples.R
+++ b/R/gen-namespace-examples.R
@@ -641,6 +641,8 @@ NULL
 #' @name torch_einsum
 #'
 #' @examples
+#' 
+#' if (FALSE) {
 #'
 #' x = torch_randn(c(5))
 #' y = torch_randn(c(4))
@@ -658,6 +660,8 @@ NULL
 #' torch_einsum('...ii->...i', list(A)) # batch diagonal
 #' A = torch_randn(c(2, 3, 4, 5))
 #' torch_einsum('...ij->...ji', list(A))$shape # batch permute
+#' 
+#' }
 NULL
 # -> einsum <-
 
-- 
GitLab


From 538119f428eb51df48f29b469953270e8e89a36f Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 4 Aug 2020 15:16:42 -0300
Subject: [PATCH 020/157] re-document

---
 man/torch_einsum.Rd | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/man/torch_einsum.Rd b/man/torch_einsum.Rd
index 1f1902fb2..c01dcc553 100644
--- a/man/torch_einsum.Rd
+++ b/man/torch_einsum.Rd
@@ -22,6 +22,8 @@ Einstein summation convention.
 \examples{
 if (torch_is_installed()) {
 
+if (FALSE) {
+
 x = torch_randn(c(5))
 y = torch_randn(c(4))
 torch_einsum('i,j->ij', list(x, y))  # outer product
@@ -38,5 +40,7 @@ A = torch_randn(c(4, 3, 3))
 torch_einsum('...ii->...i', list(A)) # batch diagonal
 A = torch_randn(c(2, 3, 4, 5))
 torch_einsum('...ij->...ji', list(A))$shape # batch permute
+
+}
 }
 }
-- 
GitLab


From 75f11b49d533ab9938d22eff405e92ea2e1e9d87 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 4 Aug 2020 15:44:23 -0300
Subject: [PATCH 021/157] implement type info functions

---
 DESCRIPTION              |  1 +
 NAMESPACE                |  2 +
 R/type-info.R            | 87 ++++++++++++++++++++++++++++++++++++++++
 man/torch_finfo.Rd       | 15 +++++++
 man/torch_iinfo.Rd       | 15 +++++++
 man/torch_result_type.Rd |  1 -
 6 files changed, 120 insertions(+), 1 deletion(-)
 create mode 100644 R/type-info.R
 create mode 100644 man/torch_finfo.Rd
 create mode 100644 man/torch_iinfo.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index dfc925790..bc5cdf483 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -107,6 +107,7 @@ Collate:
     'storage.R'
     'tensor_list.R'
     'tensor_options.R'
+    'type-info.R'
     'utils-data-collate.R'
     'utils-data-dataloader.R'
     'utils-data-enum.R'
diff --git a/NAMESPACE b/NAMESPACE
index 2b70b1ba1..7923ceeaa 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -362,6 +362,7 @@ export(torch_exp)
 export(torch_expm1)
 export(torch_eye)
 export(torch_fft)
+export(torch_finfo)
 export(torch_flatten)
 export(torch_flip)
 export(torch_float)
@@ -386,6 +387,7 @@ export(torch_hamming_window)
 export(torch_hann_window)
 export(torch_histc)
 export(torch_ifft)
+export(torch_iinfo)
 export(torch_imag)
 export(torch_index_select)
 export(torch_int)
diff --git a/R/type-info.R b/R/type-info.R
new file mode 100644
index 000000000..1b6d347f7
--- /dev/null
+++ b/R/type-info.R
@@ -0,0 +1,87 @@
+#' Integer type info
+#' 
+#' A list that represents the numerical properties of a integer
+#' type.
+#' 
+#' @param dtype dtype to get information from.
+#'
+#' @export
+torch_iinfo <- function(dtype) {
+  
+  if (dtype == torch_int32()) {
+    list(
+      bits = 32,
+      max = bit64::as.integer64("2147483647"),
+      min = bit64::as.integer64("-2147483648")
+    )
+  } else if (dtype == torch_int64()) {
+    
+    list(
+      bits = 64,
+      max = bit64::as.integer64("9223372036854775807"),
+      min = bit64::as.integer64("-9223372036854775808")
+    )
+    
+  } else if (dtype == torch_int16()) {
+    
+    list(
+      bits = 16,
+      max = 32767L,
+      min = -32768L
+    )
+    
+  } else {
+    
+    value_error("dtype must be an integer type.")
+    
+  }
+  
+}
+
+#' Floating point type info
+#' 
+#' A list that represents the numerical properties of a 
+#' floating point torch.dtype
+#' 
+#' @param dtype dtype to check information
+#'
+#' @export
+torch_finfo <- function(dtype) {
+  
+  if (dtype == torch_float32()) {
+    
+    list(
+      bits = 32,
+      max = 3.4028234663852886e+38,
+      min = -3.4028234663852886e+38,
+      eps = 1.1920928955078125e-07,
+      tiny = 1.1754943508222875e-38
+    )
+    
+  } else if (dtype == torch_float64()) {
+    
+    list(
+      bits = 64,
+      max = 1.7976931348623157e+308,
+      min = -1.7976931348623157e+308,
+      eps = 2.220446049250313e-16,
+      tiny = 2.2250738585072014e-308
+    )
+    
+  } else if (dtype == torch_float16()) {
+    
+    list(
+      bits = 16,
+      max = 65504.0,
+      min = -65504.0,
+      eps = 0.0009765625,
+      tiny = 6.103515625e-05  
+    )
+    
+  } else {
+    
+    value_error("dtype must be a float type.")
+  }
+  
+}
+
diff --git a/man/torch_finfo.Rd b/man/torch_finfo.Rd
new file mode 100644
index 000000000..9b004bfc0
--- /dev/null
+++ b/man/torch_finfo.Rd
@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/type-info.R
+\name{torch_finfo}
+\alias{torch_finfo}
+\title{Floating point type info}
+\usage{
+torch_finfo(dtype)
+}
+\arguments{
+\item{dtype}{dtype to check information}
+}
+\description{
+A list that represents the numerical properties of a
+floating point torch.dtype
+}
diff --git a/man/torch_iinfo.Rd b/man/torch_iinfo.Rd
new file mode 100644
index 000000000..93dca0e10
--- /dev/null
+++ b/man/torch_iinfo.Rd
@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/type-info.R
+\name{torch_iinfo}
+\alias{torch_iinfo}
+\title{Integer type info}
+\usage{
+torch_iinfo(dtype)
+}
+\arguments{
+\item{dtype}{dtype to get information from.}
+}
+\description{
+A list that represents the numerical properties of a integer
+type.
+}
diff --git a/man/torch_result_type.Rd b/man/torch_result_type.Rd
index 42eaa4c3e..00e366db4 100644
--- a/man/torch_result_type.Rd
+++ b/man/torch_result_type.Rd
@@ -24,6 +24,5 @@ for more information on the type promotion logic.
 \dontrun{
 
 torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_int()), 1.0)
-# torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_uint8()), torch_tensor(1))
 }
 }
-- 
GitLab


From 31bf8761fc0a1b66f6de6515a1eb03d6e60ac038 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 11 Aug 2020 21:27:32 -0300
Subject: [PATCH 022/157] add support for manual seeds

---
 R/RcppExports.R                   |  4 ++++
 R/generator.R                     |  9 +++++++++
 lantern/include/lantern/lantern.h |  3 +++
 lantern/src/Generator.cpp         |  5 +++++
 lantern/src/Scalar.cpp            |  3 ++-
 src/RcppExports.cpp               | 11 +++++++++++
 src/generator.cpp                 | 12 ++++++++++++
 src/lantern/lantern.h             |  3 +++
 tests/testthat/test-generator.R   | 11 +++++++++++
 9 files changed, 60 insertions(+), 1 deletion(-)

diff --git a/R/RcppExports.R b/R/RcppExports.R
index 4d9873b9b..3c8c7a439 100644
--- a/R/RcppExports.R
+++ b/R/RcppExports.R
@@ -6885,6 +6885,10 @@ cpp_generator_set_current_seed <- function(generator, seed) {
     invisible(.Call('_torch_cpp_generator_set_current_seed', PACKAGE = 'torchpkg', generator, seed))
 }
 
+cpp_torch_manual_seed <- function(seed) {
+    invisible(.Call('_torch_cpp_torch_manual_seed', PACKAGE = 'torchpkg', seed))
+}
+
 enquos0 <- function(env) {
     .Call('_torch_enquos0', PACKAGE = 'torchpkg', env)
 }
diff --git a/R/generator.R b/R/generator.R
index 1daf22fe5..2a631d6bd 100644
--- a/R/generator.R
+++ b/R/generator.R
@@ -57,3 +57,12 @@ is_torch_generator <- function(x) {
   inherits(x, "torch_generator")
 }
 
+#' Sets the seed for generating random numbers.
+#' 
+#' @param seed integer seed.
+#' 
+#' @export
+torch_manual_seed <- function(seed) {
+  cpp_torch_manual_seed(as.character(seed))
+}
+
diff --git a/lantern/include/lantern/lantern.h b/lantern/include/lantern/lantern.h
index 6f086249d..43566c6ce 100644
--- a/lantern/include/lantern/lantern.h
+++ b/lantern/include/lantern/lantern.h
@@ -468,6 +468,8 @@ extern "C"
   HOST_API void lantern_Tensor_index_put_tensor_ (void* self, void* index, void* rhs) { _lantern_Tensor_index_put_tensor_(self, index, rhs); LANTERN_HOST_HANDLER}
   LANTERN_API void (LANTERN_PTR _lantern_Tensor_index_put_scalar_) (void* self, void* index, void* rhs);
   HOST_API void lantern_Tensor_index_put_scalar_ (void* self, void* index, void* rhs) { _lantern_Tensor_index_put_scalar_(self, index, rhs); LANTERN_HOST_HANDLER}
+  LANTERN_API void (LANTERN_PTR _lantern_manual_seed) (int64_t seed);
+  HOST_API void lantern_manual_seed (int64_t seed) {_lantern_manual_seed(seed); LANTERN_HOST_HANDLER}
   /* Autogen Headers -- Start */
   LANTERN_API void* (LANTERN_PTR _lantern__cast_byte_tensor_bool)(void* self, void* non_blocking);
   HOST_API void* lantern__cast_byte_tensor_bool(void* self, void* non_blocking) { void* ret = _lantern__cast_byte_tensor_bool(self, non_blocking); LANTERN_HOST_HANDLER return ret; }
@@ -4175,6 +4177,7 @@ bool lanternInit(const std::string &libPath, std::string *pError)
   LOAD_SYMBOL(_lantern_const_char_delete);
   LOAD_SYMBOL(_lantern_Tensor_index_put_tensor_);
   LOAD_SYMBOL(_lantern_Tensor_index_put_scalar_);
+  LOAD_SYMBOL(_lantern_manual_seed);
   /* Autogen Symbols -- Start */
   LOAD_SYMBOL(_lantern__cast_byte_tensor_bool)
   LOAD_SYMBOL(_lantern__cast_char_tensor_bool)
diff --git a/lantern/src/Generator.cpp b/lantern/src/Generator.cpp
index 2bdac78c7..bac252ab2 100644
--- a/lantern/src/Generator.cpp
+++ b/lantern/src/Generator.cpp
@@ -28,4 +28,9 @@ void _lantern_Generator_set_current_seed(void *generator, uint64_t seed)
   LANTERN_FUNCTION_START
   reinterpret_cast<LanternObject<std::shared_ptr<torch::Generator>> *>(generator)->get()->set_current_seed(seed);
   LANTERN_FUNCTION_END_VOID
+}
+
+void _lantern_manual_seed (int64_t seed)
+{
+  torch::manual_seed(seed);
 }
\ No newline at end of file
diff --git a/lantern/src/Scalar.cpp b/lantern/src/Scalar.cpp
index 94062f4f3..ddd2fc077 100644
--- a/lantern/src/Scalar.cpp
+++ b/lantern/src/Scalar.cpp
@@ -76,4 +76,5 @@ void *_lantern_Scalar_nullopt()
   LANTERN_FUNCTION_START
   return (void *)new LanternObject<c10::optional<torch::Scalar>>(c10::nullopt);
   LANTERN_FUNCTION_END
-}
\ No newline at end of file
+}
+
diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
index 3e5ef12ba..6b1dcb558 100644
--- a/src/RcppExports.cpp
+++ b/src/RcppExports.cpp
@@ -23001,6 +23001,16 @@ BEGIN_RCPP
     return R_NilValue;
 END_RCPP
 }
+// cpp_torch_manual_seed
+void cpp_torch_manual_seed(std::string seed);
+RcppExport SEXP _torch_cpp_torch_manual_seed(SEXP seedSEXP) {
+BEGIN_RCPP
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< std::string >::type seed(seedSEXP);
+    cpp_torch_manual_seed(seed);
+    return R_NilValue;
+END_RCPP
+}
 // enquos0
 std::vector<Rcpp::RObject> enquos0(Rcpp::Environment env);
 RcppExport SEXP _torch_enquos0(SEXP envSEXP) {
@@ -25488,6 +25498,7 @@ static const R_CallMethodDef CallEntries[] = {
     {"_torch_cpp_torch_generator", (DL_FUNC) &_torch_cpp_torch_generator, 0},
     {"_torch_cpp_generator_current_seed", (DL_FUNC) &_torch_cpp_generator_current_seed, 1},
     {"_torch_cpp_generator_set_current_seed", (DL_FUNC) &_torch_cpp_generator_set_current_seed, 2},
+    {"_torch_cpp_torch_manual_seed", (DL_FUNC) &_torch_cpp_torch_manual_seed, 1},
     {"_torch_enquos0", (DL_FUNC) &_torch_enquos0, 1},
     {"_torch_evaluate_slices", (DL_FUNC) &_torch_evaluate_slices, 2},
     {"_torch_Tensor_slice", (DL_FUNC) &_torch_Tensor_slice, 4},
diff --git a/src/generator.cpp b/src/generator.cpp
index 86750acc3..dc36c8a62 100644
--- a/src/generator.cpp
+++ b/src/generator.cpp
@@ -26,3 +26,15 @@ void cpp_generator_set_current_seed (Rcpp::XPtr<XPtrTorchGenerator> generator, s
   
   lantern_Generator_set_current_seed(generator->get(), value);
 }
+
+// [[Rcpp::export]]
+void cpp_torch_manual_seed (std::string seed)
+{
+ 
+  int64_t value;
+  std::istringstream iss(seed);
+  iss >> value;
+  
+  lantern_manual_seed(value);
+  
+}
diff --git a/src/lantern/lantern.h b/src/lantern/lantern.h
index 6f086249d..43566c6ce 100644
--- a/src/lantern/lantern.h
+++ b/src/lantern/lantern.h
@@ -468,6 +468,8 @@ extern "C"
   HOST_API void lantern_Tensor_index_put_tensor_ (void* self, void* index, void* rhs) { _lantern_Tensor_index_put_tensor_(self, index, rhs); LANTERN_HOST_HANDLER}
   LANTERN_API void (LANTERN_PTR _lantern_Tensor_index_put_scalar_) (void* self, void* index, void* rhs);
   HOST_API void lantern_Tensor_index_put_scalar_ (void* self, void* index, void* rhs) { _lantern_Tensor_index_put_scalar_(self, index, rhs); LANTERN_HOST_HANDLER}
+  LANTERN_API void (LANTERN_PTR _lantern_manual_seed) (int64_t seed);
+  HOST_API void lantern_manual_seed (int64_t seed) {_lantern_manual_seed(seed); LANTERN_HOST_HANDLER}
   /* Autogen Headers -- Start */
   LANTERN_API void* (LANTERN_PTR _lantern__cast_byte_tensor_bool)(void* self, void* non_blocking);
   HOST_API void* lantern__cast_byte_tensor_bool(void* self, void* non_blocking) { void* ret = _lantern__cast_byte_tensor_bool(self, non_blocking); LANTERN_HOST_HANDLER return ret; }
@@ -4175,6 +4177,7 @@ bool lanternInit(const std::string &libPath, std::string *pError)
   LOAD_SYMBOL(_lantern_const_char_delete);
   LOAD_SYMBOL(_lantern_Tensor_index_put_tensor_);
   LOAD_SYMBOL(_lantern_Tensor_index_put_scalar_);
+  LOAD_SYMBOL(_lantern_manual_seed);
   /* Autogen Symbols -- Start */
   LOAD_SYMBOL(_lantern__cast_byte_tensor_bool)
   LOAD_SYMBOL(_lantern__cast_char_tensor_bool)
diff --git a/tests/testthat/test-generator.R b/tests/testthat/test-generator.R
index cdea3d5c4..74b9bf763 100644
--- a/tests/testthat/test-generator.R
+++ b/tests/testthat/test-generator.R
@@ -8,3 +8,14 @@ test_that("can create and use simple generators", {
   x$set_current_seed(bit64::as.integer64(123456789101112))
   expect_equal(x$current_seed(), bit64::as.integer64(123456789101112))
 })
+
+test_that("manual_seed works", {
+  
+  torch_manual_seed(1L)
+  a <- torch_randn(1)
+  torch_manual_seed(1L)
+  b <- torch_randn(1)
+  
+  expect_equal_to_tensor(a, b)
+  
+})
\ No newline at end of file
-- 
GitLab


From 9c5db05717beac0e2a5bddfc0bfeef44e177e1a4 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 11 Aug 2020 21:29:35 -0300
Subject: [PATCH 023/157] redocument

---
 NAMESPACE                |  1 +
 man/torch_manual_seed.Rd | 14 ++++++++++++++
 2 files changed, 15 insertions(+)
 create mode 100644 man/torch_manual_seed.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 632fb58f1..769eaf5d6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -425,6 +425,7 @@ export(torch_lstsq)
 export(torch_lt)
 export(torch_lu)
 export(torch_lu_solve)
+export(torch_manual_seed)
 export(torch_masked_select)
 export(torch_matmul)
 export(torch_matrix_power)
diff --git a/man/torch_manual_seed.Rd b/man/torch_manual_seed.Rd
new file mode 100644
index 000000000..9ed92be43
--- /dev/null
+++ b/man/torch_manual_seed.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/generator.R
+\name{torch_manual_seed}
+\alias{torch_manual_seed}
+\title{Sets the seed for generating random numbers.}
+\usage{
+torch_manual_seed(seed)
+}
+\arguments{
+\item{seed}{integer seed.}
+}
+\description{
+Sets the seed for generating random numbers.
+}
-- 
GitLab


From 7ecd81187888bea7aed28d03e2d46c99b9613b21 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 13 Aug 2020 16:41:37 -0300
Subject: [PATCH 024/157] add max pool 3d

---
 R/nn-pooling.R | 68 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)

diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index c2e683707..281d1b693 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -139,3 +139,71 @@ nn_max_pool2d <- nn_module(
   }
 )
 
+#' Applies a 3D max pooling over an input signal composed of several input
+#' planes.
+#' 
+#' In the simplest case, the output value of the layer with input size \eqn{(N, C, D, H, W)},
+#' output \eqn{(N, C, D_{out}, H_{out}, W_{out})} and `kernel_size` \eqn{(kD, kH, kW)}
+#' can be precisely described as:
+#' 
+#' \deqn{
+#'   \begin{aligned}
+#' \text{out}(N_i, C_j, d, h, w) ={} & \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
+#' & \text{input}(N_i, C_j, \text{stride[0]} \times d + k,
+#'                \text{stride[1]} \times h + m, \text{stride[2]} \times w + n)
+#' \end{aligned}
+#' }
+#' 
+#' If `padding` is non-zero, then the input is implicitly zero-padded on both sides
+#' for `padding` number of points. `dilation` controls the spacing between the kernel points.
+#' It is harder to describe, but this `link`_ has a nice visualization of what `dilation` does.
+#' The parameters `kernel_size`, `stride`, `padding`, `dilation` can either be:
+#'  - a single `int` -- in which case the same value is used for the depth, height and width dimension
+#'  - a `tuple` of three ints -- in which case, the first `int` is used for the depth dimension,
+#'    the second `int` for the height dimension and the third `int` for the width dimension
+#' 
+#' @param kernel_size the size of the window to take a max over
+#' @param stride the stride of the window. Default value is `kernel_size`
+#' @param padding implicit zero padding to be added on all three sides
+#' @param dilation a parameter that controls the stride of elements in the window
+#' @param return_indices if `TRUE`, will return the max indices along with the outputs.
+#'   Useful for `torch_nn.MaxUnpool3d` later
+#' @param ceil_mode: when TRUE, will use `ceil` instead of `floor` to compute the output shape
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})}
+#' - Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
+#' \deqn{
+#'   D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{dilation}[0] \times
+#'     (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor
+#' }
+#' 
+#' \deqn{
+#'   H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{dilation}[1] \times
+#'     (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor
+#' }
+#' 
+#' \deqn{
+#'   W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{dilation}[2] \times
+#'     (\text{kernel\_size}[2] - 1) - 1}{\text{stride}[2]} + 1\right\rfloor
+#' }
+#' 
+#' @examples
+#' # pool of square window of size=3, stride=2
+#' m <- nn_max_pool3d(3, stride=2)
+#' # pool of non-square window
+#' m <- nn_max_pool3d((3, 2, 2), stride=(2, 1, 2))
+#' input <- torch_randn(20, 16, 50,44, 31)
+#' output <- m(input)
+#' 
+#' @export
+nn_max_pool3d <- nn_module(
+  "nn_max_pool3d",
+  inherit = nn_max_pool_nd,
+  forward = function(input) {
+    nnf_max_pool3d(input, self$kernel_size, self$stride,
+                 self$padding, self$dilation, self$ceil_mode,
+                 self$return_indices)
+  }
+)
+
-- 
GitLab


From 41041b018e12332ee9b73436ced3c57c69026601 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 13 Aug 2020 16:51:19 -0300
Subject: [PATCH 025/157] max unpool 1d

---
 R/nn-pooling.R | 64 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)

diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index 281d1b693..10a5ba6b0 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -207,3 +207,67 @@ nn_max_pool3d <- nn_module(
   }
 )
 
+#' Computes a partial inverse of `MaxPool1d`.
+#' 
+#' `MaxPool1d` is not fully invertible, since the non-maximal values are lost.
+#' `MaxUnpool1d` takes in as input the output of `MaxPool1d`
+#' including the indices of the maximal values and computes a partial inverse
+#' in which all non-maximal values are set to zero.
+#' 
+#' @note `MaxPool1d` 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 `output_size` in the forward call.
+#'   See the Inputs and Example below.
+#'   
+#' @param kernel_size (int or tuple): Size of the max pooling window.
+#' @param stride (int or tuple): Stride of the max pooling window.
+#'   It is set to `kernel_size` by default.
+#' @param padding (int or tuple): Padding that was added to the input
+#' 
+#' @section Inputs:
+#' 
+#' - `input`: the input Tensor to invert
+#' - `indices`: the indices given out by [nn_max_pool1d()]
+#' - `output_size` (optional): the targeted output size
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, C, H_{in})}
+#' - Output: \eqn{(N, C, H_{out})}, where
+#' \deqn{
+#'   H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{kernel\_size}[0]
+#' }
+#' or as given by `output_size` in the call operator
+#' 
+#' @examples
+#' pool <- nn_max_pool1d(2, stride=2, return_indices=TRUE)
+#' unpool <- nn_max_unpool1d(2, stride=2)
+#' 
+#' input <- torch_tensor(matrix(1:8/1, nrow = 1))
+#' out <- pool(input)
+#' unpool(out[[1]], out[[2]])
+#' 
+#' # Example showcasing the use of output_size
+#' input <- torch_tensor(matrix(1:8/1, nrow = 1))
+#' out <- pool(input)
+#' unpool(out[[1]], out[[2]], output_size=input$size())
+#' unpool(out[[1]], out[[2]])
+#' 
+#' @export
+nn_max_unpool1d <- nn_module(
+  "nn_max_unpool1d",
+  initialize = function(kernel_size, stride = NULL, padding = 0) {
+    self$kernel_size = nn_util_single(kernel_size)
+    
+    if (is.null(stride))
+      stride <- kernel_size
+    
+    self$stride = nn_util_single(stride)
+    self$padding = nn_util_single(padding)    
+  },
+  forward = function(input, indices, output_size = NULL) {
+    nnf_max_unpool1d(input, indices, self$kernel_size, self$stride,
+                 self$padding, output_size)
+  }
+)
+
-- 
GitLab


From e109c87862f9e72ef96e3b967f68f013ab01c02a Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 13 Aug 2020 17:06:28 -0300
Subject: [PATCH 026/157] fix docs

---
 NAMESPACE              |  2 +
 R/nn-pooling.R         |  6 +--
 R/nnf-pooling.R        |  4 +-
 man/nn_max_pool3d.Rd   | 87 ++++++++++++++++++++++++++++++++++++++++++
 man/nn_max_unpool1d.Rd | 67 ++++++++++++++++++++++++++++++++
 5 files changed, 161 insertions(+), 5 deletions(-)
 create mode 100644 man/nn_max_pool3d.Rd
 create mode 100644 man/nn_max_unpool1d.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 632fb58f1..cee139092 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -145,6 +145,8 @@ export(nn_log_sigmoid)
 export(nn_log_softmax)
 export(nn_max_pool1d)
 export(nn_max_pool2d)
+export(nn_max_pool3d)
+export(nn_max_unpool1d)
 export(nn_module)
 export(nn_module_list)
 export(nn_multihead_attention)
diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index 10a5ba6b0..7035c3ff7 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -192,7 +192,7 @@ nn_max_pool2d <- nn_module(
 #' # pool of square window of size=3, stride=2
 #' m <- nn_max_pool3d(3, stride=2)
 #' # pool of non-square window
-#' m <- nn_max_pool3d((3, 2, 2), stride=(2, 1, 2))
+#' m <- nn_max_pool3d(c(3, 2, 2), stride=c(2, 1, 2))
 #' input <- torch_randn(20, 16, 50,44, 31)
 #' output <- m(input)
 #' 
@@ -243,12 +243,12 @@ nn_max_pool3d <- nn_module(
 #' pool <- nn_max_pool1d(2, stride=2, return_indices=TRUE)
 #' unpool <- nn_max_unpool1d(2, stride=2)
 #' 
-#' input <- torch_tensor(matrix(1:8/1, nrow = 1))
+#' input <- torch_tensor(array(1:8/1, dim = c(1,1,8)))
 #' out <- pool(input)
 #' unpool(out[[1]], out[[2]])
 #' 
 #' # Example showcasing the use of output_size
-#' input <- torch_tensor(matrix(1:8/1, nrow = 1))
+#' input <- torch_tensor(array(1:8/1, dim = c(1,1,8)))
 #' out <- pool(input)
 #' unpool(out[[1]], out[[2]], output_size=input$size())
 #' unpool(out[[1]], out[[2]])
diff --git a/R/nnf-pooling.R b/R/nnf-pooling.R
index b2bf1f7f0..cec080578 100644
--- a/R/nnf-pooling.R
+++ b/R/nnf-pooling.R
@@ -256,8 +256,8 @@ unpool_output_size <- function(input, kernel_size, stride, padding, output_size)
     }
     
     for (d in seq_along(kernel_size)) {
-      min_size <- default_size[d] - stride[d]
-      max_size <- default_size[d] + stride[d]
+      min_size <- default_size[[d]] - stride[d]
+      max_size <- default_size[[d]] + stride[d]
     }
     
     ret <- output_size
diff --git a/man/nn_max_pool3d.Rd b/man/nn_max_pool3d.Rd
new file mode 100644
index 000000000..543a05092
--- /dev/null
+++ b/man/nn_max_pool3d.Rd
@@ -0,0 +1,87 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_max_pool3d}
+\alias{nn_max_pool3d}
+\title{Applies a 3D max pooling over an input signal composed of several input
+planes.}
+\usage{
+nn_max_pool3d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  dilation = 1,
+  return_indices = FALSE,
+  ceil_mode = FALSE
+)
+}
+\arguments{
+\item{kernel_size}{the size of the window to take a max over}
+
+\item{stride}{the stride of the window. Default value is \code{kernel_size}}
+
+\item{padding}{implicit zero padding to be added on all three sides}
+
+\item{dilation}{a parameter that controls the stride of elements in the window}
+
+\item{return_indices}{if \code{TRUE}, will return the max indices along with the outputs.
+Useful for \code{torch_nn.MaxUnpool3d} later}
+
+\item{ceil_mode:}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+}
+\description{
+In the simplest case, the output value of the layer with input size \eqn{(N, C, D, H, W)},
+output \eqn{(N, C, D_{out}, H_{out}, W_{out})} and \code{kernel_size} \eqn{(kD, kH, kW)}
+can be precisely described as:
+}
+\details{
+\deqn{
+  \begin{aligned}
+\text{out}(N_i, C_j, d, h, w) ={} & \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
+& \text{input}(N_i, C_j, \text{stride[0]} \times d + k,
+               \text{stride[1]} \times h + m, \text{stride[2]} \times w + n)
+\end{aligned}
+}
+
+If \code{padding} is non-zero, then the input is implicitly zero-padded on both sides
+for \code{padding} number of points. \code{dilation} controls the spacing between the kernel points.
+It is harder to describe, but this \code{link}_ has a nice visualization of what \code{dilation} does.
+The parameters \code{kernel_size}, \code{stride}, \code{padding}, \code{dilation} can either be:
+\itemize{
+\item a single \code{int} -- in which case the same value is used for the depth, height and width dimension
+\item a \code{tuple} of three ints -- in which case, the first \code{int} is used for the depth dimension,
+the second \code{int} for the height dimension and the third \code{int} for the width dimension
+}
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})}
+\item Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
+\deqn{
+  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{dilation}[0] \times
+    (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor
+}
+}
+
+\deqn{
+  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{dilation}[1] \times
+    (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor
+}
+
+\deqn{
+  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{dilation}[2] \times
+    (\text{kernel\_size}[2] - 1) - 1}{\text{stride}[2]} + 1\right\rfloor
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+# pool of square window of size=3, stride=2
+m <- nn_max_pool3d(3, stride=2)
+# pool of non-square window
+m <- nn_max_pool3d(c(3, 2, 2), stride=c(2, 1, 2))
+input <- torch_randn(20, 16, 50,44, 31)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_max_unpool1d.Rd b/man/nn_max_unpool1d.Rd
new file mode 100644
index 000000000..8c76febd9
--- /dev/null
+++ b/man/nn_max_unpool1d.Rd
@@ -0,0 +1,67 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_max_unpool1d}
+\alias{nn_max_unpool1d}
+\title{Computes a partial inverse of \code{MaxPool1d}.}
+\usage{
+nn_max_unpool1d(kernel_size, stride = NULL, padding = 0)
+}
+\arguments{
+\item{kernel_size}{(int or tuple): Size of the max pooling window.}
+
+\item{stride}{(int or tuple): Stride of the max pooling window.
+It is set to \code{kernel_size} by default.}
+
+\item{padding}{(int or tuple): Padding that was added to the input}
+}
+\description{
+\code{MaxPool1d} is not fully invertible, since the non-maximal values are lost.
+\code{MaxUnpool1d} takes in as input the output of \code{MaxPool1d}
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero.
+}
+\note{
+\code{MaxPool1d} 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} in the forward call.
+See the Inputs and Example below.
+}
+\section{Inputs}{
+
+\itemize{
+\item \code{input}: the input Tensor to invert
+\item \code{indices}: the indices given out by \code{\link[=nn_max_pool1d]{nn_max_pool1d()}}
+\item \code{output_size} (optional): the targeted output size
+}
+}
+
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, H_{in})}
+\item Output: \eqn{(N, C, H_{out})}, where
+\deqn{
+  H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{kernel\_size}[0]
+}
+or as given by \code{output_size} in the call operator
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+pool <- nn_max_pool1d(2, stride=2, return_indices=TRUE)
+unpool <- nn_max_unpool1d(2, stride=2)
+
+input <- torch_tensor(array(1:8/1, dim = c(1,1,8)))
+out <- pool(input)
+unpool(out[[1]], out[[2]])
+
+# Example showcasing the use of output_size
+input <- torch_tensor(array(1:8/1, dim = c(1,1,8)))
+out <- pool(input)
+unpool(out[[1]], out[[2]], output_size=input$size())
+unpool(out[[1]], out[[2]])
+
+}
+}
-- 
GitLab


From ef6862de9344e9a009ae6a8790a8883e4b569f74 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 13 Aug 2020 17:14:35 -0300
Subject: [PATCH 027/157] nn_max_unpool2d

---
 NAMESPACE              |  1 +
 R/nn-pooling.R         | 66 +++++++++++++++++++++++++++++++++++++++++
 man/nn_max_unpool2d.Rd | 67 ++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 134 insertions(+)
 create mode 100644 man/nn_max_unpool2d.Rd

diff --git a/NAMESPACE b/NAMESPACE
index cee139092..7ea65982a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -147,6 +147,7 @@ export(nn_max_pool1d)
 export(nn_max_pool2d)
 export(nn_max_pool3d)
 export(nn_max_unpool1d)
+export(nn_max_unpool2d)
 export(nn_module)
 export(nn_module_list)
 export(nn_multihead_attention)
diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index 7035c3ff7..bdc57b409 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -271,3 +271,69 @@ nn_max_unpool1d <- nn_module(
   }
 )
 
+
+#' Computes a partial inverse of `MaxPool2d`.
+#' 
+#' `MaxPool2d` is not fully invertible, since the non-maximal values are lost.
+#' `MaxUnpool2d` takes in as input the output of `MaxPool2d`
+#' including the indices of the maximal values and computes a partial inverse
+#' in which all non-maximal values are set to zero.
+#' 
+#' @note `MaxPool2d` 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 `output_size` in the forward call.
+#'   See the Inputs and Example below.
+#'   
+#' @param kernel_size (int or tuple): Size of the max pooling window.
+#' @param stride (int or tuple): Stride of the max pooling window.
+#'   It is set to `kernel_size` by default.
+#' @param padding (int or tuple): Padding that was added to the input
+#' 
+#' @section Inputs:
+#' - `input`: the input Tensor to invert
+#' - `indices`: the indices given out by [nn_max_pool2d()]
+#' - `output_size` (optional): the targeted output size
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, C, H_{in}, W_{in})}
+#' - Output: \eqn{(N, C, H_{out}, W_{out})}, where
+#' \deqn{
+#'   H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
+#' }
+#' \deqn{
+#'   W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
+#' }
+#' or as given by `output_size` in the call operator
+#' 
+#' @examples
+#' 
+#' pool <- nn_max_pool2d(2, stride=2, return_indices=TRUE)
+#' unpool <- nn_max_unpool2d(2, stride=2)
+#' input <- torch_randn(1,1,4,4)
+#' out <- pool(input)
+#' unpool(out[[1]], out[[2]])
+#' 
+#' # specify a different output size than input size
+#' unpool(out[[1]], out[[2]], output_size=c(1, 1, 5, 5))
+#' 
+#' @export
+nn_max_unpool2d <- nn_module(
+  "nn_max_unpool2d",
+  initialize = function(kernel_size, stride = NULL, padding = 0) {
+    self$kernel_size = nn_util_pair(kernel_size)
+    
+    if (is.null(stride))
+      stride <- kernel_size
+    
+    self$stride = nn_util_pair(stride)
+    self$padding = nn_util_pair(padding)    
+  },
+  forward = function(input, indices, output_size = NULL) {
+    nnf_max_unpool2d(input, indices, self$kernel_size, self$stride,
+                     self$padding, output_size)
+  }
+)
+
+
+
diff --git a/man/nn_max_unpool2d.Rd b/man/nn_max_unpool2d.Rd
new file mode 100644
index 000000000..562ea2494
--- /dev/null
+++ b/man/nn_max_unpool2d.Rd
@@ -0,0 +1,67 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_max_unpool2d}
+\alias{nn_max_unpool2d}
+\title{Computes a partial inverse of \code{MaxPool2d}.}
+\usage{
+nn_max_unpool2d(kernel_size, stride = NULL, padding = 0)
+}
+\arguments{
+\item{kernel_size}{(int or tuple): Size of the max pooling window.}
+
+\item{stride}{(int or tuple): Stride of the max pooling window.
+It is set to \code{kernel_size} by default.}
+
+\item{padding}{(int or tuple): Padding that was added to the input}
+}
+\description{
+\code{MaxPool2d} is not fully invertible, since the non-maximal values are lost.
+\code{MaxUnpool2d} takes in as input the output of \code{MaxPool2d}
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero.
+}
+\note{
+\code{MaxPool2d} 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} in the forward call.
+See the Inputs and Example below.
+}
+\section{Inputs}{
+
+\itemize{
+\item \code{input}: the input Tensor to invert
+\item \code{indices}: the indices given out by \code{\link[=nn_max_pool2d]{nn_max_pool2d()}}
+\item \code{output_size} (optional): the targeted output size
+}
+}
+
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, H_{in}, W_{in})}
+\item Output: \eqn{(N, C, H_{out}, W_{out})}, where
+\deqn{
+  H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
+}
+\deqn{
+  W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
+}
+or as given by \code{output_size} in the call operator
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+
+pool <- nn_max_pool2d(2, stride=2, return_indices=TRUE)
+unpool <- nn_max_unpool2d(2, stride=2)
+input <- torch_randn(1,1,4,4)
+out <- pool(input)
+unpool(out[[1]], out[[2]])
+
+# specify a different output size than input size
+unpool(out[[1]], out[[2]], output_size=c(1, 1, 5, 5))
+
+}
+}
-- 
GitLab


From 4ec8b416648ea86ecea8cee0c5aa11460a01291e Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 13 Aug 2020 17:31:21 -0300
Subject: [PATCH 028/157] add nn_max_unpool3d

---
 NAMESPACE              |  1 +
 R/nn-pooling.R         | 66 ++++++++++++++++++++++++++++++++++++++++
 man/nn_max_unpool3d.Rd | 69 ++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 136 insertions(+)
 create mode 100644 man/nn_max_unpool3d.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 7ea65982a..51e0ab977 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -148,6 +148,7 @@ export(nn_max_pool2d)
 export(nn_max_pool3d)
 export(nn_max_unpool1d)
 export(nn_max_unpool2d)
+export(nn_max_unpool3d)
 export(nn_module)
 export(nn_module_list)
 export(nn_multihead_attention)
diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index bdc57b409..5b26b9449 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -336,4 +336,70 @@ nn_max_unpool2d <- nn_module(
 )
 
 
+#' Computes a partial inverse of `MaxPool3d`.
+#' 
+#' `MaxPool3d` is not fully invertible, since the non-maximal values are lost.
+#' `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.
+#' 
+#' @note `MaxPool3d` 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 `output_size` in the forward call.
+#' See the Inputs section below.
+#' 
+#' @param kernel_size (int or tuple): Size of the max pooling window.
+#' @param stride (int or tuple): Stride of the max pooling window.
+#'   It is set to `kernel_size` by default.
+#'   padding (int or tuple): Padding that was added to the input
+#' 
+#' @section Inputs:
+#' - `input`: the input Tensor to invert
+#' - `indices`: the indices given out by [nn_max_pool3d()]
+#' - `output_size` (optional): the targeted output size
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})}
+#' - Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
+#' 
+#' \deqn{
+#'   D_{out} = (D_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
+#' }
+#' \deqn{
+#'   H_{out} = (H_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
+#' }
+#' \deqn{
+#'   W_{out} = (W_{in} - 1) \times \text{stride[2]} - 2 \times \text{padding[2]} + \text{kernel\_size[2]}
+#' }
+#' 
+#' or as given by `output_size` in the call operator
+#' 
+#' @examples
+#'   
+#' # pool of square window of size=3, stride=2
+#' pool <- nn_max_pool3d(3, stride=2, return_indices=TRUE)
+#' unpool <- nn_max_unpool3d(3, stride=2)
+#' out <- pool(torch_randn(20, 16, 51, 33, 15))
+#' unpooled_output <- unpool(out[[1]], out[[2]])
+#' unpooled_output$size()
+#' 
+#' @export
+nn_max_unpool3d <- nn_module(
+  "nn_max_unpool3d", 
+  initialize = function(kernel_size, stride = NULL, padding = 0) {
+    self$kernel_size = nn_util_triple(kernel_size)
+    
+    if (is.null(stride))
+      stride <- kernel_size
+    
+    self$stride = nn_util_triple(stride)
+    self$padding = nn_util_triple(padding)    
+  },
+  forward = function(input, indices, output_size = NULL) {
+    nnf_max_unpool3d(input, indices, self$kernel_size, self$stride,
+                     self$padding, output_size)
+  }
+)
+
 
diff --git a/man/nn_max_unpool3d.Rd b/man/nn_max_unpool3d.Rd
new file mode 100644
index 000000000..daf8a77ed
--- /dev/null
+++ b/man/nn_max_unpool3d.Rd
@@ -0,0 +1,69 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_max_unpool3d}
+\alias{nn_max_unpool3d}
+\title{Computes a partial inverse of \code{MaxPool3d}.}
+\usage{
+nn_max_unpool3d(kernel_size, stride = NULL, padding = 0)
+}
+\arguments{
+\item{kernel_size}{(int or tuple): Size of the max pooling window.}
+
+\item{stride}{(int or tuple): Stride of the max pooling window.
+It is set to \code{kernel_size} by default.
+padding (int or tuple): Padding that was added to the input}
+}
+\description{
+\code{MaxPool3d} is not fully invertible, since the non-maximal values are lost.
+\code{MaxUnpool3d} takes in as input the output of \code{MaxPool3d}
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero.
+}
+\note{
+\code{MaxPool3d} 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} in the forward call.
+See the Inputs section below.
+}
+\section{Inputs}{
+
+\itemize{
+\item \code{input}: the input Tensor to invert
+\item \code{indices}: the indices given out by \code{\link[=nn_max_pool3d]{nn_max_pool3d()}}
+\item \code{output_size} (optional): the targeted output size
+}
+}
+
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})}
+\item Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
+}
+
+\deqn{
+  D_{out} = (D_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
+}
+\deqn{
+  H_{out} = (H_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
+}
+\deqn{
+  W_{out} = (W_{in} - 1) \times \text{stride[2]} - 2 \times \text{padding[2]} + \text{kernel\_size[2]}
+}
+
+or as given by \code{output_size} in the call operator
+}
+
+\examples{
+if (torch_is_installed()) {
+  
+# pool of square window of size=3, stride=2
+pool <- nn_max_pool3d(3, stride=2, return_indices=TRUE)
+unpool <- nn_max_unpool3d(3, stride=2)
+out <- pool(torch_randn(20, 16, 51, 33, 15))
+unpooled_output <- unpool(out[[1]], out[[2]])
+unpooled_output$size()
+
+}
+}
-- 
GitLab


From d693cb467f56071457be81f50829ece928555928 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 13 Aug 2020 17:47:48 -0300
Subject: [PATCH 029/157] add avg pooling layers

---
 NAMESPACE            |   3 +
 R/nn-pooling.R       | 223 +++++++++++++++++++++++++++++++++++++++++++
 man/nn_avg_pool1d.Rd |  65 +++++++++++++
 man/nn_avg_pool2d.Rd |  79 +++++++++++++++
 man/nn_avg_pool3d.Rd |  87 +++++++++++++++++
 5 files changed, 457 insertions(+)
 create mode 100644 man/nn_avg_pool1d.Rd
 create mode 100644 man/nn_avg_pool2d.Rd
 create mode 100644 man/nn_avg_pool3d.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 51e0ab977..447e2ca30 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -100,6 +100,9 @@ export(is_torch_layout)
 export(is_torch_memory_format)
 export(is_torch_qscheme)
 export(nn_adaptive_log_softmax_with_loss)
+export(nn_avg_pool1d)
+export(nn_avg_pool2d)
+export(nn_avg_pool3d)
 export(nn_batch_norm1d)
 export(nn_batch_norm2d)
 export(nn_bce_loss)
diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index 5b26b9449..6757ce8d3 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -402,4 +402,227 @@ nn_max_unpool3d <- nn_module(
   }
 )
 
+#' Applies a 1D average pooling over an input signal composed of several
+#' input planes.
+#' 
+#' In the simplest case, the output value of the layer with input size \eqn{(N, C, L)},
+#' output \eqn{(N, C, L_{out})} and `kernel_size` \eqn{k}
+#' can be precisely described as:
+#'   
+#' \deqn{
+#'   \text{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1}
+#' \text{input}(N_i, C_j, \text{stride} \times l + m)
+#' }
+#' 
+#' If `padding` is non-zero, then the input is implicitly zero-padded on both sides
+#' for `padding` number of points.
+#' 
+#' The parameters `kernel_size`, `stride`, `padding` can each be
+#' an `int` or a one-element tuple.
+#' 
+#' @param kernel_size the size of the window
+#' @param stride the stride of the window. Default value is `kernel_size`
+#' @param padding implicit zero padding to be added on both sides
+#' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape
+#' @param count_include_pad: when TRUE, will include the zero-padding in the averaging calculation
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, C, L_{in})}
+#' - Output: \eqn{(N, C, L_{out})}, where
+#' 
+#' \deqn{
+#'   L_{out} = \left\lfloor \frac{L_{in} +
+#'       2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor
+#' }
+#' 
+#' @examples
+#'   
+#' # pool with window of size=3, stride=2
+#' m <- nn_avg_pool1d(3, stride=2)
+#' m(torch_randn(1, 1, 8))
+#' 
+#' @export
+nn_avg_pool1d <- nn_module(
+  "nn_avg_pool1d", 
+  initialize = function(kernel_size, stride = NULL, padding = 0, ceil_mode = FALSE,
+                        count_include_pad= TRUE) {
+    
+    self$kernel_size <- nn_util_single(kernel_size)
+    
+    if (is.null(stride))
+      stride <- kernel_size
+    
+    self$stride <- nn_util_single(stride)
+    
+    self$padding <- nn_util_single(padding)
+    self$ceil_mode <- ceil_mode
+    self$count_include_pad <- count_include_pad
+    
+  },
+  forward = function(input) {
+    nnf_avg_pool1d(
+      input, self$kernel_size, self$stride, self$padding, self$ceil_mode,
+      self$count_include_pad)
+  }
+)
 
+#' Applies a 2D average pooling over an input signal composed of several input
+#' planes.
+#' 
+#' In the simplest case, the output value of the layer with input size \eqn{(N, C, H, W)},
+#' output \eqn{(N, C, H_{out}, W_{out})} and `kernel_size` \eqn{(kH, kW)}
+#' can be precisely described as:
+#'   
+#' \deqn{
+#'   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)
+#' }
+#' 
+#' If `padding` is non-zero, then the input is implicitly zero-padded on both sides
+#' for `padding` number of points.
+#' 
+#' The parameters `kernel_size`, `stride`, `padding` can either be:
+#'   
+#' - a single `int` -- in which case the same value is used for the height and width dimension
+#' - a `tuple` of two ints -- in which case, the first `int` is used for the height dimension,
+#' and the second `int` for the width dimension
+#' 
+#' 
+#' @param kernel_size the size of the window
+#' @param stride the stride of the window. Default value is `kernel_size`
+#' @param padding implicit zero padding to be added on both sides
+#' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape
+#' @param count_include_pad when TRUE, will include the zero-padding in the averaging calculation
+#' @param divisor_override if specified, it will be used as divisor, otherwise `kernel_size` will be used
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, C, H_{in}, W_{in})}
+#' - Output: \eqn{(N, C, H_{out}, W_{out})}, where
+#' 
+#' \deqn{
+#'   H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \text{padding}[0] -
+#'       \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
+#' }
+#' \deqn{
+#'   W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \text{padding}[1] -
+#'       \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor
+#' }
+#' 
+#' @examples
+#'   
+#' # pool of square window of size=3, stride=2
+#' m <- nn_avg_pool2d(3, stride=2)
+#' # pool of non-square window
+#' m <- nn_avg_pool2d(c(3, 2), stride=c(2, 1))
+#' input <- torch_randn(20, 16, 50, 32)
+#' output <- m(input)
+#' 
+#' @export
+nn_avg_pool2d <- nn_module(
+  "nn_avg_pool2d",
+  initialize = function(kernel_size, stride = NULL, padding = 0, ceil_mode = FALSE,
+                        count_include_pad= TRUE, divisor_override = NULL) {
+    
+    self$kernel_size <- kernel_size
+    
+    if (is.null(stride))
+      stride <- kernel_size
+    
+    self$stride <- stride
+    
+    self$padding <- padding
+    self$ceil_mode <- ceil_mode
+    self$count_include_pad <- count_include_pad
+    self$divisor_override <- divisor_override
+    
+  },
+  forward = function(input) {
+    nnf_avg_pool2d(
+      input, self$kernel_size, self$stride, self$padding, self$ceil_mode,
+      self$count_include_pad, self$divisor_override)
+  }
+)
+
+#' Applies a 3D average pooling over an input signal composed of several input
+#' planes.
+#' 
+#' In the simplest case, the output value of the layer with input size \eqn{(N, C, D, H, W)},
+#' output \eqn{(N, C, D_{out}, H_{out}, W_{out})} and `kernel_size` \eqn{(kD, kH, kW)}
+#' can be precisely described as:
+#'   
+#' \deqn{
+#'   \begin{aligned}
+#' \text{out}(N_i, C_j, d, h, w) ={} & \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
+#' & \frac{\text{input}(N_i, C_j, \text{stride}[0] \times d + k,
+#'                      \text{stride}[1] \times h + m, \text{stride}[2] \times w + n)}
+#' {kD \times kH \times kW}
+#' \end{aligned}
+#' }
+#' 
+#' If `padding` is non-zero, then the input is implicitly zero-padded on all three sides
+#' for `padding` number of points.
+#' 
+#' The parameters `kernel_size`, `stride` can either be:
+#'   
+#' - a single `int` -- in which case the same value is used for the depth, height and width dimension
+#' - a `tuple` of three ints -- in which case, the first `int` is used for the depth dimension,
+#' the second `int` for the height dimension and the third `int` for the width dimension
+#' 
+#' @param kernel_size the size of the window
+#' @param stride the stride of the window. Default value is `kernel_size`
+#' @param padding implicit zero padding to be added on all three sides
+#' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape
+#' @param count_include_pad when TRUE, will include the zero-padding in the averaging calculation
+#' @param divisor_override if specified, it will be used as divisor, otherwise `kernel_size` will be used
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})}
+#' - Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
+#' 
+#' \deqn{
+#'   D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] -
+#'       \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
+#' }
+#' \deqn{
+#'   H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] -
+#'       \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor
+#' }
+#' \deqn{
+#'   W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] -
+#'       \text{kernel\_size}[2]}{\text{stride}[2]} + 1\right\rfloor
+#' }
+#' 
+#' @examples
+#'   
+#' # pool of square window of size=3, stride=2
+#' m = nn_avg_pool3d(3, stride=2)
+#' # pool of non-square window
+#' m = nn_avg_pool3d(c(3, 2, 2), stride=c(2, 1, 2))
+#' input = torch_randn(20, 16, 50,44, 31)
+#' output = m(input)
+#' 
+#' @export
+nn_avg_pool3d <- nn_module(
+  "nn_avg_pool3d",
+  initialize = function(kernel_size, stride = NULL, padding = 0, ceil_mode = FALSE,
+                        count_include_pad= TRUE, divisor_override = NULL) {
+    
+    self$kernel_size <- kernel_size
+    
+    if (is.null(stride))
+      stride <- kernel_size
+    
+    self$stride <- stride
+    
+    self$padding <- padding
+    self$ceil_mode <- ceil_mode
+    self$count_include_pad <- count_include_pad
+    self$divisor_override <- divisor_override
+    
+  },
+  forward = function(input) {
+    nnf_avg_pool3d(
+      input, self$kernel_size, self$stride, self$padding, self$ceil_mode,
+      self$count_include_pad, self$divisor_override)
+  }
+)
\ No newline at end of file
diff --git a/man/nn_avg_pool1d.Rd b/man/nn_avg_pool1d.Rd
new file mode 100644
index 000000000..60e9f4ec5
--- /dev/null
+++ b/man/nn_avg_pool1d.Rd
@@ -0,0 +1,65 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_avg_pool1d}
+\alias{nn_avg_pool1d}
+\title{Applies a 1D average pooling over an input signal composed of several
+input planes.}
+\usage{
+nn_avg_pool1d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE
+)
+}
+\arguments{
+\item{kernel_size}{the size of the window}
+
+\item{stride}{the stride of the window. Default value is \code{kernel_size}}
+
+\item{padding}{implicit zero padding to be added on both sides}
+
+\item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+
+\item{count_include_pad:}{when TRUE, will include the zero-padding in the averaging calculation}
+}
+\description{
+In the simplest case, the output value of the layer with input size \eqn{(N, C, L)},
+output \eqn{(N, C, L_{out})} and \code{kernel_size} \eqn{k}
+can be precisely described as:
+
+\deqn{
+  \text{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1}
+\text{input}(N_i, C_j, \text{stride} \times l + m)
+}
+}
+\details{
+If \code{padding} is non-zero, then the input is implicitly zero-padded on both sides
+for \code{padding} number of points.
+
+The parameters \code{kernel_size}, \code{stride}, \code{padding} can each be
+an \code{int} or a one-element tuple.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, L_{in})}
+\item Output: \eqn{(N, C, L_{out})}, where
+}
+
+\deqn{
+  L_{out} = \left\lfloor \frac{L_{in} +
+      2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+  
+# pool with window of size=3, stride=2
+m <- nn_avg_pool1d(3, stride=2)
+m(torch_randn(1, 1, 8))
+
+}
+}
diff --git a/man/nn_avg_pool2d.Rd b/man/nn_avg_pool2d.Rd
new file mode 100644
index 000000000..cc59cff42
--- /dev/null
+++ b/man/nn_avg_pool2d.Rd
@@ -0,0 +1,79 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_avg_pool2d}
+\alias{nn_avg_pool2d}
+\title{Applies a 2D average pooling over an input signal composed of several input
+planes.}
+\usage{
+nn_avg_pool2d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE,
+  divisor_override = NULL
+)
+}
+\arguments{
+\item{kernel_size}{the size of the window}
+
+\item{stride}{the stride of the window. Default value is \code{kernel_size}}
+
+\item{padding}{implicit zero padding to be added on both sides}
+
+\item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+
+\item{count_include_pad}{when TRUE, will include the zero-padding in the averaging calculation}
+
+\item{divisor_override}{if specified, it will be used as divisor, otherwise \code{kernel_size} will be used}
+}
+\description{
+In the simplest case, the output value of the layer with input size \eqn{(N, C, H, W)},
+output \eqn{(N, C, H_{out}, W_{out})} and \code{kernel_size} \eqn{(kH, kW)}
+can be precisely described as:
+
+\deqn{
+  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)
+}
+}
+\details{
+If \code{padding} is non-zero, then the input is implicitly zero-padded on both sides
+for \code{padding} number of points.
+
+The parameters \code{kernel_size}, \code{stride}, \code{padding} can either be:
+\itemize{
+\item a single \code{int} -- in which case the same value is used for the height and width dimension
+\item a \code{tuple} of two ints -- in which case, the first \code{int} is used for the height dimension,
+and the second \code{int} for the width dimension
+}
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, H_{in}, W_{in})}
+\item Output: \eqn{(N, C, H_{out}, W_{out})}, where
+}
+
+\deqn{
+  H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \text{padding}[0] -
+      \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
+}
+\deqn{
+  W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \text{padding}[1] -
+      \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+  
+# pool of square window of size=3, stride=2
+m <- nn_avg_pool2d(3, stride=2)
+# pool of non-square window
+m <- nn_avg_pool2d(c(3, 2), stride=c(2, 1))
+input <- torch_randn(20, 16, 50, 32)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_avg_pool3d.Rd b/man/nn_avg_pool3d.Rd
new file mode 100644
index 000000000..1205df5d9
--- /dev/null
+++ b/man/nn_avg_pool3d.Rd
@@ -0,0 +1,87 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_avg_pool3d}
+\alias{nn_avg_pool3d}
+\title{Applies a 3D average pooling over an input signal composed of several input
+planes.}
+\usage{
+nn_avg_pool3d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE,
+  divisor_override = NULL
+)
+}
+\arguments{
+\item{kernel_size}{the size of the window}
+
+\item{stride}{the stride of the window. Default value is \code{kernel_size}}
+
+\item{padding}{implicit zero padding to be added on all three sides}
+
+\item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+
+\item{count_include_pad}{when TRUE, will include the zero-padding in the averaging calculation}
+
+\item{divisor_override}{if specified, it will be used as divisor, otherwise \code{kernel_size} will be used}
+}
+\description{
+In the simplest case, the output value of the layer with input size \eqn{(N, C, D, H, W)},
+output \eqn{(N, C, D_{out}, H_{out}, W_{out})} and \code{kernel_size} \eqn{(kD, kH, kW)}
+can be precisely described as:
+
+\deqn{
+  \begin{aligned}
+\text{out}(N_i, C_j, d, h, w) ={} & \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
+& \frac{\text{input}(N_i, C_j, \text{stride}[0] \times d + k,
+                     \text{stride}[1] \times h + m, \text{stride}[2] \times w + n)}
+{kD \times kH \times kW}
+\end{aligned}
+}
+}
+\details{
+If \code{padding} is non-zero, then the input is implicitly zero-padded on all three sides
+for \code{padding} number of points.
+
+The parameters \code{kernel_size}, \code{stride} can either be:
+\itemize{
+\item a single \code{int} -- in which case the same value is used for the depth, height and width dimension
+\item a \code{tuple} of three ints -- in which case, the first \code{int} is used for the depth dimension,
+the second \code{int} for the height dimension and the third \code{int} for the width dimension
+}
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})}
+\item Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
+}
+
+\deqn{
+  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] -
+      \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
+}
+\deqn{
+  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] -
+      \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor
+}
+\deqn{
+  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] -
+      \text{kernel\_size}[2]}{\text{stride}[2]} + 1\right\rfloor
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+  
+# pool of square window of size=3, stride=2
+m = nn_avg_pool3d(3, stride=2)
+# pool of non-square window
+m = nn_avg_pool3d(c(3, 2, 2), stride=c(2, 1, 2))
+input = torch_randn(20, 16, 50,44, 31)
+output = m(input)
+
+}
+}
-- 
GitLab


From 120bdcbfb8b4ddbfce716ea60f85f16c90c063dd Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 13 Aug 2020 18:02:03 -0300
Subject: [PATCH 030/157] docs tweaks

---
 R/nn-pooling.R         | 6 +++---
 man/nn_avg_pool1d.Rd   | 2 +-
 man/nn_max_pool3d.Rd   | 2 +-
 man/nn_max_unpool3d.Rd | 5 +++--
 4 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index 6757ce8d3..6b8eccf63 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -168,7 +168,7 @@ nn_max_pool2d <- nn_module(
 #' @param dilation a parameter that controls the stride of elements in the window
 #' @param return_indices if `TRUE`, will return the max indices along with the outputs.
 #'   Useful for `torch_nn.MaxUnpool3d` later
-#' @param ceil_mode: when TRUE, will use `ceil` instead of `floor` to compute the output shape
+#' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape
 #' 
 #' @section Shape:
 #' - Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})}
@@ -352,7 +352,7 @@ nn_max_unpool2d <- nn_module(
 #' @param kernel_size (int or tuple): Size of the max pooling window.
 #' @param stride (int or tuple): Stride of the max pooling window.
 #'   It is set to `kernel_size` by default.
-#'   padding (int or tuple): Padding that was added to the input
+#' @param padding (int or tuple): Padding that was added to the input
 #' 
 #' @section Inputs:
 #' - `input`: the input Tensor to invert
@@ -424,7 +424,7 @@ nn_max_unpool3d <- nn_module(
 #' @param stride the stride of the window. Default value is `kernel_size`
 #' @param padding implicit zero padding to be added on both sides
 #' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape
-#' @param count_include_pad: when TRUE, will include the zero-padding in the averaging calculation
+#' @param count_include_pad when TRUE, will include the zero-padding in the averaging calculation
 #' 
 #' @section Shape:
 #' - Input: \eqn{(N, C, L_{in})}
diff --git a/man/nn_avg_pool1d.Rd b/man/nn_avg_pool1d.Rd
index 60e9f4ec5..70e9880c0 100644
--- a/man/nn_avg_pool1d.Rd
+++ b/man/nn_avg_pool1d.Rd
@@ -22,7 +22,7 @@ nn_avg_pool1d(
 
 \item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
 
-\item{count_include_pad:}{when TRUE, will include the zero-padding in the averaging calculation}
+\item{count_include_pad}{when TRUE, will include the zero-padding in the averaging calculation}
 }
 \description{
 In the simplest case, the output value of the layer with input size \eqn{(N, C, L)},
diff --git a/man/nn_max_pool3d.Rd b/man/nn_max_pool3d.Rd
index 543a05092..00689f915 100644
--- a/man/nn_max_pool3d.Rd
+++ b/man/nn_max_pool3d.Rd
@@ -26,7 +26,7 @@ nn_max_pool3d(
 \item{return_indices}{if \code{TRUE}, will return the max indices along with the outputs.
 Useful for \code{torch_nn.MaxUnpool3d} later}
 
-\item{ceil_mode:}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+\item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
 }
 \description{
 In the simplest case, the output value of the layer with input size \eqn{(N, C, D, H, W)},
diff --git a/man/nn_max_unpool3d.Rd b/man/nn_max_unpool3d.Rd
index daf8a77ed..b53d193a0 100644
--- a/man/nn_max_unpool3d.Rd
+++ b/man/nn_max_unpool3d.Rd
@@ -10,8 +10,9 @@ nn_max_unpool3d(kernel_size, stride = NULL, padding = 0)
 \item{kernel_size}{(int or tuple): Size of the max pooling window.}
 
 \item{stride}{(int or tuple): Stride of the max pooling window.
-It is set to \code{kernel_size} by default.
-padding (int or tuple): Padding that was added to the input}
+It is set to \code{kernel_size} by default.}
+
+\item{padding}{(int or tuple): Padding that was added to the input}
 }
 \description{
 \code{MaxPool3d} is not fully invertible, since the non-maximal values are lost.
-- 
GitLab


From 94a8cba364dfcd65933a967c516cc99da057109e Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 14 Aug 2020 11:46:59 -0300
Subject: [PATCH 031/157] add fractional maxpool

---
 NAMESPACE                       |   2 +
 R/nn-pooling.R                  | 136 ++++++++++++++++++++++++++++++++
 R/nnf-pooling.R                 |   6 +-
 man/nn_fractional_max_pool2d.Rd |  46 +++++++++++
 man/nn_fractional_max_pool3d.Rd |  46 +++++++++++
 5 files changed, 233 insertions(+), 3 deletions(-)
 create mode 100644 man/nn_fractional_max_pool2d.Rd
 create mode 100644 man/nn_fractional_max_pool3d.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 447e2ca30..08fd52a49 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -120,6 +120,8 @@ export(nn_dropout2d)
 export(nn_dropout3d)
 export(nn_elu)
 export(nn_embedding)
+export(nn_fractional_max_pool2d)
+export(nn_fractional_max_pool3d)
 export(nn_gelu)
 export(nn_glu)
 export(nn_hardshrink)
diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index 6b8eccf63..bcada1001 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -625,4 +625,140 @@ nn_avg_pool3d <- nn_module(
       input, self$kernel_size, self$stride, self$padding, self$ceil_mode,
       self$count_include_pad, self$divisor_override)
   }
+)
+
+#' Applies a 2D fractional max pooling over an input signal composed of several input planes.
+#' 
+#' Fractional MaxPooling is described in detail in the paper 
+#' [Fractional MaxPooling](https://arxiv.org/abs/1412.6071) by Ben Graham
+#' 
+#' The max-pooling operation is applied in \eqn{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.
+#' 
+#' @param kernel_size 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 `(kh, kw)`
+#' @param output_size the target output size of the image of the form `oH x oW`.
+#'   Can be a tuple `(oH, oW)` or a single number oH for a square image `oH x oH`
+#' @param output_ratio 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)
+#' @param return_indices if `TRUE`, will return the indices along with the outputs.
+#'   Useful to pass to [nn_max_unpool2d()]. Default: `FALSE`
+#' 
+#' @examples
+#' # pool of square window of size=3, and target output size 13x12
+#' m = nn_fractional_max_pool2d(3, output_size=c(13, 12))
+#' # pool of square window and target output size being half of input image size
+#' m = nn_fractional_max_pool2d(3, output_ratio=c(0.5, 0.5))
+#' input = torch_randn(20, 16, 50, 32)
+#' output = m(input)
+#' 
+#' @export
+nn_fractional_max_pool2d <- nn_module(
+  "nn_fractional_max_pool2d",
+  initialize = function(kernel_size, output_size = NULL,
+                        output_ratio = NULL,
+                        return_indices = FALSE) {
+    
+    random_samples <- NULL
+    
+    self$kernel_size <- nn_util_pair(kernel_size)
+    self$return_indices <- return_indices
+    self$register_buffer('random_samples', random_samples)
+    
+    if (!is.null(output_size))
+      output_size <- nn_util_pair(output_size)
+    
+    self$output_size <- output_size
+    
+    if (!is.null(output_ratio))
+      output_ratio <- nn_util_pair(output_ratio)
+    
+    self$output_ratio <- output_ratio
+    
+    
+    if (is.null(output_ratio) && is.null(output_size))
+      value_error("both output_size and output_ratio are NULL")
+    
+    if (!is.null(output_ratio) && !is.null(output_size))
+      value_error("both output_size and oytput_ratio are not NULL")
+    
+    if (!is.null(output_ratio) && (output_ratio > 1 || output_ratio < 0))
+      value_error("output_ratio must be between 0 and 1.")
+    
+  },
+  forward = function(input) {
+    nnf_fractional_max_pool2d(
+      input, self$kernel_size, self$output_size, self$output_ratio,
+      self$return_indices,
+      random_samples=self$random_samples)
+  }
+)
+
+#' Applies a 3D fractional max pooling over an input signal composed of several input planes.
+#' 
+#' Fractional MaxPooling is described in detail in the paper 
+#' [Fractional MaxPooling](https://arxiv.org/abs/1412.6071) by Ben Graham
+#' 
+#' The max-pooling operation is applied in \eqn{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.
+#' 
+#' @param kernel_size 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 `(kt x kh x kw)`
+#' @param output_size the target output size of the image of the form `oT x oH x oW`.
+#'   Can be a tuple `(oT, oH, oW)` or a single number oH for a square image `oH x oH x oH`
+#' @param output_ratio 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)
+#' @param return_indices if `TRUE`, will return the indices along with the outputs.
+#'   Useful to pass to [nn_max_unpool3d()]. Default: `FALSE`
+#' 
+#' @examples
+#' # pool of cubic window of size=3, and target output size 13x12x11
+#' m = nn_fractional_max_pool3d(3, output_size=c(13, 12, 11))
+#' # pool of cubic window and target output size being half of input size
+#' m = nn_fractional_max_pool3d(3, output_ratio=c(0.5, 0.5, 0.5))
+#' input = torch_randn(20, 16, 50, 32, 16)
+#' output = m(input)
+#' 
+#' @export  
+nn_fractional_max_pool3d <- nn_module(
+  "nn_fractional_max_pool3d",
+  initialize = function(kernel_size, output_size = NULL,
+                        output_ratio = NULL,
+                        return_indices = FALSE) {
+    
+    random_samples <- NULL
+    
+    self$kernel_size <- nn_util_triple(kernel_size)
+    self$return_indices <- return_indices
+    self$register_buffer('random_samples', random_samples)
+    
+    if (!is.null(output_size))
+      output_size <- nn_util_triple(output_size)
+    
+    self$output_size <- output_size
+    
+    if (!is.null(output_ratio))
+      output_ratio <- nn_util_triple(output_ratio)
+    
+    self$output_ratio <- output_ratio
+    
+    
+    if (is.null(output_ratio) && is.null(output_size))
+      value_error("both output_size and output_ratio are NULL")
+    
+    if (!is.null(output_ratio) && !is.null(output_size))
+      value_error("both output_size and oytput_ratio are not NULL")
+    
+    if (!is.null(output_ratio) && (output_ratio > 1 || output_ratio < 0))
+      value_error("output_ratio must be between 0 and 1.")
+    
+  },
+  forward = function(input) {
+    nnf_fractional_max_pool3d(
+      input, self$kernel_size, self$output_size, self$output_ratio,
+      self$return_indices,
+      random_samples=self$random_samples)
+  }
 )
\ No newline at end of file
diff --git a/R/nnf-pooling.R b/R/nnf-pooling.R
index cec080578..7e29d1dad 100644
--- a/R/nnf-pooling.R
+++ b/R/nnf-pooling.R
@@ -375,7 +375,7 @@ nnf_fractional_max_pool2d <- function(input, kernel_size, output_size=NULL,
   
   if (is.null(random_samples)) {
     random_samples <- torch_rand(input$size(1), input$size(2), 2, 
-                                 dtype = input$dtype, device = input$device)
+                                 dtype = input$dtype(), device = input$device())
   }
   
   res <- torch_fractional_max_pool2d(self = input, kernel_size = kernel_size, 
@@ -428,8 +428,8 @@ nnf_fractional_max_pool3d <- function(input, kernel_size, output_size = NULL, ou
   }
   
   if (is.null(random_samples)) {
-    random_samples <- torch_rand(input$size(1), input$size(2), 3, dtype = input$dtype, 
-                                 device = input$device)
+    random_samples <- torch_rand(input$size(1), input$size(2), 3, dtype = input$dtype(), 
+                                 device = input$device())
   }
   
   res <- torch_fractional_max_pool3d(
diff --git a/man/nn_fractional_max_pool2d.Rd b/man/nn_fractional_max_pool2d.Rd
new file mode 100644
index 000000000..d52652725
--- /dev/null
+++ b/man/nn_fractional_max_pool2d.Rd
@@ -0,0 +1,46 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_fractional_max_pool2d}
+\alias{nn_fractional_max_pool2d}
+\title{Applies a 2D fractional max pooling over an input signal composed of several input planes.}
+\usage{
+nn_fractional_max_pool2d(
+  kernel_size,
+  output_size = NULL,
+  output_ratio = NULL,
+  return_indices = FALSE
+)
+}
+\arguments{
+\item{kernel_size}{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 \verb{(kh, kw)}}
+
+\item{output_size}{the target output size of the image of the form \verb{oH x oW}.
+Can be a tuple \verb{(oH, oW)} or a single number oH for a square image \verb{oH x oH}}
+
+\item{output_ratio}{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)}
+
+\item{return_indices}{if \code{TRUE}, will return the indices along with the outputs.
+Useful to pass to \code{\link[=nn_max_unpool2d]{nn_max_unpool2d()}}. Default: \code{FALSE}}
+}
+\description{
+Fractional MaxPooling is described in detail in the paper
+\href{https://arxiv.org/abs/1412.6071}{Fractional MaxPooling} by Ben Graham
+}
+\details{
+The max-pooling operation is applied in \eqn{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.
+}
+\examples{
+if (torch_is_installed()) {
+# pool of square window of size=3, and target output size 13x12
+m = nn_fractional_max_pool2d(3, output_size=c(13, 12))
+# pool of square window and target output size being half of input image size
+m = nn_fractional_max_pool2d(3, output_ratio=c(0.5, 0.5))
+input = torch_randn(20, 16, 50, 32)
+output = m(input)
+
+}
+}
diff --git a/man/nn_fractional_max_pool3d.Rd b/man/nn_fractional_max_pool3d.Rd
new file mode 100644
index 000000000..16d8771c4
--- /dev/null
+++ b/man/nn_fractional_max_pool3d.Rd
@@ -0,0 +1,46 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_fractional_max_pool3d}
+\alias{nn_fractional_max_pool3d}
+\title{Applies a 3D fractional max pooling over an input signal composed of several input planes.}
+\usage{
+nn_fractional_max_pool3d(
+  kernel_size,
+  output_size = NULL,
+  output_ratio = NULL,
+  return_indices = FALSE
+)
+}
+\arguments{
+\item{kernel_size}{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 \verb{(kt x kh x kw)}}
+
+\item{output_size}{the target output size of the image of the form \verb{oT x oH x oW}.
+Can be a tuple \verb{(oT, oH, oW)} or a single number oH for a square image \verb{oH x oH x oH}}
+
+\item{output_ratio}{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)}
+
+\item{return_indices}{if \code{TRUE}, will return the indices along with the outputs.
+Useful to pass to \code{\link[=nn_max_unpool3d]{nn_max_unpool3d()}}. Default: \code{FALSE}}
+}
+\description{
+Fractional MaxPooling is described in detail in the paper
+\href{https://arxiv.org/abs/1412.6071}{Fractional MaxPooling} by Ben Graham
+}
+\details{
+The max-pooling operation is applied in \eqn{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.
+}
+\examples{
+if (torch_is_installed()) {
+# pool of cubic window of size=3, and target output size 13x12x11
+m = nn_fractional_max_pool3d(3, output_size=c(13, 12, 11))
+# pool of cubic window and target output size being half of input size
+m = nn_fractional_max_pool3d(3, output_ratio=c(0.5, 0.5, 0.5))
+input = torch_randn(20, 16, 50, 32, 16)
+output = m(input)
+
+}
+}
-- 
GitLab


From 516e0a3fc815c1e344bd71a44c901e757ddf2d3f Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 17 Aug 2020 09:37:30 -0300
Subject: [PATCH 032/157] add more pooling layers

---
 NAMESPACE                     |   5 +
 R/nn-pooling.R                | 208 ++++++++++++++++++++++++++++++++++
 man/nn_adaptive_max_pool1d.Rd |  27 +++++
 man/nn_adaptive_max_pool2d.Rd |  34 ++++++
 man/nn_adaptive_max_pool3d.Rd |  34 ++++++
 man/nn_lp_pool1d.Rd           |  54 +++++++++
 man/nn_lp_pool2d.Rd           |  67 +++++++++++
 7 files changed, 429 insertions(+)
 create mode 100644 man/nn_adaptive_max_pool1d.Rd
 create mode 100644 man/nn_adaptive_max_pool2d.Rd
 create mode 100644 man/nn_adaptive_max_pool3d.Rd
 create mode 100644 man/nn_lp_pool1d.Rd
 create mode 100644 man/nn_lp_pool2d.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 08fd52a49..6ed66d5a8 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -100,6 +100,9 @@ export(is_torch_layout)
 export(is_torch_memory_format)
 export(is_torch_qscheme)
 export(nn_adaptive_log_softmax_with_loss)
+export(nn_adaptive_max_pool1d)
+export(nn_adaptive_max_pool2d)
+export(nn_adaptive_max_pool3d)
 export(nn_avg_pool1d)
 export(nn_avg_pool2d)
 export(nn_avg_pool3d)
@@ -148,6 +151,8 @@ export(nn_leaky_relu)
 export(nn_linear)
 export(nn_log_sigmoid)
 export(nn_log_softmax)
+export(nn_lp_pool1d)
+export(nn_lp_pool2d)
 export(nn_max_pool1d)
 export(nn_max_pool2d)
 export(nn_max_pool3d)
diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index bcada1001..43e2b8d45 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -761,4 +761,212 @@ nn_fractional_max_pool3d <- nn_module(
       self$return_indices,
       random_samples=self$random_samples)
   }
+)
+
+lp_pool_nd <- nn_module(
+  "lp_pool_nd",
+  initialize = function(norm_type, kernel_size, stride = NULL,
+                        ceil_mode = FALSE) {
+    
+    self$norm_type <- norm_type
+    self$kernel_size <- kernel_size
+    self$stride <- stride
+    self$ceil_mode <- ceil_mode
+    
+  }
+)
+
+#' Applies a 1D power-average pooling over an input signal composed of several input
+#' planes.
+#' 
+#' On each window, the function computed is:
+#'   
+#' \deqn{
+#'   f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
+#' }
+#' 
+#' - At p = \eqn{\infty}, one gets Max Pooling
+#' - At p = 1, one gets Sum Pooling (which is proportional to Average Pooling)
+#' 
+#' @note If the sum to the power of `p` is zero, the gradient of this function is
+#' not defined. This implementation will set the gradient to zero in this case.
+#' 
+#' @param kernel_size a single int, the size of the window
+#' @param stride a single int, the stride of the window. Default value is `kernel_size`
+#' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, C, L_{in})}
+#' - Output: \eqn{(N, C, L_{out})}, where
+#' 
+#' \deqn{
+#'   L_{out} = \left\lfloor\frac{L_{in} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor
+#' }
+#' 
+#' @examples
+#' # power-2 pool of window of length 3, with stride 2.
+#' m <- nn_lp_pool1d(2, 3, stride=2)
+#' input <- torch_randn(20, 16, 50)
+#' output <- m(input)
+#' 
+#' @export
+nn_lp_pool1d <- nn_module(
+  "nn_lp_pool1d",
+  inherit = lp_pool_nd,
+  forward = function(input) {
+    nnf_lp_pool1d(input, self$norm_type, self$kernel_size,
+                self$stride, self$ceil_mode)
+  }
+)
+
+#' Applies a 2D power-average pooling over an input signal composed of several input
+#' planes.
+#' 
+#' On each window, the function computed is:
+#'   
+#' \deqn{
+#'   f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
+#' }
+#' 
+#' - At p = \eqn{\infty}, one gets Max Pooling
+#' - At p = 1, one gets Sum Pooling (which is proportional to average pooling)
+#' 
+#' The parameters `kernel_size`, `stride` can either be:
+#'   
+#' - a single `int` -- in which case the same value is used for the height and width dimension
+#' - a `tuple` of two ints -- in which case, the first `int` is used for the height dimension,
+#' and the second `int` for the width dimension
+#' 
+#' @note If the sum to the power of `p` is zero, the gradient of this function is
+#' not defined. This implementation will set the gradient to zero in this case.
+#' 
+#' @param kernel_size the size of the window
+#' @param stride the stride of the window. Default value is `kernel_size`
+#' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape
+#' 
+#' @section Shape:
+#' 
+#' - Input: \eqn{(N, C, H_{in}, W_{in})}
+#' - Output: \eqn{(N, C, H_{out}, W_{out})}, where
+#' 
+#' \deqn{
+#'   H_{out} = \left\lfloor\frac{H_{in} - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
+#' }
+#' \deqn{
+#'   W_{out} = \left\lfloor\frac{W_{in} - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor
+#' }
+#' 
+#' @examples
+#'   
+#' # power-2 pool of square window of size=3, stride=2
+#' m <- nn_lp_pool2d(2, 3, stride=2)
+#' # pool of non-square window of power 1.2
+#' m <- nn_lp_pool2d(1.2, c(3, 2), stride=c(2, 1))
+#' input <- torch_randn(20, 16, 50, 32)
+#' output <- m(input)
+#' 
+#' @export
+nn_lp_pool2d <- nn_module(
+  "nn_lp_pool2d",
+  inherit = lp_pool_nd,
+  forward = function(input) {
+    nnf_lp_pool2d(input, self$norm_type, self$kernel_size,
+                self$stride, self$ceil_mode)
+  }
+)
+
+#' Applies a 1D adaptive max pooling over an input signal composed of several input planes.
+#' 
+#' The output size is H, for any input size.
+#' The number of output features is equal to the number of input planes.
+#' 
+#' @param output_size the target output size H
+#' @param return_indices if `TRUE`, will return the indices along with the outputs.
+#'   Useful to pass to [nn_max_unpool1d()]. Default: `FALSE`
+#' 
+#' @examples
+#' # target output size of 5
+#' m <- nn_adaptive_max_pool1d(5)
+#' input <- torch_randn(1, 64, 8)
+#' output <- m(input)
+#' 
+#' @export
+nn_adaptive_max_pool1d <- nn_module(
+  "nn_adaptive_max_pool1d",
+  initialize = function(output_size, return_indices = FALSE) {
+    self$output_size <- output_size
+    self$return_indices <- return_indices
+  },
+  forward = function(input) {
+    nnf_adaptive_max_pool1d(input, self$output_size, self$return_indices)
+  }
+)
+
+#' Applies a 2D adaptive max pooling over an input signal composed of several input planes.
+#' 
+#' 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.
+#' 
+#' @param output_size 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 `int`, or `None` which means the size will
+#'   be the same as that of the input.
+#' @param return_indices if `TRUE`, will return the indices along with the outputs.
+#'   Useful to pass to [nn_max_unpool2d()]. Default: `FALSE`
+#' 
+#' @examples 
+#' # target output size of 5x7
+#' m <- nn_adaptive_max_pool2d(c(5,7))
+#' input <- torch_randn(1, 64, 8, 9)
+#' output <- m(input)
+#' # target output size of 7x7 (square)
+#' m <- nn_adaptive_max_pool2d(7)
+#' input <- torch_randn(1, 64, 10, 9)
+#' output <- m(input)
+#' 
+#' @export
+nn_adaptive_max_pool2d <- nn_module(
+  "nn_adaptive_max_pool2d",
+  initialize = function(output_size, return_indices = FALSE) {
+    self$output_size <- nn_util_pair(output_size)
+    self$return_indices <- return_indices
+  },
+  forward = function(input) {
+    nnf_adaptive_max_pool2d(input, self$output_size, self$return_indices)
+  }
+)
+
+#' Applies a 3D adaptive max pooling over an input signal composed of several input planes.
+#' 
+#' 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.
+#' 
+#' 
+#' @param output_size 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 `int`, or `None` which means the size will
+#'   be the same as that of the input.
+#' @param return_indices if `TRUE`, will return the indices along with the outputs.
+#'   Useful to pass to [nn_max_unpool3d()]. Default: `FALSE`
+#' 
+#' @examples
+#' # target output size of 5x7x9
+#' m <- nn_adaptive_max_pool3d(c(5,7,9))
+#' input <- torch_randn(1, 64, 8, 9, 10)
+#' output <- m(input)
+#' # target output size of 7x7x7 (cube)
+#' m <- nn_adaptive_max_pool3d(7)
+#' input <- torch_randn(1, 64, 10, 9, 8)
+#' output <- m(input)
+#' 
+#' @export
+nn_adaptive_max_pool3d <- nn_module(
+  "nn_adaptive_max_pool3d",
+  initialize = function(output_size, return_indices = FALSE) {
+    self$output_size <- nn_util_triple(output_size)
+    self$return_indices <- return_indices
+  },
+  forward = function(input) {
+    nnf_adaptive_max_pool3d(input, self$output_size, self$return_indices)
+  }
 )
\ No newline at end of file
diff --git a/man/nn_adaptive_max_pool1d.Rd b/man/nn_adaptive_max_pool1d.Rd
new file mode 100644
index 000000000..f0302911c
--- /dev/null
+++ b/man/nn_adaptive_max_pool1d.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_adaptive_max_pool1d}
+\alias{nn_adaptive_max_pool1d}
+\title{Applies a 1D adaptive max pooling over an input signal composed of several input planes.}
+\usage{
+nn_adaptive_max_pool1d(output_size, return_indices = FALSE)
+}
+\arguments{
+\item{output_size}{the target output size H}
+
+\item{return_indices}{if \code{TRUE}, will return the indices along with the outputs.
+Useful to pass to \code{\link[=nn_max_unpool1d]{nn_max_unpool1d()}}. Default: \code{FALSE}}
+}
+\description{
+The output size is H, for any input size.
+The number of output features is equal to the number of input planes.
+}
+\examples{
+if (torch_is_installed()) {
+# target output size of 5
+m <- nn_adaptive_max_pool1d(5)
+input <- torch_randn(1, 64, 8)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_adaptive_max_pool2d.Rd b/man/nn_adaptive_max_pool2d.Rd
new file mode 100644
index 000000000..39ba4a7e7
--- /dev/null
+++ b/man/nn_adaptive_max_pool2d.Rd
@@ -0,0 +1,34 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_adaptive_max_pool2d}
+\alias{nn_adaptive_max_pool2d}
+\title{Applies a 2D adaptive max pooling over an input signal composed of several input planes.}
+\usage{
+nn_adaptive_max_pool2d(output_size, return_indices = FALSE)
+}
+\arguments{
+\item{output_size}{the target output size of the image of the form H x W.
+Can be a tuple \verb{(H, W)} or a single H for a square image H x H.
+H and W can be either a \code{int}, or \code{None} which means the size will
+be the same as that of the input.}
+
+\item{return_indices}{if \code{TRUE}, will return the indices along with the outputs.
+Useful to pass to \code{\link[=nn_max_unpool2d]{nn_max_unpool2d()}}. Default: \code{FALSE}}
+}
+\description{
+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.
+}
+\examples{
+if (torch_is_installed()) {
+# target output size of 5x7
+m <- nn_adaptive_max_pool2d(c(5,7))
+input <- torch_randn(1, 64, 8, 9)
+output <- m(input)
+# target output size of 7x7 (square)
+m <- nn_adaptive_max_pool2d(7)
+input <- torch_randn(1, 64, 10, 9)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_adaptive_max_pool3d.Rd b/man/nn_adaptive_max_pool3d.Rd
new file mode 100644
index 000000000..4c2d7ebc0
--- /dev/null
+++ b/man/nn_adaptive_max_pool3d.Rd
@@ -0,0 +1,34 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_adaptive_max_pool3d}
+\alias{nn_adaptive_max_pool3d}
+\title{Applies a 3D adaptive max pooling over an input signal composed of several input planes.}
+\usage{
+nn_adaptive_max_pool3d(output_size, return_indices = FALSE)
+}
+\arguments{
+\item{output_size}{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}, or \code{None} which means the size will
+be the same as that of the input.}
+
+\item{return_indices}{if \code{TRUE}, will return the indices along with the outputs.
+Useful to pass to \code{\link[=nn_max_unpool3d]{nn_max_unpool3d()}}. Default: \code{FALSE}}
+}
+\description{
+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.
+}
+\examples{
+if (torch_is_installed()) {
+# target output size of 5x7x9
+m <- nn_adaptive_max_pool3d(c(5,7,9))
+input <- torch_randn(1, 64, 8, 9, 10)
+output <- m(input)
+# target output size of 7x7x7 (cube)
+m <- nn_adaptive_max_pool3d(7)
+input <- torch_randn(1, 64, 10, 9, 8)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_lp_pool1d.Rd b/man/nn_lp_pool1d.Rd
new file mode 100644
index 000000000..73e719e7f
--- /dev/null
+++ b/man/nn_lp_pool1d.Rd
@@ -0,0 +1,54 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_lp_pool1d}
+\alias{nn_lp_pool1d}
+\title{Applies a 1D power-average pooling over an input signal composed of several input
+planes.}
+\usage{
+nn_lp_pool1d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
+}
+\arguments{
+\item{kernel_size}{a single int, the size of the window}
+
+\item{stride}{a single int, the stride of the window. Default value is \code{kernel_size}}
+
+\item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+}
+\description{
+On each window, the function computed is:
+
+\deqn{
+  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
+}
+}
+\details{
+\itemize{
+\item At p = \eqn{\infty}, one gets Max Pooling
+\item At p = 1, one gets Sum Pooling (which is proportional to Average Pooling)
+}
+}
+\note{
+If the sum to the power of \code{p} is zero, the gradient of this function is
+not defined. This implementation will set the gradient to zero in this case.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, L_{in})}
+\item Output: \eqn{(N, C, L_{out})}, where
+}
+
+\deqn{
+  L_{out} = \left\lfloor\frac{L_{in} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+# power-2 pool of window of length 3, with stride 2.
+m <- nn_lp_pool1d(2, 3, stride=2)
+input <- torch_randn(20, 16, 50)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_lp_pool2d.Rd b/man/nn_lp_pool2d.Rd
new file mode 100644
index 000000000..4c4f0a509
--- /dev/null
+++ b/man/nn_lp_pool2d.Rd
@@ -0,0 +1,67 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_lp_pool2d}
+\alias{nn_lp_pool2d}
+\title{Applies a 2D power-average pooling over an input signal composed of several input
+planes.}
+\usage{
+nn_lp_pool2d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
+}
+\arguments{
+\item{kernel_size}{the size of the window}
+
+\item{stride}{the stride of the window. Default value is \code{kernel_size}}
+
+\item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+}
+\description{
+On each window, the function computed is:
+
+\deqn{
+  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
+}
+}
+\details{
+\itemize{
+\item At p = \eqn{\infty}, one gets Max Pooling
+\item At p = 1, one gets Sum Pooling (which is proportional to average pooling)
+}
+
+The parameters \code{kernel_size}, \code{stride} can either be:
+\itemize{
+\item a single \code{int} -- in which case the same value is used for the height and width dimension
+\item a \code{tuple} of two ints -- in which case, the first \code{int} is used for the height dimension,
+and the second \code{int} for the width dimension
+}
+}
+\note{
+If the sum to the power of \code{p} is zero, the gradient of this function is
+not defined. This implementation will set the gradient to zero in this case.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, H_{in}, W_{in})}
+\item Output: \eqn{(N, C, H_{out}, W_{out})}, where
+}
+
+\deqn{
+  H_{out} = \left\lfloor\frac{H_{in} - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
+}
+\deqn{
+  W_{out} = \left\lfloor\frac{W_{in} - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+  
+# power-2 pool of square window of size=3, stride=2
+m <- nn_lp_pool2d(2, 3, stride=2)
+# pool of non-square window of power 1.2
+m <- nn_lp_pool2d(1.2, c(3, 2), stride=c(2, 1))
+input <- torch_randn(20, 16, 50, 32)
+output <- m(input)
+
+}
+}
-- 
GitLab


From c22687202ec6216975c38b325a1271f1ca954124 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 17 Aug 2020 10:33:18 -0300
Subject: [PATCH 033/157] add a few more avg pooling layers

---
 NAMESPACE                     |  3 ++
 R/nn-pooling.R                | 86 +++++++++++++++++++++++++++++++++++
 man/nn_adaptive_avg_pool1d.Rd | 24 ++++++++++
 man/nn_adaptive_avg_pool2d.Rd | 31 +++++++++++++
 man/nn_adaptive_avg_pool3d.Rd | 31 +++++++++++++
 5 files changed, 175 insertions(+)
 create mode 100644 man/nn_adaptive_avg_pool1d.Rd
 create mode 100644 man/nn_adaptive_avg_pool2d.Rd
 create mode 100644 man/nn_adaptive_avg_pool3d.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 6ed66d5a8..51071d1f5 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -99,6 +99,9 @@ export(is_torch_dtype)
 export(is_torch_layout)
 export(is_torch_memory_format)
 export(is_torch_qscheme)
+export(nn_adaptive_avg_pool1d)
+export(nn_adaptive_avg_pool2d)
+export(nn_adaptive_avg_pool3d)
 export(nn_adaptive_log_softmax_with_loss)
 export(nn_adaptive_max_pool1d)
 export(nn_adaptive_max_pool2d)
diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index 43e2b8d45..1bd3d797b 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -969,4 +969,90 @@ nn_adaptive_max_pool3d <- nn_module(
   forward = function(input) {
     nnf_adaptive_max_pool3d(input, self$output_size, self$return_indices)
   }
+)
+
+#' Applies a 1D adaptive average pooling over an input signal composed of several input planes.
+#' 
+#' The output size is H, for any input size.
+#' The number of output features is equal to the number of input planes.
+#' 
+#' @param output_size the target output size H
+#' 
+#' @examples 
+#' # target output size of 5
+#' m = nn_adaptive_avg_pool1d(5)
+#' input <- torch_randn(1, 64, 8)
+#' output <- m(input)
+#' 
+#' @export
+nn_adaptive_avg_pool1d <- nn_module(
+  "nn_adaptive_avg_pool1d",
+  initialize = function(output_size) {
+    self$output_size <- output_size
+  },
+  forward = function(input) {
+    nnf_adaptive_avg_pool1d(input, self$output_size)
+  }
+)
+
+#' Applies a 2D adaptive average pooling over an input signal composed of several input planes.
+#' 
+#' 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.
+#' 
+#' @param output_size 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 `int`, or `NULL` which means the size will
+#'   be the same as that of the input.
+#' 
+#' @examples
+#' # target output size of 5x7
+#' m <- nn_adaptive_avg_pool2d(c(5,7))
+#' input <- torch_randn(1, 64, 8, 9)
+#' output <- m(input)
+#' # target output size of 7x7 (square)
+#' m <- nn_adaptive_avg_pool2d(7)
+#' input <- torch_randn(1, 64, 10, 9)
+#' output <- m(input)
+#' 
+#' @export
+nn_adaptive_avg_pool2d <- nn_module(
+  "nn_adaptive_avg_pool2d",
+  initialize = function(output_size) {
+    self$output_size <- nn_util_pair(output_size)
+  },
+  forward = function(input) {
+    nnf_adaptive_avg_pool2d(input, self$output_size)
+  }
+)
+
+#' Applies a 3D adaptive average pooling over an input signal composed of several input planes.
+#' 
+#' 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.
+#' 
+#' @param output_size 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 `int`, or `None` which means the size will
+#'   be the same as that of the input.
+#' 
+#' @examples
+#' # target output size of 5x7x9
+#' m <- nn_adaptive_avg_pool3d(c(5,7,9))
+#' input <- torch_randn(1, 64, 8, 9, 10)
+#' output <- m(input)
+#' # target output size of 7x7x7 (cube)
+#' m <- nn_adaptive_avg_pool3d(7)
+#' input <- torch_randn(1, 64, 10, 9, 8)
+#' output <- m(input)
+#' 
+#' @export
+nn_adaptive_avg_pool3d <- nn_module(
+  "nn_adaptive_avg_pool3d",
+  initialize = function(output_size) {
+    self$output_size <- nn_util_triple(output_size)
+  },
+  forward = function(input) {
+    nnf_adaptive_avg_pool3d(input, self$output_size)
+  }
 )
\ No newline at end of file
diff --git a/man/nn_adaptive_avg_pool1d.Rd b/man/nn_adaptive_avg_pool1d.Rd
new file mode 100644
index 000000000..55c5c22d3
--- /dev/null
+++ b/man/nn_adaptive_avg_pool1d.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_adaptive_avg_pool1d}
+\alias{nn_adaptive_avg_pool1d}
+\title{Applies a 1D adaptive average pooling over an input signal composed of several input planes.}
+\usage{
+nn_adaptive_avg_pool1d(output_size)
+}
+\arguments{
+\item{output_size}{the target output size H}
+}
+\description{
+The output size is H, for any input size.
+The number of output features is equal to the number of input planes.
+}
+\examples{
+if (torch_is_installed()) {
+# target output size of 5
+m = nn_adaptive_avg_pool1d(5)
+input <- torch_randn(1, 64, 8)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_adaptive_avg_pool2d.Rd b/man/nn_adaptive_avg_pool2d.Rd
new file mode 100644
index 000000000..46d95ed73
--- /dev/null
+++ b/man/nn_adaptive_avg_pool2d.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_adaptive_avg_pool2d}
+\alias{nn_adaptive_avg_pool2d}
+\title{Applies a 2D adaptive average pooling over an input signal composed of several input planes.}
+\usage{
+nn_adaptive_avg_pool2d(output_size)
+}
+\arguments{
+\item{output_size}{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}, or \code{NULL} which means the size will
+be the same as that of the input.}
+}
+\description{
+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.
+}
+\examples{
+if (torch_is_installed()) {
+# target output size of 5x7
+m <- nn_adaptive_avg_pool2d(c(5,7))
+input <- torch_randn(1, 64, 8, 9)
+output <- m(input)
+# target output size of 7x7 (square)
+m <- nn_adaptive_avg_pool2d(7)
+input <- torch_randn(1, 64, 10, 9)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_adaptive_avg_pool3d.Rd b/man/nn_adaptive_avg_pool3d.Rd
new file mode 100644
index 000000000..ce1796d21
--- /dev/null
+++ b/man/nn_adaptive_avg_pool3d.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_adaptive_avg_pool3d}
+\alias{nn_adaptive_avg_pool3d}
+\title{Applies a 3D adaptive average pooling over an input signal composed of several input planes.}
+\usage{
+nn_adaptive_avg_pool3d(output_size)
+}
+\arguments{
+\item{output_size}{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}, or \code{None} which means the size will
+be the same as that of the input.}
+}
+\description{
+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.
+}
+\examples{
+if (torch_is_installed()) {
+# target output size of 5x7x9
+m <- nn_adaptive_avg_pool3d(c(5,7,9))
+input <- torch_randn(1, 64, 8, 9, 10)
+output <- m(input)
+# target output size of 7x7x7 (cube)
+m <- nn_adaptive_avg_pool3d(7)
+input <- torch_randn(1, 64, 10, 9, 8)
+output <- m(input)
+
+}
+}
-- 
GitLab


From ca01eba5fcfcf346a8452b1c81aaf32fa37a2a81 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 18 Aug 2020 10:45:19 -0300
Subject: [PATCH 034/157] use a better error message fr embedding fix #220

---
 R/codegen-utils.R                        | 12 ++++++++++--
 tests/testthat/test-dim-error-messages.R | 13 +++++++++++++
 2 files changed, 23 insertions(+), 2 deletions(-)

diff --git a/R/codegen-utils.R b/R/codegen-utils.R
index 76744c9e1..ea4fc73ed 100644
--- a/R/codegen-utils.R
+++ b/R/codegen-utils.R
@@ -16,17 +16,25 @@ as_1_based_dim <- function(x) {
 
 as_1_based_tensor_list <- function(x) {
   tensors <- x$to_r()
-  tensors <- lapply(tensors, function(x) x$sub(1L, 0L))
+  tensors <- lapply(tensors, as_1_based_tensor)
   torch_tensor_list(tensors)
 }
 
+as_1_based_tensor <- function(x) {
+  e <- torch_min(torch_abs(x))$to(dtype = torch_int())
+  if (as.numeric(e) == 0)
+    runtime_error("Indices/Index start at 1 and got a 0.")
+  
+  x - (x > 0)$to(dtype = x$dtype())
+}
+
 argument_to_torch_type <- function(obj, expected_types, arg_name) {
   
   if (is.name(obj))
     return(NULL)
   
   if (any(arg_name == c("index", "indices", "dims")) && any("Tensor" == expected_types) && is_torch_tensor(obj))
-    return(list(get("ptr", obj$sub(1L, alpha = 1L), inherits = FALSE), "Tensor"))
+    return(list(get("ptr", as_1_based_tensor(obj), inherits = FALSE), "Tensor"))
   
   if (any("Tensor" == expected_types) && is_torch_tensor(obj))
     return(list(get("ptr", obj, inherits = FALSE), "Tensor"))
diff --git a/tests/testthat/test-dim-error-messages.R b/tests/testthat/test-dim-error-messages.R
index caa8656e5..4ee5327ed 100644
--- a/tests/testthat/test-dim-error-messages.R
+++ b/tests/testthat/test-dim-error-messages.R
@@ -171,4 +171,17 @@ test_that("tensordot error message", {
     fixed = TRUE
   )
   
+})
+
+test_that("embedding returns a better error message", {
+  
+  e <- nn_embedding(10, 3)
+  x <- torch_tensor(c(0, 1, 2, 3), dtype = torch_long())
+  
+  expect_error(
+    e(x),
+    regex = "Indices/Index start at 1 and got a 0.",
+    class = "runtime_error"
+  )
+  
 })
\ No newline at end of file
-- 
GitLab


From 9e6c5e1394c95dc2c89b2dfb3a2e314798b35ef2 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 18 Aug 2020 11:39:35 -0300
Subject: [PATCH 035/157] small fix R  cmd check warnings

---
 R/codegen-utils.R          | 9 ++++++---
 R/gen-namespace-examples.R | 2 +-
 R/nn-pooling.R             | 4 ++++
 man/nn_lp_pool1d.Rd        | 3 +++
 man/nn_lp_pool2d.Rd        | 3 +++
 man/torch_take.Rd          | 2 +-
 6 files changed, 18 insertions(+), 5 deletions(-)

diff --git a/R/codegen-utils.R b/R/codegen-utils.R
index ea4fc73ed..8d91ecec4 100644
--- a/R/codegen-utils.R
+++ b/R/codegen-utils.R
@@ -21,9 +21,12 @@ as_1_based_tensor_list <- function(x) {
 }
 
 as_1_based_tensor <- function(x) {
-  e <- torch_min(torch_abs(x))$to(dtype = torch_int())
-  if (as.numeric(e) == 0)
-    runtime_error("Indices/Index start at 1 and got a 0.")
+  
+  if (!any(x$shape == 0)) {
+    e <- torch_min(torch_abs(x))$to(dtype = torch_int())
+    if (as.numeric(e) == 0)
+      runtime_error("Indices/Index start at 1 and got a 0.")  
+  }
   
   x - (x > 0)$to(dtype = x$dtype())
 }
diff --git a/R/gen-namespace-examples.R b/R/gen-namespace-examples.R
index adad21063..b4325298a 100644
--- a/R/gen-namespace-examples.R
+++ b/R/gen-namespace-examples.R
@@ -2314,7 +2314,7 @@ NULL
 #' @examples
 #'
 #' src = torch_tensor(matrix(c(4,3,5,6,7,8), ncol = 3, byrow = TRUE))
-#' torch_take(src, torch_tensor(c(0, 2, 5), dtype = torch_int64()))
+#' torch_take(src, torch_tensor(c(1, 2, 5), dtype = torch_int64()))
 NULL
 # -> take <-
 
diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index 1bd3d797b..6743a8815 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -791,6 +791,8 @@ lp_pool_nd <- nn_module(
 #' @note If the sum to the power of `p` is zero, the gradient of this function is
 #' not defined. This implementation will set the gradient to zero in this case.
 #' 
+#' @param norm_type if inf than one gets max pooling if 0 you get sum pooling (
+#'   proportional to the avg pooling)
 #' @param kernel_size a single int, the size of the window
 #' @param stride a single int, the stride of the window. Default value is `kernel_size`
 #' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape
@@ -840,6 +842,8 @@ nn_lp_pool1d <- nn_module(
 #' @note If the sum to the power of `p` is zero, the gradient of this function is
 #' not defined. This implementation will set the gradient to zero in this case.
 #' 
+#' @param norm_type if inf than one gets max pooling if 0 you get sum pooling (
+#'   proportional to the avg pooling)
 #' @param kernel_size the size of the window
 #' @param stride the stride of the window. Default value is `kernel_size`
 #' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape
diff --git a/man/nn_lp_pool1d.Rd b/man/nn_lp_pool1d.Rd
index 73e719e7f..e73ad8c3a 100644
--- a/man/nn_lp_pool1d.Rd
+++ b/man/nn_lp_pool1d.Rd
@@ -8,6 +8,9 @@ planes.}
 nn_lp_pool1d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
 }
 \arguments{
+\item{norm_type}{if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)}
+
 \item{kernel_size}{a single int, the size of the window}
 
 \item{stride}{a single int, the stride of the window. Default value is \code{kernel_size}}
diff --git a/man/nn_lp_pool2d.Rd b/man/nn_lp_pool2d.Rd
index 4c4f0a509..460d672ef 100644
--- a/man/nn_lp_pool2d.Rd
+++ b/man/nn_lp_pool2d.Rd
@@ -8,6 +8,9 @@ planes.}
 nn_lp_pool2d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
 }
 \arguments{
+\item{norm_type}{if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)}
+
 \item{kernel_size}{the size of the window}
 
 \item{stride}{the stride of the window. Default value is \code{kernel_size}}
diff --git a/man/torch_take.Rd b/man/torch_take.Rd
index cea95bd46..0de56ae01 100644
--- a/man/torch_take.Rd
+++ b/man/torch_take.Rd
@@ -24,6 +24,6 @@ takes the same shape as the indices.
 if (torch_is_installed()) {
 
 src = torch_tensor(matrix(c(4,3,5,6,7,8), ncol = 3, byrow = TRUE))
-torch_take(src, torch_tensor(c(0, 2, 5), dtype = torch_int64()))
+torch_take(src, torch_tensor(c(1, 2, 5), dtype = torch_int64()))
 }
 }
-- 
GitLab


From e28599b64297576e2f7c0bd0f569bd7756a177e0 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 18 Aug 2020 17:28:20 -0300
Subject: [PATCH 036/157] allows loading a state dict saved in python with
 torch.save

---
 R/RcppExports.R                      |   4 +++
 R/save.R                             |   9 ++++++
 lantern/include/lantern/lantern.h    |  25 ++++++++++++++++
 lantern/src/Save.cpp                 |  43 ++++++++++++++++++++++++++-
 src/RcppExports.cpp                  |  12 ++++++++
 src/lantern/lantern.h                |  25 ++++++++++++++++
 src/save.cpp                         |  24 +++++++++++++++
 tests/testthat/assets/state_dict.pth | Bin 0 -> 1155 bytes
 tests/testthat/test-save.R           |  15 ++++++++++
 9 files changed, 156 insertions(+), 1 deletion(-)
 create mode 100644 tests/testthat/assets/state_dict.pth

diff --git a/R/RcppExports.R b/R/RcppExports.R
index 3c8c7a439..c9671a8b2 100644
--- a/R/RcppExports.R
+++ b/R/RcppExports.R
@@ -7041,6 +7041,10 @@ cpp_tensor_load <- function(s) {
     .Call('_torch_cpp_tensor_load', PACKAGE = 'torchpkg', s)
 }
 
+cpp_load_state_dict <- function(path) {
+    .Call('_torch_cpp_load_state_dict', PACKAGE = 'torchpkg', path)
+}
+
 cpp_torch_scalar <- function(x) {
     .Call('_torch_cpp_torch_scalar', PACKAGE = 'torchpkg', x)
 }
diff --git a/R/save.R b/R/save.R
index 69c86ef6d..cb28749fb 100644
--- a/R/save.R
+++ b/R/save.R
@@ -65,3 +65,12 @@ torch_load_module <- function(obj) {
   obj$module$load_state_dict(obj$state_dict)
   obj$module
 }
+
+load_state_dict <- function(path) {
+  path <- normalizePath(path)
+  o <- cpp_load_state_dict(path)
+  
+  values <- TensorList$new(ptr = o$values)$to_r()
+  values <- setNames(values, o$keys)
+  values
+}
diff --git a/lantern/include/lantern/lantern.h b/lantern/include/lantern/lantern.h
index 43566c6ce..aa085e9fb 100644
--- a/lantern/include/lantern/lantern.h
+++ b/lantern/include/lantern/lantern.h
@@ -470,6 +470,28 @@ extern "C"
   HOST_API void lantern_Tensor_index_put_scalar_ (void* self, void* index, void* rhs) { _lantern_Tensor_index_put_scalar_(self, index, rhs); LANTERN_HOST_HANDLER}
   LANTERN_API void (LANTERN_PTR _lantern_manual_seed) (int64_t seed);
   HOST_API void lantern_manual_seed (int64_t seed) {_lantern_manual_seed(seed); LANTERN_HOST_HANDLER}
+
+  LANTERN_API void* (LANTERN_PTR _lantern_load_state_dict) (const char * path);
+  HOST_API void * lantern_load_state_dict (const char * path)
+  {
+    void * ret = _lantern_load_state_dict(path);
+    LANTERN_HOST_HANDLER return ret;
+  }
+
+  LANTERN_API void* (LANTERN_PTR _lantern_get_state_dict_keys) (void * ivalue);
+  HOST_API void* lantern_get_state_dict_keys (void* ivalue)
+  {
+    void * ret = _lantern_get_state_dict_keys(ivalue);
+    LANTERN_HOST_HANDLER return ret;
+  }
+
+  LANTERN_API void* (LANTERN_PTR _lantern_get_state_dict_values) (void * ivalue);
+  HOST_API void* lantern_get_state_dict_values (void* ivalue)
+  {
+    void * ret = _lantern_get_state_dict_values(ivalue);
+    LANTERN_HOST_HANDLER return ret;
+  }
+
   /* Autogen Headers -- Start */
   LANTERN_API void* (LANTERN_PTR _lantern__cast_byte_tensor_bool)(void* self, void* non_blocking);
   HOST_API void* lantern__cast_byte_tensor_bool(void* self, void* non_blocking) { void* ret = _lantern__cast_byte_tensor_bool(self, non_blocking); LANTERN_HOST_HANDLER return ret; }
@@ -4178,6 +4200,9 @@ bool lanternInit(const std::string &libPath, std::string *pError)
   LOAD_SYMBOL(_lantern_Tensor_index_put_tensor_);
   LOAD_SYMBOL(_lantern_Tensor_index_put_scalar_);
   LOAD_SYMBOL(_lantern_manual_seed);
+  LOAD_SYMBOL(_lantern_load_state_dict);
+  LOAD_SYMBOL(_lantern_get_state_dict_keys);
+  LOAD_SYMBOL(_lantern_get_state_dict_values);
   /* Autogen Symbols -- Start */
   LOAD_SYMBOL(_lantern__cast_byte_tensor_bool)
   LOAD_SYMBOL(_lantern__cast_char_tensor_bool)
diff --git a/lantern/src/Save.cpp b/lantern/src/Save.cpp
index 94728aba0..0211b74b9 100644
--- a/lantern/src/Save.cpp
+++ b/lantern/src/Save.cpp
@@ -66,4 +66,45 @@ void _lantern_test_print (void * x)
     auto t = reinterpret_cast<LanternObject<torch::Tensor> *>(x)->get();
     std::cout << t << std::endl;
     LANTERN_FUNCTION_END_VOID
-}
\ No newline at end of file
+}
+
+void* _lantern_load_state_dict (const char * path)
+{
+    LANTERN_FUNCTION_START
+    std::ifstream file(path);
+    std::vector<char> data((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
+    torch::IValue ivalue = torch::pickle_load(data);   
+    return (void*) new LanternObject<torch::IValue>(ivalue);
+    LANTERN_FUNCTION_END
+}
+
+void* _lantern_get_state_dict_keys (void * ivalue)
+{
+    LANTERN_FUNCTION_START
+    auto iv = reinterpret_cast<LanternObject<torch::IValue>*>(ivalue)->get();
+    auto d = iv.toGenericDict();
+    auto keys = new std::vector<std::string>;
+    for (auto i = d.begin(); i != d.end(); ++i)
+    {
+        std::string key = i->key().toString()->string();
+        keys->push_back(key);
+    }
+    return (void*) keys;
+    LANTERN_FUNCTION_END
+}
+
+void * _lantern_get_state_dict_values (void* ivalue)
+{
+    LANTERN_FUNCTION_START
+    auto iv = reinterpret_cast<LanternObject<torch::IValue>*>(ivalue)->get();
+    auto d = iv.toGenericDict();
+    auto values = new LanternObject<std::vector<torch::Tensor>>;
+    for (auto i = d.begin(); i != d.end(); ++i)
+    {
+        torch::Tensor value = i->value().toTensor();
+        values->get().push_back(value);
+    }
+    return (void*) values;
+    LANTERN_FUNCTION_END
+}
+
diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
index 6b1dcb558..d764e4981 100644
--- a/src/RcppExports.cpp
+++ b/src/RcppExports.cpp
@@ -23426,6 +23426,17 @@ BEGIN_RCPP
     return rcpp_result_gen;
 END_RCPP
 }
+// cpp_load_state_dict
+Rcpp::List cpp_load_state_dict(std::string path);
+RcppExport SEXP _torch_cpp_load_state_dict(SEXP pathSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< std::string >::type path(pathSEXP);
+    rcpp_result_gen = Rcpp::wrap(cpp_load_state_dict(path));
+    return rcpp_result_gen;
+END_RCPP
+}
 // cpp_torch_scalar
 Rcpp::XPtr<XPtrTorchScalar> cpp_torch_scalar(SEXP x);
 RcppExport SEXP _torch_cpp_torch_scalar(SEXP xSEXP) {
@@ -25537,6 +25548,7 @@ static const R_CallMethodDef CallEntries[] = {
     {"_torch_cpp_torch_reduction_sum", (DL_FUNC) &_torch_cpp_torch_reduction_sum, 0},
     {"_torch_cpp_tensor_save", (DL_FUNC) &_torch_cpp_tensor_save, 1},
     {"_torch_cpp_tensor_load", (DL_FUNC) &_torch_cpp_tensor_load, 1},
+    {"_torch_cpp_load_state_dict", (DL_FUNC) &_torch_cpp_load_state_dict, 1},
     {"_torch_cpp_torch_scalar", (DL_FUNC) &_torch_cpp_torch_scalar, 1},
     {"_torch_cpp_torch_scalar_dtype", (DL_FUNC) &_torch_cpp_torch_scalar_dtype, 1},
     {"_torch_cpp_torch_scalar_to_int", (DL_FUNC) &_torch_cpp_torch_scalar_to_int, 1},
diff --git a/src/lantern/lantern.h b/src/lantern/lantern.h
index 43566c6ce..aa085e9fb 100644
--- a/src/lantern/lantern.h
+++ b/src/lantern/lantern.h
@@ -470,6 +470,28 @@ extern "C"
   HOST_API void lantern_Tensor_index_put_scalar_ (void* self, void* index, void* rhs) { _lantern_Tensor_index_put_scalar_(self, index, rhs); LANTERN_HOST_HANDLER}
   LANTERN_API void (LANTERN_PTR _lantern_manual_seed) (int64_t seed);
   HOST_API void lantern_manual_seed (int64_t seed) {_lantern_manual_seed(seed); LANTERN_HOST_HANDLER}
+
+  LANTERN_API void* (LANTERN_PTR _lantern_load_state_dict) (const char * path);
+  HOST_API void * lantern_load_state_dict (const char * path)
+  {
+    void * ret = _lantern_load_state_dict(path);
+    LANTERN_HOST_HANDLER return ret;
+  }
+
+  LANTERN_API void* (LANTERN_PTR _lantern_get_state_dict_keys) (void * ivalue);
+  HOST_API void* lantern_get_state_dict_keys (void* ivalue)
+  {
+    void * ret = _lantern_get_state_dict_keys(ivalue);
+    LANTERN_HOST_HANDLER return ret;
+  }
+
+  LANTERN_API void* (LANTERN_PTR _lantern_get_state_dict_values) (void * ivalue);
+  HOST_API void* lantern_get_state_dict_values (void* ivalue)
+  {
+    void * ret = _lantern_get_state_dict_values(ivalue);
+    LANTERN_HOST_HANDLER return ret;
+  }
+
   /* Autogen Headers -- Start */
   LANTERN_API void* (LANTERN_PTR _lantern__cast_byte_tensor_bool)(void* self, void* non_blocking);
   HOST_API void* lantern__cast_byte_tensor_bool(void* self, void* non_blocking) { void* ret = _lantern__cast_byte_tensor_bool(self, non_blocking); LANTERN_HOST_HANDLER return ret; }
@@ -4178,6 +4200,9 @@ bool lanternInit(const std::string &libPath, std::string *pError)
   LOAD_SYMBOL(_lantern_Tensor_index_put_tensor_);
   LOAD_SYMBOL(_lantern_Tensor_index_put_scalar_);
   LOAD_SYMBOL(_lantern_manual_seed);
+  LOAD_SYMBOL(_lantern_load_state_dict);
+  LOAD_SYMBOL(_lantern_get_state_dict_keys);
+  LOAD_SYMBOL(_lantern_get_state_dict_values);
   /* Autogen Symbols -- Start */
   LOAD_SYMBOL(_lantern__cast_byte_tensor_bool)
   LOAD_SYMBOL(_lantern__cast_char_tensor_bool)
diff --git a/src/save.cpp b/src/save.cpp
index db26fc7c2..563862a89 100644
--- a/src/save.cpp
+++ b/src/save.cpp
@@ -16,3 +16,27 @@ Rcpp::XPtr<XPtrTorchTensor> cpp_tensor_load (std::string s)
   XPtrTorchTensor t = lantern_tensor_load(s.c_str());
   return make_xptr<XPtrTorchTensor>(t);
 }
+
+// [[Rcpp::export]]
+Rcpp::List cpp_load_state_dict (std::string path)
+{
+  XPtrTorch v = lantern_load_state_dict(path.c_str());
+  
+  XPtrTorchTensorList values = lantern_get_state_dict_values(v.get());
+  
+  XPtrTorch s = lantern_get_state_dict_keys(v.get());
+  int size = lantern_vector_string_size(s.get());
+  
+  std::vector<std::string> keys;
+  for (int i = 0; i < size; i++)
+  {
+    keys.push_back(std::string(lantern_vector_string_at(s.get(), i)));
+  }
+  
+  Rcpp::List L = Rcpp::List::create(
+    Rcpp::Named("keys") = keys, 
+    Rcpp::Named("values") = make_xptr<XPtrTorchTensorList>(values)
+  );
+  
+  return L;
+}
diff --git a/tests/testthat/assets/state_dict.pth b/tests/testthat/assets/state_dict.pth
new file mode 100644
index 0000000000000000000000000000000000000000..7b88a21fb8124fc244e957dadf85352fbe56bcbf
GIT binary patch
literal 1155
zcmWIWW@cev;NW1u00Im`42ea_8JT6N`YDMeiFyUuIc`pT3{fbcfvL8TK_h~Nfq@}E
zFSWRkF}WnaC^<tfzO*DWr<g0gC^e}xGbbg!BsH%%zbL-Uh^vrELnDHnfq|j8B)=#z
zJ++V-qJ_&XCqJ<y7$V43$P&TNz`$T=Vqj)sXkl(@VPR};XjI4=!OXzGkX%q&$mY#o
zQpg_Y&EU=K&DvJT;mzvJ*jC8dnVg)TlarcUl9``Z%;jH{l3J9S;*yzM!d1wn8C1w!
zQpgij$P0FFNqK&8AzucQhDHW+2C{W#W`+g^2Ii(lh5Q*{YXsod2tuq8>dXL{CR|b|
z5>zN!su$qR&ao;oiiwSpfdPbZhpG&N0ZORqLwpFg(#=T+Erd~m8-v5Zz~DeG2Dv&=
zP>9`zfq?;p@wwUxo2%jR;^t&VlB*HkU|?vlCl`a<U$9APJ;?1KjNASE4D4|Cm!%dJ
zXXfX*IWdErY|I5R<>I{bP$mWj1`rPLW&~02Jb@gq0vNdjJ%^y{Mh;&Y6y0Hr3~)Dr
z@)vqaKsO25r>G|JFkv+b9y{nJA^R4T$q>#ugJcpkoC3Vr*mR&O<d}7#ECvQpjt6mI
rG(RJV$G{*2%AgDk3<2J3piIu72vq=*4)A7W1F2$RU||5Mho}VrB)!!E

literal 0
HcmV?d00001

diff --git a/tests/testthat/test-save.R b/tests/testthat/test-save.R
index dd8636fce..68ce43d98 100644
--- a/tests/testthat/test-save.R
+++ b/tests/testthat/test-save.R
@@ -135,3 +135,18 @@ test_that("save alexnet like model", {
   
 })
 
+test_that("load a state dict created in python", {
+
+  # the state dict was create in python with  
+  # ones = torch.ones(3, 5)
+  # twos = torch.ones(3, 5) * 2
+  # value = {'ones': ones, 'twos': twos}
+  # torch.save(value, "assets/state_dict.pth", _use_new_zipfile_serialization=True)
+  
+  dict <- load_state_dict("assets/state_dict.pth")
+  expect_equal(names(dict), c("ones", "twos"))
+  expect_equal_to_tensor(dict$ones, torch_ones(3, 5))
+  expect_equal_to_tensor(dict$twos, torch_ones(3, 5) * 2)
+  
+})
+
-- 
GitLab


From e172a91fcf579d623530f288a98763dac8dcc937 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 18 Aug 2020 19:13:08 -0300
Subject: [PATCH 037/157] use names <- instead

---
 R/save.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/save.R b/R/save.R
index cb28749fb..992491521 100644
--- a/R/save.R
+++ b/R/save.R
@@ -71,6 +71,6 @@ load_state_dict <- function(path) {
   o <- cpp_load_state_dict(path)
   
   values <- TensorList$new(ptr = o$values)$to_r()
-  values <- setNames(values, o$keys)
+  names(values) <- o$keys
   values
 }
-- 
GitLab


From b1b4ca0b776662cc4d3c52e9ade409fe604797c7 Mon Sep 17 00:00:00 2001
From: skeydan <keydana@gmx.de>
Date: Wed, 19 Aug 2020 09:29:46 +0200
Subject: [PATCH 038/157] add nnf_sigmoid

---
 NAMESPACE          |  1 +
 R/nnf-activation.R | 12 ++++++++++++
 man/nnf_sigmoid.Rd | 15 +++++++++++++++
 3 files changed, 28 insertions(+)
 create mode 100644 man/nnf_sigmoid.Rd

diff --git a/NAMESPACE b/NAMESPACE
index c40ae8a97..688b9a5b4 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -275,6 +275,7 @@ export(nnf_rrelu)
 export(nnf_rrelu_)
 export(nnf_selu)
 export(nnf_selu_)
+export(nnf_sigmoid)
 export(nnf_smooth_l1_loss)
 export(nnf_soft_margin_loss)
 export(nnf_softmax)
diff --git a/R/nnf-activation.R b/R/nnf-activation.R
index a9f0dcdbc..5b2a62fa4 100644
--- a/R/nnf-activation.R
+++ b/R/nnf-activation.R
@@ -727,3 +727,15 @@ nnf_multi_head_attention_forward <- function(
     return(list(attn_output, NULL))
   }
 }
+
+#' Sigmoid
+#'
+#' Applies element-wise \eqn{Sigmoid(x_i) = \frac{1}{1 + exp(-x_i)}}
+#' 
+#' @inheritParams nnf_elu
+#'
+#' @export
+nnf_sigmoid <- function(input) {
+  torch_sigmoid(input)
+}
+
diff --git a/man/nnf_sigmoid.Rd b/man/nnf_sigmoid.Rd
new file mode 100644
index 000000000..ddd654e56
--- /dev/null
+++ b/man/nnf_sigmoid.Rd
@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nnf-activation.R
+\name{nnf_sigmoid}
+\alias{nnf_sigmoid}
+\title{Sigmoid}
+\usage{
+nnf_sigmoid(input)
+}
+\arguments{
+\item{input}{(N,*) tensor, where * means, any number of additional
+dimensions}
+}
+\description{
+Applies element-wise \eqn{Sigmoid(x_i) = \frac{1}{1 + exp(-x_i)}}
+}
-- 
GitLab


From 6006de218f477beea9ff89534c1627a5b378da58 Mon Sep 17 00:00:00 2001
From: skeydan <keydana@gmx.de>
Date: Wed, 19 Aug 2020 16:22:07 +0200
Subject: [PATCH 039/157] default to long

---
 R/tensor.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/tensor.R b/R/tensor.R
index 9b5e06f6b..831fdfb04 100644
--- a/R/tensor.R
+++ b/R/tensor.R
@@ -15,7 +15,7 @@ Tensor <- R7Class(
       if (is.null(dtype)) {
         
         if (is.integer(data)) {
-          dtype <- torch_int()
+          dtype <- torch_long()
         } else if (is.double(data)) {
           dtype <- torch_float() # default to float
         } else if (is.logical(data)) {
-- 
GitLab


From c10e2f4f29eeab2d94a04be33cb2ca266e002688 Mon Sep 17 00:00:00 2001
From: skeydan <keydana@gmx.de>
Date: Wed, 19 Aug 2020 20:32:07 +0200
Subject: [PATCH 040/157] fix tests

---
 tests/testthat/test-indexing.R | 10 +++++-----
 tests/testthat/test-tensor.R   |  6 +++---
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/tests/testthat/test-indexing.R b/tests/testthat/test-indexing.R
index e87b08c48..8cd1b1afc 100644
--- a/tests/testthat/test-indexing.R
+++ b/tests/testthat/test-indexing.R
@@ -8,9 +8,9 @@ test_that("[ works", {
   expect_equal(as_array(x[1:10:2,,]), as_array(x)[seq(1,10, by = 2),,])
   
   x <- torch_tensor(0:9)
-  expect_equal(as_array(x[-1]), 9)
-  expect_equal(as_array(x[-2:10]), c(8,9))
-  expect_equal(as_array(x[2:N]), c(1:9))
+  expect_equal(as_array(x[-1]$to(dtype = torch_int())), 9)
+  expect_equal(as_array(x[-2:10]$to(dtype = torch_int())), c(8,9))
+  expect_equal(as_array(x[2:N]$to(dtype = torch_int())), c(1:9))
   
   x <- torch_randn(c(10,10,10,10))
   expect_equal(as_array(x[1,..]), as_array(x)[1,,,])
@@ -26,10 +26,10 @@ test_that("[ works", {
   
   x <- torch_tensor(1:10)
   y <- 1:10
-  expect_equal_to_r(x[c(1,3,2,5)], y[c(1,3,2,5)])
+  expect_equal_to_r(x[c(1,3,2,5)]$to(dtype = torch_int()), y[c(1,3,2,5)])
   
   index <- 1:3
-  expect_equal_to_r(x[index], y[index])
+  expect_equal_to_r(x[index]$to(dtype = torch_int()), y[index])
   
   x <- torch_randn(10, 10)
   x[c(2,3,1), c(3,2,1)]
diff --git a/tests/testthat/test-tensor.R b/tests/testthat/test-tensor.R
index 72bda733a..8688db356 100644
--- a/tests/testthat/test-tensor.R
+++ b/tests/testthat/test-tensor.R
@@ -39,13 +39,13 @@ test_that("Numeric tensors", {
 test_that("Integer tensors", {
   
   x <- 1:4
-  expect_equal_to_r(torch_tensor(x), x)
+  expect_equal_to_r(torch_tensor(x)$to(dtype = torch_int()), x)
   
   x <- matrix(c(1:4), ncol = 2)
-  expect_equal_to_r(torch_tensor(x), x)
+  expect_equal_to_r(torch_tensor(x)$to(dtype = torch_int()), x)
   
   x <- array(c(1:8), dim = c(2,2,2))
-  expect_equal_to_r(torch_tensor(x), x)
+  expect_equal_to_r(torch_tensor(x)$to(dtype = torch_int()), x)
   
 })
 
-- 
GitLab


From 7f903341ce83cd71643346abfb75f73e3e8f6a16 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 19 Aug 2020 16:17:09 -0300
Subject: [PATCH 041/157] read in binary mode to fix on windows

---
 lantern/src/Save.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/lantern/src/Save.cpp b/lantern/src/Save.cpp
index 0211b74b9..acb0b2952 100644
--- a/lantern/src/Save.cpp
+++ b/lantern/src/Save.cpp
@@ -71,7 +71,7 @@ void _lantern_test_print (void * x)
 void* _lantern_load_state_dict (const char * path)
 {
     LANTERN_FUNCTION_START
-    std::ifstream file(path);
+    std::ifstream file(path, std::ios::binary);
     std::vector<char> data((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
     torch::IValue ivalue = torch::pickle_load(data);   
     return (void*) new LanternObject<torch::IValue>(ivalue);
-- 
GitLab


From 83c2bf6f203fe4ab979ee6262f979c3de98ef99c Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 19 Aug 2020 16:39:57 -0300
Subject: [PATCH 042/157] add deleters so memory get freed

---
 lantern/include/lantern/lantern.h | 16 ++++++++++++++++
 lantern/src/Delete.cpp            | 14 ++++++++++++++
 src/lantern/lantern.h             | 16 ++++++++++++++++
 src/save.cpp                      |  8 +++++---
 src/torch_types.h                 | 14 ++++++++++++++
 5 files changed, 65 insertions(+), 3 deletions(-)

diff --git a/lantern/include/lantern/lantern.h b/lantern/include/lantern/lantern.h
index aa085e9fb..d9eb823ad 100644
--- a/lantern/include/lantern/lantern.h
+++ b/lantern/include/lantern/lantern.h
@@ -492,6 +492,20 @@ extern "C"
     LANTERN_HOST_HANDLER return ret;
   }
 
+  LANTERN_API void (LANTERN_PTR _lantern_IValue_delete) (void * x);
+  HOST_API void lantern_IValue_delete (void* x)
+  {
+    _lantern_get_state_dict_values(x);
+    LANTERN_HOST_HANDLER;
+  }
+
+  LANTERN_API void (LANTERN_PTR _lantern_vector_string_delete) (void * x);
+  HOST_API void lantern_vector_string_delete (void* x)
+  {
+    _lantern_vector_string_delete(x);
+    LANTERN_HOST_HANDLER;
+  }
+
   /* Autogen Headers -- Start */
   LANTERN_API void* (LANTERN_PTR _lantern__cast_byte_tensor_bool)(void* self, void* non_blocking);
   HOST_API void* lantern__cast_byte_tensor_bool(void* self, void* non_blocking) { void* ret = _lantern__cast_byte_tensor_bool(self, non_blocking); LANTERN_HOST_HANDLER return ret; }
@@ -4203,6 +4217,8 @@ bool lanternInit(const std::string &libPath, std::string *pError)
   LOAD_SYMBOL(_lantern_load_state_dict);
   LOAD_SYMBOL(_lantern_get_state_dict_keys);
   LOAD_SYMBOL(_lantern_get_state_dict_values);
+  LOAD_SYMBOL(_lantern_IValue_delete);
+  LOAD_SYMBOL(_lantern_vector_string_delete);
   /* Autogen Symbols -- Start */
   LOAD_SYMBOL(_lantern__cast_byte_tensor_bool)
   LOAD_SYMBOL(_lantern__cast_char_tensor_bool)
diff --git a/lantern/src/Delete.cpp b/lantern/src/Delete.cpp
index 083afa58f..2cd53ab91 100644
--- a/lantern/src/Delete.cpp
+++ b/lantern/src/Delete.cpp
@@ -180,4 +180,18 @@ void _lantern_const_char_delete (const char * x)
   LANTERN_FUNCTION_START
   delete []x;
   LANTERN_FUNCTION_END_VOID
+}
+
+void _lantern_IValue_delete (void * x)
+{
+  LANTERN_FUNCTION_START
+  lantern_delete<LanternObject<torch::IValue>>(x);
+  LANTERN_FUNCTION_END_VOID
+}
+
+void _lantern_vector_string_delete (void * x)
+{
+  LANTERN_FUNCTION_START
+  lantern_delete<std::vector<std::string>>(x);
+  LANTERN_FUNCTION_END_VOID
 }
\ No newline at end of file
diff --git a/src/lantern/lantern.h b/src/lantern/lantern.h
index aa085e9fb..d9eb823ad 100644
--- a/src/lantern/lantern.h
+++ b/src/lantern/lantern.h
@@ -492,6 +492,20 @@ extern "C"
     LANTERN_HOST_HANDLER return ret;
   }
 
+  LANTERN_API void (LANTERN_PTR _lantern_IValue_delete) (void * x);
+  HOST_API void lantern_IValue_delete (void* x)
+  {
+    _lantern_get_state_dict_values(x);
+    LANTERN_HOST_HANDLER;
+  }
+
+  LANTERN_API void (LANTERN_PTR _lantern_vector_string_delete) (void * x);
+  HOST_API void lantern_vector_string_delete (void* x)
+  {
+    _lantern_vector_string_delete(x);
+    LANTERN_HOST_HANDLER;
+  }
+
   /* Autogen Headers -- Start */
   LANTERN_API void* (LANTERN_PTR _lantern__cast_byte_tensor_bool)(void* self, void* non_blocking);
   HOST_API void* lantern__cast_byte_tensor_bool(void* self, void* non_blocking) { void* ret = _lantern__cast_byte_tensor_bool(self, non_blocking); LANTERN_HOST_HANDLER return ret; }
@@ -4203,6 +4217,8 @@ bool lanternInit(const std::string &libPath, std::string *pError)
   LOAD_SYMBOL(_lantern_load_state_dict);
   LOAD_SYMBOL(_lantern_get_state_dict_keys);
   LOAD_SYMBOL(_lantern_get_state_dict_values);
+  LOAD_SYMBOL(_lantern_IValue_delete);
+  LOAD_SYMBOL(_lantern_vector_string_delete);
   /* Autogen Symbols -- Start */
   LOAD_SYMBOL(_lantern__cast_byte_tensor_bool)
   LOAD_SYMBOL(_lantern__cast_char_tensor_bool)
diff --git a/src/save.cpp b/src/save.cpp
index 563862a89..8d08ae791 100644
--- a/src/save.cpp
+++ b/src/save.cpp
@@ -20,17 +20,19 @@ Rcpp::XPtr<XPtrTorchTensor> cpp_tensor_load (std::string s)
 // [[Rcpp::export]]
 Rcpp::List cpp_load_state_dict (std::string path)
 {
-  XPtrTorch v = lantern_load_state_dict(path.c_str());
+  XPtrTorchIValue v = lantern_load_state_dict(path.c_str());
   
   XPtrTorchTensorList values = lantern_get_state_dict_values(v.get());
   
-  XPtrTorch s = lantern_get_state_dict_keys(v.get());
+  XPtrTorchvector_string s = lantern_get_state_dict_keys(v.get());
   int size = lantern_vector_string_size(s.get());
   
   std::vector<std::string> keys;
   for (int i = 0; i < size; i++)
   {
-    keys.push_back(std::string(lantern_vector_string_at(s.get(), i)));
+    const char * k = lantern_vector_string_at(s.get(), i);
+    keys.push_back(std::string(k));
+    lantern_const_char_delete(k);
   }
   
   Rcpp::List L = Rcpp::List::create(
diff --git a/src/torch_types.h b/src/torch_types.h
index e55d0a6ae..ce363bf7e 100644
--- a/src/torch_types.h
+++ b/src/torch_types.h
@@ -194,6 +194,20 @@ public:
   }
 };
 
+class XPtrTorchIValue : public XPtrTorch {
+public:
+  XPtrTorchIValue (void * x) : XPtrTorch {NULL} {
+    this->set(std::shared_ptr<void>(x, lantern_IValue_delete));
+  }
+};
+
+class XPtrTorchvector_string : public XPtrTorch {
+public:
+  XPtrTorchvector_string (void * x) : XPtrTorch {NULL} {
+    this->set(std::shared_ptr<void>(x, lantern_vector_string_delete));
+  }
+};
+
 template<class T>
 class nullable {
 public:
-- 
GitLab


From 702091a5a9386c532e49d4a10cbbccace3e0c7f1 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 19 Aug 2020 16:58:44 -0300
Subject: [PATCH 043/157] add dcocumentation and export

---
 NAMESPACE              |  1 +
 R/save.R               | 15 +++++++++++++++
 man/load_state_dict.Rd | 24 ++++++++++++++++++++++++
 3 files changed, 40 insertions(+)
 create mode 100644 man/load_state_dict.Rd

diff --git a/NAMESPACE b/NAMESPACE
index c40ae8a97..938093473 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -99,6 +99,7 @@ export(is_torch_dtype)
 export(is_torch_layout)
 export(is_torch_memory_format)
 export(is_torch_qscheme)
+export(load_state_dict)
 export(nn_adaptive_avg_pool1d)
 export(nn_adaptive_avg_pool2d)
 export(nn_adaptive_avg_pool3d)
diff --git a/R/save.R b/R/save.R
index 992491521..ff498cd2e 100644
--- a/R/save.R
+++ b/R/save.R
@@ -66,6 +66,21 @@ torch_load_module <- function(obj) {
   obj$module
 }
 
+#' Load a state dict file
+#' 
+#' This function should only be used to load models saved in python.
+#' For it to work correctly you need to use `torch.save` with the flag:
+#' `_use_new_zipfile_serialization=True` and also remove all `nn.Parameter`
+#' classes from the tensors in the dict. 
+#' 
+#' The above might change with development of [this](https://github.com/pytorch/pytorch/issues/37213) 
+#' in pytorch's C++ api.
+#' 
+#' @param path to the state dict file
+#' 
+#' @return a named list of tensors.
+#'
+#' @export
 load_state_dict <- function(path) {
   path <- normalizePath(path)
   o <- cpp_load_state_dict(path)
diff --git a/man/load_state_dict.Rd b/man/load_state_dict.Rd
new file mode 100644
index 000000000..4fc5ae4f1
--- /dev/null
+++ b/man/load_state_dict.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/save.R
+\name{load_state_dict}
+\alias{load_state_dict}
+\title{Load a state dict file}
+\usage{
+load_state_dict(path)
+}
+\arguments{
+\item{path}{to the state dict file}
+}
+\value{
+a named list of tensors.
+}
+\description{
+This function should only be used to load models saved in python.
+For it to work correctly you need to use \code{torch.save} with the flag:
+\verb{_use_new_zipfile_serialization=True} and also remove all \code{nn.Parameter}
+classes from the tensors in the dict.
+}
+\details{
+The above might change with development of \href{https://github.com/pytorch/pytorch/issues/37213}{this}
+in pytorch's C++ api.
+}
-- 
GitLab


From ef09c93ede2026a8dc6e96bab1663dd5cc95e8a2 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 20 Aug 2020 11:43:34 -0300
Subject: [PATCH 044/157] fix #226

---
 R/optim.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/optim.R b/R/optim.R
index 12e083750..9d6295a78 100644
--- a/R/optim.R
+++ b/R/optim.R
@@ -58,7 +58,7 @@ Optimizer <- R6::R6Class(
         
         if (is_optim_required(self$defaults[[nm]]) && !nm %in% names(param_group)) {
           value_error("parameter group didn't specify a value of required ",
-                      "optimization parameter {name}")
+                      "optimization parameter {nm}")
         } else if (!nm %in% names(param_group)) {
           param_group[[nm]] <- self$defaults[[nm]]
         }
-- 
GitLab


From 25044b465e9e529df8c4d1325cac6fdb270397a3 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 20 Aug 2020 15:46:40 -0300
Subject: [PATCH 045/157] add warning message to mse loss

---
 R/conditions.R                 | 12 ++++++------
 R/nnf-loss.R                   | 10 ++++++++++
 tests/testthat/test-nnf-loss.R | 17 +++++++++++++++++
 3 files changed, 33 insertions(+), 6 deletions(-)
 create mode 100644 tests/testthat/test-nnf-loss.R

diff --git a/R/conditions.R b/R/conditions.R
index bf3de8df8..43d267ceb 100644
--- a/R/conditions.R
+++ b/R/conditions.R
@@ -6,16 +6,16 @@ runtime_error <- function(..., env = rlang::caller_env()) {
   rlang::abort(glue::glue(..., .envir = env), class = "runtime_error")
 }
 
-not_implemented_error <- function(...) {
-  rlang::abort(glue::glue(...), class = "not_implemented_error")
+not_implemented_error <- function(..., env = rlang::caller_env()) {
+  rlang::abort(glue::glue(..., .envir = env), class = "not_implemented_error")
 }
 
-warn <- function(...) {
-  rlang::warn(glue::glue(...), class = "warning")
+warn <- function(..., env = rlang::caller_env()) {
+  rlang::warn(glue::glue(..., .envir = env), class = "warning")
 }
 
-stop_iteration_error <- function(...) {
-  rlang::abort(glue::glue(...), class = "stop_iteration_error")
+stop_iteration_error <- function(..., env = rlang::caller_env()) {
+  rlang::abort(glue::glue(..., .envir = env), class = "stop_iteration_error")
 }
 
 inform <- rlang::inform
diff --git a/R/nnf-loss.R b/R/nnf-loss.R
index 7ded46150..b24a42d6c 100644
--- a/R/nnf-loss.R
+++ b/R/nnf-loss.R
@@ -67,6 +67,16 @@ nnf_kl_div <- function(input, target, reduction = "mean") {
 #' @export
 nnf_mse_loss <- function(input, target, reduction = "mean") {
   
+  if (!all(target$shape == input$shape)) {
+    
+    target_shape <- paste0("(", paste(target$shape, collapse = ","), ")")
+    input_shape <- paste0("(", paste(input$shape, collapse = ","), ")")
+    
+    warn("Using a target size {target_shape} that is different to the input size {input_shape}. ",
+         "This will likely lead to incorrect results due to broadcasting. ",
+         "Please ensure they have the same size.")
+  }
+  
   if (target$requires_grad) {
     ret <- (input - target) ^ 2
     if (!is.null(reduction)) {
diff --git a/tests/testthat/test-nnf-loss.R b/tests/testthat/test-nnf-loss.R
new file mode 100644
index 000000000..221123f95
--- /dev/null
+++ b/tests/testthat/test-nnf-loss.R
@@ -0,0 +1,17 @@
+test_that("nnf_mse_loss", {
+
+  x <- torch_tensor(c(1,2,3))
+  y <- torch_tensor(c(2,3,4))
+  
+  o <- nnf_mse_loss(x, y)
+  
+  expect_equal_to_r(o, 1)
+  
+  y <- y$unsqueeze(2)
+  
+  expect_warning(
+    nnf_mse_loss(x, y),
+    regexp = "target size"
+  )
+  
+})
-- 
GitLab


From c63f99afbc593a227c9ac21a90f922f3672792ba Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 21 Aug 2020 13:51:25 -0300
Subject: [PATCH 046/157] match arg, fix #224

---
 R/nnf-loss.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/R/nnf-loss.R b/R/nnf-loss.R
index b24a42d6c..fc52fd0fc 100644
--- a/R/nnf-loss.R
+++ b/R/nnf-loss.R
@@ -416,6 +416,7 @@ nnf_nll_loss <- function(input, target, weight = NULL, ignore_index = -100,
 #' @export
 nnf_cross_entropy <- function(input, target, weight=NULL, ignore_index=-100, 
                               reduction=c("mean", "sum", "none")) {
+  reduction <- match.arg(reduction)
   torch_nll_loss(self = torch_log_softmax(input, 1), target = target, weight = weight, 
                  reduction = reduction_enum(reduction), ignore_index = ignore_index)
 }
-- 
GitLab


From 17b454448e753f8a9cb90943b5f1c3c2d775c0a4 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 21 Aug 2020 13:52:16 -0300
Subject: [PATCH 047/157] also match arg here

---
 R/nnf-loss.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/R/nnf-loss.R b/R/nnf-loss.R
index fc52fd0fc..fbdd30986 100644
--- a/R/nnf-loss.R
+++ b/R/nnf-loss.R
@@ -438,6 +438,7 @@ nnf_cross_entropy <- function(input, target, weight=NULL, ignore_index=-100,
 nnf_binary_cross_entropy_with_logits <- function(input, target, weight = NULL, 
                                                  reduction = c("mean", "sum", "none"), 
                                                  pos_weight = NULL) {
+  reduction <- match.arg(reduction)
   torch_binary_cross_entropy_with_logits(input, target, weight, pos_weight, 
                                          reduction_enum(reduction))
 }
-- 
GitLab


From 5c1fc2239a3b2e6bf98da2e45d41813bac5d7182 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 21 Aug 2020 17:21:41 -0300
Subject: [PATCH 048/157] implement integer 64 convertion

---
 R/tensor.R                        |  3 +++
 lantern/include/lantern/lantern.h | 10 ++++++++++
 lantern/src/Tensor.cpp            |  8 ++++++++
 src/lantern/lantern.h             | 10 ++++++++++
 src/tensor.cpp                    | 18 +++++++++++++++++-
 tests/testthat/test-tensor.R      | 16 ++++++++++++++++
 6 files changed, 64 insertions(+), 1 deletion(-)

diff --git a/R/tensor.R b/R/tensor.R
index 831fdfb04..cd8fa73d1 100644
--- a/R/tensor.R
+++ b/R/tensor.R
@@ -174,6 +174,9 @@ as_array.torch_tensor <- function(x) {
     out <- aperm(array(a$vec, dim = rev(a$dim)), seq(length(a$dim), 1))
   }
   
+  if (x$dtype() == torch_long() && !inherits(out, "integer64"))
+    class(out) <- c(class(out), "integer64")
+  
   out
 }
 
diff --git a/lantern/include/lantern/lantern.h b/lantern/include/lantern/lantern.h
index d9eb823ad..d1fa85f29 100644
--- a/lantern/include/lantern/lantern.h
+++ b/lantern/include/lantern/lantern.h
@@ -506,6 +506,15 @@ extern "C"
     LANTERN_HOST_HANDLER;
   }
 
+  LANTERN_API int64_t *(LANTERN_PTR _lantern_Tensor_data_ptr_int64_t)(void *self);
+  HOST_API int64_t* lantern_Tensor_data_ptr_int64_t (void* self)
+  {
+    int64_t* ret = _lantern_Tensor_data_ptr_int64_t(self);
+    LANTERN_HOST_HANDLER;
+    return ret;
+  }
+
+
   /* Autogen Headers -- Start */
   LANTERN_API void* (LANTERN_PTR _lantern__cast_byte_tensor_bool)(void* self, void* non_blocking);
   HOST_API void* lantern__cast_byte_tensor_bool(void* self, void* non_blocking) { void* ret = _lantern__cast_byte_tensor_bool(self, non_blocking); LANTERN_HOST_HANDLER return ret; }
@@ -4219,6 +4228,7 @@ bool lanternInit(const std::string &libPath, std::string *pError)
   LOAD_SYMBOL(_lantern_get_state_dict_values);
   LOAD_SYMBOL(_lantern_IValue_delete);
   LOAD_SYMBOL(_lantern_vector_string_delete);
+  LOAD_SYMBOL(_lantern_Tensor_data_ptr_int64_t);
   /* Autogen Symbols -- Start */
   LOAD_SYMBOL(_lantern__cast_byte_tensor_bool)
   LOAD_SYMBOL(_lantern__cast_char_tensor_bool)
diff --git a/lantern/src/Tensor.cpp b/lantern/src/Tensor.cpp
index 9a99adeee..b5f3a323b 100644
--- a/lantern/src/Tensor.cpp
+++ b/lantern/src/Tensor.cpp
@@ -98,6 +98,14 @@ uint8_t *_lantern_Tensor_data_ptr_uint8_t(void *self)
   LANTERN_FUNCTION_END
 }
 
+int64_t *_lantern_Tensor_data_ptr_int64_t(void *self)
+{
+  LANTERN_FUNCTION_START
+  torch::Tensor x = reinterpret_cast<LanternObject<torch::Tensor> *>(self)->get();
+  return x.data_ptr<int64_t>();
+  LANTERN_FUNCTION_END
+}
+
 int32_t *_lantern_Tensor_data_ptr_int32_t(void *self)
 {
   LANTERN_FUNCTION_START
diff --git a/src/lantern/lantern.h b/src/lantern/lantern.h
index d9eb823ad..d1fa85f29 100644
--- a/src/lantern/lantern.h
+++ b/src/lantern/lantern.h
@@ -506,6 +506,15 @@ extern "C"
     LANTERN_HOST_HANDLER;
   }
 
+  LANTERN_API int64_t *(LANTERN_PTR _lantern_Tensor_data_ptr_int64_t)(void *self);
+  HOST_API int64_t* lantern_Tensor_data_ptr_int64_t (void* self)
+  {
+    int64_t* ret = _lantern_Tensor_data_ptr_int64_t(self);
+    LANTERN_HOST_HANDLER;
+    return ret;
+  }
+
+
   /* Autogen Headers -- Start */
   LANTERN_API void* (LANTERN_PTR _lantern__cast_byte_tensor_bool)(void* self, void* non_blocking);
   HOST_API void* lantern__cast_byte_tensor_bool(void* self, void* non_blocking) { void* ret = _lantern__cast_byte_tensor_bool(self, non_blocking); LANTERN_HOST_HANDLER return ret; }
@@ -4219,6 +4228,7 @@ bool lanternInit(const std::string &libPath, std::string *pError)
   LOAD_SYMBOL(_lantern_get_state_dict_values);
   LOAD_SYMBOL(_lantern_IValue_delete);
   LOAD_SYMBOL(_lantern_vector_string_delete);
+  LOAD_SYMBOL(_lantern_Tensor_data_ptr_int64_t);
   /* Autogen Symbols -- Start */
   LOAD_SYMBOL(_lantern__cast_byte_tensor_bool)
   LOAD_SYMBOL(_lantern__cast_char_tensor_bool)
diff --git a/src/tensor.cpp b/src/tensor.cpp
index f694d9ebb..17c39f6e5 100644
--- a/src/tensor.cpp
+++ b/src/tensor.cpp
@@ -105,6 +105,22 @@ Rcpp::List tensor_to_r_array_int32_t (XPtrTorchTensor x) {
   return Rcpp::List::create(Rcpp::Named("vec") = vec, Rcpp::Named("dim") = tensor_dimensions(x));
 }
 
+Rcpp::List tensor_to_r_array_int64_t (XPtrTorchTensor x)
+{
+  XPtrTorchTensor ten = lantern_Tensor_contiguous(x.get());
+  auto d_ptr = lantern_Tensor_data_ptr_int64_t(ten.get());
+  
+  int64_t len = lantern_Tensor_numel(ten.get());
+  Rcpp::NumericVector vec(len);         // storage vehicle we return them in
+  
+  // transfers values 'keeping bits' but changing type
+  // using reinterpret_cast would get us a warning
+  std::memcpy(&(vec[0]), d_ptr, len * sizeof(double));
+  
+  vec.attr("class") = "integer64"; 
+  return Rcpp::List::create(Rcpp::Named("vec") = vec, Rcpp::Named("dim") = tensor_dimensions(x));
+}
+
 Rcpp::List tensor_to_r_array_bool (XPtrTorchTensor x) {
   XPtrTorchTensor ten = lantern_Tensor_contiguous(x.get());
   auto d_ptr = lantern_Tensor_data_ptr_bool(ten.get());
@@ -142,7 +158,7 @@ Rcpp::List cpp_as_array (Rcpp::XPtr<XPtrTorchTensor> x) {
   }
   
   if (dtype == "Long") {
-    return tensor_to_r_array_int32_t(*x.get());
+    return tensor_to_r_array_int64_t(*x.get());
   }
   
   Rcpp::stop("dtype not handled");
diff --git a/tests/testthat/test-tensor.R b/tests/testthat/test-tensor.R
index 8688db356..1c4b33cb2 100644
--- a/tests/testthat/test-tensor.R
+++ b/tests/testthat/test-tensor.R
@@ -47,6 +47,22 @@ test_that("Integer tensors", {
   x <- array(c(1:8), dim = c(2,2,2))
   expect_equal_to_r(torch_tensor(x)$to(dtype = torch_int()), x)
   
+  x <- 1:5
+  expect_equal_to_r(torch_tensor(1:5), bit64::as.integer64(x))
+  
+  x <- matrix(c(1:4), ncol = 2)
+  e <- bit64::as.integer64(x)
+  e <- matrix(e, ncol = 2)
+  class(e) <- c(class(e), "integer64")
+  expect_equal_to_r(torch_tensor(x), e)
+  
+  x <- array(c(1:8), dim = c(2,2,2))
+  e <- bit64::as.integer64(x)
+  e <- array(e, dim = dim(x))
+  class(e) <- c(class(e), "integer64")
+  
+  expect_equal_to_r(torch_tensor(x), e)
+  
 })
 
 test_that("Logical tensors", {
-- 
GitLab


From 5ab86d54ece47376f000ac4d2f24256d1e02c9b6 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 21 Aug 2020 20:21:41 -0300
Subject: [PATCH 049/157] interger64 -> torch_long

---
 R/RcppExports.R              |  4 ++--
 R/tensor.R                   |  6 ++++--
 src/RcppExports.cpp          |  9 +++++----
 src/indexing.cpp             |  6 +++---
 src/tensor.cpp               | 32 +++++++++++++++++++++++++-------
 tests/testthat/test-tensor.R | 16 ++++++++++++++++
 6 files changed, 55 insertions(+), 18 deletions(-)

diff --git a/R/RcppExports.R b/R/RcppExports.R
index c9671a8b2..df7bcb30b 100644
--- a/R/RcppExports.R
+++ b/R/RcppExports.R
@@ -7089,8 +7089,8 @@ cpp_torch_tensor_dtype <- function(x) {
     .Call('_torch_cpp_torch_tensor_dtype', PACKAGE = 'torchpkg', x)
 }
 
-cpp_torch_tensor <- function(x, dim, options, requires_grad) {
-    .Call('_torch_cpp_torch_tensor', PACKAGE = 'torchpkg', x, dim, options, requires_grad)
+cpp_torch_tensor <- function(x, dim, options, requires_grad, is_integer64) {
+    .Call('_torch_cpp_torch_tensor', PACKAGE = 'torchpkg', x, dim, options, requires_grad, is_integer64)
 }
 
 cpp_as_array <- function(x) {
diff --git a/R/tensor.R b/R/tensor.R
index cd8fa73d1..79d8bbcdf 100644
--- a/R/tensor.R
+++ b/R/tensor.R
@@ -16,11 +16,13 @@ Tensor <- R7Class(
         
         if (is.integer(data)) {
           dtype <- torch_long()
+        } else if (bit64::is.integer64(data)) {
+          dtype <- torch_long()
         } else if (is.double(data)) {
           dtype <- torch_float() # default to float
         } else if (is.logical(data)) {
           dtype <- torch_bool()
-        }
+        } 
         
       }
       
@@ -36,7 +38,7 @@ Tensor <- R7Class(
       
       
       self$ptr <- cpp_torch_tensor(data, rev(dimension), options$ptr, 
-                                   requires_grad)
+                                   requires_grad, inherits(data, "integer64"))
     },
     print = function() {
       cat(sprintf("torch_tensor \n"))
diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
index d764e4981..fdf00e1d2 100644
--- a/src/RcppExports.cpp
+++ b/src/RcppExports.cpp
@@ -23558,8 +23558,8 @@ BEGIN_RCPP
 END_RCPP
 }
 // cpp_torch_tensor
-Rcpp::XPtr<XPtrTorchTensor> cpp_torch_tensor(SEXP x, std::vector<std::int64_t> dim, Rcpp::XPtr<XPtrTorchTensorOptions> options, bool requires_grad);
-RcppExport SEXP _torch_cpp_torch_tensor(SEXP xSEXP, SEXP dimSEXP, SEXP optionsSEXP, SEXP requires_gradSEXP) {
+Rcpp::XPtr<XPtrTorchTensor> cpp_torch_tensor(SEXP x, std::vector<std::int64_t> dim, Rcpp::XPtr<XPtrTorchTensorOptions> options, bool requires_grad, bool is_integer64);
+RcppExport SEXP _torch_cpp_torch_tensor(SEXP xSEXP, SEXP dimSEXP, SEXP optionsSEXP, SEXP requires_gradSEXP, SEXP is_integer64SEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
     Rcpp::RNGScope rcpp_rngScope_gen;
@@ -23567,7 +23567,8 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< std::vector<std::int64_t> >::type dim(dimSEXP);
     Rcpp::traits::input_parameter< Rcpp::XPtr<XPtrTorchTensorOptions> >::type options(optionsSEXP);
     Rcpp::traits::input_parameter< bool >::type requires_grad(requires_gradSEXP);
-    rcpp_result_gen = Rcpp::wrap(cpp_torch_tensor(x, dim, options, requires_grad));
+    Rcpp::traits::input_parameter< bool >::type is_integer64(is_integer64SEXP);
+    rcpp_result_gen = Rcpp::wrap(cpp_torch_tensor(x, dim, options, requires_grad, is_integer64));
     return rcpp_result_gen;
 END_RCPP
 }
@@ -25560,7 +25561,7 @@ static const R_CallMethodDef CallEntries[] = {
     {"_torch_cpp_Storage_data_ptr", (DL_FUNC) &_torch_cpp_Storage_data_ptr, 1},
     {"_torch_cpp_torch_tensor_print", (DL_FUNC) &_torch_cpp_torch_tensor_print, 1},
     {"_torch_cpp_torch_tensor_dtype", (DL_FUNC) &_torch_cpp_torch_tensor_dtype, 1},
-    {"_torch_cpp_torch_tensor", (DL_FUNC) &_torch_cpp_torch_tensor, 4},
+    {"_torch_cpp_torch_tensor", (DL_FUNC) &_torch_cpp_torch_tensor, 5},
     {"_torch_cpp_as_array", (DL_FUNC) &_torch_cpp_as_array, 1},
     {"_torch_cpp_tensor_dim", (DL_FUNC) &_torch_cpp_tensor_dim, 1},
     {"_torch_cpp_tensor_numel", (DL_FUNC) &_torch_cpp_tensor_numel, 1},
diff --git a/src/indexing.cpp b/src/indexing.cpp
index 06a1f9219..0b2021fe3 100644
--- a/src/indexing.cpp
+++ b/src/indexing.cpp
@@ -86,7 +86,7 @@ std::vector<Rcpp::RObject> evaluate_slices (std::vector<Rcpp::RObject> quosures,
 
 Rcpp::XPtr<XPtrTorchTensor> cpp_torch_tensor (SEXP x, std::vector<std::int64_t> dim,
                                               Rcpp::XPtr<XPtrTorchTensorOptions> options,
-                                              bool requires_grad);
+                                              bool requires_grad, bool is_integer64);
 
 XPtrTorchTensorIndex slices_to_index (std::vector<Rcpp::RObject> slices, bool drop)
 {
@@ -192,7 +192,7 @@ XPtrTorchTensorIndex slices_to_index (std::vector<Rcpp::RObject> slices, bool dr
       options = lantern_TensorOptions_dtype(options.get(), XPtrTorchDtype(lantern_Dtype_int64()).get());
       std::vector<int64_t> dim = {LENGTH(slice)};
       
-      Rcpp::XPtr<XPtrTorchTensor> tensor = cpp_torch_tensor(v, dim, make_xptr<XPtrTorchTensorOptions>(options), false);
+      Rcpp::XPtr<XPtrTorchTensor> tensor = cpp_torch_tensor(v, dim, make_xptr<XPtrTorchTensorOptions>(options), false, false);
       
       lantern_TensorIndex_append_tensor(index.get(), tensor->get());
       continue;
@@ -209,7 +209,7 @@ XPtrTorchTensorIndex slices_to_index (std::vector<Rcpp::RObject> slices, bool dr
       options = lantern_TensorOptions_dtype(options.get(), XPtrTorchDtype(lantern_Dtype_bool()).get());
       std::vector<int64_t> dim = {LENGTH(slice)};
       
-      Rcpp::XPtr<XPtrTorchTensor> tensor = cpp_torch_tensor(v, dim, make_xptr<XPtrTorchTensorOptions>(options), false);
+      Rcpp::XPtr<XPtrTorchTensor> tensor = cpp_torch_tensor(v, dim, make_xptr<XPtrTorchTensorOptions>(options), false, false);
       
       lantern_TensorIndex_append_tensor(index.get(), tensor->get());
       continue;
diff --git a/src/tensor.cpp b/src/tensor.cpp
index 17c39f6e5..f6df6ec88 100644
--- a/src/tensor.cpp
+++ b/src/tensor.cpp
@@ -29,10 +29,17 @@ XPtrTorchTensor tensor_from_r_array (const SEXP x, std::vector<int64_t> dim, std
 
   XPtrTorchTensorOptions options = lantern_TensorOptions();
   
-  if (dtype == "double") {
+  if (dtype == "double") 
+  {
     options = lantern_TensorOptions_dtype(options.get(), XPtrTorchDtype(lantern_Dtype_float64()).get());
-  } else if (dtype == "int") {
+  } 
+  else if (dtype == "int") 
+  {
     options = lantern_TensorOptions_dtype(options.get(), XPtrTorchDtype(lantern_Dtype_int32()).get());
+  } 
+  else if (dtype == "int64")
+  {
+    options = lantern_TensorOptions_dtype(options.get(), XPtrTorchDtype(lantern_Dtype_int64()).get());
   }
 
   options = lantern_TensorOptions_device(options.get(), XPtrTorchDevice(lantern_Device("cpu", 0, false)).get());
@@ -55,17 +62,28 @@ XPtrTorchTensor tensor_from_r_array (const SEXP x, std::vector<int64_t> dim, std
 // [[Rcpp::export]]
 Rcpp::XPtr<XPtrTorchTensor> cpp_torch_tensor (SEXP x, std::vector<std::int64_t> dim,
                                             Rcpp::XPtr<XPtrTorchTensorOptions> options,
-                                            bool requires_grad) {
+                                            bool requires_grad, bool is_integer64) {
 
   XPtrTorchTensor tensor(nullptr);
 
-  if (TYPEOF(x) == INTSXP) {
+  if (TYPEOF(x) == INTSXP) 
+  {
     tensor = tensor_from_r_array<INTSXP>(x, dim, "int");
-  } else if (TYPEOF(x) == REALSXP) {
+  } 
+  else if (TYPEOF(x) == REALSXP && !is_integer64) 
+  {
     tensor = tensor_from_r_array<REALSXP>(x, dim, "double");
-  } else if (TYPEOF(x) == LGLSXP) {
+  } 
+  else if (TYPEOF(x) == REALSXP && is_integer64)
+  {
+    tensor = tensor_from_r_array<REALSXP>(x, dim, "int64");
+  }
+  else if (TYPEOF(x) == LGLSXP) 
+  {
     tensor = tensor_from_r_array<LGLSXP>(x, dim, "int");
-  } else {
+  } 
+  else 
+  {
     Rcpp::stop("R type not handled");
   };
   
diff --git a/tests/testthat/test-tensor.R b/tests/testthat/test-tensor.R
index 1c4b33cb2..74e848b69 100644
--- a/tests/testthat/test-tensor.R
+++ b/tests/testthat/test-tensor.R
@@ -63,6 +63,22 @@ test_that("Integer tensors", {
   
   expect_equal_to_r(torch_tensor(x), e)
   
+  x <- 1:5
+  expect_equal_to_r(torch_tensor(bit64::as.integer64(x)), bit64::as.integer64(x))
+  
+  x <- matrix(c(1:4), ncol = 2)
+  e <- bit64::as.integer64(x)
+  e <- matrix(e, ncol = 2)
+  class(e) <- c(class(e), "integer64")
+  expect_equal_to_r(torch_tensor(e), e)
+  
+  x <- array(c(1:8), dim = c(2,2,2))
+  e <- bit64::as.integer64(x)
+  e <- array(e, dim = dim(x))
+  class(e) <- c(class(e), "integer64")
+  
+  expect_equal_to_r(torch_tensor(e), e)
+  
 })
 
 test_that("Logical tensors", {
-- 
GitLab


From e412762281cb062ea56bab18bfde05dfef129b20 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 24 Aug 2020 11:24:41 -0300
Subject: [PATCH 050/157] default convertion to integer and add an as.integer64
 method.

---
 NAMESPACE                    |  2 ++
 R/tensor.R                   | 36 ++++++++++++++++++++++++++----------
 tests/testthat/test-tensor.R | 30 ++++++++----------------------
 3 files changed, 36 insertions(+), 32 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 9f8ae7e18..20b428e29 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -41,6 +41,7 @@ S3method(as.character,torch_dimname_list)
 S3method(as.character,torch_dtype)
 S3method(as.double,torch_tensor)
 S3method(as.integer,torch_tensor)
+S3method(as.integer64,torch_tensor)
 S3method(as.logical,torch_tensor)
 S3method(as.numeric,torch_tensor)
 S3method(as_array,torch_tensor)
@@ -569,6 +570,7 @@ export(torch_zeros_like)
 export(with_enable_grad)
 export(with_no_grad)
 importFrom(Rcpp,sourceCpp)
+importFrom(bit64,as.integer64)
 importFrom(rlang,":=")
 importFrom(rlang,env_bind)
 importFrom(rlang,env_get)
diff --git a/R/tensor.R b/R/tensor.R
index 79d8bbcdf..cfb4761b8 100644
--- a/R/tensor.R
+++ b/R/tensor.R
@@ -156,16 +156,7 @@ as.array.torch_tensor <- function(x, ...) {
   as_array(x)
 }
 
-#' @export
-as_array.torch_tensor <- function(x) {
-  
-  if (x$device()$type == "cuda")
-    runtime_error("Can't convert cuda tensor to R. Convert to cpu tensor before.")
-  
-  # dequantize before converting
-  if (x$is_quantized())
-    x <- x$dequantize()
-  
+as_array_impl <- function(x) {
   a <- cpp_as_array(x$ptr)
   
   if (length(a$dim) <= 1L) {
@@ -182,6 +173,25 @@ as_array.torch_tensor <- function(x) {
   out
 }
 
+#' @export
+as_array.torch_tensor <- function(x) {
+  
+  if (x$device()$type == "cuda")
+    runtime_error("Can't convert cuda tensor to R. Convert to cpu tensor before.")
+  
+  # dequantize before converting
+  if (x$is_quantized())
+    x <- x$dequantize()
+  
+  # auto convert to int32 if long.
+  if (x$dtype() == torch_long())
+    x <- x$to(dtype = torch_int32())
+  
+  out <- as_array_impl(x)
+  
+  out
+}
+
 is_torch_tensor <- function(x) {
   inherits(x, "torch_tensor")
 }
@@ -190,3 +200,9 @@ is_undefined_tensor <- function(x) {
   cpp_tensor_is_undefined(x$ptr)
 }
 
+#' @importFrom bit64 as.integer64
+#' @export
+as.integer64.torch_tensor <- function(x, keep.names = FALSE, ...) {
+  x <- x$to(dtype = torch_long())
+  as_array_impl(x)
+}
\ No newline at end of file
diff --git a/tests/testthat/test-tensor.R b/tests/testthat/test-tensor.R
index 74e848b69..0d97aa999 100644
--- a/tests/testthat/test-tensor.R
+++ b/tests/testthat/test-tensor.R
@@ -48,36 +48,22 @@ test_that("Integer tensors", {
   expect_equal_to_r(torch_tensor(x)$to(dtype = torch_int()), x)
   
   x <- 1:5
-  expect_equal_to_r(torch_tensor(1:5), bit64::as.integer64(x))
+  expect_equal_to_r(torch_tensor(1:5), x)
   
   x <- matrix(c(1:4), ncol = 2)
-  e <- bit64::as.integer64(x)
-  e <- matrix(e, ncol = 2)
-  class(e) <- c(class(e), "integer64")
-  expect_equal_to_r(torch_tensor(x), e)
+  expect_equal_to_r(torch_tensor(x), x)
   
   x <- array(c(1:8), dim = c(2,2,2))
-  e <- bit64::as.integer64(x)
-  e <- array(e, dim = dim(x))
-  class(e) <- c(class(e), "integer64")
-  
-  expect_equal_to_r(torch_tensor(x), e)
+  expect_equal_to_r(torch_tensor(x), x)
   
   x <- 1:5
-  expect_equal_to_r(torch_tensor(bit64::as.integer64(x)), bit64::as.integer64(x))
-  
-  x <- matrix(c(1:4), ncol = 2)
-  e <- bit64::as.integer64(x)
-  e <- matrix(e, ncol = 2)
-  class(e) <- c(class(e), "integer64")
-  expect_equal_to_r(torch_tensor(e), e)
+  expect_equal_to_r(torch_tensor(bit64::as.integer64(x)), x)
   
   x <- array(c(1:8), dim = c(2,2,2))
-  e <- bit64::as.integer64(x)
-  e <- array(e, dim = dim(x))
-  class(e) <- c(class(e), "integer64")
-  
-  expect_equal_to_r(torch_tensor(e), e)
+  o <- as.integer64(torch_tensor(x))
+  expect_s3_class(o, "integer64")
+  expect_s3_class(o, "array")
+  expect_equal(dim(o), dim(x))
   
 })
 
-- 
GitLab


From cc60037a6673eac64c52fbe6add60fa6c31bc863 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 24 Aug 2020 12:22:50 -0300
Subject: [PATCH 051/157] use bit64 as dependency

---
 DESCRIPTION | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 386c89deb..056401c1c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -28,7 +28,8 @@ Imports:
     rlang,
     methods,
     utils,
-    stats
+    stats,
+    bit64
 RoxygenNote: 7.1.1
 Roxygen: list(markdown = TRUE)
 Suggests:
@@ -36,7 +37,6 @@ Suggests:
     covr,
     knitr,
     rmarkdown,
-    bit64,
     magrittr,
     glue
 VignetteBuilder: knitr
-- 
GitLab


From 3637c4ee44933551390f08516cc0d765d617c5d2 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 24 Aug 2020 13:07:52 -0300
Subject: [PATCH 052/157] use dev number

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 056401c1c..26c20cccd 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: torch
 Type: Package
 Title: Tensors and Neural Networks with 'GPU' Acceleration
-Version: 0.0.1
+Version: 0.0.1.9000
 Authors@R: c(
     person("Daniel", "Falbel", email = "daniel@rstudio.com", role = c("aut", "cre", "cph")),
     person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cph")),
-- 
GitLab


From aca7f4ac9e4078d1209fc2425fc54c032cf8ba67 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 24 Aug 2020 13:08:05 -0300
Subject: [PATCH 053/157] add test for overflowed values

---
 tests/testthat/test-tensor.R | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tests/testthat/test-tensor.R b/tests/testthat/test-tensor.R
index 0d97aa999..6b34bb69a 100644
--- a/tests/testthat/test-tensor.R
+++ b/tests/testthat/test-tensor.R
@@ -65,6 +65,12 @@ test_that("Integer tensors", {
   expect_s3_class(o, "array")
   expect_equal(dim(o), dim(x))
   
+  x <- as.integer64(.Machine$integer)*2
+  y <- torch_tensor(x)
+  z <- as.integer64(y)
+  
+  expect_equal(as.integer64(z), x)
+  
 })
 
 test_that("Logical tensors", {
-- 
GitLab


From 5512f032465c2c40436e3f2248dfd16631de09af Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 24 Aug 2020 14:26:51 -0300
Subject: [PATCH 054/157] start names with 0 and dont add prefix to match
 pytorch's

---
 R/nn.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/nn.R b/R/nn.R
index 75c6e78d4..ee9beab7f 100644
--- a/R/nn.R
+++ b/R/nn.R
@@ -14,7 +14,7 @@ nn_Module <- R6::R6Class(
     add_module = function(name, module) {
       
       if (is.numeric(name))
-        name <- paste0("m", name)
+        name <- as.character(name)
       
       private$modules_[[name]] <- module
     },
@@ -422,7 +422,7 @@ nn_sequential <- function(... , name = NULL) {
     initialize = function(...) {
       modules <- list(...)
       for (i in seq_along(modules)) {
-        self$add_module(name = i, module = modules[[i]])  
+        self$add_module(name = i - 1, module = modules[[i]])  
       }
     },
     forward = function(input) {
-- 
GitLab


From 7caace505cac3704016e7d642b5e33c1ee614e0a Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 24 Aug 2020 18:01:28 -0300
Subject: [PATCH 055/157] dont consider the generator as a module.

---
 R/nn.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/nn.R b/R/nn.R
index ee9beab7f..60a5e390a 100644
--- a/R/nn.R
+++ b/R/nn.R
@@ -246,7 +246,7 @@ is_nn_buffer <- function(x) {
 }
 
 is_nn_module <- function(x) {
-  inherits(x, "nn_module")
+  inherits(x, "nn_module") && !inherits(x, "nn_module_generator")
 }
 
 #' Base class for all neural network modules.
@@ -302,7 +302,7 @@ nn_module <- function(classname = NULL, inherit = nn_Module, ...) {
       create_nn_module_callable(instance)
     })
   )
-  attr(fun, "class") <- classes
+  attr(fun, "class") <- c(classes, "nn_module_generator")
   attr(fun, "module") <- Module
   fun
 }
@@ -420,7 +420,7 @@ nn_sequential <- function(... , name = NULL) {
   module <- nn_module(
     classname = ifelse(is.null(name), "nn_sequential", name),
     initialize = function(...) {
-      modules <- list(...)
+      modules <- rlang::list2(...)
       for (i in seq_along(modules)) {
         self$add_module(name = i - 1, module = modules[[i]])  
       }
-- 
GitLab


From 559099955829f5c792d3b4b00294a2e60e26b6b5 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 24 Aug 2020 18:01:53 -0300
Subject: [PATCH 056/157] add custom load state dict for batch norm as saved
 models dont include the num_batches_tracked buffer.

---
 R/nn-batchnorm.R                   | 8 ++++++++
 tests/testthat/test-nn-batchnorm.R | 9 +++++++++
 2 files changed, 17 insertions(+)

diff --git a/R/nn-batchnorm.R b/R/nn-batchnorm.R
index 5bd2e4324..90ad87fc6 100644
--- a/R/nn-batchnorm.R
+++ b/R/nn-batchnorm.R
@@ -48,6 +48,14 @@ nn_norm_base_ <- nn_module(
       nn_init_ones_(self$weight)
       nn_init_zeros_(self$bias)
     }
+  },
+  .load_from_state_dict = function(state_dict, prefix) {
+    
+    num_batches_tracked_key <- paste0(prefix, "num_batches_tracked")
+    if (!num_batches_tracked_key %in% names(state_dict))
+      state_dict[[num_batches_tracked_key]] <- torch_tensor(0, dtype = torch_long())
+    
+    super$.load_from_state_dict(state_dict, prefix)
   }
 )
 
diff --git a/tests/testthat/test-nn-batchnorm.R b/tests/testthat/test-nn-batchnorm.R
index 923a216a0..c26f60f44 100644
--- a/tests/testthat/test-nn-batchnorm.R
+++ b/tests/testthat/test-nn-batchnorm.R
@@ -19,3 +19,12 @@ test_that("nn_batch_norm2d", {
   x <- torch_randn(10, 10, 10)
   expect_error(m(x))
 })
+
+test_that("load state dict for batch norm", {
+  m <- nn_batch_norm2d(10)
+  s <- m$state_dict()
+  s <- s[!names(s) %in% "num_batches_tracked"]
+  m$load_state_dict(s)
+  
+  expect_length(m$state_dict(), 5)
+})
-- 
GitLab


From 47f77e11bac2b25c25c9c973443792b8481325d1 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 24 Aug 2020 18:29:21 -0300
Subject: [PATCH 057/157] fix test

---
 tests/testthat/test-nn.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/testthat/test-nn.R b/tests/testthat/test-nn.R
index 0caf8daf1..c76799009 100644
--- a/tests/testthat/test-nn.R
+++ b/tests/testthat/test-nn.R
@@ -191,7 +191,7 @@ test_that("state_dict for modules", {
   expect_equal_to_tensor(s[[6]], net$norm$running_var)
   
   
-  s <- s[-7]
+  s <- s[-6]
   expect_error(net2$load_state_dict(s), class = "value_error")
   
 })
-- 
GitLab


From 4830dbf7424651c7069557e1ca51545d37fea274 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 24 Aug 2020 18:30:14 -0300
Subject: [PATCH 058/157] allow other packages to extend nn_modules without
 having conflicts.

---
 R/nn.R | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/R/nn.R b/R/nn.R
index 60a5e390a..7833142f0 100644
--- a/R/nn.R
+++ b/R/nn.R
@@ -276,10 +276,14 @@ is_nn_module <- function(x) {
 #' )
 #' 
 #' @export
-nn_module <- function(classname = NULL, inherit = nn_Module, ...) {
+nn_module <- function(classname = NULL, inherit = nn_Module, ..., 
+                      parent_env = parent.frame()) {
   
   if (inherits(inherit, "nn_module"))
     inherit <- attr(inherit, "module")
+  
+  e <- new.env(parent = parent_env)
+  e$inherit <- inherit
     
   classes <- c(classname, "nn_module")
   
@@ -290,7 +294,8 @@ nn_module <- function(classname = NULL, inherit = nn_Module, ...) {
     public = list(
       .classes = classes,
       ...
-    )
+    ),
+    parent_env = e
   )
   
   init <- get_init(Module)
-- 
GitLab


From 7777aad3bdcf744d9a8d93cc8b5e3137b068ae34 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 25 Aug 2020 17:01:48 -0300
Subject: [PATCH 059/157] add pkgdown build script

---
 .github/workflows/pkgdown.yaml | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 .github/workflows/pkgdown.yaml

diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
new file mode 100644
index 000000000..65725b0c6
--- /dev/null
+++ b/.github/workflows/pkgdown.yaml
@@ -0,0 +1,32 @@
+on:
+  push:
+    branches: master
+
+name: pkgdown
+
+jobs:
+  pkgdown:
+    runs-on: macOS-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+      - uses: actions/checkout@v2
+
+      - uses: r-lib/actions/setup-r@master
+
+      - uses: r-lib/actions/setup-pandoc@master
+
+      - name: Install dependencies
+        run: |
+          remotes::install_deps(dependencies = TRUE)
+          install.packages("pkgdown")
+        shell: Rscript {0}
+
+      - name: Install package
+        run: R CMD INSTALL .
+
+      - name: Deploy package
+        run: |
+          git config --local user.email "actions@github.com"
+          git config --local user.name "GitHub Actions"
+          Rscript -e 'pkgdown::deploy_to_branch(new_process = FALSE)'
\ No newline at end of file
-- 
GitLab


From 861364a931225bd6b86b45f36db992014ae7fb7c Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 25 Aug 2020 17:03:30 -0300
Subject: [PATCH 060/157] install remotes

---
 .github/workflows/pkgdown.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index 65725b0c6..0e658c182 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -18,6 +18,7 @@ jobs:
 
       - name: Install dependencies
         run: |
+          install.packages("remotes")
           remotes::install_deps(dependencies = TRUE)
           install.packages("pkgdown")
         shell: Rscript {0}
-- 
GitLab


From 5bd5dde84fec6b4bd5b087408f5a5e0e1adcf4b9 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 25 Aug 2020 17:14:21 -0300
Subject: [PATCH 061/157] remove docs folder as we will autogenerate and move
 to gh-pages

---
 .gitignore                                    |    1 +
 docs/404.html                                 |  191 -
 docs/CONTRIBUTING.html                        |  228 -
 docs/LICENSE-text.html                        |  193 -
 docs/LICENSE.html                             |  197 -
 docs/articles/examples/mnist-cnn.html         |  215 -
 .../empty-anchor.js                           |   15 -
 .../header-attrs-2.1.1/header-attrs.js        |   12 -
 .../header-attrs-2.3/header-attrs.js          |   12 -
 docs/articles/examples/mnist-dcgan.html       |  296 -
 .../empty-anchor.js                           |   15 -
 .../header-attrs-2.3/header-attrs.js          |   12 -
 docs/articles/examples/mnist-mlp.html         |  203 -
 .../empty-anchor.js                           |   15 -
 .../header-attrs-2.1.1/header-attrs.js        |   12 -
 .../header-attrs-2.3/header-attrs.js          |   12 -
 docs/articles/extending-autograd.html         |  222 -
 .../empty-anchor.js                           |   15 -
 .../header-attrs-2.1.1/header-attrs.js        |   12 -
 .../header-attrs-2.3/header-attrs.js          |   12 -
 docs/articles/index.html                      |  204 -
 docs/articles/indexing.html                   |  224 -
 .../empty-anchor.js                           |   15 -
 .../header-attrs-2.1.1/header-attrs.js        |   12 -
 .../header-attrs-2.3/header-attrs.js          |   12 -
 docs/articles/loading-data.html               |  274 -
 .../empty-anchor.js                           |   15 -
 .../header-attrs-2.3/header-attrs.js          |   12 -
 docs/articles/tensor-creation.html            |  231 -
 .../empty-anchor.js                           |   15 -
 .../header-attrs-2.1.1/header-attrs.js        |   12 -
 .../header-attrs-2.3/header-attrs.js          |   12 -
 docs/articles/using-autograd.html             |  268 -
 .../empty-anchor.js                           |   15 -
 .../header-attrs-2.1.1/header-attrs.js        |   12 -
 .../header-attrs-2.3/header-attrs.js          |   12 -
 docs/authors.html                             |  206 -
 docs/bootstrap-toc.css                        |   60 -
 docs/bootstrap-toc.js                         |  159 -
 docs/docsearch.css                            |  148 -
 docs/docsearch.js                             |   85 -
 docs/index.html                               |  272 -
 docs/link.svg                                 |   12 -
 docs/pkgdown.css                              |  367 --
 docs/pkgdown.js                               |  108 -
 docs/pkgdown.yml                              |   14 -
 docs/reference/AutogradContext.html           |  306 -
 docs/reference/as_array.html                  |  205 -
 docs/reference/autograd_backward.html         |  258 -
 docs/reference/autograd_function.html         |  246 -
 docs/reference/autograd_grad.html             |  272 -
 docs/reference/autograd_set_grad_mode.html    |  205 -
 docs/reference/cuda_current_device.html       |  197 -
 docs/reference/cuda_device_count.html         |  197 -
 docs/reference/cuda_is_available.html         |  197 -
 docs/reference/dataloader.html                |  278 -
 docs/reference/dataloader_make_iter.html      |  205 -
 docs/reference/dataloader_next.html           |  205 -
 docs/reference/dataset.html                   |  230 -
 docs/reference/default_dtype.html             |  208 -
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-      <span class="fu"><a href="../../reference/nn_batch_norm2d.html">nn_batch_norm2d</a></span>(<span class="fl">256</span>),
-      <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span>(),
-      <span class="fu"><a href="../../reference/nn_conv_transpose2d.html">nn_conv_transpose2d</a></span>(<span class="fl">256</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_batch_norm2d.html">nn_batch_norm2d</a></span>(<span class="fl">128</span>),
-      <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span>(),
-      <span class="fu"><a href="../../reference/nn_conv_transpose2d.html">nn_conv_transpose2d</a></span>(<span class="fl">128</span>, <span class="no">out_channels</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">3</span>, <span class="kw">bias</span> <span class="kw">=</span> <span class="fl">FALSE</span>),
-      <span class="fu"><a href="../../reference/nn_tanh.html">nn_tanh</a></span>()
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-    <span class="no">self</span>$<span class="fu">main</span>(<span class="no">input</span>)
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-)
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-  <span class="st">"discriminator"</span>,
-  <span class="kw">initialize</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">in_channels</span>) {
-    <span class="no">self</span>$<span class="no">main</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_conv2d.html">nn_conv2d</a></span>(<span class="no">in_channels</span>, <span class="fl">16</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="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">16</span>, <span class="fl">32</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_batch_norm2d.html">nn_batch_norm2d</a></span>(<span class="fl">32</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="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">32</span>, <span class="fl">64</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_batch_norm2d.html">nn_batch_norm2d</a></span>(<span class="fl">64</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="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>$<span class="fu">sigmoid</span>(<span class="no">x</span>)
-  }
-)
-
-<span class="no">plot_gen</span> <span class="kw">&lt;-</span> <span class="kw">function</span>(<span class="no">noise</span>) {
-  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="fu">G</span>(<span class="no">noise</span>)
-  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="no">img</span>$<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>)
-}
-
-<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>))
-
-<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"><a href="https://rdrr.io/pkg/progress/man/progress_bar.html">progress_bar</a></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>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-      </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> 1.5.1.</p>
-</div>
-
-      </footer>
-</div>
-
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-  </body>
-</html>
diff --git a/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6a..000000000
--- a/docs/articles/examples/mnist-dcgan_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/docs/articles/examples/mnist-dcgan_files/header-attrs-2.3/header-attrs.js b/docs/articles/examples/mnist-dcgan_files/header-attrs-2.3/header-attrs.js
deleted file mode 100644
index dd57d92e0..000000000
--- a/docs/articles/examples/mnist-dcgan_files/header-attrs-2.3/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/examples/mnist-mlp.html b/docs/articles/examples/mnist-mlp.html
deleted file mode 100644
index db40b8ea6..000000000
--- a/docs/articles/examples/mnist-mlp.html
+++ /dev/null
@@ -1,203 +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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-  <div class="col-md-9 contents">
-    <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>
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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>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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-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
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-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
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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="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <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>
-
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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><a href="../reference/mnist_dataset.html">mnist_dataset()</a></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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-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
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-      if (linkList[j].innerHTML === "") {
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@@ -1,12 +0,0 @@
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-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
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-      <h1 data-toc-skip>Creating tensors</h1>
-            
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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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-            
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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"><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>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    </nav>
-</div>
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-  <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> 1.5.1.</p>
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diff --git a/docs/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
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diff --git a/docs/articles/using-autograd_files/header-attrs-2.1.1/header-attrs.js b/docs/articles/using-autograd_files/header-attrs-2.1.1/header-attrs.js
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-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
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-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
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-    a = h.attributes;
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-});
diff --git a/docs/articles/using-autograd_files/header-attrs-2.3/header-attrs.js b/docs/articles/using-autograd_files/header-attrs-2.3/header-attrs.js
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--- a/docs/articles/using-autograd_files/header-attrs-2.3/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
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-    while (a.length > 0) h.removeAttribute(a[0].name);
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-  <div class="contents col-md-9">
-    <div class="page-header">
-      <h1>Authors</h1>
-    </div>
-
-    <ul class="list-unstyled">
-      <li>
-        <p><strong>Daniel Falbel</strong>. Author, maintainer. 
-        </p>
-      </li>
-      <li>
-        <p><strong>Javier Luraschi</strong>. Author. 
-        </p>
-      </li>
-      <li>
-        <p><strong>Dmitriy Selivanov</strong>. Contributor. 
-        </p>
-      </li>
-      <li>
-        <p><strong>Athos Damiani</strong>. Contributor. 
-        </p>
-      </li>
-      <li>
-        <p><strong> RStudio</strong>. Copyright holder. 
-        </p>
-      </li>
-    </ul>
-
-  </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> 1.5.1.</p>
-</div>
-
-      </footer>
-   </div>
-
-  
-
-
-  </body>
-</html>
-
-
diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css
deleted file mode 100644
index 5a859415c..000000000
--- a/docs/bootstrap-toc.css
+++ /dev/null
@@ -1,60 +0,0 @@
-/*!
- * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
- * Copyright 2015 Aidan Feldman
- * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
-
-/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */
-
-/* All levels of nav */
-nav[data-toggle='toc'] .nav > li > a {
-  display: block;
-  padding: 4px 20px;
-  font-size: 13px;
-  font-weight: 500;
-  color: #767676;
-}
-nav[data-toggle='toc'] .nav > li > a:hover,
-nav[data-toggle='toc'] .nav > li > a:focus {
-  padding-left: 19px;
-  color: #563d7c;
-  text-decoration: none;
-  background-color: transparent;
-  border-left: 1px solid #563d7c;
-}
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-nav[data-toggle='toc'] .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav > .active:focus > a {
-  padding-left: 18px;
-  font-weight: bold;
-  color: #563d7c;
-  background-color: transparent;
-  border-left: 2px solid #563d7c;
-}
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-/* Nav: second level (shown on .active) */
-nav[data-toggle='toc'] .nav .nav {
-  display: none; /* Hide by default, but at >768px, show it */
-  padding-bottom: 10px;
-}
-nav[data-toggle='toc'] .nav .nav > li > a {
-  padding-top: 1px;
-  padding-bottom: 1px;
-  padding-left: 30px;
-  font-size: 12px;
-  font-weight: normal;
-}
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-nav[data-toggle='toc'] .nav .nav > li > a:focus {
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-}
-nav[data-toggle='toc'] .nav .nav > .active > a,
-nav[data-toggle='toc'] .nav .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav .nav > .active:focus > a {
-  padding-left: 28px;
-  font-weight: 500;
-}
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-/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */
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diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js
deleted file mode 100644
index 1cdd573b2..000000000
--- a/docs/bootstrap-toc.js
+++ /dev/null
@@ -1,159 +0,0 @@
-/*!
- * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
- * Copyright 2015 Aidan Feldman
- * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
-(function() {
-  'use strict';
-
-  window.Toc = {
-    helpers: {
-      // return all matching elements in the set, or their descendants
-      findOrFilter: function($el, selector) {
-        // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/
-        // http://stackoverflow.com/a/12731439/358804
-        var $descendants = $el.find(selector);
-        return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])');
-      },
-
-      generateUniqueIdBase: function(el) {
-        var text = $(el).text();
-        var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-');
-        return anchor || el.tagName.toLowerCase();
-      },
-
-      generateUniqueId: function(el) {
-        var anchorBase = this.generateUniqueIdBase(el);
-        for (var i = 0; ; i++) {
-          var anchor = anchorBase;
-          if (i > 0) {
-            // add suffix
-            anchor += '-' + i;
-          }
-          // check if ID already exists
-          if (!document.getElementById(anchor)) {
-            return anchor;
-          }
-        }
-      },
-
-      generateAnchor: function(el) {
-        if (el.id) {
-          return el.id;
-        } else {
-          var anchor = this.generateUniqueId(el);
-          el.id = anchor;
-          return anchor;
-        }
-      },
-
-      createNavList: function() {
-        return $('<ul class="nav"></ul>');
-      },
-
-      createChildNavList: function($parent) {
-        var $childList = this.createNavList();
-        $parent.append($childList);
-        return $childList;
-      },
-
-      generateNavEl: function(anchor, text) {
-        var $a = $('<a></a>');
-        $a.attr('href', '#' + anchor);
-        $a.text(text);
-        var $li = $('<li></li>');
-        $li.append($a);
-        return $li;
-      },
-
-      generateNavItem: function(headingEl) {
-        var anchor = this.generateAnchor(headingEl);
-        var $heading = $(headingEl);
-        var text = $heading.data('toc-text') || $heading.text();
-        return this.generateNavEl(anchor, text);
-      },
-
-      // Find the first heading level (`<h1>`, then `<h2>`, etc.) that has more than one element. Defaults to 1 (for `<h1>`).
-      getTopLevel: function($scope) {
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-          if ($headings.length > 1) {
-            return i;
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-        }
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-        return 1;
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-      // returns the elements for the top level, and the next below it
-      getHeadings: function($scope, topLevel) {
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-        var secondaryLevel = topLevel + 1;
-        var secondarySelector = 'h' + secondaryLevel;
-
-        return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
-      },
-
-      getNavLevel: function(el) {
-        return parseInt(el.tagName.charAt(1), 10);
-      },
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-      populateNav: function($topContext, topLevel, $headings) {
-        var $context = $topContext;
-        var $prevNav;
-
-        var helpers = this;
-        $headings.each(function(i, el) {
-          var $newNav = helpers.generateNavItem(el);
-          var navLevel = helpers.getNavLevel(el);
-
-          // determine the proper $context
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-          } else if ($prevNav && $context === $topContext) {
-            // create a new level of the tree and switch to it
-            $context = helpers.createChildNavList($prevNav);
-          } // else use the current $context
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-          $context.append($newNav);
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-          $prevNav = $newNav;
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-      parseOps: function(arg) {
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-        return opts;
-      }
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-
-    // accepts a jQuery object, or an options object
-    init: function(opts) {
-      opts = this.helpers.parseOps(opts);
-
-      // ensure that the data attribute is in place for styling
-      opts.$nav.attr('data-toggle', 'toc');
-
-      var $topContext = this.helpers.createChildNavList(opts.$nav);
-      var topLevel = this.helpers.getTopLevel(opts.$scope);
-      var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
-      this.helpers.populateNav($topContext, topLevel, $headings);
-    }
-  };
-
-  $(function() {
-    $('nav[data-toggle="toc"]').each(function(i, el) {
-      var $nav = $(el);
-      Toc.init($nav);
-    });
-  });
-})();
diff --git a/docs/docsearch.css b/docs/docsearch.css
deleted file mode 100644
index e5f1fe1df..000000000
--- a/docs/docsearch.css
+++ /dev/null
@@ -1,148 +0,0 @@
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-/*
-  Source: https://github.com/algolia/docsearch/
-  License: MIT
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-.algolia-autocomplete {
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-.algolia-autocomplete .ds-dropdown-menu {
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-  padding: .75rem 0;
-  background-color: #fff;
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-  box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175);
-}
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-@media (min-width:768px) {
-  .algolia-autocomplete .ds-dropdown-menu {
-      width: 175%
-  }
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-.algolia-autocomplete .ds-dropdown-menu::before {
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-.algolia-autocomplete .algolia-docsearch-suggestion--wrapper {
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-.algolia-autocomplete .algolia-docsearch-suggestion--text {
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-<script src="pkgdown.js"></script><meta property="og:title" content="Tensors and Neural Networks with GPU Acceleration">
-<meta property="og:description" content="Provides functionality similar to PyTorch but written entirely in R and
-    the libtorch library. Also supports low-level tensor operations and GPU acceleration.">
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-<div id="torch-" class="section level1">
-<div class="page-header"><h1 class="hasAnchor">
-<a href="#torch-" class="anchor"></a>torch <a href="https://mlverse.github.io/torch"><img src="reference/figures/torch.png" align="right" height="139"></a>
-</h1></div>
-
-<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="r"><span class="kw pkg">remotes</span><span class="kw ns">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html">install_github</a></span>(<span class="st">"mlverse/torch"</span>)</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="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</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/Uniform.html">runif</a></span>(<span class="fl">8</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">2</span>))
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="reference/torch_tensor.html">torch_tensor</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_float64</a></span>())
-<span class="no">y</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt; (1,.,.) = </span>
-<span class="co">#&gt;   0.8687  0.0157</span>
-<span class="co">#&gt;   0.4237  0.8971</span>
-<span class="co">#&gt; </span>
-<span class="co">#&gt; (2,.,.) = </span>
-<span class="co">#&gt;   0.4021  0.5509</span>
-<span class="co">#&gt;   0.3374  0.9034</span>
-<span class="co">#&gt; [ CPUDoubleType{2,2,2} ]</span>
-<span class="fu"><a href="https://rdrr.io/r/base/identical.html">identical</a></span>(<span class="no">x</span>, <span class="fu"><a href="reference/as_array.html">as_array</a></span>(<span class="no">y</span>))
-<span class="co">#&gt; [1] TRUE</span></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="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">w</span> <span class="kw">&lt;-</span> <span class="fu"><a href="reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">2</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="reference/torch_tensor.html">torch_tensor</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="no">w</span> * <span class="no">x</span> + <span class="no">b</span>
-<span class="no">y</span>$<span class="fu">backward</span>()
-<span class="no">x</span>$<span class="no">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  2</span>
-<span class="co">#&gt; [ CPUFloatType{1} ]</span>
-<span class="no">w</span>$<span class="no">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt; [ CPUFloatType{1} ]</span>
-<span class="no">b</span>$<span class="no">grad</span>
-<span class="co">#&gt; torch_tensor </span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt; [ CPUFloatType{1} ]</span></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="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">100</span>, <span class="fl">2</span>)
-<span class="no">y</span> <span class="kw">&lt;-</span> <span class="fl">0.1</span> + <span class="fl">0.5</span>*<span class="no">x</span>[,<span class="fl">1</span>] - <span class="fl">0.7</span>*<span class="no">x</span>[,<span class="fl">2</span>]
-
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-<span class="no">b</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="kw">requires_grad</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-
-<span class="no">lr</span> <span class="kw">&lt;-</span> <span class="fl">0.5</span>
-<span class="kw">for</span> (<span class="no">i</span> <span class="kw">in</span> <span class="fl">1</span>:<span class="fl">100</span>) {
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-
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-<p>Class representing the context.</p>
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-
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-<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>
-    </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>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <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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-    <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='co'># \dontrun{</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>))
-
-<span class='co'># }</span></div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-retained in the form of a DAG of functions, with edges denoting data
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-    <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$input = TRUE</code> if the <code>input</code>
-argument to <code>forward()</code> needs gradient computated w.r.t. the output.
-See <a href='AutogradContext.html'>AutogradContext</a> for more information about context methods.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</span>
-
-<span class='no'>exp2</span> <span class='kw'>&lt;-</span> <span class='fu'>autograd_function</span>(
-  <span class='kw'>forward</span> <span class='kw'>=</span> <span class='kw'>function</span>(<span class='no'>ctx</span>, <span class='no'>i</span>) {
-    <span class='no'>result</span> <span class='kw'>&lt;-</span> <span class='no'>i</span>$<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>)
-  }
-)
-
-<span class='co'># }</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>
-</div>
-
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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/docs/reference/autograd_grad.html b/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='co'># \dontrun{</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><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><div class='input'> 
-# }
-</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/docs/reference/autograd_set_grad_mode.html b/docs/reference/autograd_set_grad_mode.html
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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>
-
-    <div class="ref-description">
-    <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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diff --git a/docs/reference/cuda_current_device.html b/docs/reference/cuda_current_device.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>
-    <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">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/docs/reference/dataloader_make_iter.html b/docs/reference/dataloader_make_iter.html
deleted file mode 100644
index 8e3cf682a..000000000
--- a/docs/reference/dataloader_make_iter.html
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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='no'>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>
-
-
-  </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>
-  </div>
-</div>
-
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/docs/reference/dataloader_next.html b/docs/reference/dataloader_next.html
deleted file mode 100644
index 0ab5eef50..000000000
--- a/docs/reference/dataloader_next.html
+++ /dev/null
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diff --git a/docs/reference/dataset.html b/docs/reference/dataset.html
deleted file mode 100644
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-<meta property="og:description" content="All datasets that represent a map from keys to data samples should subclass
-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
-~torch.utils.data.Sampler implementations and the default options
-of ~torch.utils.data.DataLoader." />
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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>(<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>)</pre>
-
-    <h2 class="hasAnchor" 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>
-    </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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index d421206ea..000000000
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-        <th colspan="2">
-          <h2 id="section-all-functions" class="hasAnchor"><a href="#section-all-functions" class="anchor"></a>All functions</h2>
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-        
-        <td>
-          <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="kmnist_dataset.html">kmnist_dataset()</a></code> </p>
-        </td>
-        <td><p>Kuzushiji-MNIST</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="mnist_dataset.html">mnist_dataset()</a></code> </p>
-        </td>
-        <td><p>MNIST dataset</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_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_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_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_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_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_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_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_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_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>
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-          <p><code><a href="torch_randn.html">torch_randn</a></code> </p>
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-        
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-          <p><code><a href="torch_randn_like.html">torch_randn_like</a></code> </p>
-        </td>
-        <td><p>Randn_like</p></td>
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-        <td>
-          <p><code><a href="torch_randperm.html">torch_randperm</a></code> </p>
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-        <td>
-          <p><code><a href="torch_range.html">torch_range</a></code> </p>
-        </td>
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-          <p><code><a href="torch_real.html">torch_real</a></code> </p>
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-          <p><code><a href="torch_relu_.html">torch_relu_</a></code> </p>
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-      </tr><tr>
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-        <td>
-          <p><code><a href="torch_remainder.html">torch_remainder</a></code> </p>
-        </td>
-        <td><p>Remainder</p></td>
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-        
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-        </td>
-        <td><p>Renorm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_repeat_interleave.html">torch_repeat_interleave</a></code> </p>
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-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rot90.html">torch_rot90</a></code> </p>
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-      </tr><tr>
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-        <td>
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-        
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-          <p><code><a href="torch_rrelu_.html">torch_rrelu_</a></code> </p>
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-        
-        <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_save.html">torch_save()</a></code> </p>
-        </td>
-        <td><p>Saves an object to a disk file.</p></td>
-      </tr><tr>
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-        <td>
-          <p><code><a href="torch_selu_.html">torch_selu_</a></code> </p>
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-        <td><p>Selu_</p></td>
-      </tr><tr>
-        
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-          <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>
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-        <td>
-          <p><code><a href="torch_std.html">torch_std</a></code> </p>
-        </td>
-        <td><p>Std</p></td>
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-          <p><code><a href="torch_std_mean.html">torch_std_mean</a></code> </p>
-        </td>
-        <td><p>Std_mean</p></td>
-      </tr><tr>
-        
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-          <p><code><a href="torch_stft.html">torch_stft</a></code> </p>
-        </td>
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-        </td>
-        <td><p>Sum</p></td>
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-          <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>
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-        <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>
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-        </td>
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-        
-        <td>
-          <p><code><a href="torch_tensor.html">torch_tensor()</a></code> </p>
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-        </td>
-        <td><p>Tensordot</p></td>
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-          <p><code><a href="torch_threshold_.html">torch_threshold_</a></code> </p>
-        </td>
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-        </td>
-        <td><p>Transpose</p></td>
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-        <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>
-      </tr><tr>
-        
-        <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>
-      </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><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>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="vision_make_grid.html">vision_make_grid()</a></code> </p>
-        </td>
-        <td><p>A simplified version of torchvision.utils.make_grid.</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="with_no_grad.html">with_no_grad()</a></code> </p>
-        </td>
-        <td><p>Temporarily modify gradient recording.</p></td>
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-    <div class="page-header">
-    <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>(
-  <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>
-
-    <h2 class="hasAnchor" 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>
-
-  </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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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/docs/reference/is_dataloader.html b/docs/reference/is_dataloader.html
deleted file mode 100644
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-    <div class="hidden name"><code>is_dataloader.Rd</code></div>
-    </div>
-
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-    <p>Checks if the object is a dataloader</p>
-    </div>
-
-    <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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diff --git a/docs/reference/is_torch_dtype.html b/docs/reference/is_torch_dtype.html
deleted file mode 100644
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-    <div class="hidden name"><code>is_torch_qscheme.Rd</code></div>
-    </div>
-
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-    <p>Checks if an object is a QScheme</p>
-    </div>
-
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diff --git a/docs/reference/kmnist_dataset.html b/docs/reference/kmnist_dataset.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/dataset-mnist.R'><code>R/dataset-mnist.R</code></a></small>
-    <div class="hidden name"><code>kmnist_dataset.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The <a href='https://github.com/rois-codh/kmnist'>Kuzushiji-MNIST dataset</a>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>kmnist_dataset</span>(
-  <span class='no'>root</span>,
-  <span class='kw'>train</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>transform</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>target_transform</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>download</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>root</th>
-      <td><p>(string): Root directory of dataset where <code>KMNIST/processed/training.pt</code>
-and  <code>KMNIST/processed/test.pt</code> exist.</p></td>
-    </tr>
-    <tr>
-      <th>train</th>
-      <td><p>(bool, optional): If TRUE, creates dataset from <code>training.pt</code>,
-otherwise from <code>test.pt</code>.</p></td>
-    </tr>
-    <tr>
-      <th>transform</th>
-      <td><p>(callable, optional): A function/transform that  takes in an PIL image
-and returns a transformed version. E.g, <code>transforms.RandomCrop</code></p></td>
-    </tr>
-    <tr>
-      <th>target_transform</th>
-      <td><p>(callable, optional): A function/transform that takes in the
-target and transforms it.</p></td>
-    </tr>
-    <tr>
-      <th>download</th>
-      <td><p>(bool, optional): If true, downloads the dataset from the internet and
-puts it in root directory. If dataset is already downloaded, it is not
-downloaded again.</p></td>
-    </tr>
-    </table>
-
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diff --git a/docs/reference/mnist_dataset.html b/docs/reference/mnist_dataset.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/dataset-mnist.R'><code>R/dataset-mnist.R</code></a></small>
-    <div class="hidden name"><code>mnist_dataset.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Prepares the MNIST dataset and optionally downloads it.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>mnist_dataset</span>(
-  <span class='no'>root</span>,
-  <span class='kw'>train</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>transform</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>target_transform</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>download</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>root</th>
-      <td><p>(string): Root directory of dataset where <code>MNIST/processed/training.pt</code>
-and  <code>MNIST/processed/test.pt</code> exist.</p></td>
-    </tr>
-    <tr>
-      <th>train</th>
-      <td><p>(bool, optional): If True, creates dataset from <code>training.pt</code>,
-otherwise from <code>test.pt</code>.</p></td>
-    </tr>
-    <tr>
-      <th>transform</th>
-      <td><p>(callable, optional): A function/transform that  takes in an PIL image
-and returns a transformed version. E.g, <code>transforms.RandomCrop</code></p></td>
-    </tr>
-    <tr>
-      <th>target_transform</th>
-      <td><p>(callable, optional): A function/transform that takes in the
-target and transforms it.</p></td>
-    </tr>
-    <tr>
-      <th>download</th>
-      <td><p>(bool, optional): If true, downloads the dataset from the internet and
-puts it in root directory. If dataset is already downloaded, it is not
-downloaded again.</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>
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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diff --git a/docs/reference/nn_adaptive_log_softmax_with_loss.html b/docs/reference/nn_adaptive_log_softmax_with_loss.html
deleted file mode 100644
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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>
-
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-<meta property="og:description" content="Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D
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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='co'># \dontrun{</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>)
-
-<span class='co'># }</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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-Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift." />
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-    <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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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diff --git a/docs/reference/nn_bce_loss.html b/docs/reference/nn_bce_loss.html
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-    <li>
-      <a href="../articles/examples/mnist-dcgan.html">mnist-dcgan</a>
-    </li>
-  </ul>
-</li>
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-</li>
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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='co'># \dontrun{</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>()
-
-<span class='co'># }</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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-    <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='co'># \dontrun{</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><div class='output co'>#&gt; [1] 128  50</div><div class='input'>
-# }
-</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="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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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-\((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be
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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='co'># \dontrun{</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>)
-
-<span class='co'># }</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>
-</div>
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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diff --git a/docs/reference/nn_conv2d.html b/docs/reference/nn_conv2d.html
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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='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='co'># \dontrun{</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>)
-
-<span class='co'># }</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='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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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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='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='co'># \dontrun{</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>)
-
-<span class='co'># }</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='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='co'># \dontrun{</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>$<span class='fu'>size</span>()</div><div class='output co'>#&gt; [1]  1 16  6  6</div><div class='input'><span class='no'>output</span> <span class='kw'>&lt;-</span> <span class='fu'>upsample</span>(<span class='no'>h</span>, <span class='kw'>output_size</span><span class='kw'>=</span><span class='no'>input</span>$<span class='fu'>size</span>())
-<span class='no'>output</span>$<span class='fu'>size</span>()</div><div class='output co'>#&gt; [1]  1 16 12 12</div><div class='input'>
-# }
-</div></pre>
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-planes." />
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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='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"></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/docs/reference/nn_cross_entropy_loss.html b/docs/reference/nn_cross_entropy_loss.html
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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>
-
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-    <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='co'># \dontrun{</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>()
-
-<span class='co'># }</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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></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/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='http://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'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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diff --git a/docs/reference/nn_dropout3d.html b/docs/reference/nn_dropout3d.html
deleted file mode 100644
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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='http://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'># \dontrun{</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>)
-
-<span class='co'># }</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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-    <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>
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-
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-    <p>Applies the element-wise function:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_elu</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 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>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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-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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -0.5531  0.2969 -1.9168
-#&gt;  -0.7095 -0.1328 -0.7352
-#&gt;  -1.5311 -0.6539  0.7804
-#&gt;   1.5343  0.1139  1.1985
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -1.5311 -0.6539  0.7804
-#&gt;  -0.1120  0.9578  0.1195
-#&gt;  -0.7095 -0.1328 -0.7352
-#&gt;  -0.4247  0.6266 -0.1286
-#&gt; [ CPUFloatType{2,4,3} ]</div><div class='input'><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><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><div class='input'>
-# }
-</div></pre>
-  </div>
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-$$\mbox{GELU}(x) = x * \Phi(x)$$" />
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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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/nn_glu.html b/docs/reference/nn_glu.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-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>(<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>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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-    <div class="hidden name"><code>nn_hardshrink.Rd</code></div>
-    </div>
-
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-    <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='co'># \dontrun{</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>)
-
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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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-
-    <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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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diff --git a/docs/reference/nn_hardswish.html b/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>
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-    <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>
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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>(<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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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diff --git a/docs/reference/nn_identity.html b/docs/reference/nn_identity.html
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diff --git a/docs/reference/nn_init_calculate_gain.html b/docs/reference/nn_init_calculate_gain.html
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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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-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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_constant_</span>(<span class='no'>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
-#&gt; [ CPUFloatType{3,5} ]</div><div class='input'>
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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>
-
-    <pre class="usage"><span class='fu'>nn_init_dirac_</span>(<span class='no'>tensor</span>, <span class='kw'>groups</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>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>
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diff --git a/docs/reference/nn_init_eye_.html b/docs/reference/nn_init_eye_.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-init.R'><code>R/nn-init.R</code></a></small>
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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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-    <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>
-
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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'># \dontrun{</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><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><div class='input'>
-# }
-</div></pre>
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diff --git a/docs/reference/nn_init_kaiming_normal_.html b/docs/reference/nn_init_kaiming_normal_.html
deleted file mode 100644
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-normal distribution." />
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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='co'># \dontrun{</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><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><div class='input'>
-# }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/nn_init_kaiming_uniform_.html b/docs/reference/nn_init_kaiming_uniform_.html
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-uniform distribution." />
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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='co'># \dontrun{</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><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><div class='input'>
-# }
-</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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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/docs/reference/nn_init_normal_.html b/docs/reference/nn_init_normal_.html
deleted file mode 100644
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--- a/docs/reference/nn_init_normal_.html
+++ /dev/null
@@ -1,223 +0,0 @@
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-
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-    <table class="ref-arguments">
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-    <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>
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-
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-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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_normal_</span>(<span class='no'>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><div class='input'>
-# }
-</div></pre>
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diff --git a/docs/reference/nn_init_ones_.html b/docs/reference/nn_init_ones_.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_ones_.Rd</code></div>
-    </div>
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-    <p>Fills the input Tensor with the scalar value <code>1</code></p>
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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>
-
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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'># \dontrun{</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_ones_</span>(<span class='no'>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><div class='input'>
-# }
-</div></pre>
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diff --git a/docs/reference/nn_init_orthogonal_.html b/docs/reference/nn_init_orthogonal_.html
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-  <a href="../reference/index.html">Reference</a>
-</li>
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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='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>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'># \dontrun{</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_orthogonal_</span>(<span class='no'>w</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.2147  0.0073 -0.0312 -0.0439  0.9752
-#&gt; -0.8268  0.5222  0.0419  0.0979 -0.1802
-#&gt;  0.3963  0.5329 -0.0498  0.7371  0.1148
-#&gt; [ CPUFloatType{3,5} ]</div><div class='input'>
-# }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/nn_init_sparse_.html b/docs/reference/nn_init_sparse_.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-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"></pre>
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diff --git a/docs/reference/nn_init_trunc_normal_.html b/docs/reference/nn_init_trunc_normal_.html
deleted file mode 100644
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+++ /dev/null
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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>
-
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-    <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='co'># \dontrun{</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><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><div class='input'>
-# }
-</div></pre>
-  </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>
-
-    <h2 class="hasAnchor" 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'># \dontrun{</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><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><div class='input'>
-# }
-</div></pre>
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diff --git a/docs/reference/nn_init_xavier_normal_.html b/docs/reference/nn_init_xavier_normal_.html
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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>
-
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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 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='co'># \dontrun{</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><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><div class='input'>
-# }
-</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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diff --git a/docs/reference/nn_init_xavier_uniform_.html b/docs/reference/nn_init_xavier_uniform_.html
deleted file mode 100644
index c925e4da3..000000000
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-    <div class="hidden name"><code>nn_init_xavier_uniform_.Rd</code></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>
-
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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>
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-      <th>gain</th>
-      <td><p>an optional scaling factor</p></td>
-    </tr>
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-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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_uniform_</span>(<span class='no'>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><div class='input'>
-# }
-</div></pre>
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diff --git a/docs/reference/nn_init_zeros_.html b/docs/reference/nn_init_zeros_.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_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='co'># \dontrun{</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><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><div class='input'>
-# }
-</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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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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_linear.Rd</code></div>
-    </div>
-
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-    <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='co'># \dontrun{</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><div class='output co'>#&gt; [1] 128  30</div><div class='input'>
-# }
-</div></pre>
-  </div>
-  <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>
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-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'># \dontrun{</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_log_sigmoid</span>()
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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_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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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diff --git a/docs/reference/nn_max_pool1d.html b/docs/reference/nn_max_pool1d.html
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-    <p>Applies a 1D max pooling over an input signal composed of several input
-planes.</p>
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-
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-  <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>nn_max_unpool1d()</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'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <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>nn_max_unpool2d()</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'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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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>)</pre>
-
-    <h2 class="hasAnchor" 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>
-    </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'># \dontrun{</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>
- }
-)
-
-<span class='co'># }</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/docs/reference/nn_module_list.html b/docs/reference/nn_module_list.html
deleted file mode 100644
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-    <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='co'># \dontrun{</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>
- }
-)
-
-<span class='co'># }</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/docs/reference/nn_multihead_attention.html b/docs/reference/nn_multihead_attention.html
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-    <div class="page-header">
-    <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"></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/docs/reference/nn_prelu.html b/docs/reference/nn_prelu.html
deleted file mode 100644
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+++ /dev/null
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-<meta property="og:title" content="PReLU module — nn_prelu" />
-<meta property="og:description" content="Applies the element-wise function:
-$$
-  \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
-$$
-or
-$$
-  \mbox{PReLU}(x) =
-  \left\{ \begin{array}{ll}
-x, &amp;amp; \mbox{ if } x \geq 0 \\
-ax, &amp;amp; \mbox{ otherwise }
-\end{array}
-\right.
-$$" />
-
-
-
-
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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='co'># \dontrun{</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>)
-
-<span class='co'># }</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/docs/reference/nn_relu.html b/docs/reference/nn_relu.html
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-    <div class="hidden name"><code>nn_relu.Rd</code></div>
-    </div>
-
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-    <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>
-
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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'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.2952
-#&gt;  0.0000
-#&gt; [ CPUFloatType{2} ]</div><div class='input'>
-# }
-</div></pre>
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diff --git a/docs/reference/nn_relu6.html b/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>
-    <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>(<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="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>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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-  <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='co'># \dontrun{</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><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  Columns 1 to 9  0.1563  0.2797  0.4653  0.8483 -0.5044  0.5032 -0.2032  0.8317 -0.0350
-#&gt;   0.1632 -0.1904  0.5733  0.3198 -0.3562  0.3904 -0.6873  0.7901 -0.9472
-#&gt;  -0.6539 -0.6025  0.4797 -0.8375  0.7255 -0.4198  0.5030 -0.1419  0.2092
-#&gt; 
-#&gt; Columns 10 to 18  0.9481 -0.2581 -0.6185 -0.7787 -0.2685 -0.7144 -0.3700  0.4854  0.0008
-#&gt;   0.8805 -0.2755 -0.9071 -0.5580 -0.0061 -0.8662 -0.7541  0.7855 -0.6157
-#&gt;   0.5480  0.5395 -0.1759  0.2965  0.7805 -0.2651 -0.8080  0.2643  0.7689
-#&gt; 
-#&gt; Columns 19 to 20  0.3487 -0.1631
-#&gt;   0.2535  0.5113
-#&gt;  -0.0188  0.3766
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  Columns 1 to 9  0.1416  0.4498  0.1932  0.1637 -0.5115 -0.7125 -0.4544 -0.1721 -0.0345
-#&gt;  -0.7202  0.1668  0.1129  0.6700 -0.6155  0.2947 -0.4558  0.1086  0.1323
-#&gt;   0.3006 -0.0124 -0.0014  0.5341 -0.0718  0.3542 -0.1502  0.4042 -0.5156
-#&gt; 
-#&gt; Columns 10 to 18  0.8057  0.2908 -0.1019 -0.2748  0.2799  0.6493 -0.7338  0.1910  0.1886
-#&gt;  -0.1154  0.1593 -0.5129 -0.6648  0.1733 -0.2496 -0.0886  0.2975  0.5137
-#&gt;   0.8810  0.4194 -0.0103 -0.8285 -0.0348 -0.0102 -0.6628  0.1669 -0.4613
-#&gt; 
-#&gt; Columns 19 to 20 -0.0403  0.0080
-#&gt;  -0.0600 -0.4162
-#&gt;   0.0908  0.6937
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  Columns 1 to 9 -0.1978  0.2242  0.0024  0.3932 -0.4801 -0.4895 -0.5400 -0.0527 -0.4520
-#&gt;   0.4544  0.0302  0.4917  0.2736 -0.5769 -0.1859 -0.4959  0.0229 -0.2535
-#&gt;  -0.5535  0.3675  0.5847  0.6636 -0.3288 -0.2481 -0.1065 -0.0289 -0.5147
-#&gt; 
-#&gt; Columns 10 to 18  0.8121  0.5476 -0.5889 -0.2491  0.5971  0.3482 -0.4202  0.5075  0.0695
-#&gt;   0.8887  0.3603 -0.1642 -0.3072  0.2559 -0.0096 -0.6545  0.5044  0.5036
-#&gt;   0.6373 -0.1207  0.0495 -0.3367  0.4293  0.4361 -0.3157  0.3224  0.6757
-#&gt; 
-#&gt; Columns 19 to 20 -0.0226 -0.0955
-#&gt;   0.6364  0.2054
-#&gt;   0.1772 -0.2871
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  Columns 1 to 9 -0.5002  0.2480 -0.0165  0.4973 -0.7685  0.0885 -0.3330 -0.2697 -0.1477
-#&gt;  -0.5379  0.1719  0.2126  0.1891 -0.5105  0.2180 -0.5122 -0.1882 -0.4472
-#&gt;   0.0806  0.0901  0.5329  0.3643 -0.6769  0.4601 -0.5399 -0.3066 -0.0994
-#&gt; 
-#&gt; Columns 10 to 18  0.4677 -0.0194 -0.3609 -0.2897  0.3666  0.0276  0.0770  0.5985  0.5201
-#&gt;   0.6046 -0.2503 -0.4701 -0.1266  0.3423  0.1259 -0.2631  0.5912  0.1230
-#&gt;   0.6537 -0.2490 -0.3203 -0.3803  0.0304 -0.0077  0.1981  0.6495 -0.0583
-#&gt; 
-#&gt; Columns 19 to 20 -0.0329 -0.2124
-#&gt;   0.1306  0.0613
-#&gt;   0.0430 -0.0534
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  Columns 1 to 9 -0.3560  0.0896  0.2468  0.0908 -0.3990 -0.1175 -0.3947 -0.0834  0.1421
-#&gt;   0.1891 -0.0772  0.2671  0.0296 -0.1929 -0.2009 -0.5507 -0.2240  0.2121
-#&gt;  -0.1833  0.2226 -0.0158  0.5592 -0.5925  0.0255 -0.6282 -0.1562  0.0561
-#&gt; 
-#&gt; Columns 10 to 18  0.5649 -0.0882 -0.4652 -0.2057  0.0088  0.0349 -0.0315  0.3252  0.6167
-#&gt;   0.5626  0.3505 -0.2768 -0.4894 -0.0599  0.4348 -0.1352  0.2022  0.2273
-#&gt;   0.2647  0.0037 -0.3756 -0.3976 -0.0172 -0.1532 -0.4150  0.3451  0.3110
-#&gt; 
-#&gt; Columns 19 to 20  0.2250  0.1283
-#&gt;  -0.0717  0.2627
-#&gt;   0.1909 -0.1445
-#&gt; [ CPUFloatType{5,3,20} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  Columns 1 to 9 -0.2977 -0.3901 -0.3494 -0.6523  0.3627  0.1448 -0.3341  0.2196  0.1126
-#&gt;   0.2888 -0.7529  0.1781 -0.0379 -0.2393  0.3807  0.1044 -0.0212 -0.5096
-#&gt;  -0.1402 -0.3835 -0.2036  0.3084  0.1285 -0.3805  0.1103  0.0476  0.2100
-#&gt; 
-#&gt; Columns 10 to 18 -0.6325 -0.1108 -0.1481  0.0602  0.7081 -0.3749  0.6918 -0.4901 -0.2858
-#&gt;   0.2888  0.4654  0.2154 -0.3173  0.4848  0.3496 -0.1522 -0.0645 -0.5102
-#&gt;   0.2073  0.5197  0.0807  0.4554  0.0247 -0.2980 -0.3274 -0.1698 -0.0551
-#&gt; 
-#&gt; Columns 19 to 20  0.0842  0.0867
-#&gt;   0.1297 -0.4188
-#&gt;   0.6599 -0.5773
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  Columns 1 to 9 -0.3560  0.0896  0.2468  0.0908 -0.3990 -0.1175 -0.3947 -0.0834  0.1421
-#&gt;   0.1891 -0.0772  0.2671  0.0296 -0.1929 -0.2009 -0.5507 -0.2240  0.2121
-#&gt;  -0.1833  0.2226 -0.0158  0.5592 -0.5925  0.0255 -0.6282 -0.1562  0.0561
-#&gt; 
-#&gt; Columns 10 to 18  0.5649 -0.0882 -0.4652 -0.2057  0.0088  0.0349 -0.0315  0.3252  0.6167
-#&gt;   0.5626  0.3505 -0.2768 -0.4894 -0.0599  0.4348 -0.1352  0.2022  0.2273
-#&gt;   0.2647  0.0037 -0.3756 -0.3976 -0.0172 -0.1532 -0.4150  0.3451  0.3110
-#&gt; 
-#&gt; Columns 19 to 20  0.2250  0.1283
-#&gt;  -0.0717  0.2627
-#&gt;   0.1909 -0.1445
-#&gt; [ CPUFloatType{2,3,20} ]
-#&gt; </div><div class='input'>
-# }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.0421
-#&gt;  1.4246
-#&gt; [ CPUFloatType{2} ]</div><div class='input'>
-# }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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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>
-
-    <div class="ref-description">
-    <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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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-    <div class="page-header">
-    <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='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>
-
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-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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>)
-
-<span class='co'># }</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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-    <p>Applies the element-wise function:</p>
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-
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-
-
-    <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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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diff --git a/docs/reference/nn_softmax.html b/docs/reference/nn_softmax.html
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-    </div>
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-    <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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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-    <p>Applies SoftMax over features to each spatial location.
-When given an image of <code>Channels x Height x Width</code>, it will
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-
-
-    <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>
-
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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'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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-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='co'># \dontrun{</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>)
-
-<span class='co'># }</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-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softplus.Rd</code></div>
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-    <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='co'># \dontrun{</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>)
-
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diff --git a/docs/reference/nn_softshrink.html b/docs/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>(<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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/nn_softsign.html b/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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-    <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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-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_tanh</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>)
-
-<span class='co'># }</span></div></pre>
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-      </header>
-
-<div class="row">
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-    <div class="page-header">
-    <h1>Tanhshrink 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_tanhshrink.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Applies the element-wise function:</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_tanhshrink</span>()</pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <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>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</span>
-<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>nn_tanhshrink</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>)
-
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diff --git a/docs/reference/nn_threshold.html b/docs/reference/nn_threshold.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-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='co'># \dontrun{</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>)
-
-<span class='co'># }</span></div></pre>
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diff --git a/docs/reference/nn_utils_rnn_pack_padded_sequence.html b/docs/reference/nn_utils_rnn_pack_padded_sequence.html
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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>
-
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diff --git a/docs/reference/nn_utils_rnn_pack_sequence.html b/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='co'># \dontrun{</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>))
-
-<span class='co'># }</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/docs/reference/nn_utils_rnn_pad_packed_sequence.html b/docs/reference/nn_utils_rnn_pad_packed_sequence.html
deleted file mode 100644
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--- a/docs/reference/nn_utils_rnn_pad_packed_sequence.html
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; &lt;PackedSequence&gt;
-#&gt;   Public:
-#&gt;     batch_sizes: active binding
-#&gt;     clone: function (deep = FALSE) 
-#&gt;     data: active binding
-#&gt;     initialize: function (ptr = NULL) 
-#&gt;     ptr: externalptr
-#&gt;     sorted_indices: active binding
-#&gt;     unsorted_indices: active binding</div><div class='input'><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><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><div class='input'>
-# }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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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='co'># \dontrun{</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><div class='output co'>#&gt; [1]  25   3 300</div><div class='input'>
-# }
-</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/docs/reference/nnf_adaptive_avg_pool1d.html b/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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diff --git a/docs/reference/nnf_adaptive_max_pool2d.html b/docs/reference/nnf_adaptive_max_pool2d.html
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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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-      <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>
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-      <th>return_indices</th>
-      <td><p>whether to return pooling indices. Default: <code>FALSE</code></p></td>
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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>
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-    <tr>
-      <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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diff --git a/docs/reference/nnf_affine_grid.html b/docs/reference/nnf_affine_grid.html
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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='no'>theta</span>, <span class='no'>size</span>, <span class='kw'>align_corners</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>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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diff --git a/docs/reference/nnf_alpha_dropout.html b/docs/reference/nnf_alpha_dropout.html
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-    <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>
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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 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>
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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='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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diff --git a/docs/reference/nnf_avg_pool2d.html b/docs/reference/nnf_avg_pool2d.html
deleted file mode 100644
index 7350448eb..000000000
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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_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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-  </div>
-</div>
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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diff --git a/docs/reference/nnf_avg_pool3d.html b/docs/reference/nnf_avg_pool3d.html
deleted file mode 100644
index a82de3ee2..000000000
--- a/docs/reference/nnf_avg_pool3d.html
+++ /dev/null
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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='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>
-
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diff --git a/docs/reference/nnf_batch_norm.html b/docs/reference/nnf_batch_norm.html
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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>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/nnf_bilinear.html b/docs/reference/nnf_bilinear.html
deleted file mode 100644
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--- a/docs/reference/nnf_bilinear.html
+++ /dev/null
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-All but the last dimension of the inputs should be the same.</p></td>
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-      <td><p>\((\mbox{out\_features}, \mbox{in1\_features},
-\mbox{in2\_features})\)</p></td>
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diff --git a/docs/reference/nnf_binary_cross_entropy.html b/docs/reference/nnf_binary_cross_entropy.html
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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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-    <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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-    <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>
-
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-      <td><p>Tensor of arbitrary shape</p></td>
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-      <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/docs/reference/nnf_celu.html b/docs/reference/nnf_celu.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>
-    <div class="hidden name"><code>nnf_celu.Rd</code></div>
-    </div>
-
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-    <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/docs/reference/nnf_conv1d.html b/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>
-
-    <pre class="usage"><span class='fu'>nnf_conv1d</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 , 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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-    <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>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/nnf_conv3d.html b/docs/reference/nnf_conv3d.html
deleted file mode 100644
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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='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>
-
-
-  </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/docs/reference/nnf_conv_tbc.html b/docs/reference/nnf_conv_tbc.html
deleted file mode 100644
index b9f90e3b7..000000000
--- a/docs/reference/nnf_conv_tbc.html
+++ /dev/null
@@ -1,221 +0,0 @@
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-      <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>
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-      <td><p>bias of shape (\(\mbox{out\_channels}\))</p></td>
-    </tr>
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-      <th>pad</th>
-      <td><p>number of timesteps to pad. Default: 0</p></td>
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diff --git a/docs/reference/nnf_conv_transpose1d.html b/docs/reference/nnf_conv_transpose1d.html
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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='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>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/nnf_conv_transpose2d.html b/docs/reference/nnf_conv_transpose2d.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_conv_transpose2d.Rd</code></div>
-    </div>
-
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-    <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>
-
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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='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>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/nnf_cosine_embedding_loss.html b/docs/reference/nnf_cosine_embedding_loss.html
deleted file mode 100644
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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_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>
-
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-      <th>input1</th>
-      <td><p>the input x_1 tensor</p></td>
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-      <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>
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diff --git a/docs/reference/nnf_cosine_similarity.html b/docs/reference/nnf_cosine_similarity.html
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-    <div class="hidden name"><code>nnf_cosine_similarity.Rd</code></div>
-    </div>
-
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-    <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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-  <div class="col-md-9 contents">
-    <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='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>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/nnf_ctc_loss.html b/docs/reference/nnf_ctc_loss.html
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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_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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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-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>
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-    <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='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">
-    <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/docs/reference/nnf_dropout3d.html b/docs/reference/nnf_dropout3d.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>
-    <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>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/nnf_elu.html b/docs/reference/nnf_elu.html
deleted file mode 100644
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-$$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$.</p>
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-
-    <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>
-
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-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.7520  0.2844
-#&gt;  1.3381  0.9215
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'><span class='fu'><a href='torch_equal.html'>torch_equal</a></span>(<span class='no'>x</span>, <span class='no'>y</span>)</div><div class='output co'>#&gt; [1] TRUE</div><div class='input'>
-# }
-</div></pre>
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diff --git a/docs/reference/nnf_embedding.html b/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>
-
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-    <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">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/docs/reference/nnf_embedding_bag.html b/docs/reference/nnf_embedding_bag.html
deleted file mode 100644
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-    <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>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/nnf_fold.html b/docs/reference/nnf_fold.html
deleted file mode 100644
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-</li>
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-    <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">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/docs/reference/nnf_fractional_max_pool2d.html b/docs/reference/nnf_fractional_max_pool2d.html
deleted file mode 100644
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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>
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-
-    <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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diff --git a/docs/reference/nnf_fractional_max_pool3d.html b/docs/reference/nnf_fractional_max_pool3d.html
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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">
-      <h2 data-toc-skip>Contents</h2>
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diff --git a/docs/reference/nnf_gelu.html b/docs/reference/nnf_gelu.html
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-
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-
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-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
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-    </table>
-
-    <h2 class="hasAnchor" id="gelu-input-gt-tensor-"><a class="anchor" href="#gelu-input-gt-tensor-"></a>gelu(input) -&gt; Tensor </h2>
-
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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>
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-
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-
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-    <table class="ref-arguments">
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-    <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="hidden name"><code>nnf_grid_sample.Rd</code></div>
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-
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-    <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>
-
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diff --git a/docs/reference/nnf_group_norm.html b/docs/reference/nnf_group_norm.html
deleted file mode 100644
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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/docs/reference/nnf_gumbel_softmax.html b/docs/reference/nnf_gumbel_softmax.html
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-optionally discretizes.</p>
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-dimensions</p></td>
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-Searching for MobileNetV3.</p>
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-
-    <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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-    <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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diff --git a/docs/reference/nnf_hardtanh.html b/docs/reference/nnf_hardtanh.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>
-    <div class="hidden name"><code>nnf_hardtanh.Rd</code></div>
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-    <p>Applies the HardTanh function element-wise.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_hardtanh</span>(<span class='no'>input</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>)
-
-<span class='fu'>nnf_hardtanh_</span>(<span class='no'>input</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>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="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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-    <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='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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-      <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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diff --git a/docs/reference/nnf_instance_norm.html b/docs/reference/nnf_instance_norm.html
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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='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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diff --git a/docs/reference/nnf_interpolate.html b/docs/reference/nnf_interpolate.html
deleted file mode 100644
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-    <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>
-
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-    <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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diff --git a/docs/reference/nnf_kl_div.html b/docs/reference/nnf_kl_div.html
deleted file mode 100644
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-    <div class="hidden name"><code>nnf_kl_div.Rd</code></div>
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-
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-    <p>The Kullback-Leibler divergence Loss.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nnf_kl_div</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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-    <table class="ref-arguments">
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-      <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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-      <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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diff --git a/docs/reference/nnf_layer_norm.html b/docs/reference/nnf_layer_norm.html
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-      <th>weight</th>
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-      <td><p>the bias tensor</p></td>
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diff --git a/docs/reference/nnf_leaky_relu.html b/docs/reference/nnf_leaky_relu.html
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-\(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\)</p>
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-
-    <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>
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-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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-      <th>size</th>
-      <td><p>amount of neighbouring channels used for normalization</p></td>
-    </tr>
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-      <th>alpha</th>
-      <td><p>multiplicative factor. Default: 0.0001</p></td>
-    </tr>
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-      <th>beta</th>
-      <td><p>exponent. Default: 0.75</p></td>
-    </tr>
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-      <th>k</th>
-      <td><p>additive factor. Default: 1</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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-
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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 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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-zero, the gradient is set to zero as well.</p>
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-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
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-      <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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-proportional to the avg pooling)</p></td>
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-      <td><p>a single int, the size of the window</p></td>
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-      <td><p>a single int, the stride of the window. Default value is kernel_size</p></td>
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diff --git a/docs/reference/nnf_margin_ranking_loss.html b/docs/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>
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-
-    <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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-    <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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-      <a href="../articles/examples/mnist-cnn.html">mnist-cnn</a>
-    </li>
-    <li>
-      <a href="../articles/examples/mnist-dcgan.html">mnist-dcgan</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-      </ul>
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-        <li>
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-    <span class="fab fa fab fa-github fa-lg"></span>
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-
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-    <div class="page-header">
-    <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>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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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">
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diff --git a/docs/reference/nnf_max_pool3d.html b/docs/reference/nnf_max_pool3d.html
deleted file mode 100644
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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_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/docs/reference/nnf_max_unpool1d.html b/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/docs/reference/nnf_max_unpool2d.html b/docs/reference/nnf_max_unpool2d.html
deleted file mode 100644
index cd9c095d9..000000000
--- a/docs/reference/nnf_max_unpool2d.html
+++ /dev/null
@@ -1,232 +0,0 @@
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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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-
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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>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/nnf_max_unpool3d.html b/docs/reference/nnf_max_unpool3d.html
deleted file mode 100644
index de981fa25..000000000
--- a/docs/reference/nnf_max_unpool3d.html
+++ /dev/null
@@ -1,232 +0,0 @@
-<!-- Generated by pkgdown: do not edit by hand -->
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-  <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>
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-      <td><p>the input Tensor to invert</p></td>
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-      <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/docs/reference/nnf_mse_loss.html b/docs/reference/nnf_mse_loss.html
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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='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>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-(which is a 1D tensor of target class indices, 0 &amp;lt;= y &amp;lt;= x$size(2) - 1 )." />
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-    <h1>Multi_margin_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_multi_margin_loss.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates a criterion that optimizes a multi-class classification hinge loss
-(margin-based loss) between input x (a 2D mini-batch Tensor) and output y
-(which is a 1D tensor of target class indices, <code>0 &lt;= y &lt;= x$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>
-
-
-  </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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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
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diff --git a/docs/reference/nnf_multilabel_margin_loss.html b/docs/reference/nnf_multilabel_margin_loss.html
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-      <td><p>tensor (N,*) , same shape as the input</p></td>
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-      <td><p>weight tensor to apply on the loss.</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,
-'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>
-    <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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diff --git a/docs/reference/nnf_normalize.html b/docs/reference/nnf_normalize.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_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>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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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>
-
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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>
-    </nav>
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/docs/reference/nnf_pad.html b/docs/reference/nnf_pad.html
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-    <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='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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diff --git a/docs/reference/nnf_pairwise_distance.html b/docs/reference/nnf_pairwise_distance.html
deleted file mode 100644
index 79bfbca79..000000000
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+++ /dev/null
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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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diff --git a/docs/reference/nnf_pdist.html b/docs/reference/nnf_pdist.html
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-\(\in [0, \infty]\).</p></td>
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diff --git a/docs/reference/nnf_pixel_shuffle.html b/docs/reference/nnf_pixel_shuffle.html
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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>
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-      <td><p>(int) factor to increase spatial resolution by</p></td>
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diff --git a/docs/reference/nnf_poisson_nll_loss.html b/docs/reference/nnf_poisson_nll_loss.html
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-  <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>
-
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-    <table class="ref-arguments">
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-      <th>input</th>
-      <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>
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-      <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>
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-      <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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diff --git a/docs/reference/nnf_prelu.html b/docs/reference/nnf_prelu.html
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-\(PReLU(x) = max(0,x) + weight * min(0,x)\)
-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>
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-      <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>
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diff --git a/docs/reference/nnf_relu.html b/docs/reference/nnf_relu.html
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-
-<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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-    <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>
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-    <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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-$$SELU(x) = scale * (max(0,x) + min(0, \alpha * (exp(x) - 1)))$$,
-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>
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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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-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
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-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.3549  0.1844
-#&gt; -1.4839 -0.8035
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'><span class='fu'><a href='torch_equal.html'>torch_equal</a></span>(<span class='no'>x</span>, <span class='no'>y</span>)</div><div class='output co'>#&gt; [1] TRUE</div><div class='input'>
-# }
-</div></pre>
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diff --git a/docs/reference/nnf_smooth_l1_loss.html b/docs/reference/nnf_smooth_l1_loss.html
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-    <p>Function that uses a squared term if the absolute
-element-wise error falls below 1 and an L1 term otherwise.</p>
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-
-    <pre class="usage"><span class='fu'>nnf_smooth_l1_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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-    <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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diff --git a/docs/reference/nnf_soft_margin_loss.html b/docs/reference/nnf_soft_margin_loss.html
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-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
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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>
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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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-    <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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-      <td><p>the beta value for the Softplus formulation. Default: 1</p></td>
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-      <td><p>values above this revert to a linear function. Default: 20</p></td>
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-dimensions</p></td>
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-      <th>lambd</th>
-      <td><p>the lambda (must be no less than zero) value for the Softshrink
-formulation. Default: 0.5</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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-    <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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diff --git a/docs/reference/nnf_unfold.html b/docs/reference/nnf_unfold.html
deleted file mode 100644
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-    <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='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>
-
-  </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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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
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diff --git a/docs/reference/optim_adam.html b/docs/reference/optim_adam.html
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-    </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>
-
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-    <div class="page-header">
-    <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"></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <colgroup><col class="name" /><col class="desc" /></colgroup>
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-      <th>...</th>
-      <td><p>tensors that have the same size of the first dimension.</p></td>
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-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt; [ CPUFloatType{3} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_acos.html b/docs/reference/torch_acos.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_acos.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Acos</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="acos-input-out-none-gt-tensor-"><a class="anchor" href="#acos-input-out-none-gt-tensor-"></a>acos(input, out=None) -&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>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6137
-#&gt;  1.1598
-#&gt;  0.0958
-#&gt; -0.2733
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_acos</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2.2315
-#&gt;     nan
-#&gt;  1.4748
-#&gt;  1.8476
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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/docs/reference/torch_adaptive_avg_pool1d.html b/docs/reference/torch_adaptive_avg_pool1d.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>
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>NA 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>~torch.nn.AdaptiveAvgPool1d</code> for details and output shape.</p>
-
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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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.2160
-#&gt;  0.1973
-#&gt; -0.1795
-#&gt; -0.9024
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_add</span>(<span class='no'>a</span>, <span class='fl'>20</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  20.2160
-#&gt;  20.1973
-#&gt;  19.8204
-#&gt;  19.0976
-#&gt; [ CPUFloatType{4} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.6998
-#&gt; -0.1848
-#&gt; -0.4348
-#&gt; -0.7475
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.9213
-#&gt;  0.5193
-#&gt;  0.3855
-#&gt; -1.5317
-#&gt; [ CPUFloatType{4,1} ]</div><div class='input'><span class='fu'>torch_add</span>(<span class='no'>a</span>, <span class='no'>b</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.7785  0.7364  0.4865  0.1738
-#&gt; -1.1805  0.3345  0.0845 -0.2282
-#&gt; -1.3142  0.2007 -0.0492 -0.3619
-#&gt; -3.2315 -1.7166 -1.9665 -2.2792
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'># }
-</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></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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;   5.7025   7.7808   5.3946   0.1290   2.4487
-#&gt;  -1.9730  -3.0379  -5.4090   0.6009  -3.1469
-#&gt;   4.6785   6.4997 -11.4732   6.3957  10.2272
-#&gt; [ CPUFloatType{3,5} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3050 -0.1424  0.5617
-#&gt; -0.3424  0.0045  1.0519
-#&gt; -0.2932 -0.1885  0.4079
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_addcmul.html b/docs/reference/torch_addcmul.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_addcmul.Rd</code></div>
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-    <p>Addcmul</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 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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.2008 -0.8560  0.9351
-#&gt; -0.1664 -0.8433  0.9042
-#&gt; -0.5670 -0.9908  1.2630
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'># }
-</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>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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.4411  0.9520  5.5685
-#&gt;  2.0314  0.6255  2.2542
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'># }
-</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/docs/reference/torch_addmv.html b/docs/reference/torch_addmv.html
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-    </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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.9265
-#&gt;  1.5524
-#&gt; [ CPUFloatType{2} ]</div><div class='input'># }
-</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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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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diff --git a/docs/reference/torch_addr.html b/docs/reference/torch_addr.html
deleted file mode 100644
index 6ac26d962..000000000
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+++ /dev/null
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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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  2
-#&gt;  2  4
-#&gt;  3  6
-#&gt; [ CPUFloatType{3,2} ]</div><div class='input'># }
-</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/docs/reference/torch_allclose.html b/docs/reference/torch_allclose.html
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-    <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='co'># \dontrun{</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>)))</div><div class='output co'>#&gt; [1] FALSE</div><div class='input'><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>)))</div><div class='output co'>#&gt; [1] FALSE</div><div class='input'><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>)))</div><div class='output co'>#&gt; [1] FALSE</div><div class='input'><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><div class='output co'>#&gt; [1] TRUE</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_angle.html b/docs/reference/torch_angle.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_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"></pre>
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diff --git a/docs/reference/torch_arange.html b/docs/reference/torch_arange.html
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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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt;  4
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='fu'>torch_arange</span>(<span class='fl'>1</span>, <span class='fl'>4</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt; [ CPUFloatType{3} ]</div><div class='input'><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><div class='input'># }
-</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_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"></pre>
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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></small>
-    <div class="hidden name"><code>torch_argmin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Argmin</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 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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.6530 -1.9398 -0.7858 -0.6979
-#&gt;  1.3467  2.4378  2.4695 -0.0903
-#&gt;  0.5428 -0.8464 -0.8918 -0.2703
-#&gt;  1.0460 -0.3144  0.2131 -0.1355
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><span class='fu'>torch_argmin</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 1
-#&gt; [ CPULongType{} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.3917 -1.7360  2.1245 -0.4908
-#&gt; -1.2249 -0.1974  0.3145 -1.2540
-#&gt;  2.5169  0.8670  1.2077  0.5393
-#&gt;  0.2843 -0.6558 -0.7945  1.3721
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  0
-#&gt;  3
-#&gt;  1
-#&gt; [ CPULongType{4} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <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></small>
-    <div class="hidden name"><code>torch_argsort.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Argsort</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>
-    </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'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.8250 -0.5984 -1.2454  0.4598
-#&gt; -0.9256  0.0695 -1.6829  1.5544
-#&gt;  2.1622  0.7200  0.7667 -0.4872
-#&gt;  1.1699  0.8607  2.5965  0.0434
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  0  1  2
-#&gt;  0  1  0  3
-#&gt;  3  2  2  0
-#&gt;  2  3  3  1
-#&gt; [ CPULongType{4,4} ]</div><div class='input'># }
-</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="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.5576  0.5216 -0.6254
-#&gt;  0.5108 -0.2964  0.5801
-#&gt; -0.7827  0.2806  0.0976
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.5576 -0.6254
-#&gt;  0.5216  0.5108
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5216  0.5108
-#&gt; -0.6254 -0.2964
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'># }
-</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/docs/reference/torch_asin.html b/docs/reference/torch_asin.html
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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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3857
-#&gt;  1.8551
-#&gt;  0.4113
-#&gt;  0.7013
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_asin</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3959
-#&gt;     nan
-#&gt;  0.4239
-#&gt;  0.7773
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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/docs/reference/torch_atan.html b/docs/reference/torch_atan.html
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-    <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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.7742
-#&gt;  0.3914
-#&gt; -0.0984
-#&gt;  0.7190
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_atan</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6588
-#&gt;  0.3730
-#&gt; -0.0980
-#&gt;  0.6234
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_atan2.html b/docs/reference/torch_atan2.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_atan2.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Atan2</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 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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.7384
-#&gt;  1.5533
-#&gt;  0.0480
-#&gt;  0.5090
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.3252
-#&gt;  1.4989
-#&gt;  3.0686
-#&gt;  1.3146
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_avg_pool1d.html b/docs/reference/torch_avg_pool1d.html
deleted file mode 100644
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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 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>
-
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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></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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   3.2697 -5.0643  0.0743 -0.2398 -2.5402
-#&gt;  -0.3596 -0.1524 -2.2537 -0.6132  0.4815
-#&gt;   1.1825 -2.2500  0.8243  1.5010 -2.4894
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   0.4770 -0.8900  3.0012  2.0244  2.9934
-#&gt;  -2.0624 -0.7371 -0.6249 -1.4119 -1.0305
-#&gt;   0.4525  1.1938  1.2075  2.4423  0.5840
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -1.5483  0.0002 -0.7736 -0.1712  2.3502
-#&gt;   1.3820  1.9069 -1.1504  2.8244 -0.5037
-#&gt;  -0.7816  0.0485  3.1307 -0.7125  2.3957
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;   3.2263  1.9973 -2.7929 -0.6880 -1.8358
-#&gt;   3.9498  0.1835 -3.6300 -0.7907 -2.9265
-#&gt;   1.5720 -1.5571 -0.5235  0.2169 -0.7204
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  -1.5198 -1.4044  0.6454  1.6571  1.6412
-#&gt;   0.6481 -0.1620  0.7348 -2.5747 -1.5232
-#&gt;  -3.9663  0.6486 -0.1782 -0.2130 -0.2005
-#&gt; 
-#&gt; (6,.,.) = 
-#&gt;  -0.7923 -0.1696 -0.0210 -1.4651  0.1979
-#&gt;  -0.2874  2.4903 -2.5324  0.1213  4.3363
-#&gt;   0.8367  0.5843  2.6930 -0.5081 -0.7514
-#&gt; 
-#&gt; (7,.,.) = 
-#&gt;   0.9376 -5.8062 -2.4161 -2.2368  1.7258
-#&gt;  -0.5255  2.0584  1.1016 -2.1323 -1.1418
-#&gt;  -1.8125  0.8110 -0.2142  1.9131  2.4363
-#&gt; 
-#&gt; (8,.,.) = 
-#&gt;  -1.3094  0.0064 -0.5161  4.1986 -0.5380
-#&gt;  -4.8329 -0.6216  0.9426 -3.9339 -1.2310
-#&gt;   7.8403  0.3146 -1.0314  3.4608  2.3111
-#&gt; 
-#&gt; (9,.,.) = 
-#&gt;  -0.9910 -4.0243  4.6838 -4.8655  0.7247
-#&gt;   1.0314  0.6343  0.5493 -0.0418 -0.5915
-#&gt;   0.1801  0.7773 -1.0913  0.2247 -2.3853
-#&gt; 
-#&gt; (10,.,.) = 
-#&gt;  -1.1792  0.4361 -0.6693 -0.4414  0.9327
-#&gt;  -4.9029 -6.8475 -4.1729 -2.2513  0.6501
-#&gt;   1.3470  1.4167 -0.9282  0.5063 -2.4436
-#&gt; [ CPUFloatType{10,3,5} ]</div><div class='input'># }
-</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></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>
-
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.8765  0.8092  0.3962
-#&gt;  0.4623  0.3192  0.0298
-#&gt;  0.7755  0.1732  0.0310
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><span class='fu'>torch_bernoulli</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  1  1
-#&gt;  0  1  0
-#&gt;  1  0  0
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  1  1
-#&gt;  1  1  1
-#&gt;  1  1  1
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  0  0
-#&gt;  0  0  0
-#&gt;  0  0  0
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'># }
-</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_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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2
-#&gt;  7
-#&gt;  5
-#&gt;  3
-#&gt;  6
-#&gt; [ CPULongType{5} ]</div><div class='input'><span class='no'>weights</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000
-#&gt;  0.2500
-#&gt;  0.5000
-#&gt;  0.7500
-#&gt;  1.0000
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='fu'>torch_bincount</span>(<span class='no'>input</span>, <span class='no'>weights</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000
-#&gt;  0.0000
-#&gt;  0.0000
-#&gt;  0.7500
-#&gt;  0.0000
-#&gt;  0.5000
-#&gt;  1.0000
-#&gt;  0.2500
-#&gt; [ CPUFloatType{8} ]</div><div class='input'><span class='no'>input</span>$<span class='fu'>bincount</span>(<span class='no'>weights</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000
-#&gt;  0.0000
-#&gt;  0.0000
-#&gt;  0.7500
-#&gt;  0.0000
-#&gt;  0.5000
-#&gt;  1.0000
-#&gt;  0.2500
-#&gt; [ CPUFloatType{8} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_bitwise_and.html b/docs/reference/torch_bitwise_and.html
deleted file mode 100644
index c0d1f81d5..000000000
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-    <h1>Bitwise_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_bitwise_and.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Bitwise_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>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-and-input-other-out-none-gt-tensor-"><a class="anchor" href="#bitwise-and-input-other-out-none-gt-tensor-"></a>bitwise_and(input, other, out=None) -&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>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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deleted file mode 100644
index 1740d2d36..000000000
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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>
-    </tr>
-    </table>
-
-    <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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-      <th>input</th>
-      <td><p>NA the first input tensor</p></td>
-    </tr>
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-      <th>other</th>
-      <td><p>NA the second input tensor</p></td>
-    </tr>
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-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
-    </tr>
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-    <h2 class="hasAnchor" id="bitwise-or-input-other-out-none-gt-tensor-"><a class="anchor" href="#bitwise-or-input-other-out-none-gt-tensor-"></a>bitwise_or(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
-
-<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></small>
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-    <table class="ref-arguments">
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-    <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>
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-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor.</p></td>
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-    <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>
-
-    
-
-
-<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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-    <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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-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -1.1937  1.0490 -1.3460 -1.3636  0.2908
-#&gt;  -0.7399 -0.3916 -0.0894  1.5547 -0.5792
-#&gt;   1.6370 -1.8825  0.6914  0.4735  0.2958
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   3.0209 -2.4298  0.3410  0.0615 -2.5501
-#&gt;  -3.8228  0.7082  0.9869  0.1536  1.1400
-#&gt;   2.5718 -0.3476  1.3377  0.6290 -0.2315
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -0.2813 -0.3510 -0.6811 -0.8482  1.3861
-#&gt;   3.3843 -1.2077 -1.9622 -1.1351 -1.8477
-#&gt;   2.6732 -2.4184  0.7855  2.8759 -1.4808
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  -6.8546  1.0791  2.1027 -2.8185  0.7520
-#&gt;  -0.9041  0.8896  2.4743  0.6284  0.2519
-#&gt;   2.6052 -1.4564 -1.6375  1.3288  0.3487
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;   2.5222 -1.6164 -2.2116 -1.0754  0.7719
-#&gt;  -3.6324  2.5302  0.9988 -2.1378  0.6788
-#&gt;   5.6221  0.7932  2.1447  4.9035 -5.1887
-#&gt; 
-#&gt; (6,.,.) = 
-#&gt;   0.2683 -1.0509  2.6643 -0.2398  0.4529
-#&gt;  -2.3240 -3.0188  2.6981  1.3544  0.8555
-#&gt;  -0.4469  0.3477  1.0020  4.7555  1.9801
-#&gt; 
-#&gt; (7,.,.) = 
-#&gt;  -1.5234  0.5375  0.0234  2.3384 -3.3980
-#&gt;   1.3228  3.1686  1.4053 -2.2938  7.3319
-#&gt;   1.9968 -5.2192 -0.6723 -1.0900 -3.2833
-#&gt; 
-#&gt; (8,.,.) = 
-#&gt;  -2.3741  2.0837 -0.4425 -1.5224  2.2040
-#&gt;  -0.7937  1.1621  6.6647  0.5726  1.9161
-#&gt;  -1.2275 -0.9221 -1.9841 -0.4629  0.8082
-#&gt; 
-#&gt; (9,.,.) = 
-#&gt;  -1.4034 -0.0159 -0.7663  0.1020  0.4187
-#&gt;  -1.1376  2.4816  1.0544  1.9942 -1.1878
-#&gt;  -0.8727  0.0230 -0.5804  0.0939  0.1387
-#&gt; 
-#&gt; (10,.,.) = 
-#&gt;  -0.4053 -0.3109 -2.1835 -0.1594  1.8523
-#&gt;  -1.8857  1.2782  3.0087 -2.7136 -0.9552
-#&gt;  -1.9781  1.0313  2.1664 -2.4844 -0.7711
-#&gt; [ CPUFloatType{10,3,5} ]</div><div class='input'># }
-</div></pre>
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-    <div class="hidden name"><code>torch_broadcast_tensors.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Broadcast_tensors</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 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'># \dontrun{</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'>3</span>)$<span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
-<span class='no'>y</span> <span class='kw'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>2</span>)$<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>]]</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  1  2
-#&gt;  0  1  2
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'># }
-</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>
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-    <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'># \dontrun{</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>())</div><div class='output co'>#&gt; [1] TRUE</div><div class='input'><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><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_cartesian_prod.html b/docs/reference/torch_cartesian_prod.html
deleted file mode 100644
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+++ /dev/null
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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='co'># \dontrun{</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><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><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_cat.html b/docs/reference/torch_cat.html
deleted file mode 100644
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-    <p>Cat</p>
-    </div>
-
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-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.5078  0.8533  1.7774
-#&gt; -0.7864  1.4110  0.6703
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.5078  0.8533  1.7774
-#&gt; -0.7864  1.4110  0.6703
-#&gt;  1.5078  0.8533  1.7774
-#&gt; -0.7864  1.4110  0.6703
-#&gt;  1.5078  0.8533  1.7774
-#&gt; -0.7864  1.4110  0.6703
-#&gt; [ CPUFloatType{6,3} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.5078  0.8533  1.7774  1.5078  0.8533  1.7774  1.5078  0.8533  1.7774
-#&gt; -0.7864  1.4110  0.6703 -0.7864  1.4110  0.6703 -0.7864  1.4110  0.6703
-#&gt; [ CPUFloatType{2,9} ]</div><div class='input'># }
-</div></pre>
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-      <th>x1</th>
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-      <td><p>(Tensor) input tensor of shape \(B \times R \times M\).</p></td>
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-      <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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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</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>
-
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-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.9465
-#&gt;  1.5480
-#&gt; -0.6969
-#&gt; -0.4820
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_ceil</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt; -0
-#&gt; -0
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
-  </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>
-    <div class="hidden name"><code>torch_chain_matmul.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>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'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;   2.2025   6.9263 -12.0433  -1.8318   6.1157  -1.9091  -2.5474
-#&gt;  -9.2675   3.6580 -10.9555   3.7499  -0.9984  -2.1468  18.3629
-#&gt;  -4.3318 -10.0159  20.3315   2.5116  -9.5372   3.4920   7.3516
-#&gt; [ CPUFloatType{3,7} ]</div><div class='input'># }
-</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></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"></pre>
-  </div>
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diff --git a/docs/reference/torch_cholesky_inverse.html b/docs/reference/torch_cholesky_inverse.html
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-    <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"></pre>
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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='co'># \dontrun{</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>$<span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) <span class='co'># make symmetric positive definite</span>
-<span class='no'>u</span> <span class='kw'>=</span> <span class='fu'><a href='torch_cholesky.html'>torch_cholesky</a></span>(<span class='no'>a</span>)
-<span class='no'>a</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  4.8833 -0.7896 -0.4785
-#&gt; -0.7896  1.0348 -0.2048
-#&gt; -0.4785 -0.2048  0.8552
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><span class='no'>b</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
-<span class='no'>b</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5712 -0.1153
-#&gt; -1.2014  0.0291
-#&gt;  1.1547  0.9237
-#&gt; [ CPUFloatType{3,2} ]</div><div class='input'><span class='fu'>torch_cholesky_solve</span>(<span class='no'>b</span>, <span class='no'>u</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0975  0.1667
-#&gt; -0.8489  0.4068
-#&gt;  1.2015  1.2708
-#&gt; [ CPUFloatType{3,2} ]</div><div class='input'><span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='no'>a</span>$<span class='fu'>inverse</span>(), <span class='no'>b</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0975  0.1667
-#&gt; -0.8489  0.4068
-#&gt;  1.2015  1.2708
-#&gt; [ CPUFloatType{3,2} ]</div><div class='input'># }
-</div></pre>
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-    <table class="ref-arguments">
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-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the tensor to split</p></td>
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-      <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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-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.9506
-#&gt;  2.2284
-#&gt; -0.7040
-#&gt; -0.4355
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.5000
-#&gt;  0.5000
-#&gt; -0.5000
-#&gt; -0.4355
-#&gt; [ CPUFloatType{4} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.9982
-#&gt; -1.4524
-#&gt; -1.4201
-#&gt;  0.5077
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.9982
-#&gt;  0.5000
-#&gt;  0.5000
-#&gt;  0.5077
-#&gt; [ CPUFloatType{4} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.9805
-#&gt; -1.3783
-#&gt;  0.7469
-#&gt; -0.5865
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5000
-#&gt; -1.3783
-#&gt;  0.5000
-#&gt; -0.5865
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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/docs/reference/torch_combinations.html b/docs/reference/torch_combinations.html
deleted file mode 100644
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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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  2
-#&gt;  1  3
-#&gt;  2  3
-#&gt; [ CPUFloatType{3,2} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  2  3
-#&gt; [ CPUFloatType{1,3} ]</div><div class='input'><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><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><div class='input'># }
-</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/docs/reference/torch_conj.html b/docs/reference/torch_conj.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>
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-    <p>Conj</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="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>
-
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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_conv1d.Rd</code></div>
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-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  Columns 1 to 8   8.7199   1.0094 -13.1435   0.0848  10.3420   0.6087   4.9930  -1.3194
-#&gt;    4.9928  -0.9228  -0.9986   6.1375  -2.7563   6.2688  -4.3247   1.3281
-#&gt;   -9.2853   2.2857   5.2282  -3.3548  -1.1207   0.3963   0.6191   9.8601
-#&gt;   -2.9242  10.2787   0.3779   3.2870  -0.1333   5.6178   6.8783   0.1472
-#&gt;   -3.6903  -1.2163  11.9645  -7.1134  -7.8627   8.8960  -5.3701   6.3564
-#&gt;    6.0586 -13.3361   8.7976   5.4943  -0.7770  16.2518  -1.2318  -3.5980
-#&gt;   -2.6689   7.9637  -3.4276  -8.6586 -11.7605   7.9358   3.0729  -3.9882
-#&gt;    4.5930  -2.0741  -3.8087   2.1398   0.5135  -4.2471  -6.8810  -0.7988
-#&gt;    8.2812   1.7671   0.9025  -1.8145  -4.3674  -2.3088   0.6176   3.8401
-#&gt;    9.8749   2.9536   2.1752   0.1801  -4.7035  -4.8083  -2.0966   7.7104
-#&gt;   -4.8894  -5.0632  10.0122 -17.8657  14.6597  -3.3046 -14.7129   8.5806
-#&gt;   -8.5251 -10.4482  13.8896   8.8005   1.1426  10.7473  -8.9987  -1.4510
-#&gt;   -0.4190   6.8991  -5.3740   4.5935 -17.9217  -4.3224  -7.6038   5.2284
-#&gt;    4.8968   7.2210  -3.0931   1.1218  -3.9569  -6.7213   7.2451   0.0891
-#&gt;    2.0933   2.8404  -0.0990   2.7687  -4.0503  12.9643   9.7891   6.1938
-#&gt;   -0.1327   4.4571  -4.0763   0.9068  -4.0663 -10.0672  -0.0225   1.3389
-#&gt;   -9.7440  14.3030  -2.2208   2.3737   4.2655  -4.8818  -6.3157 -14.4964
-#&gt;   -3.8432 -18.8804   5.3663   1.4313  -1.3751  14.9081  -9.7717  -4.8676
-#&gt;   -3.7739   0.8794   1.7302 -10.1387   2.1366 -12.9728  -9.7771  22.9757
-#&gt;    2.7529   9.6604  -0.1306  -3.2509  -3.3979  -0.0270   9.5813   1.2052
-#&gt;   -1.1247  -1.7801   1.5065  -2.9020   6.6147   0.8074  -6.8228  -8.4092
-#&gt;   10.0025   6.9134  -9.1085   8.0784 -11.5030  -9.0789  10.6144  -9.2909
-#&gt;   -7.9290   6.5677   6.5561  -1.8320  11.5627  -5.7025   6.3629   4.9404
-#&gt;  -11.5437   8.3430 -13.9013  -4.2953   6.8770 -10.3218   6.7874   2.5812
-#&gt;   -1.8688  -0.4796  -2.9052   1.1717  10.4609   3.9477   2.4715   1.7198
-#&gt;   -2.8262   7.9209  -3.2288  -6.6791  -5.2385  -4.3199   2.6352   2.9522
-#&gt;    6.2078  -0.4560   6.5245  -3.6146  -1.6950  14.1103   5.0830  -5.4658
-#&gt;  -11.5664   6.8556   2.3652   4.2702   1.0913  -5.0231   1.3486 -16.3077
-#&gt;    8.8823  -0.4775  -1.9547  10.6024   1.7667  -2.6328  12.5331   9.6971
-#&gt;  -11.2919  12.3110   6.9462  -8.8800  -1.5003 -14.4211  -7.1624   7.2424
-#&gt;    2.9990  -6.5829  10.0200   5.3001   2.1273  -3.6609 -12.9631   7.9098
-#&gt;   -3.6909  11.1726   7.8032   2.4063   7.1658   4.1017   4.1518   0.2259
-#&gt;    1.0179   6.3694  -1.5287   5.6457  -8.5666  -2.8587   5.0281   7.5907
-#&gt; 
-#&gt; Columns 9 to 16   2.7100  12.4777  -0.7324  -7.0967  -8.4348   0.8085   6.8330   0.4832
-#&gt;   -8.0836  -5.8170   3.4022  -9.3509  10.5116  -4.9803   6.4073   0.3220
-#&gt;    0.1177  -4.8427  -9.6902  -4.1170   4.2751   4.1760   7.5186  -2.3325
-#&gt;   -0.2762 -10.6201  -1.7163   0.3247  21.2705  -3.6339   2.3409  -1.9524
-#&gt;   -5.4666   1.8799   7.4607   6.1014  -5.0309  -0.7846  -7.0460  -4.5347
-#&gt;  -14.8343   1.0224   2.7581   1.0455  -8.7523   6.5568   2.4569   0.8886
-#&gt;    1.1306   8.4311   6.7053   7.2054   4.8025   0.7849  12.7978  10.1318
-#&gt;    1.7782   0.2690  -2.7999   2.6726  -4.2082  -5.3613  13.9660  -4.6798
-#&gt;    2.3140  -8.0598  12.3022   4.7510   0.3521   0.5352  -5.9258   3.5649
-#&gt;   -3.9661  -1.8614   1.3968  -5.5604   3.0089  -9.1651  -4.2411   1.4253
-#&gt;   -6.1841  17.0519   3.7948   8.4733 -14.7091   8.7843  -6.6218   4.4110
-#&gt;    1.9766   7.1204 -14.9206 -10.7534  -8.4386  -3.9708   4.7609 -10.6990
-#&gt;   15.3699  -7.0543   5.9875   2.9363   9.7296  -1.8657   3.1239  -3.5793
-#&gt;   -3.2610   3.2806   7.3320   9.7861  -1.4700   4.6751  -1.0152   5.4633
-#&gt;    4.6915  -7.1968   1.9738   9.5179  -6.9344   6.6227  -1.7615  -4.0994
-#&gt;   12.9562  -5.4254  -4.4734  -3.8013   1.8657   8.0770 -13.3441   3.3371
-#&gt;   -8.7960   1.4661   7.6683 -12.3310  11.6751 -13.0036  -1.9325  -2.9310
-#&gt;   -8.7788  10.2001   7.3673  -9.2273  -6.8593  -1.2400   0.5811  -6.0189
-#&gt;    2.1427   6.4209   0.6752   9.6093   6.0686   2.9096   9.4132   6.9639
-#&gt;   -6.5691   5.4340  -2.0061 -11.7639  -5.7487 -12.4004  12.9011   3.9542
-#&gt;   -7.6302   3.3180  -3.8970 -15.9881  -3.6270  -1.2098   1.5831  -4.1935
-#&gt;   11.1702  -0.4212   1.5514   3.8853   4.7498  13.8274  -3.8186   8.3544
-#&gt;   -9.0928   2.6163  -5.1443   9.0578   6.0795  -3.2900   0.8558   5.4144
-#&gt;   -3.7635  14.0854  -0.3645  -2.8304  11.9087   6.8293   0.3143   4.1031
-#&gt;   -3.6399   3.6001   0.3297 -10.6195 -22.8159  -4.6401  -6.4364  -2.2262
-#&gt;    4.5382   0.7714  -5.7943  15.3865  -6.2597   2.9687   7.7204  -7.6626
-#&gt;   -1.1954   1.3419   3.5047   6.7636   0.5723  -3.9505   2.3417   0.7854
-#&gt;   -3.7300   4.1058  -8.3983   2.9162 -12.2176  10.6979 -12.7928  -4.0314
-#&gt;   -6.2145  -8.7189 -11.9263  -8.0741 -15.0889 -15.3207   3.2689  -9.3045
-#&gt;   -3.6201  -1.4019  -4.1480   8.8221   3.6578  -1.4179 -12.9980  -2.9236
-#&gt;    3.1550  -3.8547  -9.1263   5.0425   6.9819  -7.1466  -8.7011 -15.4836
-#&gt;    3.3937 -11.3399  -8.4114  -9.2620   2.7354 -16.1142  -0.4008  -9.2902
-#&gt;   14.1278 -11.2060   0.8862  -2.8045  -7.4519   9.9396  -3.1323   0.3984
-#&gt; 
-#&gt; Columns 17 to 24   3.1652   2.7114   3.4294   3.0415  -4.4903   7.3292  -7.9222   5.6389
-#&gt;    1.4022  -2.9618   4.4276   5.1812  -3.8627  -7.9557   1.3882  -5.7022
-#&gt;   -0.7707   0.4761   1.1342 -13.3373   0.1454  -9.6590 -10.9706  -8.7114
-#&gt;    2.3547   3.2183  11.7203  -4.3086 -10.5229   7.3772  -9.7069 -10.1956
-#&gt;    6.7410 -11.6240  -0.2382   4.6611   7.6330  -1.6570   3.7988  14.0638
-#&gt;    6.2723   5.4443   5.0403 -11.4443   9.5750   9.6919  -5.5292   4.9020
-#&gt;    3.5478   9.5459  -7.2635   0.9142   7.1907   0.9225  -0.5947   9.1271
-#&gt;   -7.7508  13.0199   0.7921  -3.7426  -1.0259  -3.3604  -5.9647   5.9305
-#&gt;    9.3377   0.7645  -0.9862  -2.8337  -1.9219  -2.1858  -2.1245  -5.5627
-#&gt;   -0.3122  -6.7192   1.9777   6.8633  12.8850  -9.8542   2.4686  -7.5124
-#&gt;   11.0900 -14.9795   3.5174  -3.2619  16.1453  -6.4522  12.5797  12.6404
-#&gt;   -5.9324  -2.8032   5.1964  -1.0824  -1.9832  11.7757  -1.7279 -12.9944
-#&gt;   -0.9892   1.0205   2.8183  -7.3203   2.3917  -2.5111   1.4563  -3.8504
-#&gt;    7.3162   1.6155   5.1525 -12.5349  11.1861  -7.4035  -3.2327  -2.9030
-#&gt;    7.1859   5.1134  -0.2741  -4.0530  -1.2997  -3.2875  -5.5183  15.7646
-#&gt;   -8.1788   2.5854   5.3686  -2.9653   8.9160  -4.7501  -5.7845  -0.0098
-#&gt;  -12.0708  -1.6919  -3.5657   2.9483  10.7586   0.2813   2.4652  -9.4908
-#&gt;    0.5983  -5.5621  -1.6855  12.0777   7.8527   6.7515  -3.6835  -2.2488
-#&gt;    4.0004  13.8937  17.0219 -16.7366   1.2763  -9.2189  12.1063   6.0094
-#&gt;   -3.6126  -3.3000  -1.6039  10.1845  -2.7015 -11.2980   8.8211  -1.4717
-#&gt;   -4.4065  -2.8283   5.3857   7.3212 -15.1150  -6.8182  -3.2339 -11.4930
-#&gt;    5.9961  14.4878  -7.1496   3.3353  -8.7106   1.7533  -4.1133  -3.2559
-#&gt;    7.3600  19.3142   8.8778  -5.9481   8.3435   0.9410  11.6001   3.9670
-#&gt;   10.8095  -7.0179  13.0225  -2.6706   6.3683  -3.4559  24.2514 -12.8641
-#&gt;    3.2058  -7.5246   7.2564   2.0544  -1.8178  -7.5897   7.9397  -2.8945
-#&gt;   -5.4792  13.4542   5.1535  -3.1458   4.5727   7.0280  -3.7451  19.4841
-#&gt;    4.7460  -2.8855 -13.2574  -4.7093  -8.3299  -5.1462  -5.0900   8.6444
-#&gt;  -10.8531  -6.6294  -9.2172  -6.9031  14.4248  -4.8154  -2.9415   1.8398
-#&gt;   -6.2820  -4.4764  -2.3139   4.8700   4.6623 -12.3261  -3.9658  -0.6185
-#&gt;    1.0061  -5.7484   3.3581   0.1569  19.8596   7.6068  -0.7496   2.7470
-#&gt;   -2.2016   6.4260  -2.1709 -11.6076  -5.6707   0.9438  -6.6296   9.2304
-#&gt;  -13.8891  -0.0952   2.0144  -1.3961  -1.1307   0.9833  -5.6071  -2.3964
-#&gt;    7.1253   3.4768  -1.7254   3.0641   2.3469   4.7614   0.0285   5.2199
-#&gt; 
-#&gt; Columns 25 to 32  -5.7640   1.9080   1.2179  -7.9288  12.6428  -4.0817  -2.0562  11.2799
-#&gt;   -2.2999   3.3650  -3.3909   4.6736   2.4997  -0.5393  -8.2946  11.0434
-#&gt;    0.4672   3.7375  -6.1090   5.6665   0.1840  -1.2466   3.4214   2.1810
-#&gt;   16.8931   1.4244  -2.1984  -6.3534   9.9076   5.2015  -8.9448   2.1251
-#&gt;    3.4824   6.7484  -2.2112   3.6076   0.1031   6.6900   7.2862  -4.4783
-#&gt;    1.2703  -7.0325 -10.5537   2.3520 -14.0131  -8.6101   2.5236   4.7246
-#&gt;    6.5031   2.1143  -7.2054  -3.5723   1.5327  -2.3223  13.5636   1.2073
-#&gt;    2.6270 -10.5701   0.3349   7.4437   1.2394 -11.9173   2.2026   1.5314
-#&gt;    3.6801 -10.6600   7.3837  -3.9585   1.3472   3.7613 -11.5850  -5.6722
-#&gt;   -0.3205   0.1430  -8.6536   5.9411   0.8691  -1.4013   0.8575   9.8608
-#&gt;    6.6989   4.2341   4.1919   9.8073 -13.1589  -0.6517  10.5697  -4.4361
-#&gt;   10.4385   1.2819 -12.3164   7.7010  -3.0824  -7.2327   3.1755   0.4660
-#&gt;   -0.6344  11.4283   1.1485   3.0558   0.5925  -0.7444  -0.7708   4.2235
-#&gt;   13.7591  -4.8287   2.2579  -7.3116  -9.8958  10.0413  -1.5291  -8.6872
-#&gt;    2.3379  15.0105   6.5214   8.6823  -7.9255  -2.5331   6.9940  -9.1324
-#&gt;   17.5126  -5.5142   8.5011  -5.0201   5.6609   9.2456  -6.1677   3.4066
-#&gt;    2.7979  -5.5795  -8.3789  -8.7183   2.8860   1.6986   1.9887   8.5494
-#&gt;    1.4318  -1.6808  -7.4402  -0.1204   7.6516   5.0772  13.7477   3.2697
-#&gt;   12.2700  15.0287  -0.2295  15.0833   0.3382 -10.1724  -0.2717 -12.9545
-#&gt;   -2.9782  -0.0518  -3.3041   0.6671  -6.4112  -0.1517  10.1593  -1.5037
-#&gt;   -8.0853 -10.9545   3.6043  -9.2561   1.8679   5.7242 -10.7073  -2.8222
-#&gt;    9.8356  -9.7044  16.5548  -3.7865  -4.3693   8.5636  -6.1174  -5.1056
-#&gt;    7.9162   3.0483  -3.3383   4.8383  -7.7680  -6.6377   3.4518  -2.8278
-#&gt;   -7.1266   7.2581  -1.7810  -8.9348   4.6869  -5.0878  -7.0578   6.4613
-#&gt;  -14.1705  -1.9353  -5.3650   2.5194   0.2712   1.1505  -7.4139 -11.8516
-#&gt;    6.0882   2.5719  -1.0495  12.0958   1.7734  -4.2679  16.0315  -4.3643
-#&gt;    5.2256  -1.9650   0.6906  -2.4795  -8.6869  -5.6020 -11.8749  -6.4854
-#&gt;    0.8150  -3.5122  -8.1720  -7.3898 -15.8716  -2.2908  14.4807  -9.1374
-#&gt;   -5.1087  -3.3034   0.2836   4.4421  -9.0222   7.1890  -0.6107 -11.7751
-#&gt;   -0.4007   4.7543  -6.2101  -4.1952  -6.2441  10.7278  12.6808   6.7488
-#&gt;   12.5466   1.6176  -6.0263   5.8591  -2.0679 -16.3913  -3.3983  -2.6660
-#&gt;   -5.7903   3.3609  -9.2619   3.9730  -6.3071  -0.9969  -5.8838  -4.9410
-#&gt;    1.3117   1.3398  10.9463  -3.0687  -8.4949   7.5141  -6.1267   0.4574
-#&gt; 
-#&gt; Columns 33 to 40   5.2150  -0.8799   8.5244   8.7060   7.5206   5.6204   3.4728  -2.8515
-#&gt;   -7.1945  -2.2905  -5.6205  -9.4417   2.0720  -7.6105   6.4689  -3.4050
-#&gt;   -3.1793   3.3743   1.3336 -13.1295  -2.0912  -1.7854   4.5045  -2.4045
-#&gt;  -12.8614   5.7727  -6.9273  -9.1353   4.9521  -4.8614   3.4216   3.9188
-#&gt;   -3.9518  -1.9401   0.1467   6.3261   1.4010   8.1285  11.6837  -2.6874
-#&gt;    3.4330  -5.8609  -5.7545   0.2943   2.2022  -3.8233   4.7208  -8.1877
-#&gt;   -4.1200  -3.3579  -1.6158  -2.2032  -4.3891   2.5318  11.8702  -1.2812
-#&gt;   -4.3724  -2.4815  -7.6249  -0.3565  -6.4236  -6.1582   3.4271  -2.6058
-#&gt;  -10.7067  -2.7378  -0.1568   0.4353  -2.4969  13.3673  -4.5670   1.2707
-#&gt;  -15.9498   4.6342  -3.5398  -2.3704   2.9344  -3.9178   7.6102   3.9654
-#&gt;    5.2910  -6.9972  -1.8721   4.3733   1.1638   4.6027   5.1656   7.6044
-#&gt;   -6.5072   0.3133  -9.6089  -2.3442  -2.4470  -1.7199   5.8528  -6.2555
-#&gt;   -4.4756   2.8104   8.7608  -6.9807  -1.2307   2.2073  -9.4628  14.5121
-#&gt;   -0.8752   0.9772  -0.6482  -1.2494   4.9640   4.4118   1.1982  -2.9184
-#&gt;   -1.4969  -2.8102   8.4710  -4.1947  13.4710  15.7840   8.0034   6.4894
-#&gt;   -0.2018  -2.0787   3.6000  -5.0466  -6.3977  -0.3397  -2.1683   5.1826
-#&gt;  -10.4754  -0.3989  -2.1770   7.0205   3.4139  -9.8878  -0.6335   6.0262
-#&gt;   -2.2778   7.0196  -4.0415  -0.7292  -0.8091  -2.1652   5.3104 -10.2415
-#&gt;   -4.7832   6.1902  -6.4281 -16.2450  -0.6198  -1.2905   7.3274  13.9358
-#&gt;    0.0250   0.0059   2.8493   7.5505   7.3472   3.1271   3.9622  14.2142
-#&gt;   -7.1615  -4.8397  -3.9963  -2.8790 -11.1888  -3.5380  -8.0007 -17.4729
-#&gt;    6.1410  -7.7925   3.5944  11.6301   6.0241   5.4524  -3.4412   1.0908
-#&gt;    9.0182  -4.6797  -7.1141   7.6652   1.9939  -8.9450   4.6569   2.0778
-#&gt;    8.6951  -0.7674   8.8390  -2.0409  -1.4114  -5.7077 -10.2438  -6.6126
-#&gt;   -0.7592  -7.9487   6.5387 -19.3390   0.3024  -2.0058   3.5009  -6.7498
-#&gt;    6.0412  -5.7854   6.8616   3.3522   2.0884   2.2317  12.6853   5.7927
-#&gt;   -1.7431 -10.2967  -2.6837  13.5075   1.5764  -0.3518   1.4739   1.8059
-#&gt;   11.0760 -16.0045   0.1230  -9.1960   4.6352  -1.8363   3.4795  -4.1980
-#&gt;   -6.6729   3.4266  -1.6780 -10.0093   6.0663   0.8971  13.7264  -5.1570
-#&gt;    2.7899  -3.6841   7.6861  -0.9843  -1.9141  -4.7011  -3.1319   7.7179
-#&gt;   -9.1538  -4.4516  -8.9869  -2.2559   1.1936   0.6045   0.1807  -3.2672
-#&gt;   -8.1173   5.0338  -8.0610   1.1754  -1.3898   1.5216  -1.7338   3.0696
-#&gt;    4.5485  -4.0602  14.4818   1.3694  -0.8208   3.5496   6.0590  -6.4393
-#&gt; 
-#&gt; Columns 41 to 48   9.4318   8.2171   4.8322   5.6457  -2.4645  -3.5706  -0.8075   4.8571
-#&gt;   -3.2900   0.4726  -2.3633   1.8494  -2.6713  -3.3732  -5.7716  -4.3053
-#&gt;   -0.2045 -11.8373 -13.0920  -5.4249   2.9510  -3.4678 -15.6115  -8.6613
-#&gt;   -1.0374  -2.1204  -9.4928  -0.0420   5.7950   3.1081  -1.5467   1.4160
-#&gt;    2.2251  -6.1046  -3.2651   2.6999   2.5576  10.5800   0.0160  -3.9492
-#&gt;    9.1404   4.3808   6.6542  -0.1789   0.4081   8.8088   0.6046  -0.0137
-#&gt;    1.1672  -9.2990   3.7059   5.6774   2.8614  -2.1775  -7.2604   0.4836
-#&gt;   -2.4184  -0.9147  -0.6182   1.0150   3.7945  -2.1332   1.4857  -7.8693
-#&gt;   -6.0545  -8.6096   1.3435   0.2288   4.7465  -0.0030  -7.0416  -1.5885
-#&gt;  -11.9655   0.3257  -4.6491   2.0680  -3.0774  -0.2861   8.1254  -4.1858
-#&gt;    0.5986  12.9359  -1.3932   3.2026   4.7596   4.9578   0.6192  -0.7744
-#&gt;    0.2748   4.1774  -1.7001  -4.7775   0.3853   0.3061  -3.2432   8.5125
-#&gt;  -11.3921 -10.8563  -7.5818  -1.9139  -3.8404   3.6539   5.9986  -0.6856
-#&gt;    7.9886  -7.6804  -0.7950   2.8417   4.4662   9.4306   0.1284  11.4431
-#&gt;   -1.4610  -1.3248   0.5573  -4.3987  -2.9653  -3.4342  -0.6057  -0.4561
-#&gt;   -1.3849  -9.5639  -2.3433   1.1506   7.7221   1.7906   3.4410   1.6867
-#&gt;   -1.6394   5.4429  -8.0806  -0.0369   0.6217  -3.6089   7.8226   6.9275
-#&gt;   13.5281  -1.9440   4.2859  -6.6300  -2.5808  10.4666   6.6420   5.9351
-#&gt;   -7.1382   0.0638  -2.3715   7.4408   9.4569   0.4644  -4.9758  -6.1014
-#&gt;   -3.6463  10.5673   2.6788   2.2272   7.1652  -0.5320 -10.1751 -13.5356
-#&gt;    6.0480  -6.9958 -12.2500  -4.8509   2.3519  -1.9778 -14.9620   3.8193
-#&gt;    4.9883  -8.4463  10.3489   3.0552   0.4646  -7.8505   1.3567   7.2660
-#&gt;   13.4826  10.6962   4.5866   4.7238   8.7162   5.6177   5.2107   6.1307
-#&gt;   -3.1070   0.8094  -0.3173   4.1823  -7.6181  -4.1219   2.5528  20.6973
-#&gt;    4.1262   0.0812  10.5087   6.9784   5.2514   3.9877 -13.4908  -0.9355
-#&gt;   -6.2435  -0.0535  -5.1731  -1.5473   4.3737  -7.7088  -2.1305  -1.1972
-#&gt;   -4.0223   3.8423  -0.1538   2.1298   3.1662   4.6899  -4.0121  -8.6478
-#&gt;    1.9742   0.8946  -9.8064  -1.7442  -1.2579  -0.8046  14.5273   9.2468
-#&gt;   -5.3971   1.5371  -2.6447   2.3005  -2.7475  -1.9236 -11.5593  -9.2965
-#&gt;    5.1362   3.7165  -5.0594   1.5171   6.5340   4.1253  13.0958   8.7482
-#&gt;   -2.3735  -2.9054   4.5685   1.3250   8.4590  16.7741   3.8116   1.4827
-#&gt;  -24.0291   1.0120  -9.4348  -3.3122   4.4437  -0.8187   1.9867  -5.7997
-#&gt;   11.1663  -8.7557   4.2210   2.3879  -1.6097   0.6809  -6.8775  -2.3145
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  Columns 1 to 6 -7.1509e-01 -4.9349e-01  2.9598e+00  7.5520e+00  4.0061e+00 -1.3150e+00
-#&gt;   6.1158e-01 -6.8831e+00  4.3357e+00  2.3711e+00  5.0670e+00  4.3449e+00
-#&gt;  -5.1513e+00 -1.9952e+00  3.0471e-01 -1.4798e+00 -2.9636e+00  1.1523e+00
-#&gt;   7.4474e-02  1.0948e+00 -6.2086e+00 -5.7473e+00  3.9698e+00 -3.6172e+00
-#&gt;  -1.0056e+01  1.9371e+00 -1.2662e+00 -1.7345e+00 -1.1842e+01  3.6392e+00
-#&gt;   3.7177e+00 -8.3396e-01 -6.0881e+00  4.9506e+00 -5.1576e+00 -4.8864e+00
-#&gt;   2.7897e+00  3.9768e+00 -1.1350e+01 -5.2308e+00 -2.3766e+00  1.1203e+01
-#&gt;   5.9575e+00  3.1428e+00 -5.9637e+00  5.7530e+00  7.2610e-01  5.6093e+00
-#&gt;   1.1167e+00  4.4847e+00  7.6154e+00 -1.3624e+00  8.2594e+00 -9.0724e+00
-#&gt;  -4.8284e+00 -1.0704e+01  2.0032e+00  2.9894e+00 -6.4011e-01  8.4435e+00
-#&gt;   5.1141e+00 -1.6564e+00  4.4674e+00  6.0807e+00 -6.4299e+00  5.6468e-01
-#&gt;   2.3772e-01  6.5366e+00  8.5935e+00  3.8245e+00 -1.4917e+01 -9.2021e-01
-#&gt;  -5.6431e+00 -1.2715e+00 -2.1304e+00  6.5598e+00 -5.9600e+00 -3.6707e+00
-#&gt;  -4.7215e+00 -1.9434e-01  2.5668e+00  1.0392e+01 -3.2271e+00 -6.6734e+00
-#&gt;  -3.0612e+00 -2.3705e+00 -7.3181e-01  9.4870e+00 -7.3197e-01 -2.5297e+00
-#&gt;  -5.7609e+00 -8.1802e+00 -7.4856e+00 -1.3887e+01  2.7613e+00 -5.9915e+00
-#&gt;   7.5561e+00  6.5578e+00  5.6733e+00  9.0824e+00  1.1058e+01  1.6211e+01
-#&gt;  -2.8835e+00 -4.0863e+00  2.6817e+00  6.0484e+00 -3.5239e+00  3.4253e+00
-#&gt;   8.4178e+00 -1.9318e+00 -8.1999e+00  5.6324e+00 -4.5409e+00  6.2325e+00
-#&gt;  -4.9633e+00  1.1019e+01  3.7780e+00  1.7794e+00 -4.6708e+00 -5.6795e-01
-#&gt;   5.0768e+00  5.8880e+00 -2.3387e+00 -2.4749e+00  4.1631e+00  2.0668e-01
-#&gt;   3.3728e+00  1.9753e+00  1.2435e+01 -9.1491e+00  6.8930e+00 -1.4042e+01
-#&gt;  -2.3147e+00  1.1081e+00 -4.7888e+00  3.3629e+00 -5.7519e+00  7.9487e+00
-#&gt;  -4.1190e+00 -6.3947e-01 -1.4461e+01 -6.3951e+00 -6.1554e+00  3.6205e+00
-#&gt;   1.3226e+00  5.5740e+00 -4.9470e+00 -3.4072e+00  5.9575e-01 -7.4203e+00
-#&gt;  -2.0237e+00  6.2413e+00 -1.3620e+01 -4.2507e+00  7.2215e+00  6.4567e+00
-#&gt;   4.3053e+00  7.2772e+00  8.3178e+00  3.6260e+00 -4.1942e+00 -1.9330e+00
-#&gt;  -1.4517e+00  7.2922e-01  4.5117e+00 -2.4268e+00  7.4411e+00  6.0721e+00
-#&gt;  -1.0672e+01 -5.0188e+00  1.7881e-01  6.3910e+00 -4.9103e+00 -1.8536e+00
-#&gt;  -6.9459e+00 -5.0136e+00 -2.8328e+00  4.1931e+00 -1.0879e+00  8.8608e+00
-#&gt;  -1.3535e+00 -6.3363e-01  6.6245e+00  8.1844e+00 -4.6843e+00 -3.0662e+00
-#&gt;  -1.7060e+00 -5.8109e+00 -2.4222e+00 -1.0309e+01 -8.6567e+00  3.7424e-01
-#&gt;  -9.0191e+00 -5.1409e+00 -2.7915e+00 -1.0410e+01 -5.4462e+00 -1.1519e+01
-#&gt; 
-#&gt; Columns 7 to 12 -5.9516e+00  7.0156e+00  5.0552e+00  8.3654e-01  4.9012e+00  3.2226e+00
-#&gt;  -2.9401e-03 -8.3668e+00 -3.8962e+00  1.0038e+01 -3.0033e+00 -8.2599e-01
-#&gt;   3.8983e+00 -8.2420e+00 -2.4075e+00  1.6270e+00 -1.6823e+01  1.3287e+00
-#&gt;  -1.0277e+00  1.8767e-01 -1.2692e+01  1.2294e+00 -1.2903e+01  1.2650e+01
-#&gt;  -4.3204e+00 -8.9256e+00 -5.2164e-01 -1.7049e+00 -2.2844e+00 -8.9570e+00
-#&gt;   5.9251e+00  2.8970e+00 -2.0009e-01 -3.1560e+00  5.2960e+00 -5.3078e+00
-#&gt;   1.7958e+00  4.7085e-02 -7.6300e+00  8.0213e+00 -1.4238e+01 -3.1199e+00
-#&gt;   4.0685e-01  8.8669e-01 -1.9305e+00  9.5970e+00  4.0827e+00 -5.9016e+00
-#&gt;   3.5766e+00  3.4390e+00 -5.6902e+00 -1.2657e+01 -5.3466e+00 -3.8703e+00
-#&gt;   1.9449e+00 -9.8307e+00  3.2450e+00  2.4425e+00 -3.7293e+00  8.7565e-01
-#&gt;  -1.5291e+00 -8.5767e+00  6.2224e+00  4.7074e+00 -1.7078e+00  3.0609e+00
-#&gt;  -3.9321e+00  6.1785e+00  5.0283e+00  4.1094e+00 -2.6448e+00  5.3437e-01
-#&gt;   1.5058e+00 -1.2001e+00 -9.9301e+00  7.9571e+00 -4.7609e+00 -1.0210e+01
-#&gt;   4.2221e+00 -3.1459e+00 -2.5754e+00  3.1745e+00 -1.0673e+01 -4.2940e+00
-#&gt;  -1.5951e+00 -2.1660e+01 -6.0590e-01  1.2761e+00 -5.5640e+00 -4.4963e+00
-#&gt;   3.5914e+00 -7.2424e+00 -4.2301e-01 -2.1719e+00 -5.9560e+00 -6.6360e+00
-#&gt;   6.0457e+00  9.1813e-01  9.8854e+00  1.5404e+01  8.7826e+00 -1.0195e+01
-#&gt;   7.1532e+00  1.1020e+01 -7.9245e-01  1.8356e-01 -1.0282e+01 -2.5896e-01
-#&gt;   4.4396e+00 -6.3894e+00 -3.1537e+00 -1.0250e+01 -9.5840e+00 -3.5273e+00
-#&gt;  -1.0148e+01 -6.4415e+00  7.3811e+00  1.6038e+01 -1.2954e+00 -7.3381e-01
-#&gt;  -1.1356e+01  1.0164e+01  3.7982e+00  1.1379e+01 -1.3215e+01 -4.7850e+00
-#&gt;  -7.0512e+00  3.6091e+00 -1.1422e+00 -7.8434e+00  8.2024e-01  3.2626e+00
-#&gt;  -5.2845e+00 -3.2287e+00  1.0512e+01  3.9971e+00 -5.6834e+00 -9.3933e+00
-#&gt;  -1.4016e+00  9.6409e-01  8.2726e+00  1.9828e+00  4.4276e+00  3.6313e+00
-#&gt;   1.9920e+00 -9.7004e-01  5.1981e+00 -9.5342e+00 -5.0760e+00  3.3793e+00
-#&gt;   2.1394e+00 -1.4262e+01  3.0598e+00  7.2985e+00  1.9970e+00 -9.2220e+00
-#&gt;  -1.0893e+01 -2.5566e+00 -6.1387e+00  1.0846e+01  6.5455e+00  1.6814e+00
-#&gt;   3.2075e+00 -1.5686e+01  1.4660e+01  6.6920e+00  2.2215e+00 -1.4393e+01
-#&gt;   3.2561e+00 -3.0157e+00 -2.8714e+00 -8.9107e-02 -8.5308e-01  9.6009e-01
-#&gt;   6.7504e+00  9.9947e+00  1.3110e+01 -1.0746e+01  8.8372e+00 -1.4285e+01
-#&gt;   9.1524e-01  2.6967e+00  1.3832e+00  7.8529e+00  4.4973e+00  4.9536e+00
-#&gt;  -2.6921e+00 -1.4808e+01 -2.9850e+00  1.8306e+00  2.6230e-01  4.2523e-01
-#&gt;  -1.2366e+01 -7.5979e-01 -2.1096e+00 -6.5637e-02  2.7424e+00 -8.5872e+00
-#&gt; 
-#&gt; Columns 13 to 18  2.7000e+00  8.5381e+00 -8.1091e+00 -3.7162e+00  2.0213e+00 -6.0458e+00
-#&gt;  -4.4232e+00 -1.0786e+01  1.4304e+01  7.0479e-01 -3.7465e+00 -1.9488e+00
-#&gt;   2.8797e+00  4.6008e-01  7.6718e+00 -2.5439e+00  5.3121e+00  1.3722e+00
-#&gt;   1.9172e+00  3.2784e+00  7.7748e+00 -1.2554e+01  6.0663e+00  3.9239e+00
-#&gt;   1.9770e+00  1.4078e+01 -6.5417e-01  9.7094e+00 -2.4134e+00  5.5477e+00
-#&gt;  -3.2059e+00  6.4861e-02  4.7822e+00 -6.6546e+00 -4.4499e+00 -1.5853e+00
-#&gt;  -2.4581e+00 -1.5303e+00  4.5577e+00 -3.0146e+00  1.0402e+01  9.5618e+00
-#&gt;  -6.2991e+00 -5.5900e+00 -5.3919e+00 -1.0916e+00  9.6959e+00 -5.6915e+00
-#&gt;   1.0740e+01  9.5805e-01 -4.2546e+00  8.1128e+00 -7.7091e+00 -2.4287e+00
-#&gt;  -1.1507e+00  5.1993e+00  8.7242e+00  1.1963e+01 -6.3006e-01 -3.7769e-01
-#&gt;  -6.9138e+00 -1.0091e+01 -1.2608e+01  8.1199e+00  1.6238e+00  3.1721e+00
-#&gt;  -1.6155e+01  7.7175e-01  1.0307e+01 -2.4856e+00  1.0481e+01 -6.2984e+00
-#&gt;   3.8670e+00 -2.1379e+00 -1.2605e+00  2.7508e+00  7.6017e-01 -8.3812e+00
-#&gt;   5.5454e+00  6.7137e+00 -1.7055e+00 -7.7823e+00 -3.4515e+00  4.7732e+00
-#&gt;   7.6535e+00  9.3814e+00 -8.8797e+00 -1.1818e+01 -8.5267e+00 -1.4578e+00
-#&gt;   2.7738e+00  1.0396e+01  3.6270e+00  3.0116e+00  2.7857e-01  1.8120e+01
-#&gt;   3.4237e+00 -1.0749e+01  5.3512e+00  2.4723e-01 -7.9703e+00  1.9826e+01
-#&gt;   6.5276e+00  9.1472e+00  2.5897e+00  5.6845e-01  2.4023e+00  9.8481e-01
-#&gt;  -8.0553e+00 -1.1198e+01 -1.0056e+01  1.4838e+00  6.0982e+00  1.8073e+00
-#&gt;   7.9459e+00 -8.7685e+00  2.4101e+00  4.0183e+00 -2.7991e-01  8.6546e+00
-#&gt;  -1.1821e+01  4.5998e+00  6.9386e-01 -4.8410e+00  3.9323e+00  3.4028e-02
-#&gt;   6.9009e+00  6.5700e+00 -2.3530e+00 -2.3630e+00 -3.5682e-01 -3.1213e+00
-#&gt;  -6.3269e+00  3.8343e+00 -7.5480e+00 -1.1199e+01 -8.8526e+00  1.1089e+01
-#&gt;  -7.9600e+00 -2.6078e-01  7.3612e+00  3.3860e+00 -5.8622e+00 -5.7458e+00
-#&gt;   1.6788e+00 -4.5105e+00  4.8601e+00  2.6536e+00  6.3036e-01  1.6862e+00
-#&gt;  -8.7453e+00  1.9969e+00 -2.7020e+00  1.2625e+00  8.6836e+00  4.4179e+00
-#&gt;  -3.8124e+00 -5.3169e+00 -1.0316e+01 -6.2008e+00 -1.0265e+01 -4.3392e+00
-#&gt;  -1.1196e+01  1.0953e+00  1.2303e+01 -3.2832e+00 -2.6740e+00 -8.8153e-02
-#&gt;  -4.3849e+00  9.3394e-02  1.0517e+01  6.6309e+00 -1.0079e-01 -2.0375e+00
-#&gt;   2.8490e+00  1.0117e+01 -5.2329e+00  9.0272e-01  4.3376e+00  7.2262e+00
-#&gt;   1.2064e+00  2.9931e+00 -1.0497e+01 -1.0496e+01  5.1515e+00 -1.6065e+00
-#&gt;   1.3753e+01 -3.8291e+00  1.2658e+01 -2.3645e+00 -1.1169e+01  4.3685e+00
-#&gt;  -2.9462e+00  3.2668e+00  4.3312e+00  4.1991e+00  6.5311e+00 -3.7727e+00
-#&gt; 
-#&gt; Columns 19 to 24 -2.6109e-01  4.8078e+00 -1.0124e+01  7.8367e+00 -4.4960e+00 -9.2124e+00
-#&gt;   3.4842e+00 -8.3740e-01  1.0619e+01  2.2888e-01 -9.4949e+00  4.2166e-01
-#&gt;   7.1282e-02  5.1503e+00  8.8166e+00 -3.2567e+00 -3.4595e+00  2.0536e+00
-#&gt;   7.7086e+00 -1.0302e+01  2.3211e+00 -1.3219e+00 -6.5876e-01 -9.8129e+00
-#&gt;  -3.6886e+00  1.1903e+01 -2.9881e+00 -7.3948e+00 -1.5627e+00  8.1673e+00
-#&gt;   1.1306e+01 -6.6918e+00 -3.6204e+00 -4.8952e+00 -9.2597e+00 -6.2369e+00
-#&gt;  -6.9676e+00 -1.6389e+00 -2.3276e+00  3.5074e+00 -2.0643e+00 -2.0283e-02
-#&gt;  -4.5878e+00 -2.5588e+00  8.6070e+00  3.8829e+00 -3.9444e+00 -3.7486e+00
-#&gt;  -5.5344e+00  8.6413e+00  8.5727e-02  9.4498e-01  8.3532e+00 -1.1366e+01
-#&gt;  -5.5513e+00  2.5488e+00  7.4590e+00 -3.5227e+00 -5.4778e+00  7.7005e+00
-#&gt;   6.5902e+00 -6.7006e+00 -1.9534e+00 -4.8660e+00 -7.5983e+00  1.9447e+01
-#&gt;  -1.2766e+01  6.2877e+00  7.3611e+00 -7.5964e+00  9.8168e+00  3.7307e+00
-#&gt;  -1.1571e+00  3.2945e+00  3.3334e+00 -6.9614e-01  1.3975e+00  8.6417e+00
-#&gt;  -2.3076e+00 -3.3192e-01  3.1283e+00 -1.2837e+01  5.1154e+00 -5.8279e+00
-#&gt;   5.5291e+00  7.0720e+00 -6.0436e-02 -5.0244e+00 -1.6342e+01  5.6834e+00
-#&gt;   4.0753e+00 -8.6386e-01 -1.2163e+00 -3.3384e-01  8.5434e+00 -6.3283e+00
-#&gt;  -8.1850e+00  7.5152e-02 -5.8536e-01  1.3266e+00 -7.6313e+00  1.1692e+01
-#&gt;   1.2035e+01  2.2418e+00  1.5214e+00  2.7325e+00 -4.7900e+00 -8.3553e+00
-#&gt;   1.3710e+00 -5.7864e+00  3.9161e+00  4.7640e+00  9.8295e-01 -8.4390e-01
-#&gt;  -5.9023e+00 -7.4171e+00 -5.0537e+00 -4.7018e+00 -8.9029e-01  9.8228e+00
-#&gt;  -9.1306e+00 -1.9524e+00  8.9289e+00  3.8039e+00  1.5887e+01 -5.0845e+00
-#&gt;   7.2528e+00 -3.6954e+00 -3.4254e+00 -4.3860e-02  5.0786e+00 -8.6250e+00
-#&gt;  -1.7334e+00  3.8103e+00 -2.5993e-01 -1.1297e+01 -1.1125e+01  6.7070e-01
-#&gt;  -1.3782e+01  5.9581e+00 -3.7445e+00  7.2762e+00  8.2591e+00  1.3241e+00
-#&gt;   7.8407e+00 -3.1490e+00 -2.0267e+00  2.5580e+00  1.0221e+01 -6.0490e+00
-#&gt;  -4.8752e-01 -9.7989e+00  3.6149e+00  6.3880e-01 -6.0927e+00  6.9155e+00
-#&gt;  -5.2781e+00  2.3281e+00 -2.9401e+00 -5.2770e+00 -4.5600e+00  4.0959e-02
-#&gt;  -1.6510e+00 -4.4628e+00 -4.4317e-01 -3.7385e+00 -7.0257e-01  1.2179e+01
-#&gt;  -1.4263e+00  2.4627e-02  1.5154e+01 -8.5295e+00  1.1547e+01 -1.7261e+00
-#&gt;   3.3542e-01  3.9312e+00 -1.0944e+01  2.7019e+00  6.4122e+00  2.6792e+00
-#&gt;   4.4669e-01  1.9376e+00  7.9560e+00 -3.4105e+00 -3.0819e+00  2.3268e+00
-#&gt;  -9.2093e+00 -2.2787e+00  6.0683e-01 -5.0750e+00 -4.6972e+00  2.2780e+01
-#&gt;   3.7401e+00  2.3183e-01 -5.3432e+00 -7.5792e+00  1.1843e+01  2.6668e+00
-#&gt; 
-#&gt; Columns 25 to 30  1.3633e+01  1.1635e+01 -1.4768e+00 -1.0947e+00 -1.4935e+00  7.3367e+00
-#&gt;   1.8347e+00 -5.9848e+00 -1.5196e+01  8.0386e+00 -8.6412e-01  5.7371e+00
-#&gt;   3.3975e-01 -8.7520e-01 -2.1590e+00  6.9778e+00  2.7767e+00  4.6087e+00
-#&gt;   1.4119e+01 -3.1869e+00 -7.2740e+00  9.6347e+00  8.3509e+00 -9.9526e+00
-#&gt;   1.7852e+00 -6.6034e+00  1.3139e+00  5.3587e+00  6.7300e+00  1.1495e+01
-#&gt;   1.0884e+01 -3.6163e+00 -6.1070e-01  9.3564e+00  1.5856e+00 -1.4446e+01
-#&gt;   4.5013e+00 -3.0506e+00 -1.3709e+00 -6.1256e+00 -1.4894e+00  6.7897e+00
-#&gt;   7.1776e+00 -3.4181e+00 -5.2673e+00 -8.4402e+00 -1.3334e+01  9.0230e+00
-#&gt;  -4.5190e+00 -8.1797e+00  1.2852e+00 -1.2342e+00  1.4442e+01  3.5929e+00
-#&gt;  -4.9083e+00 -8.2237e+00 -7.7480e+00  1.4833e+00 -3.7700e+00  8.9328e+00
-#&gt;   1.3677e+01 -1.1703e+01 -3.4264e+00  8.4007e+00  3.0622e+00 -3.9277e+00
-#&gt;  -1.2444e+00 -7.7115e+00 -1.1126e+00 -4.7788e+00 -9.1684e+00  1.7096e+00
-#&gt;  -7.9805e+00  1.9402e+00  5.8282e+00 -1.0984e+01  5.7442e-01  4.8096e+00
-#&gt;   3.5863e+00 -6.9901e+00  2.0761e+01 -6.0529e+00  1.0542e+01 -5.5653e+00
-#&gt;   4.7324e-01  1.0031e+01  1.2682e+01  5.8382e+00  2.4513e+01  1.7300e+01
-#&gt;   4.3019e+00 -3.9498e+00  4.4878e+00 -2.3613e+00  8.3003e+00 -1.0475e+01
-#&gt;   6.1743e+00 -6.1079e+00 -1.0839e+01 -4.8769e+00 -6.2840e+00  7.5247e+00
-#&gt;   1.1283e+01 -1.8325e+00 -7.0253e+00  5.0291e+00 -2.7680e+00 -7.1598e+00
-#&gt;   2.6188e-01 -6.6821e+00 -1.2407e+00  7.5910e+00 -1.4712e+01  4.1615e+00
-#&gt;   1.2098e+00  7.7760e+00  8.4132e+00 -1.8960e+00  9.5166e-02  1.1113e+01
-#&gt;  -8.5597e-01 -1.1365e+01 -6.1514e-01 -7.5014e+00 -4.4777e+00  7.6538e-01
-#&gt;  -5.3641e+00  8.4976e+00  7.7201e+00 -9.8481e+00  1.3061e+01  2.3271e+00
-#&gt;  -5.7804e-01 -2.1340e+00  6.5594e+00  1.0442e+01  2.3031e+00  5.4915e+00
-#&gt;  -2.4073e+00 -7.2948e+00 -7.1055e+00 -1.4424e+01 -1.8628e+01  6.5668e+00
-#&gt;  -1.7887e+00 -1.0561e+01 -3.0666e+00  1.3548e+01  1.8072e+00 -8.3541e+00
-#&gt;   1.4815e+01  2.1536e+00 -2.1050e+00 -2.4299e+00 -2.1874e+00  7.0530e+00
-#&gt;   1.2392e+01  1.9511e+00 -5.1508e+00  4.0476e-01  1.3191e+01  1.0705e+01
-#&gt;  -3.2279e+00  1.7266e+00 -4.3829e+00 -7.9983e+00  2.0262e+00  6.8517e+00
-#&gt;  -8.2418e+00 -8.7575e+00  1.0860e+01  1.2190e+01 -1.1073e+00  7.0043e+00
-#&gt;  -4.0054e+00  7.2396e-01 -1.3193e+00  4.3663e+00 -1.4141e+01  6.7344e+00
-#&gt;   8.4796e+00 -7.0270e+00 -5.3384e+00 -9.5140e+00 -5.6786e-01  3.8349e+00
-#&gt;  -1.6548e+01  7.2795e-01  7.1365e+00  8.1874e+00  2.1921e+00 -9.6058e+00
-#&gt;  -3.1823e+00 -8.5109e-01  4.5027e+00  4.7013e+00  8.7304e+00 -3.1466e+00
-#&gt; 
-#&gt; Columns 31 to 36 -3.0502e+00 -5.4226e+00 -1.1866e+00 -7.7360e-04  1.0046e+01 -1.4339e+01
-#&gt;   5.1785e+00  8.5924e+00  7.3401e+00 -9.1732e+00  6.5585e+00  4.2616e+00
-#&gt;   4.7791e+00  1.9069e+00  1.2301e+00 -3.1632e+00  1.0141e+01 -7.9537e+00
-#&gt;  -3.2250e+00  1.1265e+01  8.3408e+00 -1.5957e+01  1.1140e+01  3.0385e+00
-#&gt;  -5.7449e+00  3.2804e+00 -3.5045e+00  3.9281e+00 -4.7560e+00  5.1381e+00
-#&gt;  -3.3620e+00  3.1483e+00  6.3762e+00 -3.0654e+00 -5.8521e+00 -2.6027e+00
-#&gt;  -6.8715e-01 -1.3580e+01  2.9583e+00 -2.8945e+00 -2.2106e+00 -1.0597e+00
-#&gt;   2.3841e+00 -2.2713e+00  5.1123e-01  1.3546e+00  2.8196e+00 -4.8460e+00
-#&gt;   4.1300e+00 -8.9717e-01 -2.0092e+00  8.0245e+00  8.9510e+00 -5.7317e+00
-#&gt;   4.9242e+00  5.6605e+00  4.8302e-01 -1.6416e+00 -5.6424e+00 -3.1269e+00
-#&gt;  -1.7472e+01  9.0286e+00 -1.1657e+00  1.5655e+01 -1.8243e+01  1.5363e+01
-#&gt;  -1.8474e+00  4.0897e+00  7.5385e+00  6.4631e+00  1.1732e+01 -1.5875e+01
-#&gt;  -2.9430e+00  1.0304e-02 -6.3027e+00  4.3613e+00  7.5438e+00 -7.5815e+00
-#&gt;  -6.3004e+00 -2.0406e+00 -2.1198e+00 -1.7317e+00  5.3290e+00  1.1506e+00
-#&gt;   4.4363e+00 -7.1078e+00 -2.2689e+00  9.1593e+00 -1.0209e+00  1.4142e+01
-#&gt;  -1.0158e+00  3.4533e-01  1.0040e+01 -5.2964e+00 -3.8510e+00 -9.8925e+00
-#&gt;   7.0940e+00 -1.9105e+00 -2.6435e+00 -6.7222e+00  4.9688e+00 -9.4728e+00
-#&gt;  -1.4549e+01  5.3767e-01  2.3201e+00 -1.9899e+00 -5.1085e+00 -2.1957e+00
-#&gt;   9.1778e+00  1.1308e+01 -4.9178e+00 -2.6880e+00  3.7381e+00 -4.8390e-02
-#&gt;  -1.4211e+00 -4.6613e+00 -5.0277e+00  7.5841e+00 -5.6116e+00  1.0896e+01
-#&gt;  -1.3916e+00  1.0658e+00  6.3661e+00 -3.2695e+00  8.0748e+00  1.1913e+01
-#&gt;  -5.4840e-01 -7.0615e+00 -2.1461e-01  5.8277e+00  4.4998e+00  6.1279e-01
-#&gt;  -7.2273e+00 -3.3353e+00 -2.2451e-01 -4.2120e+00  1.4607e+00  2.1736e+00
-#&gt;   2.3888e+00  7.9467e+00 -1.4923e+01 -2.8997e+00 -1.4515e+00 -1.0238e+01
-#&gt;   1.9038e+00 -2.1767e+00  1.7820e+00  5.0317e+00  9.6143e+00  3.5390e+00
-#&gt;  -2.2836e+00 -6.7059e-01 -3.3277e+00  2.3968e+00 -6.2656e+00  7.1541e-02
-#&gt;   1.8024e+00 -4.6275e+00  3.8931e+00  2.2845e+00  9.2879e+00  2.6737e+00
-#&gt;  -2.7062e+00 -6.9413e+00 -2.2579e+00  9.6534e+00 -2.0443e+01  1.0024e+01
-#&gt;   7.2623e+00  3.7980e+00  6.2083e+00 -5.2031e-01  6.6343e+00  2.8769e+00
-#&gt;   4.6087e-01 -1.1342e+00 -1.0071e+01 -1.0014e+01 -1.0012e+00 -1.6799e+01
-#&gt;   1.3046e+01  1.2031e+01 -5.9934e+00  2.7704e+00  5.1693e+00 -9.1025e+00
-#&gt;   2.3357e+00  3.6832e+00  1.0729e+01  2.8181e+00  4.0101e+00  2.8323e+00
-#&gt;  -3.7210e+00  9.5575e-01  9.1658e+00 -3.9453e+00  6.6812e+00 -9.8705e+00
-#&gt; 
-#&gt; Columns 37 to 42  8.0274e+00  1.3473e+00 -7.9683e+00 -2.5021e+00 -1.4562e+00  6.9043e+00
-#&gt;   6.4800e+00 -6.4930e+00  6.5987e+00  1.6992e+00  3.3423e+00  2.9007e+00
-#&gt;   4.1236e+00  2.8806e+00  3.4910e+00  9.0047e+00  1.3609e+00 -4.1131e+00
-#&gt;   3.9045e+00 -2.7661e+00  8.0995e+00 -3.2099e+00 -6.3028e+00  3.6100e+00
-#&gt;   2.8595e+00  8.5890e+00  6.9759e+00 -3.6375e+00  1.2716e+01 -5.1055e+00
-#&gt;  -3.9744e+00 -3.7970e+00  4.5694e+00 -4.4010e+00  4.6595e+00  1.0435e+01
-#&gt;   8.7296e+00  2.9286e+00 -1.9703e+00  2.7617e+00  1.0797e+00  2.7736e+00
-#&gt;   4.3076e+00 -1.6706e+00 -7.8943e+00  7.5354e+00 -6.1941e+00  3.9286e+00
-#&gt;   1.2360e+01 -5.4219e+00  1.6949e+01 -6.1366e+00  1.3807e+01 -6.2802e+00
-#&gt;   1.7763e+00  6.6658e+00 -1.9258e+00  7.6167e+00  4.0521e+00 -4.5945e+00
-#&gt;  -8.7879e+00  2.0683e+00 -1.3782e+00 -1.0502e+00  4.1240e+00 -9.3366e+00
-#&gt;  -2.4919e+00  4.6335e+00  5.6993e-01  5.8056e+00  1.3667e+00 -3.8362e+00
-#&gt;  -7.9760e+00 -9.7185e+00  6.3919e+00  5.8327e+00 -5.5591e+00 -1.0145e+01
-#&gt;  -2.4361e+00 -1.3595e+00  5.3784e+00 -1.0564e+01  4.4731e+00 -7.7008e+00
-#&gt;  -1.2498e+00  1.1055e+01  1.4361e+01  4.4318e+00 -9.1977e-01 -1.0886e+01
-#&gt;   7.8499e+00 -4.4737e-01  4.0138e+00 -1.6863e+01  3.4583e+00 -8.8853e+00
-#&gt;   5.6519e+00  7.7085e+00  1.0316e+00 -5.9979e+00  1.2462e+00 -5.8032e-02
-#&gt;  -6.5523e+00 -4.4294e+00  1.6930e+00 -8.1491e+00 -1.3399e+00 -7.3746e-01
-#&gt;  -2.9941e+00 -7.9544e+00 -9.6285e+00  2.6299e+00  3.5760e+00 -3.2629e+00
-#&gt;   5.6632e+00  1.0023e+00 -3.3555e+00  8.9834e+00 -2.5112e-01 -3.7210e+00
-#&gt;  -1.3401e+00  1.2614e+00 -1.3780e+00 -1.5681e+00  3.4846e+00 -2.9290e+00
-#&gt;   7.9777e+00 -9.5111e+00  9.9007e+00  7.6525e-01 -7.4262e+00 -2.2465e+00
-#&gt;  -1.0764e+01  1.0823e+01 -1.8126e+01 -8.9734e+00 -3.3309e+00  7.3897e+00
-#&gt;  -2.4367e+00 -9.3883e+00  5.1017e+00  1.9732e+00  1.5514e+01 -9.8860e+00
-#&gt;   1.1193e+01 -1.3586e+01  1.2199e+01 -6.2468e+00  4.8108e+00 -4.9684e+00
-#&gt;   5.5543e+00 -7.2836e+00 -1.2312e+01  1.6544e+00 -7.8411e+00 -8.5071e-01
-#&gt;   7.2632e+00  7.6805e+00  1.0372e+01  4.6467e+00  3.2371e+00  1.8547e+01
-#&gt;   1.0000e+01  4.6672e+00  8.9041e+00  6.0691e+00  2.6341e+00 -1.5674e+00
-#&gt;   1.2061e+01  3.0764e+00  5.6641e+00  4.6102e+00 -1.0707e+01 -2.9058e-01
-#&gt;   9.3883e+00  4.1780e+00 -1.0755e+01 -3.1317e+00  1.2084e+00 -6.2495e+00
-#&gt;   1.5567e+00  1.9683e+01  1.0854e+01  2.5242e+00  3.8949e+00  4.5147e+00
-#&gt;  -8.4709e+00  2.0079e+01 -4.0331e+00  3.4815e+00 -1.0787e+01 -2.4349e+00
-#&gt;   8.2260e+00 -1.2772e+01  5.0200e+00 -5.5445e+00 -2.8086e+00 -3.2149e+00
-#&gt; 
-#&gt; Columns 43 to 48 -3.8896e+00  6.5787e-01 -1.2978e+00 -6.1221e+00  1.3124e+00 -6.3121e-01
-#&gt;   3.2791e+00 -2.4867e+00  1.8503e+00 -2.1419e+00 -2.8203e+00  6.7540e+00
-#&gt;  -3.9395e+00  8.9447e-01  4.1483e+00  2.9906e+00 -1.8324e+00 -1.3498e+01
-#&gt;   9.0459e+00  2.9160e+00  1.3991e+01 -3.4683e+00 -7.5850e+00  9.4991e-01
-#&gt;  -4.8166e+00  4.3716e+00 -4.0342e+00  1.5501e+01  6.5063e+00  1.4848e+01
-#&gt;   4.3978e+00 -1.7978e+00 -1.5569e+01  9.5838e-01 -4.2077e+00  1.1983e+01
-#&gt;  -2.2132e+00  5.1870e+00 -2.2836e+00  7.6397e+00 -3.4778e-01  6.4687e+00
-#&gt;  -3.0285e+00 -6.3110e+00 -7.4775e-01 -4.4857e+00 -6.5898e+00  1.2399e+00
-#&gt;   8.5546e+00  9.6174e+00  5.5361e+00  1.3995e+00  7.3978e+00  6.0620e+00
-#&gt;   9.5426e-01 -4.6008e-01  2.7788e+00 -1.6533e+00  7.6717e+00 -6.0897e+00
-#&gt;  -2.3392e+00 -1.4206e+00  9.6478e-01  1.2578e+00  4.4325e+00  3.5684e+00
-#&gt;  -1.2031e+01  3.2264e-01 -1.2314e+00  5.9877e+00 -1.8689e-01 -6.1992e+00
-#&gt;   3.6786e+00  4.4235e+00  7.3856e+00 -3.5121e+00  1.8254e+00 -2.7950e+00
-#&gt;   1.2859e+01  3.8752e+00  3.0138e+00  2.1199e+00 -2.3312e+00  8.4692e+00
-#&gt;   3.6537e+00 -3.2655e+00  6.4011e+00  9.5566e+00  1.0377e+00  1.9657e+00
-#&gt;   9.0634e+00 -6.8571e+00  4.1418e+00 -2.2360e+00 -4.1616e+00 -2.5369e+00
-#&gt;  -1.2561e+01 -7.0413e+00  9.6420e-01 -1.5377e+00  7.4733e+00 -6.1888e+00
-#&gt;  -1.2041e+01 -4.9432e+00 -6.8733e+00  7.2480e+00  4.7230e+00  1.5316e+00
-#&gt;   4.2213e-01  2.5705e+00 -4.9289e+00  8.1845e+00 -1.2568e+01 -6.1808e+00
-#&gt;   3.1592e+00 -2.7227e+00 -1.4107e+00  6.9731e+00  2.3468e+00 -2.5774e+00
-#&gt;   4.4478e+00 -1.9829e+00  2.7573e+00 -2.3223e+00 -1.0544e+01  2.5697e-01
-#&gt;   1.6394e+01  4.0235e+00  1.8288e+01 -1.6814e+00  2.0892e+00  1.2139e+01
-#&gt;   3.9782e+00 -2.6290e+00 -4.9761e+00  1.6872e-01 -6.4128e+00  4.5709e+00
-#&gt;   6.5372e+00  7.4668e+00 -6.9902e+00  1.1342e+01 -9.0094e+00  3.0885e+00
-#&gt;  -2.4901e+00  1.1389e+01 -6.9001e+00  8.2024e+00 -1.1017e+01 -4.0060e+00
-#&gt;  -4.9509e+00 -5.8122e+00 -1.0876e+00  7.2729e+00 -7.2931e+00  6.0226e+00
-#&gt;  -9.5747e-01  1.3897e+00 -9.5474e-01 -5.0532e+00  3.3397e+00  1.9763e+00
-#&gt;   2.7213e+00 -1.0482e+01  1.0440e-01  5.5811e+00 -1.7081e-01  3.2723e+00
-#&gt;  -4.6305e+00  2.3510e+00 -2.4568e+00 -6.0458e+00 -6.0839e+00 -7.8381e+00
-#&gt;  -1.0843e+01  6.2862e-01 -1.2367e+00  5.5577e+00 -3.5569e+00 -8.7925e-02
-#&gt;  -2.7422e+00  1.8663e+00 -5.8871e-01 -4.5779e+00 -2.7187e+00 -1.7621e+00
-#&gt;   3.8242e+00  4.7493e+00  8.6695e+00 -5.1289e+00 -1.1561e+00 -1.6367e+01
-#&gt;  -2.5585e+00  4.8995e+00  5.4630e+00 -1.4587e+00 -6.8408e+00  1.0571e+01
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  Columns 1 to 6  1.4080e+00 -1.1899e+01 -4.1353e+00 -1.4412e+00  1.1742e+01  6.9489e+00
-#&gt;  -6.5218e+00  1.3255e+01  9.3847e+00 -7.3147e+00  1.4185e+00  2.9938e+00
-#&gt;  -9.4088e+00  9.8435e+00  7.5546e+00 -1.8330e+00  5.6999e-01  2.4329e+00
-#&gt;  -2.1506e+00 -1.1168e+00  1.6671e-02  7.6816e+00  7.6185e+00 -1.3157e+01
-#&gt;  -1.4543e+00 -7.0075e+00  3.7290e+00 -6.4805e+00 -4.2380e+00 -1.1300e+01
-#&gt;  -6.9435e+00 -3.1541e+00  1.6184e+01  2.3992e+00  2.1464e+00 -2.0194e+01
-#&gt;  -2.1261e+00 -6.1874e+00 -3.1801e+00 -9.9955e+00  4.5779e+00 -3.3643e+00
-#&gt;  -1.3131e+00  6.6850e+00 -6.6825e-01 -3.8057e+00 -2.1247e-01 -1.0433e+01
-#&gt;  -5.0201e+00 -5.4811e+00  1.9074e-02 -9.6455e-01 -6.4131e+00  6.0250e+00
-#&gt;  -5.4076e+00  1.1422e+01  5.0324e+00 -2.3835e+00 -5.3782e+00  3.2404e+00
-#&gt;  -8.0323e+00  2.0626e+00 -1.0257e+01  2.4690e+00 -4.1333e+00 -1.6341e+01
-#&gt;   8.1692e-01 -4.8066e+00  6.5308e+00 -2.0152e+00  1.1058e+01 -1.1816e+01
-#&gt;  -1.1623e+00  4.4053e+00  4.9868e+00 -1.4910e+00 -8.6254e+00  8.2777e+00
-#&gt;  -8.3532e+00  2.3937e+00 -1.1702e+01  5.5676e+00 -4.8145e+00 -6.8998e+00
-#&gt;  -8.3821e+00 -2.0785e+00 -8.5879e+00 -1.1150e+01  4.8695e+00 -3.8867e+00
-#&gt;  -1.2349e+00  7.4407e+00  3.5352e+00 -2.9630e+00 -4.7452e+00  4.3327e+00
-#&gt;  -7.6731e+00  9.8579e+00 -5.3611e+00 -2.5492e-01 -8.7314e+00  4.6041e+00
-#&gt;  -5.3861e+00 -9.1160e-01  7.6677e+00  1.7004e+00  7.4107e+00 -6.2495e+00
-#&gt;  -2.6204e+00  3.1218e+00 -5.8771e+00 -9.5579e+00 -9.5801e+00 -6.1502e+00
-#&gt;   2.1210e-01  4.4249e+00 -1.3583e+01 -5.0736e+00 -1.4737e+00  9.9051e+00
-#&gt;   1.1629e+01  5.7636e+00  4.6826e+00 -2.0306e+00  3.1641e+00 -3.3694e+00
-#&gt;   4.8854e+00 -7.1501e+00 -1.1292e+01 -2.5348e+00  5.3458e+00  1.3115e+01
-#&gt;   8.2982e-01 -1.4568e+00 -8.7658e+00  6.9908e+00  1.3195e+00 -7.1090e+00
-#&gt;   1.1800e+00  1.6513e+00  6.2211e+00  2.4865e+00 -1.1569e+01  1.5910e+01
-#&gt;  -1.0067e+01 -2.4248e+00  3.5204e+00 -7.3729e+00 -2.9552e+00  9.0173e+00
-#&gt;  -3.5460e-01 -1.3474e+00  3.3893e+00  1.2785e+00  4.2084e+00 -1.0641e+01
-#&gt;  -1.9705e+00 -1.6608e+01 -3.1503e+00 -4.9205e+00 -5.1441e+00 -7.2497e+00
-#&gt;  -4.0333e+00  1.4794e+01 -1.0558e+01 -5.8430e+00 -1.0755e+01 -1.7470e-01
-#&gt;  -1.1416e+01  1.0601e+01 -3.5518e+00 -1.1902e+00 -4.6416e+00  5.6309e+00
-#&gt;  -6.7081e+00  3.0187e-01 -8.6661e-01 -5.8872e+00  2.0574e+00 -3.1048e+00
-#&gt;  -7.0169e+00 -5.9670e+00 -8.7838e+00 -1.1772e+01 -4.3665e+00 -1.3870e+01
-#&gt;   6.1087e-02  9.9251e+00  2.8213e+00  1.0690e+01 -1.8414e+00  2.7020e+00
-#&gt;   7.8980e-01 -3.7714e+00  1.1692e+01 -1.0431e+00  3.6993e-01 -3.0131e+00
-#&gt; 
-#&gt; Columns 7 to 12  4.9066e+00 -4.1308e+00  6.7264e+00 -2.3971e+00  2.5494e+00  3.4488e+00
-#&gt;   6.5412e-01  6.1340e-01  3.3838e+00 -8.8767e-01 -5.3286e-01  1.1592e+01
-#&gt;   8.5030e+00  4.7017e+00  2.5309e+00  1.8400e+00 -2.3989e-01  5.0525e+00
-#&gt;  -2.8190e+00  1.1768e+00  2.9686e+00  9.4875e-01 -2.0928e+00  2.8620e+00
-#&gt;   8.0581e+00  2.0097e+00  7.7453e-01 -2.4825e+00  5.6598e+00 -5.6388e+00
-#&gt;  -2.9977e+00  5.2568e+00 -8.4047e+00  4.8200e+00  9.3105e+00 -1.2028e+00
-#&gt;  -1.1490e+01  4.0066e+00  9.3038e+00 -1.0326e+00  8.8266e-01  8.2433e+00
-#&gt;  -1.9693e+00  9.3338e-02  6.7674e+00 -7.5838e+00 -1.2921e+00  4.9577e+00
-#&gt;   1.2455e+01  1.2788e+01 -1.2791e+00  6.4981e-01 -3.5957e+00 -1.3183e+00
-#&gt;   9.4231e+00 -2.2993e+00  4.4472e+00 -7.1652e+00 -2.1302e-02  3.9951e+00
-#&gt;  -2.4195e+00 -2.5335e+00 -4.7614e+00 -2.5586e+00  2.0540e+00  2.6202e+00
-#&gt;  -1.3300e+01  8.5292e+00  4.0447e+00 -6.2759e+00  7.4482e+00  7.2431e-01
-#&gt;   4.9680e+00 -3.3235e+00 -4.6155e-01 -1.5015e+00 -1.1129e+01  7.0367e+00
-#&gt;   1.1640e+01 -2.5281e+00 -6.0034e+00  1.1838e+01  8.8062e-01 -1.9703e+00
-#&gt;   5.5088e+00  3.4637e+00 -8.0601e+00  3.6713e+00  9.8656e+00 -4.1082e+00
-#&gt;   9.0013e-01 -7.9194e+00 -1.2030e+01  6.5047e+00 -4.0442e+00  3.4924e+00
-#&gt;   1.9017e+00  2.0474e+00  7.3447e-01  4.4541e+00 -8.6128e+00 -1.1041e+01
-#&gt;   3.9773e+00  5.6824e+00 -3.7343e+00 -6.4544e+00  5.0678e+00 -1.2625e+01
-#&gt;  -1.3919e+01 -7.6696e+00 -8.0680e+00 -7.8409e-01  1.7832e-01  1.4216e+01
-#&gt;  -8.4426e+00 -6.3279e-01  9.9402e+00  7.9904e+00  2.1366e+00  9.8434e+00
-#&gt;   1.0340e+01  2.9289e+00  7.5236e+00 -3.0736e+00  5.6299e+00  2.1383e+00
-#&gt;   4.8554e+00  2.0273e+00 -3.4944e+00  9.8330e+00 -1.5486e+00 -7.0196e+00
-#&gt;  -1.3181e+01 -6.1692e+00 -4.3927e+00 -6.9112e-01  7.3283e+00 -4.3668e+00
-#&gt;  -1.0650e+01 -1.0113e+01 -1.0493e+00 -4.0480e+00 -5.0376e+00  1.5352e+01
-#&gt;  -1.2744e+00  1.3469e+01 -8.7806e+00  4.8216e+00  4.3525e+00  7.1390e+00
-#&gt;  -7.6099e+00  8.2266e+00 -1.6163e+00 -5.2979e+00 -9.2686e-01  4.1946e+00
-#&gt;  -6.8688e+00  6.8039e+00  4.5720e+00  2.1791e+00  3.2972e+00  2.7397e+00
-#&gt;  -1.3702e+01  5.0406e+00 -2.5053e+00  1.6890e+01  2.2438e+00  7.1325e+00
-#&gt;   1.4510e+01  3.9684e+00 -2.3852e-01  4.7563e+00  3.9079e+00  1.0018e+01
-#&gt;   3.8940e+00 -7.0659e+00 -5.8890e+00  1.1028e+01 -8.5083e+00 -7.1714e+00
-#&gt;  -7.7616e+00 -1.2060e+00  1.5313e+00 -1.0961e+00  4.9758e+00 -2.1184e+00
-#&gt;   6.0980e+00 -1.2272e+00  5.8412e+00  1.2258e+00 -2.6513e+00  5.4603e+00
-#&gt;   6.5693e+00 -2.6360e+00 -1.1088e+01  3.7412e+00  4.6298e+00  2.0834e+00
-#&gt; 
-#&gt; Columns 13 to 18  4.6010e+00  4.5067e+00 -2.9038e+00  3.2502e+00 -2.4335e+00 -6.4512e+00
-#&gt;  -2.3157e+00 -6.3810e+00  1.3949e+01  9.6802e-01 -3.6449e-01  2.2528e+00
-#&gt;  -3.6608e+00 -8.8017e+00  5.9599e+00 -3.7491e+00 -7.6035e+00  6.1058e+00
-#&gt;  -4.8018e+00  2.0252e+00  4.9238e-01 -9.8625e+00  4.6778e+00 -2.1808e-01
-#&gt;   3.1025e+00  8.5312e+00 -3.7468e+00  6.2848e+00 -5.9987e-01  4.7689e+00
-#&gt;   3.6285e+00  1.5422e+01 -3.4136e+00  1.2394e+00 -6.1809e+00 -3.1180e+00
-#&gt;   2.5461e+00 -5.0396e-01  3.0065e+00 -7.7716e+00 -1.0346e+01  4.0124e+00
-#&gt;  -7.0728e-02  3.7410e+00  1.0579e+01 -4.4932e+00  1.1285e+00 -6.4617e+00
-#&gt;  -7.7204e-01 -9.0712e+00 -1.7770e+00 -9.2219e+00 -8.7108e+00 -4.0555e-01
-#&gt;   9.1434e+00  5.0025e+00  8.2765e+00  2.4627e-01 -1.8783e+00 -4.6973e+00
-#&gt;  -3.2545e+00  5.0161e+00 -3.9063e+00 -1.1353e+01  5.4595e+00 -6.0954e+00
-#&gt;  -4.9873e+00  1.4954e+01  5.8090e+00 -6.2336e-01 -4.7916e+00  3.4780e-01
-#&gt;  -4.5335e+00 -3.1057e+00  1.1861e+00  2.7490e+00  2.5365e+00  1.1029e+01
-#&gt;  -1.5909e+00  1.3848e+00 -5.3683e+00 -5.9884e+00 -3.4089e+00  6.0434e+00
-#&gt;  -1.3089e+01  6.8337e+00 -8.6937e+00 -5.4593e+00 -2.6766e+00  1.0728e+01
-#&gt;   2.8165e+00 -4.1133e+00  5.6637e+00 -4.4917e+00 -1.4141e-01 -1.4887e+00
-#&gt;   9.4678e+00  4.1728e+00  1.1035e+01  9.4205e+00 -2.1007e+00 -7.6966e-01
-#&gt;  -1.0080e+00  2.3142e+00  3.3514e+00  1.3970e+00 -8.1402e-01  8.2785e+00
-#&gt;  -3.4290e+00 -3.6558e+00 -1.9663e+00 -1.4807e+01 -5.8848e+00 -3.5223e+00
-#&gt;   9.5822e+00  5.5018e+00  1.1599e+01 -1.9601e+00 -2.5891e+00  5.5822e-01
-#&gt;   5.1442e-01  4.4396e+00 -6.9159e+00 -6.6224e+00 -7.4509e+00 -5.4584e+00
-#&gt;   3.9008e+00 -7.7933e+00 -3.8777e+00 -9.7138e+00  8.1011e+00 -1.8866e+00
-#&gt;  -6.4980e-01  1.0553e+01  1.3711e+00  3.4205e+00  6.2344e+00 -6.0786e+00
-#&gt;   1.8012e+00 -2.5126e+00 -6.1118e+00 -4.8493e+00 -2.3870e+00 -8.4272e-01
-#&gt;  -8.4451e-01  3.0856e+00 -4.3986e+00 -5.1790e+00 -1.4728e+01 -3.6166e+00
-#&gt;  -2.4438e+00  1.3056e+01  1.2298e+01 -5.6175e+00  8.8998e+00 -9.7530e-01
-#&gt;   1.0519e+00  8.9182e-01 -3.7485e+00  1.4638e+00 -2.2603e+00 -1.1745e+01
-#&gt;  -6.2835e-01 -3.9113e+00 -5.2103e-01 -1.3101e+01 -5.4891e+00 -3.1151e+00
-#&gt;   3.8050e-01 -3.9765e+00  3.3705e+00 -5.4369e+00 -7.1983e+00 -4.7236e+00
-#&gt;   5.7476e+00  4.3498e+00  1.7391e+00  9.4122e+00 -1.8259e+00  4.8131e+00
-#&gt;  -5.2306e+00  1.1402e+01  2.8967e+00  5.5618e+00 -7.5690e-01 -2.9042e+00
-#&gt;   2.3695e+00 -8.2757e+00 -4.8955e+00 -7.9524e-01 -2.1519e+00  2.2029e+00
-#&gt;   5.9470e+00  5.0308e+00 -6.3217e+00  4.2271e+00 -5.8797e+00 -1.2538e+00
-#&gt; 
-#&gt; Columns 19 to 24  4.0035e+00 -5.2533e+00 -6.4376e+00 -6.5796e+00  1.4593e+01  3.5199e+00
-#&gt;   5.3346e+00 -1.0014e+00  8.6347e-01  2.0348e+00 -5.6212e+00 -4.0300e+00
-#&gt;  -2.2729e+00 -1.5539e+00  2.4310e+00  1.7948e+00  1.5205e+00  4.4759e+00
-#&gt;  -1.0538e+01 -2.0725e+00  4.6372e+00 -3.4391e+00 -2.4950e-01 -2.3709e-01
-#&gt;   4.2838e-01  2.8630e+00  3.5305e+00  1.2936e+00 -2.5255e+00 -4.0722e+00
-#&gt;  -2.2387e+00  3.0365e+00  2.7049e+00 -1.3206e+01 -4.8693e+00  1.7665e+01
-#&gt;  -1.0460e+00  3.7885e+00 -2.7064e+00  7.5554e+00 -7.9784e+00 -1.5450e+00
-#&gt;   3.3267e+00 -2.1692e+00  5.9835e+00 -6.4481e+00 -7.5086e+00  3.9535e+00
-#&gt;  -2.4311e+00 -3.8278e+00 -5.4211e-01 -8.2869e-02  3.1314e+00 -1.1352e+01
-#&gt;   4.5032e+00 -1.3754e+00 -1.0011e+00  3.0971e+00 -6.2162e+00 -2.0818e+00
-#&gt;   1.2915e+00 -6.8684e+00  6.6977e+00 -2.4609e+00  1.3056e+00  6.7367e+00
-#&gt;  -8.9935e+00 -9.3973e+00  9.1078e+00  1.0718e+00 -1.8343e+01  2.3391e+00
-#&gt;   6.7598e-01  1.4605e+01 -1.1669e+01  1.4837e+01 -4.9263e+00 -8.4995e+00
-#&gt;  -8.4471e+00  3.9360e+00 -5.0993e+00  1.2821e+00 -5.0476e-01 -5.0610e-01
-#&gt;  -3.1034e+00 -1.9458e+00 -1.6512e+00  4.0102e+00 -1.5717e+00  1.5329e+01
-#&gt;  -9.0065e-01 -3.1436e-01 -1.4189e+00 -2.0881e+00  2.7689e+00 -9.9803e+00
-#&gt;  -1.0392e+01  6.7957e+00 -8.4666e+00  1.5654e+01  8.5171e+00 -2.7840e-02
-#&gt;   5.7474e+00 -7.4794e+00  2.5581e+00 -8.2204e+00 -2.3948e-02  4.2277e+00
-#&gt;  -8.4238e+00  6.8594e+00 -7.1296e+00  1.5019e+00 -7.6827e+00  8.1506e+00
-#&gt;  -7.1825e+00 -2.6377e+00 -4.2360e+00  1.5290e+01 -8.4564e+00 -1.9189e+00
-#&gt;  -1.6717e+00 -6.7846e+00  1.0160e+01 -1.1922e+01  4.1626e-01 -7.3792e+00
-#&gt;   5.8180e+00 -7.7029e+00  3.5438e+00 -5.4828e+00  1.0892e+01 -1.7366e+01
-#&gt;  -4.1492e+00  5.8430e+00 -8.5405e-01  9.1512e-01 -9.1028e+00  8.8594e+00
-#&gt;  -2.9390e+00  1.2791e+01 -1.4800e+01  8.0902e+00  9.1215e-02 -7.9433e+00
-#&gt;  -1.1290e+01 -4.3278e+00 -6.7345e+00 -7.3926e+00 -7.8704e+00 -7.1902e+00
-#&gt;   8.8550e-04  6.6792e+00  6.1034e+00  9.4450e-01 -8.4841e+00  9.1573e+00
-#&gt;  -5.3408e+00 -4.3279e+00 -2.6200e+00  7.4204e+00  2.9669e+00  4.2542e+00
-#&gt;  -4.2130e+00  3.1756e+00 -9.8007e-01 -3.4946e+00  3.6135e+00  4.5236e+00
-#&gt;  -3.6319e+00 -1.5918e+00 -4.6717e+00  1.3942e+00 -4.0734e+00  1.0169e-02
-#&gt;  -5.5155e-01  8.8678e+00 -2.8303e+00  4.7980e+00  1.5487e+01 -2.1578e+00
-#&gt;  -1.0643e+01 -9.3310e+00 -5.1527e+00  5.4748e+00 -2.3128e+00  1.1993e+01
-#&gt;  -4.4540e+00  8.2229e+00 -7.8324e+00  5.7167e+00 -2.1282e+01  5.6597e+00
-#&gt;   1.6077e+00  5.2613e-01  6.0516e+00 -8.0079e+00  1.0206e+01 -9.7924e+00
-#&gt; 
-#&gt; Columns 25 to 30  1.1147e+01  3.6391e+00  1.1187e+01  6.0805e+00 -8.1869e+00 -2.5905e+00
-#&gt;   5.2926e+00 -1.6961e+01 -5.4181e+00 -3.6479e+00 -4.9755e+00 -2.2046e+00
-#&gt;   4.7799e+00 -2.3298e+00 -3.9841e+00  3.9436e+00 -2.6750e+00 -2.1315e-01
-#&gt;   6.8501e+00 -8.3940e+00  8.5949e+00 -7.8190e+00 -1.7399e+00 -5.6473e+00
-#&gt;  -1.1091e+01  1.1763e+00 -3.5075e+00 -3.0827e+00  4.8371e+00  1.9554e+00
-#&gt;  -1.1648e+01 -7.4380e+00  2.7608e+00 -8.0752e-01  5.0588e+00  2.9817e-01
-#&gt;  -7.2856e+00  9.5683e+00 -1.2037e+01  4.4913e+00  2.2812e+00 -5.4674e+00
-#&gt;   3.0411e+00 -2.4650e+00 -6.0758e+00  6.5565e+00  3.3859e+00 -1.2842e+01
-#&gt;   1.2164e+01 -1.1720e+01  4.6296e+00 -4.5861e+00 -1.8319e+00 -3.1555e+00
-#&gt;   1.0095e+00  2.8889e+00 -3.4291e+00  1.0095e+00 -4.2277e+00  7.2100e+00
-#&gt;  -8.5960e+00 -2.2444e+00 -4.6157e+00  2.9012e+00  3.8361e-01 -6.3091e+00
-#&gt;  -5.5840e+00 -1.8335e+00  6.8453e-01  7.2422e+00  5.8593e+00 -6.2295e+00
-#&gt;   2.0426e+00 -3.1267e+00 -3.0833e+00  1.8640e+00 -3.1724e+00  2.6324e+00
-#&gt;   1.8683e+00 -4.9115e+00  5.0103e+00  2.4655e+00 -5.9479e+00  5.0536e+00
-#&gt;   7.5598e-02 -7.1535e+00 -4.4489e-01  4.7277e+00 -5.1581e+00  3.8179e+00
-#&gt;   1.1880e+01 -2.5464e+00  1.2672e+01 -5.9125e-02 -4.3747e+00  1.1664e+01
-#&gt;  -1.0828e+01  3.0879e+00 -1.4164e+00  2.4949e+00 -8.1771e+00 -1.1576e+00
-#&gt;  -6.1593e+00  3.6951e+00  1.3597e+01  6.8700e+00  3.7079e+00 -5.8486e+00
-#&gt;   2.5296e+00  1.7619e+00 -4.1247e+00 -7.1495e+00 -9.2967e+00 -2.0392e+00
-#&gt;   1.4032e+00  2.5314e+00 -1.4011e+01  4.0010e+00  1.7084e+01  4.9672e+00
-#&gt;  -6.2591e-01 -4.5542e+00  5.4845e+00  8.8774e+00  6.3592e+00 -5.5222e+00
-#&gt;   6.6866e+00 -6.4466e+00  3.6383e+00 -5.8592e+00 -2.6308e+00  2.5872e+00
-#&gt;  -8.4094e+00  3.9811e+00 -9.8913e+00 -2.0240e+00 -8.8618e-01  5.7595e+00
-#&gt;   8.8510e+00  4.8119e-01  5.9794e+00 -2.5156e+00 -7.9376e+00  6.2095e-03
-#&gt;   5.2374e+00 -3.6271e+00  1.6991e+01  1.2048e+00  4.1467e+00  4.2666e+00
-#&gt;  -9.1448e-01 -5.3934e+00 -2.2018e+00  4.8980e+00  3.2072e+00 -1.5705e+00
-#&gt;  -1.4598e-01 -3.9422e+00 -1.3923e+01 -2.8437e+00  4.3032e+00 -6.9459e+00
-#&gt;  -9.6621e+00 -4.9686e+00  6.9225e+00 -2.1751e+00 -1.7244e-01  1.2816e+01
-#&gt;   9.4945e+00 -1.7000e+00  5.5441e+00  9.0715e+00  2.7920e+00  1.0386e+01
-#&gt;  -7.8475e+00  5.6721e+00  2.7749e+00 -1.0268e+00  7.0137e-01  7.0984e+00
-#&gt;   3.0558e+00 -1.8220e+00  5.1956e+00 -7.3876e+00 -9.1895e+00 -3.0264e+00
-#&gt;  -3.3453e+00  9.7614e+00 -7.0201e+00  9.7704e+00  3.7403e+00  7.6579e+00
-#&gt;   1.2433e+00 -8.8167e+00  7.2628e+00 -3.4548e+00  3.0066e+00  4.6408e+00
-#&gt; 
-#&gt; Columns 31 to 36  1.6280e+00  3.6802e+00 -1.1593e+01 -8.4596e-01 -1.7400e+00  6.1596e+00
-#&gt;  -4.5575e+00  4.7161e+00  1.3676e+01  1.6125e+00  1.5714e+01  1.5754e+01
-#&gt;  -1.8076e+00  2.8713e+00  3.8487e+00 -1.0413e+00  4.4413e+00  6.8242e+00
-#&gt;   3.1493e+00  4.0503e+00 -1.4582e+01 -1.5412e+01  1.5087e+01 -2.7381e+00
-#&gt;   3.5763e+00 -6.4330e+00 -2.3951e+00  1.6168e+01 -1.4622e+01  6.6627e+00
-#&gt;  -2.4222e+00 -8.8587e+00 -3.3878e+00  1.8356e+00  2.1590e+00  6.4357e+00
-#&gt;  -5.4561e+00 -4.7786e+00 -5.5594e+00  8.7320e+00 -5.4292e+00  6.9659e+00
-#&gt;  -2.4925e+00  5.7615e+00  3.0381e+00  5.1190e+00  6.9788e+00 -3.4237e+00
-#&gt;   4.2632e+00 -1.4803e+01  4.0556e+00  6.8307e+00 -3.7379e-01  2.1806e+00
-#&gt;  -2.8327e+00 -3.2625e+00  9.2088e+00 -5.7277e+00  6.3689e+00  9.3465e+00
-#&gt;   1.1018e+01  8.0445e+00  9.3478e+00 -5.5861e+00  1.5302e-01  3.1772e+00
-#&gt;   8.2271e+00  1.5902e+01  1.8643e+01 -1.6779e+00  1.8905e+00  1.0224e+01
-#&gt;   6.3268e+00 -6.0640e+00  1.0101e+01 -5.8077e+00 -4.8274e+00  1.0012e+01
-#&gt;   5.0458e+00 -7.7735e+00 -9.2099e+00 -5.3822e+00 -2.3859e+00 -3.3018e+00
-#&gt;  -4.3210e+00  1.1542e+01 -1.9341e-01  1.0544e+00 -5.4314e+00  7.5066e+00
-#&gt;   5.5280e+00 -1.2632e+00 -4.0221e+00 -6.5578e+00 -6.1401e+00 -4.2676e+00
-#&gt;   6.8111e+00 -6.7595e-01  1.1877e+01 -4.4416e+00  7.3254e+00  9.2634e+00
-#&gt;  -9.3897e-01 -7.3323e+00 -5.6501e+00 -4.2338e+00 -1.6136e+01 -7.9817e+00
-#&gt;   5.1431e+00  7.9962e+00  1.9244e+00 -1.3078e+01  9.7229e+00  1.4527e+01
-#&gt;  -6.6730e+00  9.3903e-01  1.7861e+01  4.9748e+00  6.6223e+00  4.5511e+00
-#&gt;   5.7490e+00  4.0505e+00  1.2850e+00  1.7379e+01  6.4363e+00 -3.4994e+00
-#&gt;  -4.3550e+00 -1.3790e+00 -4.9837e-01  1.6309e+00  7.2332e+00 -9.8394e+00
-#&gt;   5.2478e-01  6.5799e-01 -9.2388e+00 -3.2802e-01 -1.0465e+01 -7.3347e-01
-#&gt;   1.2429e+00 -7.9775e+00  6.9857e+00 -4.2260e+00 -4.6722e+00  1.1488e+01
-#&gt;   1.9202e+00 -3.3812e+00 -3.2940e-01 -1.7986e-01 -4.8731e+00  3.0378e+00
-#&gt;  -1.2325e+00  1.7000e+01 -1.5116e+01  2.6105e+00  6.9141e+00  3.5265e+00
-#&gt;  -4.5038e-02 -1.0016e+00  1.0130e+01  1.7839e+01 -2.6167e+00  7.1525e+00
-#&gt;   3.8105e+00  1.5240e+01 -6.2467e+00  6.8072e+00  1.5233e+01 -1.2775e+01
-#&gt;   4.2361e+00  1.1523e+01 -4.7938e+00  2.2597e+00  9.2862e+00 -5.7732e+00
-#&gt;   8.5660e+00  1.7525e+00 -1.1605e+01 -1.7779e+01 -1.0388e+01  5.5783e+00
-#&gt;   1.7085e+00  9.5260e+00 -3.2841e-01 -8.4488e+00  9.9827e+00  2.2759e+00
-#&gt;  -8.8871e-01  1.1511e+01  7.0996e-01 -1.5810e+01  3.7052e+00  1.9111e+00
-#&gt;   1.7095e+01  6.1128e+00 -3.5600e+00  7.4983e+00 -1.8792e+00  3.0595e+00
-#&gt; 
-#&gt; Columns 37 to 42  4.0209e+00  2.8236e+00  3.2228e+00  4.9068e+00 -1.0765e+01 -1.8393e+00
-#&gt;   1.7493e+00 -1.6852e-01 -8.9894e+00 -7.7516e+00 -5.7265e+00  1.5782e+01
-#&gt;  -7.5828e-01  5.2742e+00 -6.7648e+00 -7.0245e-01 -2.9208e+00  8.0485e+00
-#&gt;   2.4751e+00  2.0166e+00 -2.7255e+00 -1.6698e+01  2.6687e+00  2.0290e+00
-#&gt;   5.5240e-01 -2.9833e+00 -1.0485e+01 -4.2613e+00 -3.7978e+00 -4.5257e+00
-#&gt;  -5.4438e+00 -4.8938e+00 -1.2284e+01  2.8564e+00  8.7059e+00 -2.1239e+00
-#&gt;   3.2328e+00 -4.8299e+00 -8.4738e+00  5.2165e+00 -6.1057e+00 -2.3035e+00
-#&gt;   9.0472e+00 -1.7072e+00  1.4483e+00  1.0006e+01  1.4309e+00  3.3362e+00
-#&gt;  -3.5516e+00 -2.4131e+00 -7.4484e+00 -9.3446e+00 -2.2438e+00 -5.8856e+00
-#&gt;   4.3916e+00  1.3265e+00 -9.2051e+00 -6.4683e+00 -1.0181e+01  1.0631e+01
-#&gt;   2.4456e+00 -6.5465e+00  6.4855e+00 -2.8299e+00 -1.2262e+00  1.0795e+01
-#&gt;  -4.5987e+00 -5.7546e+00 -3.0917e+00  1.2375e+01  8.1699e+00  4.7183e+00
-#&gt;  -9.2398e+00  1.1088e+01  1.1573e+01 -1.9905e+00 -1.2787e+01  6.5136e+00
-#&gt;  -7.4235e+00 -2.3060e-01  9.7640e+00 -1.0802e+01 -2.6236e+00  1.8933e-01
-#&gt;  -1.0108e+01 -2.2754e+00  3.0799e+00 -8.2495e+00 -6.9606e+00  9.3663e+00
-#&gt;  -7.5995e+00  7.7299e+00  3.7903e+00 -1.0401e+01  5.5817e+00  4.9853e+00
-#&gt;  -5.1197e-01  1.0524e+01 -7.7887e+00  7.9674e+00 -1.1456e+01 -1.8496e+00
-#&gt;   3.4752e+00 -7.9746e+00  1.3573e+00 -3.7112e+00 -7.2776e+00 -3.8574e+00
-#&gt;  -1.8658e+00  3.9683e+00 -1.6261e+01  3.5811e+00  3.0788e+00  1.8561e+01
-#&gt;   1.0342e+01 -2.8909e+00 -2.8083e+00  7.6817e+00 -4.0105e+00  4.0795e+00
-#&gt;   3.1097e+00 -1.7027e+01  5.6430e+00  8.0713e+00  3.7843e+00 -2.7920e+00
-#&gt;  -5.6453e+00 -8.9694e+00  1.8651e+01 -1.3044e+01 -4.0992e+00 -5.1164e+00
-#&gt;  -4.7247e+00  2.1068e+00 -6.8930e+00 -3.1100e+00 -4.0142e+00  2.4032e+00
-#&gt;  -8.0732e+00 -1.9362e+00 -1.1289e+00  1.5146e+01 -1.2932e+01  3.7773e-02
-#&gt;  -3.3798e+00 -1.0183e+01 -2.1320e+01  4.0629e+00  7.0527e+00  5.3845e+00
-#&gt;   1.3099e+01  2.9590e+00 -5.3575e+00 -1.3653e+00  1.5970e+00 -1.6306e+00
-#&gt;   6.9089e-01 -4.2401e-02  6.2614e-01  8.6173e-02  5.2735e+00  5.8539e+00
-#&gt;  -6.8782e+00 -8.2778e+00  8.0748e+00  7.1342e+00  5.1705e+00  2.6406e+00
-#&gt;   6.2406e+00 -6.6000e+00 -6.3177e+00  1.2859e+00  9.0834e+00  1.1542e+01
-#&gt;  -1.0031e+01  1.7066e+01 -4.2424e+00  1.1230e+01 -1.5471e+01 -1.2751e+01
-#&gt;  -3.2882e+00  9.8471e+00  6.1404e+00 -4.0524e+00  8.4977e+00  5.9648e+00
-#&gt;  -1.6528e+01  8.3521e+00 -9.0226e+00 -1.2466e+00  8.3971e+00  8.7195e+00
-#&gt;  -3.5397e+00  5.0278e+00  3.5069e+00  3.1022e-01  1.0142e+01 -2.0094e+00
-#&gt; 
-#&gt; Columns 43 to 48  1.9703e+00 -1.1875e+01 -8.7695e+00  1.5051e+00 -5.7029e+00 -9.7498e+00
-#&gt;  -1.3301e+00  3.2313e+00  8.9848e+00 -1.0432e+01 -4.4717e+00 -7.2384e+00
-#&gt;   7.5183e+00  1.2554e+01  7.1470e+00 -3.7589e-01 -2.4162e+00  2.9486e+00
-#&gt;   3.3490e-01  4.8699e+00  3.7211e+00 -1.0333e+01  9.6389e-01 -1.0573e+01
-#&gt;   9.7084e+00 -4.1592e+00  5.4356e+00 -6.3258e+00  3.1986e-01  1.1790e+01
-#&gt;  -9.9423e+00 -1.5987e+01 -6.0567e+00  8.4694e+00 -1.5238e+00 -1.0579e+00
-#&gt;   8.9344e+00  5.8672e+00 -7.3183e+00  4.8573e+00 -1.2020e+01  2.9273e+00
-#&gt;   1.1320e+01 -7.6255e+00 -3.5554e-01  1.0035e+00 -8.6486e+00 -6.9284e+00
-#&gt;   1.2469e+01  3.7959e+00 -6.8073e+00  1.0302e+00  1.1334e+01  3.4938e+00
-#&gt;   4.6032e+00 -2.1861e+00 -2.2557e+00 -8.5205e+00 -9.0304e+00  5.3963e+00
-#&gt;   1.2137e+00 -7.5389e+00  1.7085e+01 -6.2938e+00  2.4187e+00  8.8205e-01
-#&gt;   8.5736e+00  8.6609e+00 -7.3895e+00 -5.0710e-01 -1.4946e-01 -2.8082e+00
-#&gt;   5.7984e+00  1.0131e+01  5.3564e+00 -7.1073e+00  2.3574e+00  8.9853e-01
-#&gt;   1.5896e+00  1.8233e+00 -2.4928e+00 -2.7644e+00 -2.4445e+00  3.6724e+00
-#&gt;   3.1770e+00 -1.2907e+01 -3.4434e+00 -7.8347e+00 -9.8390e+00  7.6037e+00
-#&gt;   7.2079e+00  8.7224e+00 -6.1744e+00  6.8033e+00 -1.0701e+00 -3.7301e+00
-#&gt;  -6.1776e+00  8.1282e+00 -1.6816e+01  4.8213e-01 -1.7013e+01 -6.6154e+00
-#&gt;  -1.8096e+00 -8.0818e+00  7.6369e+00  4.6508e-01  1.3007e+00 -2.2696e+00
-#&gt;   5.6270e+00  5.2678e+00 -1.9481e+00 -5.9225e+00 -1.8582e+01  1.7727e+00
-#&gt;   7.5104e+00 -3.5144e+00  1.7004e+00 -6.3550e+00 -5.8357e+00  3.4854e-01
-#&gt;   1.6668e+01 -1.5817e+00  1.3947e+01 -2.3133e+00  8.9867e+00 -5.4141e+00
-#&gt;   5.5480e+00  4.2205e+00  2.1355e+00 -5.3126e+00  1.0447e+01 -3.6338e+00
-#&gt;  -9.6799e+00  3.6726e-01 -1.1408e+01 -5.9977e-01 -1.0750e+01  1.0392e+00
-#&gt;  -4.2397e-01 -3.0862e-01  5.3942e-01 -1.0992e+00  7.5872e+00  7.4528e+00
-#&gt;   6.6743e-01 -3.1385e+00 -1.0539e+01  1.2112e+00 -1.9948e+00  2.6654e-01
-#&gt;   1.2855e+01 -4.1853e+00 -4.4663e+00 -3.0913e+00 -2.1435e+01 -1.0684e+00
-#&gt;  -4.6279e-03  1.4366e+00 -1.3314e+01 -4.3680e+00  2.9159e+00  5.4549e-01
-#&gt;  -1.4838e+01  1.6832e+00  1.4140e+01  1.0610e+01  7.4335e+00  1.5544e+01
-#&gt;  -5.9024e+00 -5.5648e+00 -8.7734e+00 -7.4661e+00 -1.0869e+01  1.4613e+00
-#&gt;  -3.0699e+00  4.7420e+00 -4.3134e+00  5.4277e+00 -6.0626e+00  2.3346e+00
-#&gt;   3.9549e+00 -4.3669e+00 -1.1736e+01 -6.6775e+00 -7.9589e+00 -2.7425e-01
-#&gt;  -1.5342e+01  5.3356e+00 -1.0145e+01  3.7000e-01 -4.6578e+00  5.8591e+00
-#&gt;   3.7243e+00  2.5135e+00  4.8050e-01 -6.2709e+00  5.8081e+00 -4.3069e+00
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  Columns 1 to 6 -2.4260e+00 -1.6090e+01 -7.1397e+00  1.0959e+01  1.3156e+01 -3.1173e+00
-#&gt;  -8.2181e+00 -1.3502e+01  3.9274e+00 -2.2057e+00 -1.2749e+01  4.7140e+00
-#&gt;  -1.4480e+01 -2.6388e+00  1.6621e+01  2.2499e+00 -1.5909e+01  9.4288e-02
-#&gt;  -4.2047e+00  4.9874e+00  1.2744e+01 -2.2830e+00 -6.1745e+00 -3.4464e+00
-#&gt;  -1.0277e+01 -1.6134e+00 -8.3036e-02 -1.7949e+00 -1.4716e+00 -1.4627e+00
-#&gt;  -2.1850e+00 -6.9099e+00  3.0435e+00  8.3562e+00  2.9605e+00 -5.9025e+00
-#&gt;  -1.0164e+00 -4.8070e-01  2.9863e+00 -1.1825e+00 -1.4028e+01  4.0764e+00
-#&gt;  -4.2190e-02 -5.4516e+00  6.1648e+00 -5.6370e+00  4.4230e-01  5.5009e+00
-#&gt;   3.7369e+00  6.2467e+00  6.4830e-01  3.9464e+00  4.0394e+00 -1.3070e+01
-#&gt;  -3.4354e+00 -1.3006e+01  1.2709e+00  2.2926e+00 -7.9340e+00  7.8264e-01
-#&gt;  -2.8094e+00 -6.3679e-01  3.3087e+00 -4.7293e+00  1.1885e+00  8.2875e+00
-#&gt;  -9.3574e+00 -1.2533e+01  5.0777e+00  6.9207e+00  9.7479e+00  1.9588e+01
-#&gt;  -4.3638e-01  1.3491e+01 -6.4486e+00 -7.1892e+00  7.7435e-01 -9.9856e+00
-#&gt;  -1.7874e+00  9.6274e+00  1.0126e+01 -1.0308e+01  8.8940e+00 -8.6108e+00
-#&gt;  -1.1105e+01  8.8923e+00  6.5329e+00 -8.6803e+00 -7.5524e+00 -8.0187e+00
-#&gt;   4.4636e-01  4.6858e+00  1.1691e+01  1.0431e+00 -7.0344e-01 -8.0854e+00
-#&gt;  -5.0888e+00 -1.2481e+01 -4.9545e+00  5.0573e+00 -1.8414e-01  3.1990e+00
-#&gt;  -8.5629e-01 -9.0537e+00  4.4783e+00  1.1009e+01  4.8588e+00 -4.0643e+00
-#&gt;  -3.3022e+00  2.5722e+00  1.6664e+01 -2.9806e+00 -1.4120e+01 -4.9962e+00
-#&gt;  -5.7592e+00 -1.0260e+01 -8.1434e-01  4.3060e-01 -4.0868e+00  9.3831e+00
-#&gt;  -1.3060e+01 -3.0198e+00  1.7335e+01  5.5972e+00  7.4274e+00  5.9574e+00
-#&gt;   3.5454e-01  1.7126e+01 -2.0991e+00 -9.9543e+00  3.3188e-01 -6.5695e+00
-#&gt;  -3.2862e+00  3.9254e+00  1.3965e+01 -1.2239e+00 -1.8622e-01  3.0077e+00
-#&gt;   8.3303e+00 -9.2190e+00  3.1816e+00  1.9540e+00  8.2113e-01 -6.3173e+00
-#&gt;   6.4037e+00 -6.7632e+00  7.7021e+00  1.1969e+01  5.9819e+00  4.0527e+00
-#&gt;  -1.1441e+00 -7.0357e+00  5.4759e+00 -4.3076e+00 -8.1542e+00  2.4943e+00
-#&gt;  -3.9106e+00 -7.4675e-01 -6.9691e+00 -2.8430e+00 -6.4367e-01  9.7454e+00
-#&gt;  -9.9073e+00 -4.1061e+00  1.1572e+01 -1.0203e+00  2.5985e+00  1.9412e+01
-#&gt;   3.7332e+00 -7.6847e+00  9.5770e+00 -3.4412e+00  5.8689e+00  1.1729e+01
-#&gt;  -6.3888e+00  3.3612e+00  5.4892e+00  2.0501e+00  8.9452e+00  1.8355e+00
-#&gt;  -1.4714e+01 -4.1082e+00 -1.1987e+00  4.5001e-01  3.4602e+00  2.8930e+00
-#&gt;   8.4743e+00  1.4654e+01 -2.0721e+00  6.1471e+00 -3.8771e+00  3.2316e+00
-#&gt;  -2.5601e+00  1.2247e+01 -3.2863e+00 -6.9482e+00  7.3815e+00  5.2209e-01
-#&gt; 
-#&gt; Columns 7 to 12  2.7039e+00  1.7928e+01  4.7722e+00 -9.0033e+00 -9.2337e+00  4.3650e+00
-#&gt;   5.6106e-01 -3.7155e-01 -9.4022e-01  6.6389e-01 -1.0687e+00 -3.6676e+00
-#&gt;  -1.5103e+00 -4.8982e+00  2.8042e+00  3.2289e+00  1.7656e+00  1.3508e+00
-#&gt;  -1.7560e+00 -1.2062e+00 -4.4098e+00 -8.6041e+00 -9.1194e-01 -2.4839e+00
-#&gt;  -7.7607e-02 -1.4647e+00 -1.4750e+00  2.2109e-01 -9.2688e+00 -1.6978e+00
-#&gt;  -6.4080e+00 -1.4036e+01 -3.3036e+00  7.0697e+00 -2.8365e+00 -1.5730e+00
-#&gt;  -8.7269e-02  2.7889e+00  6.2123e+00  3.2134e+00 -4.1613e+00 -4.4587e+00
-#&gt;  -2.1674e+00 -1.1504e+00  5.9939e+00  1.1103e+01  1.1826e+01  5.6860e-01
-#&gt;  -6.9300e+00 -6.7035e+00 -7.3591e+00 -7.8506e+00 -2.8402e+00 -7.3483e+00
-#&gt;  -1.1257e+00  2.5082e+00  2.6712e+00 -3.2684e+00 -4.4637e+00  8.8603e-01
-#&gt;  -7.6516e-02 -1.4251e-01 -2.3874e+00  1.1411e+00 -2.4513e+00 -1.6663e+01
-#&gt;   4.6774e+00 -1.4641e+00  8.1328e-01 -1.1810e+01 -1.5955e+01  1.2982e+01
-#&gt;  -8.5431e+00  3.9702e+00  9.8489e+00  4.6478e+00  1.2068e+01  4.8349e+00
-#&gt;  -5.8179e+00 -8.1493e+00  1.6114e+00 -4.0638e+00  6.5257e+00 -5.3299e+00
-#&gt;  -7.2035e-01 -4.5514e+00 -1.9175e+00  1.3576e+00  9.6702e+00 -6.4111e+00
-#&gt;   3.2116e+00 -2.5445e+00 -4.9105e+00 -8.8980e-01  1.1377e+01 -3.6236e+00
-#&gt;   1.0368e+01  2.7730e+00  8.0303e+00 -6.5360e+00 -1.2322e+01  2.1166e+01
-#&gt;  -4.8024e+00  4.8266e+00  8.0571e-01  8.9774e-01 -8.2069e+00 -9.2629e+00
-#&gt;   2.2174e-01 -6.4808e+00 -5.2895e+00  3.6308e-01 -1.2931e+00  2.7188e+00
-#&gt;   9.5745e+00  1.2801e+00  7.4423e+00  1.8955e+00 -4.1224e+00 -4.7624e+00
-#&gt;  -5.2236e+00 -1.0175e+00 -5.3479e+00  1.6259e-02  1.3361e+00  7.1376e+00
-#&gt;  -7.6194e+00  6.1334e+00  1.1134e+00 -9.8276e+00  7.6407e+00 -1.5565e+01
-#&gt;  -1.8772e+00 -1.9989e+01 -1.6736e+01  2.7651e+00 -3.2357e+00  6.1813e+00
-#&gt;   8.6555e+00  1.2206e+01  7.3953e+00 -1.3803e+01 -9.0671e+00  1.8454e+01
-#&gt;   6.6007e+00 -5.4982e+00 -7.9111e+00 -1.0212e+01 -1.3995e+01 -2.5820e+00
-#&gt;   3.3626e+00 -2.6789e+00  7.5463e+00  8.0132e+00  7.1050e+00 -4.7084e+00
-#&gt;   3.1330e+00  1.9449e+00 -7.4689e+00  1.5632e+00 -5.9301e+00  6.4092e+00
-#&gt;   1.0197e+01 -7.0770e+00 -4.1250e+00 -8.2758e+00  1.3048e+01 -7.7559e+00
-#&gt;  -2.1499e+00 -6.4168e+00 -2.3384e+00 -1.8222e+00  1.1717e-01  3.8442e+00
-#&gt;  -6.0073e+00 -8.4099e+00  3.9294e+00 -6.3322e+00 -6.4771e+00  2.1426e+01
-#&gt;   1.1076e+01  5.8835e+00 -4.9446e+00 -4.7285e+00  2.9562e+00  1.0060e+01
-#&gt;   3.8793e+00 -1.3364e+01 -5.9460e+00 -5.2039e-01  4.9388e+00  9.8313e+00
-#&gt;  -7.5788e+00 -4.2444e+00  2.7747e+00  2.6616e+00  4.5422e+00  3.9646e-01
-#&gt; 
-#&gt; Columns 13 to 18 -6.5245e+00  2.3812e+00 -2.7277e+00 -2.9581e+00 -1.6089e+01 -1.4964e+01
-#&gt;  -5.1098e-01 -1.6415e+00 -1.0149e+01  5.5287e+00  4.1055e+00  1.2482e+00
-#&gt;  -6.7670e-01 -1.7101e+00 -4.9976e+00  1.0970e+01 -3.4336e+00 -5.6223e+00
-#&gt;  -4.6039e+00 -1.9907e+00 -2.7878e+00 -1.1976e-01  5.3809e-01  2.7278e-01
-#&gt;  -4.1515e+00  6.1336e+00 -2.5220e+00  6.6164e+00  9.2006e+00  3.1525e+00
-#&gt;  -3.0194e+00 -2.8319e+00 -2.1512e+01  2.1852e+00  9.3475e+00 -8.1556e-02
-#&gt;  -2.9080e+01  2.7905e+00  7.8276e-01  2.3943e+00  1.5815e+00 -3.1924e+00
-#&gt;  -4.5630e+00  4.8802e+00 -6.8771e+00 -8.7013e-01 -1.4001e+00  1.2690e-01
-#&gt;   5.6586e+00  8.6122e+00  1.9926e+00 -2.5550e-01  1.9946e+00  3.0214e+00
-#&gt;   5.9062e+00 -3.4057e+00  3.9138e+00 -3.8029e-01 -7.9910e+00  3.1446e+00
-#&gt;   1.0425e+01 -7.9493e+00  3.6418e-01  1.0828e+01 -8.4890e+00  2.3251e+00
-#&gt;   2.3559e+00  3.4553e+00  3.1563e-01  1.0489e+01 -6.5857e+00 -1.4040e+01
-#&gt;  -7.9710e-01  4.0536e+00 -3.0672e+00  1.1442e+00  3.5079e+00  1.6066e+00
-#&gt;  -7.5029e+00  1.2243e+00 -3.7898e-01 -1.5373e+00 -1.1446e+00  3.8205e-01
-#&gt;   4.1850e+00  2.2051e+00 -2.0903e-01  2.1436e+00  1.8623e+00 -3.8784e+00
-#&gt;  -4.1479e+00 -7.1423e+00 -3.1687e+00 -3.1377e+00  5.9941e+00  5.1574e+00
-#&gt;   8.0884e+00  5.1589e-01  4.0641e+00 -1.9410e+00 -1.3332e+01 -7.8220e+00
-#&gt;   8.4895e-01  8.6939e+00 -8.5540e+00  7.4821e+00  4.9653e+00 -1.1706e+01
-#&gt;  -1.3710e+01 -9.1676e+00 -2.5768e+00  9.1837e+00 -1.1243e+01  2.3341e-01
-#&gt;  -4.1771e+00 -2.4854e+00  2.1148e+00 -4.6356e+00 -2.6337e+00  9.0549e-01
-#&gt;   5.2531e+00 -3.7544e+00 -5.2746e+00  4.3894e-01  6.3111e+00 -1.2990e+00
-#&gt;  -3.5439e+00 -6.1807e-01  9.6415e+00 -8.0851e+00  4.2410e+00  7.7130e+00
-#&gt;  -6.7433e+00 -1.0340e+01 -8.5594e+00  2.3174e+00 -2.7162e+00 -8.6086e-01
-#&gt;  -2.7265e+00 -7.5501e+00 -3.1762e+00  1.0278e+01 -8.0296e+00  3.2575e-01
-#&gt;  -4.1769e-01 -2.5689e+00 -9.3748e+00  2.6912e-01 -4.4217e+00  2.3118e-01
-#&gt;  -4.7241e+00  3.1261e+00 -3.9767e+00 -3.7091e+00 -2.0489e+00  2.0303e+00
-#&gt;   5.1013e+00  1.4020e+01 -4.9716e+00 -9.3442e+00  1.9353e+00 -4.5956e+00
-#&gt;  -4.8233e+00 -8.7770e+00  1.9722e+01  9.5561e-01  3.9089e+00  1.3546e+01
-#&gt;   9.2608e-01  3.2052e+00 -1.4554e-01 -1.4802e+01 -5.1632e+00  6.9924e+00
-#&gt;  -5.7905e+00 -5.4141e+00  4.2075e+00  6.7678e+00 -1.7884e+01 -3.1346e+00
-#&gt;   1.0392e+01  2.7993e+00 -2.3305e+00  7.8083e+00 -1.1962e+01 -1.3522e+01
-#&gt;   5.6182e+00 -2.6997e+00  3.7665e+00  1.6071e+00 -3.7697e-01  1.3203e+00
-#&gt;  -4.3505e+00 -1.3015e+00 -1.3011e+01 -7.3237e+00  4.3552e+00  6.8349e+00
-#&gt; 
-#&gt; Columns 19 to 24 -4.3832e+00  1.1854e+00  3.7849e+00 -8.7515e-01 -7.2142e+00  2.8712e+00
-#&gt;   4.3580e+00  1.0377e+01  7.7775e+00  5.6452e+00 -5.5160e+00 -4.8094e+00
-#&gt;   1.2200e+00  2.5590e+00  7.4564e-01  8.7451e+00  8.5624e+00  4.9214e-01
-#&gt;  -4.0737e+00  1.1564e+01  3.7737e+00  5.8098e+00 -9.9780e+00  6.1329e+00
-#&gt;   8.3322e+00 -8.3092e+00 -6.6719e+00 -1.1653e+00  9.0612e+00 -6.9816e-01
-#&gt;   4.1872e+00  5.6975e+00  3.3598e+00  1.7068e+00 -1.0912e+01  1.0734e-01
-#&gt;   6.7030e+00 -6.7618e+00  1.2650e+00 -1.8857e+00  1.7936e+00  1.0692e+01
-#&gt;   1.0883e+00  4.4177e+00 -2.1538e+00  1.1302e+00 -3.1243e+00  1.4657e+00
-#&gt;   2.5484e+00  2.1354e+00 -5.3882e+00  2.5681e+00  9.9810e+00 -5.4780e+00
-#&gt;   7.7740e+00  1.0476e+00 -5.1584e+00  8.7806e-01  9.8774e-01 -1.1787e+00
-#&gt;   2.5723e+00 -1.0043e+01 -2.5171e+00  1.2171e+00  6.1552e+00  1.3662e+00
-#&gt;   4.5510e+00 -1.4972e+00  1.1797e+00  9.6993e+00  1.1976e+00 -4.3668e+00
-#&gt;   2.2118e+00 -2.7551e+00 -8.4045e-01  3.4012e-01  9.2944e+00  3.2099e+00
-#&gt;  -9.9009e+00  1.7620e+00 -3.0801e+00  7.2096e+00  8.1377e+00  8.4507e+00
-#&gt;   1.2373e+00 -2.8196e+00 -2.6450e+00 -7.0677e+00  9.0234e+00  6.5156e+00
-#&gt;  -4.0219e+00  8.0798e-01 -3.0108e+00 -1.4071e+00  6.3210e+00  6.3155e+00
-#&gt;   4.1826e+00  1.0785e+01 -7.9641e-02 -8.0781e+00 -2.8322e+00  5.8314e+00
-#&gt;  -1.6334e+00 -8.2829e-01 -5.2327e-01  9.1560e-01  2.7875e-01  1.0949e+00
-#&gt;   1.1560e+01  7.1775e+00  2.4676e+00  4.3689e+00  1.2348e+00  1.5355e+00
-#&gt;  -7.2999e+00 -8.7090e+00 -1.0109e+00  8.0552e+00  5.2843e+00 -5.5434e+00
-#&gt;  -5.6621e+00  2.0950e+00  5.6926e+00  8.5302e+00 -1.8420e+00  4.9828e+00
-#&gt;  -6.2905e+00 -3.2019e+00  3.9112e+00 -2.8016e+00 -2.4425e+00 -3.9356e+00
-#&gt;  -1.2319e+00  9.3284e+00  7.1342e+00 -2.9960e-01  3.5464e+00  9.6238e+00
-#&gt;  -2.5003e+00  3.7505e-01 -1.8017e+00 -2.4822e+00 -1.3950e+01  4.2405e+00
-#&gt;   6.2448e-01  5.0793e+00 -2.1631e+00  4.3120e+00 -6.7088e+00 -6.2485e+00
-#&gt;   1.0608e+00  8.4343e-01  1.4154e+00 -8.8833e-01 -4.9350e-02  5.2178e+00
-#&gt;   5.0803e-01 -2.5242e-02  5.2353e+00 -4.7372e+00  1.6922e+00  2.6130e+00
-#&gt;  -8.1313e-01 -1.4678e+01 -8.0687e+00 -7.8823e+00  5.9716e+00  9.7268e+00
-#&gt;  -4.1983e+00  1.7083e+00 -4.8165e+00  1.0524e+01  2.4571e+00 -5.8272e-01
-#&gt;   9.0055e+00  1.6781e+00 -3.9620e+00 -1.9208e-01  7.7751e+00  4.8966e+00
-#&gt;   5.6446e+00  1.0289e+01 -2.6237e+00 -9.7269e+00 -1.8522e-01 -1.3218e+00
-#&gt;  -3.3620e+00 -1.8233e+00 -3.1495e-01  1.1295e+00  5.1915e+00 -6.8499e+00
-#&gt;  -1.2493e+00 -3.7749e+00 -4.4679e+00  4.6988e+00  6.2839e+00  1.0712e+00
-#&gt; 
-#&gt; Columns 25 to 30 -3.7678e+00 -5.7242e+00 -6.5666e+00 -6.8208e+00 -5.8472e+00 -4.0306e-01
-#&gt;  -3.7068e+00 -1.8190e+00 -3.1648e+00  6.9638e+00  5.9712e+00  4.9328e+00
-#&gt;  -1.0388e+01 -5.5448e+00 -1.4448e+00  2.6815e+00  3.7214e+00 -3.2213e-01
-#&gt;  -3.6283e+00 -2.1454e+00  3.2638e-01  6.5090e+00  3.4894e+00  5.7031e+00
-#&gt;  -5.3661e-02  4.1527e+00  2.4702e+00 -1.5813e-01 -1.6505e+00  1.4394e+00
-#&gt;   1.6428e+01 -9.1191e+00  1.1358e+00 -3.5391e+00  8.9095e+00 -2.4697e+00
-#&gt;  -4.4852e+00  1.1873e-01 -3.0818e+00  2.8583e+00  2.7108e+00  5.9310e-01
-#&gt;   3.1718e-01  5.4803e+00 -7.4583e+00 -3.3241e+00  1.9538e+00 -9.9190e+00
-#&gt;  -5.7366e+00  9.0325e+00 -3.6218e-01  1.2094e+01 -5.1567e+00  2.4442e+00
-#&gt;  -1.0432e+01  4.0087e+00 -5.2226e+00 -3.0613e+00 -2.6841e+00  4.3142e+00
-#&gt;   1.5683e+01 -4.8294e+00 -3.7603e+00 -3.9085e+00  6.4568e+00 -9.4047e-01
-#&gt;  -8.2675e+00 -1.2175e+01 -2.7009e-01  3.1945e+00  7.9773e+00 -9.3286e+00
-#&gt;  -1.1884e+00  6.6128e-01  5.1019e+00  5.5271e+00 -1.3284e+01 -6.6113e+00
-#&gt;  -1.3139e+01  4.7815e+00  1.3458e+00  7.1244e+00 -8.1554e+00  5.0782e-01
-#&gt;   4.6021e+00  5.5465e+00  1.0623e+01 -6.7340e+00 -1.0824e+01  9.9315e+00
-#&gt;  -1.2730e+01  1.4776e+01 -2.8510e+00  1.9954e+00 -6.1477e+00  6.8560e+00
-#&gt;  -8.4356e+00  3.8520e-01 -9.1840e+00  1.9059e+00 -8.1112e+00 -5.6377e+00
-#&gt;  -7.3018e+00 -2.0424e+00  2.1029e+00 -1.0376e+00  2.2140e+00 -6.5560e+00
-#&gt;   5.9514e+00  2.8834e+00  8.2532e+00 -1.2182e-01  1.4292e+01  6.1602e+00
-#&gt;   2.3185e-01 -5.6710e+00 -1.6720e+01 -6.4373e+00 -8.8326e-01  1.2590e+00
-#&gt;  -8.3497e+00 -1.0511e+01 -9.8981e+00  1.9591e+01  7.4912e+00  4.6404e+00
-#&gt;  -6.4806e+00  9.1930e+00  3.6603e+00  6.1261e+00 -4.8635e+00 -1.2633e+01
-#&gt;   1.6575e+00  1.2219e+01  1.8400e+00 -5.0897e-01  4.8157e-01 -1.5647e+00
-#&gt;   1.0697e+01 -1.0563e+01 -1.9335e+00  5.2464e+00 -4.9309e+00  5.6560e+00
-#&gt;   2.7579e+00 -1.0974e+01  3.7751e+00  2.4941e+00  1.2820e+01  1.2087e+01
-#&gt;   1.0323e+01  9.6038e-01 -8.0909e+00 -1.2246e+01  5.8800e+00 -8.1793e+00
-#&gt;  -3.2346e-01  5.5698e+00 -4.4115e+00  9.1758e-02 -5.0301e+00  3.8046e-02
-#&gt;   5.3821e+00 -8.0975e+00  3.9839e+00 -1.6710e+00  1.3352e+01 -6.0166e+00
-#&gt;  -1.8801e+01 -7.5684e-01 -6.5811e+00  2.7727e+00  8.5961e+00  8.7461e+00
-#&gt;   5.9716e+00 -2.9117e-01  6.6215e+00 -6.3897e+00  3.0783e+00 -7.0034e+00
-#&gt;   3.3100e+00  5.9482e+00  7.1441e+00 -7.2146e+00 -1.1453e+01 -2.1975e-01
-#&gt;  -2.5174e+00  6.1783e-04  3.0374e+00  9.1811e-01  1.3462e+00  1.2635e+01
-#&gt;  -1.1931e+00 -5.5093e+00 -6.1567e+00 -2.6138e+00  2.6214e+00  1.4476e+00
-#&gt; 
-#&gt; Columns 31 to 36  7.4223e-01  7.0131e+00  1.1629e+01 -5.2268e+00 -1.1719e+01  3.1106e+00
-#&gt;   2.6765e+00  7.4811e-01  2.4381e+00 -6.1843e+00  8.3653e+00  1.0713e+01
-#&gt;   5.0892e+00  1.7012e+01  5.5125e+00 -1.1288e+01  4.5462e+00  5.6505e+00
-#&gt;   6.4937e+00  5.3456e+00  7.8426e+00 -1.6293e+01 -2.3455e+00 -5.8268e+00
-#&gt;  -1.0484e+00  3.4260e+00 -8.2399e+00  4.6738e+00 -5.2327e+00  7.4706e+00
-#&gt;   5.3485e+00 -1.8475e+00 -3.6222e+00 -1.6178e+01 -3.4572e+00  3.2129e+00
-#&gt;   6.6275e+00 -2.6183e+00  1.6539e+00  4.8539e+00 -5.4083e+00  5.7612e+00
-#&gt;  -2.7518e+00  5.2740e+00  1.5759e+01 -9.9001e+00  2.3535e+00  7.7896e+00
-#&gt;   3.7825e+00 -3.4907e-01 -3.4481e+00 -2.5707e+00 -9.7787e+00  1.5958e+01
-#&gt;  -3.8520e-01  1.4803e+01  3.4473e+00 -6.6064e+00  4.9638e+00  7.6164e+00
-#&gt;  -2.5500e+00  4.3077e+00 -1.0892e+01 -4.6185e+00  3.9399e+00  9.3895e+00
-#&gt;  -8.5954e+00  5.5902e+00  9.9969e+00  9.0837e+00 -7.0463e+00  1.6710e+00
-#&gt;   2.7788e+00  7.4384e+00  8.5017e+00 -2.4923e+00  1.0762e+01  5.9373e+00
-#&gt;   4.4875e+00  1.5368e+00 -1.6538e+00 -1.0821e+01  4.6451e+00 -1.2634e+00
-#&gt;   1.2521e+00  1.4079e+01  2.2609e+00 -1.0919e+01  8.3839e+00 -3.6383e+00
-#&gt;   1.1817e+01  2.9383e+00 -4.2285e+00  2.2827e+00 -1.1505e+01  3.6831e+00
-#&gt;   5.6361e-01  1.7811e+00 -2.0413e-01  8.8456e-01 -2.2689e+00 -3.1501e+00
-#&gt;  -9.8936e+00  3.8421e-02  1.3489e+00 -7.7164e+00  1.4918e+00  1.5608e+00
-#&gt;   3.9265e+00  1.5115e+01 -4.5694e+00 -7.6374e+00  3.5958e+00  2.6907e+00
-#&gt;  -2.7697e+00 -1.5410e-01  5.8248e+00  3.0651e+00  9.2409e+00 -4.3196e-01
-#&gt;  -3.9996e+00  3.8280e+00  7.0900e+00 -9.7899e+00 -1.5215e+00  1.3456e+01
-#&gt;   1.0321e+01 -9.7283e+00 -4.8336e+00  1.3471e+01  4.9134e+00  3.0929e+00
-#&gt;   8.3774e-01  4.2876e+00 -1.7407e+01  2.7746e+00 -6.0241e+00 -9.6043e+00
-#&gt;   3.1691e+00  8.8499e+00 -9.8104e-01  5.2230e+00 -8.6498e-01  1.2157e+01
-#&gt;   9.0028e-01 -7.4834e+00 -3.0958e+00 -4.7875e+00 -1.0619e+01  1.0254e+00
-#&gt;   5.0236e+00  1.2426e+01  1.5218e+01 -4.1620e+00 -5.4289e+00  7.4496e+00
-#&gt;   1.5874e+01 -1.8470e+00 -5.5536e+00  1.0463e+01 -1.4672e+01 -4.7601e+00
-#&gt;  -9.4839e-01  1.9456e-02 -2.4531e+01 -1.4408e+00  4.2728e+00 -7.0230e+00
-#&gt;   9.5121e+00  1.6671e+00  8.0547e-01 -7.9168e+00  4.5085e+00 -9.3673e+00
-#&gt;  -1.4331e+00 -3.4387e+00 -8.9806e+00  3.4347e-01 -1.6345e+01 -8.6711e-01
-#&gt;   3.4529e+00  7.9604e+00  7.3007e+00 -1.6455e+01 -9.1844e+00 -7.5894e+00
-#&gt;   4.7911e+00  6.6002e+00 -3.2026e+00 -9.1167e-01  3.9497e+00 -1.9500e+01
-#&gt;   1.6120e+01 -1.0085e+00  2.9139e+00  9.0391e+00 -4.1158e+00  1.2126e+01
-#&gt; 
-#&gt; Columns 37 to 42 -4.2708e+00 -6.2193e+00  4.0405e+00 -2.4626e+00  2.6114e+00 -6.6725e+00
-#&gt;  -2.6733e+00  5.4875e+00 -9.1574e-01  1.0587e+01  1.4299e+00  7.1512e+00
-#&gt;  -1.1510e+01 -1.1695e+01 -6.2071e+00 -8.2175e+00  1.9661e+00  1.0048e+01
-#&gt;  -2.6016e+00 -7.4609e+00 -5.6747e+00  9.3854e+00  9.3149e+00  5.0030e+00
-#&gt;  -1.1570e+00  8.6847e-01  4.5684e+00  1.1916e+01  4.5815e+00  3.9475e+00
-#&gt;   4.1254e+00  5.6743e+00 -2.8369e+00  8.3233e+00 -6.2624e+00 -6.4872e+00
-#&gt;   6.2880e+00 -1.0629e+01  4.8851e+00  4.4807e+00  7.8539e+00  2.9114e+00
-#&gt;   5.6523e+00 -9.5010e+00  1.1585e+01 -7.8069e+00 -1.9712e+00 -1.4402e+00
-#&gt;  -9.8762e+00  1.1291e+01  3.9450e+00  7.5963e+00  1.1955e+00  1.0262e+01
-#&gt;   6.1205e-01 -3.9418e-01  6.0771e-01  1.7916e+00  4.0193e+00  1.2516e+01
-#&gt;  -5.3330e+00  5.4140e+00  1.0709e+01  5.9039e+00 -2.1101e+00  5.9015e+00
-#&gt;   4.4010e+00 -5.8462e+00  2.4776e+00 -2.7191e-01  2.4018e+00 -5.4125e+00
-#&gt;  -1.6926e+00  3.5603e+00  6.8201e-01 -1.0361e+01 -1.1898e+00  1.4208e+01
-#&gt;  -8.8385e+00  9.4390e-01 -1.0417e-01  7.5702e-01  5.1242e+00  6.3443e+00
-#&gt;  -1.0421e+01  3.4307e+00  1.0905e+00 -3.4570e-01 -4.2475e+00  3.7947e+00
-#&gt;  -8.2029e+00 -7.5056e-02 -1.0317e+01  9.6688e-01  5.8398e+00  6.9069e+00
-#&gt;   9.2056e+00  2.6418e+00 -6.2371e+00  3.8774e-01  1.1345e+00 -3.7623e+00
-#&gt;  -4.0129e+00 -4.6145e+00 -4.4591e+00 -1.5093e-01  3.8242e+00 -2.6517e+00
-#&gt;  -7.3270e-01  3.5610e+00  9.5007e+00  5.1863e+00  5.7790e+00 -9.9534e+00
-#&gt;  -1.9771e+00 -4.4519e+00  2.3197e+00 -5.2061e+00 -4.5916e-01 -1.5046e-01
-#&gt;  -6.4935e+00 -5.2702e+00  4.9656e+00 -7.4239e+00  9.6984e-01  5.0983e+00
-#&gt;  -3.6458e+00  1.0499e+01 -3.4077e-02  2.9104e+00 -4.5600e+00  9.1534e+00
-#&gt;   9.5156e+00 -1.5982e+00  4.5251e+00  3.1438e+00  4.4952e+00 -1.8068e+01
-#&gt;  -2.3347e+00  8.9090e+00 -3.1034e+00 -5.2686e+00  1.9010e+00  3.1109e+00
-#&gt;  -1.6672e+01  7.5201e+00 -1.2067e+00  3.2212e+00 -9.8372e-02 -1.1015e+01
-#&gt;   1.0232e+01 -6.7055e+00  1.3532e+01 -2.2495e+00 -3.1926e+00 -3.1087e+00
-#&gt;  -5.6343e-01 -2.0494e+00  3.1293e+00  8.3727e+00  6.9515e+00 -7.7970e+00
-#&gt;  -3.8374e-01  4.6507e+00 -1.0472e+01 -1.6164e+00  9.6006e+00  6.4874e+00
-#&gt;  -6.7306e+00 -5.4101e+00  5.1182e+00 -7.9834e+00  9.7989e+00 -5.6655e+00
-#&gt;   3.6640e+00 -1.7696e+00 -4.1505e+00  1.7499e+00  8.3987e-01  4.7474e-01
-#&gt;  -4.6932e+00 -3.6107e+00  6.9508e+00  8.5656e+00  6.0091e+00 -3.3539e+00
-#&gt;   8.8946e+00 -6.8784e+00 -5.4160e-01  8.3567e+00 -8.4307e+00  1.9961e+00
-#&gt;  -3.1567e-01  6.1672e+00 -1.2873e+00 -3.1027e+00 -7.7037e+00  4.4967e+00
-#&gt; 
-#&gt; Columns 43 to 48  7.6586e+00 -1.1518e-01 -3.6124e+00 -6.9019e-01  5.0078e+00 -7.1079e+00
-#&gt;  -1.1350e+01  5.9555e+00  6.9061e+00  7.6141e+00  4.6341e-03 -9.8206e+00
-#&gt;  -8.3167e+00 -4.8917e+00  6.2786e-01  3.4504e+00  3.6465e+00 -8.0655e+00
-#&gt;  -9.8720e+00 -1.3697e+00  1.8162e+00 -1.2960e+00  5.4253e+00 -8.4163e+00
-#&gt;  -4.6093e+00  3.1744e-01  1.3845e+01  5.9917e+00 -6.8247e+00  1.5222e+00
-#&gt;   2.9278e+00  7.6591e+00  8.8834e+00 -7.3206e+00  3.5923e+00  1.1554e+00
-#&gt;   1.7499e+01 -7.5501e+00  1.0134e+01 -2.2161e+00  2.7105e-01 -1.2830e+01
-#&gt;   4.5848e+00 -6.8604e+00 -7.4688e-01  2.7601e+00  1.7959e+00 -4.4080e+00
-#&gt;  -6.2159e+00  1.0061e+01  8.0681e+00  4.7329e+00 -1.4557e+01 -5.9783e+00
-#&gt;  -1.4388e+01  6.6257e+00  5.5512e+00  1.4502e+01  2.5892e-01  1.5548e+00
-#&gt;  -5.8538e+00  1.7142e+00  3.0457e-01  5.8071e+00 -1.3670e+01  1.0826e+01
-#&gt;  -2.2469e+00 -8.5539e+00  2.7219e+00 -3.7882e+00  6.9489e-01  4.0771e-01
-#&gt;  -1.2400e+00 -6.6279e-01 -5.0088e+00  1.0212e+00  4.5081e+00  2.4033e+00
-#&gt;   6.0568e-01  1.0213e+00  4.9064e+00 -1.2058e+01 -5.7127e+00  1.8151e+00
-#&gt;   7.8649e-01  3.0735e+00  9.0810e+00 -1.4522e+00 -2.7879e+00 -1.1300e+01
-#&gt;  -6.7457e+00 -3.5366e+00 -8.0993e+00  2.4395e+00  3.8280e+00  2.8154e-01
-#&gt;   3.8816e-01  4.0043e+00 -1.2529e-02  2.9227e+00  1.1307e+00 -2.1515e+00
-#&gt;  -1.3813e+00 -7.7966e+00  6.1247e-01 -6.6544e+00  6.6106e-01  6.3762e+00
-#&gt;  -2.4359e+00 -4.7479e+00 -1.0501e+01  3.6424e+00 -3.3815e+00 -7.2181e+00
-#&gt;   3.5248e+00  4.5957e+00  1.1000e+01  2.9740e-01 -5.6136e+00  8.5737e+00
-#&gt;   3.5406e+00  6.4048e+00  1.1231e+01 -8.7433e+00 -7.5803e+00  1.0159e+00
-#&gt;   8.7339e+00  1.2499e+01  3.4785e-01 -1.0078e+01 -6.6816e+00 -5.8729e+00
-#&gt;   6.4248e+00 -5.9144e-01  5.8935e+00 -6.0223e+00 -8.3323e+00  5.0163e-02
-#&gt;   9.3207e+00  1.0922e+01 -2.6929e+00 -1.4812e-01  2.8792e+00  1.0079e+01
-#&gt;  -1.1273e-01 -3.4145e+00  7.7179e+00 -3.9226e+00 -3.9568e+00 -3.7776e+00
-#&gt;   6.0739e+00 -1.0924e+01  6.6230e+00  8.0777e+00  4.8874e-01 -8.7335e+00
-#&gt;   6.0373e+00 -2.6559e+00  6.3964e+00 -3.3931e+00 -4.7059e+00 -4.6125e+00
-#&gt;   1.0763e+01  3.2660e+00  1.2023e+00  1.4326e+01  2.6127e+00  5.9575e+00
-#&gt;  -5.4855e+00 -7.0947e+00  9.2428e+00  6.1904e+00 -4.3986e+00 -5.3961e+00
-#&gt;   6.7367e+00  2.6316e+00 -3.6548e+00  9.4662e+00  7.7264e+00  5.8823e+00
-#&gt;  -6.9419e-01 -7.8668e+00  8.8550e-01  1.0576e+01  6.2078e+00 -4.0643e+00
-#&gt;  -8.6446e+00  4.2446e+00 -2.4906e+00  5.2612e+00  9.2999e+00  7.0488e-01
-#&gt;  -5.8305e-01 -7.3184e+00  2.9134e+00 -4.2438e+00  1.0961e+00 -5.6040e+00
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  Columns 1 to 6 -2.3830e+00  1.5045e+01  1.0755e+01  1.9093e+00  3.4693e+00  6.0668e+00
-#&gt;  -9.1152e-01 -3.0114e+00 -2.5333e+00  5.3111e+00  7.9649e-01 -1.6620e-01
-#&gt;   3.2936e+00 -8.9781e+00 -3.2925e+00  4.2987e+00  8.5330e+00 -2.5871e+00
-#&gt;  -6.7483e+00 -1.5892e+00 -8.2601e-01  8.3264e+00  2.0445e+00  8.9829e-02
-#&gt;   7.6095e-01 -3.3332e+00  2.8401e+00 -6.2223e+00  3.0014e+00  5.3401e+00
-#&gt;   4.4991e+00  6.8539e+00 -7.9476e-01 -1.1602e+01 -1.2376e+01  5.7659e+00
-#&gt;  -3.9050e-01 -6.8403e+00  3.2067e+00  2.6319e+00  2.6960e+00  5.4775e+00
-#&gt;   2.5490e+00 -2.2545e+00  1.9085e+00  1.9497e+00 -5.6462e+00 -5.9350e+00
-#&gt;  -7.9047e+00  2.6436e+00  2.0883e+00 -4.8206e+00  1.5255e-01  9.9853e-01
-#&gt;  -6.8098e+00 -4.4973e+00 -9.4591e-01  4.4095e+00  6.2232e+00  4.2034e+00
-#&gt;   3.6400e+00  4.5510e+00 -1.4693e+00 -1.9791e+00  1.4600e+01 -1.1596e+01
-#&gt;   2.0117e+00 -7.4495e+00  1.9576e+00  1.4078e+01 -1.7327e+00 -7.9045e+00
-#&gt;  -5.4675e+00 -8.1726e+00 -2.1059e+00  6.7968e+00 -4.1634e+00  7.1000e-01
-#&gt;   3.3354e-01 -2.4415e-02  1.1163e+00  5.2675e+00  4.8768e+00 -1.1194e+01
-#&gt;   5.9835e+00  8.5104e+00  4.7339e+00  1.0464e+01  4.9154e+00  2.1113e+00
-#&gt;  -1.3915e+01 -1.9716e+00 -2.9983e+00 -1.9405e+00  2.1733e+00 -9.8436e-02
-#&gt;   7.1030e+00 -2.7523e+00  3.8541e+00 -6.1061e+00  6.9082e-01  4.5209e+00
-#&gt;   9.2541e+00 -1.7739e+00 -4.2750e-01 -9.3434e+00 -6.3315e-01  1.9368e+01
-#&gt;  -3.5566e+00 -1.6541e+00  4.1912e+00  4.9319e+00  9.8950e+00 -1.2191e+01
-#&gt;   1.1695e+01 -3.1335e+00 -3.6334e-01  5.4959e+00  1.1974e+01 -3.9265e+00
-#&gt;   5.5305e+00 -2.7223e-01 -8.4738e+00  3.1758e+00 -1.2866e+00 -6.1654e+00
-#&gt;  -5.3902e-01 -3.0807e+00 -9.3326e-01  7.4006e+00 -3.2220e+00  7.1673e+00
-#&gt;  -5.6331e-01  4.7113e-01  1.0444e+00 -5.5636e+00  7.4316e-01 -1.0495e+01
-#&gt;  -5.2943e+00 -3.6060e+00 -2.2940e+00 -2.6019e+00  1.6932e+01 -9.5823e+00
-#&gt;   1.2445e+01  1.6621e+00  1.4975e+00 -4.6527e+00  3.0234e+00 -3.0627e-01
-#&gt;   3.7138e+00 -7.4142e-01  6.6150e+00 -2.3159e-01  2.8262e+00  1.0662e+00
-#&gt;   7.6823e+00  8.5604e+00  9.3044e+00  5.1059e+00 -6.4475e+00 -5.8448e+00
-#&gt;  -3.1239e+00 -6.4697e+00  2.3946e+00  3.6733e-01 -7.1365e+00 -9.1770e+00
-#&gt;   1.0079e+01  1.0610e+00  7.7564e+00  9.0786e+00  3.1420e+00 -1.1080e+01
-#&gt;   3.1758e-02 -6.1169e+00 -2.4867e+00 -7.9838e+00  1.3619e+00 -9.4099e+00
-#&gt;   2.5039e+00  9.3481e+00  6.4029e+00  3.7382e+00 -3.6803e+00 -1.0931e+01
-#&gt;  -7.1225e+00 -5.6279e-03  5.9118e+00  1.0263e+01  2.1115e+00 -7.7262e+00
-#&gt;   2.5962e+00 -4.0675e+00  1.1823e-01  4.6199e+00 -8.2061e+00  7.5710e+00
-#&gt; 
-#&gt; Columns 7 to 12  1.8408e+01  1.5652e+00 -1.9543e+00 -2.8085e+00 -6.8231e-01  4.1349e+00
-#&gt;  -6.5164e+00  1.4820e+01 -1.1288e+00  4.5189e+00  8.7818e+00 -4.2647e+00
-#&gt;  -7.7523e+00  7.2089e+00  1.1656e+00 -2.2606e+00  2.2263e+00 -8.8486e+00
-#&gt;  -6.4213e+00 -7.8116e-01 -1.3705e+01 -1.4288e+00  7.0685e-01 -9.6210e+00
-#&gt;  -5.2904e+00 -4.3724e+00 -4.5616e+00 -2.1462e+00 -1.6026e+00  2.4151e+00
-#&gt;   7.6076e+00  1.4017e-01  7.1753e+00  1.6275e+00 -1.8538e+01 -6.3133e+00
-#&gt;  -3.0048e-01  1.1206e+00 -2.7181e+00 -8.8819e+00  1.1768e+01 -2.3927e-01
-#&gt;   3.9040e+00  1.5906e+00 -4.6165e+00  5.6143e+00 -2.3985e+00  3.8737e+00
-#&gt;  -1.8581e+00  4.4859e+00 -8.5661e-01  9.1416e+00 -7.1321e+00  1.5359e+00
-#&gt;   2.5870e+00  6.4013e+00 -3.5112e+00  3.0193e+00 -9.9813e-01  4.9914e-01
-#&gt;  -5.6749e-01 -2.5634e+00 -2.0049e+01  7.6160e+00 -5.6121e+00  1.2002e+01
-#&gt;  -5.1016e+00  4.5954e+00 -6.6194e+00  1.0558e+01  8.4648e+00  4.0876e+00
-#&gt;   1.9074e+00 -4.4138e+00  1.6856e+01 -2.3015e+00  9.8041e+00  5.1201e+00
-#&gt;   7.9012e+00 -7.8831e+00  6.1398e+00  3.3781e+00 -1.0034e+01  1.1867e+00
-#&gt;   3.8663e+00 -8.9898e+00  8.8156e+00 -1.6206e+01  1.1986e-01 -2.6682e-02
-#&gt;  -3.2041e+00 -1.4711e+01 -1.0443e+00  5.8869e-01 -9.3162e+00 -3.5679e+00
-#&gt;   8.5277e-01 -6.4199e-01 -6.1192e+00 -1.5057e+01  7.4858e+00  1.3985e+00
-#&gt;   2.5524e+00  1.4245e+01 -5.8290e+00 -2.5738e+00 -9.5414e+00 -2.3828e+00
-#&gt;  -1.0419e+01 -1.1870e+01 -3.9730e+00  1.5484e+00  6.0266e+00  8.5218e+00
-#&gt;  -3.8284e+00  1.5833e+00 -6.3376e-01 -1.2201e+01  5.7151e+00  1.7104e+00
-#&gt;  -7.7268e+00  4.3423e+00 -1.8297e+00  1.2690e+01  6.7800e-02  8.8895e+00
-#&gt;   2.4643e+00  2.1095e+00  2.8859e+00  4.3288e+00  6.2184e-01  4.8834e+00
-#&gt;   1.5286e+00 -5.3391e-01  6.7303e+00  2.7903e+00 -2.8789e-01 -4.8311e+00
-#&gt;   5.6804e+00 -1.5724e+00  4.5631e+00  1.4864e+00  1.0664e+01  7.5194e+00
-#&gt;  -5.1199e+00 -6.2939e+00 -1.4950e+00 -2.8765e+00 -3.7088e-01 -3.6455e+00
-#&gt;   9.5212e+00 -1.0249e+01  4.2535e+00 -1.1122e+00  1.0850e+01  5.5438e+00
-#&gt;  -9.8939e+00 -7.7922e+00 -6.7318e+00 -3.9111e+00 -5.4714e-02 -1.3669e+00
-#&gt;   4.4817e-01 -1.5928e+01  7.6097e-01 -9.1389e+00  8.9798e+00 -6.3461e+00
-#&gt;   1.1088e+00 -5.1600e+00  1.5623e+00  4.1705e+00 -3.6206e+00 -2.3324e+00
-#&gt;   2.9760e+00 -4.8346e-01  2.1871e+00 -7.7891e+00 -8.0293e+00 -6.5323e+00
-#&gt;  -1.4006e+01 -1.1111e+01 -9.3578e+00 -1.0229e+01 -9.0496e+00 -5.3950e+00
-#&gt;  -6.5811e+00 -4.4025e+00  7.0217e+00 -1.9153e+00  1.4510e+01 -6.2039e+00
-#&gt;  -8.7036e+00 -6.6167e+00  7.2552e+00  8.6312e+00  3.9875e+00  7.1604e-01
-#&gt; 
-#&gt; Columns 13 to 18 -4.0327e+00  1.2063e-01  3.8963e-01  1.2082e+01  4.4529e+00 -4.0982e+00
-#&gt;   7.4974e+00  1.8638e+00 -8.1714e+00 -2.7034e+00  5.9391e-01 -1.8888e+00
-#&gt;  -1.9006e+00 -4.8901e+00 -5.2321e+00 -1.3496e+01  1.1133e+00 -1.3292e+00
-#&gt;  -1.7714e+00 -6.2307e-01 -1.4108e+01  7.9459e+00  4.0040e+00  4.6520e+00
-#&gt;  -5.9653e+00 -6.6982e+00  2.3026e+00 -4.1065e+00 -9.7532e-01 -4.1826e+00
-#&gt;   9.5455e+00 -1.2973e+01  9.3023e+00  8.9305e+00 -2.2556e+00 -8.2025e+00
-#&gt;   6.3662e+00  2.3125e+00  6.1421e+00 -2.8569e+00 -6.1378e+00  3.1209e+00
-#&gt;   1.7458e+01  1.5194e+00  1.1149e+00 -1.0232e+00 -5.4569e+00  4.5391e+00
-#&gt;  -8.9644e+00  5.6622e+00 -9.7496e+00  6.8962e+00  2.9958e+00 -5.5083e+00
-#&gt;  -3.4634e+00 -4.3375e+00 -3.3078e+00 -1.6976e+00 -1.8537e+00 -8.7774e-01
-#&gt;   6.9377e+00  3.8605e+00  6.0268e+00  2.3274e+00 -8.8593e+00 -2.4879e+00
-#&gt;   6.8199e+00 -5.9747e+00 -7.2865e+00 -1.4146e+00  7.5257e+00 -2.5840e+00
-#&gt;   5.0461e+00 -6.7386e-01 -8.0948e+00 -6.2207e+00 -4.3244e+00 -6.1133e+00
-#&gt;  -2.8252e+00 -2.5870e+00  1.8028e+00 -3.6711e-01  2.0952e+00  3.6568e+00
-#&gt;   2.3060e+00 -6.2086e+00  3.9923e+00 -1.4150e+00 -8.5676e-01  5.8699e+00
-#&gt;  -1.3242e+01  6.3704e+00 -1.0038e+01  2.3710e+00 -1.4868e+01  6.5075e+00
-#&gt;  -2.8410e+00 -6.0744e+00  9.4370e+00  1.7102e+00  5.2457e+00 -7.8648e+00
-#&gt;   8.1169e+00 -9.1841e+00  8.2035e-01  4.0471e+00 -2.7658e-01 -3.2713e+00
-#&gt;   7.9194e+00 -5.5048e-02 -3.5546e+00  2.4541e+00 -9.2249e+00 -5.8647e+00
-#&gt;  -1.7885e+00  5.6227e+00  9.0222e+00 -1.3317e+01 -3.9801e-01  1.6702e+01
-#&gt;  -3.9880e+00  8.6878e+00 -2.2664e-02 -7.4790e+00 -1.3203e+00  1.8476e+01
-#&gt;  -4.4812e+00  5.0602e+00 -3.5708e+00  1.2414e+00 -2.9848e+00  1.2678e+01
-#&gt;  -4.5570e+00 -2.6359e+00  1.4333e+01  9.2020e-01  4.0976e+00  4.4490e+00
-#&gt;  -1.1185e+01  3.1190e+00 -5.5076e+00  4.0855e+00  6.6168e+00  1.6649e+00
-#&gt;  -6.8375e-01 -2.9971e+00  3.8258e+00  5.9877e+00  6.4271e+00 -5.3766e+00
-#&gt;   1.2921e+01 -1.8491e+00  1.1426e+00 -2.6788e+00 -1.8496e+01  7.4915e+00
-#&gt;  -7.3414e+00  3.5322e+00  2.5229e+00  5.7388e+00  1.1160e+01 -3.6650e+00
-#&gt;  -3.9620e+00 -4.8352e-02  7.4024e+00 -7.2069e-01 -9.0369e+00  1.8158e+01
-#&gt;   1.4811e+00 -2.8406e+00  2.1601e+00 -6.3998e+00  8.4908e+00  7.1127e+00
-#&gt;  -1.0033e+01 -8.8568e-01  4.8732e+00  1.6056e+00  6.6930e+00 -8.4509e+00
-#&gt;  -3.1729e+00 -1.4420e+01  2.9933e+00  9.6449e+00  6.0507e+00  3.3586e+00
-#&gt;  -5.9448e+00 -4.6698e+00  2.9528e+00 -4.8673e+00  3.8821e+00  2.8102e+00
-#&gt;   2.5439e+00  5.1470e-01 -2.2484e+00 -7.2786e+00 -2.3403e+00 -1.0638e+00
-#&gt; 
-#&gt; Columns 19 to 24  9.0982e+00  5.1736e+00 -2.4757e+00  1.5844e-01 -6.7332e-01  5.6665e+00
-#&gt;  -4.8000e+00 -8.6078e-01  9.5131e-01 -2.8858e+00  1.2703e+00  4.7499e-01
-#&gt;  -7.5245e+00  3.5997e+00  5.3406e+00 -3.8828e+00  5.1932e-01  7.5683e-02
-#&gt;  -8.4505e+00  1.1701e+01 -2.4831e+00 -1.4745e+01  5.4654e+00 -1.7338e+01
-#&gt;   1.0567e+01  6.2607e+00 -3.6660e+00 -8.7202e+00 -1.5690e+01 -2.2930e+00
-#&gt;   9.8490e+00  8.0237e+00 -3.6052e+00  3.3250e+00 -7.4190e+00  4.9683e+00
-#&gt;   7.4106e+00 -6.2680e+00  5.1907e-02 -1.2005e+01 -6.8563e+00  4.7717e+00
-#&gt;  -9.6498e+00 -3.7699e+00  1.3658e+00  4.0453e+00  6.3397e+00  2.1112e+00
-#&gt;   3.6101e-01  2.1361e+00 -5.9951e-01 -7.0478e+00  3.9619e+00 -1.2897e+01
-#&gt;   1.7499e+01  1.4941e+00 -1.6669e+00 -1.9513e+00 -1.1608e+01  6.2823e+00
-#&gt;   5.6085e+00  3.7327e+00 -1.0657e+01  6.8685e+00 -2.5067e+00 -2.8697e+00
-#&gt;   4.8413e+00  1.7147e+00  3.9855e+00 -2.3646e+00 -1.2719e+00  5.9345e-01
-#&gt;  -1.1901e+01  2.1154e-01  1.2861e+01  6.5933e+00  5.5342e+00 -1.1736e+01
-#&gt;  -4.1619e+00 -4.0502e+00  4.5874e+00 -5.9631e+00  6.2003e+00 -1.5919e+01
-#&gt;   3.2160e+00  6.1556e+00  5.7512e+00 -2.4275e+00  1.1169e+00 -7.0630e+00
-#&gt;   3.8809e+00 -5.3835e+00  5.9786e+00 -1.2241e+01 -1.6439e+00 -1.5627e+01
-#&gt;   1.7291e+01  2.2233e+00 -5.2146e+00  6.4464e-01 -5.5866e+00  1.7714e+01
-#&gt;   5.3964e+00  1.3962e+01  1.9331e+00 -1.3314e+00 -5.3025e+00 -2.3913e-01
-#&gt;  -7.8175e+00 -7.1026e+00 -6.0704e+00 -8.0185e-02 -2.7631e-01 -3.9497e+00
-#&gt;   2.5051e+00 -6.0993e+00 -5.4260e+00 -5.7393e+00 -3.3067e+00  9.7217e+00
-#&gt;  -1.2314e+01 -1.6606e+01  1.5940e+00 -6.1850e+00  1.7046e+01 -4.5048e-01
-#&gt;  -8.8948e+00 -5.6061e+00  5.1760e-01 -4.5070e+00  2.5624e+00 -1.1760e+01
-#&gt;  -8.3118e+00 -4.8301e-01  2.4386e+00  1.1577e+00 -5.4625e+00  9.3602e+00
-#&gt;   1.0364e+01 -1.7660e+00 -7.1697e+00  4.0364e+00  6.2778e+00  8.9278e+00
-#&gt;   9.0088e+00  3.4713e+00 -1.4689e+01 -3.7752e+00 -2.6339e-01 -5.4922e+00
-#&gt;  -6.4503e+00  7.0307e+00 -2.5707e+00 -4.4928e+00 -3.8815e+00  2.7975e+00
-#&gt;  -4.7806e+00 -1.3645e+00 -2.0122e+00 -2.8309e+00  3.8261e-01  9.4296e+00
-#&gt;   1.8947e+00 -7.1179e+00  2.2631e-02  3.1296e+00 -5.7349e-01  6.5689e+00
-#&gt;   2.1592e+00 -2.2114e+00  1.0351e+00 -1.8950e+00 -2.5478e+00 -7.2731e+00
-#&gt;   9.4142e+00  3.9147e+00 -3.7307e+00  6.2922e-01 -2.9651e+00  7.1303e+00
-#&gt;  -2.8823e-01  7.4542e+00 -4.3850e-01 -7.6315e+00 -3.2843e+00 -6.0237e+00
-#&gt;   9.5521e+00 -1.9974e+00  2.2915e+00  2.4075e+00 -7.1246e+00  3.7398e+00
-#&gt;  -4.8636e+00  1.7748e+00  9.8526e-01 -3.3251e+00  3.6598e+00 -1.3092e+01
-#&gt; 
-#&gt; Columns 25 to 30  3.1280e+00  1.2788e+00 -5.9366e+00 -4.2194e+00 -1.0509e+01  1.7713e+00
-#&gt;  -5.3974e+00 -4.3479e-01  4.8893e+00  4.0898e+00  5.8615e+00 -5.5451e+00
-#&gt;  -7.2648e+00  1.4969e+00 -6.0060e+00 -3.7986e+00  4.8079e+00  6.1958e+00
-#&gt;   7.6044e+00 -5.9375e+00 -1.1993e+01  3.3922e+00  5.0549e+00  9.1746e+00
-#&gt;   5.0515e+00 -5.0973e+00  4.8219e+00  1.0619e+00  4.5165e+00 -8.5143e+00
-#&gt;   2.8204e+00 -3.9292e+00  9.8225e-01 -6.2513e+00  3.4724e+00  5.7119e+00
-#&gt;  -5.8743e+00 -4.5231e-01 -8.1410e+00 -4.4791e+00  1.2651e+01  1.2361e+00
-#&gt;  -7.2702e+00 -2.2595e+00  6.5974e+00  3.6935e+00 -1.9022e+00  7.6575e+00
-#&gt;   9.8234e+00  2.3628e-01  2.1644e+00  1.7129e+00 -6.3852e+00 -1.3977e-03
-#&gt;   7.5595e+00 -2.3019e+00  9.2416e+00  2.9271e+00  4.4853e-01 -4.5047e+00
-#&gt;  -2.7705e+00 -7.2757e+00 -2.2790e+00 -2.7667e+00 -2.1357e+00 -3.7666e+00
-#&gt;  -1.5240e+00 -2.3198e+00 -1.0283e+01 -3.7943e+00 -9.4341e-01  6.0867e+00
-#&gt;   2.9003e-01  1.1639e+01  5.3857e+00 -3.1820e+00 -1.7843e+00  1.6590e+00
-#&gt;   1.1793e+01 -5.1962e-01 -1.2450e+01 -7.9430e+00 -1.1940e+00  2.2993e-01
-#&gt;   3.3375e+00  1.0644e+01  5.7075e+00 -6.2523e+00  1.4935e+00 -8.8530e+00
-#&gt;   7.4632e+00 -1.0253e+01 -5.3447e+00  2.2636e+00 -3.4865e+00 -2.5092e+00
-#&gt;   6.9494e+00  6.0295e+00  2.3967e+00 -4.1855e+00 -8.0001e-01 -6.9067e+00
-#&gt;   6.3739e+00  3.9918e-02 -5.2436e+00 -6.9196e+00  4.8604e+00  1.7641e+00
-#&gt;  -8.2620e+00 -6.7295e+00 -1.7506e+00  4.9274e-01 -5.8497e+00  4.8315e+00
-#&gt;  -8.1358e+00 -8.7697e+00 -1.4823e+00 -1.1388e+01 -4.1414e+00 -4.4856e-01
-#&gt;  -2.1427e+00 -8.0484e+00  1.3658e+00 -4.8648e+00 -1.7214e+00  2.5082e+00
-#&gt;   4.4764e-01 -1.6163e-01 -2.3081e+00 -3.4154e+00 -3.8190e+00 -2.5088e+00
-#&gt;   6.5203e-01 -6.3120e+00 -2.4546e+00 -4.3488e+00 -4.5031e+00 -4.1094e+00
-#&gt;   8.2619e+00  2.1131e-01  7.1995e+00  3.4129e+00 -4.1694e+00  6.2864e+00
-#&gt;  -1.2033e+01 -1.0293e+01 -8.4255e+00 -1.1377e+00 -7.0015e+00  1.5763e+00
-#&gt;   1.9668e+00  8.1584e-01  1.2777e+01  3.1784e+00 -6.6701e+00 -3.4124e-01
-#&gt;  -3.6915e+00 -5.8549e+00 -4.4516e+00  4.3056e+00  4.6994e+00 -2.7244e+00
-#&gt;  -1.6473e+00  1.9608e+00 -5.7647e+00 -7.5848e+00  4.9922e+00 -4.6011e+00
-#&gt;  -8.8363e+00  3.1260e+00 -2.4390e+00  1.9239e+00 -1.3892e+00  3.3748e-01
-#&gt;  -1.2075e+00  7.8508e+00 -3.4638e+00 -5.1220e+00 -1.4918e+01 -1.8710e+00
-#&gt;   1.6721e+00 -1.1214e+00 -1.0962e+00  2.9899e+00 -4.2842e+00 -1.8938e-01
-#&gt;  -2.8022e+00  4.7344e+00  4.4270e+00  6.5299e+00  1.0474e+01 -4.4890e+00
-#&gt;  -5.0150e+00  2.8116e+00 -1.5807e+00 -4.7746e-01 -7.7342e+00  1.1266e+00
-#&gt; 
-#&gt; Columns 31 to 36  3.5927e-01 -5.8005e-04 -1.1270e+01  9.0515e+00 -8.1627e+00  6.4540e+00
-#&gt;   3.4068e+00 -1.2006e+01  5.3405e+00  8.2016e-01  6.0324e+00  7.1262e+00
-#&gt;   3.7822e+00 -2.6076e+00  2.8620e+00 -5.2815e+00 -2.5095e+00 -1.0144e+00
-#&gt;  -8.2290e-02 -1.3884e+01  1.2595e+00 -3.4814e-02 -9.7531e+00  5.2547e-01
-#&gt;  -1.4943e+01  5.3147e+00  5.4917e+00 -1.0074e+00  9.0445e-01 -1.3210e+01
-#&gt;  -9.3631e+00 -3.5228e+00  4.4674e+00 -1.0704e+01  7.5457e+00 -6.0601e-01
-#&gt;  -1.3818e+01  5.2119e+00 -1.1345e+01  3.5436e+00  1.3895e+00  1.4796e+00
-#&gt;   2.4844e+00 -8.3415e+00  6.8261e+00  4.8637e+00  5.8557e+00  1.5788e+01
-#&gt;  -6.6571e+00 -1.9480e-01 -5.0670e+00  5.2520e-02 -9.8149e+00  1.1303e+01
-#&gt;   5.5721e+00 -6.1646e+00  1.1623e+01 -1.4157e+00 -5.3222e-01 -5.4600e+00
-#&gt;  -6.5203e+00 -6.7455e+00  7.7968e+00 -9.9367e+00  1.3339e+01 -1.4284e+01
-#&gt;  -1.4378e+00  8.9679e+00  9.4038e+00 -8.1249e-01  6.5240e-02  3.7578e+00
-#&gt;   9.4793e+00 -4.9221e+00 -1.6958e+00  2.6819e+00  2.1806e+00 -3.3611e+00
-#&gt;  -1.5861e+00  2.8447e-01 -1.2740e+01 -8.7952e+00  5.9439e+00 -3.9432e+00
-#&gt;   4.3443e+00 -6.5303e+00 -7.6826e+00 -4.1726e+00  7.0762e+00 -1.4766e+01
-#&gt;   1.7967e+00  1.0627e+01 -8.8136e+00  3.6239e+00 -9.1490e+00 -2.4560e+00
-#&gt;  -1.2485e+01  8.5619e+00  2.9441e+00  1.5589e+01 -4.7064e+00  1.8462e+00
-#&gt;  -4.0600e+00 -1.4710e+00  4.7904e+00 -1.8557e+00  8.3307e+00 -7.1752e+00
-#&gt;  -3.9976e+00 -6.5843e+00  2.6554e+00 -3.1964e+00  1.0752e+00 -4.8778e-01
-#&gt;   3.3116e+00 -1.7098e+00  3.2721e+00 -4.4763e+00 -2.2594e+00 -7.9010e+00
-#&gt;   5.7161e+00  6.5650e+00 -1.6615e+00 -1.1369e+01  4.7180e+00  5.7694e+00
-#&gt;   8.5136e+00 -5.9878e-01 -1.5651e+01  8.4650e-01 -2.4974e+00  6.7638e+00
-#&gt;  -5.8394e+00  5.7140e+00 -1.1210e+01 -2.2551e+00  1.6443e+00 -6.3403e+00
-#&gt;   4.9703e+00  1.8214e+01 -9.8342e+00 -3.6146e+00 -5.6450e-01  7.4347e-01
-#&gt;  -7.1601e+00  8.6449e+00  1.8380e+00 -6.6168e+00 -9.3282e+00  4.5143e+00
-#&gt;  -7.4665e+00 -4.2576e+00  1.4170e+01  3.6051e+00 -4.7992e+00  3.7910e-01
-#&gt;  -1.4914e+01  3.7157e+00 -8.1508e+00  7.3911e+00  1.0948e+00  4.0697e+00
-#&gt;  -1.0161e+01  3.9304e+00  3.7996e-01 -1.3344e+01  9.3577e+00 -1.5207e+01
-#&gt;  -1.7293e+00 -2.0529e+00  1.0785e+01 -9.5662e+00 -1.9826e+00  5.8388e+00
-#&gt;   2.3011e+00  4.4566e+00 -1.1964e+01 -7.5399e-01 -9.7352e+00 -6.9948e+00
-#&gt;   2.0763e+00 -1.1806e+01 -4.8473e-01  5.3601e+00  2.5147e+00  8.4158e+00
-#&gt;   7.8955e+00 -2.6015e+00  6.0200e+00 -2.1845e+00 -1.3762e+01 -6.8676e+00
-#&gt;   4.4075e+00  1.2753e+01  1.1472e+00  2.4227e+00 -1.0693e+01  4.7613e+00
-#&gt; 
-#&gt; Columns 37 to 42 -1.1020e+01  6.6465e+00 -1.9290e+00 -4.8945e+00  1.8490e+00  1.5339e+00
-#&gt;  -4.5310e+00 -1.0638e+00 -4.4853e+00  7.1065e+00 -8.2337e-01  6.9402e+00
-#&gt;   1.7499e+00 -3.6581e+00  1.2350e+00  2.8491e+00  1.7408e+00 -1.7779e-01
-#&gt;  -1.4229e-01  5.2913e-02 -2.4037e+00  8.6961e+00 -1.7492e+00  5.9939e+00
-#&gt;  -5.8223e+00  1.6047e+00  6.7784e+00  2.6981e+00 -1.2013e+01 -1.1739e-01
-#&gt;  -1.6281e+01 -1.5035e+00 -2.1397e+00 -6.4374e+00  6.2523e+00 -9.6294e+00
-#&gt;   2.9919e+00  4.3445e+00  1.8871e+00  8.1929e+00 -1.2139e+01 -1.4848e+00
-#&gt;   2.7343e+00  1.6345e+00 -3.4733e+00  3.3497e+00  6.7508e+00 -1.8764e+00
-#&gt;  -1.9815e+00  4.4974e+00 -1.5160e-02 -2.7958e+00 -4.8445e-01 -1.2368e+00
-#&gt;  -2.6236e+00 -1.4458e+01  5.1467e+00  4.9547e-01 -3.0636e+00 -5.2946e-02
-#&gt;   1.2763e+01 -6.6685e+00  2.1419e+00 -2.5938e-01  1.0607e+01  9.4662e-02
-#&gt;  -2.5186e-01  1.7765e+00  1.8338e+01  5.5510e+00 -1.2427e+00  3.7455e+00
-#&gt;   1.2176e+01 -1.7097e+01 -1.3656e+00  5.5532e+00 -7.6164e-01 -8.6892e-01
-#&gt;  -6.6048e-01 -4.9274e+00  1.1193e+01 -3.6997e+00 -6.0456e-01 -2.1174e+00
-#&gt;  -2.6654e+00 -6.7153e+00  4.3796e-01  8.9276e-01  3.0882e-01  6.6746e+00
-#&gt;  -3.7119e+00 -4.4686e+00  3.3810e+00  1.2998e+00  5.5979e+00  1.9030e+00
-#&gt;  -2.1550e-01 -7.2725e+00  3.9841e+00  1.9504e+00 -1.3238e+00 -9.6834e+00
-#&gt;  -1.7437e+01  7.4582e+00 -5.9427e+00  5.0374e-01 -2.0163e+00  6.8246e+00
-#&gt;   1.0537e+01 -6.8816e+00  3.1621e+00  4.3694e+00  1.6787e+01  4.0453e+00
-#&gt;   7.4132e+00  2.5091e+00  5.2410e+00  6.6752e+00 -7.1528e+00 -4.8371e+00
-#&gt;   6.0238e+00  1.1466e+01  2.0985e-01  1.3284e+01 -6.6727e+00  9.7171e+00
-#&gt;   1.6460e+00  1.2047e+01  1.4384e+00 -9.1026e-01 -3.0676e+00 -3.0724e-01
-#&gt;  -2.9586e+00 -8.5117e+00  2.8998e+00 -7.8197e+00 -9.8610e+00  4.7346e+00
-#&gt;   3.0021e+00 -6.0272e+00  2.6469e+00 -4.5248e+00 -1.1962e+01 -5.6412e-01
-#&gt;  -2.9277e+00  1.6119e+01 -4.9798e-01  5.0180e+00  1.2726e+01  6.9309e+00
-#&gt;  -1.4897e+00 -8.5421e+00 -9.3005e+00  6.3357e+00  2.9906e+00 -3.1236e+00
-#&gt;   3.2158e+00  1.0030e+01  7.0613e+00 -3.3065e+00 -7.8101e+00  1.4496e+00
-#&gt;  -3.6055e-01  2.4626e+00  1.1457e+01  8.1908e+00  9.5694e-01 -1.1264e+01
-#&gt;  -6.4157e+00 -2.8063e+00  6.9518e+00  9.7039e+00  1.0435e+01  2.9756e+00
-#&gt;  -2.5457e+00 -2.9342e+00 -1.4990e+00 -1.3760e+01  5.3558e+00 -1.0135e+01
-#&gt;   2.4038e+00  8.8341e+00  1.1209e+01  1.9843e+00  3.2433e+00 -2.7260e+00
-#&gt;   5.9133e+00 -8.8468e+00  3.7391e+00  3.1379e+00  7.2138e-01  4.3503e+00
-#&gt;  -9.0470e+00  2.9551e+00 -7.0717e+00  4.9854e+00  4.3781e+00  4.0203e-01
-#&gt; 
-#&gt; Columns 43 to 48 -8.8934e+00 -5.8731e+00  5.3382e+00  3.8283e+00 -7.9933e+00 -6.6487e+00
-#&gt;  -2.6201e+00  6.9511e+00 -1.7930e+00  1.0116e+01  3.7670e+00  9.1283e+00
-#&gt;   1.2525e+00  7.8141e+00 -2.4204e+00 -5.5882e+00  6.7728e+00  1.0990e+01
-#&gt;  -9.0517e+00  5.1123e+00  2.6250e+00 -3.6983e+00 -1.6602e+00  3.9326e+00
-#&gt;  -1.8962e+00  2.7367e+00 -1.8428e+00 -2.0395e+00 -5.9638e+00  1.1027e+01
-#&gt;  -4.4575e+00  1.3333e+01  3.1724e+00  7.7592e-01 -5.7508e+00  1.6969e+01
-#&gt;   8.7714e+00  1.4718e+00 -6.6618e+00 -7.8995e+00  9.0566e+00  9.4957e+00
-#&gt;   4.8238e+00  6.6510e+00 -8.4335e+00  1.5356e+00  2.9097e+00  5.2194e-02
-#&gt;   8.9002e-01  3.0509e-01  3.3963e-01 -4.1054e+00 -4.2206e+00 -3.0510e+00
-#&gt;  -1.1020e+00 -4.3733e-01 -3.6850e+00  7.9086e+00  4.6608e+00  6.0527e+00
-#&gt;   7.2235e+00  2.0171e+00 -1.2581e+01 -1.5761e+01 -6.6077e-01  2.3741e+00
-#&gt;   3.0645e+00  6.5877e+00  5.2796e+00 -7.6957e+00 -5.3312e+00  9.6305e+00
-#&gt;   2.5498e+00 -3.3503e+00 -4.4238e+00 -1.2314e+01  7.3962e+00  1.7007e+00
-#&gt;   3.7125e+00 -4.4944e+00 -3.4737e+00 -9.5028e+00 -3.9384e+00 -1.1472e+00
-#&gt;   2.9937e+00  1.5265e+00  3.5508e+00  2.3970e+00  9.5820e+00  1.5473e+00
-#&gt;  -1.3902e+00 -8.5131e+00 -1.1774e+01 -4.6225e+00  7.6096e+00 -7.9541e+00
-#&gt;   6.0351e+00  5.2485e+00  7.6588e+00  4.2709e+00  4.7346e+00  6.0520e+00
-#&gt;  -3.9830e+00  7.0588e+00  6.1215e+00  4.3871e+00 -1.1102e+01  8.4514e+00
-#&gt;  -2.8928e+00 -7.0470e+00 -9.8818e+00  1.0352e+00  4.5459e+00 -8.3517e-01
-#&gt;   7.1982e+00 -3.6810e+00 -1.4942e+00 -9.4427e+00  1.7525e+00  4.1326e+00
-#&gt;   9.4196e+00  3.0754e+00  1.7681e-01 -1.2273e+00 -7.9381e+00 -1.5840e+00
-#&gt;   9.9200e-01 -3.7385e+00 -4.5607e+00 -9.1025e+00 -1.7741e+00 -5.9780e+00
-#&gt;   3.4331e+00  6.9655e+00  2.2767e-01  5.4729e+00 -1.9004e+00  1.0142e+01
-#&gt;  -2.0826e+00 -6.2107e+00  3.6577e+00  2.0191e+00 -1.2671e+00  2.2451e+00
-#&gt;   3.1745e+00 -1.8666e+00  9.5549e-01  1.1877e+00 -5.7058e+00 -1.1480e+01
-#&gt;  -3.5149e+00  1.5049e+00 -5.9846e+00  5.1825e-01  1.1530e+01  7.2957e+00
-#&gt;   1.3030e+00  2.6066e+00  1.7635e+00 -9.6033e+00 -4.6952e-01  6.4410e-01
-#&gt;   1.0071e+01  1.2886e+00  9.9078e-02 -1.1346e+01  2.7033e+00 -1.6566e+01
-#&gt;   3.3214e+00 -8.9271e+00 -1.5329e+00  1.2822e+01  7.3061e+00 -1.0610e+01
-#&gt;  -5.5313e+00 -1.9182e+00  5.2385e-01  3.3351e+00 -2.3038e+00 -2.5890e+00
-#&gt;  -9.6380e+00 -2.2434e+00  4.5545e-01 -2.4699e+00 -8.2808e+00 -7.3125e+00
-#&gt;   5.7026e+00  2.7890e+00 -1.1418e+01 -2.5375e+00  6.9807e+00  4.0363e+00
-#&gt;  -4.7068e+00 -5.1611e+00 -9.9217e+00  2.1581e-01  5.1793e-01  1.5048e+00
-#&gt; 
-#&gt; (6,.,.) = 
-#&gt;  Columns 1 to 8  -0.1104   0.9378  -5.7425  -1.3886  -6.7944  -1.2199   2.5925  -9.2208
-#&gt;   -9.2785   1.3463   2.8895   6.0180  -2.2107  -3.1544   9.8532  -1.1469
-#&gt;   -2.0518 -13.8740  -2.3745   6.4815  -3.4840  -8.7533   2.0470  -1.2614
-#&gt;    2.8974   1.6251  -6.4410  11.9340  -4.1402   5.6601   2.2437   1.0130
-#&gt;    2.1063  -0.8892   6.5428   0.7713   1.8376   0.2168  -1.7869  -3.1655
-#&gt;    5.9192  -6.5299   3.9429  -4.7417  -2.0345  -2.1108   1.4094  -1.7040
-#&gt;   -3.1214  -9.4695   7.8207  -5.2906   3.8265  -3.1306   0.2918   8.0468
-#&gt;   -2.8765  -5.4424   5.3099   5.6219  -2.0720  -3.4595   6.4237   9.0764
-#&gt;   -6.5373   2.7311   5.2222   3.8423   3.1656   0.4355   8.0006  -2.0814
-#&gt;    1.2809   2.4835   1.5944   4.1153  -2.1983  -3.9559  -3.0186   0.6905
-#&gt;  -12.6721   7.8146  11.8049  -1.7205 -10.0410   8.1629   3.5770 -11.8054
-#&gt;    5.8688  -5.7280   6.1080   3.1844   7.2086  -9.3712  -2.7592   6.2454
-#&gt;   -5.4817   8.3479   0.4329  -4.7231  -3.7640  -0.2761  -4.6210   0.6497
-#&gt;   -2.6736  -0.6957   4.6696   0.0148  -5.5836   0.8055   0.7237  -2.8764
-#&gt;    5.1874   2.4803  -2.1401   4.0009   0.3659   0.4488   8.4825   6.3955
-#&gt;    8.2776  -6.1439  -6.7428   0.6655   4.5106  -7.2390  -1.9144  -5.6041
-#&gt;    2.6683   0.6469  -7.0735  -0.0103   9.7698  -1.6489  -1.4062   0.4159
-#&gt;   -3.5536 -11.1788   8.0697  -5.3634   0.5343   5.5538   7.8198  -7.0964
-#&gt;    7.5448   3.3799  -3.9270   0.0477   7.1459  -2.2052   0.3239   2.4638
-#&gt;   -8.3648  11.6064  -1.8365  -2.8855  -3.7194  -9.8075 -13.9457   2.6624
-#&gt;   -0.8695  -7.6880   2.9305   1.6563  -4.7491  -5.6331   7.2559   7.0108
-#&gt;   -4.5930   8.6262  10.1692  -1.9337  -1.3028   1.9578   4.1001  -1.4597
-#&gt;    5.8933   4.6195   0.6673  -3.3505   4.2870  -3.4126  -1.0499  -1.0670
-#&gt;    3.0959  11.1196 -11.3394  -2.6640  -3.7993  -3.4703  -4.4409   4.4927
-#&gt;    7.0263   1.0446  -5.2210  -0.0563   7.9781   3.2414   4.7680   0.0977
-#&gt;    8.4570   2.5366 -12.1487   8.7612  -3.6473  -6.9408  -9.0835   3.5213
-#&gt;    1.4681  12.0926   4.3367  -7.4203   5.1335  -1.2544   1.0279   2.6569
-#&gt;   -1.6785 -21.8863  -1.3839   9.7845  -9.6162  -2.6612   3.2954  -1.9311
-#&gt;    0.9169  -1.5492  -3.9720   3.8593  -0.8790   1.1036   1.5049   6.8214
-#&gt;    0.2175 -10.2392  -8.1670  -1.4911  -2.5520 -10.8965  -1.0136  -5.1678
-#&gt;    1.3053   0.3541  -2.9467   7.1714   4.1810   2.3582   4.0887   4.2044
-#&gt;    5.4859  11.4342 -12.2243   6.8399   2.0426  -0.6062  -4.4571   6.7048
-#&gt;   14.3603  -3.2200   3.9387   1.3679  -3.8398  -8.6160  -2.1590  -8.0406
-#&gt; 
-#&gt; Columns 9 to 16  -6.3904   5.2458  -4.8878   0.4777   9.6190  -4.0347   6.2520   0.1535
-#&gt;    5.3624  -4.1868  -9.0603   6.5989  -2.7368  -0.5048  -8.6676   5.6638
-#&gt;    5.2559   3.3481  -7.0247  -1.2221  11.6998   5.6450  -0.9032   6.0717
-#&gt;    0.3988   8.7230   1.8028  14.9125   0.2944  -0.6893   1.6754  -1.2014
-#&gt;    5.9161  -2.8328  11.3242  -0.0847   1.8416   2.2938  -4.5816   2.5918
-#&gt;   -1.9162  -1.6693  12.8858  -7.4261  -4.7459  21.0233  -0.7926 -13.2537
-#&gt;    0.4529  -1.3662  16.3296  -6.9726  11.5936  -2.3820 -12.0824  -0.4903
-#&gt;   -3.9553   2.5013  -4.9634 -13.3355  -3.9944  -0.6187   6.8524  -4.1107
-#&gt;    4.6945   4.5045  -8.6546  10.3891  -2.7178  -5.6847   4.6090   1.6181
-#&gt;    9.8551  -1.0476  -7.6735   9.0922   5.5801   2.0157  -3.8528  14.7383
-#&gt;   -1.8485   2.3732   5.4091  -0.7128   9.2158  -4.9523 -13.8347 -14.1544
-#&gt;    1.9425  -4.4296  -0.9140 -20.1580   3.1129  -4.2815  -1.1541  -4.7674
-#&gt;   -6.3371  -1.7931  -2.5522   3.0294  -5.5946   0.2522   2.1318   6.1523
-#&gt;   -6.9545   4.2920   3.5247  12.1743  -8.3309   1.5324  -4.0184  -2.4786
-#&gt;   -7.9861   0.6720   8.6008   8.4768  -5.7279   6.4261  -0.0801  -1.1222
-#&gt;    2.8641  11.7266  -2.5167  11.6780  -3.7370   0.2652   7.3983   3.9098
-#&gt;    9.1588  -3.0015  -3.0287   7.1194  -4.4060   0.8970  -6.1486   5.8799
-#&gt;    1.1350   4.9105   5.6519  -7.8276   5.2783   3.6180  -5.6088  -4.1530
-#&gt;   11.4123   0.2572  -2.4716  -1.5631  -4.3751  -8.8379  -9.4847 -10.3646
-#&gt;   -2.2827  -5.8110   4.0167   5.5697  -1.9213  -6.4974  -6.3152   3.8903
-#&gt;    0.8398   3.9075  -4.3331  -8.2620  -0.2703 -12.2609  -1.9502  -0.2135
-#&gt;  -12.2079  -1.3780  -1.2939  10.9357  -1.0795 -15.5522   3.0758  10.4788
-#&gt;   -1.6880  -4.5991  12.9073  -0.1889  -3.7366   3.0858  -3.7235  -6.3634
-#&gt;   14.6158 -10.9498  -2.9490  14.3120  11.5878   8.4827  10.1251   7.4441
-#&gt;   12.6866  -0.0641  -3.6279   3.0155  -9.3858 -11.3412 -11.1634  -9.6604
-#&gt;   -1.7954   3.4595   3.4319  -3.6742  -8.5487  -3.0773   3.5025   1.9246
-#&gt;   -2.6488   0.0993   6.1076  -3.1861   1.9818  -3.6518   6.4687  -0.0724
-#&gt;   -1.3800  -9.8271   2.9700   5.3824  -0.3110   7.3517  -8.2606 -11.3658
-#&gt;    7.2609   4.6560 -10.1995   8.5112  -9.4829 -10.5180  -6.1653  -0.7339
-#&gt;    6.4446   3.1358   1.7575  11.6717  15.9693  -1.8292  -1.2986  -5.1633
-#&gt;    5.2860   6.0090  -0.7773  -4.0574   2.2034   8.1937  10.7889   3.1794
-#&gt;   -2.7170   0.8778   7.5877   0.2280 -13.0934  12.6423 -10.7424   2.5844
-#&gt;   -3.7412   1.8666  -0.7106  -0.6447   2.4800  -1.3900   2.4915  -3.1562
-#&gt; 
-#&gt; Columns 17 to 24   5.4172  -8.8975   4.1241   4.2239  -9.0144   0.6235  -4.9852  -5.5359
-#&gt;   -6.1860  14.5444  12.7085  -4.9095  -1.4238  -1.5653  -4.4326  -0.0120
-#&gt;   -6.4332  15.0012   7.7659  -0.6413   3.6668   7.6917  -0.7941  -3.8065
-#&gt;   -4.8550   0.6845  -5.9768   1.8772  -7.2331   7.2820   2.7222  -3.4189
-#&gt;   -1.8828  -5.6188   3.9142 -15.6999   1.0825   3.6080   0.5859   0.2142
-#&gt;  -10.4254  -7.3322   7.6011  12.1679   2.1166  -1.3719  -0.8404  -5.9731
-#&gt;    1.6563  -8.6208   0.9193  -1.9948   4.1875   9.4993  -1.0747  -8.5283
-#&gt;    3.6731   1.8565  -0.7910   9.2543   4.0691   0.0453  -3.9086  -7.4447
-#&gt;    1.2494   1.7534   0.7568  -3.5535   5.5141   0.9877   0.9665   3.8092
-#&gt;   -7.3750   5.0936   5.8954  -5.7369  -3.3362  -4.8037  -4.0630  -7.6349
-#&gt;  -15.0747   6.5869  10.2412  -8.1852  12.6544  -6.4481 -17.7733   9.8440
-#&gt;    2.3890   5.0567   2.5480  11.3571   6.9356   1.6431  -4.5357  -2.4146
-#&gt;   11.5287   3.0665 -12.9552  10.2215 -10.5193  -0.5883   4.4301  -2.9194
-#&gt;   -4.5341   4.6156 -11.1645   5.2721   4.7755  -5.4269   8.8919  -4.1058
-#&gt;   -2.5422  -4.8962  -4.2533  -3.4713   0.4350  -0.3875   5.2587   6.7581
-#&gt;   -2.2825  -1.3043  -5.7796   0.6123  -1.6119  -9.6504   6.3327  -4.8924
-#&gt;   11.4267   3.9958  -5.8304  -0.8209  -9.3814  -1.0441   7.5002 -10.5179
-#&gt;   -7.2332  -2.1754  21.7108  -6.8779   0.6091   2.9913  -5.5717  -9.2769
-#&gt;   -8.4591   6.1532  -3.9296   0.5712  -5.5937   2.2535 -12.5900  -2.0844
-#&gt;    4.4383   5.7097  -7.7355  -5.1943   5.3091   4.7351  -3.1911  -7.3453
-#&gt;    2.7565  17.1407   7.5476   2.4632  18.5448  -0.6328   1.3705  -5.2688
-#&gt;   21.0880  -6.0673 -15.0749  -6.9460  -0.4218 -10.5325   7.6761   7.2181
-#&gt;  -13.9597  -5.9834 -13.9455   3.4727  13.5404   2.1127   6.0688   3.8601
-#&gt;   -9.5446  -6.9907 -11.8360  -0.7782  -3.4148   0.4085  -7.5634   9.1145
-#&gt;   -7.9267   4.0797  13.3777   0.9393   7.7659   7.1630  -9.1600  -2.5893
-#&gt;    6.0249  -5.8716  -5.5973   0.0046  -3.0231   1.1144  -5.0819  -5.3707
-#&gt;   -4.0046 -13.7106  -4.8585   4.0724   6.5528   8.7553   3.9177   6.3200
-#&gt;   -0.9138   6.5573   8.3198   2.7524   4.8368  -0.0342   4.3763   0.9721
-#&gt;   -4.3229  19.7568  11.2584   5.4611  -1.0099  -5.6638  -2.6794  -7.9756
-#&gt;    3.7537   1.4670  -7.9778   1.0656 -12.9156   2.0157   6.9521   0.2425
-#&gt;   -5.8054  -7.1049  -2.3444  -1.4620  -7.0817   3.2761   2.1241   0.6728
-#&gt;   -4.7399   8.6810  -0.3137  12.5848  -2.7645  -4.2000  -7.7583  -4.0197
-#&gt;    6.5134  -6.5004  -1.0067   3.1483  -7.6219  -9.4831   2.1260  -2.9960
-#&gt; 
-#&gt; Columns 25 to 32   0.9164   6.6364  -2.1096   7.6167   5.2051  -3.5309   5.7607  -3.6761
-#&gt;    5.7481  -6.2620   5.5865  -7.7751   6.8103   4.0659  -5.4968   3.3758
-#&gt;    8.8301   0.1947   1.3354 -10.1364   2.6818  -6.0935  -2.1317   5.1515
-#&gt;    3.2282  10.2814  -1.9183  -5.0089   0.9938   0.0229 -14.7824  11.1888
-#&gt;   -1.7901   0.0106   0.7380   3.1113   0.0016   3.8805   8.3862   7.7820
-#&gt;   -5.4735   9.7038   0.6526   1.3334   2.2150 -16.1218  -2.6737  12.9847
-#&gt;    7.7840  -3.3068  -1.8455   2.6265   0.7157  -3.4875   8.9484   1.1720
-#&gt;   -5.0259  12.4743  -4.9562   4.6295   0.6454 -10.6554  -1.6643  11.3980
-#&gt;    5.6431  -4.1585  -3.5017  -4.5217  -6.4074   2.7111  -0.2705   2.2997
-#&gt;   -4.3305  -5.4521  13.0141  -1.0215  -2.0584   3.4156   4.8674  12.2027
-#&gt;   -2.6645  -3.5395 -15.7281   3.9482  -7.8991   0.9436  -1.4279  -2.3085
-#&gt;  -21.3975  -6.5419  -0.5706  10.4837   9.4136 -14.3615 -14.6530   9.4419
-#&gt;   10.6879  -8.3461  -4.0221  -4.7406  -8.1651   0.4021   6.4712  -5.6879
-#&gt;    3.2240  -2.9612   0.0767  -8.3518  -6.7247   3.3232  -3.6010  -3.9666
-#&gt;   10.8131   6.4351   4.1059  -3.3593  -8.6990  -0.5441   8.8988  -9.9437
-#&gt;  -11.7232  -2.4128   4.6083  -4.7411 -12.0841   8.8367  -2.9589   0.9392
-#&gt;    6.9387 -11.6099  13.2340   0.9739   8.6156 -11.7220   7.7174   1.2421
-#&gt;   -3.5701  -0.7452  -2.5017   6.0676   1.6873  -1.5639  -3.9503   9.2895
-#&gt;   -1.7005   5.3688  -5.0744 -16.4163 -10.0591 -11.3082  -6.0288  -1.3911
-#&gt;    3.5844  -5.8183   4.4966  -0.8732  11.8576   4.8436   1.3823  -1.7042
-#&gt;   -6.3087   5.5004 -10.4076  -2.8330  -7.5559   0.0676 -10.2145  -0.1479
-#&gt;    4.1106  -1.4924  -9.4611   4.5932  -3.8251  16.5333   1.7181   0.8679
-#&gt;   -6.7011   4.7048   7.9716  -4.6916   3.7326  -8.1865 -15.0386  -4.2498
-#&gt;    2.4939  -2.1954   7.4570  -0.5020   4.1974   1.0839  11.5044  -6.8840
-#&gt;   -6.2699 -12.1483   2.7677  -7.6698   3.2706  -6.9778   1.7840 -10.8523
-#&gt;   -8.1987   9.3442  -1.6294   1.6090  -3.3236 -11.7136  -0.4056  11.0062
-#&gt;   10.5299  -1.8471   0.9151  -0.6181   4.6984  -2.4208  -5.0834  -1.4955
-#&gt;   -3.2630   2.9769   0.9128  -4.3088 -10.9615   1.7562  10.8109  -7.8176
-#&gt;   -2.5736 -10.3437  11.3399 -11.4009  -1.4814   0.4352  -0.8255  -4.6240
-#&gt;   -9.0465  11.5223   0.9622  -0.6233   3.3622  -9.5351   8.7453   3.0839
-#&gt;    0.4572   3.4590   7.2394   1.7829   4.6249  -0.4822  -1.4148  10.4267
-#&gt;    2.5418  -7.5339   9.3177  -9.9249  -0.6547  -6.0251   2.0060  -3.2288
-#&gt;   -9.2620   1.0335  -8.6599  -0.5788  -5.1153   1.5086   2.8250  -4.3265
-#&gt; 
-#&gt; Columns 33 to 40 -11.8986   2.4147  -4.4610  -0.6107   3.3662  -8.5069  -2.2273  -9.2873
-#&gt;    8.6256  -1.8284   9.2563  -3.4672  -0.0532   1.5168  -5.4890   3.4163
-#&gt;    4.2299  -1.9203   1.9084  -2.1578 -10.2429   7.3712   0.1574  -2.3494
-#&gt;   -1.5205  -7.9155   7.4539 -18.9782  19.1242  -7.3024  -4.1140   5.9851
-#&gt;   11.3259  -2.9690  -0.6773   6.6274  -1.2912   3.8586   5.5946   4.4924
-#&gt;   -1.0683   9.5736 -10.4562  27.6726 -11.6011 -16.5427  14.6381   5.0497
-#&gt;    7.0892  -9.6719  -9.7624   1.9728 -13.5009   7.0491   4.0457  -5.3395
-#&gt;   -7.4001   3.7149  -4.0817  -8.0094  -7.2760  -3.4291   3.6974  -1.5636
-#&gt;   -2.8440  -3.4581  -5.7945 -11.4628   6.9835  -9.2022   0.4153   0.5739
-#&gt;   12.5440  -7.6764   0.5098   1.7730   9.1909   8.1671   2.5764  -7.0154
-#&gt;  -15.5586   7.9803   6.0739 -10.7840 -12.0563  12.1422   7.7948  -3.1358
-#&gt;    5.9472  -2.5652  -0.9360   5.3704   1.3411   0.7240  11.4107 -11.6393
-#&gt;    8.1663  -5.4020   8.3506 -17.4386  -0.8662   2.8202   6.6108  -3.2091
-#&gt;    1.4430  -6.1873   0.6227  -7.8972  -9.4365   0.6525  -3.3555  -0.8793
-#&gt;    6.1050 -16.9247   6.6229  -5.1295 -18.5341   2.2928  -2.2324  -5.7622
-#&gt;    9.0676  -5.8671   5.1432  -8.3789   8.0141   8.3037   7.0876   6.2444
-#&gt;    9.7063   1.2566 -10.7937  -0.6233  -1.8329  -8.1201   1.5637   1.3777
-#&gt;    2.8862   2.2535  -0.1103   8.3647  -7.7593   2.0481   5.5800   3.2890
-#&gt;   -5.4787  -6.0615 -15.2475  -3.8781  -7.9978   9.5445  -4.6079  -0.9424
-#&gt;    8.5528  -0.5881   4.5892   2.4927  -3.2778   8.8345  -3.8547   1.1981
-#&gt;    0.0472  -3.2210  11.5313 -10.6498  -2.6684  -0.0521  -8.7467   0.4957
-#&gt;   -3.0700  -6.1665   6.6928 -15.0433   3.9053   1.0258  -1.8824   0.8121
-#&gt;    1.9634   3.3713  -0.2318   5.9410  -6.4410  -0.9862 -14.5837  18.1802
-#&gt;    0.7374  -9.0851  -4.4673  12.4889   7.7687   6.9073  -7.1165  -0.6474
-#&gt;   -1.6195   1.2593 -19.5939  21.1321  -5.2771   5.0347  -4.9280   4.1432
-#&gt;    3.3800   2.1993 -10.3884  -0.5506  -3.8106   6.3740   1.3545   2.2678
-#&gt;  -12.3134  -3.8762  -1.9338  -3.3150  -4.5496 -18.2740  -7.0708   5.3476
-#&gt;    6.4340  -3.6236  11.0587  11.1597 -18.1282  13.3269 -13.9402  -2.0978
-#&gt;    1.6286  -1.0577  -4.4468   7.5563   0.7859  11.6447 -11.9411  -3.2172
-#&gt;    1.3110   6.0144   1.8548   4.5404  -7.4083   5.3819   3.6763  -4.2610
-#&gt;   -7.4247  -1.7670  -4.6530  -8.1285 -10.1503  -3.5548   0.2605  -7.1283
-#&gt;    0.1491  -3.1582   8.1519   0.3861   5.1820   2.5664   3.2590  -3.3111
-#&gt;   -0.4734   2.3086   2.7129   1.1263   9.1693   9.1878  10.2963   6.6774
-#&gt; 
-#&gt; Columns 41 to 48   1.2782   3.0907   1.9709   2.3417  -4.3050   7.1534  11.8273   0.9176
-#&gt;    2.4463  10.8948   1.6810  -8.5839  -0.7853  -4.7783 -14.4342 -11.8971
-#&gt;    1.7757   5.7692   5.3072  -4.5191  -3.9519   6.7591  -0.8277  -3.8367
-#&gt;    2.7664  12.2129 -13.4740  -5.2795  16.2868  -3.6188   4.1092  -1.3353
-#&gt;    9.7374  -6.3342 -17.3494  -0.5017  -9.1257   4.6217   3.1513   3.2992
-#&gt;    4.9675   4.2960 -21.1272  20.9616  -4.4320   1.8313   6.9209   4.1419
-#&gt;   -0.6570   6.6272  -0.8664  10.3004  -7.1146  10.9799  -2.9945  -2.8342
-#&gt;   -6.3293   3.1892   2.5886   5.4950   3.4890  -8.0250  -2.8254  -7.9372
-#&gt;   -0.5784   3.8178   4.4441  -3.1310   7.4993   3.6207  -0.3728   2.2499
-#&gt;    8.4700  -1.6866  -3.0571 -16.1363   5.3445  -0.7077  -9.9704  -6.9214
-#&gt;    2.5221 -11.8837   8.1810   1.1480  -4.3493   8.9619  -4.5526   1.7608
-#&gt;  -12.1878  21.2674  -6.3019  -5.0895 -14.0484  -0.5744   2.9402  -5.4836
-#&gt;   -7.0631   5.2959   7.0089  -3.0844   3.8373  -6.6631   2.1493  -0.9894
-#&gt;    2.0537  -0.0306  -1.7520  -3.4060  11.9724   1.3590   4.3287  12.5580
-#&gt;   12.4126  -4.2870  -4.4063  -1.9916   7.4882   3.6379  -0.4937  10.1923
-#&gt;    1.7027  -6.4505  -2.7387 -16.2455  12.9007   0.3193  -9.1811   9.0046
-#&gt;    2.1747   0.5451 -11.6460 -13.3928   0.0783  -4.0526   3.3838 -17.6555
-#&gt;    4.6698   2.2650 -13.3213   6.2734 -15.1414   3.1142   4.6771  -1.2094
-#&gt;   -2.7839   0.2344   8.5908   4.0064   3.6063   3.4178  -3.9916  -4.0202
-#&gt;  -10.4147 -11.0731   1.7277  -4.9056   6.9644  -3.1199   2.0394  -2.1820
-#&gt;   -5.5745  -2.2343   5.6448   6.1782   7.3012  -7.1318 -10.1754  -5.7040
-#&gt;   -4.8389   4.2046   7.1973  -1.9157   8.2896  -0.3394 -12.3002  10.6009
-#&gt;    4.0387  -2.4189  -3.1629  -2.7025   5.4084   1.8913   2.7038   9.0425
-#&gt;   -8.2393  12.7998  14.6245   0.2574   3.2928   4.0283  -3.1100 -17.5313
-#&gt;   -9.2878  -1.9203  -6.5422   7.5714   3.6611   7.3589  -3.7316  -9.5962
-#&gt;   -1.1382  -0.0783 -10.7222  -6.2943   9.0036   2.3743  -2.7191  -0.3087
-#&gt;    0.0976   1.9307  -4.1910   5.6042   0.3211   4.8762   9.9717  11.4200
-#&gt;    5.8096  -6.6802  -3.5446  -9.9357   3.0509  -1.8566  -7.1458   5.7246
-#&gt;   -1.5739  -1.6134   0.8694 -13.7485   8.9703  -8.6950  -7.3309  -1.9867
-#&gt;   -1.3322  -1.9676  -8.1164 -11.3705  -4.6452  10.2379   7.2234  -2.2219
-#&gt;    0.1392   5.5978  -9.3507  -3.7589  -5.3242   2.3767   8.0046   2.3059
-#&gt;   -1.6345 -10.6635   2.6171  -2.6985   4.7007  -0.0966   3.1772   1.3217
-#&gt;   -9.4622   6.9922  -7.3082   4.2840   0.9404   0.6788  -3.7414   0.1027
-#&gt; 
-#&gt; (7,.,.) = 
-#&gt;  Columns 1 to 8  -8.3868 -11.6401   7.4697 -10.9227  -1.2344  -0.8900  -8.8770  -5.4839
-#&gt;   -9.1322   4.3897   0.3318  -1.2337   5.0459   2.4164  -1.0493   0.3225
-#&gt;    0.1665   5.2499  -0.4661  -2.1035  -2.2735   1.1229   1.1841   1.3380
-#&gt;   -2.3096   2.9836  -0.8848 -14.8167   6.2941  -7.0506  -2.1243  12.7500
-#&gt;   10.8477  -7.4534  -3.2469   1.2711  -1.6800   2.5467   1.2383   5.5295
-#&gt;    5.1895  -2.6360  -9.6760   9.0771 -11.6362   3.2688  -2.5761  -4.0012
-#&gt;   -6.1945  -9.9513   2.7478 -14.0589  -5.7439  -4.8767  -6.6987  16.0320
-#&gt;   -1.7465   2.0612   5.0697  -4.8202  -0.9129  -1.0709   5.3457  -3.9324
-#&gt;    3.5950  -0.1859  -6.9557  -6.4294   1.4565  -4.4959  14.1353   2.4524
-#&gt;  -12.4705   0.6877  -3.4191   6.5834   5.5183  -1.0149   1.1827   5.6798
-#&gt;   -8.8835   2.0619  -3.5445   8.5120  -7.9080   6.9911   8.6304  -8.9595
-#&gt;   -0.2742  -3.0854  -1.6339   9.4392  -2.5067   4.5785   0.2035  -5.0767
-#&gt;   -1.1434  15.9596  -7.8063  -9.7865  10.5698  -4.6604   8.0900  -5.7061
-#&gt;    4.6076   3.9945 -11.6087  -5.4445  -6.3514  -2.9826  -1.0471   3.7196
-#&gt;   12.9841   5.3269  -6.8384   0.2756  -5.1947  -6.7814   2.5640  -7.7269
-#&gt;    2.2016  -1.5726  -2.6084  -0.5966   8.7939   6.1295  -0.2765   0.4567
-#&gt;    1.7501   2.1831   2.6268   4.4627   9.1659  -1.7995  -2.6477   0.7981
-#&gt;    0.1504  -7.4290  -3.9983  -5.3382  -1.8106   0.6926  -6.7663  12.2016
-#&gt;   -8.6294  10.0757  -7.5659   6.8896  -6.0096   6.9586  -4.1332   3.6935
-#&gt;   -1.4324   4.3200   6.0882   6.4074  -4.5371 -13.4335   3.0922   2.4905
-#&gt;    6.0976  -1.1818  10.5174  -4.9773  -5.7739   6.8962   0.0486   3.7851
-#&gt;   -4.0463  -1.4940   1.7253 -16.4487   2.5028  -6.1684   4.3875   7.6539
-#&gt;    7.5972  -0.5269  -2.3114   6.6299  -8.5260  -3.5701 -16.3837   0.6222
-#&gt;   -8.7690  -5.4894  -4.1165   5.5410   4.2613   7.8740 -11.9896  -5.6817
-#&gt;   -2.9779  -3.2116 -11.2902   7.1702  -9.1442   5.8912  -4.4738   3.3884
-#&gt;    6.4202   2.3732  -0.1645  -4.8198  -0.4243  -1.2116   0.7092  -4.4674
-#&gt;    5.3454   6.0369   2.3749  -5.2803  -0.7246  -2.2842   6.0546  -0.1393
-#&gt;    1.7873   1.5501   9.4551  11.0980  -7.7700  14.2810  -6.7579  -0.5943
-#&gt;   -3.9084   9.7229  -3.9989   3.4151  -2.5499   3.7245  -2.2462   1.4619
-#&gt;   -3.2414   0.1511  12.3477   9.7800  -3.8685   3.8956 -13.8782  -1.4243
-#&gt;    5.0264   3.0037  -7.3815   9.3482  10.0306   2.9168   0.5762  -5.2767
-#&gt;    3.6499  -5.1904  -9.1577   5.1506   5.4749  -2.8899  16.6732  -6.3274
-#&gt;    3.2116  -2.8429   3.4621  -5.2504   5.2777   8.3226  -5.6161 -11.3883
-#&gt; 
-#&gt; Columns 9 to 16   0.1044   0.9257  -1.7411 -12.9353  -1.1161  -2.5187   6.6271  -4.6543
-#&gt;    6.4146   6.4546  -0.6852  -0.9331   3.4367  10.3163   1.5969   1.0194
-#&gt;    9.9608   6.6149   4.0068   1.5514   4.0437   0.5026  -0.8534   5.0826
-#&gt;    6.3817  -2.3573   1.7044   0.2486   8.7588   7.6900  -7.8460  -1.7487
-#&gt;    6.5979   5.1956  -0.4842   7.8987   2.7213   0.9117  -0.3715  -2.7825
-#&gt;    9.8294  -3.1727   8.8739  -1.6314  -3.7998   0.8782 -10.0484   8.0967
-#&gt;   -6.6380   6.3176 -10.2232   2.1496   8.0068   6.6707  -4.9896 -10.9107
-#&gt;  -19.6346   4.5560   4.6483 -10.3726  -4.4241  -0.3028  -3.2161  10.4890
-#&gt;   10.0427   9.7625   0.8205  10.1058  -0.2017   2.8767   2.2373   2.5954
-#&gt;    6.4168   2.5372   4.0594   2.1162   8.3249  14.2594   1.9229   2.2237
-#&gt;   -1.2217 -11.7178  -4.6981  -7.9874  -4.3094  12.9896  -2.1611  -1.9043
-#&gt;   -2.7255 -11.3130 -13.3637  -3.7082  -2.8801   4.3433 -12.1371  13.4229
-#&gt;   -4.5561   3.4566   4.1829  10.1662   5.0826  -6.1936  -6.7400  -4.1677
-#&gt;    4.8390  -1.7285  -2.7652  11.2970   4.4895  -6.7624  -8.2958  -6.0062
-#&gt;    4.2228   2.8737   1.6859  15.0594  -4.6857  -5.1865   7.1145 -14.9673
-#&gt;    3.8024   2.3836  -2.7731  14.1042  -3.3697   2.4403  -8.3130   2.2377
-#&gt;    2.1825   9.9036  -8.2547 -19.5848  16.7161   6.8862  11.2340  -0.6450
-#&gt;    1.9039   1.6240  -5.4858  -6.3526  -6.1712   3.0947   0.7988  12.7938
-#&gt;   -3.8852 -13.2789  -1.2641  -3.7055   4.4524   7.2319  -9.9409  -2.3498
-#&gt;  -15.0849   5.1506  -0.8828  -9.4088   8.0829  -0.0959   3.6027 -13.6473
-#&gt;   -1.5873  12.1405  -3.7498  -0.2601   1.4899   6.2359  -9.1811  -1.0767
-#&gt;   -5.0776  -0.6590  -1.7319   9.0051   4.7089   2.1270  -2.0193  -4.1000
-#&gt;   -8.3452   5.3808  -5.5685  -2.0289  -0.4346  -1.4210  -2.9761   0.2022
-#&gt;    6.6507  -1.0421  -5.2787   0.5100   9.9340  -4.4988   7.4089  -7.7939
-#&gt;   11.7048 -10.1678  -6.2539  -5.6852   1.4961   0.2818   9.4267  -2.1771
-#&gt;  -13.4982  10.8678   3.8737  -5.1592   6.8410   0.9065  -1.2240  -8.9981
-#&gt;  -13.6598   8.9172   0.6429   0.0794  -3.5587  -6.8124  -9.3900  -2.4623
-#&gt;   12.8951  -6.3400  -1.7720   6.4668   7.2949  -1.0787  -0.7375 -11.4979
-#&gt;   -4.4058   3.9306   2.9124   0.6470  10.7702  -6.2025   3.5430  -2.4585
-#&gt;   14.9334  -7.0435   9.1913  -5.1234   6.4600   6.1955   4.8577  -0.3465
-#&gt;   -4.0835  -2.4654   6.2225  -0.5559  -9.6441 -11.2900   4.6662  17.0918
-#&gt;   -4.6378   1.4535   4.4265  13.6624   9.1190  -7.6378  -0.3699  -2.8796
-#&gt;    0.9298  -4.5409   0.4691   5.7132   2.0187  -0.3962  -6.2617  -4.4942
-#&gt; 
-#&gt; Columns 17 to 24  -7.5082   0.1230  -2.7170  -0.1290   1.2378   3.9310  -6.8861  -2.4757
-#&gt;   -0.0840  -4.9533   6.8870  -7.8175  -6.2897  -1.5212  12.2053   1.7018
-#&gt;   -6.1950  -5.3380   8.6784  -1.1482 -13.9410 -14.6168   7.9137   3.0913
-#&gt;    2.9134  -8.0645   1.5240  -5.6552  -3.8008   4.0944  -0.5454  -8.5642
-#&gt;   -2.7359  -4.8647  -4.7398   3.6420   4.1403  -0.2886  -4.6023  -1.1916
-#&gt;   -4.4233   1.7021  -9.9913  12.8842   7.4480  -4.9905   0.0126   1.7723
-#&gt;    0.5143  -6.9647   8.9175   2.7165  -0.2401   2.0856   1.3035  -3.8266
-#&gt;   -1.7071  -3.9459   4.0166   1.7316   2.3844  -5.6508   0.4939   6.4315
-#&gt;   -1.7874   2.9283  -1.7397  -0.0795   3.3628  -3.7478  -3.3556   2.6575
-#&gt;    3.8874  -5.4597   1.9906  -2.7408  -3.4235   0.8202  16.5486   1.5903
-#&gt;  -13.6912   7.5320   3.8591  -2.7932   2.2483  -5.1478   9.2281  11.0863
-#&gt;   14.2475  11.0989  -1.1390  -4.1648 -10.9931  -2.8091  14.1621   8.3148
-#&gt;    4.0171 -24.1954   7.6173   6.6532  -1.9288  -2.6925   2.4648   8.5419
-#&gt;   -1.1056  -3.7104  -8.6558   7.8332   3.5572  -4.8852   5.6026  -7.8859
-#&gt;  -17.2301 -14.5154  -2.0305   0.4276  -9.1197  -0.3856  -9.3708   3.1188
-#&gt;    0.1942 -10.6688   1.5796  11.6256  -2.3149  -0.3170  -1.3611  -3.2544
-#&gt;    8.5874   3.6442   1.8841  -5.7845   3.7858   3.3507   9.5682   5.7652
-#&gt;   11.5020   7.4198   3.1556   7.7805   4.3205  -6.7041   3.4949  -0.9564
-#&gt;  -20.2656  -5.5543   8.3369   6.5278  -4.8215  -6.9655   8.4572  -8.7750
-#&gt;   -2.4090  -6.5655   0.6052  -7.9955  -2.2705   1.3170   5.0470  -6.7168
-#&gt;    2.3647   9.7501   0.1719   1.5766   3.1177   1.5264   5.3542  -6.0455
-#&gt;    5.8564  -6.1934  -3.3479  -0.8891  -4.3840  13.8145  -6.9400  -5.6010
-#&gt;  -12.5772  -2.0369  -4.0395  -4.4490   6.7978   5.7791  -2.5945  -7.7926
-#&gt;   -2.7811  12.2068   8.0313   8.7803  -3.9315   2.2293  16.3355  -9.4390
-#&gt;   -4.9157  21.3081  -4.7641  -0.2722   0.8232  -7.7434   7.1377 -10.3087
-#&gt;  -16.9968 -18.4087   6.2145   7.1472   1.6623   4.9139  -7.8575   2.1788
-#&gt;   -4.1799 -10.8815  -1.5725 -13.0412   4.1585   2.2265 -13.8721   0.6302
-#&gt;    9.3972   5.2940   2.4569   5.7679  -4.9076  12.6545   9.4319  -5.0808
-#&gt;    1.1354   0.0392  -5.1914  -9.7269   0.4172  -2.1484  14.0104 -10.9378
-#&gt;   -8.9589   5.7229  -7.1192   5.9937   2.2431  11.6666   5.2665   2.3037
-#&gt;   -9.6188  -2.3606   8.3819  -5.6156  -7.0418  -2.6749   2.5972   0.8289
-#&gt;   -0.9066 -14.1249   0.7172  -3.3330  -2.2977   3.1394  -2.7918   8.9389
-#&gt;   -7.4333  -3.1721  -8.1599   4.1023  -1.1054  -1.7108  -0.0041   4.4734
-#&gt; 
-#&gt; Columns 25 to 32   6.2032   6.7606  -4.1842   4.8856   4.8326  -4.3465  12.9020  -1.9198
-#&gt;   -4.9915   7.2119 -13.3811  12.4029   4.2098  -8.6987  -7.5963 -10.2318
-#&gt;    3.2876   0.8499 -10.0451  10.1758   9.1603   4.5728   2.2774  -5.3308
-#&gt;    6.9995  -2.8632 -17.0167   4.8331  13.0833  10.4276  -6.8767   9.1409
-#&gt;  -12.4603  -7.5971   0.9850   3.2682  -1.8272  -9.1431  11.7883 -14.3354
-#&gt;   -5.0676   0.1846  -2.6840   0.9372 -13.7782   6.9465 -10.2780  -4.0720
-#&gt;   -2.2802  -7.5383   3.5250   2.1399  11.5853  -7.8419  -4.0121  -3.3568
-#&gt;    0.4598  -6.3054  -5.5410  13.9453  -2.0206  -7.8521  -1.9166   4.1174
-#&gt;   -4.2545  -2.3393   7.3020  12.9533   1.3055   3.4879   6.2869  -5.5537
-#&gt;  -12.6416  -3.1648 -10.2565  -0.2521  -1.3447 -10.9668  -0.7903 -17.2056
-#&gt;   -3.9141  -9.5369   3.0149  21.3584  -2.6780   1.7901   5.7324 -17.4045
-#&gt;   10.1366   4.0803  -5.2090  22.8942  -9.7008  -9.1265  -3.6588  -1.8557
-#&gt;   -4.3599 -13.6794   5.6573 -10.4187  -1.9024   0.6204   1.9504  -1.9618
-#&gt;    0.9198  -9.5261   5.4805  -1.3540   2.4746   9.3641   7.0959  -0.9933
-#&gt;   -8.8033 -11.0557  13.0802  -6.0954  12.3576   8.5847  16.7252   1.8883
-#&gt;   -5.7972  -4.5999  -3.2640 -12.8288   3.2058   4.2547  -2.3899  -2.1646
-#&gt;    7.3529  -7.6689   1.1825 -13.8196   8.5992  -0.3931  -5.4558   3.0841
-#&gt;    3.8626   0.6250 -15.1615   5.8399  -9.6220   0.8828   1.3470  -3.3132
-#&gt;   -8.2905  -6.0210  -6.0656  17.1575   8.8731   1.6172   1.9336  -2.0237
-#&gt;    4.8286   1.5069  11.3829  -1.7471   5.2813   4.3474  -6.8825  -5.2249
-#&gt;    8.4692  11.5821  -8.9529  11.3588  -1.6938  -5.6908  -3.0364  -2.1104
-#&gt;    6.3942   4.8667  15.7654  -2.2110  -7.7429  18.1713  -8.2298   4.1817
-#&gt;    5.3835  -2.9582  -5.8010  -4.3772   0.1724  -4.1213  14.8204  -4.8059
-#&gt;    9.6845   0.8019 -10.6839 -13.3050 -10.2778  -5.5199   0.4439 -10.2108
-#&gt;   -7.2615   6.5667   9.5359  18.8324  -0.5216   8.7538  -9.2066  -5.9030
-#&gt;   -0.3073 -15.2456   2.1572  -3.2268  -5.6128  -1.0913  -2.9713  -4.1415
-#&gt;    5.9096  -4.1621   1.9846   9.2107  14.1094   3.7912   8.5795  -0.3664
-#&gt;    3.1452  -3.1311  11.1002  -6.0726  -3.6456   6.7602  -9.3967   9.3666
-#&gt;  -10.4010   2.7401   8.1540  12.5843   3.1052 -10.2762  -4.5395   6.4634
-#&gt;   -1.8610  -6.9201 -18.4132 -11.7961  -3.1464  -0.3100   4.7452   1.2053
-#&gt;    0.6977 -13.1071 -13.5327  13.9274   8.3351   3.5292   7.3812   5.6499
-#&gt;  -13.9419  -2.8361   9.5517 -16.0587   8.9735 -15.3313   1.8182   3.1891
-#&gt;   -8.8529   4.9793   5.3233   2.8270 -13.7933   5.3210  -6.3498  -4.2234
-#&gt; 
-#&gt; Columns 33 to 40  -4.6260   8.8620   3.4329  -8.1481  -8.1702  -0.7852   2.0633  -2.5541
-#&gt;    9.5929   2.5161  -5.7370   7.1015   2.3320  -6.9793  -8.9219   5.1713
-#&gt;    6.1647   1.8937   4.1104  19.2877   1.8289 -11.5295  -3.7615   7.3228
-#&gt;    2.2258  -5.6179   0.3055   9.3003 -11.9471  -4.2971  -6.7805  14.6903
-#&gt;   -6.3973  -2.0035  -2.7854   0.8171   7.2534  -9.8019   3.8554  -7.0032
-#&gt;    1.0929  -7.6663 -11.2568   4.7428  -4.0117  12.4513   5.8052   5.8733
-#&gt;  -17.8153  -1.4327   5.2808   1.4281   0.3969 -17.7094   3.8365 -10.8317
-#&gt;   -7.7704   4.2667  -4.6862   6.3226   0.9491  -1.5931  -0.6221   7.8181
-#&gt;   -3.7231   5.6704   4.0881  -1.1873  -4.5026  -0.5250   2.7140   0.5003
-#&gt;   12.6789   3.0519  -6.3219  -0.5452   1.5616  -9.3017 -15.5234   1.3124
-#&gt;    5.1097   6.3707  -4.6902  -3.8234  -8.2346   6.2555   7.4859 -11.0259
-#&gt;   10.0714   7.5715  -2.5473  13.3906  -2.7765  -7.9680   2.9459   6.5335
-#&gt;   -1.5787 -12.5808  -0.2462   9.8006   0.4333   0.8598  -2.3843  -9.3571
-#&gt;   -6.2396  -2.2068 -10.0107   7.9180  -2.8846  -2.9688   1.4483   2.2000
-#&gt;   -2.5841   0.5950  -0.6526  -0.3271  -2.4743  -6.3197   9.3940  -4.1160
-#&gt;    2.2653   3.4443  -8.9261  -0.0572   5.1695  -6.9099  -6.3007  -0.9485
-#&gt;    2.9831   2.9335   1.6797 -10.6045   4.4246  -4.5346 -11.1331  -8.0911
-#&gt;   -2.4504   7.1525   4.0060   9.7077   1.3683  -5.5369  -0.4538  -3.3585
-#&gt;   -0.3564  -4.4546   4.0470  -3.3142  -4.0203 -10.6082  -5.8961  10.2849
-#&gt;    0.5000  -3.4236  -2.3786  -2.6622   8.2442  -9.2194  -2.3547   1.3446
-#&gt;    2.0190   0.3457  -8.0455  11.9630   5.4565  -7.7393   3.8689   6.4768
-#&gt;    1.7497  -0.7774  -1.2803   3.1482  -8.3563   2.5110   0.5142  -6.7214
-#&gt;   -7.3846  -3.3771  -8.7545  -6.7862   8.2003  -4.0938  15.2718   3.2306
-#&gt;    8.8394  -4.8474  -1.2207  -3.2168   7.5263  -7.6474  -2.2813  -6.1993
-#&gt;    4.1752   8.8815   5.9743  -9.0465  -5.3511  -6.1333  -7.7893   7.6450
-#&gt;   -9.8636  -2.4300  -3.4376  -5.1739   8.7395  -9.5285  -2.1348  -0.2848
-#&gt;   -6.7656   0.5918  -0.9239 -10.9065  -5.3489   3.6691  15.7931  -3.7872
-#&gt;    5.8799 -10.8813  -7.1825   1.8036  10.5524  -7.3634  12.1371   2.8828
-#&gt;    5.1392   5.2809  -7.0235  -8.9886   1.4891  -8.4972  -8.1288  17.9264
-#&gt;    1.7187 -11.7695  -8.5988  -4.9949  -1.4106   0.4465  -3.4039  -0.1012
-#&gt;    1.4442  -4.1300  -1.3585  -4.7286 -16.1419  -2.8185   0.3761   9.5014
-#&gt;    5.2136  -3.7293   4.7115  -7.1455  -1.6220  -1.0989  -2.3889   5.3699
-#&gt;    3.4604  -1.1786 -13.8256   4.2584   1.8335   6.6330  -3.3588   1.7650
-#&gt; 
-#&gt; Columns 41 to 48 -16.4893   5.8363   5.3839   6.3958  -1.4048   4.1891   5.3464   2.6790
-#&gt;   -2.8933   2.2947   7.0668   1.9631   0.7939   2.5534   3.7270   0.8783
-#&gt;   -3.1012   4.6196   1.3724  -5.0885   2.5820   3.7266   3.9187  -7.3649
-#&gt;   -3.5867   4.6952   7.6381   4.0358  -9.3693  -0.2167  -3.8898   2.1728
-#&gt;    3.5989   5.3581   0.7363  -6.6457  -6.0644  -3.5028  -3.2641   4.5286
-#&gt;  -14.7376   2.8292   6.0339  -4.5955   0.7311  14.3921   0.4553  -9.5141
-#&gt;    7.2329  11.5357  -6.6458 -10.2309  12.7271  -7.7142  -5.5970   2.3869
-#&gt;   -9.5121  -4.3462   0.2946   5.5319  -0.1472   7.0475   4.4480   2.4542
-#&gt;    1.9695  -4.8489  -0.9918   1.9768  -8.7223   0.4888   0.2278  -7.7381
-#&gt;    7.7019 -10.5364   3.4470   1.3466  -1.5905 -14.6580   7.5356  -2.0517
-#&gt;    5.6417  -0.9466  -9.8949   1.3275   3.2663  -1.9140  -2.8904   5.5347
-#&gt;    3.7832  -2.9117  -5.5314   0.2180  14.6717   4.5803   9.6055  13.1461
-#&gt;    7.8353 -18.9302   3.6607   1.4060   3.8057  -9.1981   0.9597   1.2560
-#&gt;   -2.9988  -7.1067  -6.3133   7.0839   2.7961  -6.5958   9.6824  -2.1668
-#&gt;   12.1370  -7.3829  -1.9034  -2.0261   7.5014  -1.3085   0.9310   4.8365
-#&gt;   15.2301  -2.5347   1.4293   4.3834 -11.1265  -9.4680   8.2599   0.5567
-#&gt;   -6.9354  -0.8273  12.9513 -16.3247   8.7733 -10.5149  -8.3016  -3.5463
-#&gt;  -14.8789   6.2359   0.9929   5.7673  -0.0762   0.3267  -2.3356  -5.3080
-#&gt;    4.9092   5.9725  10.4473  -7.8813  -2.5593  11.1572   8.9717  -0.6405
-#&gt;    2.2686  -2.9832  -0.0737  -2.9151   3.4551   8.8344   8.4443   1.2575
-#&gt;   -5.6279  -1.6709   0.0603   4.5958  -2.0800   9.7000   7.3730   1.3542
-#&gt;    3.8505   2.0401  -9.5092  18.2622  -3.4618  -5.5624  -1.9886  -0.1771
-#&gt;   -1.8987   5.4359   3.5942  -6.1446   0.4830   9.7549  10.8543   2.9409
-#&gt;   -3.1502  11.0579  -4.4697 -10.3236  -4.4525  -3.1020 -20.5302  -8.3858
-#&gt;    2.7279  21.5577  -0.4661   5.0657   2.4091  18.8487   3.7039   4.6378
-#&gt;   -5.8267   4.8931  -3.0858   2.2141 -10.2913  12.2682   2.7239   7.4378
-#&gt;    1.9541  -1.8613   0.1603  -3.9407  -2.8795   6.0980   3.9917  -1.0544
-#&gt;    3.9225   4.6235 -17.5093  -8.6676   0.0359  -8.2765  -9.6504  -2.3546
-#&gt;   -3.0591  -0.6578  -2.2810  14.9146   1.5137   4.6708  18.2630  -1.8310
-#&gt;   -2.0140  -2.5775  -2.4864  -8.7478  -2.2091  -5.5142 -10.5906  -5.6048
-#&gt;   -5.8611  -1.2948   3.3547   0.3666 -10.2013  -2.2231   5.5278  -1.7056
-#&gt;    8.8016 -10.1636   6.4712  -8.4551  13.4827  -1.8375   6.0630   9.2747
-#&gt;    1.3997   1.9604  -6.4808   8.4185  -0.9404   6.2133   0.5617  11.8576
-#&gt; 
-#&gt; (8,.,.) = 
-#&gt;  Columns 1 to 8   9.7505  21.5211  -0.7218   3.5807   9.2035  -5.7990  -5.3427  -6.5380
-#&gt;   -9.3461  -4.3623  -9.2135   0.1155 -17.1370  11.1844  -8.7337  -8.3448
-#&gt;    4.5790  -3.9913  -6.5917   6.4134 -14.5408  -5.1644   1.6960  -7.1082
-#&gt;   -0.1047  -7.5503  -1.7047   1.2399  -2.9983   4.1608  -5.1501   2.1459
-#&gt;  -12.8589  -4.4785  -3.2548   0.9505   5.6102   1.1982  10.0071   6.0460
-#&gt;  -11.4783   5.1726  -4.2252   4.2747   1.2827 -10.9742   0.3886   2.7719
-#&gt;    4.9565  -1.0287  -9.1887  -7.2761  13.3099 -11.3184  -5.4476 -12.9089
-#&gt;    0.8144   4.3780  -7.6663  -5.0387   8.2941  -7.1678  -3.1985  -3.6510
-#&gt;    2.9263  -6.0486   2.0268   2.1189  -5.3773   0.8080  -6.3592   8.9286
-#&gt;   -8.7668   2.5708  -1.3839   7.6679  -9.7390  10.7981  10.0152   3.3428
-#&gt;    1.9171  -0.7832  -3.3217   9.5469   0.1392 -13.4776   6.0295  -2.9709
-#&gt;    0.3317   2.2112  -6.3795  -2.9447   2.0840 -22.5732  -2.1504   6.8618
-#&gt;   -6.5304  -0.3599  -4.6970   1.2913   2.6933   7.7431   1.0711   7.2043
-#&gt;   -0.4512  -4.5796   7.8038   6.7893  -3.4706   0.2633  -1.9958  -5.2133
-#&gt;    5.8139  -2.7947   0.3844   9.0265   6.0386   0.1709  -2.3162  -5.0494
-#&gt;   -9.7443  -8.1242  10.9562  -1.5658  -3.0498  11.7880   6.9681   6.9782
-#&gt;    2.8668   5.6868   7.3995   0.1941  -8.7889   8.4973  -0.6561   0.4459
-#&gt;    4.1562   5.1469   0.3420   3.6547   5.1419  -9.6600   3.7330   3.8443
-#&gt;   -2.3604   1.4771  -4.2629   0.4718   3.2103  -9.8736  -0.7177 -12.7919
-#&gt;   12.6961   3.1390  -5.6916   0.6333  -0.3446   1.4370  -2.3489 -14.2977
-#&gt;    0.3057  -4.6486  -2.6362  -7.3607  -1.4229  -4.7620  -1.7483  -6.2187
-#&gt;    6.4337  -9.8663  -2.6658  -0.2098  -3.9033   6.9333 -10.1242  -6.0069
-#&gt;  -14.3827  -8.0791   2.1936  -2.1635   7.4595  -9.9999  -4.5255 -15.1139
-#&gt;   -6.5837   0.5889   9.7503   1.1785   2.1524  11.7576   8.8130  -2.7063
-#&gt;   12.3878  -0.1523   1.3679   2.5950 -13.6549  -3.5532  -3.3741  -9.7953
-#&gt;   -0.7170  -6.3100  -8.0999   9.8903  14.4547  -6.2736   9.8533  -5.8607
-#&gt;    1.1360   1.0335  -0.0059   3.1830  -3.2109  -7.0617  -5.7204  -3.7372
-#&gt;    5.6506  -5.1117  11.7300  -3.5025  -7.5426   7.8535   7.1664  -9.1174
-#&gt;    5.7702  -0.2175   1.7750   6.0243 -21.9186   8.0276   9.3998  -4.4018
-#&gt;  -10.6650  12.4759   5.2718  -1.5027  -2.9467   7.5737  15.0786   2.4842
-#&gt;    6.5021   8.8091  -0.6611   4.7909   5.5022  -8.2502   3.1660   7.5191
-#&gt;   -8.0627  -1.6115   0.2394   6.6535   2.8433  13.6172   2.0723  19.1200
-#&gt;   -6.4064  -6.9318   2.5021   0.6963  -5.9183   5.2332   4.6153  12.9524
-#&gt; 
-#&gt; Columns 9 to 16  -7.4489  -0.8042   2.7854   8.4652  -6.6809  15.0909  19.8745   7.1479
-#&gt;   -9.3199   9.4770  -3.4972   7.5095  -0.6291  -4.8227   2.6740  -6.6423
-#&gt;   -2.7731   0.6078  -0.3694  -3.5848   4.8989   4.5288  -0.2155   2.2294
-#&gt;    0.0430   7.7739   0.5964   5.9654  -5.0387  -8.3233   6.6414  -0.9961
-#&gt;    4.0313  -1.3787   6.4081  -6.2994   2.1910  -5.4143  -3.6866  -1.5317
-#&gt;   -0.4060   3.4386  -5.3304  -1.7402  -5.4494   8.4392  -2.5724  -2.6365
-#&gt;    2.4761  -6.6946  -2.1128   2.0522   7.0601   7.0882  -7.5598  16.4945
-#&gt;    1.7845  -5.1161  -4.2109   0.0422   2.5480   2.2563 -13.5602   6.9806
-#&gt;  -10.9724   2.8955  -0.9919  -4.6993   3.9657   5.9168   2.8627  -3.5043
-#&gt;   -2.7526   3.3810   0.4070   4.7812   2.5217 -14.3968  -4.0289   3.0694
-#&gt;   11.0097  -4.5242  -2.8502  -7.9287  15.9853 -11.4852   8.3789 -10.0767
-#&gt;    1.5480  -3.0409  -0.6347   7.7452   6.0101   2.0608  -2.8751   7.2602
-#&gt;    0.0410  -5.5461   7.5252 -11.0345  12.0098 -14.8030  -7.9794   0.9225
-#&gt;    4.2580 -11.4530   3.3565  -3.9571   5.1758  -4.5969   5.6352   9.2885
-#&gt;   12.7585  -1.5618  -3.5641 -13.9890   5.7753  -1.2041  -3.7336   6.4951
-#&gt;    1.9021   3.1302  -0.2939   3.6689  -5.5221  -9.6258  -4.1210   5.6672
-#&gt;   -0.9260  -2.8063 -10.6438   5.8411   4.1513  -2.4985 -12.3546   3.7584
-#&gt;   -2.3836   2.6476  -0.4543   4.2394   5.1216   6.6579   1.9311  -3.5211
-#&gt;   -2.4128   7.1206  -3.8751 -13.5594   4.8275  -8.7876  -0.1497  -2.2992
-#&gt;    0.6666  -1.5999  -1.9034  -4.0682  11.3346  -6.7277   5.9325  15.2344
-#&gt;   -6.1739  -7.7141   1.4850   1.0927   4.3074  -3.4131  11.6982   5.0749
-#&gt;   -2.6437   2.8348  -0.0386  16.8818  -1.2101  -2.0663   8.1696   7.1242
-#&gt;    6.6301  -1.2115  -2.6788  -3.4775  -9.8312  -4.7657   6.6134  -4.9456
-#&gt;   -3.8740  10.2621  12.2994  -6.8315   2.0823 -15.4459  13.4714 -16.5075
-#&gt;   -6.1147   7.5286  -2.9784  -3.6033   4.8837  12.5250   1.7987  -0.8655
-#&gt;    9.8514  -3.1028  -6.3037   1.3988  -3.0110   1.9040 -11.5565   9.3783
-#&gt;    5.2401  -1.0755  -3.6053  -5.0613 -11.9937   5.4612   9.3271  -4.1031
-#&gt;   13.2205  -8.3787  -7.4333   1.0424   3.7193  -8.2087   1.5639   6.6348
-#&gt;    5.1053  -8.5397  -4.6884   5.3031  -1.3494   4.1826   0.5436  14.7424
-#&gt;   -0.3656  -4.0147  -4.7661  -7.8632  -4.5603   1.3236  -5.4402  -3.0846
-#&gt;    4.5304   0.9833  -2.5083  -9.7851 -14.0094  -0.4676   3.3590  -8.4788
-#&gt;   10.5364   4.5215  -1.5188   5.0515  -7.1089  -6.8000  -7.6796   2.9325
-#&gt;   -0.4371  -3.9301   3.3137   3.9294  -1.5344  -0.7612  -7.9348   8.6682
-#&gt; 
-#&gt; Columns 17 to 24  -0.1389   2.7833  -7.6417   1.0127   4.3739  15.8968  13.7240  -2.0858
-#&gt;    1.2018  -1.3251  11.9279   4.4186  -5.6396  -6.8106   1.4250  -3.8938
-#&gt;    7.1025   7.5194  15.7088  10.0171 -14.3076 -12.7381   3.2395   0.8369
-#&gt;   -4.8720  -8.5710  12.2525   3.2695  -5.5304  -5.0498  15.3786  -3.1256
-#&gt;    5.6980   6.7997  -3.1759   0.1066   2.2419   3.0356  -4.8155  -1.3508
-#&gt;  -15.3042  -7.0760   4.9612 -14.5196   6.9446  11.3609   1.7151  -9.4168
-#&gt;    2.4581  11.1489  -2.4932 -12.1783   8.0708  -0.7791   8.1304  -3.2390
-#&gt;  -11.4518   5.1643   1.6748  -0.9467  -7.1317  -7.2802   3.5551  -1.4782
-#&gt;    8.5132   6.0701   2.0084   2.9306  -7.6543   4.9825   2.7986  -1.4610
-#&gt;    7.6029  -2.1964  -4.7003   4.6741  -2.4956   4.5839  -3.3663  -0.7649
-#&gt;   16.4216   1.3188  -0.2868  -7.9091  -4.4317  11.5826  -4.2421   3.3804
-#&gt;   -0.3521   1.5658  -3.3197  -8.8169  -8.6169  -7.3379   0.2026  -4.2922
-#&gt;    4.6503   0.3193   4.8151   1.6662  -5.3378  -8.9674  -5.4164  10.2136
-#&gt;    0.3513  -0.3407   2.1979  -3.5283  -2.5753   8.7386   1.5805   5.4534
-#&gt;   10.4485  13.4950   1.1411  -0.7244  -8.2095  -4.7985  -5.4840  17.3438
-#&gt;    5.4441  -7.1625   3.4126   6.5226  -3.5294  -6.1442  -2.3265   1.3708
-#&gt;   -2.3677   4.9284 -11.7406   5.4634  -2.0317   7.2705   9.2657   1.4044
-#&gt;  -11.1751   5.8955  -3.9999  -6.3525   7.5790   1.7701   2.9943  -8.7027
-#&gt;    5.8696   2.9949  17.6991  -1.4768 -12.4445  -6.9235  -3.2380  -6.8725
-#&gt;   12.3087   3.4907  -5.1019   8.4455   7.1078  -1.3708  -3.1946  10.9005
-#&gt;   -7.0519  -0.9545   2.6750   5.3558   0.6328 -18.4314  -3.3503  -8.6130
-#&gt;    8.1444   1.3283  -1.1925  -3.6192   8.7250   0.5334   0.0338  12.2328
-#&gt;  -13.1485  -2.2758  -0.7310  -1.1975  -0.2313  -1.8796   8.2425  -5.9023
-#&gt;   -0.4631 -21.9035  -2.7409   1.5576   2.6490   8.8296 -12.3949  -3.2970
-#&gt;   16.1738  -3.6051   5.2913  -6.8463  -0.1715   3.8577  -4.4052  -7.1396
-#&gt;    8.6622  -2.5606   4.2791   0.3756  -2.5184  -2.4997   1.7716   2.0096
-#&gt;   -0.6146   6.1604   0.3380  -3.5578   5.9450  -6.0484   8.9132   2.9027
-#&gt;   11.9040 -15.3711   4.8235  -6.3088  -3.0654   5.7914 -16.9265  10.7671
-#&gt;    9.6258   0.8721   2.1063  -1.4997  -7.8480   0.8220  -1.0926  -5.7129
-#&gt;   -0.8224  -4.4742  -5.6826   3.2623  -3.7203  13.1767   5.8564   0.3523
-#&gt;   -8.5367   4.8810   4.6889  -6.7867 -10.1217  -1.6946  14.1697  10.3410
-#&gt;   -3.1274  -7.0905 -12.2435   2.7620 -10.2159  -6.9943  -0.7448  -2.0076
-#&gt;    6.4558  -4.3491   1.4251  -2.4677   2.4405  -4.2120   0.5065  -1.7806
-#&gt; 
-#&gt; Columns 25 to 32  17.1026   2.5812  -7.0203   9.4465   6.9103  -0.1044  -2.8706   1.0071
-#&gt;   -0.1017  -4.5751  -2.9160 -13.9657  -7.6557  -3.4798  -5.2746   0.7955
-#&gt;    2.6565  -3.7401   3.4362   1.4084  -8.9534   3.8798   0.9005  -5.1114
-#&gt;  -13.6695   5.2867  12.6991   4.7499  -5.6753  -3.7951  -0.4789   4.1006
-#&gt;   -3.4392  -0.5906  -1.6030   4.2723  -7.6491   0.5247  -3.4139  -9.0802
-#&gt;   -8.7874  12.8729  -2.4180  -4.9332  -6.5361   0.6753  -5.9675   1.7622
-#&gt;   -0.2850  -1.0839   6.2215   9.3789  -1.1318  -3.4875  -1.5527  -1.2908
-#&gt;    3.6416  -1.7346   6.5360  -5.6006   3.7726   3.3410   6.3362  13.5934
-#&gt;   -0.0879  -0.4969   1.7138  -9.7765  -9.0325   1.5765  -4.2677  -6.9798
-#&gt;    0.4966 -10.6000  -5.1651  -9.7292  -4.8165   6.4431  -7.8703 -12.7564
-#&gt;  -12.0424  -2.4013   8.9028  -3.3145   7.1346   3.8412   1.4704  -9.6413
-#&gt;   -3.1423  -5.1933  -0.0150   1.9071  -0.2029  -0.1701   0.1602   1.7525
-#&gt;   -1.8765   1.9230   1.4065   0.4553  -0.2087   8.1157   8.0254  -4.5533
-#&gt;   -4.3565  10.7568   7.1821   9.0265   2.6125   0.1118  -1.6544  -0.7527
-#&gt;   -0.7168  -2.0606  -8.3614  11.2642   3.2440   1.1178   4.9414  -2.3209
-#&gt;   -4.8396  -6.3713   5.5836   5.3154  10.2439   3.4682  -1.7568  -1.7856
-#&gt;    1.6700  -9.3760 -19.3531  -6.9326  -2.3024  -1.7312   2.0125   2.4404
-#&gt;    1.9675  -0.2319  -0.9638  -6.1850   0.7932   3.1152   1.3602  -7.4149
-#&gt;    3.1073  -2.1754  13.7664   0.9557  12.6638   4.6034   1.6815  -3.3305
-#&gt;    0.4340  -7.9141   5.9485   2.6441   6.5036 -10.2815  -8.1310  -2.6895
-#&gt;   -5.8532   1.4265   1.8267  -6.1927   3.4157  -7.9354   2.2704  10.0210
-#&gt;    1.6743  -1.2345   0.3427   1.3384   1.5342  -8.3515  -8.5344   5.3275
-#&gt;   -2.2453  -5.9218   2.3372   2.9031   9.8127 -13.8977   2.0153   9.5521
-#&gt;   12.5302  -2.4257  -7.4401  -5.6927   5.5491   1.6091  -0.7882  -6.3543
-#&gt;    5.6356  -1.7172   3.7151  -2.9179   3.5514   4.6399  -0.7576 -12.3475
-#&gt;   -3.5529  -8.4877  11.6536   3.9503   0.1079  -2.8133   9.7080   0.9127
-#&gt;    7.0334   5.4122   2.6707   0.3616   4.5236  -6.2855   5.1213  14.4611
-#&gt;  -11.7232 -12.7253   4.7953   3.4767   5.8241  -8.2993   0.8080 -11.5194
-#&gt;    1.3343  -6.4987   7.4096  -5.8047   7.1858   3.4141  -5.2520  -4.4160
-#&gt;   -0.1721 -14.0198  -8.1386   7.9760  -0.4833   1.1973  -5.2975  -2.7722
-#&gt;   10.6461   1.8665  -7.1903   5.6824   6.1101   2.6576   6.4654   1.1838
-#&gt;   -6.6105   5.0036   2.2007  10.3305  -6.5373   0.0834  -0.2109   2.9936
-#&gt;   -8.1512  -1.1948   3.9096  11.5425  -2.3290   4.5866   1.5263  -2.7111
-#&gt; 
-#&gt; Columns 33 to 40 -13.6757   3.2050  -1.7102   3.0423   3.4864  -6.0581  -7.0705   2.3436
-#&gt;    2.8154   6.7909  11.3422  10.5838  -1.1585   0.3751  -6.7020   3.0085
-#&gt;   -2.3170   5.7280   8.5315   5.9173  -2.9746  -7.2888   9.4413   7.2076
-#&gt;   -5.7367   7.5299  -1.4259  -1.6888 -14.4085   4.8950   5.3013   8.0799
-#&gt;    0.9883   0.7480  -2.2983  -8.1308  -4.4850  -8.3801  -8.7969   7.5343
-#&gt;   -0.8695  -1.1404  -3.4372  -2.3562  -0.8389   6.6296  -5.0157   3.7476
-#&gt;   -0.6605   1.1048  -7.0557  -4.1054   4.1530  -0.1704   9.4408  -5.8617
-#&gt;   -0.5488   4.6426  -2.6103   6.9008   6.1783   6.9762   2.5179 -14.8938
-#&gt;   -4.0633   8.2448  -3.4058  -0.3540  -8.4536  -6.8800   1.3375  -0.1555
-#&gt;    1.3970  -0.2304   0.0156  -5.2144   3.5280  -1.1255 -12.1279  -0.2955
-#&gt;   -2.0486 -10.7888   4.8500   2.6335   2.3910   5.6371  -9.2602  -6.2757
-#&gt;   -2.1683  11.4737  14.6329  13.6383   3.6652  -0.8922   1.0977 -11.2232
-#&gt;   -6.7400   3.4379  -0.9003   4.8029 -10.6919   0.7908   6.7403 -16.2157
-#&gt;   -4.8868  -2.2131   0.3401   0.4005  -6.0264  -4.2342  10.5046  -2.7749
-#&gt;   12.2537  -3.7335  -1.7123  -8.3265 -19.5502  -2.4142   1.6112   1.2166
-#&gt;   -6.8644  -1.0912  -2.1300  -4.5600  -3.7915  -4.2678   6.2871  -1.8575
-#&gt;   -1.6467  -3.3780  -2.6142  -6.3843   8.7063  -4.1567  14.1473  -0.9055
-#&gt;   -4.3967  -1.3374   5.1231   2.8412  -0.3606   1.1262  -8.2442   9.5898
-#&gt;   -1.0255   1.8714   5.5460  -3.0920   2.2492  -1.4507   3.2481  -4.1082
-#&gt;   15.2278  -1.4644  -1.6724   7.7990   5.4575  -9.2883   1.3260  -0.2767
-#&gt;    2.4734   1.0298   3.4018  19.7773   6.4603   0.6927  -2.3733  10.8856
-#&gt;    7.5390   5.9991  -1.6665  -1.1839 -15.8801  -4.4179   0.7634  -3.5811
-#&gt;    7.5150  -7.2280   4.7583  -7.6827   7.1725  -8.0871   6.3701   6.3065
-#&gt;   -4.4429 -10.0434 -13.7058  -4.5672   2.3839  -7.5390   7.9516   3.9969
-#&gt;    8.9826   8.9293   7.4789   6.6279  10.4749   2.4352  -2.6923   0.4486
-#&gt;    0.9305  -0.8680  -7.8173  -8.4552  -0.5972  -2.2555   3.4722  -5.0586
-#&gt;    0.5122  -4.1389  -4.9615   0.4793   3.6580  -3.4449   4.5336   3.1636
-#&gt;    2.0379 -19.8241   2.8024   2.6805  10.5936   2.1072   1.4258 -11.4136
-#&gt;    4.7889   6.2284  10.5768   4.5156   9.1442  -8.2460  -6.0661  -2.1584
-#&gt;  -15.9749  -3.6706   0.0012  -7.2619  -0.1787  -9.4787  11.7275   4.4441
-#&gt;   -3.1463   4.3559  -0.9214  -5.9368  -7.9699   3.1736  -0.3443  -1.0745
-#&gt;    2.4498   1.2488   6.3804  -3.6066  -0.9166  -0.3392  -0.6077  -6.2136
-#&gt;  -11.3278   5.6230  -1.1469   5.0627  -6.2094  -1.0932  -3.9888  -2.4802
-#&gt; 
-#&gt; Columns 41 to 48   7.0135   6.3294  -8.8584 -14.5510  10.0676  -0.6149  -4.1354   4.4232
-#&gt;  -10.7895  -2.7615   2.1639  20.1588   2.0257   0.0967  -6.4809   2.2671
-#&gt;   -4.5688  -2.0050   4.8668   6.7397 -11.2983   5.5228  -5.6060 -14.5368
-#&gt;   -7.8524  -5.3228  12.4785  -5.4817   0.3444  -3.9921  -2.4454  -5.6591
-#&gt;    2.3190  15.2425  -2.7544   8.7666 -13.2045  -6.8948  -1.2019  -8.1382
-#&gt;    7.4866  11.4021  -1.9954   1.7636   0.1764   4.6639   5.1616  11.7051
-#&gt;    5.7654   4.7622   5.9318   5.4125  -3.8339   5.5030   1.5205  -8.6833
-#&gt;   -3.0645  -1.9771   6.8651  -6.9690   1.3674   6.4900   2.4330   0.6932
-#&gt;    4.0131  14.1373   0.8940   3.5991  -1.6315   1.8919  -5.1338  -4.8808
-#&gt;    0.9488  10.4619  -3.4536   5.8847  -6.6616   3.8535   3.3347  -8.1186
-#&gt;   -1.3349  -1.6230  -8.1500   4.8047  16.3066   0.6186  -6.0828  -4.2537
-#&gt;  -17.0976  -8.4809  10.3442  -6.5025  -4.5308   6.4224  -4.3393  -4.1076
-#&gt;    1.9242   0.5845  19.1147   0.7265  -2.8166   2.5570  -3.9082  -3.4749
-#&gt;    3.3098   7.0949   7.2359  12.9875  -7.0863  -0.1894   5.2748 -13.0611
-#&gt;   12.1284  16.8619   8.8710   5.6396 -11.2881  -9.3478   1.6231  -0.1281
-#&gt;    4.4235   0.4590 -10.8071   9.9598 -10.6359  -3.5016   4.7264   0.5766
-#&gt;   -1.2458  -2.6800  -3.8881  -0.7222  -1.0022   1.7662   3.4330   0.8911
-#&gt;    0.1136   5.5672   0.2567  -0.0802  -3.1427   5.4188   4.5744  -6.9384
-#&gt;   -2.1668   1.9430  18.8833   9.1453   9.9037  11.1195   0.9507  -8.9267
-#&gt;    1.1640 -10.1598   3.5957  -4.8968  -5.7853   0.4013  -3.8952  -8.5320
-#&gt;  -14.0711 -19.9621   0.9685  11.0321  -2.2575   3.2575  10.8891  -0.8897
-#&gt;    0.3868   7.2838   3.2818  -0.9068  -1.4344  -2.2705 -17.2004   6.3904
-#&gt;   -1.4807   6.5627  12.2064  17.1711   0.4854   8.3901  14.3149   2.6802
-#&gt;   -6.3829 -12.7508  -9.0151   0.1801   2.1498   3.4304   5.9517  10.1215
-#&gt;   -1.7794   0.6457  -5.0495   2.3017  -2.0233  -6.5212   4.6659   5.2673
-#&gt;    4.9225  10.4069   4.0690   2.3700   1.0188  -5.1772   4.6426   8.9672
-#&gt;    3.3292   1.8687  -3.6283  -8.1369   5.5408  -5.3452 -12.8441  -0.9602
-#&gt;    3.5477 -10.2707 -13.2258  10.0899  -0.9471   0.1878   5.2863  -0.4901
-#&gt;    3.1693   2.1068  -3.6108   3.8335  -0.3474  -9.2534   2.9291  -8.8937
-#&gt;   -1.5092   0.1917   3.0646   0.2466   6.9229  11.0598   3.4176   4.7661
-#&gt;   -1.5282   1.3181   2.3549 -15.1336  -7.1468  -5.1242  -1.5188   4.6585
-#&gt;   -1.7846   4.1737  -5.0727  -4.0856  -6.7048  -6.8882   0.4729   6.3599
-#&gt;   -1.1021  -4.5408  -5.4687   5.1239  -2.5769  -4.7390  -4.1566   8.3170
-#&gt; 
-#&gt; (9,.,.) = 
-#&gt;  Columns 1 to 6 -5.9772e-01 -9.8983e+00  1.0404e+01 -6.1356e+00  3.5001e+00 -8.9590e+00
-#&gt;  -6.7052e+00 -2.4473e+00 -5.9757e+00 -6.9124e+00 -7.5695e+00  1.5501e+01
-#&gt;  -5.1638e+00 -3.0836e+00 -8.2642e+00 -1.0065e+01 -2.8839e-01  1.8010e+01
-#&gt;  -3.9167e-01 -4.5961e+00  2.2247e+00 -1.1997e+01  1.2477e+01 -5.6814e+00
-#&gt;   6.0788e+00  1.2863e+01  3.9910e+00  2.3731e+00  7.4136e-01  4.5087e+00
-#&gt;  -3.1761e+00  1.3578e+00 -4.6626e+00  2.6179e+00 -3.8575e+00  9.9712e+00
-#&gt;  -1.1396e+01  3.3664e+00  1.0453e+00  6.6824e+00 -3.1272e+00  7.3237e+00
-#&gt;   3.7370e-01 -8.0273e+00  6.5533e+00 -1.0999e+01 -9.4513e+00 -8.1806e-01
-#&gt;  -1.0576e+01 -1.6506e+00  1.9676e+00 -8.0581e+00  7.0890e+00 -4.9045e+00
-#&gt;  -4.7729e+00  1.8349e+00 -1.5519e+00  8.6583e+00 -7.6534e+00  9.1328e+00
-#&gt;   8.7335e+00  1.4546e+01 -5.9599e+00 -5.1928e+00  2.0621e+00  9.8500e+00
-#&gt;   1.0081e+01 -1.6942e+00 -6.2237e+00 -5.0624e+00  7.8544e+00  1.6970e+01
-#&gt;   1.7356e+00 -9.4270e+00 -5.2761e+00 -6.2843e+00  2.3019e+00  1.8504e+00
-#&gt;  -5.1167e+00  1.2433e+00 -1.0857e+01 -5.2793e+00  1.4291e+01 -7.3475e+00
-#&gt;   1.5092e+00  5.9874e+00 -1.4614e+01 -1.3576e+01 -3.3374e+00  1.4458e+00
-#&gt;  -5.6186e+00  3.3894e+00  2.7115e+00  8.4038e+00  1.0308e+01 -7.5663e+00
-#&gt;  -2.6028e+00 -3.0251e+00 -1.3113e+01  8.9866e+00  2.3850e+00 -2.6730e+00
-#&gt;  -8.7767e+00  1.3823e+01  4.7101e+00  6.0249e-02  1.1898e+00  8.5789e+00
-#&gt;  -6.3649e+00 -7.8938e-01 -1.2153e+01 -5.6260e+00 -6.6550e+00  1.4229e+01
-#&gt;   1.1580e+01  8.7932e+00 -2.5971e+00 -2.3929e+00 -6.2260e+00  1.0944e+00
-#&gt;  -4.3348e-01 -4.3489e+00 -5.5791e+00 -1.3491e+01  8.0694e+00  1.1862e+00
-#&gt;   1.6337e+00 -8.1348e+00 -3.0525e+00  4.3178e+00  1.4512e+01 -7.3191e+00
-#&gt;  -4.4990e+00  1.0172e+00 -2.2629e+01  4.2001e+00 -7.2138e+00  1.2539e-01
-#&gt;  -1.9345e+00 -2.1191e+00 -4.6567e+00  7.5494e+00  1.1774e+01 -6.5196e+00
-#&gt;  -7.8636e+00  1.3880e+01  1.7210e+00 -3.0380e+00  3.4229e+00 -4.0000e-01
-#&gt;   6.5259e+00 -4.9772e+00  1.7033e-01 -1.4080e+01 -6.3298e+00  3.6542e+00
-#&gt;   6.0277e-01  1.8627e+00 -5.3594e+00 -4.0737e+00 -5.3710e+00 -3.8900e+00
-#&gt;   6.8195e+00  1.4984e+01  5.6257e+00  1.4304e+01  7.8092e+00 -5.7403e-01
-#&gt;  -4.1558e+00 -5.3392e-01  3.6684e+00 -9.8107e+00 -2.4841e+00  1.7410e+00
-#&gt;   2.3968e+00 -1.2837e+01  7.0652e+00  1.3442e+01  5.5320e+00 -5.8472e+00
-#&gt;   1.0226e+00 -3.8321e+00  7.9918e-01  1.0291e+00  6.0297e+00 -4.5766e+00
-#&gt;   1.9976e+01 -9.2697e+00 -1.4449e+01  4.4617e+00 -1.1223e+01  4.3968e+00
-#&gt;   4.3422e+00 -8.1300e+00  2.7784e+00 -8.8056e+00  9.0905e+00 -3.6160e+00
-#&gt; 
-#&gt; Columns 7 to 12 -7.9886e+00 -4.1789e-01  3.4889e+00 -6.5670e+00  1.7025e+00 -6.1554e+00
-#&gt;   1.2431e+00 -3.3220e+00 -8.6634e+00  3.4699e+00  9.3183e+00  1.4846e+01
-#&gt;   1.4251e+01 -3.7282e-01 -6.0053e+00 -1.1380e+00  3.1382e+00  7.6234e+00
-#&gt;   2.7875e+00 -1.0526e+01  1.5817e+01  5.0829e+00 -1.1793e+01  3.4515e+00
-#&gt;   3.1888e-01  4.2712e+00  5.5459e+00  1.3774e+00  4.5625e+00 -2.3585e+00
-#&gt;  -3.6468e+00  9.7521e+00 -3.9350e+00 -2.4761e+00  2.9528e+00  1.9369e+00
-#&gt;   8.7279e+00 -3.0858e+00 -4.8988e+00  1.0578e+01 -2.2710e+00 -8.5113e-01
-#&gt;   6.3638e+00 -3.3849e+00 -1.4784e+01 -5.1161e+00 -2.4338e-01  5.7786e+00
-#&gt;   1.1709e+01  7.0229e+00  1.0275e+01  2.0638e+00 -8.0311e-01  2.2046e+00
-#&gt;  -1.9236e+00 -5.5674e+00  3.4492e+00  4.4238e+00  1.4859e+01  9.4732e-01
-#&gt;   7.5186e+00 -7.2396e+00 -1.1684e+01 -8.1177e+00  1.1721e+01  9.8822e+00
-#&gt;  -1.7116e+01 -2.7047e+01 -1.5828e+01  1.8199e+00  3.7760e+00  1.2151e+01
-#&gt;   1.5202e+01 -1.6115e+00  4.3183e+00 -2.2170e-01 -1.3697e+00  6.1547e+00
-#&gt;   1.0838e+01 -2.1337e+00  1.5393e+01 -3.0158e+00 -2.5116e+00 -1.3567e+00
-#&gt;   9.8020e+00  3.9353e+00  8.0539e+00  1.0331e+00  8.9065e+00  4.4400e+00
-#&gt;   1.1014e+01 -5.1721e-01  1.0056e+01 -1.8658e+00 -1.0587e-01 -1.0652e+00
-#&gt;  -9.9327e+00 -7.8304e+00  6.8668e+00  5.4561e+00  1.7317e+01 -8.1515e+00
-#&gt;  -3.6940e-02 -2.0545e-01 -4.2434e+00  2.7756e+00 -1.0222e+00  3.4477e+00
-#&gt;   7.5637e+00  5.2881e+00  5.0657e+00  8.2493e-01  2.7979e+00  4.6146e+00
-#&gt;   6.9088e+00 -1.3395e+01 -1.2399e+01  3.8654e+00  1.9648e+00  3.5629e+00
-#&gt;   6.7725e+00 -7.7367e+00  4.9227e-01  8.8902e+00 -9.8244e+00  1.1019e+01
-#&gt;  -5.6635e+00 -1.9914e+00  7.1919e+00  9.3059e+00 -3.8622e+00 -3.8176e-01
-#&gt;  -9.7490e+00  1.1049e+01  1.6366e+01 -2.3207e+00  6.3493e+00 -1.8590e+00
-#&gt;  -9.0104e+00 -1.3411e+01  1.2738e+01 -5.6306e+00 -3.5250e+00 -8.7249e+00
-#&gt;   4.4496e-03 -2.9238e+00 -6.2142e+00 -1.1927e+00 -1.8179e+00  5.8272e-01
-#&gt;   8.8736e+00  3.5496e+00  1.7175e+00  9.9175e+00  4.7218e+00  4.1414e+00
-#&gt;  -6.9808e+00  5.4581e+00  1.1534e+00 -1.2803e+01 -9.3482e+00 -5.8145e-01
-#&gt;   2.9285e+00 -5.5349e+00 -3.5046e+00  7.2675e+00  5.2552e+00 -3.8660e+00
-#&gt;  -1.4056e+00 -7.3416e+00 -1.8322e+00  6.9944e+00  7.7574e+00  8.1254e+00
-#&gt;  -1.3827e+01  1.4488e+01  1.4742e+01  9.4897e+00  1.2354e+01 -9.0142e+00
-#&gt;  -1.9852e+01 -3.4075e+00 -1.7569e+00 -9.4174e+00 -3.2770e+00 -6.5168e-01
-#&gt;  -1.2156e+01 -7.0697e-01 -7.5206e+00  1.0838e+00  9.1425e+00 -4.8583e+00
-#&gt;   8.3659e-01  4.9569e-01 -8.0905e+00  3.2208e+00  2.9091e-01  1.9936e+00
-#&gt; 
-#&gt; Columns 13 to 18  1.4268e+00 -6.4643e-01 -1.7622e+01 -9.2782e-01 -6.9855e+00 -5.9646e+00
-#&gt;   5.6406e+00  6.4896e+00  3.9732e+00  1.1957e+01  5.9071e+00 -3.4283e+00
-#&gt;   1.4863e+00 -3.2103e+00  6.5280e+00  3.0830e+00  7.4875e+00  3.9739e+00
-#&gt;  -1.3436e+01  1.1493e+01 -5.9182e+00  2.1436e+01 -3.7133e+00 -4.1458e-01
-#&gt;   7.0292e+00  4.3810e+00  1.8445e+01  9.7100e-01 -1.0219e+00  9.3279e+00
-#&gt;   1.1690e+01  9.6098e-01 -6.6718e-01 -8.3299e+00  1.1058e+01 -4.3912e+00
-#&gt;  -8.2923e-01 -3.1380e+00  5.5529e-01 -4.2088e+00 -5.2010e-01  4.0494e+00
-#&gt;  -4.9005e+00  9.1976e-01 -6.6567e+00  4.6711e+00  1.0360e-01  2.0231e+00
-#&gt;   1.0765e+00  6.4593e+00  3.4552e+00 -9.2336e+00  4.4749e+00 -3.8992e+00
-#&gt;   2.2649e+00 -1.8091e-01  1.3098e+01  2.2921e+00  6.5336e+00  6.9644e+00
-#&gt;  -5.2512e+00 -9.9104e+00  6.5997e+00  2.8382e+00 -5.9939e+00  5.7500e+00
-#&gt;  -7.6590e+00 -4.8979e+00  4.1078e+00 -9.0289e+00  2.2878e+00  6.8816e+00
-#&gt;  -1.0736e+01  6.7032e-01  6.6919e+00  6.4399e+00 -2.3449e+00  9.0975e+00
-#&gt;  -9.6398e+00 -2.0480e+00  4.3620e+00 -2.9848e+00 -2.7863e+00  3.0141e-02
-#&gt;   7.9273e+00  4.9398e+00  4.2805e+00  1.4641e+01 -5.9008e+00 -8.4110e+00
-#&gt;  -9.7235e+00  4.5998e+00  1.4957e-01  1.4007e+01  1.1557e+01  3.9815e+00
-#&gt;   1.1669e+01 -1.4081e+01  7.0342e+00 -3.7801e+00 -3.2983e+00  4.9881e+00
-#&gt;  -4.2155e-01 -8.0756e+00 -1.6138e+01 -3.7417e+00 -5.7215e+00 -6.8203e+00
-#&gt;  -6.5037e+00  5.9370e+00  8.5020e+00  1.4392e+01  1.2422e+01  6.3810e+00
-#&gt;   3.2395e+00  9.6114e+00  4.6214e+00 -2.9670e+00  6.5430e+00  8.2656e+00
-#&gt;  -3.6188e+00  1.2400e+01 -1.9658e+01  1.3046e+00 -2.5884e-01 -4.3679e+00
-#&gt;  -8.8569e+00  5.8866e+00 -1.1162e+01 -1.3357e+00 -2.4089e-02 -7.8688e+00
-#&gt;   8.4758e+00 -4.6751e-01  6.0961e+00  3.6116e+00 -9.4077e+00  1.3468e+01
-#&gt;  -5.6394e+00 -2.3949e+00 -1.3362e+01 -1.1423e+01  5.0500e+00 -6.3947e+00
-#&gt;   5.6710e+00  9.3222e+00 -4.5345e+00 -1.2849e+01  1.2083e+01 -8.1289e+00
-#&gt;  -1.7648e+00  1.4789e+01  3.0152e+00  1.3705e+01 -1.6920e+00  9.6765e+00
-#&gt;   2.4847e+00  6.5077e+00  5.2258e+00  4.6694e+00 -1.9813e+00  4.2211e+00
-#&gt;  -4.8027e+00 -8.0172e+00 -6.2700e+00  1.1320e+00 -9.2289e+00 -8.3512e+00
-#&gt;  -2.1190e+00  1.2911e+01  1.1840e+01  6.4975e+00  4.7221e+00 -1.3022e+00
-#&gt;  -2.8746e+00 -8.6969e+00  9.1544e+00  1.7319e+00  6.2875e+00 -3.9191e-02
-#&gt;  -7.6621e+00 -4.1807e+00  9.5632e+00  1.3037e+01 -7.3126e-01  3.5519e+00
-#&gt;   1.0035e+01 -9.1915e+00  2.2972e+01  8.9583e-02 -5.2422e+00  9.7728e+00
-#&gt;  -4.1256e+00  9.7167e+00  1.2161e+01  5.9556e+00  9.9613e+00  6.0163e+00
-#&gt; 
-#&gt; Columns 19 to 24 -1.4788e+00  6.5384e-01  3.4991e+00 -5.0203e+00  1.1833e+01  6.5428e+00
-#&gt;   1.0071e+01  2.6512e+00 -1.8944e+00 -8.1566e+00  3.5798e+00 -1.0335e+01
-#&gt;   3.6254e+00 -6.5613e+00  3.1483e+00  6.8949e+00 -7.4044e+00 -1.5177e+01
-#&gt;   9.3763e+00  8.3278e-01  2.8163e+00 -1.2261e+01  2.4854e+00 -6.6130e+00
-#&gt;  -6.1210e-01 -5.5153e+00 -9.4884e+00 -8.2171e+00 -3.7525e+00 -5.8954e+00
-#&gt;   8.1481e+00  5.1251e+00 -2.1194e+01  9.2614e+00  3.1435e+00 -6.1527e+00
-#&gt;  -2.6929e+00  4.8587e+00  2.8694e+00  4.9911e-04 -1.0671e+01  1.7866e+01
-#&gt;   9.1479e+00  2.6889e+00 -4.1593e+00  5.9204e+00  2.1084e+00  4.5870e+00
-#&gt;  -6.2372e+00 -8.9761e+00 -4.5223e-01 -1.2422e+01  8.3073e+00 -1.0398e+01
-#&gt;  -3.1561e+00 -7.1218e+00 -6.7626e+00 -7.2100e+00 -1.9478e+00 -1.4644e+01
-#&gt;   1.2066e+01 -2.1823e+00  5.2041e-01  4.1326e+00 -2.5256e+00  9.1427e+00
-#&gt;   5.0821e+00  5.6678e+00  3.3381e+00  1.1488e+01 -7.2279e+00  6.3522e+00
-#&gt;  -7.3032e-01 -2.6469e+00  3.1833e+00  8.2736e+00 -5.3655e+00 -6.5288e+00
-#&gt;   4.6056e+00 -2.7989e+00 -1.0808e+00  4.8777e+00  7.8457e+00 -8.4777e+00
-#&gt;  -4.3655e+00 -8.9310e+00 -1.7927e+01 -5.9276e+00 -5.2484e+00 -6.5302e+00
-#&gt;   7.2124e+00 -3.5134e+00  5.8129e+00 -8.7390e+00  1.4915e+01 -8.4247e+00
-#&gt;  -2.9340e+00  1.6258e+00 -4.3462e-01 -7.5343e+00  3.9852e+00  4.6746e+00
-#&gt;   6.7741e+00  1.7857e+00 -4.5787e+00  1.1741e+01  5.7908e+00 -5.6829e+00
-#&gt;   1.2928e+01  5.4310e-02  3.4659e+00  1.9901e+01  2.8366e+00  2.5718e+00
-#&gt;  -4.6991e+00 -3.8227e+00  3.3682e+00 -3.3209e+00 -1.0747e+01  9.3639e+00
-#&gt;   4.2007e+00 -7.2732e+00  2.2640e+00  1.5743e+00  6.7326e+00  7.5268e+00
-#&gt;  -4.4983e+00 -4.3638e+00  1.5736e+01 -1.3776e+01  4.9824e+00  3.0897e+00
-#&gt;   8.9306e+00  3.4701e+00 -2.1963e+00  1.0783e+00  2.4437e+00  1.6603e+01
-#&gt;  -1.1078e+01 -1.6464e+00  1.2810e+01 -6.5072e+00  2.1953e+00 -1.2031e+01
-#&gt;   1.7704e+00  2.0420e+00 -2.6357e+00 -3.8983e+00  1.6743e+01 -1.0175e-01
-#&gt;   5.4945e+00 -3.4841e+00 -5.5210e+00 -6.9099e+00  1.0233e+01  1.2634e+01
-#&gt;  -5.5126e+00  4.0176e+00 -1.0951e+00 -1.7160e+01 -8.5082e+00  1.4385e+01
-#&gt;   2.0308e+00 -8.7291e+00  5.6261e+00 -3.4511e+00 -9.2142e+00 -3.6996e+00
-#&gt;  -5.1241e+00 -9.3991e+00 -7.1547e+00 -3.7527e+00  5.2667e+00 -7.4117e+00
-#&gt;   2.3922e+00 -2.5907e+00  3.1423e+00  5.9142e+00 -2.4298e+00 -5.1382e+00
-#&gt;   8.3085e+00  4.0955e+00 -1.1548e+01  2.3639e+00 -7.7381e+00 -8.7379e+00
-#&gt;  -8.2212e+00 -5.8799e+00 -1.5753e+01  8.4185e-01 -1.7796e+01  2.3051e+00
-#&gt;   4.0001e+00 -4.4698e+00  1.2708e+00 -1.2979e+00  1.1418e+01 -7.5451e+00
-#&gt; 
-#&gt; Columns 25 to 30  7.9131e+00  2.7154e+00  1.4872e+01 -3.7386e+00  1.0172e+01  5.7043e+00
-#&gt;   1.2539e+01 -5.7977e+00  1.4523e+00 -9.3483e+00 -3.8128e+00  8.8916e+00
-#&gt;   5.1914e+00  7.2358e+00 -1.7755e+00 -4.7946e+00 -7.9701e+00 -4.5566e+00
-#&gt;   5.7365e+00 -4.7697e+00  3.2019e+00 -1.5064e+01 -3.9985e+00  9.4707e-01
-#&gt;   1.3723e+00  6.2656e+00 -6.1616e+00  7.3707e+00  7.5894e+00 -2.7101e+00
-#&gt;   3.7877e+00 -1.2307e+00 -1.3621e+00 -4.5134e+00 -1.5887e-01  5.9096e+00
-#&gt;  -2.1808e+00  9.1400e+00 -1.8969e+01  1.0006e+01  4.8628e+00 -4.7325e+00
-#&gt;   2.6560e+00 -7.3123e+00 -1.2357e+00 -7.4553e+00  1.0254e+01 -8.7145e+00
-#&gt;   2.8662e+00  3.2610e+00 -6.6772e+00  7.8181e-01 -1.5148e+00 -2.0216e+00
-#&gt;   4.2846e-01 -2.5294e+00  3.9553e+00 -7.1783e+00  4.7985e+00  1.1505e+01
-#&gt;   9.3605e+00 -7.1987e+00 -8.8640e+00  4.7875e+00 -2.3975e+00 -7.0165e-01
-#&gt;  -1.1350e+01  3.1453e+00 -1.0100e+00  1.1050e+00 -8.0596e+00 -5.7571e+00
-#&gt;  -9.8132e+00  1.3579e+01 -2.7146e+00  9.5315e+00 -1.0039e+01  6.6940e+00
-#&gt;   6.9763e+00  6.9958e+00  8.9224e-01  5.1560e+00  4.1708e+00 -9.1118e+00
-#&gt;   8.2931e+00  2.2381e+00 -8.5325e-01  9.0331e+00  3.3555e+00 -4.0119e-01
-#&gt;  -3.5723e+00  4.3296e-01 -9.3968e-01 -9.5574e+00  1.9474e+00 -2.2715e+00
-#&gt;  -4.5663e+00  1.2040e+00 -7.5696e+00  6.6384e+00  4.2499e+00  8.5393e+00
-#&gt;   5.1188e+00  5.2276e+00 -4.3429e-01 -7.5187e+00  4.4111e+00 -6.0097e+00
-#&gt;  -3.9696e+00 -5.2015e+00 -1.2500e+01 -2.1139e+00 -1.9498e+00  3.8756e+00
-#&gt;   1.5824e+01  1.9293e+00 -8.2049e+00  2.0788e+01  6.3813e+00 -5.7230e+00
-#&gt;   2.1565e+00 -5.5108e+00 -1.5508e+00  1.4097e+00  2.1417e+00 -1.3263e+01
-#&gt;   8.3929e+00 -6.8624e+00  6.7333e+00  2.3216e-01  6.8439e-02 -9.4227e+00
-#&gt;  -2.0929e+00  2.5265e-01 -7.1508e+00  4.1465e+00  3.3456e+00 -7.6238e+00
-#&gt;  -8.5652e+00 -7.6295e+00  3.7898e+00  7.8058e+00 -1.4628e+01  9.4347e+00
-#&gt;   1.1126e+01 -2.1903e-01 -3.5481e+00 -1.7516e+00 -9.3764e+00  5.1781e+00
-#&gt;   4.5579e-01 -9.3047e+00  1.5870e+00  2.4300e+00  1.7651e+01 -4.7332e+00
-#&gt;  -7.6143e+00  1.1278e+01 -7.4274e+00  1.3487e+01 -6.3099e-01 -2.0147e-01
-#&gt;  -1.1968e+00  8.2476e-02  5.4258e+00  1.4257e+00  9.6109e+00 -3.0527e+00
-#&gt;   5.3096e+00  8.0046e-02  7.3033e+00 -9.9037e+00  1.3863e+01  1.9194e-02
-#&gt;  -1.4189e+00  6.8598e+00  5.9213e+00 -2.9910e+00  4.5579e+00  8.9131e+00
-#&gt;   3.3798e-02  8.6234e+00 -3.4579e+00 -6.6073e+00  2.8995e+00  8.0238e+00
-#&gt;  -1.2174e+00  1.7211e-01 -7.2720e-02  5.1705e+00 -7.9263e+00  6.3274e+00
-#&gt;  -1.0282e+01  5.6483e-01  5.9698e+00 -1.0672e+00 -4.2042e+00 -1.7796e-02
-#&gt; 
-#&gt; Columns 31 to 36  1.1844e+00 -7.2629e-01 -1.8941e+00 -7.6083e+00 -4.2147e+00  2.5961e+00
-#&gt;  -4.1939e+00 -2.9817e+00 -4.4593e+00 -5.4455e+00 -5.9977e+00  1.0099e+01
-#&gt;  -4.0233e+00  6.7645e+00  2.6826e+00 -5.2496e+00  3.4417e+00  2.0870e-01
-#&gt;   4.8918e+00  1.9567e+00 -1.6991e+01  1.1052e+01 -2.3925e+00  1.4149e+01
-#&gt;   3.3314e+00 -6.8083e+00  1.0376e+01 -3.6049e+00  9.1836e+00 -1.4545e+00
-#&gt;   1.6061e-01 -1.3304e+01  9.7826e+00  8.3859e+00  8.1357e+00  9.6278e+00
-#&gt;   1.5257e+00  2.2147e+00  8.2917e+00 -5.8503e+00  8.1073e+00 -6.5636e-02
-#&gt;   4.5712e+00  1.0947e+00  2.4104e+00 -1.5979e+00 -1.6107e+01  1.4548e+00
-#&gt;  -1.9434e-01 -2.3166e+00 -2.6052e+00  9.9696e+00  4.3325e+00  1.0414e+00
-#&gt;  -1.3384e+00 -2.7895e+00  6.8319e-01  2.3454e+00 -1.4055e+00  2.0878e-01
-#&gt;   5.9838e+00  1.0921e+00  1.9156e+00 -4.6902e+00  7.7425e+00 -5.7468e+00
-#&gt;  -7.3102e+00  5.0646e+00 -3.5681e+00 -9.5191e+00 -6.9702e-01 -2.1993e+00
-#&gt;   4.7431e-01  4.3941e+00  6.1900e+00  5.5946e-01  2.3139e+00  1.3793e+00
-#&gt;   9.5830e+00 -2.6180e+00  2.3090e+00  1.0488e+01  3.5078e+00  7.5632e+00
-#&gt;   2.1537e+00  7.1923e+00  7.6483e+00 -3.7928e+00  6.7952e+00 -3.0645e+00
-#&gt;   5.4097e+00  2.5142e+00  1.7182e-01  6.4000e+00 -4.7737e-01 -1.1004e+00
-#&gt;  -4.6148e+00  4.4991e-01 -6.9146e+00 -6.9988e+00 -3.4297e+00 -1.2070e+00
-#&gt;   1.4174e+00 -4.9231e+00  6.5810e+00 -1.2322e+00 -3.7342e+00 -1.9426e+00
-#&gt;  -2.0832e+00  1.0677e+00  3.1704e+00 -4.3507e+00  1.0039e+01 -2.5501e-02
-#&gt;  -3.6884e+00  4.0975e+00  3.6172e+00 -4.0332e+00 -3.9415e+00  4.6806e+00
-#&gt;  -6.6195e-01  1.4217e+00  3.0961e+00  2.9925e+00 -7.7697e+00  6.8150e+00
-#&gt;   1.5677e+00  8.5009e-01 -1.0536e+01  5.7060e+00 -4.8438e+00  8.2975e+00
-#&gt;   1.4551e+00 -4.0907e+00  3.4321e+00 -6.6505e+00  1.5629e+01  1.1222e+01
-#&gt;  -4.7277e+00 -3.8640e+00  5.8621e+00 -3.9335e+00  1.0030e+01 -2.7991e+00
-#&gt;  -5.8399e+00 -2.7366e+00  2.3711e-01  3.0909e+00  3.6587e+00 -8.1478e+00
-#&gt;   1.2677e+01 -1.5665e-01 -1.6311e+00 -4.9779e+00  3.6424e+00  3.0476e+00
-#&gt;   2.8489e-01 -4.8924e-02  7.1833e+00 -2.2264e+00  4.6149e+00  6.1171e+00
-#&gt;  -3.6819e+00  5.9067e+00  3.2096e+00  1.0361e+00  7.6217e+00 -3.1803e+00
-#&gt;   7.7033e+00  3.5124e-02  1.5344e-01  6.7063e+00 -1.2995e+01 -2.4705e+00
-#&gt;  -7.7243e-01  5.8335e+00 -9.0640e-01  7.5684e-01  1.5670e+01 -1.5860e+00
-#&gt;   2.1432e+00  6.0483e+00  4.0227e-01 -7.1577e+00 -2.8218e+00 -5.0067e+00
-#&gt;   2.5484e-01  1.0097e+01  2.4836e+00 -1.1225e+00  5.1515e+00 -2.9331e+00
-#&gt;   2.1140e+00  1.2718e+00 -6.7375e+00  3.4060e+00  1.5925e+00  6.5458e+00
-#&gt; 
-#&gt; Columns 37 to 42 -9.0413e+00  4.2641e+00  1.5076e+01 -2.2102e+00  5.7407e-03 -2.3147e+00
-#&gt;  -2.5366e+00  9.2487e+00  5.6971e+00 -7.5793e-01  3.1941e+00  6.2136e+00
-#&gt;  -1.0855e-01  5.7570e+00 -2.9134e+00  3.9281e+00  7.0567e+00  3.6867e+00
-#&gt;  -5.7815e+00  6.1053e+00  2.5409e+00 -4.6347e+00  4.2671e+00  1.3293e+01
-#&gt;   8.7166e+00 -1.9208e+00 -2.0736e+00  1.6139e+00  2.7751e+00  4.2373e+00
-#&gt;  -7.0585e+00  8.1723e+00 -9.2192e+00 -5.7954e+00  1.1677e+01  1.4162e-02
-#&gt;   9.3435e+00  1.7496e+00 -6.9971e+00  1.2072e+01  4.4855e-02  1.0169e+01
-#&gt;  -5.4095e+00 -5.3696e+00 -1.0430e+01  5.7073e-01  1.6181e+00  3.0055e+00
-#&gt;   3.1037e+00  2.6920e+00  1.0490e+01  9.2666e+00  2.1840e+00  5.4630e+00
-#&gt;   4.2201e+00 -1.5488e+00  9.5045e+00  6.7580e+00  2.3942e+00  5.4924e+00
-#&gt;   1.5608e+01 -1.2334e+01 -2.4745e+00  8.0635e+00  4.5486e+00  3.4762e+00
-#&gt;  -5.2987e+00  3.9534e+00 -4.9090e+00 -5.6588e+00 -1.2606e+01 -2.5031e+00
-#&gt;  -9.3156e-01  6.0539e-01 -2.3783e+00  2.9253e+00  8.3484e+00  9.2735e-01
-#&gt;   7.2469e+00 -6.1220e+00 -1.6000e+00  5.0525e+00  3.5506e+00  9.3286e+00
-#&gt;   1.0778e+01 -2.7546e+00 -5.9711e+00 -6.2599e+00  1.7342e+01 -1.2956e-01
-#&gt;   8.0654e+00 -7.5368e+00  4.2084e+00 -2.8554e+00  5.2685e+00  5.8940e+00
-#&gt;  -4.1574e+00  3.0891e+00 -1.7797e-02  2.6921e+00 -6.5087e+00  3.0199e+00
-#&gt;  -1.3272e+01 -3.6118e-01 -1.0552e+01 -3.4381e-01 -5.9032e+00  2.5379e+00
-#&gt;   7.1396e+00 -2.4519e-01  1.4729e+00  2.1580e+00  4.3612e+00  6.5780e+00
-#&gt;   6.7803e+00 -6.1947e+00  5.5043e+00  1.2180e+01 -8.9490e+00 -3.8433e+00
-#&gt;  -1.3721e+00 -9.1113e+00  5.5899e+00  3.0196e+00 -9.7791e+00  6.4865e+00
-#&gt;   1.7351e+00  3.5879e+00  1.5976e+01  4.0530e+00  2.1135e+00 -5.7476e+00
-#&gt;   1.5130e+01 -1.0406e+01 -6.5211e+00 -7.1649e+00 -3.4955e+00  1.1013e+00
-#&gt;   3.6414e+00  5.7045e+00  2.1475e+01 -7.1721e+00 -7.4133e+00  8.6888e+00
-#&gt;  -1.0062e+01  2.4678e+00 -4.0043e+00 -7.9186e-01 -3.4969e+00 -3.5037e+00
-#&gt;   3.4416e+00 -3.2598e+00 -3.1747e+00 -4.2460e+00  1.4092e+01  4.3662e+00
-#&gt;   1.3994e+00  1.1805e+00  7.6355e-01  8.1165e-01 -7.8432e+00  4.3089e+00
-#&gt;   1.7457e+01 -3.9223e-01 -1.0589e+01 -5.4922e+00  1.0360e+01 -4.8000e+00
-#&gt;  -6.0066e+00 -5.0413e-01 -3.5014e+00  7.0190e+00 -6.6348e+00 -4.3041e-01
-#&gt;   5.7813e+00  9.0441e-01  8.9422e-01 -4.0966e+00  9.7021e-01 -6.7914e+00
-#&gt;  -8.4177e+00  7.5051e+00 -8.2865e+00 -5.5693e+00  3.7791e+00  4.6787e+00
-#&gt;   9.4031e-01 -5.7692e+00 -9.4047e+00 -2.7730e+00  1.0719e+01 -5.6055e+00
-#&gt;  -2.0762e+00  7.6085e+00  4.4843e-01  4.3434e+00  8.3749e+00 -4.6087e+00
-#&gt; 
-#&gt; Columns 43 to 48  4.3737e+00 -2.5087e+00  6.3889e+00 -8.0037e+00  1.2070e+01  7.6759e+00
-#&gt;  -2.0968e+00 -2.8082e+00 -1.3237e+00  1.1395e+01 -8.8094e+00 -2.2518e-01
-#&gt;   4.4428e+00 -1.5217e+00  7.1631e+00  1.0258e+01  7.0296e+00 -6.6016e+00
-#&gt;   9.3830e-01  1.0754e+00  6.3390e+00  6.4996e+00  1.1364e+00  6.5365e+00
-#&gt;  -7.3669e+00  1.2879e+00 -3.6561e+00  6.0226e+00  1.0079e+00 -5.1102e+00
-#&gt;  -3.1869e+00  6.5918e+00  2.0732e-01 -4.5766e+00 -6.3377e+00  2.8138e+00
-#&gt;   2.0437e+00 -1.0918e+01  1.2007e+01 -9.5038e+00 -3.3561e+00 -5.4644e+00
-#&gt;  -2.2200e-02 -2.5758e+00  2.5701e+00 -1.5186e+00 -1.5802e+00 -6.7054e+00
-#&gt;  -1.1495e+00  7.3191e+00 -8.1472e-01  6.5960e+00 -4.5626e+00 -6.9297e+00
-#&gt;  -3.1921e+00  1.1575e+00  1.1334e+00  1.0130e+01 -3.0875e+00  1.0981e+00
-#&gt;  -1.3860e+01 -7.7124e+00 -8.0822e+00  4.3692e+00 -1.8890e+00 -1.1706e+01
-#&gt;  -1.0236e+01 -1.4173e+01  6.1403e+00  1.3038e+00 -6.5673e+00 -4.4560e+00
-#&gt;   1.6044e+00 -2.1522e+00 -3.1384e+00  4.5684e+00 -1.2475e-01  8.4231e-01
-#&gt;  -9.8657e+00  6.5702e+00  4.0053e+00  8.1363e-01 -3.0906e+00 -8.5054e-01
-#&gt;  -6.7798e-01  6.9365e+00  6.7617e+00  7.0552e+00  5.7285e+00 -7.3362e-01
-#&gt;  -1.9092e+00 -1.2977e+00  1.3451e+00  2.6043e+00  7.1558e+00  2.2102e+00
-#&gt;   1.3258e+01 -4.5233e+00  4.2634e+00  2.9977e+00  1.4320e+00  6.0799e+00
-#&gt;  -9.4925e+00 -2.7318e-01 -5.4690e+00 -6.3278e+00  1.4818e+00  1.2148e+01
-#&gt;  -1.5550e+00 -4.9477e+00 -3.7344e+00  6.9157e+00  5.1898e+00 -2.2928e+01
-#&gt;  -4.9061e+00  2.7184e+00  2.0085e+00  1.0371e+01 -5.9143e+00  4.4246e-01
-#&gt;  -1.1608e+01  6.4426e+00  2.3071e+00 -2.4539e-01 -1.0232e+01  4.0275e+00
-#&gt;  -1.8309e+00  8.3980e+00  4.2186e+00 -3.7221e+00 -1.2211e+01  9.1400e+00
-#&gt;  -4.2552e+00  2.3704e+00  1.4545e+00 -3.9296e+00  1.6062e+00 -1.3370e+01
-#&gt;  -2.1842e+00  1.5637e+00 -5.2733e+00  1.9259e+00 -6.5270e+00  9.1398e-01
-#&gt;  -5.7691e+00 -2.3866e+00 -3.4253e+00  3.6446e+00 -7.2987e-01 -9.9818e+00
-#&gt;  -5.3362e-01 -4.7567e+00  2.5631e+00  5.8880e+00  3.7545e+00 -9.2315e+00
-#&gt;   8.1852e+00  5.2404e+00  4.6403e+00 -5.1693e+00  5.6024e+00 -1.9305e+00
-#&gt;  -1.9630e+00 -1.4028e+01  1.1234e+00 -4.9099e+00 -8.5770e+00  9.8347e+00
-#&gt;  -2.3442e+00  8.7382e-01  6.1165e+00  9.1952e+00  3.3971e+00 -4.7489e+00
-#&gt;   1.4446e+01 -5.3570e+00 -9.9253e-01  1.6825e+00  1.0498e+01 -7.8251e+00
-#&gt;  -1.2283e+00  8.3363e+00  3.4820e+00  8.8028e+00  1.2035e+01 -5.4122e+00
-#&gt;   3.3773e+00 -5.1518e+00  3.0077e+00  1.2355e+01  5.0393e+00  1.6614e+00
-#&gt;   4.3089e-01 -4.3456e+00  1.1466e+00  5.3134e+00 -2.3118e+00  8.6921e-02
-#&gt; 
-#&gt; (10,.,.) = 
-#&gt;  Columns 1 to 6 -9.5958e+00 -7.3723e+00  1.2047e+01  6.5237e-01 -4.7717e+00 -1.2016e+01
-#&gt;   4.8334e+00  5.0307e+00  8.7751e+00 -4.1145e+00 -2.9787e+00 -7.0777e+00
-#&gt;   5.3799e+00  2.5190e+00 -1.0327e+00 -3.6891e+00 -3.6897e+00 -2.2001e+00
-#&gt;   3.0633e+00 -7.7809e+00  9.6613e+00 -4.0909e+00 -3.6797e+00 -5.8602e+00
-#&gt;  -8.7687e+00  7.7281e+00  2.8332e+00 -9.3733e+00 -4.9154e+00  4.3284e+00
-#&gt;   2.6050e+00  7.4308e+00  2.4147e+00 -1.8745e+00  5.2642e+00  7.2496e+00
-#&gt;   6.1107e-01  3.7037e+00 -3.6763e-01 -3.7137e+00 -6.8335e+00 -2.2041e-02
-#&gt;   6.3727e+00 -4.0470e+00  9.8372e+00 -9.1580e-02  1.2816e+01 -7.3477e+00
-#&gt;   4.6817e+00  1.1897e+00 -4.7628e+00 -3.2549e+00 -5.2942e+00 -4.7634e+00
-#&gt;   1.2118e+00  3.4150e+00  2.0200e+01  4.0394e+00 -6.6602e+00 -4.1004e+00
-#&gt;  -1.8507e+01  1.0203e+01 -6.0837e+00  4.8159e+00 -1.6472e+00  2.0550e+00
-#&gt;  -1.1630e+00  3.0891e+00  7.1104e+00 -2.4322e+00 -2.3919e+00  2.3690e+00
-#&gt;   2.2192e+00  5.9340e+00 -4.8951e+00  1.4631e+01  3.8242e+00 -3.2868e+00
-#&gt;  -3.2127e+00 -3.8830e+00 -6.4114e+00 -5.0048e-01  8.1078e+00 -5.9243e+00
-#&gt;  -4.0585e+00 -2.5125e-01 -1.9762e+01 -5.1743e+00 -7.5492e+00  1.3552e+00
-#&gt;   6.4482e+00 -4.5000e+00 -5.4551e+00  1.3725e+01 -3.2925e+00 -9.4615e-01
-#&gt;   1.8685e-01 -7.3666e+00  1.3488e+01  1.1538e+01  2.1585e+00 -3.5440e+00
-#&gt;  -8.2571e+00 -7.2357e+00  2.6185e+00 -5.7009e+00  1.0855e+01 -6.0345e+00
-#&gt;  -1.0681e+01  7.8015e-01  1.1207e+01  2.2349e-01 -1.9994e+01 -4.4592e+00
-#&gt;  -2.9950e+00  7.2179e+00  3.6860e+00 -3.2118e+00  3.0814e+00 -8.9734e+00
-#&gt;   1.2245e+01  4.6379e+00  8.6471e-01 -1.1092e+01  1.4047e+01 -2.6863e+00
-#&gt;   3.6473e+00  9.6788e-01 -1.5797e+01 -7.5575e-01  1.0347e+00 -1.4668e+01
-#&gt;  -8.9120e+00 -7.8948e+00 -2.6262e+00 -2.0313e+00  4.8857e+00  5.1242e+00
-#&gt;   5.4599e+00  2.0633e+01  1.3699e+01 -6.1316e+00 -7.5593e+00  6.0918e+00
-#&gt;  -2.3579e+00  3.9159e+00 -2.1032e+00 -1.1156e+01 -1.1244e+01 -1.0482e+00
-#&gt;  -1.1059e+01  3.7168e+00  1.0351e+01  3.6616e+00 -2.1056e+00 -4.0807e+00
-#&gt;  -3.3395e+00  4.6382e+00 -5.7943e+00 -4.8561e-01 -4.6544e+00  2.2636e+00
-#&gt;  -4.9313e+00  1.4905e+01 -1.6279e+01  1.7440e+00  1.5006e-01  1.0771e+01
-#&gt;   1.8941e+00 -2.4229e+00  6.7721e+00 -1.3173e+01 -3.8212e+00 -9.3271e+00
-#&gt;  -9.6208e+00 -3.8180e+00  9.4949e+00  1.4090e+01  2.8480e-01  8.8882e+00
-#&gt;  -1.6255e-01 -3.3643e+00  1.2077e+01  3.0009e+00 -2.8539e-01 -2.1709e+00
-#&gt;   1.0549e+01  4.7026e+00 -8.8984e-01  2.2968e+00 -1.7090e+00  1.1730e+01
-#&gt;   5.7700e+00  5.7668e+00  8.5146e-01  1.1141e+00 -3.9964e-01 -4.2993e+00
-#&gt; 
-#&gt; Columns 7 to 12 -5.5264e+00  5.2635e+00 -4.5586e+00 -7.7724e+00 -6.7307e+00 -8.4080e+00
-#&gt;   4.2303e+00 -3.7358e-01  7.6817e-01  6.5046e+00 -3.3298e+00  1.6328e+01
-#&gt;   4.7472e+00 -1.0522e+00  1.4437e+01  6.2140e+00  3.2500e+00  7.5858e+00
-#&gt;   1.5293e+01  5.4497e+00  1.0482e-01  8.9316e-01 -2.1978e-01  5.0263e+00
-#&gt;   2.2517e+00  1.9998e+00 -2.8958e+00  8.6876e+00  5.9590e-01 -2.5562e+00
-#&gt;   1.5858e+01  2.7522e+00 -4.7113e+00  6.5628e+00 -3.8360e+00 -6.7326e+00
-#&gt;  -2.1941e+00 -6.8718e+00  3.7223e+00 -1.0419e+01 -1.1370e+00 -4.4845e-01
-#&gt;  -1.1046e+01  3.7126e-01 -4.4773e+00 -3.0009e+00  4.1116e+00  3.8538e+00
-#&gt;  -4.7099e+00 -1.8970e+00  1.4741e+00  7.1659e-01  3.7944e+00  6.2840e+00
-#&gt;  -7.3374e+00  7.9661e+00  8.4182e+00  3.5170e+00 -6.3520e+00 -1.7288e-02
-#&gt;  -1.7208e+01 -3.3698e+00  4.1292e+00 -2.0220e+00 -1.0347e+01  1.5438e+01
-#&gt;  -1.0082e+01 -6.4995e+00  5.5515e-01  5.9078e+00  7.1912e+00  3.6817e+00
-#&gt;  -2.3130e+00  8.0063e-01  1.1618e+01  9.1944e+00 -7.0368e-01  1.0089e-01
-#&gt;   1.9452e+00  4.3857e-01  9.5861e+00 -5.9947e+00  3.2025e+00 -4.1489e+00
-#&gt;   2.3898e+00  5.1869e+00  7.7287e+00  3.4755e+00  5.7937e+00  5.4252e-01
-#&gt;  -5.9263e-01  9.0569e+00  1.5395e+01 -2.7766e+00  1.0821e+01 -1.7293e+00
-#&gt;   4.5239e-01 -1.0123e+01 -3.0120e+00 -4.2275e+00 -5.5924e+00 -1.9935e+00
-#&gt;   1.8789e+00 -5.4162e+00 -1.0534e+01  3.1018e+00 -6.8032e+00 -4.4391e+00
-#&gt;  -1.0391e+00  7.1574e+00 -2.2047e+00  6.4773e+00  2.0316e+01  2.3733e+00
-#&gt;  -3.7556e+00  6.4101e+00  4.5364e+00 -4.5714e+00 -1.0283e+01  3.7152e+00
-#&gt;  -1.0023e+01  4.5924e+00 -7.0045e+00 -3.7151e+00  7.6043e+00  1.0022e+01
-#&gt;  -4.4115e+00  4.4164e-02  3.7332e+00 -1.2023e+00 -6.7839e+00  3.3401e+00
-#&gt;   4.4598e+00  7.0718e+00  2.9898e+00  3.5532e+00  1.2489e+01 -4.6912e+00
-#&gt;   7.4195e+00 -3.9552e+00 -3.8079e+00 -6.0742e+00 -6.6667e+00 -1.4535e+01
-#&gt;   3.6264e+00 -1.3222e+01 -6.8985e+00 -2.4302e+00  1.0476e+01  3.2049e+00
-#&gt;   6.4970e+00  5.5345e+00  2.7960e+00  2.5680e+00  1.0286e+01  5.2805e-01
-#&gt;   2.8223e+00  4.0463e-01 -3.0897e+00 -2.8653e+00 -4.7640e+00  3.1136e+00
-#&gt;   1.0542e+00 -1.2056e+01  2.3570e+00 -1.0653e+01 -2.1960e+00 -5.1746e+00
-#&gt;   2.5563e+00  4.3190e+00  6.0527e+00 -1.5861e+00  1.3050e+01  3.7657e+00
-#&gt;  -2.7193e+00  8.2389e+00  4.5454e+00  1.9444e+00 -6.1626e+00 -1.5945e+01
-#&gt;  -1.5180e+00 -3.8249e+00 -4.1468e+00 -2.9882e+00 -4.2244e+00 -2.6074e+00
-#&gt;   1.0457e+01  1.1844e+00  1.2966e+01  7.0765e+00 -3.6259e+00  7.7815e+00
-#&gt;   4.6665e+00  5.6338e+00  1.1487e+01  1.3988e+01  6.3386e+00 -8.3386e-01
-#&gt; 
-#&gt; Columns 13 to 18  2.0886e+00 -7.3745e-01  3.8596e+00  1.1250e+01  7.6722e+00  5.0116e+00
-#&gt;   2.1444e+00 -1.6645e+00  6.8044e-01 -4.4825e+00 -2.3838e+00 -8.9263e+00
-#&gt;   1.7066e+00  8.2733e-01  1.3864e+00 -5.2769e+00  2.1373e+00  2.8794e+00
-#&gt;  -9.2881e-01  5.1343e+00 -4.6063e-01 -6.8650e+00  5.7070e+00 -3.9509e+00
-#&gt;   8.9417e+00 -3.2170e+00 -7.5011e+00 -1.3436e+00  2.3125e+00 -1.0677e+01
-#&gt;   2.6758e+00  2.1154e+00 -1.4393e+01  1.9364e+00 -1.8340e+00 -1.4569e+00
-#&gt;   1.2783e+01 -2.6929e+00  3.6496e+00 -6.7124e+00 -3.2855e+00 -7.3697e+00
-#&gt;   3.5376e+00 -1.2442e+00  6.6557e-01 -9.2502e+00 -4.3490e+00  5.2186e+00
-#&gt;  -1.0287e+00  1.7489e+00 -6.3890e+00 -3.3085e+00  4.4065e+00  4.9332e+00
-#&gt;  -6.0900e+00 -6.4055e+00 -2.4614e+00 -3.4166e+00  3.4514e-01 -1.0389e+01
-#&gt;   1.3470e+01 -1.1791e+01 -2.7705e+00  8.8360e+00  8.9835e+00  4.3432e-02
-#&gt;  -2.2199e+00  3.6928e+00  7.5354e+00  1.7304e+00 -6.0398e-02  5.4968e+00
-#&gt;  -1.4242e+00 -8.0811e-01 -3.7795e+00 -1.4466e+01  1.3720e+01  3.5974e-01
-#&gt;  -1.2734e+00  1.6379e+00 -1.8523e+00 -1.4098e+01  1.9150e+01  4.3712e+00
-#&gt;   3.7641e+00  2.0794e+00 -4.8163e+00 -1.0482e+01  6.0374e+00 -6.2612e+00
-#&gt;  -5.7567e+00 -3.5951e-01  3.5209e+00 -9.5553e+00  7.4145e+00 -1.5226e+00
-#&gt;  -4.3966e+00 -8.0719e+00 -7.3956e-01  8.1789e-01 -5.3823e+00 -4.8919e+00
-#&gt;   5.3892e+00 -5.2176e+00 -1.4142e+01  7.2664e+00  1.4347e+01  2.4555e-01
-#&gt;   7.7653e+00 -5.8794e+00  1.2585e+01  2.1114e+00 -1.4813e+00 -7.0001e+00
-#&gt;   3.9473e+00 -5.0185e+00  1.1463e+01  7.1674e+00 -1.6456e-01  5.4728e-03
-#&gt;   2.4814e+00 -1.7935e-01  6.9283e+00 -1.1585e+01  1.4784e+00  2.3654e+00
-#&gt;  -6.4205e+00  1.1133e+01  2.9250e+00  1.7329e+00  7.0688e-01  5.7559e+00
-#&gt;   1.0456e+01 -6.8576e+00  4.8140e+00 -7.6468e+00 -2.7327e+00 -6.2993e+00
-#&gt;   6.8432e-01 -7.0138e+00  1.2649e+01 -9.6007e+00 -7.8085e+00 -2.0163e+01
-#&gt;   5.6327e-01 -9.2424e-01  7.8005e+00  1.6437e+01  4.0998e+00  1.4246e+00
-#&gt;  -3.8699e-01 -3.5711e+00  4.4379e+00 -9.7836e+00 -6.2413e+00 -2.5627e+00
-#&gt;   1.2638e+01  8.7734e-01 -3.8200e+00 -2.4395e-01 -3.4485e+00  1.2097e+00
-#&gt;  -5.7193e-01  1.1901e+01  2.9845e+00 -1.4858e+01 -6.7063e-01  2.3071e+00
-#&gt;  -9.6732e+00 -5.1042e-01  8.5809e+00 -3.6766e+00  4.3520e+00  6.9453e+00
-#&gt;   1.8797e+00 -2.1659e+00  4.6082e-01 -3.9050e+00 -7.2183e+00  1.1659e+01
-#&gt;   1.8875e-01  3.7510e+00 -4.7080e+00  2.4531e-01 -2.2246e+00 -9.3851e+00
-#&gt;  -7.1921e+00 -8.4018e+00  1.4588e+00  2.4293e+00 -7.9774e+00 -1.2957e+00
-#&gt;  -7.1650e+00  6.5995e+00 -8.2609e-01 -4.5824e+00  1.8581e+00  1.0594e+01
-#&gt; 
-#&gt; Columns 19 to 24 -1.6547e+01 -1.7455e-01 -1.3965e+01  1.1901e+01  1.1083e+01  7.0531e-01
-#&gt;  -9.7378e+00  4.9298e+00 -3.4363e+00 -8.7591e-01 -2.4670e+00 -5.0297e+00
-#&gt;   4.3643e+00 -1.5493e+00 -5.8622e+00 -7.1102e+00 -1.2048e+01  2.1534e+00
-#&gt;  -1.5390e+01  2.9438e+00  3.5965e+00 -4.0425e+00  4.1766e+00 -2.8972e-01
-#&gt;   2.0704e-01 -8.7539e-01 -6.3799e+00 -4.0346e+00 -1.2937e+01 -2.5051e+00
-#&gt;   1.3951e+00 -1.5320e+00 -2.3383e+00  8.2027e+00 -4.7966e+00  2.8239e+00
-#&gt;  -8.0676e+00 -6.4718e+00  5.0619e+00 -5.0724e+00 -6.6217e+00  8.5565e+00
-#&gt;   3.2756e+00  7.5328e+00 -5.7972e+00  9.7473e-02  3.8714e+00 -7.3040e-01
-#&gt;  -9.6483e+00 -2.0600e+00 -9.7193e+00 -4.9492e+00 -7.4333e-01 -8.6179e+00
-#&gt;  -1.0411e+01 -6.1163e+00 -6.2946e+00 -6.3084e+00 -8.5870e+00 -4.3519e+00
-#&gt;   1.3475e+01 -6.2340e+00  9.6245e-02  4.0572e+00  4.9656e+00  6.8442e+00
-#&gt;   1.2770e+01  7.3423e+00  5.9753e+00 -3.9718e+00 -5.8074e+00  1.2654e+01
-#&gt;   6.1425e+00 -1.5625e+00 -5.7367e-01 -8.6138e+00 -5.9765e+00 -2.7418e+00
-#&gt;   2.2703e+00 -2.2491e+00  8.4373e-01 -6.4280e+00  3.2190e+00  3.5527e+00
-#&gt;   1.1549e+01 -7.9122e+00 -1.8938e+01 -1.5693e+01 -4.1604e+00  1.1917e+01
-#&gt;  -5.9029e+00  9.0893e+00  7.5842e-01 -5.2862e+00  8.7295e+00 -7.4853e+00
-#&gt;  -1.2061e+01 -6.9287e+00 -3.7148e-01  5.6541e+00  1.5193e+00  3.5425e-01
-#&gt;  -5.0123e+00 -4.6697e+00 -1.0465e+00  1.5650e+00 -1.0680e+00  2.7245e+00
-#&gt;   1.0073e+01  7.4417e+00  9.5893e+00  4.2450e+00  4.2740e+00  6.7323e-01
-#&gt;   4.4783e+00 -4.2850e+00  5.4657e+00 -3.8559e+00 -6.5410e+00  3.1018e+00
-#&gt;   2.0051e+00  2.6213e+00  3.6959e+00  1.6965e+00  5.2516e+00 -1.1366e+00
-#&gt;  -1.3355e+01 -2.0926e+00  1.9470e+00 -9.4491e+00  7.5002e+00 -6.4533e+00
-#&gt;   1.8658e+01  4.7384e+00  1.5558e+00  2.8851e+00  6.7274e+00 -1.1672e+00
-#&gt;  -3.9936e+00 -6.7976e-01  3.4615e+00  1.5546e-01 -4.7202e+00  3.0028e+00
-#&gt;  -1.1919e+00 -1.3104e+00  1.5569e+00  1.2456e+01  3.1025e+00  2.3721e+00
-#&gt;   1.1173e+00  4.5066e+00 -9.3090e+00 -8.5264e-03  1.7803e+00 -6.3240e+00
-#&gt;   1.9434e+00  1.0001e+01 -4.6523e+00 -7.2178e+00  1.9133e+00  4.2573e+00
-#&gt;   1.7640e+01  2.0178e+00  1.2119e+01 -1.2353e+00 -1.2177e+00  3.3461e+00
-#&gt;  -1.1616e+01 -2.9746e+00 -7.5183e+00 -4.7508e-01  4.7900e+00  1.1289e+00
-#&gt;  -4.1454e+00 -8.5355e+00 -4.8618e+00  7.4444e+00 -6.7522e-01  1.5182e+00
-#&gt;   9.5614e+00  7.6493e-01 -1.7208e+01 -4.1176e+00  5.2517e+00  3.0220e+00
-#&gt;   7.9180e+00 -8.7570e-01 -7.2966e+00 -8.8530e+00 -1.2356e+00  9.8385e+00
-#&gt;   8.0671e-01 -7.2320e-01 -2.2440e+00  6.0880e+00  2.4059e+00 -2.9827e+00
-#&gt; 
-#&gt; Columns 25 to 30 -1.7654e+00  6.1573e+00 -4.7365e+00 -8.6320e+00 -1.0574e+01 -2.4142e+00
-#&gt;   9.6550e+00 -1.3046e+01  3.9530e+00 -7.7652e+00 -7.8142e-03 -2.3785e+00
-#&gt;   7.7095e+00 -4.5380e-02 -2.2185e+00 -2.1431e+00 -2.8480e+00 -4.1918e+00
-#&gt;  -1.2611e+00 -6.1082e+00  8.4187e+00 -1.1930e+01 -1.8830e+00  9.4992e-01
-#&gt;   2.9813e+00  1.7482e+00 -3.5632e+00  1.0422e+01  7.9552e+00 -9.4522e+00
-#&gt;   2.3804e+00 -3.7486e+00 -7.8931e+00 -8.6517e-02  3.5921e+00  5.1656e+00
-#&gt;  -1.0735e+01  2.3283e+00 -1.0351e+01  9.9058e+00 -1.6368e+01  1.7590e+01
-#&gt;  -3.3455e+00  6.6997e+00  6.1150e+00 -1.3539e+01  3.1509e+00  9.2606e+00
-#&gt;   9.9220e+00  2.6841e+00 -7.4650e+00 -3.8941e+00  4.9308e+00 -9.6910e+00
-#&gt;   1.2039e+01 -5.5931e+00  3.8058e+00 -3.7076e-02  4.8538e+00 -1.3906e+01
-#&gt;  -1.2222e+00 -5.8971e+00  5.7628e+00 -1.3399e+00  8.7426e+00  2.8881e-01
-#&gt;  -3.1671e-01 -1.3056e+00 -8.4503e+00  6.6954e+00  4.4994e+00  7.4874e-03
-#&gt;   5.8190e+00  1.4675e+01 -4.8289e+00  5.9821e+00  6.1915e+00 -1.3343e-01
-#&gt;   3.3220e-04  4.6068e+00 -4.6546e+00  5.7364e+00 -2.2543e+00  2.7028e+00
-#&gt;   9.3891e+00 -2.0363e+00 -8.9049e+00  4.3891e+00 -8.8804e+00 -9.8594e+00
-#&gt;  -1.0224e+00  5.4304e+00  5.4160e+00  1.0815e+01 -9.0729e+00  3.5370e-01
-#&gt;  -1.5242e+00 -5.8958e+00 -9.4318e+00  1.0289e+01 -2.9070e+00 -4.4505e+00
-#&gt;  -2.7731e-01 -6.3541e+00  1.7985e+00  4.7676e+00  1.1100e+01  5.3108e+00
-#&gt;  -4.6237e+00  5.7527e+00  1.9249e+00  2.4828e+00 -6.9288e+00  4.5745e+00
-#&gt;   1.0163e+01  5.8311e-01 -1.0615e+00 -6.9244e+00 -7.4802e+00 -1.2862e+01
-#&gt;  -2.9575e+00  2.7526e+00  3.0471e+00 -1.4789e+01  4.2150e-01  4.5949e+00
-#&gt;   5.3139e+00 -8.4273e-01 -1.1009e+00 -1.0658e+01  2.4117e-01 -5.2637e+00
-#&gt;  -1.8128e+00  3.4119e+00 -9.5389e+00  4.7784e+00  1.5970e+00  8.7575e+00
-#&gt;   7.7266e+00 -1.3421e+00 -1.6817e+01  6.9556e+00  8.4550e-01 -3.6458e+00
-#&gt;   2.7317e+00 -5.6457e+00 -4.8476e+00  4.2472e+00 -7.1554e+00 -4.5735e+00
-#&gt;   2.0287e+00  1.2595e+01  5.9875e+00 -6.9651e+00 -3.8292e-01  4.1640e+00
-#&gt;   4.7382e+00  1.5736e+00 -1.6172e+01  8.5885e-01 -4.5621e+00 -4.1614e+00
-#&gt;  -1.3447e+01 -8.9498e+00  1.1139e+01  1.4416e+01 -6.6645e+00  1.1075e+01
-#&gt;   1.9097e+00 -3.9173e+00  1.9079e+00 -7.1014e+00 -6.5555e+00 -1.1570e+01
-#&gt;  -6.9789e+00  1.3138e+00  2.7922e+00  1.4033e+01 -3.5511e+00 -6.7887e+00
-#&gt;   4.4467e+00  4.0878e-01 -4.3903e+00 -4.1820e+00 -1.0072e+00 -4.2013e+00
-#&gt;   5.4239e+00 -5.3318e+00  1.1106e+01  8.8659e+00 -6.3158e+00 -8.8431e+00
-#&gt;   1.7667e+00  7.0973e+00  1.4381e-01 -1.7409e+00 -1.4521e+00 -8.0895e+00
-#&gt; 
-#&gt; Columns 31 to 36  5.7877e+00 -4.6230e+00  7.2711e-01 -5.3029e+00 -1.0070e+01  1.5749e+00
-#&gt;  -6.4950e+00 -8.0376e+00 -4.5445e+00 -6.8741e-01  7.7429e+00 -1.0083e+01
-#&gt;  -1.5847e+01  1.7480e-01 -2.1960e+00 -3.0769e+00  1.8834e+00 -1.6263e+01
-#&gt;  -6.5188e+00 -4.4961e+00 -7.4398e+00  4.9623e+00 -5.0504e+00 -1.0719e+01
-#&gt;  -6.9759e+00 -2.7550e+00  3.8908e+00 -1.4866e+00  1.4092e+00  8.6068e+00
-#&gt;   1.1555e+00 -7.9262e+00 -1.7627e+00  1.4443e+01  5.2027e+00  1.9195e+01
-#&gt;  -1.1875e+01  1.7406e-01  3.8870e+00 -1.6582e+01 -7.6358e-01 -9.4129e+00
-#&gt;  -5.6199e+00  2.9915e-01 -4.2304e+00  1.3446e+00 -8.7123e+00 -8.4327e+00
-#&gt;   1.4075e+00  2.2520e+00 -5.4539e-01  1.2540e-01  2.7837e+00 -1.9737e+00
-#&gt;  -1.0888e+01  2.6625e+00 -2.2844e-01 -5.8239e+00  9.2506e+00 -4.6154e+00
-#&gt;   3.2156e-01 -4.3800e+00  1.0448e+01  1.1336e+01 -5.0925e-01  1.1347e+01
-#&gt;  -8.9026e+00  8.4311e+00 -1.1829e+00  8.5838e+00  4.1165e+00 -2.6017e+00
-#&gt;  -1.0618e+01  4.5853e+00 -8.2806e-01  3.4717e-01 -5.1240e+00 -7.8976e+00
-#&gt;  -1.1902e+01  1.0810e+01  3.4999e+00  3.2599e+00  9.5764e-01 -8.7301e+00
-#&gt;  -1.3804e+01  3.4591e+00  2.7214e-01  3.7915e+00  8.5312e-03 -5.0884e-01
-#&gt;  -8.1244e+00  1.0172e+00  1.8569e+00 -2.3522e+00  6.7773e+00 -1.2270e+01
-#&gt;   1.8376e+00  8.6732e-01  1.6401e+01 -7.6989e+00 -2.0114e-01  2.8492e+00
-#&gt;   1.0577e+00 -1.9100e+00  3.3745e+00  1.2003e+01  3.8370e-01 -1.6340e-01
-#&gt;  -2.0837e+00  8.0215e-01  4.6182e+00  2.0742e+00 -5.6774e+00  1.3857e+00
-#&gt;  -6.0330e+00  1.0794e+01  5.6998e+00 -6.3962e+00  4.2862e+00 -2.6237e+00
-#&gt;  -3.0855e+00  7.1994e+00 -2.1118e+00  8.3192e+00  1.5085e+00 -1.3735e+01
-#&gt;   2.6148e-01  1.1909e+01 -1.5958e+00 -8.5097e+00 -1.8810e+00 -1.4911e+01
-#&gt;  -8.3884e+00  3.3756e+00  1.4041e+01  9.2340e-01  8.0913e+00  2.0051e+01
-#&gt;   7.7919e+00 -1.8161e+00 -5.1652e+00 -1.1685e+01  6.0734e+00  5.4655e+00
-#&gt;   7.2264e+00  3.7018e+00 -1.2938e+00 -3.1015e+00  7.4691e+00  3.9491e+00
-#&gt;  -1.2834e+01  9.6482e-01  2.5271e+00 -7.3225e+00 -1.5531e+00  3.0103e+00
-#&gt;  -3.2939e+00 -7.1995e-01 -1.5961e-01 -8.3620e+00 -3.4409e+00  2.4556e+00
-#&gt;   8.2753e+00 -4.5557e+00  9.8521e+00  1.1992e+01  1.5305e+01 -8.5115e-01
-#&gt;  -7.4579e+00  8.0515e+00 -3.0819e+00 -1.4713e+00  9.2651e+00 -1.5457e+01
-#&gt;   9.7496e+00  4.9703e+00  1.0001e+01  9.2289e+00  1.9981e+00  8.2831e+00
-#&gt;  -1.0757e+01  3.1880e+00 -2.7738e+00  1.1525e+00 -1.5764e+01  1.4619e+00
-#&gt;  -5.5567e+00 -7.2724e+00  4.9576e+00 -2.6230e+00  5.0559e+00  9.4046e+00
-#&gt;  -7.6573e+00  3.0302e+00 -5.1165e+00  3.8154e-01  4.2957e+00 -2.9837e+00
-#&gt; 
-#&gt; Columns 37 to 42  1.4781e+01  5.8403e+00 -7.9739e+00  4.3284e-01  4.0315e+00 -1.8781e+00
-#&gt;   1.0714e+01 -3.9545e+00  5.9812e+00 -1.0373e+01  4.7588e+00  8.2855e+00
-#&gt;  -2.0618e+00 -2.1084e+00  4.1794e+00 -1.0581e+01  4.8777e+00  8.7927e+00
-#&gt;   1.1532e+01 -1.2932e+00 -6.2592e+00 -2.0058e+00  1.2718e+01 -1.0143e+01
-#&gt;  -6.9627e+00 -1.5297e+00 -4.0824e+00 -1.5353e+00 -6.1356e+00  3.2539e+00
-#&gt;   1.0944e+01 -1.2924e+00 -8.0913e+00  8.3946e-01  5.9463e+00  4.7262e+00
-#&gt;  -8.8214e-01 -7.6500e+00 -9.0881e+00  1.2230e+01  7.8738e+00  1.2728e+00
-#&gt;  -3.6937e-01 -7.4623e-01  2.8593e+00  6.0178e+00 -4.1878e+00 -6.2481e+00
-#&gt;   4.6512e+00 -3.8977e+00 -8.6991e+00 -7.2066e+00 -8.0469e+00  4.5942e+00
-#&gt;  -1.1658e-01 -2.2086e+00  4.4043e+00 -8.7130e+00 -8.1418e+00  6.6675e+00
-#&gt;   3.2866e+00 -1.4364e+01 -1.1202e+01  2.3180e+00  9.5690e-01 -2.2525e+00
-#&gt;  -6.0650e+00  4.9470e+00 -8.6046e+00 -3.2993e+00  1.0456e+01 -8.4463e+00
-#&gt;  -1.1800e+00  4.0888e+00  1.0231e+01 -6.0781e+00 -1.3315e+01  8.7547e+00
-#&gt;   5.2642e+00 -5.8803e+00  2.3019e+00  7.4136e+00 -4.8173e+00  4.0485e+00
-#&gt;  -2.2386e+00 -9.5951e+00 -1.3358e+00 -4.3168e+00 -5.1432e+00  1.0044e+01
-#&gt;   4.5064e+00 -1.4719e+01  8.1893e+00 -3.3648e-01 -9.4532e+00  3.5265e+00
-#&gt;  -8.1611e+00  4.5290e+00 -4.8454e+00  3.8065e+00  5.1539e-01 -3.5527e+00
-#&gt;   5.1429e+00 -6.1683e+00 -7.7518e+00  2.2368e+00  3.3335e+00 -3.2178e+00
-#&gt;  -7.6238e+00 -8.8446e+00  3.7186e+00 -4.0304e+00 -1.2876e+00  3.0657e+00
-#&gt;  -9.6459e+00  7.1043e+00  1.1225e+00 -8.9580e-01  3.6940e-01  8.4879e+00
-#&gt;   1.3031e+00 -6.8280e+00 -9.7464e-01 -1.7561e+00  4.3782e+00 -5.3408e+00
-#&gt;   6.9679e+00 -5.8045e+00  1.6076e+00 -1.3174e+00 -2.8242e+00  3.5411e+00
-#&gt;   4.8554e+00  5.4867e-01  4.6937e+00 -8.3519e-01  2.8432e+00  1.4558e+00
-#&gt;  -4.0727e+00  5.3502e+00 -6.1120e+00 -1.2640e+01  6.2638e+00  9.2251e+00
-#&gt;  -4.0730e+00 -4.3857e+00  4.1525e-03 -5.7744e+00  5.5562e+00  1.8900e+00
-#&gt;  -1.4340e+00 -1.0864e+00  6.5575e+00  2.4515e+00 -8.7689e+00  1.5012e-01
-#&gt;   7.8476e+00  3.2083e-01 -1.9992e+00  1.2308e+00  1.4226e+00  4.5515e+00
-#&gt;   1.4021e+00 -6.5290e+00  3.1490e+00  1.2606e+00  1.7082e-01 -2.3939e+00
-#&gt;  -6.8526e+00 -6.5598e+00  7.8596e+00 -2.0899e+00 -4.9527e+00  3.3671e+00
-#&gt;  -7.9504e+00  1.3332e+01 -5.1713e+00 -4.8172e+00  3.8252e+00 -9.7357e+00
-#&gt;   4.2879e-01  1.0015e-02 -3.9781e+00 -9.1234e+00  1.0227e+00 -1.5022e+00
-#&gt;  -9.6010e+00  7.0668e+00  9.2475e-01  3.4143e+00 -3.9469e+00  2.5812e+00
-#&gt;  -7.8083e+00  9.0833e-01  7.7153e+00 -7.5587e+00 -5.6300e-01  4.6345e+00
-#&gt; 
-#&gt; Columns 43 to 48  1.2810e+01 -1.3189e+01 -7.7055e+00 -6.3976e+00  1.5547e+01  8.8330e+00
-#&gt;  -1.5065e+01  9.7159e+00 -6.3723e+00  3.3489e-01  2.0888e+00  3.7348e+00
-#&gt;  -4.2313e+00  6.3053e+00  1.9464e+00 -8.2544e-01  8.5206e+00  5.7207e+00
-#&gt;   2.0316e-01  1.0027e+01 -5.5466e+00  9.0533e+00  1.8833e+00  5.5861e+00
-#&gt;   3.3048e+00  3.1319e+00  6.2988e+00  3.1838e+00 -2.9775e-01 -9.9813e+00
-#&gt;   1.6776e+00 -1.1691e+00 -5.3201e+00  3.5656e+00 -4.7045e+00 -8.3086e+00
-#&gt;  -4.5635e+00  2.0188e+00  9.2125e+00 -3.9032e+00 -3.8079e+00 -6.3385e+00
-#&gt;   5.3327e+00  7.2008e+00  1.8978e+00 -5.8745e+00 -3.4445e+00  5.1775e+00
-#&gt;   5.9848e+00  2.5756e+00  2.4851e+00 -4.3141e+00 -7.1245e+00 -4.7120e+00
-#&gt;  -4.2353e+00  4.1360e+00  2.4754e+00 -3.3324e+00  6.6476e+00 -2.6378e+00
-#&gt;  -3.8737e+00 -2.7430e+00 -4.2719e+00  6.1796e+00  3.7464e+00  3.1966e+00
-#&gt;  -8.9031e+00  5.3667e-02 -2.0811e+00  4.1157e+00  7.7390e+00  4.8121e+00
-#&gt;   2.6389e+00  4.2188e+00 -3.3962e+00  4.2881e+00 -2.4315e+00  9.4363e+00
-#&gt;   8.9556e+00  1.2537e+00 -2.8011e+00 -7.3613e+00  1.4005e+00 -3.8574e+00
-#&gt;   1.2019e+01  1.4726e+01  6.9024e+00 -2.1664e+00  5.1618e+00 -1.1799e+00
-#&gt;  -3.3054e+00  3.7320e-01  1.0245e+01 -1.4051e+00  1.6680e+00 -8.1630e-02
-#&gt;   2.8422e-01 -7.4191e+00  2.8573e+00  1.6843e+00  1.4833e+01 -2.8418e+00
-#&gt;  -4.4291e+00 -2.8135e+00 -1.5023e+01 -3.1354e+00  2.7697e+00 -1.9877e+00
-#&gt;  -1.1016e+00  7.1654e+00  1.2580e+00  7.7710e+00  4.1162e+00  5.1481e+00
-#&gt;  -6.9081e+00  5.2128e+00  2.9860e+00 -1.4196e+00 -1.7435e+00  8.4676e+00
-#&gt;  -5.3983e+00  1.0983e+01 -3.1357e+00 -1.0623e+01 -4.6322e+00  7.8922e-01
-#&gt;   8.3450e-01 -4.0142e-01 -7.6967e+00 -1.4488e+00 -8.4348e+00  2.2065e+00
-#&gt;   5.7479e+00  3.1151e+00  1.8005e+00 -5.7872e+00  3.0122e+00 -9.6315e+00
-#&gt;  -7.8079e+00 -7.6238e+00 -1.7575e+00  1.8205e+00  7.2539e+00  3.8008e+00
-#&gt;  -1.0109e+01 -3.5757e+00  8.2105e-01 -4.4458e+00 -5.1365e+00 -1.5818e+01
-#&gt;   4.6419e+00  8.5954e+00  8.1159e+00 -1.8950e+00  1.0888e+00  7.3860e+00
-#&gt;   1.0279e+01  4.0486e+00  3.8668e+00  6.8263e+00  4.4360e+00  1.7308e+00
-#&gt;  -1.8266e+01  3.0640e+00  4.1436e+00  8.5667e-01  6.0407e-01 -1.2738e+01
-#&gt;   7.3158e-01  8.1287e+00  8.3764e+00 -5.1014e+00  2.8116e-01 -7.9374e+00
-#&gt;   3.0224e-01 -5.2574e+00  4.2073e+00  6.6237e+00  5.8265e+00  1.7484e+00
-#&gt;   5.1284e+00  5.3158e+00  4.4600e+00  1.2923e+01  1.3637e+01 -3.8000e+00
-#&gt;   2.7022e-01 -4.0573e+00  9.2956e+00  1.5066e+00 -9.9581e+00 -8.9386e+00
-#&gt;   5.2094e+00  1.3030e+00  2.6377e+00  3.6572e+00 -1.2780e+00  1.9778e+00
-#&gt; 
-#&gt; (11,.,.) = 
-#&gt;  Columns 1 to 8   2.8781   2.2097  -1.0166   8.1775  -0.7718   8.1902  -0.9899 -11.8470
-#&gt;   -6.2581   5.5743   7.7125 -12.8765  -3.1623   0.1986  -5.9956  -9.2363
-#&gt;   -0.2172  -0.8420   9.4055 -11.2229  -1.8000  -1.2664  -1.7218  -1.0896
-#&gt;   11.4138   1.5981   0.7542   1.7948  -1.1702  -1.7719  -9.2642  -9.8508
-#&gt;   -3.8479   8.2731   3.5893  -6.5526   3.9244  -7.8203  -9.8070   3.4631
-#&gt;    9.6756   1.5901 -10.3462 -10.4983   5.3770  -0.4591  -2.7411   3.5073
-#&gt;    5.7282  -6.6532   7.0050  -3.8995  11.4720  -1.5143  -3.0646   7.9134
-#&gt;  -10.4324   2.0450   1.2011   5.9723  -2.9955   5.4474   7.2957  -3.6325
-#&gt;    8.0859   1.0551   0.5931   0.0603  -6.0898  -3.1183   5.7426  -6.1302
-#&gt;  -12.6554  11.6160   9.1281  -8.6554  -0.7018   3.1528  -6.0132  -5.0604
-#&gt;  -14.8811   9.6235  -2.2084   0.9378   5.9259   2.6533  -0.6158  -0.3892
-#&gt;   -7.6016  11.6173  -4.0455  -9.1039   7.3522   1.6756   1.2683   7.6569
-#&gt;   -4.3699  -5.2974   5.9473   7.1043  -3.2537   5.4743  -1.0449  -5.1495
-#&gt;    5.2138  -2.3339  -1.0099   0.8368   2.8119   2.7774   3.6714   0.0031
-#&gt;    8.2720  -6.8858   7.2203  -5.7084 -10.2222  -5.5913  -9.6192   0.9030
-#&gt;   -2.1772  -4.7607  14.7243   3.4797  -0.6596  -1.4458   2.4896  -5.4278
-#&gt;   -7.1641  -0.1249  -7.9006  -3.6037  -3.3484   8.6855  -5.7696   2.6918
-#&gt;   -2.5526  -4.4526  -2.4355  -3.2067  14.2889   3.3216   1.6071   0.7929
-#&gt;  -15.2051  14.2628  12.3325   3.1186  -1.8251  -7.4071  -2.9547  -1.7397
-#&gt;   -5.2387   9.3243  -7.0639  -0.8562  -4.1239   3.8398   1.4057  -1.6789
-#&gt;    0.5528  -0.8265 -10.4466  -4.1306   6.3734   1.5282  12.3649   4.0504
-#&gt;   11.1108  -3.0633  -3.6177   5.7682   5.1008  -2.4357   7.9087  -9.6357
-#&gt;  -10.3575  15.5950  -6.8540 -12.3639  -0.0412  -8.0590  -2.7639   6.2915
-#&gt;   -7.8598  14.0037  -4.7523   2.2997   6.1635   1.7026  -6.2875  -1.1226
-#&gt;    3.5824  -5.8863   4.7592  -3.0903   2.6901  -1.9886   2.6103   5.2841
-#&gt;   -1.5312   4.2905   8.7163   1.5517  -6.8520   1.7236  -6.1933  -7.1499
-#&gt;    9.4862   2.0688  -4.7118  -1.9646  -3.7517  -5.6849  -2.6916  -0.4818
-#&gt;   -6.3272  -8.3648   1.5437 -12.2026   4.5188  11.4448  -0.7743  21.8454
-#&gt;   -4.0406   3.5181   4.3892  -3.6517 -10.6121   1.8866   2.7786  -0.3579
-#&gt;  -14.2391   7.8265 -13.1909   0.1867  -0.9296   1.5074   5.8571  -1.2301
-#&gt;   -0.2945   6.9921   6.2890   1.7168  -1.7118  -6.4731  -5.1135  -1.5401
-#&gt;    2.0167  -0.0208   2.3058   4.0023  -9.0107   0.2120  -7.0342   6.9787
-#&gt;    7.1531   0.8605   9.0946  -3.1487   0.0899  -3.9209   2.6381  -3.0970
-#&gt; 
-#&gt; Columns 9 to 16  -6.1915  -8.2626  -9.0006  -4.0418  -5.2003  -5.3085  -4.1699  16.7122
-#&gt;   19.5822   3.5655  -0.5455   2.5924   2.8166  -7.1293   2.1717   0.6939
-#&gt;   -0.0666  -1.4415   2.4082   7.5363   0.5220  -1.4166   4.4682  -3.4278
-#&gt;    4.9616   0.3099  -4.0076  11.1014  -5.7296   2.0147  -7.3795   9.0969
-#&gt;   -8.2887  -3.4343   9.5883  -2.1146   0.5618  -2.2804   5.8889  -7.3778
-#&gt;   14.1456   5.9190  -2.0328  -2.6687   2.2124   0.8063   6.1982   2.2195
-#&gt;   -1.5928   0.4058   8.9747   1.6188   3.1345  -9.7028  -7.3274   6.0963
-#&gt;   10.4677   2.9256   0.9422  11.0137  -2.2644 -11.5415   7.8916   2.9541
-#&gt;   -6.0708   4.4333   3.1589  -5.0676  -7.9851   2.3739  -6.0588  -2.1832
-#&gt;   10.8193   4.0739   0.8660  -2.5729   0.8408  -8.6158   4.2908  -6.9327
-#&gt;   -6.8986   5.9385   4.4483 -11.7290 -14.5286   0.4827  17.4183  -2.5515
-#&gt;    5.1375   4.0198   8.6159  -4.3389 -13.8087   0.7683   8.3413   2.9647
-#&gt;   -6.5967  -1.5645  -1.2306  12.1490  -6.1894  14.5564   8.7421  -4.9975
-#&gt;  -11.2655   0.8737  -6.1851   0.2204  -5.8681   4.7180   5.9456   1.9183
-#&gt;  -12.2213 -15.1589  -2.7198   2.5953  -6.5069 -10.0845   9.1320 -10.0574
-#&gt;   -0.7626   1.8518  -2.9269   1.0017  12.3075   5.7738  -5.0093   9.1991
-#&gt;    3.9567  -2.3733   5.1063   4.6667  -0.3354 -10.5436 -11.7094   8.5977
-#&gt;    2.9944  -6.6322   4.3978  -6.5010  -0.0759 -10.2965   4.9730   2.9660
-#&gt;    8.9919   2.6644  -4.4370   7.2588   4.9745   1.3050  19.2494  10.3855
-#&gt;   -1.0392  -2.9545  -5.6787   3.8888  -1.4883  -5.5207   6.1828  -3.4936
-#&gt;   10.7327   4.6214  -0.5641   0.3687  -1.4311  -4.9733   3.9833   0.7176
-#&gt;   -8.3585  -5.7510  -4.0912  -2.6170  -1.7727   7.6623  -6.1395  -9.1816
-#&gt;    2.2889  -0.2266   3.1189  -2.6195   4.2082  -5.6330   9.8348   7.9776
-#&gt;    8.7933  12.3731  -3.5023   2.7876  -0.1834   2.7520 -10.9274  -2.5809
-#&gt;   11.7923   6.6071  -4.3929 -12.0979  -4.8559  -3.5806  -1.5844  13.4595
-#&gt;    0.7568  -5.8900   2.0718  14.9415   2.2399  -6.6502  14.8196   8.2244
-#&gt;   -3.6494  -1.7169   6.9579  -0.4423  -3.3180  -1.1368 -11.8920   6.4290
-#&gt;    6.7029 -10.1809  -1.9819  -5.6661  -2.7719  -2.5286  -3.1997  -9.4372
-#&gt;    8.7309  -4.0389 -11.5737  -8.1458  -3.5622  -0.5749   8.0254  -1.8079
-#&gt;  -10.1502  -2.2120  -3.8260   6.5190   6.1603   0.6438   0.1826   1.3515
-#&gt;   -3.4241  -1.1157   6.5079  11.2266  -4.7174  -7.4841   2.2192  -3.6866
-#&gt;   -1.7339  -2.9707  -5.3129  -6.4849  -7.4874   8.1912   9.1298  -6.6721
-#&gt;   -3.9521   1.4523   0.2940  -1.9947  -3.2829  16.9990   4.8489   6.0814
-#&gt; 
-#&gt; Columns 17 to 24   5.6664  11.0845 -10.2942  -4.7752  -6.0080   8.1106  -3.5690   0.9122
-#&gt;   -7.6126 -19.9690   4.6329  -8.9806  -0.4967   2.6903   7.8598  -4.5414
-#&gt;  -11.8706  -5.2885  -0.0619  -1.4086   3.6219   0.9005   1.2591  -2.8981
-#&gt;   -5.7637 -11.8687  -0.4377  -7.7378   4.4512  -7.9747  -8.7335   0.4574
-#&gt;    3.1455  -1.0820   2.9134   0.2344   3.9357 -11.2709   0.1974  -3.3359
-#&gt;  -12.8174  -2.0394  -9.5841   2.4451   7.2319  -0.3387   0.5652  -0.1930
-#&gt;  -13.7959   8.0847   3.8370  -1.3418 -10.4121   5.0823  -8.9951  -1.7491
-#&gt;   -6.6635   3.5142   1.4026 -11.0846  -2.7440   9.2802   3.5806   5.7885
-#&gt;   -1.1767  -0.9026  -8.0627  -2.0746  12.1456  -3.9612  -5.6939   3.9885
-#&gt;   -1.7274 -16.3384   7.3582  -6.4908  -0.4155  -1.4168  10.5900  -8.3264
-#&gt;   22.9371  -2.6684   7.5032  -5.1639  -0.7731 -15.6504   2.5496 -25.6584
-#&gt;   -9.9856  13.7973   0.9767 -14.3910  -3.7744   0.1570  -6.9156  -0.4604
-#&gt;   -1.7880   3.5345   1.4397   1.9685   8.2998  -7.1436   2.2520  -1.8548
-#&gt;    2.6198   6.9447   1.9977  -4.7944   7.8972  -9.6881 -14.5384   2.0402
-#&gt;    2.4234   3.0974  -5.9777  12.3375   2.4437  -5.6257   0.3055   3.5800
-#&gt;   -2.0321  -4.9167   1.8321  -1.6269  -7.0493  -7.4840  -6.6248   8.4155
-#&gt;   -6.3536  -5.2497  -0.8554  11.0275   0.3792   2.4790   6.4480  -4.4525
-#&gt;    0.3469   6.9205   4.5552  -4.9644   9.5360  -1.3360  -9.3090  -4.5213
-#&gt;   -6.4502 -11.7928  -6.1335  -8.0252   0.8029  -9.4381   5.9685  -4.3368
-#&gt;    8.8216   0.8329  -0.2735  -7.1837  -8.0822  -9.6586   1.2252   0.6287
-#&gt;   -1.3171  -6.6742   6.8636 -18.5117   5.8795   5.2577   0.1714   3.6908
-#&gt;    1.8756   8.8235  -7.2492   1.8759  -0.0843  -4.4299 -16.8106   9.7002
-#&gt;    5.3941  -1.8122  -2.9792 -10.6169   3.2304  -1.0800  -0.9455  -3.4328
-#&gt;   -7.0644  -8.4130   4.7494   5.3553   7.0956  -8.0271  20.0530  -7.7643
-#&gt;   -2.8851  -2.7643  -9.6940   0.6988 -13.5548 -11.2170  -6.3717   6.5294
-#&gt;    1.4785  -1.5019 -13.8826   2.5866  -6.0112   1.4736   5.9116  13.5663
-#&gt;    4.4456   3.7312  -3.7568  -0.6211   3.6518   0.4688  -5.2054   4.7016
-#&gt;    4.1419   7.2961  22.8483  18.7562 -13.6192  10.4419   5.3509 -19.8762
-#&gt;   -1.3481 -10.2438  -1.7149 -11.4341 -10.5016   4.5516  -6.6300  10.4442
-#&gt;    4.8445   8.3227  -5.4541   7.5567   0.8922  -0.4789   0.3967  -5.8435
-#&gt;   -4.6249  13.5624  -6.0266  -7.0758   0.9136  -2.8116   0.4097   5.0527
-#&gt;    2.1420  -5.8441   1.0169   3.2376 -15.4424 -10.0194   7.6189   0.4871
-#&gt;   -6.5184  -1.2838 -10.3528  -3.5135  -7.1717  -4.0199 -10.5834   9.9731
-#&gt; 
-#&gt; Columns 25 to 32 -10.3515   3.4975  -3.6517   7.7534   0.3283   0.2941  -7.0501  -6.8274
-#&gt;    1.4270  12.2531   9.8292  -0.3401   3.0283   1.5113   2.4921   3.2593
-#&gt;   -7.5005   7.8531   8.1607   1.5169   3.4709 -12.8539  -1.9708  12.2384
-#&gt;    2.4944   3.9962   0.7539   3.6741  -9.7667  -4.9758   3.8036   2.6912
-#&gt;    5.5318   2.7070   1.6485  -5.0410  -0.7996  -4.4982   4.1551  15.4612
-#&gt;  -10.5261  -5.4848   4.7405  -1.1069   3.9326   5.6820  -5.2473  -8.3052
-#&gt;   -1.1329  -1.5255  12.7872  -3.1154  -0.9708   2.8787   1.7427  14.3937
-#&gt;  -12.2924  -4.7862   7.1931   0.5332   7.4366   7.9451  -9.8216  -8.7823
-#&gt;   -0.7814   4.8600   0.4755  11.8892  -3.6985  -3.3411   5.5341   0.2644
-#&gt;    3.4088   5.4109   8.5450  -2.0580  -0.5300  -3.7219   7.3510   7.8470
-#&gt;    9.0335   9.4278  11.0499  -3.8728  -1.4293 -12.1467   1.2945  -5.4632
-#&gt;    6.8743  11.7608   1.6648  -4.8751  13.1060  -2.2418   4.4712   3.7576
-#&gt;    6.7995   3.0815   1.0554  -3.7866   1.1449 -10.6269  -4.1921  -1.4030
-#&gt;   -0.4489   3.0557   1.8424   2.0436  -6.4865  -3.8906   0.9938  -2.0567
-#&gt;  -10.4877  -4.4549   1.0167   0.4971  -7.5357  -6.5673 -10.2117   4.3548
-#&gt;   -0.8417   2.0043  -0.5281  -1.6514 -12.2684  -1.2485  -2.1550   2.2551
-#&gt;  -12.7666  -6.6186   0.8067   5.8773  -3.6131  -2.2668  11.4308   7.8232
-#&gt;   -9.4452  -1.5626   6.0157  -1.6627   4.5507  -0.6084  -3.8642   1.6012
-#&gt;    2.9794   0.1325   3.8421  -8.8259  -0.4939 -18.2918 -11.1140  -4.3855
-#&gt;    6.2471   4.9542   9.4580   0.6201  -2.4679   0.5176   9.5204   6.5565
-#&gt;   -0.3907   4.6630  12.7648  -0.0060   7.7318   9.3784  -0.8921   5.6945
-#&gt;   12.5322  13.0636  -1.7965   4.8947  -6.6470   2.9905   9.2692  -3.8457
-#&gt;   -0.9241 -11.4957  -1.6680  -5.9663   9.7777  -4.1905  -8.1391  -2.7973
-#&gt;    5.2600  -4.3981  -7.8513  -1.1895   4.0581  -2.9813  10.5569   5.8662
-#&gt;   -6.3160   9.7658   2.4030  10.7301  -2.1206   0.5316   2.3882   3.0174
-#&gt;   -9.0679  -6.4728   0.3893   0.7980  -4.6750  -4.5644 -10.7221   4.0892
-#&gt;    1.4679  -5.2258  -3.4626  -0.5695   2.0237  -1.4349   2.7530  -4.8818
-#&gt;   -7.9846   6.8764   4.2227   2.3961   0.2227  10.2062   9.4042   9.6672
-#&gt;    0.3163  12.4046  -0.8354   0.3742  -5.6021  -1.8028  -0.4147   4.0197
-#&gt;   -7.1034  -3.1581 -15.2460   3.4164   1.7271 -13.3808   8.1067   5.0651
-#&gt;   -5.3282  -7.1169 -14.3244  -2.9758   9.1079  -3.9552  -3.5461  -5.6140
-#&gt;   15.6531   4.8156   1.1635  -6.2082  -6.7749   4.0234  -1.0711  -5.8332
-#&gt;    4.3762  14.6048  -1.9513   1.7074  -6.0273  -8.7757  -5.1166   8.4456
-#&gt; 
-#&gt; Columns 33 to 40 -10.1023  -3.9219  -9.1799   9.5749   8.4975 -11.1930   9.7151   7.1257
-#&gt;   -4.6959  -0.7548  12.8207  10.9396   0.5109  -2.8755  13.5778  -6.5760
-#&gt;   -5.4996  -7.8202   4.7448   6.0284  -1.3823   8.4791  13.4582   9.2748
-#&gt;   -6.7925   3.9432   4.9907   6.8099   3.3344   2.6087   3.8572 -13.1837
-#&gt;    4.0631   2.9278  -2.1702   7.0277  -5.9275  -6.6336 -14.0380  -0.8810
-#&gt;   13.7508   7.2422  10.8456   3.6631  -9.4903  -0.0949  -9.4799 -13.1683
-#&gt;   -6.4126  -4.4764  -3.1920   3.6200   9.2765  -0.6455  -2.4736  -3.5236
-#&gt;    5.1701  -2.2355  -4.0393  -2.0168   9.7074  -5.0182   5.5302   0.5364
-#&gt;   -4.7535   2.4163  -7.0061  14.8368  -6.1606   2.4834  -2.1907   0.2419
-#&gt;    1.8371  -6.5680   2.0259   6.1676   1.0617  -9.2078   1.2528   6.3787
-#&gt;    9.4472  -6.5630   1.2068  10.8190  -6.5610   3.5445  -2.8692   2.9642
-#&gt;   -7.2891   6.0978  -3.3751   4.1682   2.6300   1.0304   6.1595 -13.8636
-#&gt;    0.6786  -0.7477  -7.2634  -1.8376   3.1016   5.0136   3.9515   1.5174
-#&gt;  -12.6435   4.9984   1.0216   3.7301 -11.5680   9.5201  -1.4775  -8.6870
-#&gt;   -3.6625   4.2057   5.5981   4.0501  -3.0935   9.0510  -4.4381  -1.4277
-#&gt;   -7.8689  12.5444  -3.1417  10.3896  -9.9695   3.2747   2.8322   1.7519
-#&gt;    1.8620 -12.7264   2.2756  -1.6458   1.6316 -13.8687  -4.1662  16.7878
-#&gt;    0.3420   3.7973  -1.1359  -1.4965  -4.0083  -4.1914   1.7148   2.4032
-#&gt;    6.3714   2.4896   7.8242   6.8193   7.0321   3.2046   6.8605  -4.6502
-#&gt;    2.8786 -16.2526   2.4352  -3.8882   2.2542  -2.4454  -1.5951   4.7389
-#&gt;  -23.1875  -4.1333   6.3962   6.6887  -6.1362   0.8058  15.6956   7.2013
-#&gt;   -8.7329   7.1801  -5.0613   1.1381  -2.0916  10.0014  -3.2677 -12.7174
-#&gt;   -4.5880  11.6920  14.5654   1.7693   4.4473  -4.4503  -6.5495  -8.7196
-#&gt;  -12.2589  -7.2170   7.9125   3.5010  -9.2617  -7.6637   7.5840   8.4875
-#&gt;   -5.3479  -5.0334   5.0921   6.9245  -8.3232   7.1854   4.1576  -1.5226
-#&gt;    8.1612 -11.9883  -3.6214  13.7768   3.6118  -6.4589  -4.7312  -0.0460
-#&gt;   -4.4050   2.7748  -5.1623   7.4096   5.4151  -9.0273  -6.8535  -1.7368
-#&gt;   -2.0037  -5.8586   4.3548  -7.4482 -17.8355   7.5988  -5.9774   3.9989
-#&gt;   -4.5390  -2.9569   6.4785   2.0471   4.1070  -0.4610  10.3860  -6.3458
-#&gt;    2.7225   0.7216  -7.2652  -4.7884   1.4376  -6.6834  -0.9750   8.7055
-#&gt;    6.1952   6.8069  -4.0349   3.0092  -4.8960   1.1997  -1.7488  -7.0332
-#&gt;    3.4292  -9.9456   6.0187  -7.4573   6.3376  -2.4621  -2.0052   0.2439
-#&gt;   -2.6957   6.6021  -1.6683   9.6269  -5.0222   3.0561  -0.4602  -3.9711
-#&gt; 
-#&gt; Columns 41 to 48  -2.3662  -4.1178   6.0542 -17.2736   1.1544 -14.6532  -4.2327  -1.6723
-#&gt;   -9.6362  -4.0162   8.9831   0.0999   0.5063  -5.7267  -0.4100  -3.6152
-#&gt;    4.8958 -15.0266  -0.8540  -2.6382   2.8027   3.7457   6.4793  -3.2880
-#&gt;    0.2428 -20.9852  -2.8938  -5.1603  11.2823  -5.0218   3.5444   2.0790
-#&gt;   -4.0056   2.6767 -14.2301  -2.8351  -1.5785   9.4275  -3.2266   0.2698
-#&gt;   13.7052  -6.2582 -17.5901  13.6814   2.5738   7.9126  -6.7576   3.7483
-#&gt;   -0.4129  -9.1739  -1.6229   1.1630   0.6235   4.9927   0.3573  -0.4206
-#&gt;    9.1233   3.1910  10.6905   9.6390   2.7398  -2.4940   0.5708  -2.8679
-#&gt;   -7.8422  -9.4731  -9.4229  -5.5931   0.8138   8.7929  -2.3753  -1.6864
-#&gt;   -3.2461   3.6471   0.5257 -10.9632   3.6299  -0.0675   3.1798 -15.2807
-#&gt;  -14.9918  -3.9584  -9.7075  14.5392  -5.4990  -1.5539 -12.2078   8.0035
-#&gt;    9.5730  11.4291  -7.0751   0.0573 -15.4524   8.8957   3.7455   2.1895
-#&gt;    7.8082  -3.8102   6.6591  -1.2311  -4.3900   0.9965   9.2195 -10.1490
-#&gt;    3.8526 -15.6402  -5.1694  -3.2650   5.3320   0.6164   3.4690  -5.3112
-#&gt;   -1.2557 -12.2737  -7.2549  -3.3846   7.4051   1.0831  -4.2061  -6.6609
-#&gt;    0.8970 -14.5638   8.9470  -8.6792  -1.5913  10.7282   3.4311  -6.1475
-#&gt;    4.0533   3.6346  22.0376 -11.0145  -3.3696 -10.2912   1.8133  -4.4615
-#&gt;    6.9292   1.4592 -10.4186   9.6825   1.6393   7.4733  -6.5915   1.2002
-#&gt;   -9.6357  -5.2183   6.0034   6.9522  -8.2266   2.9211  -1.4970   7.7575
-#&gt;   -4.3216  -1.2334  -8.8963  -0.6198   2.8602  -9.6112 -12.6712  -1.2095
-#&gt;   -0.2654  -4.1480   0.0734   8.8209   2.3408   0.8615  -4.3848   7.7182
-#&gt;   -7.8335 -11.9261  -1.9578  -4.8017  -4.1270  -2.7510  -7.9025   3.6308
-#&gt;   -5.1774   1.1036  -3.9699  -7.1527   4.0851   8.1275   0.2665   4.3704
-#&gt;  -10.5100   8.6654   3.9859 -11.6839  -6.7456  -1.7912   4.1879   6.8301
-#&gt;   -8.2875  -7.6335  -5.0641   3.4509  -0.7044   5.7884  -1.5903   5.4270
-#&gt;   14.7860   1.6551   6.0810  -0.1190  -0.6695  -3.8393  -2.5997  -2.9030
-#&gt;    3.0867  -8.2454  -2.4351   3.7962  -2.4292   0.2244  -0.2360   6.7647
-#&gt;    2.3410   2.5302  -3.1083  -6.5420 -13.2832   6.7880   2.6604   2.6548
-#&gt;   -0.9989  -4.2706   7.0629  -3.4784   6.5034  -6.2423   9.9963 -14.3233
-#&gt;    4.2896  -4.0390  -1.4066 -14.4308  -2.8039   9.3129  -0.1018   2.7691
-#&gt;   -2.0399   6.2666   2.7508  -5.6485  -1.2999   4.8603   2.6266  -1.8459
-#&gt;   -3.3496  -4.6115   1.7148  -2.4124  11.2194  -6.3833  11.5255  -9.6916
-#&gt;    3.6975  -7.3336  -0.8093  -4.4650  -9.5633   5.6365   0.4674  -2.7346
-#&gt; 
-#&gt; (12,.,.) = 
-#&gt;  Columns 1 to 8  -1.6277   1.2656  -1.4825  11.3328  10.6316   3.1802   2.7966   7.3602
-#&gt;    0.7563  11.4658   3.0252  -5.1515  -9.5619  -2.4433  -5.7716   3.6334
-#&gt;  -10.5882  12.0208  10.2176   0.2408  -6.5390  -8.0264  -0.8759  -1.1588
-#&gt;   -0.7622   4.3897  -1.1896  -1.7668  -8.9001   1.3332  -7.5461   8.9010
-#&gt;   -3.4753  -0.1087   8.2465  -6.7310  -5.2670  -7.9490  10.5989   5.9618
-#&gt;   -5.2578  -9.5628   3.8548  -2.9971  -7.3184  -3.6002  -3.0816 -13.1817
-#&gt;   -1.4256   1.5171  -0.5876 -11.1181  -4.1151  -5.4291   6.7526   8.3762
-#&gt;   11.6134  -3.6160   3.2140  -0.5094   0.0399  -2.3758  -3.6816   5.9869
-#&gt;   -3.0449  -2.3651  -3.8286   4.9898  -7.6807   3.3386   0.9428  -0.6329
-#&gt;   -8.3678   1.8662   0.6236  -3.0013  -9.7194  -8.1617  -1.3421   4.1308
-#&gt;   15.7022   3.1097  -0.6749 -15.2644  -1.2098  -6.0772   6.4032  -5.7096
-#&gt;    1.1139  12.4873   7.5104  -6.8317   3.5047   0.8986   8.7020   5.2628
-#&gt;   10.1008   0.3333   1.9349  -2.2046 -10.1011  -1.9751  -6.3534   0.1331
-#&gt;   -6.7504   4.4257  -8.3930   4.4600  -6.5332   1.4475   0.3728  -2.6905
-#&gt;    2.4878   5.3327  -1.4330  -2.6156  -6.1966   0.1642   9.1931   0.4174
-#&gt;   -7.9441  -2.8109 -10.7725   1.8952 -18.0593   0.0408  -9.0493  -2.1951
-#&gt;  -15.9260   9.6935   2.5494  -2.1085  -0.7622  -8.1082   2.7542  10.5612
-#&gt;  -15.6643  -9.0980   9.7987  -4.7460  -1.0128  -5.6632   4.0524  -1.4456
-#&gt;   -0.5765  -0.7434  -8.1518 -23.3519 -10.5594  -6.9221  -8.8655   1.9328
-#&gt;   -1.7641   8.7819  11.6229   2.5808   6.2782  -1.7949   8.3206  13.0015
-#&gt;    7.5008   1.6181   3.6726  -4.3403   0.8462  -0.1154  -2.3327   4.6810
-#&gt;   14.0324   2.9959 -11.2916   8.4420  -1.3429  10.1206   6.9614  -1.7276
-#&gt;   -9.8757  -3.8125  -9.9773  -9.4471   5.8427  -8.9117  -6.0019  -4.9530
-#&gt;   -7.5694  13.4387 -14.0810 -13.3506  -0.1395  -1.3370   4.0942   3.1384
-#&gt;  -15.3091   1.5466  -7.9623  -5.0435   0.9694   9.4680   9.3944  -0.9201
-#&gt;   10.9647  -3.0660  -0.2466  -3.3298 -10.6204 -13.0028  -1.8922  10.1039
-#&gt;    1.9402   5.1355  11.9231   5.3982   4.5491   2.5113   4.7216   5.9898
-#&gt;    3.4196   5.3555  -3.5012   5.1005   2.0839   0.7908  10.2228  -5.0219
-#&gt;  -12.1527   0.5536  -4.1541   5.5112  -1.1533   6.0899  -1.1417   5.2220
-#&gt;   -5.5688  -0.8625  -9.4234  -4.4074  -1.7161  -8.4068  -7.6863  -3.9451
-#&gt;    7.5764   6.6458  -1.9726  -7.0190   4.9921   6.4549   1.7532   6.0018
-#&gt;    7.9313  12.2998  -4.0938  -7.3600  -3.4463   5.2630  -3.4504  -4.2298
-#&gt;    5.3973  -4.2705  -3.6850   1.1945  -8.5218  -1.3317  -2.4017   0.3735
-#&gt; 
-#&gt; Columns 9 to 16  -9.1700  -5.4425  -9.0628   2.4983   4.2815  11.4832   3.8628 -12.8614
-#&gt;   -2.9090  -0.2501  -6.0506  14.9251   0.4494  -0.0867   5.6211   1.6150
-#&gt;   -8.0287  -5.9114  -0.7751   4.1525   0.1630   7.7034   5.3568   3.9979
-#&gt;  -13.7131  -4.1252   9.3736  11.7485   9.8657  -4.1714   1.9415  -9.1430
-#&gt;   -0.1387   3.0135   0.7491  -1.8394   0.1614   2.2556   8.3285  -0.2316
-#&gt;   -7.1668   2.9922   2.4090  15.7913  -6.2054   1.9636  -3.4500   7.7883
-#&gt;   -4.2918  -4.8643   9.0532  -4.6586   1.6853  11.3118  -0.0763  12.0158
-#&gt;   -3.6470   2.7414   1.4341   3.2723  -2.6892  -4.6365   0.6876   2.2663
-#&gt;   -1.0103  11.8353   2.4095   3.1762   3.4075   2.2852  10.2877  -2.9286
-#&gt;    0.0213  -3.4105  -5.0831  -0.0772   4.8501   1.4105   6.8952   0.7864
-#&gt;   20.3048   2.1310  -2.1970   0.2460   3.9726  -6.4222  -8.8292  -4.2873
-#&gt;   -9.2621  -9.3889  -2.6775   3.2402 -10.3275   6.7707  -0.3343  -5.5113
-#&gt;   10.8026   8.6806   0.4268  -7.1872  -6.6650   8.9344   7.0174  -5.5875
-#&gt;    1.4095   2.6411   5.9594  -5.0065   8.6666   0.3373  11.5285  -3.7100
-#&gt;    8.7947   7.9955   5.4419  -9.5228  12.7298   8.9183   5.0018   4.0518
-#&gt;    0.5228  -2.5511  -0.3390   1.5011   7.9529  -9.4144  -2.0756  -8.6872
-#&gt;   -7.0986  -6.2085  -1.4724  -1.9024  -6.0415   5.1373  -8.1891  -2.0590
-#&gt;   -8.7808  -0.9564 -12.4611  -8.2841  -4.8299  -0.9901  -2.5113  -4.2294
-#&gt;    5.1060   3.8005  10.3105  18.3401  -9.3370  -6.2471  -4.2201  -1.0089
-#&gt;   -4.3429 -12.8005  -3.6652  -3.7718   6.2987  10.2018   4.2139  10.7305
-#&gt;  -17.5902  -7.8866  12.9877   4.4011   2.7936  -7.6180  13.9074   1.7445
-#&gt;    4.2564   8.6423   2.3472  -9.2550   8.6073   9.5538   6.6635  -6.1956
-#&gt;  -13.6064  -5.4308   6.6853   3.2149  -1.3079  -3.1043  -5.8836   0.8049
-#&gt;   -1.6895   4.3386   8.2895   3.9100 -12.1941   5.3036   6.2939 -16.0732
-#&gt;   -8.1796  -5.9860  -5.0674   9.5823  -5.6035   5.2585  -8.3461   4.2852
-#&gt;   -7.3154  -9.5797   6.5789   5.3402  -3.7686   1.3971  -2.6134   1.4748
-#&gt;    0.2336   9.6401   6.4530   8.9854   9.1470  12.0268  -3.3962   2.6446
-#&gt;   10.4801  -7.9177   8.4949  -1.7847  -3.1570   4.6640  -3.5705  11.5037
-#&gt;   -3.3292  -7.2115  -7.2814   5.5860   2.5205  -4.7651   2.1381  11.7788
-#&gt;    1.8813  -1.8615  -7.5800   2.1251 -14.1831  -0.7501  -3.1613  -4.1596
-#&gt;    7.9042   5.9622   5.9364   7.8909   1.2282   4.5489 -10.1794 -10.3223
-#&gt;    1.7342   0.1356   1.8621   8.5985  10.3165   1.0473  -3.2761   5.2590
-#&gt;    2.2492   2.5793  -1.9641   3.2908  -4.4731  -0.2186   0.3898   0.2026
-#&gt; 
-#&gt; Columns 17 to 24  -4.2233   6.4903   6.4824  -1.4196   4.8213   6.1748   6.0226   2.4244
-#&gt;    3.0449   3.7639  -2.5759   6.9809  -0.7800   1.7511   3.8831   4.2390
-#&gt;    1.0488  -2.6044  -4.1870   0.7029   5.0582  13.9877   5.3120   3.5888
-#&gt;    6.0070   6.5205  -8.5075  13.3583  -0.2409   6.9363   0.4215   1.7057
-#&gt;    4.8480   3.5007  -1.8699 -10.8386   5.1291  -4.9043  -2.1371  -5.0365
-#&gt;   19.0496 -13.6680  -6.3532   9.1346  -4.0195  -3.4128   8.0442 -18.1827
-#&gt;  -10.8550 -11.0827   8.8548  -1.0606  -3.7950   6.9387   1.0117   4.5115
-#&gt;   -1.6574  -3.8386   1.0533  -3.1069  -7.0465   2.9287   9.3957   2.9272
-#&gt;    4.9568   3.9067  -6.9688  -5.0304   6.6911  -2.0881   1.5644   4.6007
-#&gt;    1.8367   7.9528   0.6834  -2.0801   4.4093  -1.0517   3.4551  12.8767
-#&gt;   -2.4516   1.4194  -6.7113   2.9893  11.2839  -8.8115 -12.6606  -2.3365
-#&gt;   -5.6027   4.1230  -0.2919 -10.3406   8.2155   1.4383  -3.7078 -15.4252
-#&gt;    6.5895   2.5759  -1.3891   0.8086 -11.1012   9.8928 -10.9215  15.6942
-#&gt;    1.6642  -1.2642  -0.4765   2.7864  -2.2443   6.1590   1.0146   2.5986
-#&gt;    1.0419   1.3659   0.6039  -5.0359  -2.2402  14.3927   9.0557   5.2491
-#&gt;    6.4229   3.5484  -1.2524  -0.4892  -3.4019   3.0809   3.7813   4.4377
-#&gt;   -5.1540   0.9143  14.0819  -1.7899   0.6947   1.4108  -3.0455   4.5929
-#&gt;  -10.6781  -6.3872  -4.5548  -6.6081  -0.5025   4.2739   5.1464  -5.9072
-#&gt;    8.7503  -5.9570   2.4831  -4.3309   4.5570   6.4887   7.9486   3.6529
-#&gt;   -6.3005   4.0479   9.9377   3.0526  -0.2363  -7.0282  -0.0508   4.9989
-#&gt;   -6.6149   4.3750  -2.5539   0.0184  -6.1014  -5.4061   6.5405   3.4628
-#&gt;   -9.9721   8.5501  -2.9886   9.5830  -8.8987  -0.2068   2.3084  -0.8445
-#&gt;    7.9700  -2.0417   7.5790 -15.0848   2.8634  -5.3935   2.9578  -3.5884
-#&gt;    3.0857  -0.8034  -2.5963  -4.5656   2.6836  -8.8144   0.8256   4.5884
-#&gt;    1.8981  -7.6407   0.2676   2.9430   9.5955  -6.7089   7.3951  -6.9108
-#&gt;    9.6305   4.2658   1.6670  -3.3669  -5.1492  -0.5657  11.6158   5.5527
-#&gt;    1.0008   7.9193   9.3322  -5.0705   4.1512   6.1594  -3.7197  -6.7994
-#&gt;   -2.6899  -4.9149  -2.1636   7.0355  -8.7861  -5.2892 -10.5869 -17.6919
-#&gt;    0.6838   0.8199   7.2066   2.8384   4.5133   5.0984   3.4396   3.6857
-#&gt;    4.4714   2.8224   7.5290  -7.9173   3.3588  -8.8742 -11.3356  -7.5563
-#&gt;    8.5606   6.7477   3.0738  -9.4245   6.5681  11.2474  -3.1492  -0.1624
-#&gt;    8.2534  -2.3553   5.9498   1.4656   7.2768  -0.2787  -7.3997   6.9294
-#&gt;    9.2180   6.3995  -3.5352   4.6713   2.0107   1.4465  -1.4998 -13.3744
-#&gt; 
-#&gt; Columns 25 to 32   1.2006 -21.4737   0.9001   5.9005   6.3590  13.1644  12.8056  -2.2806
-#&gt;    1.8015   1.9923   7.9683  -2.1466   1.5827   1.7011  -0.8395  12.8080
-#&gt;    5.4818  -0.8662   3.4282   3.0912   0.0991 -15.9086   4.2903   9.6336
-#&gt;   -4.9352   4.1994  -1.9150   3.4635  -0.2113  -2.8963   2.8749  -2.0883
-#&gt;    9.9119  -2.3160   8.6780 -10.4556  -3.6645  -2.5613  -7.9138   1.2894
-#&gt;    7.8871 -15.3103  -8.5266  -3.0464   4.2798   2.4715  -6.7049  17.0776
-#&gt;    4.4950 -10.8351   3.4407  19.0463  -7.8943  -0.6938   4.3225  -0.0173
-#&gt;    3.2397  -3.7939  -4.6765   8.3715   4.3350   2.6491   5.8642  -4.2448
-#&gt;    3.7390   0.5699   1.5634   1.3752  -8.9437  -1.8978  -5.1261  -3.9542
-#&gt;   -4.8704   5.9890  -1.4254 -10.6813   6.4396   0.1326  11.2238   8.7575
-#&gt;  -10.8583  -4.5291  11.5939  -4.8853  -2.0942  -8.0907  -3.6970   4.8505
-#&gt;   10.9864   5.7334 -10.6233   7.9893  -1.8921 -14.8004   7.8390  10.5799
-#&gt;   -0.9188  11.9645   0.7103  -1.0071  -5.8439 -12.7488   4.8880  -0.4999
-#&gt;   -9.1022   1.4576   4.3436  -4.2208  -4.4709  -6.3411  -0.1393   9.1185
-#&gt;   -7.6898  -9.3494   9.6089 -16.1926  -1.2733   9.3045  -3.8841  -5.5536
-#&gt;   -0.3512  -3.1724   1.7844  -1.9881 -15.5762   2.9981  -0.3080  -6.5510
-#&gt;   -7.1989  -0.4827  -6.9958   6.3886  11.6115   4.9651  14.6168   3.8082
-#&gt;    8.4152  -8.2617   4.9626  -1.7486  -6.7048   4.1693   2.4776  20.8345
-#&gt;   -9.3380  -1.1969  -0.6511   2.3570  -4.3794  -1.5527  -4.2422 -17.0752
-#&gt;  -13.8299   6.4868  -8.9810   0.4807  13.4931   4.3804  10.0231   1.8395
-#&gt;    4.7416  -5.4565   2.1269   8.8037   2.9015  -2.4739 -10.3819   3.0256
-#&gt;    4.5900   1.1081   7.7875   4.9756  -5.1406   9.5396   0.5284  -7.9706
-#&gt;  -10.6671  -1.8440  -5.7507  -9.6134   0.4597   2.5786 -10.8169   6.2005
-#&gt;   -2.9426  -5.4963   0.2590   6.3128   4.9737   0.8832  -0.7476  -6.4966
-#&gt;    6.2490  -8.2589   2.1780  -4.8646  -2.6697  14.5472  -7.1675  -2.4236
-#&gt;    6.2239  -4.4174  -3.0557  -0.4300  -0.0547  10.7877   2.4003 -14.0559
-#&gt;   -6.4656  -0.9594  -4.6296  10.1206  -4.0693   0.3593  -6.3911  -5.4361
-#&gt;   -7.3114 -12.1247   3.1014  10.2935  -7.8968 -12.6844  -6.3857  -8.1799
-#&gt;   -5.6425   8.9205  -3.6614  -5.8458   3.4007   3.4536   7.0519  -0.3873
-#&gt;    9.2386 -12.7774  -2.1179  -5.5670   1.9680  -9.7900  10.5361   0.4400
-#&gt;   -7.3526  -4.2630  -2.5834  -8.1674   3.0012   9.5988  -1.7162  -3.8316
-#&gt;   -8.0502  21.1164 -10.2557 -11.3802   5.5937 -13.6895   4.5989   6.8188
-#&gt;   16.1440  -3.9049   5.8081  -1.8903  -4.4917   0.1119  -8.0698   3.1317
-#&gt; 
-#&gt; Columns 33 to 40  13.8945   5.1376  11.2892   4.7304   5.2678  -3.4176  -3.9634   6.5021
-#&gt;   -8.8483  -1.0424  -7.2540  -1.0065  -9.8846  -5.2708  -4.0247   8.4387
-#&gt;  -10.8568   7.6129   8.4755  -3.1647  -4.1767   1.8637  -5.9465   6.6877
-#&gt;   -4.7889   2.6010 -11.0272  -0.9510  -7.6316   5.0427  -2.6191   1.1624
-#&gt;   -8.7421 -19.1770  -4.6337 -12.8940  -2.2656   1.9619   0.7837  -0.8762
-#&gt;   12.0341  -4.1220  -0.8017   8.1249   3.1926   1.6903 -11.3236   9.4815
-#&gt;   -9.4582  -4.6722   1.3877   2.0304   5.5673   7.5023  -1.9270  13.0872
-#&gt;   -4.0317   0.9335   8.0035   4.8700   6.0325  -2.9948  -8.9064  -1.1722
-#&gt;    0.2323   8.4465  -1.6160  -7.1798  -0.7423   1.5307   1.2431   6.8303
-#&gt;   -7.3312  -0.3445  -5.3169 -14.5565  -6.2619 -10.5918   4.3772   5.1601
-#&gt;    2.6516 -13.7986  -4.7847  -6.2661  10.1320  -5.9332   4.6941  -3.3675
-#&gt;   -7.1772  -1.6759   7.9337   0.4910   1.9311  -5.4967  -6.4506  -0.9066
-#&gt;   -1.4451   8.3609  -1.1478  -4.2416  -2.5835   3.1141   4.8414  -4.9543
-#&gt;    2.4346   2.4627   6.9711   2.8532  -1.3404  18.3892   5.8931   1.3370
-#&gt;   -0.6633  -8.7064   0.6492  -5.9467  -6.5899  10.7705  14.2726  -1.0755
-#&gt;   -8.1477  -5.0272  -2.3789  -7.4334  -8.4012   4.5336  -5.6346   1.4006
-#&gt;   -2.9836   4.9160  -1.7135   2.6437 -10.8257  -3.6766   5.8261   1.5997
-#&gt;    1.9477   1.5791   0.3320  -3.6651  -1.7538  -9.1123  -4.8502   5.0894
-#&gt;  -11.1569  -1.6841  -2.3055  -0.9117  -7.9841  -3.5817  14.3205  12.3716
-#&gt;  -10.0935  -2.0168  -5.7619   7.2712  13.5324   6.1849   6.3196 -13.0108
-#&gt;  -10.8633   2.1813  13.8934   7.0051   9.0097  -9.0596  -5.2719   4.5808
-#&gt;   -3.4282   5.9018  -7.4001  -6.6291  -0.2669  13.3171   4.1376  -2.6469
-#&gt;    6.5581  -1.5709   9.2863   6.6821  -1.6363   8.3377   6.7423  -1.2998
-#&gt;    2.4276   3.2596  -1.8186   2.4292 -10.6535 -12.4743   0.6836  12.4658
-#&gt;   -2.0732  -0.9243  -3.3457   5.6381   0.9751   2.0973  -2.0401   7.6741
-#&gt;   -3.1224  -5.8254   2.7578   5.8531  -4.7348   2.8611  -9.0410  -2.6366
-#&gt;    2.3521  -2.6690   1.4861   8.4215  12.6360   9.4422   4.0272  -4.4723
-#&gt;   -4.9759  -4.6801   3.2078   6.0508  -0.4729  12.5741   7.1624   0.3861
-#&gt;   -3.6352  -1.5695  -2.0715   1.1457  -3.7931   1.3690   1.7022  -1.2421
-#&gt;    3.2337   3.5467   6.6668  -9.6162  -6.5442   1.8203   8.3872   8.9253
-#&gt;   -6.3699  -3.4739   0.2831  -2.6326  -3.4428  -0.5782   3.6256  -2.2742
-#&gt;   -2.2679  -5.6235  -3.2640   5.4520  -7.1208   5.1629  -5.4996 -18.7610
-#&gt;   -4.0078  -3.6185   1.1123 -10.8744  -4.2643  10.0603 -11.6839  -1.0615
-#&gt; 
-#&gt; Columns 41 to 48  -5.8828   2.6631  -3.0110  -6.7809   0.0293  -9.6178  -4.4755  -2.6119
-#&gt;    4.1385  -0.2211  -5.7847  -0.0816   4.6601  -1.8591   1.6024  -4.3605
-#&gt;    4.1862   1.2995 -10.7519  -5.6809   1.6123  -1.3902   2.0055  -6.9491
-#&gt;    7.3520  -4.4029  -4.7040  13.0503 -17.7398  -1.9633   4.4114   3.0867
-#&gt;   -6.6644   3.7429   8.4155 -15.3608   9.3341   0.3042   6.8965  -2.5737
-#&gt;    3.2898   6.2717  12.4690   8.1115  -9.0811   1.5432   2.7472  -3.1159
-#&gt;   -3.8740  -6.8035   9.4345  -3.9589   0.1824   4.4107   5.2822   4.0166
-#&gt;    2.5250  -2.8588   4.2052  -0.8799  -2.6479  14.9133  -5.0021  -3.4389
-#&gt;    3.1106  -1.1804  -7.3940   4.4292  -1.1075  -1.8306   8.6412   3.1824
-#&gt;    5.9995   8.5145   9.8750 -12.1270   4.5127   2.0419   2.5088   5.1208
-#&gt;    2.3775   1.5137  -4.6429 -16.8772  17.8410   8.7111  -9.2237   4.7237
-#&gt;   -6.3879 -10.6307 -10.1143  -0.3322   7.1997   4.7498 -13.2150 -12.5384
-#&gt;   -1.1561   5.3692   5.9235  14.8841 -11.4622   7.4580  -0.6476   7.9789
-#&gt;   -2.6277   0.0278   4.4945   5.5632  -3.9244 -11.6345  13.3383  -2.1119
-#&gt;  -13.9300  -0.8732   8.3611 -12.4894  -7.5368   4.1963   4.3556  -8.3775
-#&gt;    2.0822  12.0235   5.5337  -4.5093  -0.8370  -1.8867  12.7920  -2.3058
-#&gt;    5.4194   3.3481   8.3929  -4.1561  -2.4016  -0.4821  -2.4102   5.9310
-#&gt;   -2.7429   2.2809  13.0835 -13.3393  -4.6388  -2.4268  -4.5999  -6.9089
-#&gt;   11.8154  -0.0086  -8.7581  -1.8602   6.0035   7.5693  -1.2043   9.0918
-#&gt;    3.5296  -8.8532  -8.3397  -6.5962  -0.2161   4.7971  -1.8156  -0.7327
-#&gt;    3.9167  -5.9942  -4.0317  -1.1674  15.1550  -5.2999  -0.7470  -2.4903
-#&gt;  -13.4262  -8.0015  -4.5641  11.9610  -6.9310 -14.0014   5.2533   7.4856
-#&gt;   -0.4048  10.0374  12.3274  -7.3601  13.5395 -12.6968  -3.3714  -0.7631
-#&gt;    8.6631   5.0238   5.7300  13.5159   5.2352 -13.0944   8.8396   3.8043
-#&gt;   -2.9619 -11.5246 -16.4590  -2.5229  -0.7031  -3.5418  11.5896 -11.8498
-#&gt;    6.3225   4.4412  10.7698   5.8504  -2.5050   5.8819  -1.0052  -3.7116
-#&gt;   -9.8836  -0.2882  -1.3196 -10.6948  -0.0785   9.0144  11.0455   5.9665
-#&gt;   -0.6816  -3.5955   4.0003  -4.2055   6.3928   3.0317   0.4626  -1.8370
-#&gt;    1.2728 -12.7381 -11.3945  -3.2397   9.7050  -1.7765   6.0116 -12.0875
-#&gt;    9.3059  15.8616  -3.6912  11.0371   6.4374  -8.0325  -1.6756   7.2352
-#&gt;   -1.4773   9.8128  -8.6768  -7.0641  -0.5934  10.5564   7.9960   4.2665
-#&gt;    1.8995   6.3863  -1.6942   4.8941   0.6913  12.0428  -9.7984  -1.0175
-#&gt;   -2.9466   4.8298  -9.6127  13.0258   4.2701  -3.4983   3.2971  -9.5837
-#&gt; 
-#&gt; (13,.,.) = 
-#&gt;  Columns 1 to 8 -13.7181  -0.3447   5.8356   1.5973   6.2881   7.5921  -5.4251   7.5284
-#&gt;    6.1287   1.0172   4.8493 -11.8992   0.0052   0.0865   8.0248  -0.1148
-#&gt;   -2.7358   6.5331   9.9707  -0.5685  -7.6053  -2.8306   4.6883  -3.0714
-#&gt;   10.5341   0.0862   2.1901   0.7360   5.3057  -1.7246  10.2474  -0.4840
-#&gt;   -5.9171  -1.9733  -0.1388 -11.1198   6.1604   1.0356   0.4460   0.5814
-#&gt;   -1.5607  -0.5406   7.0331   7.1030   5.0921   4.7854   3.2406  -1.8484
-#&gt;   -5.0085   2.8199   4.1907  -1.4205  -1.0750  11.5682   3.4545  10.3755
-#&gt;    3.5391   0.1704   0.2633   0.0874 -16.5464   4.8874   3.8234   0.3897
-#&gt;    2.8643  -0.8036  10.4355   0.0936   3.7378  -3.9538   3.5668  -6.0208
-#&gt;   -0.3664   0.5333   2.8271  -6.5794   7.8843   2.2596  12.7841   1.2995
-#&gt;    1.6696  17.1502 -12.6774 -21.5333  -0.3590  -2.8230   0.7653  -5.5555
-#&gt;    0.3097  -3.6061  -5.9365 -14.6621  -9.4706  -1.2509  -8.0828  16.4437
-#&gt;   -0.9067  -5.4808  -5.9482   3.4010 -11.3237  -5.6148   0.5647   2.0937
-#&gt;   -2.1402  -2.1574   1.6820   1.7839   9.6963  -5.1659  10.5187 -17.2321
-#&gt;  -12.0609   8.6689   7.5419  -5.2172  -1.5569   1.8496   9.3615 -19.1509
-#&gt;   -7.8590  -8.8998   3.7620  -0.8961   1.9522  -4.9404  13.7316  -8.6320
-#&gt;   -1.3495   1.6034   4.7689   7.2803   3.5203  12.2188  -7.4717  19.8181
-#&gt;   -5.8713   0.0045  16.6472  -7.4291   2.1585   1.1381  -5.0502  -6.4395
-#&gt;   -8.0780  10.4833  -7.7087  -4.3378  -6.3973   7.0898  -0.6432  -5.0435
-#&gt;    1.3963   3.7577 -13.2720   2.1819  -3.5963   3.7020   4.4230  11.6741
-#&gt;   15.3527  -7.6608   6.6919  -3.0026   5.1196  -7.1289   2.5166  -8.8008
-#&gt;   12.4373 -10.7005 -10.4661  -0.0378   5.0841  -9.1134   6.2372  -1.9012
-#&gt;   -3.8107  -2.2387  -2.3170   6.6957   7.6508  11.8243  -6.0962  -4.2987
-#&gt;   -6.0589  -6.3465  -3.5900   8.8230  10.4383   0.5384  -1.9179  11.7249
-#&gt;   -8.0175   8.1418   4.3234  -0.7016  -1.8186   4.3396   1.3985   3.0427
-#&gt;   -9.0953   0.0205 -16.0239   7.2141  -6.1833   5.6397  12.4842  -7.2513
-#&gt;    1.8304  -2.2553   3.7247   4.4194  -2.4612   5.6431 -11.8009  13.6524
-#&gt;   -1.0657  13.1455  -6.4824   2.7998  -2.0011   2.4593  -0.1380 -18.9450
-#&gt;    2.1227   1.4994  -2.0051   1.0570  -2.4858   9.9079  11.9125  -8.3056
-#&gt;  -18.7767   7.6160   2.8001  13.6778   9.9759  11.7203  -7.3760  14.7773
-#&gt;   -9.2775   3.5073   5.8065  -3.2242  -0.1886   1.7389  10.1169   5.1351
-#&gt;    2.8881   1.9169 -14.5476   0.0994  -0.6918   1.7538   6.6907  -2.9264
-#&gt;   -1.5610 -15.5603  -9.3431  -5.6707  -2.7868  -0.6588   0.4745   7.5594
-#&gt; 
-#&gt; Columns 9 to 16  -0.4537  13.3681 -18.0402   5.3283   7.8392  -6.5154   2.1867  -0.0730
-#&gt;    0.4234  -2.8830   8.3738   5.2371  -5.4255  -9.4456   7.8477  -3.3967
-#&gt;    0.8610  -4.0427   7.0516   8.8728  -8.9026  -6.1186   0.6182  -0.7321
-#&gt;  -12.4426  18.5727   2.2000  -3.9517   1.9837  -4.6114  12.5401  -3.7230
-#&gt;   11.1755 -13.6765   2.2766   7.0844  -5.9924   2.1159  -2.8463  -9.8425
-#&gt;    3.4147  -0.2241   1.2905   2.3795  -0.3784   1.7020  -1.0787   2.1012
-#&gt;  -12.1654   3.6320   0.2117  13.0024 -15.6153   0.4192   0.7902  11.0978
-#&gt;   -3.3314  11.4002   1.2202  11.2767  -9.8292   1.9364  -3.5906   0.8670
-#&gt;    1.3514  -4.4492  -4.2492 -10.7704   8.9424  -7.2758   0.2141  -8.3028
-#&gt;    3.5126 -18.3866  -2.6702   9.4522  -6.4155 -10.0933  14.7170  -5.0721
-#&gt;    7.3894  -9.3259  19.8691  -1.6937  -5.9989   5.7458  -7.6616   4.0485
-#&gt;   -2.3914  -0.6550  -3.2121  10.7993  -6.6451  -0.9181  -8.1194   8.8540
-#&gt;   -5.0520  11.2931   4.6047   2.6079  -2.1932   1.5243   0.4934  -4.4654
-#&gt;    3.8567  13.9962   9.6761  -5.5595   0.2366  -1.1271   9.6884  -5.6431
-#&gt;    4.9715  10.0399  18.6660   6.2897  -8.3365  -3.5119   1.8689 -19.4082
-#&gt;    1.4753  -2.3583   1.3196   4.5634   5.9071  -7.1842  11.7466   3.7223
-#&gt;   -1.7783  -7.2138 -12.7813  -4.8437  -0.8411 -10.0547  -6.0221  -7.1953
-#&gt;    3.2388  -6.6003  -9.2452   5.9463  -6.5550   6.6169  -1.9871  -0.7962
-#&gt;    0.6546   8.8318  12.1956  20.1071  -6.5240   8.5515  -3.0849   4.9281
-#&gt;    2.2310  -1.9946  14.7048   2.0993 -13.3510   1.7606   4.6692   0.4065
-#&gt;   -7.1707   0.6846   3.2662  -1.9376  -3.8787  -3.6412   4.8520   2.4025
-#&gt;    2.3054   7.0808  10.2049 -20.7992  -2.9452  -0.6283   9.2520   1.4946
-#&gt;    9.4116  -2.0544  11.1145   8.0121  -7.4675  15.5080  -6.3952   9.1342
-#&gt;   -4.9404 -15.8663  -9.9113   0.3530   3.1372  -6.2037   3.0748   9.5932
-#&gt;    4.7657  -7.6916  -1.1407   2.0595   8.4072  -2.1366   2.2217   1.0098
-#&gt;    4.1171   3.3204   5.5477  16.9107 -11.5887   2.8421   1.1560  -8.0653
-#&gt;    6.5559   9.8319   0.0996  -2.4849  -0.1135  10.7154 -12.6500  -6.1913
-#&gt;   -1.2950 -24.7869  11.4024   2.9801 -18.1488  -3.0154   3.3089   2.1303
-#&gt;    1.2271   3.9828  -0.3498   6.1050  -2.0436  -0.7848   4.1063 -10.9277
-#&gt;  -10.3081 -13.2193  -5.8505  -0.1730   4.0578   2.0895  -1.7765  10.1179
-#&gt;   14.2637  11.8619  -5.1396   0.1210  -2.3210   0.0329  -7.7430 -10.3766
-#&gt;   -6.8249   6.6138  12.0710  -1.9869   5.8455  -0.9538   2.8660   5.0700
-#&gt;    1.6378   4.3483   8.2711  -2.6529   7.7409  -0.4693  -0.2296   3.8611
-#&gt; 
-#&gt; Columns 17 to 24 -18.2529  -0.7843  11.8839  -4.1371  -7.5558   7.9947  11.7894   2.7651
-#&gt;   12.3867   4.1103  -4.3933  -6.1404  11.4483  -3.2177  -6.0363  -4.0282
-#&gt;   13.4974   2.2192   5.8247   3.9458   6.4274   4.3778  -9.2454  -6.9374
-#&gt;   10.8095  -0.0647   6.7780  -4.2264  10.1764  -9.3652   8.2440   5.3695
-#&gt;    1.5547   1.4251 -10.5475   3.5811   0.9859   4.3994   2.8293  -1.6326
-#&gt;    7.2752  -7.2462  -9.2140   1.8691 -10.8841   9.8740   4.3779 -14.9532
-#&gt;  -10.0497  -4.1462   5.6859  -1.9422  -7.2311  -2.2039  -6.7706  -7.7515
-#&gt;   -7.2611   0.3434  10.8226  -2.5999 -11.7364  -1.3330 -11.1642  -6.6722
-#&gt;   -0.5093  -2.7057   1.1337   5.9100  -0.0395   8.2051   8.4493   2.2405
-#&gt;    3.0575   8.8751  -8.7246  -5.4412   6.1884   3.0971  -1.2062  15.2766
-#&gt;   -9.0075   6.7520 -11.1594  -3.4083  11.5826  10.3835 -13.9912   5.7969
-#&gt;   11.1581   3.9730   1.6344   9.1667  -8.0174  -3.3163   1.8599 -14.1742
-#&gt;    8.8165 -10.4774   5.9238  -7.9778  15.0429  -2.0832  -8.3252  -0.3596
-#&gt;    7.0435 -16.1194   0.4444   3.5330   2.8708   7.6799  -3.6777   4.3081
-#&gt;  -11.0742 -13.8376   2.3848  -3.8527  10.4867   1.3087 -13.1818   8.7516
-#&gt;    9.0784   2.0644   4.0886   4.4662   0.3837   4.4890   3.2950   7.3878
-#&gt;   -3.0825   2.8900   1.5499  -1.8036  -6.2011  -5.9667   3.2099  18.0180
-#&gt;   -6.6086  -0.4012  -0.6056  -1.2367  -2.0511   6.0924  -0.1952  -1.4324
-#&gt;   -5.3393   5.1967  -0.9222  -0.1756  -2.3408   2.1642  -3.2054   3.4058
-#&gt;   -2.7763  -1.9330   5.2968   6.1661  11.7368  -1.3482 -13.2555  10.1649
-#&gt;    7.7419  -1.5774   6.6621   4.8569  -2.8487  -1.1811  -1.2904  -3.5283
-#&gt;   -2.1136  -3.8623  10.4048   5.8220  10.9268  -1.0691  -0.8613   6.4316
-#&gt;   -5.7798   1.2923 -15.6228   1.4064  -8.7560  -1.9435   0.4758  -2.0679
-#&gt;    9.5343   4.2179  -6.6049  -5.6621  -1.8436   2.4079   3.7554  -6.3271
-#&gt;    4.9440   5.6170  -2.8385   6.9373  -4.0377   1.8346   3.6950   0.9082
-#&gt;   -9.6127   7.4395   3.0402  -2.2246  -7.6659  -1.1280  -1.8699   9.9940
-#&gt;  -12.7051   4.4045  -3.1659  -2.2974  -1.9191  -4.9103   1.0011 -13.5765
-#&gt;    1.8682  -9.5825 -10.4569 -11.7557   4.8390   6.9839  -4.2444   9.4414
-#&gt;    6.5605   1.6008  -2.1709   3.3722   1.2255  -0.9195   0.8182  14.2683
-#&gt;   -5.5613 -12.4914  -7.0133  -0.1660  -9.0405  12.4403   5.1551   7.0832
-#&gt;   -7.8657  -4.8879   2.4015  -0.1641  -8.1200  -4.2953  -0.9813  -2.0122
-#&gt;   13.1090  -1.1549  -9.9288   3.4933  10.4903  -6.2657   4.1032  11.1627
-#&gt;   22.9732   5.6470   3.7499   6.1426   6.4469   4.5686   8.4451 -10.3352
-#&gt; 
-#&gt; Columns 25 to 32   5.7054  -5.3441  -6.0978   6.0447 -10.4055   8.0234  -0.7850  -5.8711
-#&gt;    4.4482   5.9026   8.5244  -6.5094  -4.0751   6.0330  -6.4294   0.0522
-#&gt;    9.7296   3.1358   8.8016  -8.0296   5.5832  -3.2571   5.4091   4.3074
-#&gt;   -5.8150   4.7967  -0.4531   0.9397  -2.1079   3.9001  -4.1233   0.6805
-#&gt;    1.8793   6.4777  -0.0286  10.9774  -0.6168  -2.8688  -0.0493  11.3595
-#&gt;   16.6737   1.7602 -10.4666 -16.6184   8.4079  11.1176  -7.8161  12.0327
-#&gt;   13.1589 -15.4014   3.1226   7.8401  11.9626 -20.9714   6.7551   3.3441
-#&gt;    5.5559  -8.3731 -10.7067   2.6777  -6.6518   6.9165   5.1903  -0.0387
-#&gt;    0.6119   9.3752   1.1777  10.1055  -7.7612   2.4682  -2.6647  -3.2515
-#&gt;    1.4933   0.8544   6.1785   1.2085   0.6575  -1.3207   0.0227   5.0657
-#&gt;    3.8068  -3.8701   6.7254   6.9575  -7.4438   5.2901   1.0952  -7.0580
-#&gt;   10.7262   7.8740   0.3326 -21.5471  -7.1646  -4.9007   9.6586   9.2646
-#&gt;    0.9556  -0.0027  11.4575  -1.3609  -1.2580  -5.2391   2.3976   8.6294
-#&gt;   -0.6346   4.0469   3.8204  -2.8324  -1.9279  -6.9823   0.8844   9.6346
-#&gt;   10.8041  -2.5132   4.4946   3.0518  -0.3110   0.5100   2.4100  -2.7544
-#&gt;    1.7201  -0.0440  -3.2218   2.4878   0.9819   0.4591  -1.7396   4.6635
-#&gt;    3.1439  -8.0099  -2.2478   2.0788  19.5492 -13.2788  -6.1485  10.1090
-#&gt;   17.1915   1.1694  -0.3155  -3.8923  -4.0340  -0.6389   0.5847  10.7342
-#&gt;   -3.1465  -5.3648   4.2524  -2.8767   4.0003   2.2823   3.7417  -0.6386
-#&gt;   -7.0934  -3.8575   1.9482   8.5610  -4.5604 -11.2780   8.2424  -1.5578
-#&gt;    1.1857  -1.3874   1.4302   2.4410 -17.7996  -2.9959  -4.0117  -7.0299
-#&gt;   -4.5234  -1.2063   1.6614   6.9609  -5.7689  -0.8851  -1.3269  -8.5309
-#&gt;   -1.9256   3.0458  -3.4664 -10.7231   5.4695  -2.0249   1.7123   5.4240
-#&gt;  -15.2104   4.3967  -0.5276   2.4381  -4.1284   6.0441   3.7916  -6.4473
-#&gt;    7.1233   0.2088   0.9726  -8.5699  -3.5048  -4.0555 -10.4901  -1.2846
-#&gt;    1.7306   0.8744  -4.3449  13.0051  -0.1197   2.0478  -3.5184   5.3571
-#&gt;   -3.3896  -4.1441  -3.2758  -1.9248   3.6815  -1.8899   4.8495   0.0962
-#&gt;   -0.2889  -3.1917   0.6236  -3.3692  14.2575  -8.5865 -10.0056  -3.6139
-#&gt;   -3.2592   8.1670   2.5470  -6.6863  -1.6065  -5.4853  -9.3110  -1.9122
-#&gt;    6.8977  -4.4494  -8.8062  11.3942  14.2317  -5.9056  -6.1580   0.3269
-#&gt;    1.0336  -5.3697 -12.3649 -15.0611   5.9503  13.7932  10.8067   4.7749
-#&gt;   -2.3155  12.2920   2.8133 -11.2705  -3.1521  -5.7135   4.6695  -6.1455
-#&gt;    2.0441  -0.5978   2.9946   2.2039   1.6005   0.5476 -10.1008   5.1450
-#&gt; 
-#&gt; Columns 33 to 40  13.9888   1.6621  -6.2028  -5.8881 -15.0765  -7.4038  -6.6962  -5.2960
-#&gt;   -2.1251  -0.9159   0.2313   6.4890   5.8609   1.1657  -1.4320   1.8357
-#&gt;  -13.0969  -3.3009   4.5870   0.1656   6.5823   4.0142   0.2058   5.6121
-#&gt;    4.0445  -7.3677  -3.3419   6.4198  -2.8480   5.5827  13.7324   2.4249
-#&gt;  -18.3887 -10.2412  13.6661   7.2739   0.7158   3.9746  -4.5782   9.0107
-#&gt;    1.4559 -11.6066  -6.2290  -2.9388  -5.0435  -1.1411  -1.3263  -4.6638
-#&gt;   -9.8511  -7.0693  11.5878   9.9078  -6.7654  -3.8763   0.2616   1.9187
-#&gt;    6.3136  -5.5531  -0.5147   2.4884   5.5770   0.8150  -1.4526  -5.0775
-#&gt;  -13.1313   3.6136  -6.0809   1.1632   6.8753   4.5192   2.9325  -4.9329
-#&gt;  -16.4135  -5.5086   5.1798   0.9344   4.7721  -5.9660  -6.8268  12.8024
-#&gt;   -7.5546   8.2615   3.2973  -7.1439   6.9062  -3.2143   3.7799  -3.6713
-#&gt;  -10.0234  -4.3302   6.5380  -3.5359  -0.6433 -12.9074   0.1519   6.9613
-#&gt;    1.6368  -3.8118   7.6408  -0.7032   9.1176   7.9364  -6.1997   0.0139
-#&gt;  -11.1897   8.9903   6.2937  -8.6550   0.1846   3.1229   7.4796  -9.4778
-#&gt;  -13.8819 -10.0532  17.6174  -8.0613  -1.6298   9.8393  -9.3358  -9.0107
-#&gt;   -1.8548   1.0385  -0.4659   6.4678  10.0948  -3.8695  14.7695   8.0527
-#&gt;   -8.6322  -9.9610  -2.1486   5.3907  -7.8803  -8.2907 -17.0719   9.2418
-#&gt;   -5.7645  -4.1250  -0.8757  -5.3394  -1.9818   0.1242   1.4888   1.3666
-#&gt;    4.2515  -3.5361   3.5730   9.5375   8.4103   2.8748  11.2373   2.8970
-#&gt;   -7.1270   7.0362   6.7197   0.2017  -9.1090  -3.3480 -11.7893  -5.8566
-#&gt;   -0.3841   5.4366  -0.9445  -2.6105  -5.0244   3.3680  13.2543  -1.5195
-#&gt;    1.0747  14.1734   1.1987  -1.7408   4.8632   1.5659   5.5709 -19.8471
-#&gt;    1.6983  -1.2366   4.8798  -2.5299  -9.1657   0.9159   2.1604  -8.2412
-#&gt;   -1.0699  -3.8091  -0.6495   0.3911 -17.1536  -1.6623   1.0929   8.2718
-#&gt;   -1.9780   0.0814   0.5713   5.0296  -6.1497   2.7823   4.6570   2.8901
-#&gt;    9.9274 -15.5823  11.1468  13.4251  -6.8414  -1.5312  -4.1873  -3.8815
-#&gt;   -3.2455  -0.2084  -6.4184   2.6211  -4.3341  -1.4415  -4.0072 -10.8088
-#&gt;   -5.4387  -5.7921   0.6411  -5.3342  -2.9096 -17.0201   8.5314   9.1331
-#&gt;   -9.3609   2.5416  -0.6532   0.8389   3.4634  -5.3714   1.6504   6.9934
-#&gt;    1.2955  -0.9298   0.3603  -5.5105  -4.1873  -4.4538   2.8055  14.2132
-#&gt;  -10.8144  -7.4770  -1.9756  -0.7660   0.2557   5.3072   3.9560  -1.3456
-#&gt;  -12.0214   2.5167   8.1731  -1.4205  -1.2927   8.0969  -3.6897  16.6704
-#&gt;    1.7988   0.0107   8.2771   4.0196   2.4219  -0.4908   1.5162   0.4107
-#&gt; 
-#&gt; Columns 41 to 48   6.8223  -1.1953 -16.5700  -0.2202  -1.8059  -4.1714   2.2586   3.1392
-#&gt;   -9.7408  -4.4622   9.2715   9.7497  -9.3702  -6.9846   1.4185   0.2367
-#&gt;   -1.3460  -4.0343   6.5043   8.9333  -2.8347  -0.7131  -4.1512  -1.6062
-#&gt;    0.1806   3.0887  -2.6297  -1.5396   3.7892  -3.0346  -1.3348   9.8749
-#&gt;   -6.5430   1.0502   4.3606   7.9445   2.2091  -2.4708  -2.1457 -11.0325
-#&gt;   -0.3296  -1.0538  19.1806  -0.6681   7.7155   1.0968  14.9470   9.6190
-#&gt;  -13.6597   1.3308   4.1055  18.2011   5.4537   1.0127  -9.1060  -0.3762
-#&gt;   -0.2162  -1.0438   5.0688  -8.3406   2.6659  -4.0165  -0.4390  -0.7727
-#&gt;    0.0441  -5.9841  -4.3727  12.3835  -6.2386  11.5710  -3.6306   8.3481
-#&gt;   -1.1854   2.9303  -2.0192   6.8946  -1.6485  -7.4601  -0.4546  -1.8196
-#&gt;  -12.3307  -2.6553  18.9656  -0.5494   0.6465  -8.9811  -3.0752 -17.2511
-#&gt;   -5.0318   0.9506  13.3649  -5.5659  -9.9188 -11.2742 -10.3484 -13.8310
-#&gt;   -7.5226   8.9456  -1.2620 -12.0812   6.8642   5.7276 -10.8331   1.1360
-#&gt;  -10.2075  10.5311   7.0963  -2.4166  -4.3044   9.0821  -1.8509   4.5489
-#&gt;  -10.6392   2.3903   7.6245  -5.2061   1.1225   0.5899   0.8800  -0.8110
-#&gt;   14.0755   5.1738  -7.7262   3.4819 -10.6563  11.5738 -15.0048  10.6674
-#&gt;    4.9958   8.7628 -13.1731  -2.1885   5.2119  -3.2639 -12.4037   3.4309
-#&gt;    6.2419  10.6203  11.5762   3.5668  -7.6028  -9.1614   4.8107  -7.8572
-#&gt;   -0.7675 -12.4405   2.2323  -6.8869   3.5631  -2.5244  -6.3250   8.6155
-#&gt;   -4.1113   2.0419   1.0660  -4.4972   4.0218  -6.5370  -6.7348  -5.9009
-#&gt;   -0.9550   8.0446   7.0813   0.4395 -12.6257  -4.2030   3.9091  -0.8361
-#&gt;   -0.6885   7.8596 -12.6022  -7.9810  -2.3373  11.8033   1.6770  -1.6105
-#&gt;   -6.5064   4.1790   4.0999   1.1366   7.9675  11.6719   3.3407   6.3710
-#&gt;   -1.5531 -11.0727  -1.7039  10.3451   7.2884   6.7369   0.9745  10.8481
-#&gt;   17.5924  -8.7264  11.3075   2.8726 -14.5478 -11.4686  -3.0124  10.2257
-#&gt;    7.9230  -6.6112   1.0433  -3.9745   6.6238  -8.8205 -12.9009   0.8202
-#&gt;   -4.9997  -3.1634 -10.4045   6.3738   4.9820  12.5160  -2.0337  -2.6327
-#&gt;   -5.0487  -8.2076  19.7135   2.4629  -0.8365   2.6996  -0.6732  -1.3441
-#&gt;    7.2947  -0.0056   5.3458  -0.1922 -20.1747 -14.5637  -0.0136  -0.7484
-#&gt;    5.6158  -6.5718   1.9386  -6.6233  14.2731   6.9216  -3.8060   3.3327
-#&gt;    4.9116  -5.7868 -13.6616 -10.8264   3.4249   4.6447   6.1558   2.3811
-#&gt;   -0.3448  12.0279  -1.1617  -2.9150  -6.2155 -14.8863  -4.9204  10.1638
-#&gt;    2.2497  -1.4470   0.6458  -3.5691  -0.2670  -2.3919  -8.6552  -3.1209
-#&gt; 
-#&gt; (14,.,.) = 
-#&gt;  Columns 1 to 8  -2.1444   0.6275  -5.3745   7.5895  16.7731 -13.2129 -10.5422   9.0688
-#&gt;   -8.6767 -10.2206   9.7955   6.8703   2.7551 -14.1020  -4.8347 -11.2143
-#&gt;   -7.6052 -18.8394   2.9669   5.0451   4.2223  -1.5257   7.6646  -7.3693
-#&gt;  -10.0294  -4.8756  15.5625   8.1806   3.2329  -9.9985   2.3995   2.0965
-#&gt;    4.9436   1.4775  -3.2586  -7.1490 -10.9452   7.2870   3.4860   0.2213
-#&gt;    6.0503 -10.1194  16.5172   0.6114 -16.4727   1.2303   4.3059  -0.2589
-#&gt;   -1.6701   3.0303   2.6328   1.9484  -4.5294   1.2428  -4.5693  -9.4682
-#&gt;    5.1566  -4.0968  -5.4200   5.1647  -1.7696  -1.5406  -3.8415  -1.7984
-#&gt;    3.6765   3.4669  -5.7077   0.1770  -7.3732  -7.5296  -0.5793  -2.3397
-#&gt;   -5.7430  -8.4634  -5.5900   1.6437   6.9694 -11.7347  -6.2177  -4.6205
-#&gt;    0.6710  -4.0882   9.8013  -1.7805   1.2103  15.6890  -3.8652  -8.9470
-#&gt;   12.5851   4.4384  10.4783   1.7717   1.1894  14.1159 -11.6373   0.3138
-#&gt;    0.3266  -3.3944 -12.8845  -4.9403  -1.4425  -2.2774   4.4732  -4.2001
-#&gt;    7.7831  -0.6257  12.6476  -4.7705  -1.3084   3.2592  -4.7172   3.3935
-#&gt;    0.2951 -13.8237  -0.6221  -3.9925  -6.7304   4.5764  -0.5526  -9.1893
-#&gt;   -3.1852  -3.4848  -5.6748  -3.6255  12.7578   2.8069   1.2936   9.8560
-#&gt;   -8.7286  10.1477   3.0263  -0.5387   2.2513  -3.9209  -9.0792  -0.1861
-#&gt;    8.2871  -9.3489   6.1300   5.8867   0.3383  16.0642  13.6681   5.7864
-#&gt;   -2.4832 -11.1671   4.5390  16.8200   1.3044  -0.3483  -5.8973 -11.7801
-#&gt;    1.2391  -1.0048   2.2145  11.8020  -6.3125  -1.3823  -4.2399 -10.7427
-#&gt;    6.6959  -6.8043   7.1455   3.5484  -2.3540  -6.0205  -8.9290  -1.8643
-#&gt;   -0.3061  12.4510  10.5427  -7.4929   0.3782 -12.1543  -9.1551   6.2211
-#&gt;    9.7317   4.5159  10.3419  -2.1879  -2.6870   7.7015  -1.9451   4.4211
-#&gt;    1.7871  -0.0642 -10.1845   0.0613  -3.8851  -5.8144  -6.2688   1.7174
-#&gt;   13.4599  -2.8482   8.3512  16.3706   3.3594   5.3914   1.4781 -11.9550
-#&gt;   -4.2157 -11.1060 -12.5199   8.8629  -1.2847  -3.2273  -1.1830  -2.4803
-#&gt;    2.6273  15.6500   5.7461 -16.2158 -15.0618  -6.3968  -4.2644  -2.5628
-#&gt;   -4.7286   5.6862  14.9851  -0.5636  -7.2220  -2.7243  -2.4637   2.2551
-#&gt;   -3.3462  -7.6389   1.9538   5.6908   4.4791  -4.6541  -5.4730 -10.1804
-#&gt;    2.2027   2.9326  -3.4673  -5.2041  11.2478   4.3450  -5.5164  13.3213
-#&gt;   13.4535   6.0531   0.5279  -0.7927  -3.0388  -6.7141  -5.2771   0.3138
-#&gt;   -9.7670   6.7562  -5.5766  -8.8379   5.7746   2.1539   0.0484 -15.2464
-#&gt;    0.5020  -6.4378  -6.9533  -6.7794   6.2663  -0.8423  -2.0734   5.1950
-#&gt; 
-#&gt; Columns 9 to 16  -0.9762   3.0234   1.0104   7.0104   6.8566 -10.5834  16.1784  -4.9472
-#&gt;   -3.3504  -8.0560   1.4816   4.3687  -7.1903   3.3486   1.2851  -8.6248
-#&gt;   -5.5563  -8.5923   5.3647   3.1766  -4.4003  -2.6062  -1.3784 -10.4659
-#&gt;  -10.4741  -5.5723  -0.6460  13.2420   3.4350 -11.2556   0.8239 -15.1732
-#&gt;   -4.9091 -10.6420  12.9322  -3.6824  -3.0656   1.6655  -5.3298   4.0569
-#&gt;  -13.2547   6.9211   6.5194 -13.3752  -5.8592   6.8385 -10.8662   6.6610
-#&gt;   -6.7226  -2.5321   0.3613   3.7425   0.5684  -2.5713  -4.4943  10.9557
-#&gt;   -3.2104   1.4335  -3.7294   3.8372  -5.0969   5.9416   8.7966  -0.9288
-#&gt;   -7.9040   5.0802   1.7175   9.3299 -16.1795  -0.6950  -4.9105  -7.8795
-#&gt;   -1.7059 -23.6254   8.1958  13.6085  -8.8340   5.5690   3.1202  -6.7429
-#&gt;    6.1610   0.7591  -0.4275 -16.4001  -3.7464  14.8188   2.7395  -6.1317
-#&gt;   -4.6173 -10.0567   8.0694  -2.3891  -5.2332   2.1577  -2.2042   5.9672
-#&gt;    5.1777  -7.5451  -6.8466  -0.0412   4.9360   5.7510   4.0687  -7.6527
-#&gt;   -3.6797  -2.3772   2.3120  -1.6865  -4.3676   8.5093  -5.7215  -7.1319
-#&gt;    0.6331  -0.7750   5.7840  -3.2438   2.5939  -0.9735   1.9388  -7.4345
-#&gt;   -3.6778  -6.0983 -16.4988  10.7501  -8.7596   6.7864  -1.5069  -5.2718
-#&gt;   12.5570 -12.5301   3.2381  10.6822  -0.4238  -1.2423   1.9058   0.9420
-#&gt;   -8.1062  -3.3461   1.2314  -5.4680  -1.8716  14.2853   2.3487   4.5719
-#&gt;   -7.1079 -11.9893   0.3655   4.2615   4.4804   6.5035  -8.9245  -4.1921
-#&gt;    2.1747  -8.1742   7.0437   1.0222   2.6713  -3.7678  -6.9374   9.9402
-#&gt;   -1.1591  -3.6203  -0.1846  -5.2856  -8.4578  -5.6804  -3.7338   7.4090
-#&gt;   -4.8591  16.9304  -8.2518   6.5114   2.1960  -4.1031  -0.9657  -2.2481
-#&gt;    5.8799 -13.4325   1.9374  -7.1436  -4.0939  -1.5348  -4.6633   2.8750
-#&gt;   11.9135  -5.9931  10.6113  -0.2672   7.9714  -6.8968   5.2002  14.0565
-#&gt;   -5.4820  -0.7658   0.9432  -0.8519  -2.5855  -5.5044 -12.3018   1.7075
-#&gt;   -6.5375  -9.9194 -10.3241  11.1755   3.7264   0.8428   6.9324   4.6708
-#&gt;    1.6639  13.2267  11.1546   1.4382 -10.2988 -11.9187  -4.2637  -0.9742
-#&gt;    7.6500   3.4899   4.6013  -9.5289  -8.5190   1.7298  -7.5066   1.5496
-#&gt;   -4.4808  -9.7392   1.8155   3.2473  -9.2740   2.8275  -5.9945 -12.2909
-#&gt;    4.8999  -8.4155  -6.2310  -7.1038   6.0395  -7.5361   8.3684  -1.2237
-#&gt;   -0.3172   2.4322  10.8408   4.7848  -1.3948  -2.0308  -3.4644  -3.6158
-#&gt;    1.2032  -0.9063  -0.4967  -4.0826   1.0183  -3.8041   1.0185  -9.7255
-#&gt;   -4.6066   0.2925 -11.5063  -3.3608   3.6180   1.6864   2.4220  -4.2550
-#&gt; 
-#&gt; Columns 17 to 24   3.7132   2.7026  -1.0514  -6.3857   5.8852   9.2122  -7.7985 -10.9549
-#&gt;   -3.5542 -10.4813 -10.9183   5.2912   0.7644  -1.3522   5.8959   4.7606
-#&gt;   -1.8724  -4.6788  -9.0386  -0.9991   8.7160   1.1418   3.4454   0.1548
-#&gt;    0.3223  -5.1287  -7.9411   2.5696  13.8285 -13.6017 -11.8921  -1.6590
-#&gt;   -7.4142   8.6525  -8.4901  11.6494  -4.1533   0.5475  -0.9908  -3.4346
-#&gt;   -4.0823  -2.7602  -2.7770   7.8444  -8.3679  -1.6173  -2.7454  -4.3434
-#&gt;   -8.7138  -5.5199   5.0951  -9.3132  10.9098  -0.3270  -5.0873 -10.6834
-#&gt;    1.7729   0.1606   3.5235  -3.2069  -1.4126   5.3862   1.6555   6.0412
-#&gt;   -2.3556  -3.7612  -5.9476  -0.6006   8.3621  -6.4094   0.4973   8.1502
-#&gt;    5.8877  -8.2092 -14.1709   4.3547  -1.2038   5.5549   6.8928   1.8750
-#&gt;    2.7522  11.6348 -15.4113  14.9461  -7.2504   3.8405  -1.0440   1.2750
-#&gt;   -2.4977   6.1348  -1.9506 -13.9133   4.9634   8.0606  10.5353  -9.5552
-#&gt;    3.4657   0.2648  -3.3996  -1.7110   5.0830  -2.7579   0.3588   4.0522
-#&gt;   -2.9026  -0.4903   4.1745   1.7057  12.6265 -10.0762  -9.1472  -2.8776
-#&gt;  -24.6963   4.9028   2.2497  12.7402   6.2606   0.9909   5.0337   3.1554
-#&gt;    2.6343  -1.5731  -0.3098   1.5872   8.9305  -9.5032  -3.1197  -4.7093
-#&gt;   11.4809 -10.5834   7.0071  -0.9855   0.1514  11.3191  16.7707  -4.3523
-#&gt;   -5.0535  12.5095  -3.0928  -3.5276   3.5058  -5.5731  -6.5917  -3.1749
-#&gt;   -4.4402  -4.3286  -2.5488   4.6506  -4.4229  -2.9584  -2.2505   0.4066
-#&gt;   -1.5600  -3.5898   2.8236  -0.6024   4.6846   4.3762   5.7524   1.0433
-#&gt;  -14.8293  -1.8766   0.7403  -6.9910  16.1613   1.9846  -0.6524   2.4959
-#&gt;   -5.4296   0.8858  -2.0244   0.4708  10.3446  -7.2269 -11.1847   6.7621
-#&gt;   -6.3072  -7.2304   5.8156   6.9135  -6.5860  -9.2921  -0.1708  -7.5872
-#&gt;   -1.2483 -13.9717  -4.7192  -0.4497  -5.7567   0.0720  -3.2322  -5.1351
-#&gt;   -3.1657  -4.7625   8.0419  -5.4016  -1.2495  -8.2174   9.3439  -3.0667
-#&gt;   -1.3187   1.6499  -6.5667   4.9880   1.5150   0.8097  -3.4620  -2.6438
-#&gt;   -2.3831  -2.7993   7.8687   7.6837   5.1708  -8.4030  16.2167   2.6972
-#&gt;   -1.5868   2.9036   0.2707   3.7706   8.1882  14.5434   5.2464  -5.5609
-#&gt;    5.3385 -10.0304   7.2175  -3.0437  11.3225   2.2313  12.7802   2.7522
-#&gt;    2.7248   4.7965  -6.9295   1.4670  -5.0119  18.2201  -6.2492 -13.9647
-#&gt;   -0.5215   6.2616   6.8941   3.0859   5.0107   0.5953   7.2855   2.0261
-#&gt;    4.9730 -10.6661   2.0325   5.6798 -10.4148   6.9674  -0.3137  -1.7415
-#&gt;    1.3191   1.6653  -0.9755  -6.9042   1.9775   1.5980  -2.9887  -7.3644
-#&gt; 
-#&gt; Columns 25 to 32  -2.2584  -3.1411  -1.7190   5.6135  -0.7605  -1.8872   8.8467   0.3317
-#&gt;    1.8911   2.5071   4.7714   2.8800 -11.7468  10.1048  -1.3142  -1.2634
-#&gt;   -0.3020  -6.2638   0.6670   0.2206   1.6318  -0.1777   3.8507   1.0090
-#&gt;    3.9061  11.3403  -0.1531   2.3443  -0.2155  13.3499 -17.7884   5.8076
-#&gt;    4.6891   4.9715   1.8122  -3.1096  15.1079  -8.8061   5.2626  -1.7620
-#&gt;   11.8195  13.2659  -0.9978  -3.7929  10.2876 -14.6448   0.8412   5.7287
-#&gt;    4.9830  -6.6374   0.5644  -1.2754  -0.3460  -2.1989 -15.5581   7.8374
-#&gt;    3.7161  -4.0108  -5.7117   0.2989  -4.1622  -9.0537  -7.2892  12.5500
-#&gt;   -4.2194  12.9871  -8.7733  -1.4598  -6.5197  -3.6219  -2.2843  -3.0821
-#&gt;    5.8795   0.5183   3.4822   1.5533  -7.1084  -0.2438   8.3458   1.0638
-#&gt;   -4.5325 -12.4497  -9.7003  13.0726   2.2119 -18.1307  11.2652  -3.5084
-#&gt;  -12.6648  -0.2436   6.2022  -5.6211  12.8947  -5.3681 -11.4360   6.7527
-#&gt;  -10.3917  -4.2899  -4.2763   3.9326  -9.5481   8.7687  -0.3994   1.4870
-#&gt;   -1.9819  -3.9799   0.4570   0.6859   8.2083  -2.0543  -8.9140   3.7974
-#&gt;    7.5153  -4.8186  -5.2606  -6.9110  19.4118  -2.6405   1.9254  -4.9273
-#&gt;   -7.9555  12.0409  -8.3323  -0.7393  -3.4030  11.7891  -4.2925   2.8215
-#&gt;   10.9420   6.2364   4.0612  -0.0166  -4.1987   6.9281  -6.5285  -7.9298
-#&gt;    8.7943   1.2502   4.2291  -1.3830  10.3950  -2.8952  -1.2324   7.2852
-#&gt;    5.0857  -6.5046 -11.5604   3.5139  -9.5032   1.5154  -8.9062   1.5487
-#&gt;   -4.3588 -12.7515   4.9994   7.2508  -1.5145   3.3324  -0.5210  -0.6055
-#&gt;    3.6695  -6.7156  -2.2916  -3.3440  -8.8127  -3.9514  -8.6936   5.4232
-#&gt;  -17.4519   0.3753  -0.1577   0.2455  -3.8726   2.1221   3.0209  -8.5284
-#&gt;    7.7403   3.7682  11.5437  -1.9562  11.3489   2.4882 -14.7405   5.0131
-#&gt;   -3.4348  -6.1867  -2.1967  -0.9979 -15.9601  12.7055   7.6482  -5.3109
-#&gt;    7.6323   3.3177   1.1548  -1.6291  -1.5787  -4.0459  -5.3410  -8.6709
-#&gt;    1.7209   5.6033  -4.2626  -4.5748   5.7546  -4.3819  -3.5714   9.4888
-#&gt;   -7.2977   4.9754   0.1989  -1.4958  -2.5134   2.0138  -8.3286  -1.0149
-#&gt;   -5.3932  -8.4312  -1.7728  -1.3764  -1.8622  -2.0587   1.6540 -13.3436
-#&gt;   -0.6438  -4.0675   9.3477  -2.1537  -6.8739   6.6785  -3.6679   2.0648
-#&gt;    3.0280  14.1391  -0.7212   6.4096   9.0701  -3.9991  10.6811 -11.6501
-#&gt;    7.7576   3.7023   2.2892  -3.6257  11.5897  -3.1216 -11.6945   3.9960
-#&gt;    0.0091   1.1377   5.4606   3.3294   2.3344   0.6012  -3.5069   1.8844
-#&gt;  -14.2707  -0.6768  -4.0255  -3.9383   3.2496  -0.0379   8.5746   0.7396
-#&gt; 
-#&gt; Columns 33 to 40  -7.0296 -12.8663  14.3987   3.2322   9.2232   0.7883  -1.6867   7.8755
-#&gt;   -1.5344   4.4558   8.8551   0.2860   3.9697   4.3488   3.3198  -2.3698
-#&gt;    1.2647   5.6437   5.3609   1.3164  -0.4757   2.6660   5.7626   0.0191
-#&gt;   -6.5931   5.5725   7.9819  -2.8026  -7.9575  -0.1073   0.7064   6.7801
-#&gt;   -6.1830  -5.9598  -9.5774 -15.6217  -0.8016   4.2489   1.4874   1.1143
-#&gt;    5.8825   9.6267  -5.7148   1.8366  -3.2282   0.2424  -7.8084   9.1993
-#&gt;   -3.0657  -5.0323   7.8977   0.8028 -12.1188  -3.9341  -4.0435   5.0439
-#&gt;    2.6450   6.5642   4.8798   1.0387  -9.5429   0.2942   2.1092  -2.6703
-#&gt;   -2.1335   3.0644  -8.9395  -4.4608   3.8032   9.3958  14.5904   2.0776
-#&gt;   -4.7665   0.4154  -1.4496   1.7975   9.1528  -1.9465   6.8242   2.5062
-#&gt;   -6.3660  -3.0849  -4.3183 -17.0690   5.4184   8.2195  -5.6109  -3.5211
-#&gt;   -6.4113   2.3761   4.4897   7.6312  -2.9152  -0.5913  -7.5612  10.6324
-#&gt;    7.5895  10.0746 -10.9153   0.2363  -1.3659   4.2210   4.9896  -9.9209
-#&gt;   -5.0598  12.0518  -9.0581  -0.0027  -3.1009   6.3472  -0.5132  -1.0659
-#&gt;    0.3511  -1.0510  -7.4713  -7.5874   8.9896  19.5600  10.1285   4.5457
-#&gt;    7.4178   3.2575  -6.0450   1.1417  -5.1602 -17.7806  -3.1945  -1.6117
-#&gt;   -4.3079  -1.4211   0.9433  25.7182  16.7786  -0.5493  -1.7191   3.3322
-#&gt;   -8.1885   1.3356   1.4096  -8.2148  -2.4566  -1.6299  -9.5060  -3.4289
-#&gt;    3.3245   5.9569   5.9231  -0.3552 -18.8013  -5.1879   2.9843  -2.1808
-#&gt;   -8.7187  -7.0404  -4.1315  13.2902   6.9334   6.2543   1.4562  -8.1287
-#&gt;   -6.5858  10.9018  20.6336  10.7662   0.3862  -2.1395  -6.7924  -5.6453
-#&gt;   -2.4868  -2.3452  -6.7193  -4.8742   9.7127  -1.3753   4.8112   3.5227
-#&gt;    0.2052   6.9031   5.0824  -2.2491  -0.1196   0.8441  -9.4147   1.7011
-#&gt;   -8.9176  -3.0189   9.5077   4.9989  -1.1380  -5.3146  -5.5733  -6.7260
-#&gt;   -0.4082   4.1821   5.2904   2.3013   1.2743  -2.9406  -6.0362  -3.0038
-#&gt;   -2.0832  -1.8657   4.9447   1.4180   0.1416  -0.1899   4.1653  -3.4298
-#&gt;   -4.8597   0.9150   0.4392  -4.8070   7.4141  14.8686   3.7364  10.1229
-#&gt;    8.3424  -6.1335   4.3202  -2.1732 -11.5991 -13.4142   1.2317  -8.0982
-#&gt;   -0.2153   6.0834   3.4220  10.8916   4.3969   8.2267  10.9871  -7.5181
-#&gt;    2.3822  -8.7823   4.6057  10.1561  -1.5710 -10.0309  -2.0746  -2.2866
-#&gt;   -5.8263  -0.6575  -6.0233  -4.0320  -7.1885   4.3454   9.4840   7.2089
-#&gt;   13.3708   2.2496  -7.7976   8.7236   5.5935   5.6348   4.9756   5.5582
-#&gt;    5.9179   1.3443  -3.3823  -1.2847   8.5173   0.9727  -0.8258  -1.4907
-#&gt; 
-#&gt; Columns 41 to 48  -4.8874   4.2648   1.4254 -11.1819  -7.5432 -11.3109  -9.2888   0.4967
-#&gt;    9.5153  11.7349  -2.2791   0.8585  -2.8558   0.2460   1.0491   1.4689
-#&gt;    9.3801  15.6711   2.0565   4.8049   3.5439   4.0364  15.1548  -3.6749
-#&gt;   16.3325  -9.3679   2.0192  -3.3858  -9.8849   2.9303   1.8756   4.9476
-#&gt;   -0.8684   4.5410  -9.3713  -3.4632  12.9519   2.7012  12.1265  -1.4925
-#&gt;  -10.3416  -2.8101  -0.1286  -2.5805  10.9838   4.5101  -2.4895  11.1958
-#&gt;   -2.4855  -0.2830  -1.4296  -3.1296  -0.3691  11.4104  -3.1588 -15.5533
-#&gt;   -8.7580  -3.4773  10.9539  -2.5102   3.5234   5.4549  -5.1429   0.9529
-#&gt;    8.7106   8.7735  -1.1035  -5.3187 -10.2647   3.4108 -11.0404  -0.5582
-#&gt;    1.5927  12.1160  -0.9894  -0.0483  -0.4213 -11.2137   3.4887  -5.2136
-#&gt;    8.0233  -7.5385  -1.8384  -3.2708  12.0102 -10.5743   9.0551  -0.5432
-#&gt;    2.3194  -5.6273  -8.4390   9.7349   4.6735   9.8609  -3.3499  -5.7117
-#&gt;    8.2240   9.1067   0.2126   0.6903  -3.8826   2.3915   8.0595  -2.0850
-#&gt;    8.5066   5.3333  -5.2188 -13.0931  -3.6299   0.0899  -1.9622  -4.6138
-#&gt;    8.9595   7.7200 -12.6849 -11.6411  11.2263  -0.6814  -1.3068   4.1395
-#&gt;   -3.7121  -0.5109   7.3844   4.4809  -0.1424  12.3762   1.8442   3.6132
-#&gt;    3.2045  -0.0386  -0.3754   5.7350  -9.9900 -16.1272   2.7240 -17.6808
-#&gt;   -2.4684   3.9191  -5.6213  -6.9191   7.2749  -3.8292 -12.5974  10.8177
-#&gt;    0.0444   1.4745  -0.4502  -0.5448   3.1292   4.7605  12.5336  -0.3326
-#&gt;   15.8767   2.8549  -9.2265  -2.5690  -2.1676   1.7361   7.1797  -3.3801
-#&gt;   -4.3841   5.1055   6.1935   1.2127   0.0729   3.1731  -5.4489   2.8111
-#&gt;   14.3846   1.3005  -4.7567 -11.0473 -13.8530   3.5960 -16.5615  -2.5091
-#&gt;   -2.9694  -2.3522  -5.3276  -5.7980   4.1376  -3.9642  14.9809  -3.5241
-#&gt;    5.5171  -2.1734   0.9172   4.5474   1.9186  -9.3068   0.5903 -19.9337
-#&gt;   -9.6287  -6.7558 -12.1460  -1.5988  -3.5005   0.7460 -12.0347   4.5429
-#&gt;   -3.4691  -4.8915   8.4898   1.0439   6.9208   8.3621   3.7009   2.0818
-#&gt;    2.2342  -6.7275 -13.9861  -5.2344  -2.4440   7.9633   3.3043   1.9516
-#&gt;    3.4051  -5.0082  12.2827  14.6407   3.5717   1.9640   6.0396  -9.4632
-#&gt;   -9.2561   4.1927  -4.7531  -2.0654  -6.8197   1.7003  -2.1707  -0.6996
-#&gt;   -6.5002   9.6357   6.3987   5.9241  -6.2198 -14.6919   6.5156  -6.7869
-#&gt;   -4.5410  -3.4889  -8.7651  -1.1678   3.2435  -0.3625  -6.3166   3.1699
-#&gt;   -1.0508 -10.6551  -7.6276  12.8458   1.8464  -5.5304  10.2748 -19.5096
-#&gt;   -7.3590   3.5982   3.9005   4.6067  -1.3096  10.7647   1.2693   2.7367
-#&gt; 
-#&gt; (15,.,.) = 
-#&gt;  Columns 1 to 8   7.1115  -4.8512   6.6240  -2.8056  -3.3284  -8.8784  -1.8778   2.6504
-#&gt;    2.0700   2.2446   3.5921  -8.6059  11.0003   4.2419  -2.6831 -13.7168
-#&gt;   -3.0170  -4.9289  -6.9572  -6.9360   3.4179  10.0614  -1.7287  -5.8838
-#&gt;    6.8548 -18.6215   4.7617   1.7003   5.2943 -13.6111   7.5037   7.0371
-#&gt;    0.2961   6.8685  -6.2783   5.1014   0.8988   2.8195   3.8814   6.8040
-#&gt;   -4.8547   5.2219   4.0477  16.4903 -12.9067   4.1274  18.0589   2.3969
-#&gt;   -3.1119  -5.7625   2.6623   1.4139   2.1222  -0.2198  10.5954   7.2307
-#&gt;   -7.6110   2.3029  -1.1292  -7.6671  -3.8505   7.4110   3.5311 -16.9414
-#&gt;    1.3999  -9.8199   3.7912   5.3425   5.5118   5.7815   1.5328   8.6705
-#&gt;    3.1219   5.7515   1.3880  -0.1989   5.4088   0.5510 -11.7477  -0.2784
-#&gt;  -14.2673  10.1715  -8.1456   1.4596  -2.2529  16.0602   3.6823 -10.3038
-#&gt;   11.0010  -7.4895 -21.9005   7.8930   0.6953  -2.4494  -1.8775  -1.9337
-#&gt;    8.0189  -9.5857   0.1912  -0.3771  13.6362   1.4489  -6.2672  -7.9932
-#&gt;   -2.5327  -3.8656  -7.2625  12.7074   0.4264   1.5368   5.6500  -3.9857
-#&gt;    1.8156   1.5734   0.2571   1.3652   5.4486   9.7826   3.4503 -10.3677
-#&gt;   -3.8268  -7.5919   8.7224  -7.5668  -4.8322   2.4466  -1.4175  -2.6361
-#&gt;    8.6276  -4.3907   2.3532 -11.3650   0.8983  -8.9388 -21.6246  -0.2398
-#&gt;    5.3831   5.5249   1.9599   0.7754 -11.9672  -3.6293   0.7607  -1.5836
-#&gt;  -15.1287  -0.2102  -8.7870  -9.7320   3.5318   3.8834   3.2313  13.0888
-#&gt;    2.2043   1.2443  -6.2095  -0.5036   8.3035  -2.9883 -11.5146  -0.9311
-#&gt;   -2.2014  -4.3662  -9.8363  -0.0592   2.2272   7.1386  -2.3986  -9.8353
-#&gt;    7.3829 -15.6095   4.8038  13.5743   8.6284  -4.1394  -8.4229  -2.4527
-#&gt;  -13.6528   4.5778  -9.1974  10.2745   5.6171   1.3481   0.0050   2.3714
-#&gt;    5.1346   5.7869  -3.6878   5.2730  12.8479  -8.4027  -0.2686  12.3093
-#&gt;   -1.9531   1.3696  -5.5243  -0.3543  -4.0368   0.4145   1.2231   6.5016
-#&gt;   -4.4855   3.5932   0.2520  -5.4919   1.7162  -2.1047   3.5553 -10.0349
-#&gt;   -0.7338 -12.2822   3.8291   1.8916  -5.6240   4.1292  13.4338   8.6450
-#&gt;    4.1223   8.7543  -3.3275 -10.5468   0.5172  11.4885   8.5203   4.6293
-#&gt;    0.8531   9.3552  -4.1095  -4.7907   0.7956  -1.3426  -3.6576  -5.8040
-#&gt;    3.3943  -0.0591  -5.4711   1.9219   1.3790  -3.3002 -14.7059   9.0316
-#&gt;    0.1388  -3.6049 -10.5187  -4.0452  -4.5910   0.0103   2.7861  -6.4096
-#&gt;   -6.2126  -1.2538   5.6032   1.9384   8.2004   3.6508   5.4220  -2.7547
-#&gt;    1.4816  -4.9908  -3.5770   5.7046   5.3249   7.0368  -4.7861  -8.5862
-#&gt; 
-#&gt; Columns 9 to 16   7.3293   0.7758  11.2714   0.4524  10.0081  -1.8373  -6.1661  -5.1543
-#&gt;   -5.8472  -0.0340   6.9379  -9.0681   0.1452  -5.2057 -12.2126  -1.6486
-#&gt;    2.3437  -8.1255   4.6813   4.4023  -1.8926  -3.2216   1.2384  -9.5795
-#&gt;   11.8843 -12.4493  13.3344  -0.9240 -10.9565   4.5401  -3.0791   7.8568
-#&gt;   -9.3088   5.4911  -0.1078   0.6599   0.1283   2.3134   8.2952   5.1707
-#&gt;    4.1178   8.3648  -9.3887  -6.1874  10.2946  -2.3948  -1.2944   1.9575
-#&gt;    3.9512  -2.3895  -3.5280   6.1398   4.6451  -8.5465  -1.5480  -6.9995
-#&gt;    2.2374   3.9606  -5.4656   4.2810   4.2215  -8.9381  -2.8852  -8.8617
-#&gt;   -3.6895  -4.0458   6.1441 -12.8404  -8.1936   1.2354  -1.3595   1.0117
-#&gt;   -0.8024  -6.1211   2.9179   0.7460   3.9652   0.3023   0.6151   5.0391
-#&gt;  -11.5725  28.7443 -21.9473  12.8849  -7.2069  -3.0946 -10.9281  -3.5617
-#&gt;   -5.4936  -0.1804  -4.5155   5.1270  -0.6444  -8.3817  -5.6771 -16.6725
-#&gt;    2.3760  -7.0744  -3.9384  -0.7086  -7.3069   4.5342   2.1592   2.9307
-#&gt;   18.0141  -6.5316   8.1737  -1.6791  -7.9068  -0.4408  -1.8226   5.0003
-#&gt;    7.7288  10.7306   6.2264   3.0890   7.9790  -2.7280   5.4462  15.1681
-#&gt;    8.5514  -4.4508   5.4930  -0.7188   3.4840   1.9186   1.2016  11.9940
-#&gt;    4.5466  -9.5580  14.4330  -8.1170   8.1214   3.3552  -7.4451   0.8764
-#&gt;   -6.6361  -2.7121   0.2718   2.8905   5.6385   1.1932   0.1248  -9.6453
-#&gt;    4.6519  10.0845 -11.9125   7.3490 -12.6394  -5.6982  -3.9589  -5.8524
-#&gt;    7.5585   4.5320   4.8395   8.3544  -6.1310   1.5636  -1.5205   3.5457
-#&gt;    8.8557  -3.6722  -4.3582  11.1995 -13.1770  -5.3377   0.8860  -7.7558
-#&gt;    9.9494  -9.0174   6.8326  -8.1302 -15.9986  -1.7770  -6.3664  14.5632
-#&gt;   12.8819  16.6322   0.9788   0.6587  -9.5508  -0.4219   1.1572   1.0094
-#&gt;  -13.2621  -4.8329  -3.3790   0.6607  -3.8674  19.4960  -2.4050   1.7530
-#&gt;    8.2321   3.4733   4.7779  -5.3348   0.2874   1.8593  -7.6614  -6.7333
-#&gt;    9.0961   5.4900  -4.4653   6.5680   0.2827  -4.5207   3.0002   8.9991
-#&gt;   -4.3711   8.0910  14.0509  -7.3617  -4.7490   9.0630  -1.7344  -2.7312
-#&gt;   -1.7179   1.9318  -9.6681  16.6352   4.7708  -6.8043  -6.1491  -0.2504
-#&gt;   14.4281 -10.5506  18.0719   0.6034   2.2336   0.7469   4.2527   3.9240
-#&gt;    3.4098   3.1782  -1.2625   5.2707   3.1843   3.9704   6.9703   3.3117
-#&gt;    1.1391   6.7281   8.8927   5.2655   5.8992  -1.3787   1.0948  -1.4335
-#&gt;   -7.3976   0.4969  -0.9509  -7.0835  14.7301  -5.6787   4.1450  15.2954
-#&gt;   12.4407  -4.6198  -0.9390  -2.1177  -4.9047  -1.7441   7.3410  10.5900
-#&gt; 
-#&gt; Columns 17 to 24  -7.8632  -2.9569  -7.4812 -13.7157   8.0637   2.3197 -11.0591   6.0891
-#&gt;    5.2080  13.6162   9.5811   9.7852   5.6479  -0.6554  -1.9055   6.7459
-#&gt;    0.0767   2.2985  -3.4298   3.6582   3.5657   1.8876   4.9415  -0.0905
-#&gt;   -0.8436  -8.1971   5.6436  -0.8189  -2.7185  -6.8124   1.8173   1.5997
-#&gt;   16.2083  16.5765  -9.1467   1.2284 -12.6130  -4.5548   2.0418   1.2082
-#&gt;   -0.3672  -0.1228  -0.0400  -4.1638   0.4942  -6.9269   7.8788  -6.5652
-#&gt;   13.4752  -1.3951  -7.7839  -2.8148  10.6688   3.2956   5.4087  -6.8197
-#&gt;   -5.1719   3.6068   8.4387   2.3832   9.2946   7.0327  -3.0611  -2.2567
-#&gt;    1.8095  11.6708  -3.7466   6.6039   0.9131  -5.9405  12.7895  -1.3867
-#&gt;   16.9034  14.6173  -1.4816  12.0900  -9.7003  -8.1873 -10.4125   3.2021
-#&gt;   18.8224  -3.7732  -8.4614  17.2741 -12.3936  11.3341  -9.2802   9.3729
-#&gt;    2.1984   4.4343  -2.0045  17.9349  12.9631 -10.8768   5.0915  -1.7478
-#&gt;   -1.1283  10.2250  -0.7795   9.2527   2.0497  -6.3417   6.1346  -4.9914
-#&gt;    3.7214 -17.4149  -2.9213  -3.1648  -9.2139  -1.9090  16.3691  -1.6833
-#&gt;   17.7869   1.2456  -8.1943  -6.7983 -12.6561   1.0241   8.7451   1.9875
-#&gt;    4.7919  -7.9875   5.3562  -4.6082 -12.4467  -9.7171   4.8647  -3.7000
-#&gt;    8.2106   7.4021  -7.2296  14.3541   0.3103  -1.5755   0.0523 -11.7865
-#&gt;    4.0221  -0.2243  -7.8152   2.0820   3.3237  -8.2121  -3.1208   0.4177
-#&gt;    2.1557   6.9841   3.9537   3.1003  -4.4127  13.4479   6.2930   9.3346
-#&gt;    6.3638  -7.4629 -22.5791   1.1678  -7.8419  11.4958   5.5791  -2.9846
-#&gt;   -8.2702 -13.1500   3.4012   4.5281  11.8472   9.1611  -0.4623   4.2150
-#&gt;    5.4411 -12.5264   6.7563  -2.7371   5.6250  -2.9614   7.0123   4.2767
-#&gt;    5.8710  -6.3890   1.2708 -12.6806 -11.1545   2.2729   2.5553  14.9886
-#&gt;    3.2095   8.6801  -0.5554   8.1789  -1.8506   0.1934 -27.5572  -1.5930
-#&gt;   -6.8670  -1.6240 -12.6488  -5.6393   6.8329  -1.8703   6.1925  -2.0215
-#&gt;    6.8801   3.9142  -2.3834  -8.1555   2.7157   7.8278   2.2930  -6.3260
-#&gt;   -2.3847  -1.3235  -3.3893  -0.3491   4.1305  -3.4685  10.7051   2.8873
-#&gt;    8.2645 -10.5847  -0.8847   1.2763 -10.3263   7.4525  -9.3715   3.6793
-#&gt;   -2.3518  -0.1390   1.0705  -5.8702  -6.4483  -4.0465   8.3043   5.2560
-#&gt;    3.2468   8.2362 -10.9262  -6.5290 -12.8279  -5.7303  -1.3841  -0.0960
-#&gt;   -3.0723  10.6398  12.0713   0.7000  -3.5167 -14.5697   3.6254  -2.7057
-#&gt;    0.0036  -1.4688   3.9714  -0.6494 -15.3354  -3.4704   3.0982  -7.4453
-#&gt;    0.4233   3.8652   1.7919 -11.7475   5.7635 -13.0052   8.0559  -2.3109
-#&gt; 
-#&gt; Columns 25 to 32   1.6111   9.8913  -5.1667   1.7823   4.4968  11.4306   4.7141   4.1485
-#&gt;   11.6637  -6.4701   7.3143  -4.6603   5.1235  -7.4082   8.3548  -0.5569
-#&gt;    0.6283  -6.8813   2.5591  -1.5133   4.7381  -2.9992   9.0115  -5.5502
-#&gt;   -5.2239  -4.3276   5.1755  -0.0235   4.7770  -7.2770  -5.2854   5.8824
-#&gt;   -8.7289  -0.1826   4.4126  -2.3563  -3.5984  -9.1043   9.6573  -0.8086
-#&gt;    4.8235   5.1700   1.8015   4.0285   7.1082  -7.7678   5.7203 -16.4164
-#&gt;    4.0143   7.2377   4.8562   9.9579  -4.2431   0.6437  -3.0818  -3.3971
-#&gt;   -1.1889   6.8866   4.1312   3.2933  -1.7267   1.8717  -1.0299  -4.4844
-#&gt;    7.9196   5.0489  -2.6998  -6.7225  -5.8232  -7.8611  -8.9995   8.6625
-#&gt;    6.5469  -5.5016   5.5969  -7.3770   1.7542 -13.9674   6.6170  -3.4024
-#&gt;   -3.2858  -0.3681  14.5794  -3.3098  12.1049  -8.3324  -0.8725 -12.8950
-#&gt;   -4.5755   8.1768  10.8769  -4.6412  -1.5418   6.8211   5.3414  -1.7760
-#&gt;    4.8843   2.1762  -8.7745  -0.1388   2.1083   1.3516 -11.3251  10.4455
-#&gt;    3.3188   0.7462   2.9958   3.4060   2.7944  -4.2444   4.0027  -0.6496
-#&gt;    3.7560 -13.5172   1.9928  10.8493  -1.9686   0.5085   3.2974   7.6224
-#&gt;    0.7598  -7.6283  -2.8274   4.6157   4.2587  -4.9954  -2.3554   0.9759
-#&gt;   -3.5939   7.8641   0.4557   9.6153 -11.5511   0.3652   5.8262   1.3749
-#&gt;   -1.5936  -5.9849  -0.1979  -1.2771  -2.1348 -12.1289   1.5487  -8.8455
-#&gt;    7.6842   4.1295   8.3468  10.5882   5.1853 -11.4518   6.2370  -5.1755
-#&gt;    1.1376  -0.9603  11.4751   7.1682   0.3050  13.5715  11.1495   2.8284
-#&gt;    8.3730  10.5045  14.0180 -11.2211  -1.3625   2.3138   3.8882  -6.8152
-#&gt;    3.7236  -1.7148  -7.6170 -10.5873  -3.2708  10.6142  -7.5766   8.5804
-#&gt;   -7.0491   2.5127   8.0336   9.0789   0.4238   6.1985  12.7871 -13.7677
-#&gt;    2.8779  10.3715 -17.5331 -10.4388  -4.2289  -2.8544   3.5366 -13.0072
-#&gt;   14.9275   8.7207   3.2939   4.4447   3.7926  -9.0021   9.4168  -1.2667
-#&gt;   -5.4648   7.6661  13.5957  10.1384   1.0440  -2.1800   3.8088   2.7490
-#&gt;   -6.1931   2.9256   6.1095   2.7975   6.8424  10.0426   6.3487   9.9140
-#&gt;    5.5017  -2.1072   6.9378   2.6380  -1.2445  -0.7814  -0.1116 -12.2251
-#&gt;    7.5641  -1.5218  12.6226   3.0393   4.7455 -10.6471   8.8348   8.3849
-#&gt;  -10.8081   6.4331  -0.9774   6.6743   2.1463   2.1770  -8.2339 -16.4321
-#&gt;   -4.9075  -7.1971   2.3138   7.9496   2.5724   2.2093   3.0592  -2.0602
-#&gt;    2.3171  -3.5195   3.2429   3.8005  -9.6169   5.8675  -6.2395   5.4772
-#&gt;   -4.0414   7.8393  -3.1173  -0.2646   2.4630   4.9677  -1.7572   7.2229
-#&gt; 
-#&gt; Columns 33 to 40  -1.1876 -10.1433  -6.3296  -0.4366  -8.4276   4.5743  10.7330   9.1425
-#&gt;    5.0169   1.3334  11.1390  -3.0555  -5.2267   2.4215   3.0188 -13.6635
-#&gt;   -2.9091   7.9281   9.3381   8.1316  -7.7611  -3.3668   7.0043   7.1997
-#&gt;   -3.4941  -8.8710  22.0236   2.1432 -11.5798  13.7316  10.7663  -0.7452
-#&gt;    0.7552  17.7075  -3.7739  -8.6597   7.8503  -3.0094  -2.7635   0.9666
-#&gt;  -10.9439  -3.6854   1.2291 -11.6378  14.9099   0.2285 -15.1566  -4.2762
-#&gt;   -0.5783  15.5054   3.0954  -3.3504 -13.4509  -2.3075   9.3905   1.3299
-#&gt;    2.8292  -1.6403   8.7995  -4.3770  -1.7240  -1.7656  -8.9716  -2.7895
-#&gt;    2.3974  13.9734   1.0183  -8.1300   0.0174   6.9550   8.2570  -8.0587
-#&gt;    3.0301  11.8596   8.8648  -3.7870  -8.1850  -2.5475  -4.2095  -0.0444
-#&gt;   10.4876  10.5542  -4.7367   4.3377 -11.2366  -0.7845   0.3486  -2.2428
-#&gt;   -5.8378   2.4098  -0.9600  -4.4779   0.1891  -6.8651  -9.0412  -1.2870
-#&gt;    0.5132   3.5568   7.9932   3.6603  -3.2506  -9.6204  -3.2829  11.5460
-#&gt;    4.3603  -4.4309   2.1633  -4.6632  -2.0197  10.3826 -10.8853  18.2003
-#&gt;    7.5312   9.4417 -14.0978 -10.5704   7.4753   2.4065 -12.0700   8.9292
-#&gt;   -5.9442   5.7016  10.2508   5.5452  -4.8535  -2.5900  -5.1088   4.2898
-#&gt;  -18.8976   5.1532  -1.7340   8.3101   5.6807  -2.8783  -3.3924   0.9158
-#&gt;   -4.6655  -2.7845   2.2621  -5.1463  11.9983   5.4542   5.0857   3.3174
-#&gt;    4.7172   8.5207   8.1565   2.4791  -7.0028  -0.8125  -4.5575  -3.3890
-#&gt;    4.9781   3.8925  -6.7012  -3.3485  -9.6601  -9.7996   0.6632   4.9651
-#&gt;    6.2889  -6.5221  -0.1579   0.8559  -3.5223  -2.9480  -8.2845  -3.6279
-#&gt;   16.2025  -4.6872   0.9619  -2.2647  -0.9014  -1.9281  10.6382   1.3064
-#&gt;  -11.7320  -8.8520  -8.2607  -1.3817   9.6871   3.5138 -12.0845   0.8755
-#&gt;   -1.4128  -0.8471  -8.2075   6.5583  -9.8319   0.0534  -2.4484   3.2296
-#&gt;    0.1093   5.4801  -5.7720  -4.4883   1.0138  11.8541  -5.7959 -13.5700
-#&gt;   -2.7510   3.9824   7.7852  -8.5101  -0.6763  -0.6188  -4.2763  -4.1366
-#&gt;   -7.6959   1.4529  -2.4865 -10.2923   0.1622   3.2543   0.6042  -8.5631
-#&gt;   -5.8255   3.2380  -5.6855  10.0065 -13.5559 -18.4401  -6.9620   6.1546
-#&gt;   14.2976  -3.6410   2.4764 -14.9836  -9.5727  12.6544 -12.3706  -4.0015
-#&gt;   -4.1990   1.5840  -2.1817  14.1879  -4.8348 -12.4974   6.0161  17.1821
-#&gt;   -0.7287   3.5166   6.5444  -5.4296   2.1586  -1.0379  -4.0262   1.5960
-#&gt;   -3.1356   4.7152  -3.5899  -3.5598   4.9465 -12.6657  -0.7683  -1.2444
-#&gt;    4.7171   0.5994   0.7119  -1.2080  -0.6086 -13.2839  -5.4848   2.1458
-#&gt; 
-#&gt; Columns 41 to 48  12.1076   4.1011  -2.1738 -12.9176   6.0466  -1.2080   4.1916 -14.2161
-#&gt;  -11.0032  -2.0739  -9.6891   9.1633  -6.7289  -0.9261 -11.6838  -9.1522
-#&gt;   -2.8997  -6.1992   0.2605  -0.8266   3.4657   6.5743  -7.7402  -0.3860
-#&gt;   -9.4210   0.0803 -11.6765   7.1376   2.6870  -8.4538  -5.3343  -1.7722
-#&gt;   -6.3655 -12.2643   8.3479  -0.7230 -13.5265   0.0571  -4.1167  -0.2769
-#&gt;    7.3242  -2.8557   6.1877   5.6059  -0.9670   0.8556   8.1311   3.8170
-#&gt;    0.9440  -6.0001   5.4027   3.7200   8.2664  -4.4506  -8.2239   3.3115
-#&gt;    1.1671   1.2033  -0.1148   4.6368   9.2238  -8.6745   9.8473   1.3444
-#&gt;   -7.4524  -2.3227   2.5718   0.3380   4.2563  -6.7630  -0.9309 -14.5055
-#&gt;  -10.5321  -0.5466   3.3322   8.5984 -15.1215  -3.8004  -5.8031  -4.9785
-#&gt;    0.5278  -2.1349   0.5429   6.7788  -7.7084  -6.8593   3.6495   5.3098
-#&gt;   -3.9173 -12.6153   6.4391  -2.6274   5.9788  -2.6989   5.6161   4.0751
-#&gt;   -9.5523   9.0388  -3.6559   0.7355  -6.6148   2.9918  -4.4288  10.3985
-#&gt;    0.3431  -0.1633   9.4491  -0.1662   0.1703  -8.1686   3.9250  -1.9232
-#&gt;    1.5288  -2.3083  -5.0019  -2.2136 -13.1283  -6.2529 -12.6464   3.3033
-#&gt;    1.8642  -1.6308   0.4104  -2.1203   6.6278  -7.2271   9.7675  -0.6935
-#&gt;   -3.5142  -5.4236   0.4054   4.4837  -1.8545  -1.1056 -16.1740 -11.8274
-#&gt;    7.2128   0.4841   1.1405   1.7895  -2.4622   1.0410   5.6031  -9.5526
-#&gt;   -5.1216  -4.1475   0.7678  -7.3741   7.0402  -3.9091   5.0237   1.8940
-#&gt;   -2.9024   6.0801  -1.3226  -1.9505  -7.1316  -1.0240  -7.6500   1.4707
-#&gt;   -4.6808  -3.3517  -0.6546  -1.4612   6.0778   8.9817   7.7796 -10.4063
-#&gt;   -3.9926   9.0640  -7.9229   4.0447   2.5237 -11.6426  -7.8623 -10.9089
-#&gt;    4.1280  -9.8034   9.1217   5.1864   5.1205  -6.8890  -4.8244  -0.4584
-#&gt;   -0.4377   6.9769   8.7374 -10.3751   9.5877  14.0208   1.6259  -4.7922
-#&gt;    5.0793  -7.7669   5.9387 -13.6609   4.1338   1.2238  10.1254  -4.0397
-#&gt;    4.3210  -7.1856  -3.9623   2.0145  -3.4725 -12.8630   3.5290   2.1779
-#&gt;    0.4560  -4.6627   1.9266   2.4260  -2.8334  -6.1060   1.7071   4.3015
-#&gt;    5.2938  -0.4437  -0.2218   4.4239   6.5981  10.4136   4.6831   3.9865
-#&gt;    0.0827  -6.5887   1.4364  -9.5022 -10.8417  -7.3839  10.5732  -8.4682
-#&gt;   -0.0789  -5.1351  12.8619 -10.3998  11.1001  -0.8602  -1.0153  -5.6380
-#&gt;   -0.8525  -8.5984  -4.2362   2.2819   5.7807   3.9225   5.1029  -3.2233
-#&gt;   -6.7520   2.5125  -7.0239   0.2492 -17.7612   7.3457  -5.1606  21.6853
-#&gt;   -1.2298  -2.5437   6.2544  -6.8056  -5.4504  -6.6582   7.4377   4.6695
-#&gt; 
-#&gt; (16,.,.) = 
-#&gt;  Columns 1 to 8  -1.3204   0.6526  10.5092  -5.6327  11.6096   5.4670   4.2140   0.0346
-#&gt;    5.5739  -3.2615 -10.9422  15.8088  -5.3255  -9.1013  -4.6959  -0.5321
-#&gt;   -1.4791   6.4599  -3.5330  18.5985  -7.7456  -0.7409   4.7767  -1.2939
-#&gt;    2.1681   2.9503   8.4330  -5.2447 -17.1378   7.1880 -10.5660   0.0754
-#&gt;   -7.0732  14.9318   3.6820  -3.9117 -10.7969  -8.7032   2.4902   0.1089
-#&gt;   -6.4173   8.3155  -8.7507  -5.8865 -10.0633   6.2456  -5.8225  -4.2588
-#&gt;   -0.2864   3.1815   8.6395  -0.3138   0.3553  -5.9444  -0.0749  -2.6012
-#&gt;    1.0320  -8.3929   1.9943   1.1173   5.1423  -6.1033   0.8580   4.4847
-#&gt;    4.8267   6.8426   0.9700  -0.1088  -1.3790   4.9978   2.2415  -0.3459
-#&gt;    3.9409   0.6129 -12.8229  13.8631  -6.3605 -12.7444  -0.1254   3.9160
-#&gt;    0.0052  12.2717   2.5303  -3.5619  -2.9978   1.4443   1.1008  -9.0313
-#&gt;    2.6251  -6.9490  14.1019   9.4191 -15.9656  -6.0713  10.5775  -3.8202
-#&gt;   -1.0193  -6.3644   1.8182  11.5701  -0.1826  -6.2748  -2.8998  16.5332
-#&gt;   -9.2020  19.8385  -5.9341  12.3887 -10.9639   9.4341  -1.0446   8.9571
-#&gt;  -14.0495  11.1773   9.4797   2.2405 -11.3045 -14.3312  -0.5920   8.3753
-#&gt;    7.1392   4.5487 -10.9330  -3.0501   2.0338   0.5864  -6.0851   6.9316
-#&gt;    2.6657   0.9035  -5.8575  -3.1847   9.1681  -1.4086   2.6142  13.2898
-#&gt;   -2.2047  10.0648   8.4240   1.1256  -2.6297  -0.3316   4.8572  -6.3603
-#&gt;    9.5895 -10.9603   2.9531   4.1686  -4.6578   1.8648  -3.9940  -0.8700
-#&gt;    7.0689  -1.4863   0.9992   7.1206  -0.5830  -4.1616   6.5459   7.8172
-#&gt;    9.3923  -5.3182  -5.8125   1.4640   1.8993   9.1349   3.1091   0.5354
-#&gt;    5.0575   3.5568   4.8846  -3.3064   4.8651   3.0455  -4.5414   6.5744
-#&gt;  -12.5866   3.3297   1.1292  -7.9103  -4.2261  -5.4005   4.1291   1.8921
-#&gt;   10.1886 -21.3950 -15.0034   5.4307  -1.9230   2.3486 -17.8280  -3.1337
-#&gt;   10.0871  13.7585  -9.1602   2.4039  -4.3966   6.6193  -0.0315  -5.2115
-#&gt;  -10.1880   3.9860   5.6668 -11.3652   2.0414  -4.1076  -1.0125   8.7918
-#&gt;    0.1394   5.1834   9.7742 -11.5945   1.0495  -1.1102   5.8734  -1.9149
-#&gt;  -17.5434  -6.5157   1.6953   4.7723  -0.9014   0.5178   6.9369   2.9006
-#&gt;   -0.3901   9.4402 -11.9338  12.7419  -8.8317  -5.6769   6.9202   0.9321
-#&gt;   -7.3379  -7.1466  -0.9682   0.3887   4.4588   8.6968   2.9510   1.6989
-#&gt;   -0.7674  -1.7466   3.9111   2.1979 -10.2515  -4.2773   9.5866   1.0826
-#&gt;   -1.5783   7.1853  -6.7278  -9.1641 -15.5977 -10.6462   3.5289   5.9880
-#&gt;   -4.7147   7.8592  -9.0302  -5.2358  -3.3409  -1.7656  -4.6556  -2.6884
-#&gt; 
-#&gt; Columns 9 to 16   1.0254  10.3361  11.6621   5.4113  -5.4113   1.7748  -6.8710  -9.8104
-#&gt;    0.4625   4.9036   5.4948  -4.6719  -3.3553  -0.6379   0.6686  12.4877
-#&gt;   -7.4537  -5.7242  -1.2048  -7.6518 -10.1702  -7.5392   1.4355  12.3038
-#&gt;   -0.9118   1.9426 -22.7376  -2.0421  -3.2993   6.2670  -8.1239   7.8196
-#&gt;    3.0716  -2.4960  -0.7130 -13.1407   9.6765  10.1313  14.9288  -0.7429
-#&gt;    7.0794  -9.5038   2.5003  -5.2492  -1.7645  -1.3024  -3.8371   8.9790
-#&gt;   -5.1030  -6.9457  -0.9513   0.6588  -1.2483  -1.0004   2.7857  -3.1608
-#&gt;   -2.9562  -6.1328   3.7558   8.8549 -16.1291  -5.9037  -3.9408   6.5159
-#&gt;   -1.6588   3.0108 -12.8467  -1.6978 -15.3076   9.3569   4.3810  -2.0955
-#&gt;   -4.3906   0.5790   2.3439 -12.0252  -5.3709   9.9952  10.5764  10.3523
-#&gt;    6.5793   1.0548  11.9372  -2.0547   8.4397  -1.5919   3.8448   2.3324
-#&gt;   -1.5664   9.7998  22.0340   8.1030 -11.4524 -15.2741   0.8491  -3.5748
-#&gt;  -11.9949  -1.1028  -1.6206  -2.0485  -9.2684  -1.9960   6.5735  15.1946
-#&gt;    0.5942  -1.1728  -5.7841   3.6862  -7.8729   5.5735  -7.0033  -1.6853
-#&gt;   -1.7875  -9.1322  -7.6958 -18.2343   4.8327   9.3955   3.9783  -3.5601
-#&gt;  -10.6473  -8.1347 -16.5191   3.3761  -4.2651  15.5675   4.2293   0.2734
-#&gt;    7.0049  13.8239  -7.1527   1.1260  -1.0699  -1.6748  -1.0046  -0.9413
-#&gt;    7.5757   2.4887  13.2990  -6.5295   1.1039   4.3652  -0.2265   7.1046
-#&gt;   -5.9953 -13.3316  -5.4457  -8.3539   4.9143  -7.1104  -1.9930   8.8147
-#&gt;    8.4529   7.0763   6.0135   4.3569  11.1647   2.8066 -11.1874 -16.3402
-#&gt;    2.9189  -8.3507   4.3038   3.2874  -5.8044  -2.3187 -17.9639  -7.2240
-#&gt;    3.0913   7.9585  -5.1885   3.2803  -2.8590   9.8295  -2.5579 -16.5575
-#&gt;    2.7880   2.8779   0.6772  -9.4118   8.2246  -8.6007  -2.6125  -6.7030
-#&gt;  -10.5021  -5.8253   2.1096  -1.5385   2.2725  -4.3075   1.2849   1.6933
-#&gt;   13.3302 -10.1520  -1.0219  -0.6172   9.4727   3.5504  -4.7962  -8.7593
-#&gt;    3.4697  -6.5768  -6.3320  -1.2067   4.5735   2.2156   4.8014  -4.8593
-#&gt;    1.3233   2.2615  -7.9351  -8.0059  -1.7954  -6.8646  -4.6570  -2.4058
-#&gt;    0.3469   0.1460  17.7335   4.3834   9.7596  -7.1045   2.4103  -7.7036
-#&gt;    1.1120  -6.3162  -1.7628  -1.7410  -3.0155   7.6801   0.8479  -1.0478
-#&gt;    4.1956  10.0632   1.7495  -5.7685   3.5873  -0.3796   8.8077  -4.3659
-#&gt;   -5.1423   1.6058   2.3845  -8.5698 -10.1795  -4.4278   7.6315   3.5999
-#&gt;  -12.7859  -1.8685 -14.3316   3.0497   7.8189  -6.5503   9.9442   5.3623
-#&gt;   -4.5302  -7.2908  -7.1151   4.5172  -1.1260   6.3266   7.6171  -7.9990
-#&gt; 
-#&gt; Columns 17 to 24   2.8025   2.8271   2.6480   3.4959   5.0904   2.8226  -0.0594  13.4359
-#&gt;  -14.2048  -9.2178   2.9372  -0.3767  -5.0131  -6.1643   1.8080  -4.0727
-#&gt;    1.7859  -2.5607  -1.9930   8.0479  -0.7414   3.8054   5.1039  -2.8461
-#&gt;   10.4622  -6.9029 -11.8365  10.8772  -2.8240  -2.7649   7.2829  -5.8833
-#&gt;  -14.1812 -10.3616  -4.2358   3.8300   1.7081   3.1369  -2.0032  -6.1442
-#&gt;   -0.5696   2.5405 -15.5316  -5.1835  -6.0208   2.9822  -0.5974  20.8739
-#&gt;   -5.6804  -8.5035  -1.6632  -0.6585  -2.4957   3.7135  14.2337  -4.5331
-#&gt;    1.7501   0.6361  -5.0097  -6.8476  -5.1944  12.4870  -3.7354   0.9425
-#&gt;    5.1651 -11.6754   1.1540   1.5580   7.1286   4.3611  -2.7560 -18.3934
-#&gt;  -17.0134 -11.4174   0.8793  -0.6620   4.0115   1.4018  -0.8441   3.0652
-#&gt;  -10.4883  -1.5086   8.5144   3.9381  -1.8614  13.6181  -9.9868   7.9517
-#&gt;  -10.4059  22.3095   2.7550  -2.2265   1.2176   4.2821   5.2935  -1.3582
-#&gt;    4.4460  -3.1476  -7.2154  -9.6695   3.5336  -1.6278  -9.2159 -10.0800
-#&gt;    8.1050   2.0959  -3.9473  -0.7106  -2.8918   5.4461  -3.6736  -1.2730
-#&gt;   -6.1247  -1.4076  -4.4631   1.3634   5.7326  -2.7705 -12.1562  -6.0150
-#&gt;    6.4228  -7.8171   5.9113   3.0675   2.0606  -1.1603   4.6238  -8.2037
-#&gt;   -1.6866  -5.6572   0.2234   4.5182   3.1158 -13.9857   6.7237   4.4937
-#&gt;    0.1855  -2.4545  -7.3087   1.1111  -3.9793   6.8628   6.6371  11.8543
-#&gt;   -6.6008   0.6008   4.6997  -1.5596   0.2766  -3.9507  -0.4521   3.7588
-#&gt;   -6.1623   4.5682   6.6610   0.1241  10.9521   1.7854   2.6624  -2.3684
-#&gt;    8.5402  15.9268   5.0533  -1.7578   0.7582   5.4772  -1.6012 -10.3979
-#&gt;   11.4607   1.5538  -7.0335   1.5337   5.4602   3.1144  -6.8919 -15.1536
-#&gt;   -0.9874  11.4116  -1.7802 -16.8985  -2.0688 -12.0846  -7.8504   7.7643
-#&gt;   -4.8618  -0.8623   6.5522  -3.0675   0.4466  -6.2224  -1.0398   3.3669
-#&gt;   -6.9103  -0.4912  13.6113   8.4300   5.0445  -1.1812  12.4340   5.0129
-#&gt;    2.9676  -3.5193  -6.5441  -1.1419  -0.4146  -2.7309  -3.9625   0.2427
-#&gt;   -0.5066  -4.1027   2.8202   1.5772  12.5458  -7.5701  -1.1209  -7.5376
-#&gt;   -2.0766   4.6149  10.3450   6.7406  -4.9339   5.7498  17.3834   2.1269
-#&gt;   -9.6193  -1.3648  16.9052   6.0183   4.4245  -0.6685   2.6400  -7.4417
-#&gt;    4.9282   9.5929  -7.0839  -1.3978  -6.6698  -8.3408   0.2958   5.9292
-#&gt;   -6.4539   0.5636  -5.9307  -1.6424   5.9629   3.1537  -4.6600   5.7245
-#&gt;   -4.9213   0.7057   9.0855   3.7485   3.8237 -11.5914  -0.5496  -9.8527
-#&gt;    2.9095   5.2565  -2.3803   9.6923  -1.2148  -3.3360  -1.4157 -13.0961
-#&gt; 
-#&gt; Columns 25 to 32  17.6035  -1.0098   0.4745   5.2585  -8.8529   8.4093  -2.0189 -14.7016
-#&gt;   -4.3281 -14.1630   5.7770   3.3682  -7.6735   3.5629   0.3521  13.0974
-#&gt;   -9.4802  -2.8482  -2.6903  -2.4335  15.1989  -3.3284 -11.1240  16.1645
-#&gt;   -6.6805 -17.1317   0.4984   7.8714  -7.6200  -5.2047   4.7747   4.5009
-#&gt;   -5.8401  -6.7577  -3.1531  -3.6990  10.3882  -6.1229   0.1506  14.0069
-#&gt;   -3.9239  -1.7848  15.8552 -18.3749   4.1166   7.0067  -9.0793   7.1111
-#&gt;    3.9034  -3.2623   6.3512   6.5142  14.1725  -5.2678  -7.8942  10.7899
-#&gt;    4.7258   0.0566   4.4063  -9.2343  -4.1122  10.0212  -5.9066  -8.8272
-#&gt;    7.2739  -0.5225  -9.5228  12.9427   8.1354   1.2012   1.9712  12.4760
-#&gt;   -7.0641  -7.0924  -7.2254   6.4615  -2.3076  -5.9760   7.1168  12.0361
-#&gt;  -15.8013   4.0385  -0.1108   4.2316  10.0294 -14.1306   3.5020   7.7976
-#&gt;   -3.4861   0.6688   1.7526 -18.0820   5.2449 -15.6297 -13.8289   3.2798
-#&gt;    5.3501   8.1111   3.2815   4.4101   8.1744  -0.5013  -2.3403   2.6503
-#&gt;    1.1117   9.9882  11.2948  10.9378   7.5708 -11.2358   6.5127   2.0092
-#&gt;   -9.1659  -7.0353   6.4879   2.5423  13.5808   1.9107   8.5529   9.1137
-#&gt;    5.4336 -12.5380 -12.8151  16.0256 -12.6789  -4.6559  13.5395   1.8768
-#&gt;    9.7476  -7.3086  -6.5087  12.6011   1.0711  -4.2273  -2.4932  -3.8373
-#&gt;    7.3071  -3.0686   4.7835 -14.8320   1.5951 -10.5570 -17.9316   8.8383
-#&gt;  -12.3674  -5.0916   6.1914   3.6960   2.2219   6.4669   6.3904  11.3875
-#&gt;   -0.5713   3.2452  -1.6545   4.4855   6.2768  -1.4021  -2.1622  -7.2800
-#&gt;    6.0130  -2.4392   2.8221   5.5003   2.2283  -5.5170   2.9483  -8.5641
-#&gt;    3.1298   7.8611  -2.8450  13.6667  -4.9227   0.3805   7.6739  -5.1870
-#&gt;    2.8829  -0.7680  12.2108   3.1766  -4.0264   7.3800   3.9469 -11.9077
-#&gt;    8.4839  -6.2682  -5.1891  10.8249  -7.2002  -7.9541   3.9664  -1.7241
-#&gt;    0.0130  -6.5531  -3.9633 -11.3358  -2.3152  -5.5856  -5.8848  14.1289
-#&gt;    0.2054  -2.6318   3.5308   4.7872   6.3370  13.6606  -7.1487  -0.2621
-#&gt;    5.9394   4.4685  -0.4925   6.0674   1.9031   6.0924   3.2171   2.2223
-#&gt;  -12.7919   0.8670   0.7464   9.2208   5.1413  -3.4920  -0.8628  -1.1500
-#&gt;  -10.9959   2.0829  -6.2836  -3.3308  -1.2404   0.3261   3.4622   6.7565
-#&gt;   -1.7894   7.0095  -4.7989   5.9668  14.7276  -0.0832  -1.1292  -2.3796
-#&gt;   -3.8813  -4.9567   4.5870 -20.2590  -3.6941  11.4605   5.8353   5.5101
-#&gt;  -21.2222  -2.7852  -0.2706  -3.5601 -10.8011  -0.5643   7.5233   2.2526
-#&gt;   -2.8226  -1.7783  -0.7867  -6.6398   6.8352   1.3618  -7.3804  -0.3211
-#&gt; 
-#&gt; Columns 33 to 40   4.6767  -5.6769  -8.2998   1.8646 -11.7082  -0.1739   0.7321   0.9858
-#&gt;    9.6309  -4.0555  -4.5445  -6.0392  -1.8424  -4.5782  -2.2394   2.7480
-#&gt;   19.3465   1.4504  -0.5011   0.6825   8.3566  -6.0887 -12.4131   1.4882
-#&gt;   15.4626  -0.9147  -2.8020  10.4818   0.1867  -2.4863  -4.4813   8.9666
-#&gt;   -2.2579  -4.4858  -2.6382  -8.7673  -2.4578   8.6333  -0.1472   0.8210
-#&gt;   -3.8017  -4.5528   2.1585   0.3108   4.3367   4.4167  -4.1404  -7.1562
-#&gt;    6.1018  -1.3892   2.6067  11.0037  -0.8936  -5.2789  -3.8202  -8.6666
-#&gt;    9.7097   3.3408   0.3116   3.6396   0.1723  -5.4994  -3.0563  -3.6130
-#&gt;    4.9959   1.7683  -1.3992  -1.3118  -4.7137  -3.1537   2.8307   8.6702
-#&gt;    5.5646  -1.9904  -0.2973 -14.9815   3.3672   0.3944   0.3536   3.0087
-#&gt;  -18.5556   0.8547   3.2588  -8.9740  -5.4412  -7.7150  -7.0575   5.6699
-#&gt;  -12.5947  -4.6393  -2.0987 -10.0344   2.7829   5.2220  -0.2026  -6.7648
-#&gt;   13.9009  10.4672   0.7112  -1.6072  -6.0489  -4.2060  -7.4302  11.4814
-#&gt;    2.8680   2.9533   3.4821   3.1926   2.0164  -6.2120  -8.4158   2.3738
-#&gt;    2.9694  -8.4894  -3.6779  -3.0176  -9.0022   7.1106   1.5780   7.5124
-#&gt;    7.1181   7.5552  -9.9423   5.9438   4.1525  -1.6902  -6.9626   0.7560
-#&gt;    3.2141  -9.8982  -6.5783  -6.5020   5.5751  -0.7571   1.3244  -4.2656
-#&gt;   -1.6315  -6.8276  10.5276   2.9332   0.0299  -0.9580   0.6356  -1.0171
-#&gt;    6.1094  11.5435  -2.5057   1.0803  -5.3372 -16.7403  -5.9370   0.2371
-#&gt;  -10.7809  -6.6780 -11.6158  -7.0993   1.7148   0.5335  -0.7110  -5.7289
-#&gt;    4.4232   8.1946  -2.5558   8.8174   9.0208  -4.4722  -3.7291 -11.3726
-#&gt;   -5.6536   1.3000  -6.5301   3.3230  -7.4231   4.5805   7.0784  11.0849
-#&gt;   -1.1154   0.2432  -2.7664   1.8290  12.1905   4.9875   2.9038 -13.9286
-#&gt;   -4.1042   6.4505  -2.1113   6.7506   6.6225   9.8238  11.0429  -0.3648
-#&gt;  -10.7701  -6.5126   3.9260   0.4233  -5.3073  -2.3215   4.7964  -4.2986
-#&gt;   10.9507   4.4344 -11.7721   3.5976 -10.9204   3.9153  -7.7275  -1.1018
-#&gt;   -1.1800  -1.4308  -7.2643  -2.7054  -0.8770   3.8441  -2.1754  -8.6870
-#&gt;  -10.8437  -0.3657  -7.6537   7.2054   7.0568   0.1700 -12.9408  -9.0044
-#&gt;   -0.5383  -5.7443  -2.7455  -9.1649  -1.6883  -5.2865  -0.0477   2.8188
-#&gt;   -3.5584   3.7999  -4.8056   5.4437   8.3543  -2.5686 -14.0578   2.3122
-#&gt;    3.3393  -6.5970  -3.9285  -7.5108  -5.0091   1.1976   3.0261   1.5317
-#&gt;    6.3473  -0.3590   3.6051  -7.7109   6.6993   0.2420   0.8268   9.6148
-#&gt;    2.3679  -4.2571  -7.5652  -2.6319  -0.2594   3.5161  -3.8648   1.3730
-#&gt; 
-#&gt; Columns 41 to 48  -8.9004  -2.4431  10.1769 -11.5539   0.9727   1.1449  -4.1664  -5.9406
-#&gt;    0.7273  -1.5841  -4.8308   0.8003   4.3019  -3.4016   8.7224  -4.3127
-#&gt;    9.4124  -5.4304  -7.3879  -1.5780  -0.8244  -9.7549   7.1619  -1.2881
-#&gt;    4.4182  -9.6812   2.0931  -7.2892  -9.6957  -6.2069  13.2915 -14.1783
-#&gt;   12.7210  16.2381   1.1936  -1.2641   2.7008   7.9641  -5.9156   2.2741
-#&gt;   -0.3840  -4.3539   0.3929   4.1084  13.3946  -1.5061 -16.0275  14.1161
-#&gt;    4.8527   4.2228  -5.5224 -10.0346   0.8754 -11.9943   3.7959  -1.2697
-#&gt;   -3.2294  -7.6962  -0.5004   0.1692  -2.6538  -2.7955   4.5124   7.7744
-#&gt;   -1.2764  -5.2906  -6.6582   1.0561   1.0314   8.8017  -1.1811  -5.1169
-#&gt;   -0.8060  11.0308  -2.5343   4.9971  -3.6082   3.8307   1.2113  -1.5236
-#&gt;    8.1704   4.0161  -3.9638  -0.7323  -7.8175   3.4363  -1.1549   2.2999
-#&gt;   18.6318  -2.9746  -5.0073  -6.5272  -2.8055  -3.2255  -4.9634   6.8455
-#&gt;   -2.7224  -6.3045   1.2384  10.8459  -4.7152   2.5874  11.1960   3.3570
-#&gt;   -0.6983 -11.5129  -7.0210   0.7556  -6.1628  -4.8726   9.0647  -7.1177
-#&gt;    6.3566   5.4712  -5.9771   5.7913  -4.8866   0.0971   5.9906  -0.9292
-#&gt;    0.4678  -0.3558   2.8748   7.5943  -7.6338  -2.0429   9.8464  -6.7635
-#&gt;  -14.0334   6.1741   0.1270  -3.7588  -5.0111   2.7787  -5.4135   5.7406
-#&gt;   12.3765   2.0786   6.4566   3.8771   3.3312 -17.2266  -4.5957  -4.6832
-#&gt;   -4.6396  -8.2818   1.9074 -12.3811 -14.7881   0.2181  13.7469   6.0143
-#&gt;   -2.1437   8.6999  -7.6948  -5.9722   1.3065  -2.1438   1.2666  -2.7880
-#&gt;    8.8771 -19.2104 -12.9171  -1.5808   9.4828  -5.6920   7.1086   1.3148
-#&gt;   -3.7578  -9.0183   0.3922   3.3510  -2.0988  -1.6591   7.8891 -20.0991
-#&gt;   -2.7687  -1.7540   1.9076  -5.1849   0.4355   5.3135  -2.5350  -6.0803
-#&gt;    0.0246   7.8145   5.0346  -6.8477  10.7157  -0.9533  -9.1985  -8.1825
-#&gt;   -1.9543  -8.1319  -8.0247 -20.4680  13.8195 -11.3091   1.6414   6.3563
-#&gt;   -1.4511  11.1988   2.4813  -9.8037   3.3185   6.6449   6.7901  -5.9648
-#&gt;   -7.7734  -3.5544  -5.1077 -13.9153   1.2176   7.7291   1.5552   1.4795
-#&gt;    8.3889   9.3231  -4.9893   1.0248   3.7695  -7.8579  10.9412   6.6266
-#&gt;   -5.4727  -5.4548  -6.1984  -7.9609   4.9564  -3.3660  19.5644   1.3405
-#&gt;    0.5967  -2.9063  10.0813   2.3446  -2.4747   6.2081  -2.8591   8.8000
-#&gt;   -7.9574  -9.9399   4.6892 -10.6437  -9.4975   6.5943   8.4337   4.7842
-#&gt;   -0.9742   9.0712 -15.3261   2.3812  -8.8730  17.1299   4.1943  13.8919
-#&gt;   10.7144  -5.8989   3.4063  -3.5016   9.2069   1.1498   4.2816  -0.5729
-#&gt; 
-#&gt; (17,.,.) = 
-#&gt;  Columns 1 to 8  -5.1619  -3.0181   0.0799   8.0886  -8.0772  -1.7640  -2.0343  -4.7349
-#&gt;   -9.5859  -4.2881  -1.5816  -0.3175   5.1352  -4.7297   4.2629 -17.4556
-#&gt;    0.1543  -4.0294   1.6310  -3.1471  -1.8329  -1.6866   5.3869  -2.4516
-#&gt;   -0.6062   7.6543 -11.9250  -3.3041   6.7607 -15.7408  10.0127  -9.7765
-#&gt;   -8.9783   3.2641   6.1305  -6.3902 -11.9548   6.8638  -3.3556   9.9042
-#&gt;    5.1950  -8.6193  -2.3250   1.6673  -5.1070   4.9004   1.0199   1.3473
-#&gt;   -2.8416  -3.7654   0.7706   1.6454   7.8840   0.3698  -6.4068  -1.4886
-#&gt;   -5.5793  -6.7922  -8.8654  23.0614  10.3158  -4.6445   0.7068  -2.0719
-#&gt;  -10.3018  -3.6898   6.1985  -1.7263  -4.9523  -4.9590   4.9585  -3.1475
-#&gt;  -15.3576  -6.5329   6.7014  -3.4137  -5.3419  -1.9853   5.9655  -9.1938
-#&gt;   -9.9914  10.6697   9.6767  -1.4626   8.7081  10.7835  -8.2493   2.8484
-#&gt;    4.6417  -4.1499   3.1414   5.5009   6.9503  11.8197  -5.4029   4.0689
-#&gt;    5.8440  -9.3735   5.0352   5.2992  -9.7783 -13.5972   0.1664   2.9280
-#&gt;   11.3548   6.7934  -0.6160  -0.2442  -1.3540 -15.6663   9.3547  -3.9605
-#&gt;   -0.2968  -2.4413   7.3948   3.9985 -12.6692  -1.9594  -0.7751   3.0247
-#&gt;    9.6975   0.2138   3.5373  -0.5736  -1.4443  -8.1797  13.9032  -4.1398
-#&gt;    1.3615  -8.6565   4.6917   1.6749   7.3299   4.0300   1.2357   9.4734
-#&gt;   -4.4466   5.9717   3.1726   4.6943  -6.6801   8.4973   4.4991  -1.2390
-#&gt;   -2.3091  -0.4119  15.5392   3.3148   7.9750  -1.5371  -7.4485   3.0937
-#&gt;    3.5304  10.8551  -6.4733 -10.7545  12.2401   2.2745  -1.5117  -1.7315
-#&gt;   -7.3637  10.8645 -15.1257   9.1840  20.0610  -3.7527   0.8773 -10.2257
-#&gt;    1.5459  10.0743  -5.4409   2.1041  -3.2769 -16.7276   4.5645 -10.5682
-#&gt;   -2.6268   4.3734   0.6472   1.6766   0.9398  -2.4559  -1.0414  10.8499
-#&gt;  -10.7332  -0.2520  -1.4968  -9.6132   3.9767  -2.8039  -5.3973  -5.8692
-#&gt;   14.2206   3.5955   9.6403  -6.4169   9.3426   8.1698   2.8643  -2.2282
-#&gt;   -6.4186  -4.2460  -2.9623   9.3977   3.6590 -11.1579  -3.4319   6.0393
-#&gt;    1.2881  -1.7220  -1.5405  -2.2051  -1.8137   3.1895   2.4844   2.4990
-#&gt;    8.5819  -4.9393   6.3451  -0.7898  10.0815   7.1112  -4.4164   7.8334
-#&gt;    4.4946   4.7311   4.1540   1.7233   4.4152  -6.0693  11.5300 -11.6184
-#&gt;    0.9200  -7.2788   8.5793  -9.7418  -3.6820  11.7213  -5.6273  14.2378
-#&gt;   -2.9730  -8.6553   5.6509   4.2432  -3.6835   8.6053   6.7605   8.3489
-#&gt;    6.7401 -10.0715  -0.1860 -10.4713  -3.0209  -3.4074  -5.3498   0.4218
-#&gt;    8.3541   3.9618   5.6562   1.1163 -10.3233  -7.6167   2.0936  -3.4023
-#&gt; 
-#&gt; Columns 9 to 16   2.6554   7.9167  11.0304  -4.0841   8.3659 -15.0617   1.0746   0.0951
-#&gt;   10.2653  -7.1887  -4.6526  -3.0189  -2.7806   3.2218   1.7490   0.8503
-#&gt;   -2.5758 -11.4871  -2.7451   2.1423  -5.5929  -9.2981  -1.2714   5.3440
-#&gt;   15.2209 -20.7210   7.8777  -4.8968  -3.0089  -8.5371   6.3820   5.9276
-#&gt;   -4.4465   2.5734  -0.8134   2.3880  -4.4097   3.2617   5.6593  -8.9384
-#&gt;  -10.4354  -1.8856  14.2443   0.8415   0.0422   6.6552  -0.5176  -6.7742
-#&gt;  -10.1508   6.2166   2.2943 -11.7513   1.5491   8.8356   7.0511   5.0354
-#&gt;    2.0353 -10.3984   6.9557 -10.8480  18.4747  -3.5018   3.2486  -3.2049
-#&gt;    1.1831  -2.6879  -5.1247  10.9413 -21.6096  -4.6595  -1.9645   6.3144
-#&gt;    4.4255  -5.3163  -0.3537  -1.4157  -8.4213   2.9359   3.8514  -9.3721
-#&gt;   -4.2098   5.0282 -18.7914  -4.1104   8.0676  -1.0900   7.6226  -6.8346
-#&gt;   -4.2074 -13.9924   1.3014  -3.5194   8.7787   6.4364   7.0054  -7.3548
-#&gt;   -7.7226  -9.0022  -1.0856   5.7499  -1.0572   0.2483  -8.3625   9.5794
-#&gt;   -1.4326 -10.7731   3.7636  13.0417 -10.8303   9.9199  -4.1907  -3.1137
-#&gt;   -7.6242   1.8568  -9.7423  -1.0967  -1.8745   8.7429  -6.1002  -4.2548
-#&gt;    5.0080 -12.6321  -5.0495  10.8333 -13.4834   1.3995  -9.5204   5.1213
-#&gt;    2.6434   4.7095   0.6623   3.0407 -15.8481  -1.2436   1.1847  -6.4874
-#&gt;    7.3450   6.5187   9.4047  -3.9367   3.4945  -6.0047  -0.9545  -0.7230
-#&gt;   -8.0573 -17.1685  -9.0478  -9.2035  -0.3623  -1.2031   5.7464   6.7034
-#&gt;   -6.2174   2.1358  -1.1809  -0.1612   0.1096  11.0414  -3.0298  -0.0556
-#&gt;    8.8962 -16.2860   3.3317   1.7542   4.2848  -3.9125  -1.7668   1.5290
-#&gt;   -0.6201   5.7843   0.1410   1.3853  -9.2887   3.0838  -6.3960   2.8845
-#&gt;   -4.3300  -8.9639   2.3970  -1.8609   5.3062   2.0718   9.1274  -8.2183
-#&gt;   -2.6742   9.8989   3.7277  -1.4399 -16.5996   9.0688  12.1121   6.0464
-#&gt;    1.4948   6.4696 -10.6562  12.1046  -9.5973  10.5317   2.4813  -1.4759
-#&gt;   -7.1796 -11.7486  -2.1333  -1.0915   6.6922  -7.2241   2.8495  -3.5509
-#&gt;   -1.6976   4.0630  -2.1573  -6.1899  10.4746   4.8966   3.6920   1.2625
-#&gt;   -5.1499  10.9102 -10.2700  11.9868   2.2540  18.0478 -14.5344  -3.5789
-#&gt;    6.3958 -15.7501  -3.0503  10.2427   2.8136   3.6015  -5.5024  -8.6063
-#&gt;   -4.2474   6.5401   6.0854   3.6492  -5.1034 -14.2622  -2.2923   1.7127
-#&gt;    6.0082   0.6665   6.6702   1.6794   2.8281  -5.3877   1.5180   0.3557
-#&gt;  -10.4044  -5.7640  -3.0339   5.7415  12.9303   6.6334  -7.6317  -9.7096
-#&gt;  -11.3651 -15.4415 -11.4166  10.0479   1.4643  -3.6533  -3.1368  -0.9498
-#&gt; 
-#&gt; Columns 17 to 24   4.1676   7.5346 -10.3955  -4.6839   0.8027   2.8550   0.9751  -5.7473
-#&gt;   -6.4199  -7.4061   3.9227  -1.3790   5.2387   9.0546   3.6188 -12.4515
-#&gt;   -3.0704  -2.0439   9.9159   4.2188   5.0221   9.8404   5.9538  -6.1228
-#&gt;   -4.2774  -4.6437  -1.9673   0.0261   4.6949  -3.4775  -3.4377  -1.4731
-#&gt;   -0.7676   1.8158  -6.7767   1.5923  -8.7248  -6.2934  -4.0206  -8.6869
-#&gt;    5.2254   0.5085  -4.9254  11.4029   3.0626   0.1749 -13.6412   2.6948
-#&gt;   -4.2016   2.6745   1.2933 -11.6407   9.3893  -8.2027  -0.5136  -2.2459
-#&gt;   -2.9029  -1.7087   6.3701   0.0140   2.1192   9.2754   6.8887   8.9853
-#&gt;   -1.8979  -2.3461   1.6134   6.0133  -6.7247  -0.7846  -8.3433 -10.7952
-#&gt;    5.7948  -0.8582  -1.8358  -7.5462   0.4105   4.3216  -1.8319 -11.7112
-#&gt;    0.0190   3.6595   0.9984   2.8579 -10.6178  -6.5399 -16.9250   2.6083
-#&gt;  -14.4299  13.9719   3.9606  -7.8635   6.7253   7.4283  -2.4251   9.9554
-#&gt;    4.1462  -0.4103   8.6328 -13.0772  -3.6970  -0.9119   8.4376  -2.4958
-#&gt;   10.0345   1.5690   1.6743  -1.7376 -13.0662  -8.2072   5.0286  -3.3454
-#&gt;   -2.9310   1.0582  -0.1027  11.4803   2.2111 -19.6883  -3.5387 -17.9479
-#&gt;    0.9670   0.4862   1.6640  -4.9562  -4.3110  -1.3840  10.0940  -9.0717
-#&gt;   -4.9501   9.6821   5.8758  -7.8606  -7.7860  -1.8677   7.3119   3.8837
-#&gt;    2.2149  -6.3069  -6.0500   2.8637   0.0099   2.4581   3.6942   2.2841
-#&gt;  -11.5380  -3.9158  -1.4166  -2.4726  11.1947   6.1733   1.3971  -4.6914
-#&gt;   -0.1422   1.8758   5.2678  -6.8755  -2.8271  -4.3313  -3.3322   0.3057
-#&gt;    0.0091  -2.7056  11.6045  -1.7608  -1.0983  -0.1272  16.8650   4.9916
-#&gt;   10.2011   2.1264  -2.9585  -1.9620  -9.1845  -8.9957  -7.8044  -9.0668
-#&gt;   -6.6551  10.0978   2.3357  -5.9963   3.1873  -2.3427   5.4463  -5.4590
-#&gt;    4.8718   7.0416 -10.5680 -16.6755  -2.4014   2.2601  -2.7843  -4.7637
-#&gt;  -14.4118  -6.3285 -14.8899  14.0403  11.6604   6.6104   1.4341  -2.7390
-#&gt;   -5.8211   9.2186  -4.2027  -1.8181   0.1697  -5.8698   3.5700   2.3641
-#&gt;   -7.4844   6.4635   2.6496  -1.5543   4.3140  -8.8012  -2.4610   1.3795
-#&gt;   -0.4812  13.2961   7.8098   4.3972  -3.9960  -9.7672  -4.6588  -7.2766
-#&gt;   -6.0338  -7.8509   2.8048   7.2771   2.8969  -1.5515   6.3580  -4.0975
-#&gt;    8.6986  14.3351  -9.7717  -4.0023 -13.9180  -0.9266  -3.7760   5.1942
-#&gt;   -6.2422   2.0891  -3.1558   1.5595   2.0403   2.2207  -3.0024  -3.1902
-#&gt;   -1.3010  -5.1145   7.8635  -2.7449  -3.6258  -4.4641  -4.6369  -3.9438
-#&gt;    6.8597   5.6082  -5.5376   1.7434 -13.2703  -3.8859   0.5757  -2.0066
-#&gt; 
-#&gt; Columns 25 to 32   1.0254   7.0910  14.8846  -3.1377  -1.7441  -0.0101   8.2309  13.6490
-#&gt;    7.2203  -9.8544   4.2957   0.8403  -5.4216   4.2901   0.5307 -11.0900
-#&gt;   -3.2518  -5.0485  12.4350 -10.1260   3.0470   5.9382  -5.1991  -3.0149
-#&gt;    0.9825 -17.6493  -3.7067   5.6643  -9.3720   9.9860  -2.0499 -10.5845
-#&gt;   -5.4938  -0.9989  -7.3844   1.0720  -2.2592   6.6725 -11.4328   2.6314
-#&gt;   -3.4160  -1.9221   6.9001  -9.0317  -1.7597   0.1183 -10.0590  -6.1533
-#&gt;   -1.4411   2.9178   6.7159  -5.2199   3.7244   6.8215  -3.6982   7.9301
-#&gt;   -6.6415   3.1656  13.6420 -11.6915  -3.5711   5.6936  -2.9921   3.3072
-#&gt;   -9.9602   2.2012   0.1391   5.7160   4.5033   4.3581  -0.8962   1.0365
-#&gt;   -2.7040 -12.5892  -0.7467   6.4007   3.1365   0.2761   5.1091  -4.8358
-#&gt;   -6.5507  13.0072  -8.1460  -0.7292   9.5301  -1.2198   5.1274  13.7180
-#&gt;   -1.8198   2.6547  17.5326 -10.1833  -5.6534   0.1305  -8.4429   6.1219
-#&gt;   -3.2205   8.2305  -6.3872   8.7776   8.8897   2.1674  -2.3012   3.0600
-#&gt;   -2.2893   4.2262   4.3550  -0.6973   3.3189   5.1070  -4.3752   0.7677
-#&gt;   -1.8420   9.5921  -1.2874   5.4161   8.3827   4.1311   7.7046  -4.6980
-#&gt;   -2.2840  -7.7288 -11.9842   0.1884   1.9551   0.8840   0.4875  -0.9882
-#&gt;    5.5980  -6.7492  -0.0901  -2.6437  -0.4772  10.5760  -1.4162   2.2015
-#&gt;    1.7957  -4.1445   0.9496 -10.8582  -6.8151   4.5452  -3.4300   6.9712
-#&gt;  -12.0713  -3.2485  -0.1581   2.2096   9.8587  11.0674  13.6746  -0.3345
-#&gt;   -0.0934   4.7471   6.2557  10.1419  -4.7220  -6.5351   3.8809  -4.5939
-#&gt;   -2.1335   4.1217  11.6893   5.7794 -12.5167   3.2000   3.4059  -2.0594
-#&gt;    4.6066  10.4228  -5.0172   5.2087   2.2536  -7.1337   3.2033  -2.7008
-#&gt;    7.1901  -6.3060   3.5671  -9.6330  -5.9182  10.4501  -0.6683  -0.4104
-#&gt;    3.0606   8.3018  -4.4403  10.3565  -6.3468  -4.1858   9.2453  12.9026
-#&gt;   -1.5092 -10.2147  -2.1994  -0.7522   6.9462 -12.3251   4.1127  -4.6304
-#&gt;   -3.3659  -2.5790   1.9965  -0.7867   7.8040  11.6107  10.2200   0.8229
-#&gt;    3.3383   7.1378  10.7486  -3.0431   0.0634   2.2515 -10.1307  -7.2413
-#&gt;   -2.3418  12.8547  -2.7102  -2.8552   7.1522  -3.7697   3.2059  -6.8201
-#&gt;   -0.0820 -11.7380  13.9148  -0.6083   8.5250  -1.8080   2.8137 -12.9253
-#&gt;   -5.3931  -4.7689  -5.3929  -7.5490   9.0840   3.3297   3.7127  10.0785
-#&gt;   -0.8460  -2.7423   6.4142  -7.6215  -7.6490  -2.2948  -0.1514   5.8941
-#&gt;    3.4158  -9.1364   1.2171   3.4272  -1.3323  -1.6521  -5.7197  -1.3973
-#&gt;    1.8469   0.4327  -5.5370   1.6241  10.8228   0.9583 -11.2766   2.1041
-#&gt; 
-#&gt; Columns 33 to 40 -14.3575  -6.2215  -2.6464   3.5467   3.0407  -4.6165   3.2398   5.9551
-#&gt;   -0.0480 -11.2296   9.0606  -8.4787   3.7471   9.2177  -4.2168  -8.3942
-#&gt;   -7.6122  -3.3571   9.6595   5.2166   9.2910   1.9303   0.2418   2.0756
-#&gt;   -5.9336  -9.8716  16.4470  -1.7783  -4.9414   9.2061  -4.3183   3.6377
-#&gt;    4.0196  -1.7859  -6.7206  -3.6580  -2.8390  -2.7614   2.3717  -4.0672
-#&gt;    8.6838  -8.0013  -4.4193 -15.6865   4.0136   5.3373   4.8009 -15.2233
-#&gt;   -4.8134  -6.2068  -1.7466   5.2689   2.3783  -4.6048 -13.0424  -1.3871
-#&gt;    2.1823  -1.4709   4.6472  -4.9556   3.6358  -1.6127  -4.5299  -0.7508
-#&gt;   -7.7999   8.5013  -0.5993   8.4254  -4.4798 -12.0772  13.9128   7.1798
-#&gt;   -2.4574  -7.7704  -6.0134  -1.1115  -2.3742   5.8104  -0.0299  -7.7799
-#&gt;    4.7580   9.0302  -0.8664   2.2599  -4.7359   6.7417  -5.0842  -6.4733
-#&gt;   14.2720   0.5971 -11.0018  -0.0784  12.7015  -7.7272  -1.3485   8.8818
-#&gt;   10.4907   0.4184  -0.7159  -1.4922   1.9790  -9.6921   3.3352  -4.8613
-#&gt;   -2.7084   3.6704   0.9111   5.4527  -7.8099  -1.5995   4.5562  -1.6489
-#&gt;   -2.1861  -3.2265   4.8851   2.3370 -12.4613   3.2311  15.8262  -3.8380
-#&gt;   -1.0990   4.2719   3.0757  15.0411  -5.6176  -3.2691  -6.7996   1.1006
-#&gt;   -8.8203  -4.1936  -1.6645  -2.6186  10.6776   9.8482   6.9322 -12.3674
-#&gt;    5.5215  -8.9044  -3.4960  -6.0829  -1.6044   2.5236   0.0788  -5.7065
-#&gt;   -1.2669   0.4632  13.2928  11.7140  -7.4080  -6.8600  -8.0006  -4.2027
-#&gt;   -4.0752  -4.7452   0.7452   6.4075  -1.4182  10.2600   3.5309  -8.4121
-#&gt;   -4.2307   1.1806   9.6492  15.4468   5.8637   6.8539  -8.5205   1.0018
-#&gt;   -3.8457  -0.7070   6.1170   2.9409  -7.0939  -3.4753   1.9503   6.2271
-#&gt;   -5.9679  -1.6525   0.3704  -5.1216  -8.2476   5.7906  -7.2179   1.3314
-#&gt;  -11.1109  -3.0333 -14.4224   1.7890  -2.4105  -0.1108   0.3998  -5.0452
-#&gt;   -7.0083   4.6308  -0.7722  11.1411   3.5653  -0.4934   6.9186  -8.6089
-#&gt;    2.0942  -7.0346   7.1834  -1.7625  -9.4952  -0.1650  -0.6904  -3.1321
-#&gt;    0.0324   6.7720   4.1951  -2.1648   1.2437   6.1136   1.2496   7.0027
-#&gt;   10.8015   5.0426   0.4105  14.0700   6.3069  12.8383 -11.9664 -13.9583
-#&gt;    1.3122   4.0592   2.7471   6.4240   1.6898   1.8227   0.5049   4.1766
-#&gt;   -1.7909  -5.0770  -1.5484   7.6894   5.3256 -14.3788   4.5347   0.7098
-#&gt;   -3.2952  -1.6346   2.2955   2.4030   1.0500  -8.9837   6.7502   6.9802
-#&gt;    6.8395  -5.0656   0.1906  -9.0577   9.8598   5.1781  -7.2335  -0.2278
-#&gt;    4.4538   2.7797  -0.9497  -6.1691   5.6912 -10.2034   3.1724   3.2141
-#&gt; 
-#&gt; Columns 41 to 48  -8.6441   2.4011   5.3406   0.5169  -7.1458 -10.6428   3.4734   6.4883
-#&gt;    3.6561  -1.3602  -4.9829  14.9695   7.3692   1.5609   3.6538  -0.9603
-#&gt;    9.7197   3.6295  -1.4647   4.9346   9.6288   7.8971   3.1743  -8.6251
-#&gt;    1.6472   2.3387 -15.3470  17.4664  14.3885   0.9156  -9.6962  -6.3569
-#&gt;   11.7460   6.7559   5.7597   1.3397  -0.2342   7.9527   4.8621   5.7302
-#&gt;    5.2752  -6.7882   7.0607   4.4035  -7.4108   2.5544 -11.0435   1.9892
-#&gt;    0.3894   5.1207   3.2670   0.8916  -1.3623   0.8533  -0.3910  -2.0448
-#&gt;  -14.1813   3.4252  -6.7175  -0.2159  -4.8677   0.9509  -6.7635  -3.2218
-#&gt;   -4.9690  13.1662   4.1110   8.9466   6.0124   3.0692  10.9720  -6.5359
-#&gt;    0.5593   3.6655   7.2326   6.2879   7.1621   6.6863   1.0141  16.3214
-#&gt;   12.3352  -7.1740  -4.8923  -5.6806  -6.4280  10.6362  -5.3967   3.5949
-#&gt;   -2.2432 -12.8000 -11.8296 -16.0490  -9.8189  12.8758  -2.4351  -7.8443
-#&gt;   -4.5688   7.1990   0.5583  -1.7811  14.3203   4.1463  -2.6914  -4.7332
-#&gt;   -1.3859   5.0359  -1.3397   1.9056   9.3781   7.6273  -7.1196  -5.7528
-#&gt;   12.7389  11.5307  10.7647   0.5788  12.7882   1.0392  -4.6635  -2.4167
-#&gt;   -7.0239  16.5722  -6.2209   3.7139   8.4041   1.7262  11.3945  -4.0328
-#&gt;    2.1784  -2.5491  -0.0794   9.1677  -5.9280  -6.5202   5.9558  12.9000
-#&gt;    0.6541  -2.0004 -10.7711   1.4766  -6.1706   7.5175   4.1388   1.1855
-#&gt;   -5.8622  -0.4412   4.2065  22.7852  10.0497   7.5345  -7.5494  -9.1347
-#&gt;    7.6072   8.8231  -5.0044 -11.2194  -0.6387   0.7680   1.4248   6.9167
-#&gt;  -12.1577   3.3665 -14.2950  -4.3271   3.3356   6.4653   4.0655  -2.3738
-#&gt;   -6.7812  10.3407   3.4721  -3.3608  10.9246  -4.7196   0.0555   2.4327
-#&gt;   -5.3465  -8.1440  -9.7443  10.5662  -1.7553   4.5819  -9.2733   0.5123
-#&gt;   -5.5077  -4.3240  -2.6952   1.6386 -13.8442  -3.9715  -3.8010  -1.9999
-#&gt;   -4.1681  -4.8682   1.0707   4.2970  -5.5541  -2.3680  10.4819 -12.6713
-#&gt;   -8.8408   7.5186   1.9883   4.9950  10.6310   3.2479   1.3208   0.8368
-#&gt;    3.6220   9.5142  -0.6823   1.2705  -5.9588 -16.6398  -2.1621   1.5515
-#&gt;   16.8932   7.8655   0.1815 -15.7166  -8.2259  -9.0776  12.2167  -0.3076
-#&gt;   -6.6233   3.8283   1.9062  -0.7773   8.5527   3.2746   7.6873  -3.1137
-#&gt;   -4.6619  -5.6619   7.5286  -3.1911  -1.0284  10.5853   6.7247   7.2033
-#&gt;    1.2962  -2.9593   6.1153   2.5834  -6.6286  -5.0059  -6.8562  -4.3241
-#&gt;   15.2824 -16.6599  -1.0556  -0.1420  10.4592   3.5020  -5.7064   8.5650
-#&gt;   -5.1108   4.7232  -1.9738  -6.1196   2.2868   3.7609   9.3567 -10.4940
-#&gt; 
-#&gt; (18,.,.) = 
-#&gt;  Columns 1 to 8  -2.1182  -0.7571   7.9467   2.1898   4.7717  25.2638  -0.8232  -7.9312
-#&gt;    1.4757  -8.4381   4.6898  -6.4652  12.7088   6.1968  -6.5510  -3.3435
-#&gt;   -1.0880   3.1956   5.9717  -2.7051   2.0740  -1.9199   2.2675  -1.2013
-#&gt;   -1.3924   8.2906  -2.5384  11.9476  -3.7197   7.0748  -6.8147  -9.3733
-#&gt;    5.5619  -1.2401   1.4920  -6.0486   3.3838  -1.9039  -6.3562   8.2120
-#&gt;   -0.6116  -4.2774   9.4702   1.4873  17.8344  -8.8499 -29.3098   3.4220
-#&gt;   -3.7875  -1.9810  -6.1796  10.2906  -1.9900  -2.1061   7.1188   6.1529
-#&gt;   -2.5599  -9.5376  -2.6383   2.9431  -3.7980   5.2267  -1.7671  -3.9193
-#&gt;    4.8052   1.8797   8.5406  -8.1130  -2.0360  12.9946   2.4048  -4.7191
-#&gt;    2.9974 -10.4339  -0.2404 -13.4410   7.9839   3.1934  -4.1069   3.5502
-#&gt;   -1.6361  -4.6846  -3.8339  -1.8360  20.3230  -7.6295  -4.0841  -7.0594
-#&gt;   -9.9150  -3.2720   6.0693   3.1899   3.3531   1.6007   1.5613   1.2052
-#&gt;   -8.4328  -4.1565  -8.0653  -2.6708  -9.2769   1.4444   8.3375   8.4450
-#&gt;   -3.2125   0.9822   0.4917  12.6375   3.2021   0.8483  -0.6230   4.8014
-#&gt;   -0.2609   3.1864  -1.5156   6.5577  12.8700   7.6108  -4.1719  -4.2897
-#&gt;    3.2358  -6.7246   3.5930  -4.1780  -4.8662  -1.1375  -4.2261 -14.1053
-#&gt;   10.4043   1.6688  -7.9842  -8.4965  -2.1641   5.7481  -5.0010  12.2231
-#&gt;   -3.2635  -2.9199   6.9172  -5.2742   3.3919   0.9880  -7.8094   6.6050
-#&gt;   -2.4746  -0.2135   7.4969  -0.3032  11.5303 -13.9905  -0.9217  -5.6705
-#&gt;   -3.7569   2.5001  -6.3367  14.1089   8.5051  -3.3212  10.8588   5.3911
-#&gt;   -4.0467  -4.7554   1.1179   9.3319  -3.1277  -0.4392  -0.8629   2.8097
-#&gt;   -1.7547   2.4296   3.7441   5.2022  -5.6253   8.2018  16.8028  -5.5757
-#&gt;    0.0685  -0.7017   3.6165   9.9757  13.0693  -0.2408  -5.1801 -11.3338
-#&gt;   -5.8028  -5.4429  -7.2817 -11.3863  -0.9092  -0.2677   4.8687   3.4034
-#&gt;    2.2659   4.6321   7.0772   4.8961  16.6779   0.9971  -7.9687  -5.9066
-#&gt;   -4.8906  -2.4658 -11.6296  -1.2893   2.6321   0.9346  -0.6113 -13.0913
-#&gt;    8.9434   9.2545   3.0081   7.0109  -0.4382  12.3121   3.2274 -14.7190
-#&gt;   -0.4862   1.9033  -5.7876   4.4347   7.4138 -26.2398   9.5332   9.6503
-#&gt;    2.2402   3.9792   3.3721  10.4018   6.3583  -4.3108  -1.3508  -1.7882
-#&gt;    7.4722   2.3650  -1.8423 -17.8048  -1.3112  -4.7984   3.7357   9.3711
-#&gt;   -4.0211  14.1959   4.6885  -7.2354   3.9748   7.9475  -8.8620  -6.2914
-#&gt;   -3.7000  -6.9855  -3.8212   8.3691   0.8111 -11.8869  -3.7272   0.7643
-#&gt;   -4.7139  -6.8645  -0.7060   2.7541  -4.5484   3.5338  -8.6274  -1.2171
-#&gt; 
-#&gt; Columns 9 to 16   7.2647  -8.6888  11.7926  -1.0136   1.5494   2.4462  -6.5419  11.3079
-#&gt;    4.5663  12.5308   0.7116  -2.8596   5.5737  -9.2472   5.8513  -9.5661
-#&gt;  -13.3512   1.3591  -1.0610   0.4476   5.8431   1.2887  -1.1533  -1.8306
-#&gt;   -3.8562  -1.3592  10.7865  14.0710 -13.6829 -10.5686   3.1663  -0.1743
-#&gt;   -6.0634   8.4946   1.7074  -4.0652   1.0906  -0.4083  -2.4810  -2.0329
-#&gt;   22.3303   8.4309   5.9973  -7.3569 -11.3795   8.3505   2.1217  -1.1199
-#&gt;   -7.1720   2.0795   6.0179   1.7678   7.1983   0.3938  -4.3298   5.9715
-#&gt;    4.9156  -5.4124   0.4973  -9.1705   4.4407   4.9347  -1.4694  -2.2564
-#&gt;   -4.8952   9.7767   2.9930  -5.5948   7.6400  -5.4677   1.7944  -2.3845
-#&gt;   -1.0263  10.0324  -7.4797   2.0402   9.0892  -4.2136  -4.2335  -7.1426
-#&gt;    2.9696  16.1909 -12.5469 -11.5150  -1.9482  11.0889  -3.9540  -3.3895
-#&gt;    2.2270  -4.5721 -18.1313  -9.1965   2.6634  18.9085   7.9679  -2.6307
-#&gt;  -20.6692   4.9193   1.0881  10.1452   3.7314  -4.5919   1.2086  -2.2627
-#&gt;   -9.9781   7.9977  -5.2585  11.1988  -2.5901  -5.4626   3.3733   5.7326
-#&gt;   -2.7484  12.7465  -0.2040  13.1155  -6.3980  -9.1547  -0.0957   2.8694
-#&gt;   -0.1539  -3.9398  -0.4655   8.6836   2.9089 -17.5699  -2.7059   2.7102
-#&gt;    4.0578  -1.0165 -17.0126  -5.9745   9.3175 -15.3041   3.0811   7.9012
-#&gt;    3.4738   2.0146   2.5935  -9.4803  -0.1053   0.6432  -0.7223  -6.0844
-#&gt;   -6.5118  -1.8916   9.7646   0.6670  -2.9842  -7.9055  -6.2935  10.3625
-#&gt;   -6.9629   4.8729 -21.6642  -4.4527  15.1960  -1.6819   5.9706   0.5120
-#&gt;    2.6332  -3.0433 -16.7360   2.7591  11.2852   1.8029   1.4326   5.5599
-#&gt;    3.3250  -9.9069  -0.7334   9.8337   6.9544  -8.0702   4.9554  -4.5893
-#&gt;   11.7242   9.3968   2.5485   0.8957  -2.3584  -5.4113   2.9040   6.0120
-#&gt;   -2.5418   9.6976  -6.0312   4.0430  11.2914  -7.4692   2.5382   4.2934
-#&gt;    2.3915  -0.9626  -0.1069 -11.9796   9.2055  -1.7904   0.1402   6.1305
-#&gt;    8.3409   0.2522  13.4007  -0.3983   2.9108  -8.6546  -7.6775   7.6755
-#&gt;   -1.1018   1.2193   2.2627 -10.3144   0.4891   5.3719   4.6967   9.7798
-#&gt;    9.2502  -9.7482 -14.2606   5.1587   0.8067  -4.0890   8.0564   4.0071
-#&gt;   -4.5599  -3.6730   0.2758  -0.8335  11.5419  -2.6657  -3.4265   7.6750
-#&gt;    3.7316 -15.9019   6.4014   2.8236   5.3996  -3.1120 -14.9505   0.0564
-#&gt;   -5.4596   1.8753   0.0063  -7.5509 -16.1388   4.6096   3.8762  -3.2336
-#&gt;   -8.9335   8.1773   4.1663  12.9117  -4.6454   2.1488   2.8809  -9.6851
-#&gt;    0.0477 -10.4034   6.2636  -0.4742   5.5335   0.5257   0.8917   2.3452
-#&gt; 
-#&gt; Columns 17 to 24  11.0573  13.7312   5.8543   4.8489  -2.6569  -3.6729  -2.0829   6.7676
-#&gt;   -2.7833  -7.6586  -4.0285  -6.8365  -4.4145   3.2945  -7.1827  -4.5094
-#&gt;    1.2952   1.4334  -2.6366  -1.1614 -13.9862  -0.6042   4.2555  -2.1690
-#&gt;    6.4549  -7.4690  -2.0481  -4.5348  -7.2859   8.3759  -7.9426  15.4676
-#&gt;  -18.3143  -2.0021  -5.8181  -0.4990   2.7677  -6.0340  14.8655 -11.5795
-#&gt;    1.4829   5.7384  -3.3054 -11.3290  -0.5048 -14.0581  12.6753  -3.5082
-#&gt;  -11.5949   1.0957 -17.6710   4.4140  -6.1736  -2.9811   9.0966 -13.4329
-#&gt;    4.3069   2.3311  -6.4558   0.6288 -10.9957   1.1390   3.1576   1.7888
-#&gt;   -0.2856  -6.8539  12.5494  -3.4223   0.8451   7.7194   3.4557  -3.7051
-#&gt;   -7.3951  -7.1409  -9.8258  -3.3382  -3.3691   0.7643   7.8610  -1.5240
-#&gt;   10.8178   8.4922   2.1662 -14.1131   4.2186  10.6684  12.8027 -13.3816
-#&gt;   -0.2625   5.7901  -7.1812   5.2922  -3.0609  -7.8868  10.9752   4.4107
-#&gt;    1.2552  -1.4243   1.2744   4.8681  -4.7870   8.5247  -2.4831   7.4701
-#&gt;    0.1306  10.3847   0.1616   3.1268  -4.7955   2.5798  -0.0894  -5.6855
-#&gt;    5.3339  25.9790  14.3555   7.6033   0.2488   0.1268  -0.5005  -2.1643
-#&gt;   -9.2570  -6.3087  -3.6075  -7.1626   0.3360   6.7071   0.3442   1.5326
-#&gt;    1.8577  -2.1719  -8.1049 -13.1077  -7.9606 -11.6935  -6.9597   5.5981
-#&gt;   -1.2713   4.8206   3.2899  -1.6654 -19.0327  -9.0100   4.2865  -5.6547
-#&gt;    4.1511   4.4826  -2.2632  -7.7519   2.3801   7.0907   6.0796  -5.8401
-#&gt;    3.5021   1.0276  -6.6806   1.2568  -2.0366   7.1988  -6.7076  -1.0068
-#&gt;   -2.5280   0.8024  -8.9132  10.7897  -2.8374   6.7471  -2.2640   0.6762
-#&gt;    6.0122 -12.5526   0.2823   1.9227   3.5617  13.1899 -14.5711  -1.3615
-#&gt;   -5.3664  21.6509   0.7484   3.8145   0.2056 -14.7323  -4.4145  -4.0317
-#&gt;  -15.9122  -8.8177  -0.5277   4.1409  20.5291   3.4526  -2.0654  10.7065
-#&gt;   -3.5763  -0.3758   5.0681  -7.6710   4.2845   1.7020   3.9231  -2.4412
-#&gt;    2.4172   5.0061  -5.2222  -2.6070   0.8065  -3.1587  -3.6731   5.6538
-#&gt;    2.0505   5.3885   6.5308   6.9079  -0.5731   2.4897   5.5510  -0.2221
-#&gt;   -1.2687  -6.4637  -0.7992   1.5713  11.7883   4.8409   7.4817   5.2496
-#&gt;    2.1072   2.8074  -2.4123   6.6889  -1.5585  10.1925  -1.7233  -6.7870
-#&gt;   -0.0777  -0.0870  -2.5451  -2.8325  -0.0159 -10.7361   4.9466  -2.9215
-#&gt;    0.8042   6.5573  -3.6933  -4.7547  -6.9004  -1.5150   6.4858  10.4649
-#&gt;    0.4677   4.9717   2.9189   1.6511  17.4041  10.3360   7.9191   9.0941
-#&gt;    1.1544  -7.1980  -5.6384   2.1168   6.5097   8.4443   3.2848  -2.8482
-#&gt; 
-#&gt; Columns 25 to 32   1.0632  -2.7192  10.7800  -1.4988   5.6075  -1.0201  24.4864  -4.5952
-#&gt;    1.8964  -4.6640  -5.0406   5.0747 -20.3201   6.5519  -6.2392 -14.4074
-#&gt;    1.1198  -9.0152   0.1442  -4.7465   2.0033   3.4777  -3.9870  -4.5185
-#&gt;   -2.9254  -8.6776   1.1509   8.1511  -1.6471   1.3036  -9.9595   1.6227
-#&gt;   -1.9584  -1.3598   0.2078   3.9571  10.6007  -5.8023   2.1676   6.2004
-#&gt;    8.0944  -9.1235  -7.1985  -4.1770   2.2347  14.1464  -2.0111 -22.4899
-#&gt;   -2.8796  -7.7015  -6.2215  -5.4362  11.5978  -6.0972  -7.0790  -2.9885
-#&gt;   -3.1749   4.9953  -3.3213  -7.7443  -8.5786   4.4992   3.6853  -9.6041
-#&gt;   -5.9079  -3.8342 -12.4933   9.6378  -6.8800  -8.5215   0.6795   3.3179
-#&gt;   -2.3786  -2.7458   3.6221  10.2273  -4.5791   1.8903   4.0407  -1.8440
-#&gt;    3.4746  -7.2704  -5.6237   2.9026  -1.7574  -6.0075  -5.9611   3.7171
-#&gt;    5.2485  -5.6597  -6.8140  -4.6195  -1.5528  -3.7034  -3.9570  -0.2235
-#&gt;   -0.6517   6.5427  -2.0068   0.1044  -2.7763   1.1070  -7.6801   3.5222
-#&gt;    6.5342  -7.2092  -3.5259   3.8781   2.7967  -2.8619  -6.4781   1.9573
-#&gt;   -3.2627  -8.2156   5.7127 -10.2309   8.4198   1.0317  -8.5008 -11.6586
-#&gt;    2.6354  -4.9628   6.0390   5.1588  -0.0314   5.3271  16.1069   2.2121
-#&gt;   -6.7892   1.7971   3.7045  10.1105 -11.8865  -2.2256  -2.0193  -3.8336
-#&gt;    2.3820  -6.5229  -9.5870  17.5375   7.5020   4.4041  -4.9949  -2.7408
-#&gt;   -6.9702  -5.4410  -1.7742  -8.2750 -13.7040  -1.1796  -7.8330 -20.4477
-#&gt;    2.6058   9.6906  -4.7918   5.4476  -9.4555   6.6318   1.0639  16.5680
-#&gt;   11.1500  -8.0351  -3.4691  -2.8308 -11.5355   2.5864   3.8273   3.3524
-#&gt;    1.0210  -2.1057  -3.7304   2.2107  -3.7075  -5.8099  -8.6483   7.5339
-#&gt;   -0.2247  -5.4163  -0.5882 -14.3024  -2.6323   1.0308  -3.6861 -16.4198
-#&gt;    8.8517  -5.0068   4.0431   5.2185  -7.5437   2.1399   9.1219  -7.3401
-#&gt;   -1.0232  -2.8021  -7.3895   2.2813  -8.9037   0.7960  10.9585   3.6894
-#&gt;   -3.5527   6.3734  11.3317  -9.4079   4.1024   9.1470   5.1007 -10.9093
-#&gt;    1.7934   2.0417  -7.4359  -0.9415  -7.7304 -14.8421   4.6599  15.0996
-#&gt;   -3.7730   3.0577   2.7603 -14.7906   6.3743  12.6716 -12.3185   3.3731
-#&gt;   -8.9482   7.4187  -3.6600   3.4782  -1.1062   7.5387   8.5087  13.0561
-#&gt;   -3.1593  -1.4216   6.9764  -0.7125  14.8323  -3.5689   9.6071  -3.1786
-#&gt;   -5.4040  -7.6017   4.8639  -5.5065  -6.7502   5.9897   1.7524   4.7436
-#&gt;   -0.7224   6.5160  13.5542  -0.8484   4.8272   9.3635   0.0067   8.9889
-#&gt;    3.8936   8.1327  -1.3547  -5.1174   7.6414   0.9399   4.8738   6.6594
-#&gt; 
-#&gt; Columns 33 to 40  -9.2635  -7.2035  -6.8341  -3.8920  11.1846   0.3289   8.8149   1.3694
-#&gt;  -11.8951   3.4905   3.6577   6.1760   2.5567  -3.8852   5.0694   3.1206
-#&gt;    5.2271   1.2251   9.4451   7.1253  -0.0516  -0.6160   2.3369  -2.2727
-#&gt;    2.7091  -4.3054  17.7565  -1.2457  13.8621  -3.6399  11.4166  -8.5968
-#&gt;    5.7349   1.3999   3.5504   7.1171 -14.1684  -3.7598  -9.0061   4.2168
-#&gt;    0.1764  20.1954   2.7542  12.2646   6.3112  -0.7967   3.3466  -0.7696
-#&gt;    8.9047  -5.0562   0.5008   5.8380  -5.7228   6.8445  -3.6734  18.9400
-#&gt;    2.0629  -8.6991  -0.5225 -10.7714   9.7581  -8.4159   7.7689   3.5440
-#&gt;  -17.0136   1.7361  10.8883   5.8647   6.5579   5.2743   5.7465  -1.6654
-#&gt;   -1.0095  -2.9798   5.6213   9.2175  -6.1612  -3.8507  -2.1239  -0.5416
-#&gt;    5.0362   1.0972  -7.7023  -1.9197  -4.8732 -11.5382  -0.7454  -2.0414
-#&gt;    5.5874  -1.5368  -4.8733  -3.8002  -6.0718   0.0737   0.5264   8.3143
-#&gt;    1.9344  -3.4319   5.4247  -5.5361  -2.4694   9.2598  -3.9390 -12.0095
-#&gt;    7.2665  -5.3043  20.2393  -5.8789  -3.3490  12.6675   3.9846   1.7936
-#&gt;   12.2226   4.7088  -0.0720   3.2021  -5.8620   5.8119  -6.5830  16.4504
-#&gt;    4.5032   5.2409  10.1898  -5.6131   0.3540   1.0364 -11.1512  -9.0271
-#&gt;   -6.4820 -11.0132  -6.5388   4.7742 -18.1064   6.5336  -3.4887  -0.7135
-#&gt;   -3.6589  -0.3688  -0.8579  -2.5890  -5.6719  -7.9088   3.0846 -10.2442
-#&gt;   -4.3303   0.2712  -4.2583  10.9800   3.5294  -0.0587  -5.5945  -1.6424
-#&gt;    7.3768   0.5957   0.5841  -7.9975 -16.0498   1.6262   4.4650  13.6902
-#&gt;   -5.2461  -8.1827  12.8812  -1.8316  -0.6669   0.6355  20.4224  12.9001
-#&gt;   -1.3292   1.1440  11.3888 -18.0718   8.5401   7.8767  15.9205  -3.3331
-#&gt;   -6.4445  -0.6811   2.3454   7.0941  -0.0060  10.7454  -2.9552   3.8102
-#&gt;   -8.8245   6.8739   3.8587   8.6120   1.8066   5.9996  -5.2777  -3.7399
-#&gt;   -3.8900  12.8585 -10.1478  14.4859  -4.3676  -2.4779   9.6047  13.7051
-#&gt;   -0.3521 -13.9371 -10.2086   0.6534   5.3738 -10.6879  -4.5834   9.1824
-#&gt;   -3.5589  12.1856   0.6645   2.0483   2.5583   8.6065   4.0014   2.7410
-#&gt;   23.3597   6.5069  -0.3860   5.0167 -13.4162  -3.9814  -3.4062  14.9261
-#&gt;    3.4224  -7.6012   2.4267   2.0239  -5.9643  -2.4389   8.4784  12.2254
-#&gt;    8.1131 -10.8963 -11.4393   7.0195  -8.6953  -4.0921  -9.2943 -10.3610
-#&gt;    9.7696   4.3104  -9.8152  -5.1547   7.7941  -1.7377 -12.3977  -1.6889
-#&gt;   14.6476   7.0514  -2.3186   2.1118  -2.5128  16.7693 -11.8469   6.5283
-#&gt;    7.4444  -1.3278   5.5839   2.1411   3.1325  -7.2029   6.9434  -0.3647
-#&gt; 
-#&gt; Columns 41 to 48   6.3361  -8.3823  14.6838  -2.5564   2.3135 -11.0018  -3.4430  -6.6691
-#&gt;    9.5879   9.2590  -5.3786   3.0516  -6.5846  10.0990 -11.7321   3.3944
-#&gt;   -3.6460  13.8559   1.0598  -0.7469   1.6902   6.6729  -1.9580   2.0151
-#&gt;   -0.0767  11.7552   7.3769  -8.4430   3.5126   3.5257   5.5583  -0.5369
-#&gt;   -3.4907  -0.4182  -8.7510  -5.0566  -3.7087  -1.6293  -1.2193  -4.2151
-#&gt;    0.7292  12.5892  -7.2307  -1.5323   7.5259  -0.2826  -6.2068   1.0094
-#&gt;    0.1214  -0.8100  -0.6030   6.5839  -7.0462  -0.7055  13.5199  -9.9241
-#&gt;    7.8897  -1.4335   0.9386   3.6702   4.7301  -1.1972   6.3694  -6.8673
-#&gt;    0.1027   7.4085   0.0746  -4.1141   1.1504   7.1094  -7.2309  -3.6300
-#&gt;   11.9246   4.9357 -16.8775   4.4258   1.9653   3.3235  -9.4068  -1.7021
-#&gt;   -2.9280   1.6684  -8.8813  -3.6751   1.3775   6.1987  -7.1506   7.4461
-#&gt;   -0.2259   1.0554  -2.3421  -6.5382   4.4263   8.2247   3.2295   2.3804
-#&gt;  -10.8960  12.0267   2.7160   8.1598  -2.4184   9.6094  -2.0357  -3.3505
-#&gt;  -15.7383   9.7148  12.3839   4.5777   2.4778   1.9414  -2.6358  -8.7548
-#&gt;   -5.0241   5.4350   5.5382   3.1705   5.4128  -0.5800  -2.6863   5.3659
-#&gt;  -10.4657   2.7522  -7.5623  -1.2090   1.4477   6.0935  -0.7693  -1.6742
-#&gt;   22.0229  -5.2352 -11.4991   9.3870  -3.7281  -6.6134  -0.3707   9.4902
-#&gt;   -7.5351   7.1026  -4.9276  -8.8025  -3.7069   4.6289  -4.7223 -12.3302
-#&gt;    7.2135   3.2702 -11.6549  18.1010   4.2276   4.9779   1.6759   7.6809
-#&gt;    8.9515  -8.4539  -1.6544  10.5667 -11.8874  -8.4563  -1.8262   2.5703
-#&gt;  -13.5672 -11.6889   9.8639   6.4763  -3.5790   4.2325  10.7076  -8.6775
-#&gt;  -10.4942   1.1181  11.4040   1.1981  -9.4173  -2.4131  -4.2072  -2.0181
-#&gt;   -1.2752  -1.4358   6.2455   8.8270  -0.5674   4.2308   0.0401   5.4706
-#&gt;    1.2251  -9.2584   2.5148  13.4763  -3.8339   3.0532   3.8232   8.1582
-#&gt;   11.2414   0.5207 -14.2519  -8.0120   0.9716  -5.9536  -3.9727   6.0368
-#&gt;    0.1865  -8.3975  -2.1859   1.7352   6.8353  -3.5377   6.7373   3.0838
-#&gt;   10.4572  11.4622   8.1965  -0.7065  -2.8599   2.4068  -5.4784   3.3010
-#&gt;  -17.6197 -19.1179  -5.1196   8.2346  -3.9124  -1.4653   7.1807   3.3478
-#&gt;   13.8698  -7.8992  -7.9530   1.0119   4.6210  -1.1669  -1.1689  -2.1323
-#&gt;    0.2055  -7.5565  -5.8767   6.8536   6.6724  -9.7350   0.5113   5.2973
-#&gt;   13.9984  11.7311   4.0220  -6.5534   6.5149  -0.9068 -13.3340   4.0862
-#&gt;    6.8044  -8.1622  -1.4375   4.8625   5.6870   6.0109   0.3636   6.5547
-#&gt;  -11.3849   1.3162   3.0693  -3.2302   6.4743  -1.3361   8.0931   2.7210
-#&gt; 
-#&gt; (19,.,.) = 
-#&gt;  Columns 1 to 8   7.9069   8.2314 -15.8607 -17.8247   1.8460   3.3960   1.7360   0.4683
-#&gt;   -8.9073   3.4705 -10.9638   4.2166   2.0623   8.7358   4.3075   7.7731
-#&gt;   -3.2392  -4.2938  -1.1854   5.3059  -9.1970  -3.2163  -0.8529  -5.6473
-#&gt;  -16.0983  -5.4826  -5.9025   7.2389  -1.9904  -0.2595  -5.9837  -2.8110
-#&gt;   -0.7603  -5.9232   5.6491   1.1754  -5.2810  -9.1353  -5.6206   1.7050
-#&gt;   -3.8353  -2.9147  -0.8285   3.0347   7.9952   5.0474   2.4767   2.0987
-#&gt;   -4.2395  -7.5736   6.2816  -0.5787  -3.1490  -2.5334   3.8162  -0.3144
-#&gt;   -1.0432  10.8327  -2.2433   3.8450   8.5869   8.0106   6.9709   5.5028
-#&gt;   -3.6809   8.2100   1.3975   4.3335  -8.6595 -15.4878   2.6603  -6.5403
-#&gt;  -13.4476   0.9709   6.4793   4.2022  -5.8665   1.9948   3.1041  11.2352
-#&gt;   17.0529 -18.9924  -5.9974   1.5673  10.0528  -9.8677 -11.7180   4.0176
-#&gt;    2.5034 -11.2738 -11.7232   3.2905   3.9547   0.6555  -2.0560  11.3394
-#&gt;    2.0134   5.7511   4.8171  21.8050  -7.7353  -1.7456   1.4701  -0.3785
-#&gt;   -3.1590  -2.1877   2.9348  11.7882 -17.0002  -8.5149   6.3678  -8.5830
-#&gt;    7.2730  -5.6195   0.4421   3.1140 -16.6905   3.8076  -8.8719 -10.0481
-#&gt;   -5.8159   3.5791   3.6580   2.3512 -11.1311  -0.7581   6.1737 -15.1651
-#&gt;  -10.3767   2.9377   1.5156  11.9794  -1.1671   7.3783  12.5935  -1.0580
-#&gt;    7.6018   3.8385  -9.4392 -16.1210  -4.3064  -3.2163  -9.3067   0.9529
-#&gt;   -0.3496  -7.4393  -6.1523   4.5940   5.0168   5.7267  -5.7149   2.5463
-#&gt;  -10.4903  -5.9530  -0.8680   1.7640 -11.1686   1.3398   8.3867   4.7253
-#&gt;   -2.5409   5.8450   1.7522   5.4105   1.0961   1.1265   8.4920   2.9826
-#&gt;   -5.9085  -3.9603   4.3299  -6.5190  -5.3264  -9.2896   3.0530   0.7985
-#&gt;    6.2176 -12.0171  -0.4960   3.7811   2.7566  -2.6141  -2.2421  -2.5439
-#&gt;    5.8788   3.4545   4.1160  12.4822   4.4258  11.6593   2.6726   0.7957
-#&gt;   -9.7333  -7.4100 -12.2648 -10.8202 -10.6378  -1.1734   1.2867  -4.5883
-#&gt;   -3.5399  -5.8196   2.7657   8.1652   3.0322   1.3477   3.9744   1.1902
-#&gt;   -0.0897   5.3256   5.4750   1.2325   7.7582  -2.1776  11.7394   0.1351
-#&gt;   -8.1216 -10.0971  15.2709  10.7041  -0.3764  12.1821  -8.2312  -6.5013
-#&gt;  -19.4967   7.9488  -6.2680  -1.6368 -13.4774  -4.1064   9.2371   1.7931
-#&gt;   -0.0632   3.0925   6.8879   8.9557   4.6627  -4.3163  -8.4271  -0.9522
-#&gt;   10.2018  -0.9176  -4.9775   7.8032  15.2119   6.8883  -1.4054   0.6085
-#&gt;   -9.2351  -1.6457   5.4440  11.2551  -2.2972   1.8527   4.7612   0.0586
-#&gt;   -2.7470  -3.5868   2.2028   5.0616  -1.9473  -6.4087   3.4935  -2.2491
-#&gt; 
-#&gt; Columns 9 to 16  -5.4244  -2.9245  -1.2107  -9.0027  13.3219   1.9440   4.8561  -2.7183
-#&gt;   10.7535   4.1678   1.8800   5.5829 -10.7058   8.6258  -1.2581  -6.0093
-#&gt;    5.9274  -2.4308   4.2193   7.6270   5.6584  -2.0767  -6.9968  -5.9731
-#&gt;    0.4131   5.1239   8.8736  -4.7305 -11.9331   9.0935  -3.7310 -17.8027
-#&gt;   -5.1119  -4.9878 -10.6180   0.6381   8.9182  -6.2042  -9.7135  -3.2330
-#&gt;    3.2885  -1.8013   2.8075 -17.3621  -5.3055   0.9743 -14.3689  10.6096
-#&gt;    8.6763  -2.0296   1.8713 -12.6399   5.9778 -13.2539  -0.1304  -9.0192
-#&gt;    6.8218   3.4581   4.1998   2.8683   5.3528   0.3076   4.8818  -2.1856
-#&gt;   -1.0621  -3.8601 -16.4815   6.7831   0.5187  -4.3644 -10.2156  -4.1790
-#&gt;   10.7836  -1.4334  -6.2758   8.9640   0.5866   5.1194  -1.1981 -10.0573
-#&gt;  -11.2556  -3.9902   1.0401  15.9763  -3.9429   6.0554   8.2533  -3.9805
-#&gt;    4.6950  -4.2295  14.3786  -0.8173  17.1867   5.5266  -0.8495  -2.7889
-#&gt;    6.8932   5.7003  -3.2936  13.3559   3.7617   1.0504   7.2782  -8.0423
-#&gt;   -0.6424  -2.4865   3.1472  -4.1264  -5.5375   4.5062 -11.4587  -8.3921
-#&gt;  -10.7727  -7.5424  -0.4159  15.8691   2.7726  -8.9574 -10.0827  -4.1641
-#&gt;   -3.5679   5.6040  -0.5242   4.5327   2.4081   1.7839  -4.5119  -8.2861
-#&gt;   14.1972   5.9486   5.8421  -3.0795   0.4700   9.4044   6.9189  -8.0553
-#&gt;   -2.5634  -8.9776 -10.4028  -6.9258   9.4904  10.9099   4.0087  10.6379
-#&gt;    5.2233  -6.9021  14.9642  14.7190   4.9868  -5.5645   1.4895 -11.4390
-#&gt;    2.5536  -1.9752   2.8385  14.3668  -6.3358  -2.8437   0.8566  -7.5364
-#&gt;    3.3009   6.6402   2.2603  -5.8293  -1.8706   2.2362  -2.2903  -6.8235
-#&gt;  -16.8731   8.3490  -2.8018   1.8419  -7.3747   2.7683  -6.5963  -2.6283
-#&gt;   -2.8859  -5.0357  11.7609 -12.2117  -0.3431  -4.5218  -3.4076  -6.0098
-#&gt;    8.7292   0.1320   3.4142  -3.4457  -2.2028  -1.9678   0.8707  -6.1005
-#&gt;    7.0865 -11.2925  -4.8071  -3.5456  -3.9779  -6.9031  -7.8760   9.7066
-#&gt;   -2.3910  13.3094   8.4412   7.7155   5.9992   3.4252   4.0100  -3.7850
-#&gt;   -5.6154   2.0339   3.5498  -7.0280   4.2935 -12.5404 -11.6149 -14.0983
-#&gt;  -10.1337  -2.7636  -8.9362 -10.0790 -15.9228   4.6759  -3.2626  -1.6180
-#&gt;    4.8460  -2.3104   7.6842   5.6354  -3.9253  -1.7831  -8.9399  -4.0914
-#&gt;   -2.5983  -0.2068   0.7000  -9.6218   5.8294 -10.0757   8.0538   4.3348
-#&gt;    1.8360  -1.5328   3.6088   3.5472   9.7461  -3.1073 -18.6002 -10.2768
-#&gt;    1.2851   0.6742   9.5674   7.6433  -6.4800  -3.8342  -3.2553  -7.6469
-#&gt;   -1.3759  15.5534  13.5354   4.8163   4.8285  -2.8745  -5.3420  -1.2828
-#&gt; 
-#&gt; Columns 17 to 24  -0.5167  -3.2736   3.6042   9.2083   9.7675  -3.9386   1.3428   3.4470
-#&gt;    2.4704   2.8545   6.8275   3.5003  -0.2367   5.6338   1.1046   4.6771
-#&gt;   -3.0332   2.1101   8.6318  -4.8976 -14.9192  -1.6236   2.2431   2.0142
-#&gt;    0.4408   1.3938   3.2056  -8.0020  -2.1119   6.5324  -2.4020   9.4199
-#&gt;   -9.3392   9.3710  -3.2274  -4.2743  -0.2528  -5.7441  19.9280  -2.5542
-#&gt;    1.9263   1.6250   2.8807   0.3688 -14.1765  -6.7254  10.7015   9.2226
-#&gt;    1.9163  -0.4107   8.0362 -10.4422  -0.5098   3.4652   5.8927   2.2033
-#&gt;    2.4038  -8.7245   2.6648   2.7030   1.3872  -6.1252  -9.6656   0.5002
-#&gt;    0.1946  -2.0269   1.5145   1.2279  -0.1315   1.4630   0.6094   1.6623
-#&gt;   -6.3521   0.4316  -4.7299   0.3104  -1.4829  -1.6261   0.6357   7.8922
-#&gt;  -11.5962  10.6036   1.4772  -5.9238   0.6079  -0.2997  15.3069   3.6901
-#&gt;    2.1367  -1.4953  12.1796  -5.4230  -6.6698  -5.3625   8.1637  -3.4637
-#&gt;   -4.0125   3.2110   5.4136   0.7173 -13.9764   6.4144 -11.7281   4.7523
-#&gt;   -0.8757   6.4271   6.8656   5.0033  -9.3574   4.0220   1.5025   8.2151
-#&gt;  -14.3414   4.9516  -3.8332   1.1615   3.7050   3.6838   0.3889   7.1748
-#&gt;    3.4352  -8.0321  -0.5548  -7.1979  -2.1331  -0.9266   0.7498   3.3833
-#&gt;   -3.8955   1.0812  -7.4346   9.9988   8.9725  -0.6261 -14.2812 -16.3892
-#&gt;    0.9259  -3.9578   9.6653  -1.8063  -2.1143  -7.3721   9.1487   4.5222
-#&gt;   -7.4901   0.1527  14.3195   1.1760   7.2353  -5.5672   2.5676  13.1659
-#&gt;   -0.1484  12.3372  -1.2780  -1.0691   1.3121  10.2539  -5.5109   0.9131
-#&gt;    5.1531  -5.7138   0.8301  -4.5160   7.7156  -4.6745  -5.6056 -10.4448
-#&gt;   10.0189   6.8799   3.0023   0.4315   0.7961  14.9472  -0.0927  -6.6039
-#&gt;    9.3146   7.4606   6.3168   2.5325  15.7218 -14.6869   6.3143  -8.6685
-#&gt;   -3.7615  -2.7482 -14.5030 -11.5257   2.6656   0.5920   4.0818  -3.0660
-#&gt;    1.9609   0.6213   2.8665   0.1718   8.8866   1.9372   0.4757   7.3429
-#&gt;   -4.1544   4.6766   0.7745   1.2428   9.3632  -0.3896  -4.7325   2.0222
-#&gt;    1.3315  -3.0702   1.4970  -9.3075   5.4004  -2.4335   1.7001  -3.5945
-#&gt;  -12.0965  -2.4795  -3.4041  -5.4235 -13.7871  -3.7242   2.2135   0.9609
-#&gt;   -1.4595  -7.6355   2.5596   6.0541  -0.2605   6.7192  -2.9285  14.5957
-#&gt;  -13.5363  -0.7604  -2.6655   3.2770   0.5550  -3.3883  -0.0947 -17.2695
-#&gt;  -12.8532 -10.6036  -5.2803  -5.4334  -0.8143   0.9354  -2.8281  -0.3836
-#&gt;    1.2841   8.5728 -15.6670  -2.3541  -9.3281  11.0663   9.8001   8.9577
-#&gt;    1.2819   0.9044   5.6131   2.0612  -6.8050   1.4621  12.6808  -3.0845
-#&gt; 
-#&gt; Columns 25 to 32  18.7151  -3.8094 -10.6858  -7.2112  10.5903   2.8308  -5.1495  -6.9245
-#&gt;   -8.1932 -10.3093  -4.9610   6.5884   3.5473  -1.5878   1.4735  11.9831
-#&gt;    1.6399  -2.0138   3.1374   2.5061  -0.7592 -12.6079   5.2005   9.6991
-#&gt;   -0.2601   2.4913  -0.1172   3.2673  -1.0057  -5.0404 -15.6786  29.6677
-#&gt;    6.3120  -2.2729   8.2883   8.2263   4.6611 -11.1320   7.2619   0.2219
-#&gt;   -0.0609   7.8129   3.8104  -6.3285   4.6025  -4.4485  -0.0333  20.5367
-#&gt;   21.4471   1.3245 -10.1189   6.9064  -6.3160  -3.4137   5.9507  10.0966
-#&gt;    2.1005   3.0113  -7.5420   1.7446 -10.8668   0.2345   2.9597   7.5663
-#&gt;    6.8733   1.2057  -1.3649  -2.9656   0.2376   6.2730  -2.6192   3.3051
-#&gt;    2.5055  -4.9166   1.1617  10.2103   5.4102  -3.6294   8.0282  -3.7121
-#&gt;    6.1556 -18.8554  -6.8870  16.7942   0.8996  -4.3907  15.7691  -6.0708
-#&gt;  -13.7109  -5.4970   2.0404  12.2095  -1.1657  -5.2565 -15.3310  13.6764
-#&gt;   -2.0914   5.5632   5.5576   6.7898  -2.8911   6.3307  -3.9102  -2.4087
-#&gt;    5.1732   2.5589  14.9252  -4.7789  -0.9729  -1.8489  -9.2545  10.3432
-#&gt;    0.5507  -0.1272  14.3721  -2.8788   7.3230  -8.7260   4.2822  -8.2377
-#&gt;    7.8071   6.3932   6.5513   3.2569   0.9689  -0.3189  -4.6282   9.9274
-#&gt;   -7.9111   1.6218 -14.3454   7.2757   9.5468  -7.2799   4.2477  -2.9991
-#&gt;    1.4842  -7.5389 -11.1014  -1.6944   7.2728  -9.5720 -10.4276   4.1355
-#&gt;   -3.4725  -0.5684   0.1559   2.7386  -5.8742   4.7850   5.0468   1.2748
-#&gt;    0.1762   6.7808   6.3146   7.9698  -2.0042  -0.3079   6.2638  -6.0213
-#&gt;    7.8444  -1.9231   9.2642   1.0918 -10.7200   6.6738  -5.5230   9.9495
-#&gt;   -1.0943   7.7873   4.2141  -8.8941   0.5661   8.1359  -8.9883  -2.9023
-#&gt;   -2.6989   9.5537  13.1507  -1.4590  -5.3198  -5.8421   3.6497   2.1718
-#&gt;    8.8567  -5.6070   5.4076  -1.1994   1.1786   6.1395  -2.2800 -11.5192
-#&gt;    3.2517  -4.9864   5.4578 -13.7910   0.8607   0.3237   2.9753  -1.2993
-#&gt;    3.8434   5.3917   4.0140   5.5966   4.2340  -6.8201  11.1240  -2.5896
-#&gt;    3.2052  10.5745  -3.6056   2.5925  -1.4456  -0.6922  -3.8502  16.5866
-#&gt;   -2.4905   0.8548   5.1917   8.9457   7.4349 -11.4280  17.5558 -10.8432
-#&gt;   -8.6278  -1.5267  10.5918  -1.0952   0.6434   4.8316  -0.5702  -4.1505
-#&gt;    2.0603  15.5785  -1.0938   0.4554  14.3984   0.0487   1.9924  -8.3948
-#&gt;   -4.8434  -0.7336   7.9365  -3.0920  -0.1015  -6.8206  -2.6112   8.2260
-#&gt;  -10.5381   6.8931   5.0467  10.2215  -5.0572 -10.9092  15.1842  -1.2059
-#&gt;    3.0102   6.5824  11.8250   0.1481   5.1619   6.7905  -6.3298   6.1166
-#&gt; 
-#&gt; Columns 33 to 40 -13.0929  10.7086 -10.6413  -3.2434  -4.7143  18.9670  11.5604   0.6759
-#&gt;    3.8253   5.3724  -3.8026   1.2284   3.1021   3.1633 -10.6655   4.8320
-#&gt;    9.6050  15.6168  -0.1603   5.9876   5.5583   3.9613  -3.5428   2.9187
-#&gt;    0.0542  11.3364 -16.0973   3.8528  18.8930   0.8423 -19.0941   3.0001
-#&gt;    4.6167  -7.5432   1.4097  11.1482  -7.2509   2.3051  -1.6934  -2.9962
-#&gt;   -1.2676   3.3272   4.4583   2.2233   0.6304  -1.1169  -3.7036 -12.3604
-#&gt;   -7.3089   2.9711  -4.3308  18.0142  -7.9470  -0.1779  -2.5158 -10.6243
-#&gt;   -8.7892  -0.0394  -2.9864   0.9498  -9.3213 -11.2037   1.7052   3.1814
-#&gt;   -8.1533   6.6281   4.9492  13.8490  -3.8301   7.7220   1.4083  -1.9021
-#&gt;    9.1308  -7.0064  -1.9722  -2.4951  -0.6262  -2.2968  -8.2641   4.3387
-#&gt;    3.6544 -28.7199   7.4428  -0.3025   8.9910   8.9597 -18.8920  -2.7463
-#&gt;   -4.9109   2.1997  -5.8295  -5.6553 -10.4926  -5.4169  -0.9827   5.2563
-#&gt;    4.6452   3.1934   3.9736   3.3958   4.4744  -5.4590   7.4215   2.6005
-#&gt;   -0.9471   5.3609   0.7467   4.1787  10.4314   0.9719  -8.7019  11.9533
-#&gt;   12.1445   3.5598   5.1703   1.6544  14.4675   9.4715   3.7597  -2.1770
-#&gt;   -8.2620  12.5158 -12.3281   0.5927  -1.2875   3.3224 -15.3013  -1.1989
-#&gt;    2.0916  -0.5344 -13.9363  -4.2687   1.7251   2.1584   9.0830  -2.3336
-#&gt;   -5.1937  -0.4773  -5.7731  -0.3001 -10.2828   9.0828   1.6291 -10.7334
-#&gt;   -1.9560  -4.0323   3.8732   9.6128  12.4645   5.0971 -12.0427  -6.5134
-#&gt;    7.2082 -10.4481  10.9988  -6.3425  -1.5460  -7.8643   8.3994  15.8663
-#&gt;  -15.5418   7.9106   0.1194  -4.9896 -12.1432  -4.8835 -13.2222  11.1480
-#&gt;   -6.0650  12.4503   1.2084  -0.6934   4.0231   5.1878   7.6225  16.0133
-#&gt;   10.0894   3.7950   2.7662   6.9198  12.7698   9.4798  -1.6231   2.8290
-#&gt;    3.9802   3.8951  -5.1209 -12.1517  -6.8346  11.1322 -12.7089  -8.3542
-#&gt;  -14.0842  -6.4819  -3.2150  -0.6622  -8.4714  -3.2800 -11.8791 -14.5002
-#&gt;   -3.6597   0.2235   1.5656   1.3861   8.3664  -1.1531   3.4840   4.8625
-#&gt;   -0.0208   4.5306  -4.4620   5.6068  -3.5328   0.9448   7.0014  -8.7854
-#&gt;    8.1730 -16.0610  -4.7102  -6.8084   6.8921 -13.9539  -2.5970   0.8457
-#&gt;   -7.5805  -7.3242   0.9772  -3.8022  -2.8765 -21.1915  -8.8806   8.6112
-#&gt;    2.7900  -1.2628 -10.5153   0.7526  11.9589  11.3937   0.9338  -0.7113
-#&gt;    3.3273  -4.8510 -16.1544 -11.6446   4.8889   1.0366  -9.9487  -8.3986
-#&gt;    7.2784 -17.8624   2.1096  -7.7101   3.0077 -21.8409  -7.6522  -5.3485
-#&gt;   -9.4605  12.9928  -5.7377   8.1991  -3.6538  -4.7890  -3.8185   1.3486
-#&gt; 
-#&gt; Columns 41 to 48  -1.2806   4.4719   3.2787  -6.8226  -2.5020  -0.3241  16.2354   4.4872
-#&gt;    4.2604  11.2756   1.4287  16.5132   1.0703  -2.5217  -3.6417  -3.5623
-#&gt;   -5.9802  -4.1072   5.3458   8.4859   5.6440  -5.0054  -5.6247  -2.7213
-#&gt;  -13.6875  12.1878   8.7390   5.3186  16.4883 -17.4650  15.0589  -7.1149
-#&gt;    8.6370   6.1348   6.9977   5.9094  -3.5747  -0.2621 -14.0265  -8.1669
-#&gt;    5.1393   4.4540  -1.6370 -10.1522   3.2873   4.1298   7.4150   9.1745
-#&gt;   -3.5731 -10.3298   9.7454   5.4470   7.8061  -1.7168   3.2278   8.5204
-#&gt;  -21.1075  -2.1749  -7.7596  -2.9712   1.7288   3.6917  16.6576   3.8277
-#&gt;    2.7863  12.8813  21.6354   5.3553  -1.5177   2.0300  -9.4604 -10.3778
-#&gt;  -14.4875  11.1262  -0.4382  11.3462  -7.3018   2.3005 -12.4743  -5.6966
-#&gt;   12.2622   7.4871  -5.5479   1.3534   6.5592   7.8683   6.0947  -3.2516
-#&gt;    5.1640 -17.4172   4.4021  -9.6653  10.3575   1.2316  -6.2373   9.5555
-#&gt;  -16.6553  -1.8077   2.1062  -4.1136   0.4310   0.2189  -1.6960 -16.2303
-#&gt;   -4.1715   1.8463   9.8158   2.6802   1.2722  -8.7310  10.8009  -9.0684
-#&gt;    7.0178  12.7044  13.5289   3.1725  -2.0315  -5.1303   0.0483  -5.9967
-#&gt;   -7.4459  -7.6461   1.2485   0.4100   4.6413  -3.3643  -1.9663  -8.7889
-#&gt;   -1.4376   0.2320 -14.3036   7.3219 -10.1662  13.2323  -4.9297   6.0043
-#&gt;   -2.9853   6.5275  -3.6477  -2.9087  -4.8125   1.1970   5.2914  -0.7746
-#&gt;   -1.9150   0.9732   1.9533   8.5727  21.2805   1.1917   0.2164   2.1555
-#&gt;    1.4902   8.4288  -2.4849  -2.1220  -5.5417  -1.3303  -0.5162   7.3754
-#&gt;   -8.6690 -13.9623   0.6408  -1.8519   2.7357 -11.0654  12.4976  -2.1276
-#&gt;    2.2020  12.4450  13.9324   1.6783  -4.6593  -5.5748   5.2634  -8.7318
-#&gt;    6.9535  -3.0555  -0.3675  11.4615  -7.1063   8.8430   2.9909   8.8720
-#&gt;  -12.3394 -23.7992 -11.7108   2.3299   7.7491   0.7197   2.0416   0.8095
-#&gt;   15.6655   1.4036  12.8402  -3.1910   9.5950  -5.7898  -0.3393   4.2886
-#&gt;  -11.1935  -0.6632  -4.5321  -4.1002   7.3329 -10.4798   8.2632  -4.2149
-#&gt;    6.5294   6.4735   2.2356  -5.7527  -3.6129   8.0644   5.7421   3.7550
-#&gt;    5.3824 -15.5725  -6.2125   6.5763   5.5347  -8.4960   2.9947   3.0953
-#&gt;    0.0087  13.1358   1.9827   7.6309   1.6621 -10.9873  -4.9114  -9.1532
-#&gt;   -5.0495 -20.4267   3.4558  -1.2676   6.2794  -2.6602  -7.8177   8.3995
-#&gt;   -5.3475  -8.1220  -0.6322  -4.6946   7.1616   7.0391   7.9409  -1.3812
-#&gt;    9.0524   1.8260 -11.0054   8.7754  -8.6430   5.8534 -23.3776   1.1368
-#&gt;    8.2543  -9.6404   8.8720  -3.6102   9.5666  -5.0479  -3.4277  -8.2915
-#&gt; 
-#&gt; (20,.,.) = 
-#&gt;  Columns 1 to 6  2.3410e+00  9.7388e+00  1.6557e+01  1.1382e+01  3.7641e+00  5.1080e+00
-#&gt;   9.7553e-01 -8.4447e+00  1.3123e+00 -1.5725e+01 -4.9083e+00  4.1044e+00
-#&gt;  -3.1703e+00 -2.2135e+00  4.4341e+00 -1.2445e+01 -3.2447e+00  3.6457e+00
-#&gt;   1.2177e+01  1.5420e+00  5.5418e+00 -2.3071e+01 -1.8939e+00  1.7252e+01
-#&gt;  -1.0726e+01 -4.2155e+00  6.5425e+00 -2.0108e+00 -4.7632e+00 -1.3778e+00
-#&gt;   1.2218e+01 -3.6186e+00 -4.9115e+00  5.6926e+00  3.1619e+00 -1.5542e+01
-#&gt;   1.1675e+00  1.8411e+00  1.7625e+01 -1.9243e+00 -1.8360e+00  5.9005e+00
-#&gt;  -2.1542e+00  3.3940e-01 -4.9044e-01  7.3563e+00  9.6633e+00 -3.6952e+00
-#&gt;   8.6159e-02 -9.9189e+00 -3.8149e+00 -8.9140e+00  2.6082e+00 -6.3848e+00
-#&gt;  -1.0125e+01  1.6594e-01 -2.5587e+00 -6.3884e+00 -1.0598e+01 -5.6895e+00
-#&gt;  -1.2456e+01  1.3919e+01  8.3685e-01  1.1546e+00 -3.0186e+00 -3.8498e+00
-#&gt;  -2.6311e+00 -2.4535e+00  6.6286e+00 -1.7185e+00 -5.9649e+00 -2.6087e+00
-#&gt;  -1.0975e+01 -2.2859e+00 -4.9682e+00  1.6251e+00 -8.6998e-01  9.8512e-01
-#&gt;   2.8754e+00  2.6769e+00  2.8849e+00  9.1190e-02  4.8219e+00  5.3024e+00
-#&gt;  -1.0107e+01  8.5539e+00 -2.2039e+00  1.1878e+01  3.7640e+00  1.7298e+00
-#&gt;   5.3624e+00 -4.1503e+00  1.0721e+00 -9.7069e+00 -2.6360e+00  6.1107e+00
-#&gt;  -4.1990e+00  5.3183e+00  9.7740e+00 -1.4839e-02 -2.9959e+00 -9.8873e+00
-#&gt;   7.1682e-01 -6.7365e+00  1.7742e+01 -2.0493e+00 -4.0481e+00 -2.4705e+00
-#&gt;   5.6811e+00  2.1429e+00 -1.9895e+00 -6.0990e+00  7.1933e+00  1.0245e+01
-#&gt;  -1.0327e+01  1.1902e+01  8.1764e+00  2.9050e+00  3.5568e+00  4.8856e+00
-#&gt;   9.8634e+00 -7.3801e+00 -1.8072e+00 -1.8462e+01 -9.1422e+00  8.2301e+00
-#&gt;   7.4528e+00 -3.2333e+00 -2.7164e+00 -2.6745e+00  4.9729e+00  8.2682e+00
-#&gt;   9.8821e+00  6.4739e+00 -2.9487e-01 -9.3617e-01  1.5004e+01  1.2282e+00
-#&gt;  -4.8038e-01  7.8481e+00  3.1612e+00 -1.7786e+01 -7.0473e+00 -4.7113e+00
-#&gt;   1.3829e+01 -1.1918e+00  4.7191e+00 -9.9064e+00  3.7553e+00 -5.6204e-01
-#&gt;  -2.0430e+00  1.0027e+01  7.2484e+00  7.6063e-01  6.9578e+00  4.1638e+00
-#&gt;  -1.2486e+00 -3.1506e+00  7.0924e+00  6.9274e+00  1.1315e+01 -6.7493e-01
-#&gt;   4.5251e+00 -1.1089e+00 -4.6790e+00 -4.9297e+00 -2.0166e+01 -3.0425e+00
-#&gt;   2.0341e+00 -5.0193e+00 -1.5881e+00  1.4271e+00 -5.1175e-01 -1.9088e-01
-#&gt;   4.3895e+00 -3.2604e-01  2.8351e+00  6.5263e+00 -6.4667e+00 -3.6202e+00
-#&gt;  -4.6403e-01  1.6272e+00  3.1020e+00  6.5796e+00  1.4924e+01  4.7444e+00
-#&gt;  -8.6713e+00  7.4556e+00 -1.9416e+01 -6.8462e+00 -1.1393e+01 -6.3260e+00
-#&gt;   8.3200e+00 -1.1023e+01  3.5368e+00 -1.8314e+00 -7.4983e+00 -1.0943e-02
-#&gt; 
-#&gt; Columns 7 to 12 -2.4804e+00 -1.8602e-01  7.1954e-01  2.9089e+00  1.7618e+00 -1.3436e+01
-#&gt;   1.7067e+00  8.4794e+00 -2.6234e+00  7.7493e+00 -6.4802e+00  2.0458e+00
-#&gt;   5.1771e+00  3.1818e-01 -1.7405e+00 -6.4634e-01 -7.8719e+00 -2.0841e+00
-#&gt;  -8.7515e+00  8.5336e-01 -1.5769e+01 -5.3437e+00 -3.2970e+00 -2.0343e+00
-#&gt;   2.2304e+00 -5.4520e+00  1.9013e+00  2.4374e-01 -2.1490e-01 -1.4346e+01
-#&gt;  -2.6801e+00 -1.8525e+01  2.5054e-01  1.1545e+00  1.2923e+00 -2.2221e+00
-#&gt;   5.4859e+00  3.2670e+00  5.4010e+00  1.0159e+00  2.6045e+00 -3.3450e+00
-#&gt;  -5.5536e-01  1.9618e+00  3.5680e+00  8.0874e+00  9.1611e+00 -2.3273e+00
-#&gt;  -8.0280e+00  1.5038e+00 -2.8943e-01  6.2901e+00 -5.8491e+00 -1.6066e+00
-#&gt;   7.5733e-01 -8.2033e+00 -9.2451e-01  7.4625e+00 -6.7360e+00 -8.9020e+00
-#&gt;   1.2189e+01 -1.8027e+00  1.6689e+01  3.4474e+00  2.9138e+00  1.7912e+00
-#&gt;   1.3682e+01 -1.6062e+00  9.7473e+00  4.5117e+00  5.6799e+00 -5.4646e+00
-#&gt;   6.8369e+00  1.3800e+01 -6.7010e+00 -2.2776e+00  6.9862e+00  7.4298e+00
-#&gt;   5.5939e+00  7.5994e+00 -9.1281e+00 -1.0513e+01  3.1338e+00  2.2780e+00
-#&gt;   4.0438e+00  9.6674e+00 -8.9905e+00 -6.4321e+00 -2.2459e+00 -2.7442e+00
-#&gt;   1.7242e+00  3.3175e+00 -1.9010e+01 -5.4166e+00  2.1368e+00  1.3751e+00
-#&gt;   9.9083e-01 -4.8365e+00  4.8378e-01  1.6902e+00  2.2087e+00 -3.4764e-01
-#&gt;   5.4890e-02 -7.2534e+00 -2.5185e+00  1.2493e+01 -2.4334e+00 -6.3627e+00
-#&gt;   9.5054e+00 -4.4612e+00  1.8223e+01 -3.7394e-01  3.6688e+00  7.5635e+00
-#&gt;   2.2095e+00  4.6699e+00  9.9971e+00 -6.1401e+00 -1.0550e+01 -1.5797e+00
-#&gt;  -9.8198e+00  9.8286e+00  5.4587e+00  1.0334e+00 -3.0479e+00  1.7580e+00
-#&gt;  -1.2700e+00  2.6403e+01 -4.4089e+00 -8.6787e+00 -8.4800e+00  4.4394e+00
-#&gt;   2.8741e+00 -1.9863e+00 -3.2514e+00 -8.6687e+00  9.8180e-01 -1.7195e+00
-#&gt;  -5.3990e+00  2.8465e+00  1.2402e+01 -8.9504e+00  2.4825e+00 -1.1775e+00
-#&gt;  -4.4550e+00 -8.1371e+00  6.7530e+00  3.7530e+00 -1.0986e+01 -2.4960e+00
-#&gt;  -2.8496e+00  2.0082e+00 -4.6388e-01 -9.5946e-01  1.0177e+01 -3.2097e+00
-#&gt;  -9.3333e-01  1.1898e+00 -4.7260e+00 -5.8988e+00  1.5007e+00  3.4544e+00
-#&gt;   3.4987e+00  3.1301e+00  1.5726e-01 -1.7080e+00  4.4588e+00  4.7642e+00
-#&gt;  -2.8878e+00 -6.6251e-01 -3.1073e+00 -3.9279e+00 -6.6434e+00 -1.0017e+00
-#&gt;   7.0556e+00 -1.6597e+01  2.5636e+00 -1.2606e+01  7.0497e+00 -1.9386e+00
-#&gt;  -1.7508e+00 -1.2447e+01 -1.0717e+01  5.4835e+00  5.0638e+00 -3.2764e+00
-#&gt;   4.4112e+00 -5.0044e+00 -1.2282e+01 -1.1170e+01 -6.7855e+00 -6.1712e-01
-#&gt;   4.2148e+00  1.2460e+01 -8.7559e+00 -2.6166e+00 -1.9169e-02  1.4165e-02
-#&gt; 
-#&gt; Columns 13 to 18  6.2091e+00 -8.8143e+00  7.2104e-01  7.2597e+00 -1.5449e+00 -3.9369e+00
-#&gt;  -9.3598e+00  5.4552e+00 -2.3879e+00 -1.3020e-01 -1.2810e+00 -6.2295e+00
-#&gt;   1.6856e+00  5.1225e+00 -6.9954e+00  1.0107e+00  1.1691e+00 -1.7933e+00
-#&gt;  -3.2094e+00  4.2271e+00 -1.9460e-01  6.9871e-01 -1.8737e+00 -6.9162e+00
-#&gt;  -5.5509e-01  1.1511e+01  9.7265e-01 -4.1372e+00  6.0545e+00  4.0498e+00
-#&gt;  -6.5004e+00  3.4623e+00  3.2891e+00 -1.6722e+01 -3.2627e+00  1.3829e+01
-#&gt;  -2.0242e+00  1.3205e+01 -8.8209e+00  9.6122e-01 -4.1757e+00 -2.7641e+00
-#&gt;   1.6212e+00 -1.6687e+00 -4.6750e+00  2.5037e+00  2.8769e+00 -8.8224e-01
-#&gt;   6.9457e-02 -9.3542e+00  1.1674e+00  6.2896e-01 -1.2544e+01 -5.5968e+00
-#&gt;  -6.5634e+00  9.7293e+00  6.9997e-01 -4.0996e+00  7.8806e+00  1.4738e-01
-#&gt;   8.3325e+00 -7.4368e+00 -1.0828e+00  4.0310e+00  1.0186e+01  1.6531e+01
-#&gt;  -9.2470e+00  1.6434e+00 -1.0851e+01 -2.7858e+00 -4.7217e+00  1.2983e+00
-#&gt;   3.9226e+00 -3.9145e+00  2.3867e+00  1.5888e+01  6.1320e+00  5.3633e+00
-#&gt;   9.3941e-01 -3.2628e+00 -1.4717e+00 -9.6640e-01  9.1844e-01  5.7049e+00
-#&gt;   1.4595e+00  1.0113e+00  6.7419e+00 -4.7996e+00  4.4700e+00  1.5168e+01
-#&gt;  -4.1207e-01  7.1577e+00  7.2850e+00  3.0012e+00  6.6942e+00  3.8092e+00
-#&gt;  -5.2720e+00  1.0192e+01  2.8500e-01 -8.5123e+00  1.5029e+00 -1.8070e+00
-#&gt;  -8.1098e+00  1.7250e+00 -1.1236e+01 -7.7844e+00 -5.8833e+00  3.2155e+00
-#&gt;   1.0499e+01  1.0083e+01  6.7967e+00  4.1068e+00  1.0419e+01 -5.3592e+00
-#&gt;   2.2096e+00  2.1852e+00 -1.7720e+00  6.6781e+00  9.3576e+00  2.2509e+00
-#&gt;   5.5971e+00  3.2116e+00 -1.7596e+01  6.4026e+00 -3.3209e+00 -8.8648e+00
-#&gt;  -1.5210e+00 -1.3915e+01 -2.6886e+00  1.4786e+01 -4.6967e+00 -7.7192e+00
-#&gt;  -7.6820e-01  8.3450e+00 -1.9640e+00 -6.6878e+00 -2.4184e+00 -7.9350e-01
-#&gt;   7.4614e+00  5.3085e-01  5.5515e+00  6.6437e+00  5.0550e+00 -1.3595e+01
-#&gt;  -7.7221e+00 -4.6417e+00  3.9403e-01 -1.3851e+01 -1.0311e+01 -1.3360e+00
-#&gt;   6.6917e+00  1.2243e+01  1.4173e+01  2.8141e+00  1.1076e+01  5.8287e+00
-#&gt;   1.0787e+00  2.0636e+00 -1.0247e+00  3.1193e+00 -9.4799e+00  8.2093e+00
-#&gt;  -1.5041e+00  7.0780e+00 -7.8316e+00 -1.5371e+01  7.1263e+00  5.9010e+00
-#&gt;  -8.2715e+00  9.9196e-01  2.1497e+00 -2.1559e+00 -1.2710e+00 -1.9854e+00
-#&gt;   1.1778e+01  1.7334e+00  3.4908e+00 -2.5709e+00  1.1166e+01 -7.3452e+00
-#&gt;  -5.5082e-01 -4.1049e+00  2.4903e+00  8.0472e+00 -3.3471e+00  3.5109e+00
-#&gt;  -5.2418e+00  6.6022e-01  3.3127e+00 -5.4575e+00  1.7177e+01  6.8944e+00
-#&gt;   1.4118e+00  3.3244e+00  4.7817e+00  1.2690e+01  1.1262e+01  5.2541e+00
-#&gt; 
-#&gt; Columns 19 to 24  5.7987e+00  9.9046e-01  1.3476e+01  2.2373e+00  4.8450e+00 -3.3785e-01
-#&gt;  -6.4077e+00 -5.5996e+00  7.7242e+00 -4.9139e+00 -1.0795e+01 -2.7210e+00
-#&gt;  -7.8080e+00 -9.8749e+00 -8.0328e-01 -5.6685e+00 -6.5051e+00 -7.3282e+00
-#&gt;   4.4262e+00 -4.0880e+00 -3.1434e+00 -1.5922e+00 -3.5972e+00 -4.8698e+00
-#&gt;   8.9376e+00 -1.6125e+00 -3.0707e+00  2.6647e+00 -8.6386e+00  1.0014e+01
-#&gt;   1.0788e+01 -8.3395e+00  9.1440e+00 -2.9792e-01 -5.8211e+00  5.8759e+00
-#&gt;  -6.4088e+00 -1.2216e+01 -6.1322e+00 -6.9714e+00 -1.8267e+00  3.8377e-01
-#&gt;   6.6123e+00 -1.4050e+01  4.3107e+00  5.4709e+00  2.8144e+00 -4.5840e+00
-#&gt;   1.3259e+00 -9.2734e+00 -6.1174e+00 -1.4116e+00  8.0506e+00 -1.7683e+00
-#&gt;  -1.3442e+01 -1.9701e+00  7.1781e+00  4.8789e+00 -1.0749e+01  4.3147e+00
-#&gt;  -1.0874e+01 -2.7071e+00  5.0782e+00  3.2553e+00 -3.3053e+00  5.0959e+00
-#&gt;   4.7742e+00 -9.7450e-01  4.0312e+00  4.8259e+00  2.0546e+00  1.6256e+01
-#&gt;  -6.2039e+00 -7.7300e+00  2.7282e+00  9.7221e-01  1.7280e+00 -1.1206e+01
-#&gt;  -3.4114e+00 -5.4780e+00  1.9617e+00  1.9247e+00 -1.0711e+00 -6.1604e-01
-#&gt;  -4.0607e+00  6.2097e+00 -6.0131e-01  1.2949e+01 -9.1161e-01 -5.7228e+00
-#&gt;  -4.4931e+00 -6.3089e+00 -1.1048e+00 -3.4241e+00 -5.9472e+00 -1.1120e+01
-#&gt;   1.4240e+00 -6.8421e+00  5.2643e+00  1.2383e+00  3.9429e+00 -1.4067e+00
-#&gt;  -5.4825e-01 -1.4219e+01 -7.1825e-01 -3.0608e+00 -4.8196e+00  1.2558e+01
-#&gt;  -9.2407e+00 -1.0295e+01 -4.5680e+00 -6.3542e-01 -5.6528e+00 -1.1106e+01
-#&gt;   2.5570e+00  1.3100e+01  3.2021e+00  3.3889e+00  7.0477e+00  6.1366e+00
-#&gt;  -3.4840e+00  6.5269e-01 -1.1508e+01  3.1331e-01 -2.6091e+00  8.6547e+00
-#&gt;  -3.7476e+00 -1.5001e+00  4.7892e+00 -7.2544e+00  6.8576e+00 -2.3800e+00
-#&gt;   1.2855e+01  4.0347e+00 -4.9090e+00  1.6522e+00 -3.1490e+00  7.2887e-01
-#&gt;  -1.1071e+00 -1.1343e+00  7.0469e+00 -4.3051e+00 -6.4920e+00 -2.1248e+00
-#&gt;  -6.6440e+00 -2.3582e+00 -5.1569e+00  1.7230e-03  4.1819e+00  2.6481e+00
-#&gt;   8.3807e+00 -1.0780e+01 -4.4174e+00  6.4726e+00 -1.4760e+00 -1.2222e+01
-#&gt;   1.2455e+01  1.3503e+01  6.7132e+00  3.2466e+00  9.0439e+00 -1.0962e+00
-#&gt;  -7.0192e+00  1.2580e+01  7.1967e+00 -9.6692e-01 -2.7230e+00  7.7308e+00
-#&gt;  -7.6719e+00  7.7843e+00  5.2237e+00  4.2453e+00  6.7228e-01 -9.5117e-01
-#&gt;   3.6038e+00 -6.0937e+00 -1.4692e+00 -8.3313e+00  1.5697e+00  4.7399e+00
-#&gt;  -1.2687e+00  1.0278e+01  8.8936e+00  2.8074e+00  2.1382e+00  3.5530e+00
-#&gt;  -1.9756e-01  9.7066e+00  3.1387e+00  7.1569e+00  2.4309e+00 -8.4276e+00
-#&gt;   7.7145e+00 -3.3162e+00  9.2129e+00  1.5138e+00 -6.6110e+00 -5.4324e+00
-#&gt; 
-#&gt; Columns 25 to 30  3.3448e+00  9.3022e+00  9.5999e+00  6.9354e+00 -5.0616e+00 -6.9058e+00
-#&gt;   5.9573e+00  3.5726e-01 -9.9542e+00 -3.3779e+00  6.1937e-01  6.9114e+00
-#&gt;   2.3182e+00  3.1634e+00 -5.6326e+00 -9.8146e+00  1.1031e+00  4.6291e+00
-#&gt;   3.7577e+00  2.9056e+00 -1.2893e+01 -7.0990e+00 -5.8789e+00  5.4299e+00
-#&gt;  -2.0569e+00 -1.2289e+01 -1.2072e+01 -8.9648e+00  6.1291e+00 -4.8387e+00
-#&gt;   2.4939e+00  3.9368e+00 -3.3553e+00 -3.7930e+00  2.2403e+00  1.5174e+00
-#&gt;   4.7722e+00 -2.3783e+00 -3.4526e+00 -7.4857e-01  4.7284e+00 -5.9305e-01
-#&gt;  -4.4239e+00  2.2117e+00  1.6915e+00  1.6951e+00  6.1919e-01  7.1786e+00
-#&gt;  -8.2375e+00 -1.1487e+01 -3.2686e+00  1.9129e+00  3.4600e+00  6.1822e+00
-#&gt;   9.6635e+00 -9.8398e+00 -1.4344e+01  1.1570e+00  3.5877e+00 -3.4732e+00
-#&gt;   6.8537e+00 -1.6598e+00  1.4938e+00 -5.8247e+00  1.0250e+01 -5.8323e+00
-#&gt;   7.7056e+00  4.1740e+00 -1.0906e+01 -3.1704e+00  2.1178e+00  7.9223e+00
-#&gt;  -5.4461e+00 -4.5399e-01 -2.1419e+00  3.8774e+00 -1.9193e+00  5.1422e+00
-#&gt;  -3.3196e+00 -2.4690e-01  2.8738e+00 -2.3391e+00  8.6590e+00 -3.8078e+00
-#&gt;  -1.1259e+01 -8.6266e+00 -2.9897e+00 -6.1473e+00  4.7037e+00  4.2867e-03
-#&gt;   1.7899e-01  2.0520e+00  3.4490e+00 -6.9371e+00  2.1701e+00 -6.4096e+00
-#&gt;   1.5743e+01  1.5727e+00 -8.0126e+00 -1.6293e+00 -6.9487e-01  9.7779e-01
-#&gt;   3.3199e+00  4.9184e+00  9.6237e-01 -1.3034e+00 -6.9815e-01  1.5008e+00
-#&gt;   1.7309e+00  1.7355e+00  3.3040e+00 -1.1595e+01  6.4257e+00 -2.0536e+00
-#&gt;   9.0737e-01 -3.9525e+00  4.7663e-01  6.2257e+00 -3.6348e+00  7.5429e-01
-#&gt;  -1.6843e+01 -2.9076e+00  5.7143e+00  5.1020e+00  5.1055e+00  6.6208e+00
-#&gt;  -1.3249e+01 -3.3027e+00  1.0028e+00  1.5995e+01  2.1968e+00  4.1618e-01
-#&gt;   1.8869e-01  4.9186e+00  6.6267e+00 -1.1144e+01  1.2518e+01 -9.4801e+00
-#&gt;   7.2734e+00 -4.7865e+00  7.6977e+00 -2.5835e+00  6.9857e+00 -1.1586e+01
-#&gt;   1.9629e+00 -7.9237e+00  8.7230e-01  3.6833e+00  1.0750e+01  7.8505e-01
-#&gt;   2.2178e+00  2.6605e+00 -3.7676e+00 -8.0763e+00  2.7502e+00 -1.2980e+00
-#&gt;  -3.3262e+00 -2.9508e+00 -6.5470e+00  1.2192e+00 -5.2483e+00  1.0321e+00
-#&gt;   1.7810e+01 -1.1750e-01  4.5557e-01 -8.7464e+00  1.0301e+01  1.7402e+00
-#&gt;  -1.9575e+00 -7.2531e+00 -5.6767e+00  4.7833e+00  1.0424e+00 -8.0447e-01
-#&gt;   9.4049e+00  4.8694e+00 -3.4308e+00 -4.0935e+00  7.7406e-01 -8.3420e+00
-#&gt;  -1.0877e+00  5.9013e-01 -8.3452e+00 -6.0349e+00 -2.7546e+00 -1.5708e+00
-#&gt;  -4.6523e+00 -1.1413e+01 -7.7647e+00 -1.9390e-01  2.4253e+00  9.0860e+00
-#&gt;  -5.4974e+00  2.3994e+00 -4.8689e+00  2.3564e+00  1.8606e+00 -4.4984e+00
-#&gt; 
-#&gt; Columns 31 to 36 -5.3996e+00 -4.0268e+00  1.0155e+01 -9.1129e+00 -1.8529e+00 -5.9859e+00
-#&gt;  -1.1870e+00  9.0568e+00 -5.8302e+00  2.2678e+00  1.4472e+01 -7.0498e-01
-#&gt;  -3.1906e+00 -3.1934e+00 -5.5015e+00  1.1824e+00  1.1202e+01  5.9822e-01
-#&gt;  -4.0763e+00  5.6502e+00 -1.1138e+01 -5.5295e+00 -6.0794e+00 -9.0080e+00
-#&gt;   1.4124e+01 -8.0207e+00 -1.2152e+00 -2.9232e+00 -1.2306e+01 -6.1385e-01
-#&gt;  -1.5200e-01  1.1691e+00  5.9120e+00  9.2071e+00 -1.2299e+01  1.2687e+01
-#&gt;  -4.5410e+00 -7.0710e+00 -6.6179e+00 -9.8775e-01  9.3494e-02  9.7834e-01
-#&gt;  -8.4071e+00 -4.0261e-01  3.7053e+00  3.0223e+00  1.0222e+01  5.3209e+00
-#&gt;   3.2476e+00 -7.2387e-01 -8.1169e-02  5.4877e+00 -4.6037e+00 -3.2524e+00
-#&gt;   7.5073e+00  7.5331e+00  3.5078e-01 -1.7766e+01  6.9871e-01  2.0369e-01
-#&gt;   2.2511e-01 -2.8367e+00  2.4152e+00 -2.4106e+00 -9.4229e+00  5.5880e+00
-#&gt;   4.5797e+00 -3.7804e+00  4.6730e+00  1.4458e+01 -1.3785e-01  5.0291e+00
-#&gt;  -4.1167e+00  7.9227e+00 -4.6733e+00 -7.1373e+00  1.6804e+01  4.3820e+00
-#&gt;   8.5466e-01 -1.0724e+00 -9.3670e+00  1.3986e+00 -2.3463e+00  6.8835e+00
-#&gt;   1.4297e+00 -4.2350e+00  9.0299e-01 -2.1366e+00 -5.3894e+00 -2.7163e+00
-#&gt;  -5.1330e+00 -1.9521e+00 -1.0124e+01 -9.9508e+00 -5.3707e-01 -1.4966e+00
-#&gt;   4.0181e+00  1.2901e+01  1.0945e+01 -1.3669e+01 -8.6388e+00 -4.9692e+00
-#&gt;   2.7508e+00  4.1519e+00 -2.2471e-01  6.2458e+00 -1.2785e+01 -8.8307e+00
-#&gt;  -7.6881e+00 -5.7679e+00 -7.7874e+00 -5.1019e+00  1.2219e+01  1.0732e+01
-#&gt;   1.0853e-01 -5.1944e+00 -5.5256e+00 -4.7699e+00  6.8243e+00  6.7839e+00
-#&gt;   6.1342e+00 -7.3736e+00 -4.8876e+00  6.9786e+00  1.0532e+01  1.9532e+01
-#&gt;  -7.2484e+00  4.8691e+00 -6.1163e+00 -2.4217e+00  9.1861e-01 -7.0110e+00
-#&gt;   9.6960e+00 -1.0111e+01  4.1335e+00  8.7717e+00 -4.5262e+00 -1.9480e-01
-#&gt;   1.0519e+01 -2.2790e+00 -5.3841e+00 -1.5754e+01 -4.0010e-01  5.5570e+00
-#&gt;  -1.7870e+00 -4.3120e+00 -8.3646e+00  5.1878e+00 -3.8105e+00  5.4791e+00
-#&gt;  -6.1213e+00  2.6515e+00  3.8603e+00 -7.3960e+00 -2.4629e+00 -3.2340e-01
-#&gt;   2.4277e+00 -6.0967e+00 -6.1727e-01  9.6043e+00  8.1445e-02 -1.4710e-01
-#&gt;  -1.2415e+00  1.0929e+01 -8.9700e+00 -8.4613e-01  1.9625e+01  3.9038e+00
-#&gt;  -2.6456e+00  4.6997e+00 -1.1893e+01 -3.1487e-01  1.3500e+01 -5.4387e+00
-#&gt;   3.8680e+00  1.5947e-01  1.4209e+01 -1.6989e+01 -9.0062e+00  3.9879e+00
-#&gt;   9.3004e-02 -1.7697e+00 -4.5250e+00  7.9326e+00 -4.7444e+00 -9.6158e+00
-#&gt;   4.5809e+00 -5.6403e+00 -5.0796e-02 -5.9721e+00  7.1729e+00  1.8613e+00
-#&gt;  -5.4809e+00  1.7845e+00 -8.3083e+00 -5.4888e+00 -2.0179e+00  4.4420e+00
-#&gt; 
-#&gt; Columns 37 to 42 -4.7855e+00 -8.5175e+00 -2.1857e-01  8.4637e-01  4.3660e-01 -4.9466e+00
-#&gt;   6.6450e-01  4.6346e+00  2.6782e+00  1.2447e+00  3.8391e+00 -8.0498e-01
-#&gt;  -1.1130e+01  4.2234e+00  7.1485e-01 -1.3026e+00 -3.9174e+00  4.1765e-01
-#&gt;   8.3580e-01  1.3614e+01  3.9566e+00 -1.0713e+01  3.2567e+00 -8.4020e+00
-#&gt;   7.6823e+00  3.8936e+00  3.1150e+00  1.7484e-01  4.1188e+00  1.7145e+01
-#&gt;   2.6063e+00 -2.1592e+00  3.8965e+00  2.8466e+00 -1.9440e+00 -3.9970e+00
-#&gt;  -4.6261e+00  1.2012e+01  1.5563e+00 -3.5953e-01 -1.2297e+01  2.0953e+00
-#&gt;  -5.7136e+00 -1.6435e+00 -3.1510e+00  3.9118e+00 -6.4690e+00 -2.4583e+00
-#&gt;   1.0818e+01 -1.7416e+00 -9.2704e-03 -7.6917e+00  7.7324e+00 -2.0817e+00
-#&gt;   7.8892e-01  1.1853e+01  1.3597e+00  1.0491e-01 -9.5310e-01  5.4994e+00
-#&gt;   2.7781e+00  8.3081e-01  1.2500e+00 -2.1802e+00 -1.1053e+01  1.8365e+01
-#&gt;  -5.6311e+00 -9.8486e+00  2.1665e+00 -2.4590e+00 -2.2154e+00  6.3714e+00
-#&gt;  -4.4185e+00  9.0754e+00 -4.9723e+00  4.0641e+00 -2.4477e-01  1.1559e+00
-#&gt;   3.8127e+00  4.3389e+00  1.8134e-01 -1.1438e+01  1.4193e+00  9.3104e+00
-#&gt;  -2.5857e+00  1.2633e+00  5.8720e+00 -2.4309e+00 -1.2344e-01  7.6392e+00
-#&gt;   1.4477e+00  1.0082e+01  2.5753e+00 -3.5404e+00  1.1207e+01 -2.6013e+00
-#&gt;   2.7662e+00  1.3309e+01 -1.3517e+00  8.3357e+00 -5.1607e+00  1.3805e+00
-#&gt;  -5.8706e+00 -1.7318e+00  2.8528e+00  1.9490e+00  2.3670e+00 -3.3641e+00
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-#&gt;   1.2455e+01 -3.8683e+00 -1.1434e+01 -6.7846e+00  5.1093e-01  1.0225e+01
-#&gt;  -6.8801e+00  1.1190e+01  5.6877e+00 -1.1347e+01 -8.6186e+00 -8.9230e-01
-#&gt;   6.7188e+00 -1.9456e+00  2.7723e+00 -2.1238e-01  5.3955e+00  1.3889e+01
-#&gt;   2.5153e+00  1.3421e+01  1.0657e+01  1.0813e+01 -4.0389e+00  4.3639e+00
-#&gt;   2.3169e+00 -6.5107e+00  1.1071e+00  5.0577e+00 -1.9199e+00  2.6549e+00
-#&gt;   3.7111e-01  4.1470e+00 -1.4876e+00  6.1349e+00 -3.8993e+00  1.3496e+01
-#&gt;   3.9903e+00 -2.5372e+00  2.3113e+00 -1.3299e+01  1.5658e+01 -2.1184e+00
-#&gt; 
-#&gt; Columns 43 to 48 -5.3974e+00  1.1470e+01  8.0168e+00 -3.1737e+00  1.8088e+01  6.7016e+00
-#&gt;   8.4993e+00  7.5901e+00 -3.8522e+00 -1.5282e+01  2.7711e+00 -3.9009e+00
-#&gt;   5.4798e+00  8.2084e+00 -4.3640e+00 -5.6298e+00  2.8553e+00  6.0013e-01
-#&gt;   1.5117e+01 -3.8010e+00 -6.0661e-01 -8.6124e+00  7.9563e+00 -8.5322e+00
-#&gt;   1.0926e+00 -5.7196e+00 -7.8125e+00  2.9962e+00 -2.4641e+00 -3.9728e+00
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-#&gt;   7.8986e+00 -2.0966e+00 -4.5563e+00 -9.9288e-01 -3.9173e+00  9.6752e-01
-#&gt;  -2.1302e+00 -3.0563e+00  5.9163e+00 -7.8072e+00 -5.2031e+00  4.8729e+00
-#&gt;  -3.2703e-01 -5.2523e+00 -1.7050e+00  6.8670e-01  6.2048e+00  2.3407e-01
-#&gt;   3.8850e-02  9.0948e+00 -3.6616e+00 -6.1142e+00  7.1880e+00 -1.5549e-01
-#&gt;  -6.8444e+00  4.9850e+00  2.1235e+00  4.4228e+00 -3.7574e+00 -8.3262e+00
-#&gt;  -3.4195e+00  7.0761e-01 -6.9093e+00  2.0876e+00 -1.3823e+00  1.0538e+01
-#&gt;   7.4552e+00 -2.3935e+00  1.1926e+00 -3.8090e+00 -8.4963e+00  6.3465e-01
-#&gt;  -2.4364e+00 -1.1607e+00 -6.0250e+00  5.9558e+00 -3.0977e+00  1.4288e+00
-#&gt;   1.1530e+01 -5.9603e+00 -4.1689e+00  1.2215e+01 -5.4808e+00  1.5768e+00
-#&gt;   3.7910e+00 -1.1363e+00  2.6201e+00 -3.1267e+00 -7.8219e+00 -8.4765e-01
-#&gt;   2.4809e+00 -2.6889e+00  9.0631e+00  1.3237e+00  6.6355e+00  4.8937e+00
-#&gt;   2.8647e+00  1.6486e-01 -3.0638e+00  3.1515e+00  1.1612e+01  2.6685e+00
-#&gt;   2.0890e+00  8.1913e+00 -5.3012e+00 -7.3151e+00 -5.1472e+00 -5.9437e+00
-#&gt;   3.8042e-01  9.8934e-01 -1.1415e-01  4.0065e+00 -2.8091e-02  4.7139e+00
-#&gt;  -1.7802e+00  4.7301e+00  1.3032e-02 -3.6905e+00 -6.6128e+00  5.0625e+00
-#&gt;   3.5504e+00 -8.2332e+00  3.7096e-01  9.8524e-02  3.4324e+00  2.8376e+00
-#&gt;  -9.2314e+00 -6.9470e+00  7.7655e-01 -5.2664e-01 -8.1657e+00  3.3885e+00
-#&gt;  -1.4360e+01  1.4785e+01 -3.7140e+00 -6.3802e+00  6.6014e+00 -1.1859e+01
-#&gt;  -7.1300e+00  1.4353e+01 -9.8361e+00  9.5068e+00  4.5462e+00 -4.0918e+00
-#&gt;   2.4090e+00  2.6027e+00  8.3217e-01 -2.4805e+00 -4.2917e+00 -5.1048e+00
-#&gt;  -3.8890e+00 -9.2622e+00 -2.4179e+00  9.1678e+00 -1.0741e+00  1.4275e+00
-#&gt;   1.0725e+01  3.6622e+00 -1.0390e+01  3.2040e+00 -8.1849e+00 -1.4154e-04
-#&gt;   2.5473e-01  1.4962e+01 -6.1097e+00  7.3571e+00  3.6925e+00 -1.6461e+00
-#&gt;  -4.1509e+00  6.1366e+00  3.2962e+00 -5.4747e-01  3.5650e+00  2.8045e+00
-#&gt;  -4.0917e+00 -2.1213e+00 -6.7569e+00  2.4749e+00  4.6886e+00  1.8253e+00
-#&gt;   4.5376e+00 -1.0110e+01  1.1122e+01 -1.1604e+00 -8.3908e+00 -3.9016e+00
-#&gt;  -4.6760e-01  1.4584e+00  5.2119e+00 -3.8599e+00 -8.4865e+00  2.6378e+00
-#&gt; [ CPUFloatType{20,33,48} ]</div><div class='input'># }
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,1,.,.) = 
-#&gt;  -1.2050  3.5601 -7.8325 -0.6184 -5.9300
-#&gt;   6.9992  0.3900  1.3901 -2.5574  6.5135
-#&gt;  -2.3490  3.9786  4.1985 -9.6240  8.3693
-#&gt;   0.1293  0.5186 -2.7322  9.4996 -1.1448
-#&gt;  -5.2164 -12.5999  8.9623 -8.9888  7.3018
-#&gt; 
-#&gt; (1,2,.,.) = 
-#&gt;  -0.8450 -8.2603 -2.9019 -5.5598 -4.9509
-#&gt;  -5.6410  1.2666 -5.8790  2.1525 -1.3928
-#&gt;  -0.2024  2.1010  6.3255 -0.5656 -2.5797
-#&gt;  -0.3285  6.7826  5.8021 -1.7078  7.7191
-#&gt;  -0.0922 -8.5321 -5.4983  0.7221 -3.5291
-#&gt; 
-#&gt; (1,3,.,.) = 
-#&gt;   2.5094  9.2626  5.0731 -3.6366  3.3318
-#&gt;  -0.2142  2.5216 -0.6932  8.3285 -0.4724
-#&gt;  -2.7548 -2.3855  1.6016 -9.5280 -0.9662
-#&gt;   1.6404 -7.5112  0.5470 -3.7868  0.9206
-#&gt;   0.0776 -1.8698  3.2148 -2.8353 -1.9503
-#&gt; 
-#&gt; (1,4,.,.) = 
-#&gt;   -1.0442  10.8465   5.7719   2.8028   0.3426
-#&gt;   -1.9633   3.7087  -3.1929  -2.0919  -8.2913
-#&gt;   -0.9261  -3.8192   9.3410  -9.4944   8.6766
-#&gt;   -5.9383  -2.2824  -4.0878 -10.5887  -2.2831
-#&gt;    6.7283   2.9823   0.6066   0.9479  -1.2099
-#&gt; 
-#&gt; (1,5,.,.) = 
-#&gt;  -3.4877 -1.5824 -6.4475 -1.1290 -2.3183
-#&gt;  -2.2229 -5.1762  9.2085 -3.5268 -3.8315
-#&gt;  -6.3696  2.3858 -12.1739 -2.6510 -3.0961
-#&gt;   4.4813  3.8243 -0.4043  2.4245 -0.3913
-#&gt;   0.6648 -2.4358  0.4671 -3.7840 -3.5538
-#&gt; 
-#&gt; (1,6,.,.) = 
-#&gt;   0.8825  0.3176 -6.6245 -6.0051  2.4430
-#&gt;  -2.0265 -0.9768  6.7438 -7.4899  2.0777
-#&gt;  -6.8935 -2.2333 -3.3011  2.0151  2.5786
-#&gt;   0.2469 -4.8563  2.3537  0.5968  3.6600
-#&gt;  -1.4781  0.2248 -3.1730 -6.7766 -3.0347
-#&gt; 
-#&gt; (1,7,.,.) = 
-#&gt;   1.5889 -4.5167  0.0906  7.6198  1.8365
-#&gt;   5.0933 -2.3551 -2.6166 -7.5808 -2.0514
-#&gt;   4.0381  6.9844 -2.3409  2.5503  6.2012
-#&gt;   6.1860  8.5588  0.8248  1.6048 -4.7339
-#&gt;  -2.7566  3.9998  8.0454 -1.6114 -0.9366
-#&gt; 
-#&gt; (1,8,.,.) = 
-#&gt;   4.3153 -3.2445  0.4090  1.5033  6.4462
-#&gt;   6.4586  2.8058 -3.3607  5.9048 -2.4407
-#&gt;  -3.9344  4.6081  5.3624 -0.2189 -0.3718
-#&gt;  -4.3796  5.2653 -0.4628  8.5329 -6.1232
-#&gt;   0.7405  5.8740  5.0339 -0.6710  0.8794
-#&gt; [ CPUFloatType{1,8,5,5} ]</div><div class='input'># }
-</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/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</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_conv3d.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <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'># \dontrun{
-
-# filters = torch_randn(c(33, 16, 3, 3, 3))
-# inputs = torch_randn(c(20, 16, 50, 10, 20))
-# nnf_conv3d(inputs, filters)
-# }
-</div></pre>
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-    <div class="hidden name"><code>torch_conv_tbc.Rd</code></div>
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-    <p>Conv_tbc</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>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>
-
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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>
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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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  Columns 1 to 8   1.6673  -7.0390   6.6254 -13.9097  -9.2569  16.1860   3.0554 -10.1673
-#&gt;    0.5097   0.4955   5.0436  -2.2605   3.2619   1.0813   8.2840   0.2337
-#&gt;   -2.5402  -1.4119  -6.6792   0.1580 -12.0189  -2.8290   5.7440  10.1772
-#&gt;   -6.6198   3.2866  -5.4540   2.0456   3.6355   7.4837 -10.9005  -5.6650
-#&gt;   -4.6747   5.7736  -5.4007   1.2693  11.4762  -4.7003 -11.3653  10.1400
-#&gt;   -7.4153   3.9516  -1.1868  -8.5232   6.7928  -4.0613 -12.1954  -0.3727
-#&gt;    3.3955   1.0648  -4.0864 -17.0257  -3.4968  -4.9347 -25.7271  21.9273
-#&gt;    4.0335  -6.6207  -3.6258   2.1134   2.8998  -6.8774  10.1487 -13.0325
-#&gt;   -0.7558  -1.2329   0.9330  12.2378  -7.8136  -0.6589   7.9927   1.7098
-#&gt;   -7.7476  11.0338 -19.6308 -11.0443  -1.9108  -9.0969 -28.1443 -17.9797
-#&gt;    2.7401   3.4970  -6.8374  -8.1578   1.5898   9.3208   4.2085   2.1895
-#&gt;    3.1280  -2.2744   3.1126   3.8573  -5.6051   2.0268   2.0747  -4.4871
-#&gt;    3.2771   4.8169   5.4240  -9.3082 -13.5079  -0.9002  -4.1523   5.5984
-#&gt;   -3.5472   5.3228  -0.3093   4.4044   0.9189  -6.4752  -6.5480   7.4419
-#&gt;    4.2013  -5.9650  -2.9532  -1.8045   4.5317   0.8231   5.2075  -0.9755
-#&gt;    1.6888   6.3345   8.0313  -4.3153  -1.1394   6.0972 -11.7238  -4.2819
-#&gt;   -0.4871   7.4014  -7.3978  -2.9028  -5.0449  -7.1657  -3.8541  12.1393
-#&gt;    0.8300  -2.1989  18.5210  -6.7047   1.8052 -12.0026  -3.6008  12.7004
-#&gt;   -5.7435  -4.3290  -5.1741  -4.9028  10.4417  -1.7813  22.8982   4.6736
-#&gt;   -1.8184   1.6866   2.4972 -10.6010  -1.6615   4.2066   1.2256   1.8704
-#&gt;   -0.1898   7.4767  21.4571  -1.9761   8.4871  -4.1088  -4.3248   3.2695
-#&gt;    2.8483 -11.0110   7.3260   0.7999   3.4976   5.7566  12.8856   4.1038
-#&gt;    6.9826   9.8364  -0.9694  -7.2072   2.1751  -9.7158 -20.3140  -4.7768
-#&gt;    0.2967  -5.7820   1.4100  -1.7626  -0.5567  -2.8442  11.3496   0.9707
-#&gt;    2.5259   7.6391   2.8814   8.4676  -8.6886 -16.8321   4.9696 -23.1633
-#&gt;   -3.6094  -7.7429  -3.9817 -10.3633 -14.8211 -17.5294  -7.3543   1.4741
-#&gt;   -3.6305   1.5408  -2.2006   8.1134   1.1769  -3.0465   5.4047  -7.1063
-#&gt;   -0.2056  -3.0883   2.6383   1.4320  -0.4309  12.6957  20.8133  -1.0188
-#&gt;    0.7445  -2.1118   1.9698  -2.5180   0.3591 -17.7520  -5.9807  -7.7320
-#&gt;    3.2831  -7.6201  -3.1870  -8.1050  -8.9224  -8.1959  -4.7006 -10.0807
-#&gt;   -1.7069  -4.7109   5.5420   0.4770   3.4143  15.3951  -8.6150 -10.6463
-#&gt;    4.9333   8.1781   2.6415   5.0800 -18.8387 -13.3438   0.6360  16.3387
-#&gt;    2.4939   8.4147  -4.0838  -7.4578  -5.3779  -6.8781   3.7359   9.3849
-#&gt; 
-#&gt; Columns 9 to 16   4.5019   6.9288 -21.3335   0.1317  -3.8836   8.3590  -0.8443 -14.3927
-#&gt;  -16.2857 -11.4820  -1.4024  -4.5153   1.9758   0.6438  -2.7761  10.1361
-#&gt;    6.2420  -0.6045  -9.7481   9.3241  -3.6135 -18.6855   6.7881 -13.9915
-#&gt;   -5.0487 -20.3735  28.4884   3.9854  20.9909  -0.9224   7.0288   0.5621
-#&gt;   -3.5373 -15.3569 -16.2579  16.5925  -7.9983  -9.0862  -6.5943   0.2070
-#&gt;   13.7029   5.4600  -7.5984  -6.4349   7.3174  15.5161   2.0219  -1.0355
-#&gt;    3.6252 -20.4715  -3.8647 -20.7537   7.9750   0.7964 -23.7706   9.1045
-#&gt;   -8.7950   4.0828  -4.5432  -6.1630  -5.2080 -14.8888   9.1260  -5.9252
-#&gt;    2.9966   4.8565   6.0109  -8.5393  19.8787   3.5916   4.0040 -11.9161
-#&gt;  -11.2591  -1.4102   8.5272  -8.9441  10.3345 -13.4799  -0.9897  15.2467
-#&gt;   -0.6632   8.7770 -15.7039 -13.8364   1.2821  -0.6675   0.4520  -0.1015
-#&gt;   17.6055  15.4246  -8.5307  13.7545   6.1392   2.1994   5.0420  -3.2646
-#&gt;    5.2685  -6.2487   3.4035  -6.7103   4.0975   7.0198  -4.6465  -7.1480
-#&gt;    7.5268  -3.9966   9.9433  -3.9344  -6.8677   6.9691 -16.2891  22.7894
-#&gt;    5.2153  16.0680  16.4236  18.3746  -5.5733  -1.2358  -6.8528   1.5499
-#&gt;    4.9569  -3.0487  -1.9484   0.2809   6.0391   0.1817   2.8151 -11.2639
-#&gt;   29.2747   7.6625  -8.7079   2.9776  -6.0190  -6.8860  -3.6460   2.3650
-#&gt;    8.1998  -6.1037  -6.8587  -6.0042   4.2655  10.6292  -6.2967   0.6930
-#&gt;  -14.4762  16.4459  -5.9406   2.0809   1.8835  -3.1317  -8.9321   7.7105
-#&gt;  -10.7999  18.7483  17.0834 -10.3893  -8.3545 -21.9094   4.0324   2.8529
-#&gt;   -6.2272  -3.0918   1.9956  -4.4372  -6.3339   5.5328  -6.0873 -10.6416
-#&gt;   -5.9817  -6.9611  12.4265  -5.7492   3.2616  -6.5960   6.5149   9.2240
-#&gt;    8.0986  13.9525  21.4184  -4.3470   9.5946 -18.5461  -4.5604 -15.4378
-#&gt;    3.7024  -0.7089   2.5686  -0.7054   6.0643  12.1628   1.9096   6.4987
-#&gt;   10.7530   8.5200  -5.6235   2.7819   6.0993 -11.6488  18.3623  -5.3475
-#&gt;   -0.5536  -8.5774   0.8194   7.4602 -11.3240   5.9768  -0.3840  19.7081
-#&gt;    0.0850   0.9243   2.3494  12.6000  -6.7075   8.3957  -7.9544  15.4305
-#&gt;    1.4948   1.6475 -10.0262  -0.6992 -20.6828   5.6993   3.6267  -8.4266
-#&gt;   -3.9348   2.5818   4.7236   2.2962  -8.2360   3.9196   1.1975  14.9994
-#&gt;    7.2291 -14.4824  -7.3407  26.6378   4.6584  -1.7192  -4.7126  -6.6773
-#&gt;    3.6147  -1.7117   3.2719  -0.1214   3.2613 -12.0086   0.1217 -11.8695
-#&gt;    2.6794  -8.8757  20.7463  13.6375 -16.4280  14.3362 -13.4708   9.1810
-#&gt;   11.7337   6.5971   4.8227  -7.0015 -12.9038  22.7038 -19.3795  -5.8170
-#&gt; 
-#&gt; Columns 17 to 24   1.2900 -12.9553  -5.3724  15.7169 -10.8972 -11.2200  15.5498  -5.0368
-#&gt;    2.0881   8.0263  -3.9599   9.3983 -10.2026   2.6811  -5.9319  -7.8959
-#&gt;   14.9066   7.4782  -0.0191  -0.6530  -7.1498   9.4691 -11.9981  15.5602
-#&gt;   14.5616   5.2064   2.5051   8.9144  17.6842 -13.5671  -3.9489   7.7915
-#&gt;   -9.7128   6.7477  -3.1993   1.6146   4.5125   5.5407  -8.2644  13.3508
-#&gt;   -1.6102  11.9611   3.4677  -4.9038 -10.2262 -14.4330  -1.2227 -12.5192
-#&gt;   12.0007 -14.9848   0.9463   3.5031  17.4539   9.3853  -1.6176  -9.6670
-#&gt;  -18.4721   3.3622   8.1907  -2.8999  -0.2637  -4.8769   1.5459  16.6135
-#&gt;    2.1102   5.4149  -1.7087  -8.4103  12.7386  -9.9078  -9.3740  12.7742
-#&gt;    5.3657   5.1944  12.6113   8.4549  15.0561 -10.8250  11.2875  -8.3614
-#&gt;    4.6304   9.5156 -12.6676  -0.9019 -10.6211   9.9370   3.3044   3.2810
-#&gt;   -4.6163  17.0280   1.7505   6.2276 -11.3447  -6.5945   1.7422   0.4499
-#&gt;   -3.0405  -5.3669  -1.8315  -2.1907  -6.5904  -0.9806  -0.6572  17.8599
-#&gt;    4.4193  18.8522  10.2451   2.9497  21.5558   0.1101  -5.1855  -9.5943
-#&gt;   10.4257  13.2620   2.2022 -19.5122   2.7581  -3.0927   1.9384   7.3883
-#&gt;   10.6498  -4.7587   2.6286  -6.5227  -4.8643 -16.4109  11.9423 -26.0725
-#&gt;    3.3000   9.5719  11.0230   9.4557  -4.9966  -4.4449   4.4763 -18.4214
-#&gt;  -15.8333  -7.3848   4.0740   6.5697  -5.7416  -4.4711   8.0335  -3.5673
-#&gt;   11.2547  11.7943 -27.4378  -5.9246   8.8010  20.3112  -3.0324  16.8786
-#&gt;   -2.6789   4.0453   0.9162  12.4067   5.2146  -2.5160  -9.9002  16.7632
-#&gt;  -19.3769  -4.4897   5.8856   5.8640 -14.5450  11.0825   1.5232  -3.6449
-#&gt;    6.0268  12.5299  13.8870 -16.7846  -9.3446   0.2853   0.7077 -11.7750
-#&gt;  -12.7468  10.7160  31.0325  34.9867  -5.8856 -11.4765 -10.4610  -2.5913
-#&gt;    9.3896  -6.9599  -7.6279  -5.4562   0.0194  -6.9496  -2.8263  -5.2451
-#&gt;    4.0190  -7.9305  -7.1466  13.0450  -5.5047  -4.4661   0.9274  -4.7775
-#&gt;  -13.4600  -0.2809  -0.7337   9.6314   1.7702   3.1068   9.6836  -3.6410
-#&gt;   -0.2111   0.3837 -10.4137  -3.0894   8.8677  -2.2090   7.1865  -3.8667
-#&gt;   13.4651   7.1765  -1.6467  -4.9560  -9.6018   8.1471  -7.0450  17.0260
-#&gt;  -17.4430  -1.8719  -8.0025 -15.9723  11.6767  -7.6636  -7.0144   3.6021
-#&gt;   18.1120  -0.0250 -18.4019   9.8127  -2.1751  -2.7134   8.3464  -1.5143
-#&gt;   -3.8167  10.2692   6.5966   7.9059   1.1906  10.1638   1.1750  -2.4618
-#&gt;    0.1265 -15.9027   2.7108  -6.4089   4.9851   1.2107 -11.7264  -1.2454
-#&gt;    0.4524   0.2076   8.5678  -6.2080   0.6733  -7.2406   0.8338   3.5937
-#&gt; 
-#&gt; Columns 25 to 32  -0.0293  14.3552   3.6278   7.7933  -5.4766  12.6476   1.8703  -2.8825
-#&gt;   -3.2107  -9.0870   7.0099   5.4586   2.7359   2.2581  -3.8164   8.9624
-#&gt;   13.3699  -4.2088  -4.7406  -8.7636  -5.9517   2.2329   1.8833  21.2654
-#&gt;   12.8826  -7.6350  -5.2774   2.4938   9.3194 -13.5257   5.9729  20.7365
-#&gt;   10.1223   4.4283 -13.0912  -9.2791  -1.8947   0.4621   3.4288   8.0413
-#&gt;   -9.1683   7.7603   3.0516  -5.1836   2.5889   8.7409   5.4927   4.1831
-#&gt;   -1.1417  10.9090  -7.1233  -3.5366  -1.2165  -3.2714   3.8553   1.1704
-#&gt;    6.5912   4.1697   7.7293   7.9883  -9.9514  -5.4976  12.7131  -0.1949
-#&gt;   -4.4844  -5.7362  -3.9648  10.1807  -6.2640   5.3054  -4.7080   4.0852
-#&gt;   12.6666  12.5517  -4.1955   7.5249  -0.7672   3.2016   6.5978  20.8497
-#&gt;   -7.9074   7.2881  -4.8798  -1.4548  -8.7517   3.9573   9.5329  -4.8086
-#&gt;    0.3169  -3.8601   1.0305   0.5803   1.2628   4.3491 -12.1400  -2.7427
-#&gt;   -0.3467  13.3886  -6.3979   2.0452   2.3282  -2.0779  -2.4584 -16.2387
-#&gt;   -5.7151   9.2725  -0.0182   1.8764   6.9025  -7.1871 -10.9513  -6.7978
-#&gt;    2.4439  -1.6886  -7.7059   0.1886  17.1840  -6.1218  -5.3057   5.4783
-#&gt;  -16.9525   6.0691  16.1657 -11.4185   3.4004  10.6037   4.7744   2.5440
-#&gt;    9.5940   2.7641   7.8744  19.0521  -3.7261  14.7517   3.5746  -0.4117
-#&gt;    0.4406 -12.6979  -6.8351   3.1805 -11.3419  -1.0762   9.5960  -4.1767
-#&gt;  -11.9938  -6.4985   9.3770   6.9766  13.3734  12.1043  14.3930  -7.0519
-#&gt;   -2.6697  -2.4679   1.1577   3.0986   6.3140 -21.5625  -0.3885   0.4263
-#&gt;    0.3668  -3.9383  20.8603   6.6954  -2.7366  -0.2231  -0.5172 -13.0651
-#&gt;   19.9507   1.6934   7.1942 -15.3310   3.2378   8.5819 -14.4676   0.2683
-#&gt;   -2.5942   1.2458   9.2131   9.8679   8.3943 -12.4564   4.6223   3.0705
-#&gt;    2.0545  -1.3666  -0.5177  -9.2698  -7.6781  -1.9081  -3.4399  -3.9338
-#&gt;    8.1274  -0.1854   5.2153 -11.7028   8.3669   4.8400 -13.4963  11.7893
-#&gt;   -0.1239   6.0862  -7.2274   0.1266   2.1916  -9.3351 -11.9587  -7.4673
-#&gt;  -20.4607   4.0279   7.3662  -0.7345   5.7650   3.7344  -0.5221  -2.1129
-#&gt;   11.4275 -12.4868  13.0824  10.5353   4.0451   1.8503  18.2519  -0.7406
-#&gt;   13.0047 -15.2421   9.2275   1.4917 -11.4757  -5.3868   3.8877   4.4101
-#&gt;  -10.2052  18.6921 -13.4181 -20.0699  12.8999 -12.0904   0.3806  -5.5696
-#&gt;   -2.4651 -10.7724   8.9007   1.9734  -6.8980 -11.9957   6.5855  -4.4643
-#&gt;   -1.0077  -4.1095  -2.1013  -0.5406   5.8052   9.8388 -20.2044  -2.6470
-#&gt;  -13.4139  -2.1838  -1.5024  -6.3311  -6.9503   3.3761   7.3990   7.9767
-#&gt; 
-#&gt; Columns 33 to 40   1.8592   0.9761  -2.6963  -3.8153  -1.8090  29.3572 -11.8402  11.8519
-#&gt;   13.3483   5.1859   4.9052  -0.4473  12.8364   2.1392  11.2240   1.1636
-#&gt;    2.9127  -0.4139  -9.8866   2.1386  -0.3520  -3.4992  15.7872  -1.9768
-#&gt;   -8.7932  10.8440  -0.3113   9.1708   9.1623 -17.6051  14.4912  -2.6571
-#&gt;   -5.4569  14.1891   1.8065  17.6668   2.9545  -3.7614  26.8721   6.9820
-#&gt;    5.6159 -18.5910  -0.6680  -9.2854   2.9860   3.0446 -10.3332  -9.6543
-#&gt;    6.3406   1.3053  -2.6143   0.2845   7.4428   8.5844   1.7969  -1.5125
-#&gt;   10.9239   9.0027  22.6637  18.8975  -4.2188  12.3108  26.5707   8.6210
-#&gt;    1.2633  -9.0421   0.9745   2.6553 -11.1772 -12.4188   1.3170  -5.5598
-#&gt;  -12.8508   6.2380  -4.0621  14.0566  16.2944  -9.8447   8.1251  11.8212
-#&gt;   10.8548  17.6606  -0.6996  -0.6495   1.4041   2.4018   7.0875   3.6098
-#&gt;   -3.0109  -0.2962   6.1093   6.9493  -6.2817   4.9856   9.9639   0.5589
-#&gt;    0.5565  -0.0766   1.2112   1.3211 -15.6654   0.7365  -1.7787 -11.4870
-#&gt;    1.2893 -10.7174  -7.8088  -3.3270  -0.2953 -10.0667  10.5948  -3.7164
-#&gt;   -7.5186  -7.9848 -14.5698   2.6338   6.9504  -1.8349  14.9450   9.0353
-#&gt;   -3.8914   2.3522  -3.9384 -14.0598  11.8851   3.7743  -4.7451 -18.7160
-#&gt;   -7.9114   4.4290   1.2984  -6.1245  10.8040   9.1649   9.3862   3.8943
-#&gt;   13.5192  -0.3540  11.7615 -10.4322 -10.5759   5.1959 -10.5739  -2.5550
-#&gt;    4.1348 -11.0223  -7.5457  -1.8305  10.4051   9.8118   5.1208   5.6186
-#&gt;   -4.8263  -6.7968   5.5902  12.7691   7.9257   1.9996   6.5328   8.3577
-#&gt;    0.7681   7.4137  12.9171  -3.9795  -2.3992   5.0543  -2.1804   6.8998
-#&gt;    7.7960  -0.6246  -0.4729   2.6995   5.4509  -0.4801   2.1876   4.9933
-#&gt;   -6.3639   0.3813  12.9830  -2.4914  -6.7726   1.7444   4.6179  18.1880
-#&gt;    6.7140   2.4883   8.6126  18.9305 -10.5894   3.3141 -21.9243   6.1176
-#&gt;    0.2413  -8.9027   3.3162 -13.0962 -12.3184  -3.8753 -23.0307   9.0538
-#&gt;    0.0690 -15.0865   1.8602  -8.6315   0.5089  -0.7437  -5.9029   3.0767
-#&gt;    7.1451  -8.2692 -11.5116  -8.8415  -2.0170  -7.0324  -3.6529 -10.3245
-#&gt;    6.1436  -0.5680   4.2794  -0.3316  -7.4252 -10.7011   7.0821   7.7291
-#&gt;   -1.3997   6.5191  -0.3735  -6.5781 -10.0386  -6.9226  -2.5003  -9.5659
-#&gt;   -0.0958  -0.5062   2.8923  -3.4108  13.0374  -2.9370 -10.4329  -3.1501
-#&gt;   -8.9609   2.8826   0.2373   5.7849  -1.4304  -5.0798  12.6846  12.8611
-#&gt;   -0.7236  -1.0731  -3.0510  -9.1343 -18.2925   1.9743  -0.5286  -7.9524
-#&gt;   10.3919  13.0850   0.9242  -0.9788  -6.7773   0.8554  -4.7509  -5.3867
-#&gt; 
-#&gt; Columns 41 to 48   2.9985 -15.5869  14.0373  10.4037 -11.1567   6.1454   3.8270   7.7380
-#&gt;   -1.6961 -13.4523   2.7170  -5.3499  -5.4221  10.5192   9.8349 -12.1479
-#&gt;    6.2730  22.6802  -7.0960 -14.5570 -13.0734  24.1451   3.9818   4.1208
-#&gt;   -1.6407   8.4934  -6.2687  -4.5555  -9.5029  15.1876   8.3357  -3.0061
-#&gt;  -12.4818  22.4958  -6.0299  -8.6670 -10.3577   2.4604  10.0247 -10.8954
-#&gt;   -6.8458  -6.1891   1.1851   2.8739   2.8533 -10.4645   1.1883  -5.4889
-#&gt;  -18.7008  15.1108   8.5953  -5.3394  -2.5032   6.4586   3.9125   3.6250
-#&gt;    2.3395   4.7180 -12.1364  -2.4026   6.8779   8.2134 -22.8381  -3.5587
-#&gt;   15.7945  -2.3467   5.5252   4.7041  -6.9610  -8.8453  -2.2314  11.2754
-#&gt;   -4.7374 -10.4254  10.9580 -13.0100  -6.7119   0.3756  16.1945   8.9202
-#&gt;   -6.7409   4.5078   4.3692  -4.6630  -5.9395  -3.9312  -5.5752   1.4460
-#&gt;    1.1583   8.8820   2.3125  -8.9832   1.3572   5.7707   4.3816 -12.6141
-#&gt;    1.1008   6.2556  -5.6111  -7.0873   0.3703   3.5847   0.6188  -5.6186
-#&gt;   -5.2835  -8.3637  -0.4618   5.6316  -1.7833  -8.5794  27.2491 -10.4892
-#&gt;   20.8741   8.0639 -15.5463  -7.4028   3.6106  -1.1026   0.9431  -4.4649
-#&gt;  -11.5054  -5.7812  19.4337   8.4593   7.6994 -14.5653   3.4009  -3.5659
-#&gt;  -13.8089 -11.7804  10.2397  -9.4642  -1.2912 -14.0060  -6.6083 -11.9624
-#&gt;   -8.1229  -1.9699  11.3045   4.4870  -1.1037   9.2278  -1.8346  -6.5917
-#&gt;    8.1041 -13.1606 -10.0848   2.4079  -2.9753 -14.2536  -8.2830  25.1459
-#&gt;   -0.6315  11.0338 -11.0389   4.5759  -1.7932  18.5247  -1.9834  -1.4612
-#&gt;  -11.3549  -0.3709  18.6718  14.9252  -3.7786  -3.9237  -4.9956  18.7164
-#&gt;    9.1790   5.0087  -4.8780 -10.6788  -4.4748  -7.9521   2.3956  11.5146
-#&gt;  -14.9233  15.5074  13.5288   6.9501  -3.6375   6.9654  -4.0685  -8.8993
-#&gt;   -1.0245   4.7855 -17.6042   4.1208   1.0548  -8.5788  -1.7149   1.0637
-#&gt;   -5.1705  13.9681   1.5986  15.8308  -3.9914   8.3166   1.1126 -11.7575
-#&gt;    2.7832 -21.6206   0.3791  -5.4350  18.9783   0.2022   6.9384 -23.6424
-#&gt;   -1.1030  -4.5934  -1.3159   2.3002   2.8800  -6.3804  19.1025 -11.9186
-#&gt;    1.8217   2.0252  -7.3752   7.9834  -7.1141  -2.5236  -6.1048  14.2583
-#&gt;   -7.0049   7.8784 -14.4774  -6.2137  10.1269 -15.6804  -6.8904  -4.6614
-#&gt;   10.0098   8.4671   2.8254   9.2529 -16.7819  -3.6309  18.5456 -10.9203
-#&gt;   -0.6490  -6.3074   0.1846   4.3354   9.1514 -12.6226  -3.6332   2.4962
-#&gt;   11.7775  -1.8143   1.0238 -24.0774  13.3624   0.3635   1.7397  -2.5824
-#&gt;  -13.1913   2.5277 -12.8994  11.0974  -3.0293   2.3099  -0.8103  -0.4676
-#&gt; 
-#&gt; Columns 49 to 54  -8.7345 -14.2799  19.6350  13.5499   1.2315  -0.6506
-#&gt;    1.8484  -3.2535  -1.7324  -4.3937   4.1733  -0.5664
-#&gt;    4.5108  13.2596  -4.4172   1.1377   3.8892  10.2091
-#&gt;    7.5502   1.0322  -9.1656 -15.8731   5.8408  -5.2419
-#&gt;   -2.2194   8.1269  11.4847  -5.2739  -5.0256   0.6433
-#&gt;   -4.1628  -0.1577   1.1322   9.3402   4.0405  -2.5579
-#&gt;   -2.4551  21.4342  -0.1518   0.2222  -0.9053  -2.7780
-#&gt;   10.6593  -5.1491   0.3012   6.6177  -8.4856 -11.1581
-#&gt;  -11.0929   5.7385  -7.6207  -0.4959  -1.9499   2.3844
-#&gt;   -4.4337  -0.3689  -3.9270  -5.0881   3.1659  -2.1273
-#&gt;    1.6437   0.2100  -3.8414  -0.0949  -6.4092  -2.9916
-#&gt;  -10.3230  11.3656 -11.9414  11.8128  -5.7713   2.6200
-#&gt;   -4.4394  -3.3487   2.7528   3.8788  -1.4185  -3.0869
-#&gt;   15.5630  -6.5025  -3.7371  -6.6396  15.1147  -0.3786
-#&gt;   -3.5993  -1.2830 -12.9225  -9.8014   6.9267   7.7926
-#&gt;    1.7097   5.5124  -1.5683  -0.6643  -3.9920   5.6530
-#&gt;   -6.5705   5.0899  10.4892  -1.9026   2.3790  -1.7570
-#&gt;   -9.6865  -3.1503   5.3201  -1.7256   1.1293 -11.1168
-#&gt;   -4.3125   4.4370 -12.0474  -2.7226  -2.3004  -5.8733
-#&gt;    6.1387  -0.5534  -3.6291   9.1612   7.0297   1.6557
-#&gt;   -7.3084   9.0659   4.9779  -3.0977  -8.6063   0.9893
-#&gt;   20.1320  -8.8056 -12.3684   6.0840  -1.6641   4.9350
-#&gt;   -3.0457   9.5258 -15.0322   7.7813   1.9794  -3.8203
-#&gt;    0.5432  11.5130 -26.2873   3.9583   8.1876   1.2593
-#&gt;   -5.0762  12.8825  -2.9607   6.7716   2.5277  -4.8843
-#&gt;   -3.7113   7.3289  -1.8610   6.7798   4.0303  -2.3700
-#&gt;    1.6202  -2.6948  -2.9366  -5.8387   7.3190   3.1959
-#&gt;    1.5668  -3.8051  15.0303  -7.3860  -2.8871  -3.9352
-#&gt;   -2.9899 -11.8343  -5.7724  -2.9391  -0.1798  -5.7964
-#&gt;   -2.9931   4.5864   7.4006 -21.0496 -10.1332   5.1122
-#&gt;   20.1991  -6.5378 -11.0948   0.1378   7.5001  -2.8968
-#&gt;   -2.7824 -17.3546  -6.3185  -4.0368  12.6433   7.2955
-#&gt;    0.4172  -4.4497   9.9093  -2.1454   8.5557  -5.9379
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  Columns 1 to 8   4.7596  -2.3708   1.3418  10.7392 -17.9585   1.6765   9.6306   3.8409
-#&gt;    2.5987 -11.9234   5.6093 -15.4016   8.0875 -11.7553 -11.3902   8.7209
-#&gt;  -11.8239   2.1317  -9.5167   8.5529  -5.5999  -8.7316  -8.2675  12.9377
-#&gt;  -10.1575   1.3394  -2.0061   6.9250   0.4979  -6.0003   2.4058   8.1599
-#&gt;  -12.7172  -1.2893 -14.0448  -3.4130   1.7811 -20.7635  -1.9964  -6.3883
-#&gt;   -1.9728  17.3032   8.5093   4.1580  -6.2205  18.0032  -1.7450   2.6589
-#&gt;    3.5242 -16.6260   7.9907  -1.1886 -14.1955  -7.1470   7.9479   7.5243
-#&gt;    0.5199  -7.8098  -5.8438  -1.0146  -5.0564  12.4495  -1.4228 -13.6972
-#&gt;    0.1183   2.4802  12.7326  -6.0799  -1.6831  -6.8250   9.4823  10.3730
-#&gt;  -12.6137  -9.4658  -8.8782  -6.6302  -1.4513   2.4207  23.0161  -8.5375
-#&gt;   -1.3446  -0.7345  -8.8468   5.0346   0.8501  -2.3404  -1.5003  -9.4550
-#&gt;    0.1520  -2.6062   4.4482  -8.2256  -5.3967  -2.2162  -3.8969 -12.9057
-#&gt;    7.7093  -0.7853   7.2272   3.5096   1.8355  16.3071  -9.8703  -3.0199
-#&gt;    1.7039  -7.8727   4.3188  -6.3996   9.3464 -11.0304 -13.0502   8.8442
-#&gt;   -5.0000  -0.4485  -5.8625   0.0639   0.5489  -8.9442   3.0572 -10.4764
-#&gt;    2.1024   1.1575  -7.3141  -3.1504  16.4440   1.9490   4.5007   1.5405
-#&gt;    2.2730   3.5882  10.6477  -8.7302   5.1281  -5.5718   1.3978  -8.2277
-#&gt;   13.8522  -0.6762  10.2911  -0.2770  -7.3265  -7.6784   1.6779   2.8196
-#&gt;   -3.8064  -8.9778  -0.1520   1.8026  -7.8690  -4.4951  12.5004   1.7195
-#&gt;  -14.5756   4.0345  -6.2921   5.8808 -14.3969   9.0118  -9.3838  -3.6941
-#&gt;    4.7236  -4.2033  -8.3519  -4.8034  -9.7167   9.4050  -4.0289 -11.0073
-#&gt;    5.3240  -7.0412   1.8191 -11.3021  12.7159   9.0398   8.1060   2.1124
-#&gt;   -6.4629  -2.5036  -7.5835   3.1017 -11.9905  -8.9950  11.2799  -2.9150
-#&gt;    4.2767   4.3477   1.0800  -2.5998   2.8277  10.7435   9.3785  -9.6822
-#&gt;   -4.4303  20.7324 -13.0990   6.1930  -6.2529   0.7377  -7.4001   1.3349
-#&gt;    4.4720   2.8303  13.6389  -7.9152   8.4899  -3.1532   8.3466   0.4572
-#&gt;   -8.3623  15.7370 -10.7719   4.4032   9.9055   3.7759  -2.8595  -1.7905
-#&gt;   -4.8775   1.8915  -0.8530  10.2657 -15.9343  10.3426 -17.7020   2.0729
-#&gt;    0.5613   7.1272  19.9651  10.9519  14.0965   2.4315  13.4485  -1.4175
-#&gt;    4.8714   8.1319 -10.5989   0.3411  -9.3492   7.6087   9.2715   9.6984
-#&gt;   -1.3565  -7.4533  -4.8182 -17.3904  -0.5276  10.7067  -8.2810  -3.0651
-#&gt;    5.9905   1.0488   8.9671  16.8183  11.3654  -6.4070   3.9435  19.8416
-#&gt;    3.5665  -2.9801  -1.3262   8.9776   9.5841   2.2091  -2.9954  -3.0973
-#&gt; 
-#&gt; Columns 9 to 16  -8.8073  -4.6426 -13.9884  -8.2320  -8.5217  -0.4775  -7.4151  -0.6725
-#&gt;   10.0546   4.3717  -1.5863   1.3989   7.1575  32.8837   3.4143  -0.7769
-#&gt;   -8.8521  -9.9139 -19.5135 -24.9521   0.7116 -11.7181  -5.9171  -5.1497
-#&gt;   -3.9503 -13.6314  -5.8326  17.8691   6.6317  -7.6286   4.8017  10.8725
-#&gt;  -12.6786 -19.4017  -9.4109   2.1331   6.1469  11.5832   4.8107   5.5907
-#&gt;   23.6291  -7.2367   7.5458   1.4301   9.4437  -3.4435   9.0182   6.6427
-#&gt;    2.9611   0.3858  -5.4467   4.0315 -21.9230 -10.6043   3.1202   1.5852
-#&gt;    3.9447   3.1710 -12.9762   8.3977   9.4892  -0.5182   2.5873  17.2142
-#&gt;   -1.3194 -13.1223   6.6894   7.0331   6.4437 -12.6453 -14.5226  -3.8072
-#&gt;  -10.4038  -7.1690  14.0111  -8.5985  -3.2720   0.6783   8.7759   8.8802
-#&gt;    2.2255   7.0874  -0.6579  -2.0451  -3.7498   0.6559   5.8038   3.7002
-#&gt;   14.7065 -16.0033  -5.9809   5.9349   7.9587  -9.9855 -17.9542  -3.7742
-#&gt;   -1.4432   1.0548 -14.3342  13.1105   2.1546  -9.1868  11.2042  -0.9932
-#&gt;   12.1984 -12.8600  -2.5919  -0.8897  11.8771  10.0617  -8.9914 -11.5302
-#&gt;    7.5468 -12.1222 -26.0768  -6.2028  -0.7591  -1.2010  -6.0798 -11.1108
-#&gt;    8.7007  20.9135  -2.9024   1.6970 -11.4094   8.3639  10.0946   6.3963
-#&gt;   12.4040   4.4726  24.0844  -5.9889   9.6856   1.4792  -1.8792   2.4935
-#&gt;    2.5080  18.3730  -7.6850   9.7258 -11.3493  13.4590  -1.7244   9.6702
-#&gt;   -5.4163 -14.2738  -4.3608  10.3636   3.8761 -12.1021  -6.0958   1.4944
-#&gt;   -5.7651 -10.1263 -17.2028   5.7702  -3.2691  -9.1889  -2.5732  -3.3792
-#&gt;  -10.0545   4.1091  14.0733 -10.4982  -5.7342   2.3171  -6.4773   3.3353
-#&gt;    2.6782  -2.8125   2.7590  -1.9897  -9.1181  11.7984  -9.0359   1.9233
-#&gt;   -5.2344  10.2287  -2.8782   1.6119  -0.4775  -8.1459   1.1576  -9.2421
-#&gt;   21.9949  -8.0431   1.7145   4.7288 -22.4366   4.0880 -12.0808   1.6562
-#&gt;    7.5187   9.7269   0.6820  -2.6167  -1.1286  -1.0346 -18.5821  -5.9096
-#&gt;    9.3526  -5.6035   5.2310   9.7429  -3.3496 -10.7880  -7.9118 -15.5445
-#&gt;   13.6427  -0.6605  11.3279 -13.0369   9.5204  -4.8265   1.9385 -12.8543
-#&gt;   -1.0619 -13.6995  -2.8276   4.1690  18.6644  -5.6350  -5.1124   1.0305
-#&gt;   17.6611  11.3794   4.5687  20.1350   3.8588  -1.0378   1.2913   5.7805
-#&gt;   -9.2243   5.0020   3.6845   5.8329 -21.8466  -0.6807   0.5413  -0.8968
-#&gt;   -0.6459  -3.9581  -4.2478  -0.6667  -8.4845  -8.5001   2.8692   8.0881
-#&gt;   -4.8527   8.2332  -5.3847 -15.9781  14.2163 -13.9345  -3.4176  -7.2562
-#&gt;   -1.8429   9.3069  -3.8057  -1.8225 -20.4709   2.7353   2.2712  -5.1033
-#&gt; 
-#&gt; Columns 17 to 24  11.0662 -14.5526   0.0697 -19.0220   4.6790   3.9423  -2.6753  -2.9578
-#&gt;   22.7239   6.9675   0.6424   3.7429  16.1882  -1.5087 -16.0539   0.1969
-#&gt;   -2.5550  -3.6210  -3.0403   3.2895  -9.0184  -1.8333  -1.3289   3.3007
-#&gt;   -0.8008 -13.9414  -3.3939 -18.7868  -4.2458   9.7082   1.1865 -18.0323
-#&gt;   -5.0873  11.8188   7.8642  11.5776 -16.0727   9.4908   8.4264 -15.9399
-#&gt;    9.5338   0.5156   0.8218  24.7352  21.4326  -1.3723  -3.8085   4.0040
-#&gt;   -3.6941  13.9830   3.1614  -2.4057  -5.2300  12.6373   0.7486 -10.2209
-#&gt;    9.6279   5.2914  -2.1667  -2.6882  -8.5979 -17.3494  14.1774   4.5505
-#&gt;   -4.9814 -17.5009 -15.7751   2.3943 -14.3330  15.0807 -10.8200  -6.3016
-#&gt;    1.0375   7.5819   1.8242  -2.1025   3.4078  -3.6889  -3.6870  13.7473
-#&gt;  -10.5505   8.0478   7.4250 -13.4552 -14.5107   3.5241  -1.5158   4.6435
-#&gt;  -17.6329  -8.9843  -0.5239  11.9668 -16.0605  -6.9913  -9.6883   2.4846
-#&gt;    3.9703  10.5117   2.4437  -9.5863  -8.5419   4.4804  15.5980  -8.0862
-#&gt;   -3.8186   2.0125   2.0543  10.0792  -7.5268  -4.8376   5.2885   7.0757
-#&gt;  -12.5689  -8.2614 -13.2759  12.4033 -19.5609   3.0369 -12.8527  -5.7830
-#&gt;   -4.6646   0.1257   6.0242   4.2538  14.7014  -8.8032  -5.5753 -10.4496
-#&gt;  -10.2244  -0.1577  13.0210  10.7659  -2.0343  -4.4945  -2.3216  -4.1657
-#&gt;    0.5649  -8.4057  26.7812  -6.8665   1.8525   5.4294  -5.2585   4.8750
-#&gt;   -3.3987   2.3718 -11.2156  -6.4853 -24.5315   2.5088  -9.4710   4.6227
-#&gt;   -7.9132   1.8410  11.1107 -11.2720  -4.2379  -2.7606  -5.2530   5.4750
-#&gt;   -1.3218  10.2854   1.9285 -11.3502 -11.5231 -10.5135  -5.7751   1.5308
-#&gt;   -4.0835  -6.0362   1.4618  -5.1609   6.1913 -23.7162   6.2317  14.7307
-#&gt;  -17.7677 -17.0161 -13.5697   2.4813   8.5010  -6.2259 -17.0806 -15.3741
-#&gt;   11.8556  -4.8654 -11.5342  -4.0967  11.5691   1.4684   7.8547   9.3808
-#&gt;    1.8603 -12.9068   8.3558  -8.8895  23.3471 -17.4276   1.8093   7.6683
-#&gt;    9.5079   3.8055   7.7854  20.9576  -6.5707  15.5529   2.2398  21.4755
-#&gt;   -5.8233 -11.2661 -21.2273   1.8201   1.9201   0.9845 -15.9731   7.6355
-#&gt;    4.1302  -6.0695  -8.4113 -10.1005 -17.6905 -16.4190   6.3698 -12.3462
-#&gt;  -17.9427  15.6452  -5.7567 -10.7185  12.8775  12.0741  21.4769   6.2621
-#&gt;   17.3807   3.2854   2.0233   0.4028   7.7633   4.6181   7.1242 -18.0502
-#&gt;   -2.6197 -14.2332   0.0485   9.7610   3.9811 -18.7429  12.5354  -6.3579
-#&gt;   -9.0786   0.7897   3.0636 -13.3762   1.8459   8.2314  11.0418   3.6651
-#&gt;   -2.2425   2.0437  -5.5021  -3.8781   9.9642   2.8891  -2.2660 -11.2317
-#&gt; 
-#&gt; Columns 25 to 32 -32.6698  -8.6093  -5.0129 -11.3094  24.4550  -0.5704   4.8933  -5.5201
-#&gt;    1.2721   0.6487 -10.9427  11.6122  12.3350   0.6388   1.1434   3.0152
-#&gt;    8.1651 -14.7024 -10.9904   2.9376   5.4284  11.7591  15.3603   5.0108
-#&gt;   21.2289 -14.6108   3.7578  23.7265   0.1149   3.1634  -7.0561 -25.7672
-#&gt;   -6.4865   1.1524  13.9062  17.8211   4.4666   6.4054   4.1235   7.4927
-#&gt;  -24.0737 -10.2488  -0.7837 -19.7711  -3.5405 -14.7599 -11.0616  -5.3042
-#&gt;    0.7789 -17.5263 -15.1668   8.9913  -2.4021   9.1531   9.4817   7.4751
-#&gt;   -6.0258  -2.9461  13.5621  15.0060  -5.9288  -7.2522  13.6002  -4.5906
-#&gt;   -5.3995 -12.3453  -2.9668 -10.6944   0.0278   3.2880  -3.7492   5.9309
-#&gt;    7.2386  11.6521   6.7375   4.7726  10.0783  -2.9344 -19.2203  -3.8975
-#&gt;   -3.8018   5.9055  -9.5356 -17.2161   1.6056  -3.9030  -2.1836   6.9117
-#&gt;  -22.5193   1.6938  -3.8648   6.0373   6.0638 -18.1423   2.2888   1.0071
-#&gt;   -0.0912   4.5126  12.5903   5.7911  -4.7895 -15.4148  -1.4881  -7.6777
-#&gt;    8.1668  -1.7108 -10.8280  16.8029   8.0118   0.1464   0.8490 -14.1483
-#&gt;    9.1446   1.4189   0.1367  -3.3847   9.2224  -2.9130   2.0731 -13.4869
-#&gt;   -1.2765  21.6822   5.4889  -5.0004  -3.4493  -2.9209  -9.9478   7.0307
-#&gt;   -4.0638   6.2533  -9.5217  -2.7831  -0.7303  -1.4290   7.1134   8.5808
-#&gt;   -5.4981 -17.6882  -6.5339   9.6844   6.3867   3.6524   4.6800   2.2742
-#&gt;   -1.4602   1.0576   5.5146 -13.2516 -14.4008  13.8107 -18.6747   5.9339
-#&gt;    4.9861  -4.7771  20.6300  15.5541  -6.0074   5.1128  -0.4189  -2.9462
-#&gt;   -1.4486  16.5589  19.4602   1.9526   3.5924  -7.0477  -4.7282   1.1304
-#&gt;   -0.3443   9.8070 -16.0824  -8.1581   3.0776 -12.2659  11.0605 -22.4701
-#&gt;    8.4359  13.0354  13.1142  -1.8719 -10.8537   3.0869  11.9608   4.5571
-#&gt;  -11.3341  -7.3854  -0.7040  -8.9241  13.4767  -0.2498   1.4465   7.7940
-#&gt;   -9.5916   2.3081   9.4823  -5.6463  -4.7764 -17.7450  14.1080  17.3467
-#&gt;   -0.5413   2.8060  10.5147   1.7546  -3.5436  -6.1599 -14.4421  12.4286
-#&gt;    1.0812  -9.0801  -1.7089 -18.4518   2.6798  -9.2463  -9.5171   6.6413
-#&gt;    4.7788  -0.4133   2.3009   0.4398  -5.0574  -4.7202  -5.6557 -13.3648
-#&gt;   12.6261  -3.5589  -2.4662   1.1209 -23.9096  -2.2812   6.0163  -7.3213
-#&gt;    1.1171  20.2254  -3.8290 -15.7041   5.4705  -1.6709  -2.7804  10.5089
-#&gt;    9.1564   5.4900  -7.7571  14.2231   5.9711  -1.7668  -1.0377 -10.1896
-#&gt;    4.7588  -0.2267  -7.0410  -8.5436 -12.5272 -17.1023  18.5268  -5.4560
-#&gt;    8.0987  -4.0980  -0.5273 -11.0374  -7.9019  10.1087   0.3397  -2.1266
-#&gt; 
-#&gt; Columns 33 to 40  -0.6891   5.1512  -5.9616   8.3684  -6.5541   6.7590  -1.5969  -3.3233
-#&gt;   -7.1381  -5.8954   2.3722   1.1176  -1.5182  -0.4858   4.1055   0.5757
-#&gt;  -12.1200   1.3603   4.2367   6.1071   7.8496   0.7433   7.9517   7.7278
-#&gt;   -6.7656  -8.5868   2.2282  -0.5914  -4.4096   6.2110  -1.2708  26.3763
-#&gt;    1.6977  -3.0817   0.9547  11.4690   8.4347  -0.5228   9.5944  -1.6138
-#&gt;    4.3577  -4.5182  -3.2134  -9.1625  -9.4456  -3.9361  -7.6599  -1.6247
-#&gt;    2.2651   6.7368   9.1694   7.3736  11.9476 -25.9909   7.7422   7.7991
-#&gt;    6.8059  -2.5797 -14.0586  -0.4316   4.3446   1.4599  14.3628 -19.3045
-#&gt;   -2.8454  -5.5364  -2.1157  -6.6131  -0.8127 -10.4145  -5.6694  10.0690
-#&gt;   12.9586   1.9260  -3.7913   6.1513   1.6149   0.6863 -12.8163  14.7614
-#&gt;  -17.7495   0.5206   3.7497   1.8556   7.8428  -7.2770   0.9337  -3.4212
-#&gt;  -13.5710   6.9932  -2.0936  -2.0107  -1.9852  -1.7724  17.9422   7.7792
-#&gt;   -3.7335  -7.4195  -2.4935  -3.5388   2.8353  -8.7390   6.6221  -8.8004
-#&gt;   -9.6807 -15.3747   4.5706  -4.4656  -1.0659  -1.2077  -4.3275   0.8064
-#&gt;   -1.5919   8.4855   4.3687   2.5481   3.1221   1.6635   7.3577  -9.0880
-#&gt;    0.5298   7.4672   4.1995   9.2957   0.1546   5.4831 -13.1099  -8.7616
-#&gt;   -9.7556 -26.8600  -9.2641 -11.0434 -10.5407 -10.8140  -0.6515  -5.8294
-#&gt;   -0.2582   5.4815   3.4851  -1.8528   9.1895   5.7904   2.5789  15.6803
-#&gt;    6.5863   3.9697   9.1019  -7.4410  -7.5782 -16.5352  -7.4639  -8.7216
-#&gt;    3.1715   6.5688  -5.2957  -4.3494  -4.3843  17.6800   6.5693 -15.0530
-#&gt;    8.9078  -1.9871   4.1219   7.5827  17.6163  14.7036  -0.9792  -9.6755
-#&gt;  -16.9230  -0.4241  -8.5176  -0.6277 -15.3566 -11.9403 -10.3961   3.3311
-#&gt;    1.5606  -8.6993  -6.6938  -7.1477  12.3101 -12.5791  -3.6695 -11.3222
-#&gt;   -7.9792   4.5259   9.5120   1.5035   1.6864   1.8410  -7.3137  -4.2054
-#&gt;   -5.8180  10.1696   4.6879   5.7464  -1.5702  28.5283 -13.1240   6.4315
-#&gt;    2.2603   3.7824  10.2709  -9.1696   6.4035   7.2668   7.3593  -2.1308
-#&gt;   -0.7404  -3.1743   9.5376 -12.0170  -5.0920   3.1090   5.7006  -8.4534
-#&gt;   -6.4615 -25.2439 -10.9172  -2.2400   4.7356  -2.1891   2.6827 -10.5127
-#&gt;    9.5131   7.4656  -6.5773   4.6907   4.1973  -3.3962   7.0490  -7.4300
-#&gt;   16.4267   3.5051  10.1579   7.0891   9.1914   6.3337  -2.7389  14.1222
-#&gt;    1.3694   0.3559   1.9932  -3.0715   0.0079  -0.8437  -4.3533   3.1633
-#&gt;    2.0113  -3.4719  11.3453  -3.5211  -6.7155 -10.9955  -4.1942   2.4041
-#&gt;   11.0641 -13.8099   4.6128   2.8432  11.7525   3.2607   1.2708  -2.1138
-#&gt; 
-#&gt; Columns 41 to 48  -0.1047   7.1639 -12.7312 -14.9411  -1.7243 -12.6823  -5.2909  -1.5054
-#&gt;   -0.6198  -5.0780   3.3587  -3.7096   9.7318 -11.5688  -6.0776  -5.0235
-#&gt;    3.5723  -9.2005   2.5989 -21.5485 -12.2536  17.0624   1.4070  -7.9798
-#&gt;  -12.1266   2.2245  -1.4824  -2.2374   6.7505  22.1302 -10.3626  16.0516
-#&gt;   12.9833 -10.8692   8.1235   6.8576   1.9169   5.8651  10.7781   1.8127
-#&gt;   13.4862  14.4447   7.6561   0.8456  -6.2709 -10.2808 -14.2315  -5.1059
-#&gt;  -11.6531   3.8384 -13.5760 -15.2587  -0.4386 -11.6019  -3.1254   1.9812
-#&gt;  -14.4318  -8.3302  -5.7316   1.3431   3.1469  -0.5234   2.0978   5.0587
-#&gt;   11.3827  12.2602  13.5625   8.1668 -13.3577  -5.9555 -10.2404  -0.4668
-#&gt;  -20.0554   6.6514  -5.7848  -3.7281   2.9242  -1.2270  -3.3500   2.0820
-#&gt;   13.5036   4.9700  14.1329  -3.3614  -2.4387   4.3636   5.2511 -26.7314
-#&gt;   12.3755  -6.0302  13.0706   7.0045  -4.3136 -20.3503   6.1174  -7.6721
-#&gt;  -11.2236   2.2306   0.2531   6.8231  -1.9899   3.6850   2.7379  -7.5945
-#&gt;    7.6483  -7.3374   9.9872   4.2223  12.5574   4.1922  11.5467  -1.8445
-#&gt;    6.6880   1.3918   6.2407 -23.0986   3.5472  15.1577   6.0838  -9.5015
-#&gt;  -25.3432  21.5193   0.5058  -5.4625 -14.2084  -2.0676  14.9385   2.1116
-#&gt;    5.4331   0.7524  -4.1156  10.5738 -15.1925 -10.3904  11.5019   8.5438
-#&gt;   -0.5497 -12.5371   5.6004  -4.6952  15.8923  -8.7153  -3.9162  -8.2549
-#&gt;   -0.2119  -0.2621   3.6054 -26.5559  -0.8848 -10.6993   3.7716 -17.5107
-#&gt;  -19.2452  -4.8849   2.6568 -10.7631  -3.6147  18.3777  10.0724  -3.3694
-#&gt;   -8.3158 -15.7886  12.6284   4.6058  -7.6070  -6.1975  23.2880  18.2722
-#&gt;   -0.4041  10.9108  -5.3681   8.0119  -4.9525  14.1613  12.6936  -3.8161
-#&gt;  -14.9089   6.0440  14.4983  -9.9542 -14.4793  -4.1608   2.9876  28.2071
-#&gt;   -7.5434  14.3462 -13.1011  -0.0543  -0.1406   1.4744  -9.4161  -4.9771
-#&gt;  -14.9983  13.5030  10.9352 -10.9594  -6.3634   6.1297 -18.0191   1.8998
-#&gt;    3.8310 -10.9236  -3.8793   6.5608   5.8434  -7.0148 -15.9521 -13.2939
-#&gt;   17.6064  -5.7444  -0.3266  -0.0692 -11.4574   5.3437 -12.4108  -8.0583
-#&gt;   10.7164  -5.7833   1.8178  15.0389   3.0004  -2.2704  -3.4458   4.3220
-#&gt;    4.8007   3.8398  -4.0033  10.0028   4.0875   9.7484 -17.8828 -12.7776
-#&gt;   10.3227  -1.1501  -2.4754 -20.4586  13.4596  -9.2950   1.5691   2.9662
-#&gt;   -6.1917  -0.8440  -3.0441  14.1943  -4.7117   2.9086   9.5871   7.8146
-#&gt;  -20.5611  12.6771  -7.1091   2.4571  -3.8733  -0.2544   7.2356  -6.9050
-#&gt;    3.2720   2.4587   0.8837 -15.8807   4.8638  -8.7061  -8.4504  -8.2424
-#&gt; 
-#&gt; Columns 49 to 54  21.8557   4.4202  10.8168   7.5174  -3.7758   2.1122
-#&gt;   -2.8456 -15.9854   2.0339   7.9619   0.4806  -0.4653
-#&gt;   -9.5906   5.3457   2.7396   8.0575  -3.6745   1.8922
-#&gt;    9.1292   8.5034 -25.1697  -2.3926   3.7174  -3.3314
-#&gt;    5.9401  -1.1539   3.8906   7.1575   0.5660   2.4047
-#&gt;   -5.3541  -2.6976   4.3906   2.5245   0.3295   2.7928
-#&gt;  -10.4245   7.0469  -6.8186  -4.2369   2.9327   2.8947
-#&gt;    3.0145   0.9534   5.5193   7.3609   0.7635   1.8795
-#&gt;    1.0380   8.5256 -16.1735  -1.1572  -3.3035  -0.8527
-#&gt;   -5.8692 -11.7066 -13.6596   4.0496  10.6453   1.8254
-#&gt;   -5.8070  -8.3313  21.3003   0.7767  -2.3350   3.4440
-#&gt;  -12.4272 -14.2899   1.9738   2.6648  -7.6391   2.7511
-#&gt;  -16.9396  -9.4806   4.9570   0.1588   3.8355   3.2884
-#&gt;    5.2529  -7.1969  -4.2218  -6.2748   2.3049  -1.3600
-#&gt;  -13.7700  15.0260 -11.5880  -3.3049  -0.6822  -3.0384
-#&gt;  -10.5514  -9.3660   5.5534   1.1381  -2.6856  -1.1547
-#&gt;   -2.5532  -0.4677   5.0003   5.1773   0.7936  -0.4262
-#&gt;    6.8253  -5.2287  -6.6104   1.5442  -2.8864  -0.4845
-#&gt;   11.6100  14.6819  -9.3433  -6.4061 -13.6912  -0.5850
-#&gt;    0.3866  -5.1808   4.5291   7.7504   1.9457  -1.1015
-#&gt;    0.5982  -8.9206   4.5865   0.8641   1.1949   1.2615
-#&gt;   -5.3683  -4.6591   0.5351  -8.2208  -2.7109   3.5416
-#&gt;  -13.6418 -10.2297  -9.1510   5.2640   2.1182  -4.0404
-#&gt;    4.0355  -8.2871  -9.8668  -5.2217  -8.3299   0.6824
-#&gt;   -3.4248  -5.1316   9.4518   0.5314   0.5506  -2.2042
-#&gt;    1.9089  -0.8906  -2.7357   3.9258   4.9725  -0.2202
-#&gt;    7.4031  -6.5030  -0.5144   0.6044   1.5107  -1.8649
-#&gt;   -2.6418   0.4153   3.0424   8.7083   1.7534  -0.0252
-#&gt;    4.0239   3.5966  -7.6770   0.8856   5.5774  -2.7345
-#&gt;    2.3516 -11.4263   0.2037  -6.9024   2.5088   3.9246
-#&gt;   11.4006  -1.0101  -2.1060  -2.4315   0.8993   1.6014
-#&gt;  -14.6229  -1.0623  11.6151  -9.1313   6.1282   1.5521
-#&gt;   -2.1762 -12.8544  -2.2436   5.0173   2.8554  -0.1953
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  Columns 1 to 8   5.0065  -7.5648   4.3697   5.4119   1.1572  17.5422  -0.4933   3.3253
-#&gt;    4.5796   7.3903  -1.7693  -2.0851   2.1409   3.3564   1.4481  -4.8963
-#&gt;    1.6970  -2.0240   0.1673 -13.3189  10.1225   4.5772  12.1823 -12.9104
-#&gt;    4.4423   0.3706   1.9800 -14.1760   1.7023  -7.2400   0.9277 -10.3556
-#&gt;   -4.9464   5.5538   3.4204  -2.9786  -1.0806  -6.1542  -0.2451  -9.0744
-#&gt;   11.9943  -1.9920   5.2217  -4.9694  -3.6795   7.3552  11.9307   8.9454
-#&gt;   -6.8636   5.1646   3.3317  -2.5187  -3.2920   2.8625   8.4681  -1.5043
-#&gt;   -3.4725  -0.3777   0.8911  -0.7854  -1.9812  -1.6493 -27.8762   2.8427
-#&gt;    4.7066  -3.2704  -0.3828  -6.5696   7.0008  -5.8864  13.5979 -16.6016
-#&gt;    5.4407   3.1746  19.6670   0.1120  -2.7427 -11.9844   1.0637   5.8977
-#&gt;   -7.6526  -4.7646   7.5172   2.7917 -15.0073   1.5688  -6.8070   5.4306
-#&gt;    2.9191   1.0430   0.1736  -4.6040  -3.2349  18.2567  -0.5415 -12.4009
-#&gt;   -2.2711   4.5532   4.4744  -0.5926   2.2176  14.9981 -11.5919  14.1614
-#&gt;   -5.8899   2.6850   0.1409  -2.7625  -0.4703  14.8921  -3.4326  -3.3055
-#&gt;    0.4423  -8.7097 -10.6624   8.3148   6.7439  -2.4797  12.4699 -20.2065
-#&gt;    8.0021   0.3554   0.3091  -3.3935  12.4163  -1.2953   4.3594  -4.7604
-#&gt;   -2.1517  -1.1949   6.7236  -9.2434 -11.6224   6.0957  10.3943  -1.4627
-#&gt;    0.7060  -1.5135 -20.7254  -6.8302  -3.2998  15.6868  -5.5653  -9.2229
-#&gt;   -5.0176  -0.9663  -1.7610  15.8706  -1.0764 -10.6421  -5.5809  -3.3236
-#&gt;    1.9067 -10.7770  -3.6526  10.3817   9.1903   6.5684 -17.1229  -7.5234
-#&gt;    1.2750  -3.1628  -1.8613   8.5306   1.5362   0.4427  -4.1968  -0.8201
-#&gt;   -2.1752  -1.8308   6.5429   7.1769  10.1203 -15.6256  -3.5803   4.7920
-#&gt;   -3.9520   1.5366  -1.0068  -8.6163  -5.8413   5.2089  -2.8411  -5.1499
-#&gt;    4.3731   3.5496  -1.5140   6.9102  -1.4003   8.9362  -6.4642  -4.5631
-#&gt;    4.2081  -9.6222  -3.5010   0.5712  -2.3747   6.0067  -4.5879   8.2174
-#&gt;    6.5613   5.2677  -1.1577  15.0782  11.2545  -8.9979   2.4309   3.5133
-#&gt;    4.0774  -0.2183  -7.1818  -3.6122   4.8120   5.3588   7.6044   0.1746
-#&gt;   -8.7328 -10.5805   1.2447   3.5645   6.8815  -6.6055   1.7821  -3.7090
-#&gt;   -6.2692  -0.6318  -4.2597  -8.9461  -2.7332  -6.6875  -7.8548   4.0098
-#&gt;   -0.3510   3.9007 -14.3480  -0.3358  -9.3153  -3.4372  -6.5250   6.8921
-#&gt;   -1.2713   2.7064   5.8096  -5.4592   1.9853   5.8544  -6.8093   2.7735
-#&gt;   -2.0104  -1.4024  -2.1828  -2.0720  10.3598   8.6430  -0.3503   3.7573
-#&gt;   -4.4944   2.3952  -2.2312  -6.9977  -4.7600   2.7363  -0.5609   6.7697
-#&gt; 
-#&gt; Columns 9 to 16  -6.9998  10.6761   8.6290   1.0619   4.6502  -8.7584  -8.5783   5.7564
-#&gt;    1.2974  -3.5524   6.9653 -19.2955  -4.2965   3.1079  13.4470  -4.0555
-#&gt;   -5.2354   4.2810  -0.9473  18.6950  -8.9680   9.0909 -22.2445 -16.3682
-#&gt;   -1.2449   6.2986 -12.2026  19.6986  -8.0719   4.8052 -13.2800 -24.9230
-#&gt;    3.1749   5.6027  -5.8446   4.7225  -1.5331   9.1026   6.1800   6.6361
-#&gt;   10.8301  14.3328  -7.5849  17.7657   6.8040   2.6532  -6.7402   2.3008
-#&gt;   -2.4575  -5.3909   3.3260  12.3631  10.6478   1.0650  -6.7919   6.5352
-#&gt;   -6.5352  -1.1951  -2.9045  -4.2426   1.1777  -9.2049   1.3871  22.7300
-#&gt;   -6.4794   8.2347 -10.4154   4.7242  -8.1403   7.2454 -13.2459   0.8178
-#&gt;   -1.7903  -5.8577   0.6970   9.9278   5.4227   9.8878 -14.7169  -0.7110
-#&gt;    6.4007  -7.5623 -10.7833   4.0560   5.7243   6.1790   6.8927  -6.2734
-#&gt;    2.0545  -3.5389   2.0349 -10.0992  -3.0352  -4.9398   2.8822   0.6213
-#&gt;    9.8403   9.5956 -10.4498  -1.1934   5.3889  -9.9235  -3.0167 -12.1756
-#&gt;   -1.9861  -9.2124   6.4746  -6.2048  12.4562  -2.5963   7.2102  -7.0540
-#&gt;    6.5246   4.7411 -12.6090   2.8226 -13.1843   7.1695  -9.3513  -5.5817
-#&gt;   12.1244   6.2580  -2.8042  -7.4370   5.9493  12.8463   5.8486  -8.9301
-#&gt;    2.3902  -0.9217   6.3379   3.5433   7.7250  -5.8269   6.4756   0.2072
-#&gt;    4.1890   0.4687  13.0917  -8.7310  -4.4070 -12.8233  -3.4196   5.6194
-#&gt;    7.1521  -6.5397   0.6541  -6.3224  10.2388   4.7956 -12.6320   4.4017
-#&gt;   -1.5785  10.3988 -10.7020   4.6607  -7.1149  -2.0043  -1.2336  -6.8194
-#&gt;   -2.8968   2.4503  -5.4942 -14.4237   6.6551  11.6778   8.5156  11.1045
-#&gt;   -1.1568  -2.7098   1.0471  -3.7183   4.8246  -1.9663  -4.6640  -1.1049
-#&gt;   -8.5518   7.5451 -15.9437  -1.2995   0.7863   6.2623   1.3795   1.0162
-#&gt;   -1.5262   8.2184   1.0894   1.9374  -4.0241  -0.2278 -15.7278 -12.5403
-#&gt;   -3.6396  -0.5381  -9.3408   4.3941 -12.5852  11.0130   7.4217  -5.1584
-#&gt;    3.5167  -0.9388   8.3914  -6.8105  -2.8391 -21.0392   0.0891   6.5242
-#&gt;    6.7332  -3.6219 -10.7829   7.0740   7.6049   5.0294  -0.9974  -3.7430
-#&gt;    8.4995  11.3926 -10.2824  -2.8517  -9.0784  11.1800   2.0026  -2.0633
-#&gt;    4.7858 -14.6920   7.6764  14.7249   7.3598  -4.3916  -7.6974  -6.0448
-#&gt;    4.9977  -2.4565   6.1780  -5.7729  -1.9491   7.0341   9.1896   2.2460
-#&gt;   -3.9617   0.7266  -8.6600   0.5977   9.7916   8.5833  11.3199  -4.5682
-#&gt;    3.0461   7.4758   2.2648   3.1652  15.9415  -9.5217  -7.9929 -14.9009
-#&gt;    0.0593   8.0995   5.7684  11.2536  -1.8930   0.8747  -0.8771 -17.4723
-#&gt; 
-#&gt; Columns 17 to 24   2.9991  -7.0031  -4.3098   3.9696  -3.8698   3.7981  -3.4638  -5.8145
-#&gt;    9.8929  -0.6985   1.5678  -7.6750  -1.0575   3.5687  10.1861  -2.8345
-#&gt;   -3.5003  11.2459  -9.4071   7.3753  -0.4676  -7.4027  -6.5143   4.4750
-#&gt;    2.7744   3.1267  -5.5507  -3.7926   4.7195  -4.2368 -16.2356  11.4313
-#&gt;   14.7289   3.2852  -5.1273 -26.9714  -5.7715 -11.4138  -6.7957   3.3423
-#&gt;   -0.2818   5.4439  -0.5775  -0.4554  -2.5376  18.6859  12.4762 -13.8969
-#&gt;    4.9449   1.3183  -0.1317  -5.1529   3.8452   9.2586   1.8964  13.9726
-#&gt;   -1.6536   0.3676 -13.3478  -4.2053   0.0647  -6.0252   9.3051  -6.1649
-#&gt;   -5.5615  14.3067  -1.6848  12.2309  14.4537  -8.1157  -0.8579   1.5930
-#&gt;   -0.9635  -1.4980   4.2655 -21.3527  -7.6679  -7.1719  -0.3917   1.2707
-#&gt;    3.1651   2.3176   4.5623  -2.1620   2.1102   1.5631   6.1667 -12.4041
-#&gt;   -3.5800  15.3647 -13.4311  -7.2799   0.5813  -0.9263  17.0942 -18.8560
-#&gt;   -1.9807   1.5200 -10.6795  -0.0452  -4.0646  16.5639  -0.9831 -21.0682
-#&gt;   -0.0563   2.6941 -12.5726 -15.9221  -5.0472  11.8357   9.6567   2.6775
-#&gt;   -0.7082  15.5344 -10.7737 -14.3932 -12.7231 -17.7728  -9.7606   3.9705
-#&gt;  -11.7585  -0.7036   6.9729   2.0480  -1.0557  13.8488  -3.0825   1.0844
-#&gt;   -5.2574   2.7561  -4.0692  -7.9613  -7.0095  11.1284  14.2156  -2.0798
-#&gt;   12.7254 -12.6184  -4.4636  10.5271   8.7149  -3.2069   5.7323   4.0469
-#&gt;  -14.2750   1.7038  10.0903  -2.5975  15.6578 -16.2574  15.1585 -11.0779
-#&gt;   12.1564  -2.2589 -12.5739 -10.4091  -6.1980  -5.2244   4.4709  -3.7656
-#&gt;  -18.4174 -15.6443  -1.5797  -5.4252   6.9559   2.8517  16.7162 -15.9417
-#&gt;   -9.5823  11.4253   6.4563  -7.3173  11.0476   4.3020   4.4361  -3.1137
-#&gt;    2.2054   7.7500   4.4457 -11.8929 -18.6036  10.0097  11.4196   5.3231
-#&gt;   -1.3877 -14.1901  10.9835   5.7080   2.7517  -2.3153   2.5598   3.1702
-#&gt;   11.1839  -7.5466   6.8821  12.2485   3.6899  10.8344  -6.7247   2.7138
-#&gt;    6.8281 -12.7237 -12.4240   0.8836 -10.6440   2.3363   0.7084   4.0937
-#&gt;   -3.7046   3.6566   3.7686  17.9101   2.9497   3.7957 -10.0212   7.2813
-#&gt;  -15.1208   3.2913  10.3041  11.0956   2.9664   5.2545   3.8653   5.1548
-#&gt;    0.9173 -13.3204   5.8748   4.8021   4.3568  -2.2284  -1.8059   4.8992
-#&gt;   -1.8480  -7.9347   0.0099  11.5403  -5.5167 -14.6196 -13.5348  -3.0772
-#&gt;    6.2677   3.2872  -3.0857 -13.0179 -17.1925  -3.1941   4.9096 -10.0129
-#&gt;   -4.5396   7.1775  -5.0795  -2.1652  -4.6862   9.7565   4.7690  -8.4607
-#&gt;   -6.4992 -17.9611  -0.7486   8.4730   0.2359  14.2265  -4.9010  13.5942
-#&gt; 
-#&gt; Columns 25 to 32  -0.4502  -2.3367   5.3966  -1.9736  14.0377   3.0119  -8.4109 -13.9355
-#&gt;    3.9575  -4.0850  -0.7513  -8.3361  -0.8708   9.1271   0.5462 -14.4554
-#&gt;   -6.7282   1.3936   7.7278  15.0878   3.4429  -3.3617  -7.3842  -6.6403
-#&gt;   -6.4748 -13.2080   3.8655  10.9370  -5.1134 -14.1147   8.0535 -11.6929
-#&gt;  -12.5487  -1.3291   3.7207  -1.6684 -11.1489  -1.3516   9.4046  -2.0490
-#&gt;    5.3655   9.5782 -17.2964  -9.7356   0.9319  11.7845  -0.2503  -2.2241
-#&gt;   -4.9538 -22.1325   8.2270  17.2945  -4.1733  -6.3520  -0.8176  -5.2402
-#&gt;   -4.1229  10.7951   3.8800 -14.9516 -13.4749 -13.0130  -2.2828  -2.7846
-#&gt;    4.6756  -9.9426  -6.9022   4.8072  15.7269  12.2702  -3.2373  -5.9807
-#&gt;    4.7910  -8.1889   3.8486  -5.5455 -18.6664   8.1700   7.6792  -5.5944
-#&gt;    5.4961  -5.3140 -14.3234   2.9961   0.9553   2.8977  -3.2754  10.3027
-#&gt;   -0.4717   7.9712 -12.2355  -3.7001  11.2613   4.8899 -19.9430   3.0561
-#&gt;   10.5788  11.4136  -4.8488  -8.5414   1.8690   1.0350  -0.8902  -6.1415
-#&gt;   10.3371 -10.8624 -12.7971   1.8257  -3.5686  -1.8141  -6.7775  -5.4592
-#&gt;    4.4009  -3.1121   4.6466  18.3852  11.2915  -3.8269  -7.2640   2.5717
-#&gt;   13.3067  -0.2645  -2.1777  -3.1643   4.2687  12.0935  10.4656  19.6666
-#&gt;   -8.0351  -4.7948  -8.3152 -14.3852  -4.9098   0.2674 -12.8918  -5.0915
-#&gt;   -6.9013  -3.8669   6.1630  -0.5587   1.0668  -7.0706   6.9699   8.8962
-#&gt;    4.4448  -4.7124 -12.8896  14.1769   3.3869  -5.7435  -8.1179  -5.2362
-#&gt;    4.2559  16.4625   2.6017  -3.6140  -1.8479 -22.0223  -7.7596  -0.5762
-#&gt;   -7.4018  16.2872 -12.9994  -7.2869  -7.2918  13.4027  18.9048  17.6126
-#&gt;    1.9429   6.2656   1.1654  -6.0094   7.2305  -3.4192  -6.5580 -11.7218
-#&gt;   -1.4432  -3.2813  -9.4318 -10.5674  -1.7319  17.8822  14.0319  -6.5661
-#&gt;   -4.2170   9.2337   2.8840   1.7105  -0.0033   4.7875   7.6331   0.7086
-#&gt;    1.5496  -4.3581 -11.1832  -1.6342  15.4827   0.6272   0.9326  19.2356
-#&gt;   -2.5731  -0.6965  15.7973   2.5878 -13.0473  -8.7761  -7.2038  10.3990
-#&gt;    0.1765  -2.1546  -2.9154  17.0378   1.3992   1.8228 -11.6180  11.1169
-#&gt;  -19.8042   5.9646 -15.4726 -11.0236  -6.0788   4.6658  -4.0302  -6.4444
-#&gt;  -16.1552  12.7106   8.4312   1.0798  -3.3709 -10.7590  18.7330   1.0471
-#&gt;    2.8258 -19.5791   2.6700  24.3105   0.5782  -0.9183  -3.7057  -0.9869
-#&gt;    2.3721   4.8013  -6.0742  -5.4514  -2.3220   0.5604  13.2491   5.0343
-#&gt;    8.4863   3.8528   6.6870   0.3344  12.9178  28.9992   1.1368  -3.5024
-#&gt;    7.6834  -2.3466  -9.0723  -5.1307  -6.5264  14.4130   9.0037  -5.7521
-#&gt; 
-#&gt; Columns 33 to 40   6.1166   3.4755   3.0107  10.4820   2.2313  21.4302  -0.3769   7.2492
-#&gt;    7.5885   2.9510  -5.9707  -3.1287  -6.9030   0.4736  -9.9255 -11.9223
-#&gt;   -7.3547  -2.6745  16.3513  -9.9079  -7.4035  15.0191   6.9734  -0.3176
-#&gt;   -3.9057   7.4514  -3.5686  -0.0520   5.5190  -8.5440  16.0547  -6.0800
-#&gt;   -3.9551   9.4788   2.8746 -14.6228  -8.6366 -17.5136  -2.6790  -0.4776
-#&gt;   -9.5146  -5.2213  11.0260 -14.8032   6.0196   1.7087   6.6660  14.5329
-#&gt;   -5.6591   6.8087 -15.9567  -2.8290  -4.5220  -7.0795   4.1778  -3.3880
-#&gt;    2.3287   9.4812   1.4361   2.4778  -4.0854 -14.8061 -15.7904 -13.1929
-#&gt;   12.3652 -12.8175   4.8446   9.9093   2.9067  24.6350  18.0183   2.1561
-#&gt;  -12.6300  -1.2267  -5.1545  12.1703  -2.8878  -1.3358   8.3011  -2.3132
-#&gt;   -0.8691  -1.6677  17.0963   2.4648  -7.9536 -11.2632  -4.9028  -4.6315
-#&gt;   -2.7268   0.6535  15.4282   8.6797   3.8988   5.2600   4.8597   1.6352
-#&gt;   -8.4937  -0.0901   5.7381   5.0044  11.3519  -8.4415  13.9622  -1.7646
-#&gt;  -16.1659   4.4432 -11.6796  -0.2280   7.2947   0.4916  23.7616   4.4628
-#&gt;   -7.2801   7.3802   3.8974  -3.3115   3.7231   2.3252   9.6789   3.7708
-#&gt;   -3.8670 -11.5865   1.2037  -4.6438   7.7230  -3.4565  -6.9513   5.7064
-#&gt;  -12.7542 -12.4351   2.0368  -1.3465   7.0032   5.8571   1.5565   0.1240
-#&gt;    2.4653  -3.8384  -6.6116  -0.0328  -2.6110   9.6295  -1.0983  -7.9041
-#&gt;   18.4993  -2.1162  -7.7738   0.2191   8.9795  11.0376   7.3325   9.2761
-#&gt;    3.1431 -11.9472   0.6696   7.9047   1.0182   4.9934  -7.1324  -9.2449
-#&gt;   12.1020   1.9057  -1.7627   9.8111   6.0275  -2.3225  -1.9654  -5.9898
-#&gt;   -5.4913  -1.6481  21.1171  15.0078  -6.5409  -2.4634  -9.6712  -3.9974
-#&gt;  -27.1950  -3.4378  -0.7142  -6.0477   9.2275 -22.3768 -14.0585   7.8989
-#&gt;    0.9481  11.1605   5.6543  -4.2198   5.2720   9.0667  -8.2835   0.0860
-#&gt;   -1.8083 -11.5910   3.3375 -21.1419   0.6927   7.6406 -25.4208  12.1868
-#&gt;    2.0968  -8.3505   5.4435   8.9627  10.5742  14.8554  12.7098  -5.0643
-#&gt;   -0.4590  -4.4083  -0.0300  -4.9880   8.3520  -3.7739  19.8174  -0.7580
-#&gt;   10.4876   2.0805   1.9403   2.2905  18.1642  -4.9934  -3.4718  -1.4246
-#&gt;  -16.6475  11.8381  -9.6935 -20.3861   8.1342  -1.1187   8.6158  10.3533
-#&gt;    5.5304  -1.2605 -15.8330 -13.0794  11.5950 -10.1415  -4.5288  -9.6068
-#&gt;    1.0786  -3.2658  16.3202   0.3046  -4.1398  -7.0007  -3.2427  -0.0090
-#&gt;    2.3637  -7.6107   4.8096 -14.9503  16.2826  -1.3325   3.9388  11.2878
-#&gt;   -5.3557  16.9209   0.8013  -1.4738   3.4657  -9.9022  -1.0349  -4.0073
-#&gt; 
-#&gt; Columns 41 to 48   1.3345 -11.9278   4.8337 -20.2754   3.6607  -7.6054 -10.6370   0.7393
-#&gt;   18.4758   4.1840   5.1066   0.5228   4.7843  25.5798   1.4430  -4.8073
-#&gt;    2.2567 -12.8952   0.7386  -6.6147 -16.7057  21.4841  -9.8661  -0.3491
-#&gt;    0.1938   8.7895 -10.5219  -5.8892  -1.2568  25.8267  -9.5847 -17.0484
-#&gt;    0.5941  13.5316  12.5077  11.4497 -15.2622   7.9676   3.8970  -5.2587
-#&gt;    3.5475  -0.9832   4.7096  10.4700   7.5481  -6.0933  -1.1715   0.8234
-#&gt;  -15.7558  10.3998  -3.9478  -2.2627  -3.1319 -13.5418  22.4130   5.0423
-#&gt;  -15.0093   0.8777   8.9129  -0.8943   6.5934  -8.7079  -9.1901   1.2953
-#&gt;    0.7930 -18.1611  -9.1900  -1.9323  -4.9864   0.0681  -2.9314   1.2715
-#&gt;    8.6339   5.4117  14.6414   2.9027  18.3501   9.9014  -8.9372  -0.7209
-#&gt;    4.2320   3.9845   7.9440   3.8098  -7.0927   6.7669  -6.9893  17.8406
-#&gt;   -7.9320 -18.7648  -7.3420   2.7295   5.3951   3.3670  -8.2192   4.4860
-#&gt;   -2.9330  20.8977   5.6355  -2.1919 -12.0907  -3.9535   6.5782   8.8423
-#&gt;   -9.3347 -10.6555 -17.5191  -6.0478  -6.2106   1.9033  17.4623 -13.6784
-#&gt;    1.0635 -20.0535  -9.3549 -19.6059   0.9665   5.6258   2.6148 -15.1398
-#&gt;    7.1171  13.7309  10.8487  -5.9154   2.0694  -2.6388   2.8409   7.6188
-#&gt;   -9.5625  -5.8672  -2.7635   9.1451  -1.4415 -10.1499   8.3045  -3.1570
-#&gt;   -0.8245  -1.6012   8.4346   7.0643   0.2006  -6.0378  -5.0282   7.1434
-#&gt;  -20.1260 -14.5046  -4.1219 -16.9426   2.5031   7.4219  -3.9130  -0.1316
-#&gt;   -9.7300  -6.6407 -14.1674  -9.5041   4.6654  13.3797 -17.6050   5.9562
-#&gt;   -5.4223 -14.4687   7.3019   5.0101 -12.4661 -10.5039  -1.3772   9.8727
-#&gt;   -1.9127 -18.7884   8.1959   1.4009  -0.5677  -3.3554   7.6146  -4.5400
-#&gt;   -8.3890  -4.7103 -10.4115 -10.5468 -11.2971   6.0386  -0.3137  -4.2605
-#&gt;   -0.4814  11.7382   8.6822   6.9266   7.6994  -3.8786  13.3594   5.3836
-#&gt;    6.7302  -2.0560   2.8992  -1.6217  -7.0485  -3.6673  -8.4619  20.2970
-#&gt;    1.4750   1.6396   1.5047  14.3273  12.3591  -3.2715   6.9656  -9.5265
-#&gt;   11.8076   1.5431 -13.8370  -2.2326  -7.8101  -4.1678   6.3293 -11.9680
-#&gt;   -4.2533  -2.1013 -11.6261  -1.8696  -1.4513  -3.3382   0.7499  -3.3817
-#&gt;    1.1298  16.2342  -3.1134   0.6689   2.9858  -5.9440   6.0355  -6.6737
-#&gt;   -2.8073  11.9571  12.6337   8.9343   4.8625   2.5022   5.9198   4.3786
-#&gt;   -1.4268   0.1202  -1.7580   5.3506   5.1749   1.9549  -8.0605  -6.0836
-#&gt;   -2.4020   8.9237   7.2497 -13.5399  -2.3874  12.1784   4.0796  16.0535
-#&gt;    0.6426  23.0241   2.1876  -4.6716  -2.3914   4.1827   5.9588  12.7554
-#&gt; 
-#&gt; Columns 49 to 54 -14.4496  -8.8349   1.4433   2.1690  -3.7491   0.9587
-#&gt;   -7.7497  -6.5779  -8.7464   9.5712  -5.7366  -5.5686
-#&gt;    4.6983   5.0069  -3.8794  11.1549  -4.5174   4.5328
-#&gt;    1.5492  -0.2539  -1.7878  -1.7838   2.4953  -0.5657
-#&gt;    3.1202   0.9545   3.0343  -1.6115  -8.4593   5.7329
-#&gt;   -4.5750   7.9842   3.8035 -13.5802  -5.4309  -4.1773
-#&gt;   -5.8305   6.6706   5.9399  -4.3707   6.1034  -0.6240
-#&gt;    3.9163  -7.3941  -6.3986  -1.3349  -4.0819  -4.4972
-#&gt;    4.2833   1.5292  -2.7047   0.2604  -0.3884   3.3314
-#&gt;    7.3679  18.8472  -7.1299  -6.6655   1.7560  -7.6865
-#&gt;   -8.4181  -8.1825   2.7599   5.0031  -9.8373  -0.7030
-#&gt;   -1.7533   7.0408   1.4097   4.1171  -2.0185   0.9093
-#&gt;  -15.0229   2.6766  -0.4257  -7.9381  -2.5720   4.4902
-#&gt;    0.2040   1.1599  13.5789  -7.6558  10.2325  -6.6165
-#&gt;    4.6438  -3.2434  10.0951   3.6194  -4.4035  -0.0836
-#&gt;   -9.2463   2.0903  -0.0023  10.8511  -6.4154  -5.2644
-#&gt;    2.7451 -13.9360  -9.0759  -7.8088   2.8181  -5.4235
-#&gt;    3.7136  -6.3668  -1.2021   2.3239   7.6207  -3.6235
-#&gt;   -0.2203   3.0907 -11.3325  -1.1252 -11.7583  -3.8903
-#&gt;    7.2152   0.2197  -1.1622  12.1529  -2.1452   3.1754
-#&gt;   -9.8983   2.5051  -0.5053   1.2530   1.9963  -3.8447
-#&gt;   -6.6937   8.3050   0.6103   1.0726  -4.1585  -3.8931
-#&gt;   -0.9202   5.0524   0.0446   8.4999   8.9261  -1.5784
-#&gt;    4.6479  -1.3398  11.7661  -1.6349   0.1920  -0.1233
-#&gt;    8.7547  -3.9087   3.6918   5.1973   3.0588   2.7503
-#&gt;   16.5450   9.8928   0.2408   0.5691   6.6154   1.0948
-#&gt;    0.1952 -11.5720  13.2281  -6.8009   3.6701   0.9840
-#&gt;    2.0523 -17.0915  -1.5819  -4.7299  -3.5012  -0.7101
-#&gt;    7.6151  -2.8723   3.9770  -5.7623   2.6486  -0.8233
-#&gt;   -3.1527   4.0325   8.9116  -5.7383  -0.0428   7.4691
-#&gt;    7.4466   9.5679  -3.7968   6.6464   2.2457  -3.8793
-#&gt;  -13.5084   7.2839  -5.4541  -4.2447   0.3259   3.5467
-#&gt;    7.9819 -10.1110   6.5147  -0.2204   2.2264  -2.8546
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  Columns 1 to 8  -2.1050  12.3998  -0.4774   1.2383   5.8022  -6.9281  -5.4728   6.0727
-#&gt;   -1.8157   0.5752   1.5176   9.4034  10.4397  -3.4623  -8.0909   8.9566
-#&gt;    2.8523  -1.4397  -2.0096   0.4399  -2.9058  13.6328   1.0217  13.0719
-#&gt;   -1.5294   2.2925  -6.5809  -2.0880 -19.8867  14.5897 -13.2773 -11.1911
-#&gt;    6.7109  -7.0110   0.6445  -0.2109   1.6469   9.4859  -5.7427 -10.0133
-#&gt;   -4.8364   5.8289   4.6019   3.3430  -1.8501 -10.7135  21.6716 -15.6815
-#&gt;    7.1862   0.7821  12.1798   8.3415  -3.7003   6.7931  -1.0732  -6.0702
-#&gt;    6.0033  -0.7861  -5.6532   8.0808   9.2785  12.5299  14.3445   3.3772
-#&gt;   -2.2405   4.2441   2.1795   2.5741 -12.6495   2.4456   6.2146  -5.1256
-#&gt;    3.2793  13.7299   3.0425  -3.4000   3.7587  10.1255   7.9639 -18.8988
-#&gt;    0.8084  -8.2614   1.0017  10.9275  10.0781   5.7191  -3.9948   0.1607
-#&gt;   -1.2355  -1.4259  -3.7475   1.8783   0.8166   0.9565  -4.1186   5.8185
-#&gt;    1.4094 -12.0985 -11.9980   7.8775   9.0769  15.9720  -4.7439   2.9385
-#&gt;    1.5571   0.0629  -0.1035  -1.5539  -3.8787 -14.4867  -3.6049 -22.4549
-#&gt;    4.0708  -4.1490   0.1908  -5.8443  -4.5887  -0.4553  -0.2272  -8.7493
-#&gt;   -4.0910  10.3983   0.2122   2.4811 -10.1094   2.2425 -10.9766   5.7353
-#&gt;   -1.1070  -3.0927  -0.9344   0.5191   3.5854   0.6241  -0.4108  -3.0465
-#&gt;   -6.5693   4.7755  -9.9835   3.8776  -1.0840   8.8799 -12.0977  14.3494
-#&gt;    1.4439   5.9479  14.3636  -2.7812  -2.9505  -1.6697  -2.3293 -10.8652
-#&gt;    5.3586   0.2086  -5.2899  -5.4280   9.1076  -3.8572   5.6281   6.1843
-#&gt;    2.3577   3.8243  -3.7754   0.7548  -6.3366  -7.6611   0.5793   1.8813
-#&gt;   -1.0511   7.6017 -20.3287   7.5251  -7.2557   5.9654   8.1025  14.8469
-#&gt;    7.8334   0.2638  -5.3560   5.1233  -0.2849   1.2744  -9.1953  -2.2904
-#&gt;   -3.4615   2.4915  -1.4092  -8.7137   1.1823   4.8117   8.3513  16.4244
-#&gt;   -3.6477   8.8516  -8.0689   0.4341   8.3788   2.0504  -8.3104  11.5212
-#&gt;   -5.6302   0.9044   3.8023  -4.9904  18.6028 -11.3102   7.7670   3.6899
-#&gt;   -0.8651   1.9924   8.2783  -7.4464   4.8625 -15.3295   0.5469  -9.0181
-#&gt;    0.7463  -6.4614   1.3538   3.3951   2.1724  12.7520   7.3761 -11.6228
-#&gt;    2.6962 -12.6195  -3.5614   6.3831  -0.7390   4.9695   7.9886   9.1374
-#&gt;    0.5365   6.8245  -1.8546  -7.4207 -12.1718  12.5915  -8.2897   5.3908
-#&gt;    0.0312   4.4716  -0.3820   3.3666 -15.6750  -0.7308  -5.7290 -10.0158
-#&gt;    0.0913  -9.0728 -10.2081   0.3705   0.5208  -4.9135 -13.3187  -3.3307
-#&gt;   -2.4412  -8.5937   2.6922   0.0637   7.2697  10.2367  -0.7692   7.1194
-#&gt; 
-#&gt; Columns 9 to 16   1.4035   1.4040   4.8057 -16.6953 -10.7570  15.9873  -3.9211   2.1780
-#&gt;   -1.6966  -5.1725  19.3307  -6.3238   6.2951   4.6960  -2.6081  20.1427
-#&gt;   -1.5471  -7.7275   3.1516  -0.9020 -10.0211  -7.9036   1.2798   0.7613
-#&gt;   -0.2585  -4.4660  -6.3808  -6.9989   7.8038   0.3097   3.0570  -5.2005
-#&gt;    1.4197   1.7325 -17.5488  -3.1597  13.1648   2.9228  -3.4837 -14.5672
-#&gt;   15.8725  23.6275  -5.4269 -23.7333 -21.2530   0.0843   1.8968  14.8612
-#&gt;   13.6775  10.2984 -13.2271 -13.6772   5.7547   2.0044  -3.7326 -19.6737
-#&gt;  -16.4962   6.2314   5.0742  -0.6590   0.0642   0.3928 -11.1059  -7.1018
-#&gt;   11.2382   0.8385  -1.0045  -4.1813 -10.6938   4.4621  14.5863 -18.2117
-#&gt;    1.6609  -5.2850   5.2872   0.7011  -0.5309 -15.8560   4.4899 -23.4390
-#&gt;  -14.5825   4.0461  11.6341 -17.4078  -7.0555  -5.2670   0.8999  -5.6901
-#&gt;    2.8568   6.2582  10.6664 -14.0320   4.2543   4.6557  -4.4138   2.9445
-#&gt;   -5.7549  11.1731  -8.0999 -14.7524   7.7781  -2.1008 -18.4997   2.9257
-#&gt;    2.8536   8.2467  13.3471   0.4846   5.7097   1.9348  -6.6551  -8.1418
-#&gt;   -3.3299  -4.0816  13.1217  16.2571  -1.7483   7.3860  14.2170 -11.9363
-#&gt;   11.0684   5.0762   5.3195   0.1432  12.0400  -3.7210   2.7910  -1.7928
-#&gt;   19.4745  -2.3785   8.6529  -4.5426  -2.4921   7.6448   0.1893  -0.8252
-#&gt;   -1.9241  -9.2395   5.6588  -7.6330  -0.7623  -0.6414   9.5515   8.5972
-#&gt;    9.1962  -6.1070   6.8629 -12.9698  -1.6276  24.5720  16.0840 -17.1156
-#&gt;  -19.7998   6.6669  12.4325   8.5895   4.8028  -7.0879 -12.0306   1.7905
-#&gt;   -9.7381 -10.4174  12.7223  17.6191  16.4640   1.4797  -2.2388   4.2665
-#&gt;   -3.5749   1.9684   4.4351   0.8422   3.2918 -15.7919 -17.1336  -9.5118
-#&gt;   -5.4865  18.5721  13.7783   1.7104   9.1968   8.7916   5.1292  -8.2372
-#&gt;  -10.1325  19.9818   2.2621  -2.6759   1.3810  10.1955   1.6041   0.8605
-#&gt;   -8.0232   8.5082  -8.4552  -5.3418   2.3370  13.6287   2.0805   8.9783
-#&gt;   11.1102  -8.4891   2.2244   2.1921  -7.0178 -18.9312 -15.8038   1.8319
-#&gt;    6.3255  -1.7980 -12.3073   0.5514   2.2092   2.9887  -5.8563   2.6762
-#&gt;  -15.6557  -3.5616   2.8305  -8.2303 -19.6977  11.7540   1.1495   1.8021
-#&gt;   -2.1927  17.4035  -0.7762   0.3718  -5.3043  -6.2854  -6.9760 -15.9494
-#&gt;   15.0398 -13.4662 -24.0948   1.7141  14.0322  16.5744   0.9768  -0.3527
-#&gt;  -14.3824   5.7630  15.4500  -0.7237   2.4711   6.4872   2.1575  -3.1848
-#&gt;   10.7607  -2.2199   4.4435   4.3157   6.8573   2.1049 -20.8255  23.3302
-#&gt;   -2.3852  14.4152   1.5751  -5.2666  -9.6740  -7.5906   0.5588  12.2082
-#&gt; 
-#&gt; Columns 17 to 24  -0.3194 -14.9899 -11.1432   7.6206  -2.6515  20.3641   6.9513   1.0261
-#&gt;    8.1171  -7.2204 -20.3917  -8.9583  -0.4736   9.6779  12.4881  -6.0153
-#&gt;    7.8626  -5.3077  -1.0013  -4.2854  -3.4871  12.5138  -4.1042  -4.1187
-#&gt;   -0.2872 -14.5971 -15.9643   6.7193  11.3281  16.3837  -6.2956  -8.5059
-#&gt;    7.8821  -2.9166  11.2147 -13.4587  -1.7634   8.1924  -8.6742  10.9291
-#&gt;   -5.3366   3.4671   7.0700  10.0996  -3.0505   5.1970  -6.2224  -0.0521
-#&gt;    3.1443  13.5610   8.5994 -11.0686   4.1030   1.0456 -10.7196  -4.6311
-#&gt;   -5.1903   1.7303 -10.9142  -3.1094   1.8742 -16.7151  -0.4450  14.1182
-#&gt;   -5.8055  -7.5304   3.0536  -5.1155  -7.8851  18.8317  -2.9984  -7.9829
-#&gt;   -9.4636   0.1752   1.8338  -3.9810 -18.1357   7.3099   3.1274 -19.7982
-#&gt;   -1.5617  16.0187  -1.5374 -20.1662  -5.5443   7.1038  -4.8856   3.5684
-#&gt;    4.4075 -20.2555  15.7781   2.6051  16.9903  -1.7933  -4.4984  -6.9582
-#&gt;   -0.3479  13.9096 -15.6244 -11.1793  -3.5875 -15.4238  -7.4633   8.6940
-#&gt;  -12.2850  -4.8526   8.2597  -7.7774  10.2423  10.6756  15.3577   3.9057
-#&gt;   -9.5595 -12.0798   5.7455  13.8555   2.1398  11.5047   2.4477  -7.0594
-#&gt;    6.9720  -3.8493  11.8396  11.0641  -2.8332  -0.1463  -7.4761  -6.3588
-#&gt;    5.5957   1.3788  12.8145 -16.4845  -2.3937  -4.1697   2.6650   9.6846
-#&gt;   16.3475  -1.5722  -3.2263  -1.4248   2.0256  -1.4739   4.0604 -10.2989
-#&gt;   -0.6586   3.4046   4.6824   0.4668  -9.5215  -0.6462  -1.9205   0.3976
-#&gt;  -16.8921   9.5151   0.8390  14.2056  -3.7218   9.2721  -5.7017  -8.5205
-#&gt;  -14.3077   9.3742   3.4875   9.6195  -6.0802  -3.7222  10.8635   5.9279
-#&gt;   -2.1217  -0.1907   4.4814   6.3114   4.5167  -3.5535   2.8435   9.9919
-#&gt;  -13.9939  15.1288   4.2053  -9.5740   1.5628   3.6481  -1.3641  -3.7322
-#&gt;    8.7557   8.7261  25.1453   0.8178   4.6255  -6.7057  -8.6511  -0.1091
-#&gt;    7.4447  -1.8325   3.1293   7.0337  -2.7863   1.6286 -16.0444  -3.8087
-#&gt;   -2.8873   2.8116   0.0587   3.4188  -9.5266 -14.8540   4.2804  -8.2651
-#&gt;   -9.8895  -6.4253 -10.2173   5.8122  14.4629  -1.7608 -11.0493   3.2803
-#&gt;   -1.0160 -13.9493  -9.9600 -15.3795  -3.2849   5.6923  -7.4746  12.0561
-#&gt;    2.1805   4.5089  -2.3862  -4.1503  10.5217 -16.1267  -6.3030   6.9384
-#&gt;   -1.3073   7.8117   7.1677   2.6476  -8.0955   8.7233   2.8246   4.7810
-#&gt;  -18.4368  -2.3778   0.1608  13.3684   6.7485   6.4731   6.1167  -1.2516
-#&gt;   -0.6043  24.9120  -1.4423  -6.7582   8.9642  -8.6571 -14.0482  16.8610
-#&gt;   17.7286  20.2208  18.4647  -7.7855   1.3575  -9.3874  -0.5325 -11.0693
-#&gt; 
-#&gt; Columns 25 to 32   5.6326   5.8350 -13.5859   2.6041  -4.8413  -0.6250  -6.9518  -5.0680
-#&gt;  -12.4608  -1.0832  -3.9246  -6.0014 -14.6060   9.3799  -5.2067   9.0054
-#&gt;   -9.7539   1.2320  -1.4374  -8.5207 -10.2753  -2.1604  -1.0834  -9.2973
-#&gt;   -2.7700  -3.0238  -6.5836  22.4792  -6.0836  -3.0262   9.0822 -13.0279
-#&gt;   -3.6857  -8.2444  -9.6928  -5.6210  -3.9370   3.0351   7.6838 -11.3569
-#&gt;   -0.2301   6.7172  -1.5944  -1.4519   0.7078 -22.6055  -0.0719  -9.2618
-#&gt;   22.1931 -19.8122  -7.2434  -1.8270  -7.0690 -20.1464 -11.8704   2.4956
-#&gt;    8.5558 -13.4251   9.5341  -4.0036  -0.1540   8.6128   7.1111   7.8143
-#&gt;    7.0072   4.0444 -14.5680 -15.6934  16.3534  -8.4037   2.4889 -12.2590
-#&gt;    4.0391  13.6764 -10.0684   6.1171  -5.3206 -15.5514   5.7784  -2.1322
-#&gt;   -0.0031  -7.7351  -9.6109   6.7777  10.3609  -5.9732  -5.8301   0.0472
-#&gt;   -7.5225  -8.9016  -3.5149 -12.1527 -13.5420 -10.9205   1.6544 -16.7605
-#&gt;    0.7409  -9.4100   4.2403   6.1743  13.0698   4.1074  -2.2450  11.6677
-#&gt;    7.2575   5.4975   1.5036   8.9499  -6.6605   1.1732  10.0563  -2.5328
-#&gt;  -13.7980   2.2803 -11.4877  -8.7844 -11.2398 -18.5173  21.1523 -24.5849
-#&gt;  -13.6781   4.3191  -1.2661   3.1900  -9.3190  -2.6247   2.3197  -7.3712
-#&gt;   14.6047  -0.4196   6.9357  15.3953   5.5046  -5.5453   0.5608  -0.1292
-#&gt;    7.9832   2.5611  -7.4931   8.8954 -12.2209   9.0451 -13.8076  15.4440
-#&gt;   16.5112   0.9398   2.9589  -0.9302  10.1620  -6.0823  -2.3119  11.8207
-#&gt;   -2.4773  11.4658   4.3734 -14.3540  -4.6328   6.7320  10.7169  -2.2529
-#&gt;    5.1290   7.3429  -6.4964  -1.1527   9.2697  11.9739  -7.9363   7.1066
-#&gt;   -6.5489  -4.9425  -4.4167  11.7435  -4.6573   3.6331  -4.8614  -0.2404
-#&gt;    4.7502 -13.9168   8.9244   1.8197  -3.4975   2.8928   5.0875 -16.2945
-#&gt;    8.9716 -22.2495  -7.6811 -13.3519  -6.7208  -3.6837  -6.2659 -10.6813
-#&gt;   10.0090  -2.3997  -3.9190  11.7128 -11.4862   2.7779  10.6134 -17.2960
-#&gt;    5.2759  10.7843   6.3188  -9.5183   3.7632 -13.0112   7.6870  -9.9708
-#&gt;  -17.1446   0.4763   1.7853   8.8129   0.8247 -12.7194  13.7973 -10.3702
-#&gt;   -2.9073   7.9717  -1.6721   4.9187  17.0543  27.8806  -3.1481   3.5462
-#&gt;    0.6223  -5.8077  21.9198  14.0404   1.6793  11.8098  -2.5856   5.5326
-#&gt;   -1.5546   8.4663 -19.7711  -3.5745  -9.0930   2.5057  -8.8058   1.5028
-#&gt;    2.9323   2.4301   0.4083   1.3978   2.1401  -3.1432  -9.2657  10.6113
-#&gt;   -8.3944   2.1382   3.2571   5.1617   4.1159  11.8319  -2.4654   3.5631
-#&gt;   -9.5940  -3.8198  -0.0708   0.2324  -0.3559   5.0562  -9.3026  -0.0156
-#&gt; 
-#&gt; Columns 33 to 40   5.4751  -4.9685  -1.1140  12.0255  -2.7069   0.1429  -3.9142  -1.8313
-#&gt;  -21.6627   7.3637  -1.2541 -10.8029  -6.5101   4.8416  -2.5360   5.8329
-#&gt;    0.8076  18.1017  -4.4381  -0.4216  -1.6496   0.0826   8.9062  -9.5129
-#&gt;   11.0231  -3.6290  -7.2440   1.0281   5.9202  -2.9915  -0.1684  -6.0654
-#&gt;   -1.8620   4.0979   3.5261  -2.2876  -3.8825  -4.7818  30.0266  -5.1267
-#&gt;    9.2627  -9.3801  -6.3897   4.1681   3.5414  -1.2313   2.2550  -7.4358
-#&gt;    3.8479   0.4866  21.6011  11.6342  -7.4530   9.2773   4.7980   4.0954
-#&gt;  -12.4142   0.9372  -1.0704  -8.8271 -10.6478   7.2930   3.4420  -7.5093
-#&gt;    0.1698  -2.8374  -9.1786   3.2934   9.4190   9.5889 -12.3461  -6.6323
-#&gt;    6.0238 -16.7210  -2.1618   6.7680   9.0894 -11.5117   9.2601   4.0035
-#&gt;    7.0340  -5.0686  -6.7381   6.7828   3.2271  -1.7524   6.9165  -1.2493
-#&gt;   -2.5774  -9.4541   5.8497 -10.5635   7.0962   4.1460  -3.2895  -4.5333
-#&gt;   -0.0374  -5.8546   0.1963  -1.3632 -10.0908   9.7387  -1.9190  -8.9648
-#&gt;    2.5153  -6.6226   7.6842  -8.7523  -0.5876  -4.2538   0.6357   7.1436
-#&gt;   15.0600   8.3653  -5.3016  -8.9727   1.9099  -4.7034 -14.8080   6.8321
-#&gt;    5.2180   1.6998   9.6705  -1.0297  -0.2300 -18.0583  -5.9676   2.8278
-#&gt;   -6.6438  -3.8793  10.7047   1.1690 -10.4911  -4.8902  -8.9453   2.6225
-#&gt;    2.6865  -7.0715  17.3035  -1.0292   8.6419   1.5127  11.3743   4.6567
-#&gt;   -0.8823   1.9922  -1.6805   2.6441   7.0532  -3.5863   1.5426  -9.0900
-#&gt;    3.4192  22.3566 -13.5973  -0.9803  -0.6945 -13.5652  -6.6271  -8.6951
-#&gt;   -4.7106  -1.8860   4.7969  -3.3994  -0.0136  -1.0474   7.1799 -12.5076
-#&gt;   -3.5109  -6.8928 -10.2952 -27.2561  -4.6149   1.7142 -12.5523  -1.0221
-#&gt;   -6.7093  10.3142   8.2573   2.4934  -1.7516 -11.4688  -2.4966 -15.7521
-#&gt;    7.1038  -8.7811   1.0369  -5.1111  10.7257  -0.7682  -2.9512  -2.0453
-#&gt;  -11.9724  15.1011  -3.2683   5.3593  10.3720  -2.2955   6.3947   4.5287
-#&gt;   -6.6439   6.2459  -5.5014  -5.6457   0.1835  12.0601   2.1755  11.0305
-#&gt;    1.2823  -1.0151   0.4840   9.5057   6.6848  -7.5415   3.5770   1.3109
-#&gt;    7.9927  -9.9826   1.0853 -13.3779  -7.0886   3.5114  -5.8115  -1.8867
-#&gt;   -1.5721  -1.4694   5.1739  -5.5387  13.7887   3.5508  14.0987   8.0543
-#&gt;   11.2043   6.1202   1.7812  17.8911   9.0120   2.1963  15.4762  12.7197
-#&gt;    8.6884 -13.1554  -4.0781   6.2083  10.8189  -9.2288  -7.2779   3.9982
-#&gt;   -8.0184  16.5070   5.8675  -2.3072 -16.1820  18.5364  -5.8738 -16.4123
-#&gt;    3.2305  -0.1945   8.0007  15.5950   6.0886   3.3087  -5.3623   0.7048
-#&gt; 
-#&gt; Columns 41 to 48  -8.6006  -4.8646  27.5523  -1.5430 -15.4093   7.7829   7.3981  -0.3793
-#&gt;  -15.8642  -0.2972  -3.1149  -0.9991  -3.9445 -12.1184 -10.9670   6.3001
-#&gt;   -0.7089 -11.8555   2.6656   0.5943  16.2644  -5.9812 -12.9371  -1.6014
-#&gt;  -12.7581  -0.4289   6.1141  -8.4463  -2.0868  -6.8926  -2.6126  -9.0016
-#&gt;   -8.2506  21.2704  -4.0491  -2.2546  12.1547   0.1667  -8.4576  10.7930
-#&gt;   16.0594   4.1562  10.0196  -7.5173  -0.3256  16.0685  -0.1030 -16.9110
-#&gt;   -0.6021  12.6787   2.3527  -8.8386  -7.3754  13.4047  -8.9777  -5.7262
-#&gt;   -2.1249 -14.3050   5.0873   6.6320  -6.5322 -16.8134  -7.4323  -3.4885
-#&gt;   12.5125 -10.3117  18.7218   4.0142   2.5446   8.3031   0.4404   3.8229
-#&gt;   -0.8576   1.2573   0.1647  -5.6551  25.1561   2.0695 -17.7567 -22.7935
-#&gt;   17.9452 -11.9911   9.8603   7.5845  -2.0050   6.4060  -4.7783   0.6968
-#&gt;    3.0729  -2.5976  17.7593  -0.2200 -14.6376  11.7562  12.4318 -22.8368
-#&gt;   -3.3752  -8.7275  22.4086  10.6214  -6.4601  17.0018   1.2447  -9.9543
-#&gt;  -17.1848   7.0295 -20.1981  15.9027 -13.7001  14.6122 -12.5801   8.7190
-#&gt;    7.3749  -8.2294 -10.3922   5.2209  10.2326  -1.1034  -2.3768   5.0779
-#&gt;    6.5220   6.8331  -5.5146 -26.3487  10.0968   7.4076   4.2510  -0.1241
-#&gt;  -16.3551  23.9117 -12.9491  -6.2878  -8.0376  -8.9809 -11.6969 -16.2643
-#&gt;   -3.3320  -7.2563   7.0096  -5.1591 -10.8903  -8.3051  11.5121  -4.8205
-#&gt;   30.1463 -27.2953 -10.3255   1.2407  -3.8466 -11.4208  10.3090 -21.3064
-#&gt;   16.6340 -28.7060   7.5365  -0.2053  -9.2341  -6.5734 -10.7766  -2.7444
-#&gt;   11.6982  -2.6341 -11.3039  -7.5007   5.1457   7.6688  26.6237 -15.1442
-#&gt;   -9.7540  -8.5738 -14.3518  -5.7013  13.9404  -3.8985  -0.1138   8.8463
-#&gt;  -10.4308  11.0512  -5.5217 -22.5346   1.6473  -5.2179  -9.4511  -7.9082
-#&gt;   -2.4912   0.3161 -11.0292   6.5892   0.1307  -1.6834  -1.0987   1.6923
-#&gt;   -3.9673  -7.9401  22.3718  -2.3691  11.7177 -11.3505   6.1523   3.1161
-#&gt;    2.6399  13.7468  -0.3763  18.8045   8.5708   4.0262  -8.7736   4.2290
-#&gt;   -2.8576   7.1759  -8.0101  10.8422   6.3865   5.0936   1.2168   9.2354
-#&gt;  -10.8633  -1.0824  -0.8987  11.4758 -11.9743 -10.4646  13.7240  -1.7358
-#&gt;  -14.1178  21.2416   3.7955  24.8035   6.0983   0.3335   3.4862   4.4295
-#&gt;   11.0182   5.3442  -3.7926 -20.3325  23.6837  -3.2902  -8.2127  -9.5710
-#&gt;    1.1583   2.6508 -16.1883   2.1579 -12.3988  16.1296   1.6853   2.6362
-#&gt;   -4.6801  -1.2721   3.6902   8.4217  20.9060 -13.4412  18.0424  -1.5549
-#&gt;   -8.8554  17.1304  -6.1151   8.3124 -10.3070  -2.1585 -10.7280   1.6565
-#&gt; 
-#&gt; Columns 49 to 54  -6.7632   9.0205   3.9832   1.1877   3.4006   5.9775
-#&gt;   -8.4520  -5.9237   2.4574   0.5103  -1.2747  -0.2187
-#&gt;   -6.3469   0.8673  -5.0039  -2.8374  -1.5800   0.5130
-#&gt;  -15.8223 -10.4915  -2.9986  -0.9827  -1.1368  -2.5114
-#&gt;  -10.8001   7.0442   9.2466   8.6179   2.7842   0.3649
-#&gt;   20.2535  11.2160   3.6390  13.7110  13.4839   8.3034
-#&gt;    4.1736  -9.9043  -5.7932  -7.1145   1.6513  -1.3660
-#&gt;   -2.9477  -6.7830   2.2032   4.6185   4.4635   2.1922
-#&gt;  -19.0045   9.1668  -3.2519   5.0947  -1.5906   1.6095
-#&gt;   -5.0198  -7.6808 -18.1146   5.1944   4.6247   2.0399
-#&gt;    8.0772  10.3144  -3.3351   4.8508  -1.3147   1.5389
-#&gt;    6.1753  -1.1323   7.2598   9.2451  -4.0144   4.3835
-#&gt;    2.9787   0.5584   7.9027  -0.5738  -0.4446   3.5570
-#&gt;   -2.4050 -19.9975  -0.9384  -1.0172   1.1957  -3.5614
-#&gt;   16.0283  11.9335   1.4864  13.4882   0.2626   2.1828
-#&gt;    6.7011   1.7985  -3.5908   3.4675   0.3625   1.4105
-#&gt;   14.4403   6.5076   8.2722  12.7859   4.8887   5.4573
-#&gt;   -4.6987  -9.9025  -0.4740  -6.8387  -0.7232  -0.5840
-#&gt;   -0.4707   3.2551  -7.0753   1.6114  -1.6163  -0.1045
-#&gt;   -3.9348   7.0254  -7.9377  -5.2363   0.3976  -1.0753
-#&gt;   -5.7204   6.0980   1.9556  -4.8694   0.6084  -1.2481
-#&gt;   -8.0397   4.9181   1.6892  -0.0915   0.5730   0.9259
-#&gt;    5.1109  -1.9765   3.3636   4.5646   6.2376   3.4933
-#&gt;    6.6943 -21.3563  -4.1890  -4.2812  -1.6969  -2.0259
-#&gt;   15.2933 -11.0076  -7.0053  -1.5960  -3.4357  -2.7896
-#&gt;   -1.0028 -14.8088  -0.0199  -5.7920  -1.0041  -0.3028
-#&gt;   11.7314  -0.1637  -1.2221  -3.7700  -1.0029  -3.3862
-#&gt;   -2.0893   6.2374  18.0681  -0.3367  -1.2742  -0.1907
-#&gt;   -0.0101  -6.8336  -1.6090  -7.7200  -3.4319  -1.2397
-#&gt;    5.7481  -7.0355   4.3746   0.9063  -8.7526  -4.9835
-#&gt;    7.4789  -5.0154   6.5255   6.2043   1.7799   4.7899
-#&gt;   -6.0105   7.1342  -0.0061  -2.5677  -8.1663   1.6100
-#&gt;   -0.1814 -12.1102  -1.1473  -2.8114   7.7898  -4.2462
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  Columns 1 to 6 -6.4262e+00 -1.0302e+01 -8.0426e+00 -3.3373e-01  1.1466e+01 -1.5697e+01
-#&gt;  -1.1519e+00  2.0001e+00 -4.2894e+00 -7.6826e-01 -1.1227e+01  2.5261e+00
-#&gt;   1.6970e+00 -3.3100e+00 -4.4895e+00  1.3259e+00  1.8962e+01  8.1508e+00
-#&gt;   2.2075e+00  2.1765e+00  1.4384e+00  1.5917e+01 -1.1309e+01  7.0364e+00
-#&gt;   3.8093e+00 -3.8368e+00 -1.6455e+01  1.2298e+00 -1.0367e+01 -1.5784e+01
-#&gt;  -9.8932e-01 -5.0788e+00 -1.5136e+01 -9.8048e+00 -6.8520e+00  1.4605e+01
-#&gt;  -4.7910e+00 -5.1260e+00  1.6453e+00 -5.3460e+00  8.2288e+00  2.0704e+00
-#&gt;   6.3422e+00  9.8608e+00  5.8286e+00 -5.4466e+00  5.3061e+00 -8.3891e+00
-#&gt;  -4.0661e+00 -2.3066e+00  2.0202e+00 -1.3354e+00  8.2298e+00 -7.6327e-01
-#&gt;  -1.5477e+00  7.6396e-01  4.5311e+00  3.5211e+00 -1.4649e+01  7.8986e+00
-#&gt;   2.5916e+00  2.3080e+00  6.7754e+00 -1.1042e+01 -1.3366e+01 -3.8321e+00
-#&gt;   2.5285e+00  4.0808e+00  8.8407e+00 -6.2941e+00  1.3328e-02  3.6616e+00
-#&gt;   4.6916e+00 -5.0228e+00  7.1309e+00 -4.2475e+00  1.1867e+01 -8.2675e+00
-#&gt;   8.0642e+00  7.6726e+00  5.1226e-01  4.8608e+00  2.9767e+00  2.1985e+00
-#&gt;  -4.2219e+00  6.4403e+00  1.7694e+00  4.1442e+00  7.6349e-01 -1.0233e+01
-#&gt;  -9.3862e+00 -3.3176e+00 -1.1481e+01  2.5465e+00 -3.9686e+00  1.6527e+00
-#&gt;   8.8190e-01 -3.4220e+00  1.1836e+00 -2.9330e+00  1.0608e+01  1.6665e+01
-#&gt;  -1.4457e+01 -2.0386e+00 -7.8769e+00  9.1628e+00 -1.2645e+01  8.8112e+00
-#&gt;   3.3646e+00  2.8787e-01 -2.8648e+00  9.7906e+00 -4.8234e-01  2.3953e+01
-#&gt;   5.5200e+00  8.0760e+00  8.1474e+00  1.6808e+01  3.8783e+00 -2.1914e+00
-#&gt;  -2.4432e+00 -6.1003e+00 -3.4941e+00  9.1001e-01 -1.7641e+01 -9.1526e+00
-#&gt;   7.2679e+00  2.8391e+01 -5.7809e-01  6.8612e+00  1.4567e+01  3.5080e+00
-#&gt;   6.7239e+00  6.1337e+00  1.0413e+01  1.2382e+00  4.5004e+00  2.7951e+00
-#&gt;  -2.6557e+00 -6.5517e+00  2.6339e+00 -3.3769e+00  4.9165e+00  1.9107e+00
-#&gt;   4.2102e+00 -3.3543e+00 -1.2687e+01  2.5395e+00  2.5638e+00  1.9196e+01
-#&gt;  -3.8726e+00  2.0134e-01  4.1404e+00 -6.0846e+00  3.6685e+00 -6.0531e+00
-#&gt;   2.7909e-01  1.0917e+00 -7.4160e-01 -2.7103e-01  8.1912e-05 -3.0494e+00
-#&gt;   7.3136e+00  6.4701e+00  1.0309e+01  7.2169e+00  2.8521e+00 -2.0348e+00
-#&gt;  -2.8647e+00 -3.3199e+00 -1.1787e+01 -2.0081e+00 -4.9858e+00 -7.1792e+00
-#&gt;  -9.1410e+00 -1.2388e+01  3.4625e+00  1.7626e+01 -1.7789e+01  2.6632e+00
-#&gt;   5.0764e+00  6.8630e+00  8.2448e+00 -1.5367e+01 -6.3745e+00 -8.8637e+00
-#&gt;   3.8776e+00 -6.5466e+00 -1.8389e+01 -6.6696e+00  5.8418e+00 -6.7904e+00
-#&gt;  -3.2302e+00 -8.0956e+00 -5.7334e+00  1.4126e+00 -2.7921e+00 -3.9389e+00
-#&gt; 
-#&gt; Columns 7 to 12 -4.0465e+00  8.8923e-01 -1.4303e+01 -9.4046e+00  6.4015e+00  1.5997e+01
-#&gt;  -2.9243e+00  1.7137e+00  4.3121e+00 -7.3565e+00 -1.0214e+01 -1.2201e+01
-#&gt;   7.1019e+00  5.0987e+00  1.9010e+00 -2.4699e+00 -3.0563e+00  2.9805e+00
-#&gt;  -8.1475e-01  8.9229e-01  1.4172e+00 -3.4651e+00  5.2722e+00  1.2982e+00
-#&gt;  -5.4192e+00 -9.6536e+00  6.7028e+00 -2.9846e+00  3.8598e+00 -8.2039e+00
-#&gt;   1.7103e+01  4.1835e+00 -1.9952e+00  5.5272e+00 -1.5506e+00 -9.4011e+00
-#&gt;   2.2505e+00 -1.9357e+01 -2.2002e+00  2.4192e+00  1.0321e+01  1.7182e+01
-#&gt;  -1.0851e+01  9.3675e+00 -1.4618e+00 -1.9284e+00 -3.1685e+00  9.2756e+00
-#&gt;   1.1996e+01 -8.0681e+00  3.1487e+00  4.6738e+00  7.7214e-01  9.2540e+00
-#&gt;  -2.0899e+01 -2.4664e+00 -1.0292e+01 -2.3162e+00  4.3699e-01  3.0183e+00
-#&gt;  -7.5234e+00  4.0622e+00  2.8611e+00 -5.0612e-02 -2.7615e+00  1.0727e+01
-#&gt;  -6.7269e+00  1.7070e+01  1.3479e+00  2.8591e+00  1.3784e+00  1.5057e+00
-#&gt;  -2.5754e+00  1.4918e+00 -1.6116e+00  9.1770e+00 -2.8859e+00  6.1093e+00
-#&gt;   4.5459e+00 -6.6762e+00  1.0926e+01 -8.5546e+00  5.9326e-01 -4.5555e+00
-#&gt;   2.1496e+00 -6.6026e+00  2.0679e+00 -2.2635e-01  1.2354e+01  8.0611e+00
-#&gt;  -1.0398e+00  2.4987e+00 -6.6752e+00  1.6018e-02 -5.0908e+00 -7.8365e-01
-#&gt;   2.1959e+00  6.0708e+00  4.3137e+00  2.1468e+00 -6.7604e+00 -2.5864e+00
-#&gt;  -7.0599e+00  5.5481e+00 -4.1306e+00 -1.1196e+01  7.3052e-02  2.0858e+00
-#&gt;  -1.9106e+01 -4.3791e+00 -1.5648e+01 -3.9139e+00  4.2754e+00  1.4006e+01
-#&gt;  -1.2236e-01  1.2929e+01  1.3368e+01 -7.0120e-01  8.7745e+00 -6.1214e+00
-#&gt;  -1.8933e+01 -2.1145e-01 -5.3975e+00 -3.9796e+00 -9.0409e+00 -8.7610e+00
-#&gt;   7.7373e+00 -2.1278e+00  3.6165e+00  7.6281e+00  4.9305e+00  7.7253e+00
-#&gt;   8.2651e+00  1.7974e+01  1.0775e+01 -1.3353e+00  8.9461e+00 -3.0251e+00
-#&gt;   4.0353e+00 -5.0770e+00  7.8504e-01  8.8365e+00 -3.8695e+00  1.3443e+01
-#&gt;  -7.6197e+00  1.1457e+01 -8.3000e+00 -1.5143e+01 -4.2876e+00 -8.1806e-01
-#&gt;   3.5782e+00 -2.8537e+00  9.9314e+00  2.0893e+00 -3.4795e+00 -1.2507e+01
-#&gt;   6.0520e+00 -1.2071e+01  9.9488e-01  2.2217e+00  5.7808e+00 -1.6132e+01
-#&gt;   1.4790e+01  1.4483e+00 -1.9808e+00 -3.8774e-01 -8.9233e+00  4.7249e+00
-#&gt;   1.2036e+01  2.3744e+00  3.1593e+00  1.5919e+00  5.9775e+00  2.4469e+00
-#&gt;  -1.0775e+01 -9.3839e+00 -1.6414e+01  5.2102e+00  1.5185e+01  5.9463e+00
-#&gt;  -3.5521e+00 -7.9817e+00 -2.9207e+00  5.7468e+00 -1.1332e+01  8.4551e+00
-#&gt;  -1.0195e+01  1.2005e+01 -3.6723e+00  1.1780e+01 -1.6271e+00  7.6696e+00
-#&gt;   2.1713e+01  5.2783e+00  9.3111e+00  8.0497e+00 -1.6087e+00  4.5775e+00
-#&gt; 
-#&gt; Columns 13 to 18 -1.8652e+00 -1.6296e+01  1.8492e+01 -8.9194e+00 -2.5883e+01  1.1869e+00
-#&gt;  -6.7993e+00  5.6378e+00  1.4581e+01 -4.0838e+00  6.1944e+00 -2.9574e+00
-#&gt;   2.2431e+01 -2.6491e+00 -1.2315e+01 -4.1996e+00 -2.0293e+00  9.9904e+00
-#&gt;  -1.8455e+00  3.2875e-01 -6.2301e+00  1.0648e+01 -3.1354e+00  4.8623e+00
-#&gt;  -2.4562e+00 -4.0854e+00 -1.4479e+01 -4.5397e+00 -1.3717e+01 -3.2325e-01
-#&gt;   1.6040e+01  9.0584e+00 -8.3440e+00  3.6967e+00  9.8770e+00  2.2980e+00
-#&gt;  -1.2911e+01  1.2589e+01  6.9104e+00  3.5571e+00 -5.1544e+00 -7.4146e+00
-#&gt;   6.1413e+00 -1.8190e-01 -3.3456e-01 -2.6882e+01 -1.4690e+01 -1.1079e+01
-#&gt;   1.1708e+01 -9.3690e+00 -1.1440e+01  9.3169e+00  7.7772e+00 -1.8925e+01
-#&gt;   1.5197e+01  1.3778e+01 -5.0570e+00 -9.0739e-01 -6.1582e+00 -2.7271e+00
-#&gt;  -2.1772e+00 -1.6658e+01 -6.2780e-01  5.7757e+00 -1.0697e+01  2.5060e+00
-#&gt;  -1.1280e+01  1.2305e+01  5.9221e+00 -3.2115e+00 -7.5718e+00  8.3008e+00
-#&gt;  -1.5762e+01 -2.2615e+00  1.2355e+01 -1.1957e+01 -9.3039e+00  6.8012e+00
-#&gt;  -8.7712e+00  8.6509e+00  7.4090e-01  9.2350e+00  1.9766e+00 -1.6338e+01
-#&gt;   1.4290e+01 -3.2918e+00 -1.5809e+01  1.0007e+00  6.8876e+00 -3.0997e+00
-#&gt;   1.4291e+01  1.4412e+01 -8.0414e+00  9.9127e-02 -4.8554e+00  3.1393e+00
-#&gt;  -9.8472e+00  9.9958e-01  2.5397e-01 -4.2138e+00 -1.1304e+00 -1.6261e-01
-#&gt;  -1.1017e+01 -4.3670e+00  1.7037e+01  8.7364e+00  1.3894e-01  7.6550e+00
-#&gt;   3.4132e+00 -3.2857e+01  2.2227e+00  5.0719e+00  2.8524e+00 -1.2048e+01
-#&gt;   1.4289e+01  4.6767e+00 -1.4386e+01 -8.2073e+00 -1.0569e+01 -1.3552e+01
-#&gt;   3.5160e+00  1.1464e+01  7.5946e+00 -5.3213e+00 -1.9107e+01 -1.2373e+01
-#&gt;   3.4990e+00 -7.3915e+00 -1.1307e+01 -6.4806e+00 -5.4687e+00 -2.5775e+00
-#&gt;  -2.8884e+00  2.8935e+01 -6.5814e+00  1.8260e+00 -8.2682e+00 -1.2801e+01
-#&gt;   1.1532e+00  3.3882e+00 -1.7850e+01  2.8981e+00  1.5333e+01 -9.5075e-01
-#&gt;   5.5637e+00  1.7700e+01  7.4352e+00  8.8628e+00  7.4742e+00  2.0415e+01
-#&gt;   2.9779e+00  2.2275e+01 -1.2943e+00  6.2881e+00  2.5515e+01 -1.5408e+01
-#&gt;  -1.9378e+00  5.2831e+00  2.9522e+00  1.0344e+01  4.0745e+00 -1.2546e+01
-#&gt;   1.8626e+00 -2.3987e+01  4.6421e-01 -5.2962e+00 -1.8111e+00  4.6379e+00
-#&gt;  -8.5860e+00  9.5225e+00  9.0563e+00 -7.0315e+00  4.4661e+00  8.3436e+00
-#&gt;   9.7812e+00  5.3378e+00  1.3720e+01  1.2092e+01  1.2569e+01  6.4263e+00
-#&gt;   6.8753e+00 -2.3651e-01 -7.5481e+00  5.3557e-01  2.3002e+00 -2.2790e+01
-#&gt;   2.4410e-01 -1.2898e+01  1.6192e+01 -1.8877e+00  3.8370e+00  6.8946e+00
-#&gt;  -5.2318e-01 -5.6432e+00 -6.6090e+00  4.1998e-01  5.3408e+00  1.3252e+01
-#&gt; 
-#&gt; Columns 19 to 24 -1.4445e+01 -7.1449e+00 -1.5830e+01 -8.5493e+00 -1.3246e+01 -1.0818e+01
-#&gt;  -5.8257e+00  2.5380e+00  4.0747e+00  3.3717e+00  1.2595e+01 -3.3998e+00
-#&gt;  -7.3894e+00 -4.2604e+00 -1.2218e+01 -4.3442e+00  9.4085e+00 -2.5070e+00
-#&gt;  -4.1200e+00 -3.6416e+00  9.7555e+00  2.0301e+01  4.9697e+00 -1.1485e+01
-#&gt;  -3.8639e+00  3.4220e+00 -1.6872e+01  3.9867e+00  2.3141e+01  1.2290e+01
-#&gt;   1.7509e+01  1.1767e+01 -8.2266e-01 -3.9060e+00 -1.0922e+01 -1.0966e+01
-#&gt;   7.7470e+00 -1.4266e+01  3.9405e+00 -6.0676e+00  1.2489e+00  8.5741e+00
-#&gt;  -1.4520e+00 -7.8036e+00 -8.0052e+00  4.7120e+00  1.9998e+00  9.7785e+00
-#&gt;   7.1853e+00 -5.4099e+00 -1.1661e+00 -9.6124e-01 -3.5060e+00  2.8660e+00
-#&gt;  -4.3456e+00  1.4829e+01  1.3369e+01 -6.0624e+00 -6.6249e+00 -4.8913e+00
-#&gt;   6.3133e+00 -5.3194e-01 -1.5952e+00 -2.1070e+00  2.6445e+00  5.3696e+00
-#&gt;  -1.2118e+01  5.5716e+00  4.6971e-01 -4.8059e+00 -5.0967e+00 -9.4180e+00
-#&gt;   1.5625e+01 -2.5577e+00 -1.1879e+01 -4.4374e-01 -8.2021e+00  2.3238e+00
-#&gt;  -4.9951e+00  1.4574e+01  1.0015e+01  6.7822e+00  1.8982e+00 -9.4548e+00
-#&gt;  -1.3176e+01 -7.8126e+00 -1.8460e+00 -1.2364e+00  8.4664e-01 -1.9703e+01
-#&gt;   1.4479e+00  1.4478e+01  5.8062e+00  9.4571e-01  8.8584e+00 -5.1504e+00
-#&gt;   1.1455e+00 -6.0675e+00  9.1297e+00 -1.1173e+01 -4.6109e+00  5.5358e-01
-#&gt;  -3.5382e+00  9.8266e+00  1.4570e+01  7.3645e+00 -3.1711e+00  8.0725e+00
-#&gt;  -1.3910e+01 -1.2044e+01 -1.6904e+01 -6.0675e+00 -4.3612e+00 -8.3484e+00
-#&gt;  -4.3631e+00  5.4232e+00  3.4341e+00  2.0606e+00 -5.7591e+00 -3.5410e+00
-#&gt;  -8.8304e+00  2.7338e+00  5.3586e+00 -4.3458e+00  8.0168e+00  4.1562e+00
-#&gt;   3.4144e+00  1.1629e+01 -4.1753e+00 -2.2655e+00  2.4787e-01 -1.4080e+01
-#&gt;   1.0627e+01  7.8785e+00  1.1896e+01 -7.2031e+00 -4.6135e+00 -2.0067e+00
-#&gt;   1.1385e+01  1.1738e+01 -1.7749e+01  1.7336e+01  8.0784e+00  7.6059e+00
-#&gt;   1.2071e+01  9.1543e+00 -9.2724e+00 -3.8312e+00  7.5362e-03 -8.5426e+00
-#&gt;   1.0760e+01  6.5716e+00  5.0156e+00 -6.8049e+00 -1.5221e+00 -1.8318e+00
-#&gt;   5.8628e+00 -5.8969e+00 -2.5412e+00 -7.0412e+00 -7.1329e+00 -4.1548e+00
-#&gt;  -5.9491e+00 -4.2658e+00 -1.1386e+01  4.8017e+00 -4.1747e+00 -1.5459e+00
-#&gt;   6.2477e+00  3.3169e+00  1.0762e+01  8.2661e+00 -4.6610e+00  1.3204e+01
-#&gt;   1.6609e+00  8.2084e-01 -2.4961e+00 -1.3010e+00  5.8499e+00  1.2042e+00
-#&gt;  -1.8975e+01  9.0164e+00  6.3102e+00  1.0104e+01 -4.1855e+00 -2.9264e+00
-#&gt;   1.7456e+01  2.2383e+00 -7.5770e+00  1.1101e+01 -7.0678e+00 -7.7324e+00
-#&gt;   1.2254e+01  1.9148e+01 -9.5767e+00  1.3521e+01  5.4999e+00  1.3138e+01
-#&gt; 
-#&gt; Columns 25 to 30  4.7672e+00  3.9393e+00  5.8296e+00 -3.1557e+00  9.4979e+00 -2.1713e+01
-#&gt;  -6.2923e+00  1.0280e+01 -9.7095e+00  2.7831e+00  3.1785e-01  1.5338e+00
-#&gt;  -1.8244e+00  2.1574e+00  1.1629e+01  8.0770e+00  4.8884e-01  1.8520e+01
-#&gt;   1.5629e-02 -1.8364e+00  1.8026e+01  1.5302e+01 -2.8889e+00  1.3968e-01
-#&gt;   1.1179e+00 -9.3442e+00  2.7496e+00 -6.8790e+00 -4.7582e+00 -1.4035e+01
-#&gt;  -6.6229e-01  6.1338e-01 -6.1479e+00 -9.7994e+00 -1.1166e+01 -3.6618e+01
-#&gt;  -1.2571e+01  2.6986e+00  3.4589e+00  1.0057e+01  6.3649e+00  1.6529e+00
-#&gt;   1.3031e+01  4.0839e+00  2.0510e+00 -1.5870e+00 -9.3332e+00  9.5409e+00
-#&gt;   1.5391e+00 -2.6677e+00  9.4479e+00  1.2192e+01 -1.5544e+01  4.3714e-01
-#&gt;  -5.6149e+00  1.4890e+01  2.8167e+01  2.1890e+01  3.7060e+00 -2.3475e+00
-#&gt;   3.1602e+00 -6.4318e+00 -5.0051e-01 -7.0032e+00  2.8286e+00 -5.1985e+00
-#&gt;   2.1729e+01  4.4250e+00  5.0968e+00  6.9575e+00  1.7531e+00 -2.5253e+00
-#&gt;  -1.0086e+00 -2.7643e-01  4.9174e+00  3.8020e+00  1.1939e+01 -2.0344e+00
-#&gt;  -6.7084e+00 -3.3351e+00  7.7175e-01  2.1211e+01  2.6425e+01  6.7001e+00
-#&gt;   8.5745e+00 -6.3598e+00  4.2359e+00 -6.8517e-02 -8.5386e-01  5.5816e+00
-#&gt;  -8.9783e+00  4.1437e-01 -8.1550e+00 -8.5186e+00 -3.6995e-01 -4.0838e+00
-#&gt;  -9.3662e+00 -9.4380e+00 -1.6104e+01 -1.6740e+01 -1.4065e+01 -1.4101e+01
-#&gt;   4.0266e+00 -6.0926e+00 -2.1354e+00 -1.2275e+01 -3.8985e+00 -4.7642e+00
-#&gt;   7.2370e-01 -8.8531e+00  3.0463e+00  1.1427e+01 -4.3989e-01  3.9344e+00
-#&gt;   1.6464e+01  1.1835e+01  9.4309e+00  1.2652e+01  4.9765e+00  2.0312e+01
-#&gt;  -5.0763e+00 -5.5975e+00 -5.2981e-01 -9.1223e+00  7.8726e+00 -1.1486e+01
-#&gt;  -5.5547e+00  4.3114e+00  1.8073e+00  1.1570e+01  9.9499e+00  6.8600e+00
-#&gt;  -6.6898e-01 -3.5121e+00  1.2643e+00  3.2227e+00 -3.4610e+00  1.8418e+01
-#&gt;   4.0002e+00  1.0894e+00  8.4338e+00 -5.1922e+00  3.8902e+00 -6.6107e+00
-#&gt;   3.9277e+00 -6.1210e+00 -1.3713e+01 -1.8275e+00 -3.6425e+00  1.0173e+01
-#&gt;  -3.0410e+00  1.9244e+01  1.1407e+01  2.2155e+01  3.5196e+00  1.0021e+01
-#&gt;  -1.0214e+01 -3.6439e+00  5.5281e+00 -1.3027e+01  1.2913e+01 -8.8776e-01
-#&gt;  -1.2979e+00 -2.1701e+01 -3.9833e+00 -1.3435e+01  1.7802e+01  5.0667e+00
-#&gt;  -8.7162e+00 -4.8520e+00  4.9603e+00 -6.5037e+00 -7.7042e+00 -2.6138e+00
-#&gt;   2.7359e+00 -1.6787e+00  1.8766e+01 -6.5021e+00 -6.3636e+00 -1.6554e+01
-#&gt;  -2.1235e+00  3.6546e+00  2.7977e+00  2.9845e+00  4.1591e-01  1.8891e+00
-#&gt;  -1.6986e+01  5.1112e-01 -5.8523e+00 -8.3724e+00  1.6497e+01  1.3320e+00
-#&gt;   3.8910e+00  1.3113e+00 -4.9165e+00 -1.6934e+01  1.6983e+00 -9.6708e+00
-#&gt; 
-#&gt; Columns 31 to 36 -2.1679e+01 -2.4109e+01  8.4593e+00  7.2496e+00 -7.6315e+00 -2.8294e+00
-#&gt;   9.3554e+00 -1.6916e+01 -9.3243e+00 -4.0114e+00 -7.3527e+00  4.0655e-01
-#&gt;   1.4200e+01  1.5864e+01 -5.9292e+00  1.3862e+01  2.3485e+00  2.4782e+00
-#&gt;  -1.8995e+00  2.3539e+01 -2.6220e-02  1.8460e+01 -1.2601e+00  1.1129e+01
-#&gt;  -1.4494e+01 -9.6573e-01 -5.0733e+00 -1.0772e+01 -2.2918e+01 -3.4051e+00
-#&gt;  -2.4427e+01  5.6512e-02 -2.4875e+00 -1.4292e+01 -5.5592e+00 -1.3762e+01
-#&gt;  -1.4701e+00  8.7661e+00  3.7526e+00  3.6377e+00 -1.2703e+01 -9.5928e+00
-#&gt;  -1.0701e+01 -1.4778e+01 -3.5459e+00 -1.4791e+01 -1.0391e+01  1.0136e+00
-#&gt;  -9.1675e+00 -1.6266e-01  1.1090e+01  1.5069e+01  1.9667e+01 -7.0169e+00
-#&gt;   2.6015e+01 -2.2708e+00 -1.2905e+01  1.6435e+00 -2.3520e+00 -6.1251e+00
-#&gt;  -1.7792e+00 -2.3453e+01 -1.9723e+00 -4.5929e+00 -1.6097e+01 -4.4947e+00
-#&gt;  -5.4744e+00 -6.4192e-01  1.6510e+01 -2.6668e+00  2.4133e+00 -2.6066e+00
-#&gt;  -1.9293e+00  1.5536e+01  1.1604e+01 -1.0907e+01 -1.0784e+01  1.3530e+01
-#&gt;   1.2010e+01 -9.5282e+00 -7.4836e+00  8.5354e+00  1.5636e+01  6.4028e-01
-#&gt;  -8.9697e-01  1.3402e+01 -8.8782e+00 -7.5711e+00  1.0922e+01 -1.6372e+00
-#&gt;  -8.5683e+00  7.1512e+00 -3.3600e-01 -8.1503e+00 -1.5620e+01  2.9184e-01
-#&gt;  -7.3744e+00 -9.9613e+00  3.9222e+00 -3.9316e+00 -4.0211e+00 -1.2877e+01
-#&gt;   3.4622e+00 -2.0458e+00  1.0160e+01  1.9177e+01 -1.0452e+01 -7.6392e+00
-#&gt;  -1.7070e+00 -1.3017e+01 -3.2206e+00  4.0881e+00 -4.6195e+00  1.0797e+00
-#&gt;   1.7468e+01  9.2982e+00 -1.2351e+01  2.2376e+00 -3.9956e+00  1.3261e+01
-#&gt;  -4.4173e+00 -1.4719e+01 -9.7518e+00 -1.7765e+01  1.4858e+00 -3.2349e+00
-#&gt;   3.7165e+00  5.9601e-01  1.4029e+01 -1.0961e+01  8.7351e+00  7.7419e+00
-#&gt;  -6.1524e+00  1.3197e+01  3.9808e+00  4.3193e+00 -1.2020e+00 -1.2825e+01
-#&gt;  -5.0571e-01 -1.0672e+01  1.8364e+01  8.0442e+00  1.3081e+01  8.4952e+00
-#&gt;   4.4564e+00  5.5969e+00  5.6686e+00 -1.1134e+00 -7.3456e+00  7.7669e+00
-#&gt;   1.3500e+01  4.4614e+00  4.9841e+00  8.9211e+00  2.0098e+01  3.8844e+00
-#&gt;   3.6128e+00  1.1122e+01 -4.4044e-01 -3.6093e+00  1.3210e+01  8.5443e+00
-#&gt;  -1.8090e-01 -1.0985e+01 -4.1344e+00 -5.0467e+00 -1.5144e+01 -1.9789e+00
-#&gt;  -1.3420e+01 -7.0009e+00  8.1848e-01 -5.0013e+00  1.8268e+01  8.2374e+00
-#&gt;   6.3996e+00  1.8506e+01 -1.1341e+01  7.0303e+00 -1.0490e+01 -4.4531e+00
-#&gt;   5.5464e+00 -1.7529e+01 -7.0866e+00 -8.8303e-01 -5.3377e+00 -7.3746e+00
-#&gt;  -8.8998e+00  1.7904e+00  4.3107e+00 -1.4987e+01 -1.3366e+00  5.2522e+00
-#&gt;   8.7654e+00 -7.8483e-01 -7.9464e+00  1.0005e+01 -1.1502e+01 -2.4911e+00
-#&gt; 
-#&gt; Columns 37 to 42 -1.2982e+01 -4.2126e+00 -3.8256e+00  3.6204e+00  3.1385e+00 -3.8542e+00
-#&gt;  -7.1096e-01 -1.1357e+01  1.9772e+01  3.5039e+00  4.9852e+00  1.4891e+01
-#&gt;   1.5609e+00 -2.5245e+00 -8.8441e+00  4.7378e+00  3.7088e+00  8.7780e+00
-#&gt;   7.1751e+00 -5.4980e+00 -1.6342e+01  8.2999e+00  2.4324e+00 -7.6220e+00
-#&gt;   1.0249e+01  1.2807e+01 -6.5801e+00 -5.8649e+00 -4.9927e+00  2.6560e+00
-#&gt;  -2.1048e+01  6.4206e+00  1.0280e+01  5.0778e+00  1.0445e+01  4.2637e+00
-#&gt;   1.2026e+01 -6.7137e+00 -8.5624e+00  2.7173e+00  3.2165e+00 -3.5499e+00
-#&gt;  -2.1791e+00 -2.1789e+00  4.3917e+00 -1.3265e+01  5.4625e+00  1.8335e+01
-#&gt;   7.3245e+00 -5.1060e+00  7.6107e-01  6.8573e+00  1.4891e+01 -4.1370e+00
-#&gt;   1.1617e+00  9.7119e+00 -6.5697e+00  3.8368e-01 -1.1239e+00 -7.7994e+00
-#&gt;   4.0031e+00 -3.4288e+00 -1.2127e+00 -7.2412e+00  2.3004e-01  8.5065e-01
-#&gt;   5.2983e+00  5.3690e+00  5.7870e+00 -2.9230e+00  4.3193e+00  4.7594e+00
-#&gt;   1.4966e+01 -4.6703e+00 -2.1251e-01 -1.6869e+01 -6.9216e+00  9.3986e+00
-#&gt;   1.6732e+00 -2.7369e+00  5.2217e+00 -2.9938e+00  5.3384e-01 -3.6932e+00
-#&gt;   6.5449e+00  4.4988e-02 -8.0596e+00 -1.0725e+01  9.6437e+00 -1.2317e+01
-#&gt;   1.0285e+00  8.7999e+00  1.4183e+01  1.0116e+01  9.0127e-01 -5.8294e+00
-#&gt;   4.4251e-01  1.2525e-01 -8.3928e-01  8.1308e+00  1.8535e+00 -1.1609e+01
-#&gt;  -1.7475e+01 -2.8897e-01  6.4601e+00  9.6699e+00  2.5221e-01 -1.0648e+01
-#&gt;   3.5728e+00 -6.6077e+00 -1.7886e+01  4.0742e+00  1.7618e+00 -3.7558e+00
-#&gt;  -2.0264e+00 -2.7089e-01  4.3377e+00 -5.0581e+00  2.4265e-01  8.6139e+00
-#&gt;  -9.2931e-01  6.4229e+00  8.4352e+00  6.6326e-01  1.1871e+00  1.4929e+00
-#&gt;   6.7805e+00 -1.0345e+00  1.5609e+01 -1.3094e+01 -1.1205e+01  5.9305e+00
-#&gt;   3.6864e+00  1.0229e+01  1.1679e+01  4.0239e+00 -4.9793e+00 -2.2810e+01
-#&gt;   1.8718e+00 -2.4655e+00 -4.8008e+00  9.0004e+00 -3.6858e+00  6.8290e+00
-#&gt;  -5.7660e+00  2.6562e+01 -6.6253e+00  5.0441e+00 -3.5265e+00 -3.7034e+00
-#&gt;  -1.9097e-01 -7.5070e+00 -2.1687e+00 -1.3804e+01 -1.7359e+00  1.9275e+00
-#&gt;  -7.2095e+00 -9.9872e+00  3.7455e+00  4.0918e+00 -9.8750e+00  3.0196e-01
-#&gt;   5.1022e+00  1.0027e+01 -1.4910e+00  1.2692e+01 -6.9118e+00  7.6790e+00
-#&gt;   1.3758e+00 -7.4730e+00 -1.0146e+01  1.7413e+00 -2.0952e+00 -3.0648e+00
-#&gt;   3.3219e+00  4.3010e-02 -1.1769e+01 -5.4523e+00 -8.0371e+00 -1.5348e+01
-#&gt;  -4.3930e-01 -1.0443e+01  8.5679e+00  1.2639e+00 -1.0131e+01 -7.0613e+00
-#&gt;   1.1237e+01 -1.5008e+01 -1.1160e+01 -1.6202e+00 -1.6306e+01 -3.6980e+00
-#&gt;  -4.0589e+00 -8.3484e-06  2.1629e+00  1.3816e+01 -3.1079e+00  4.6911e+00
-#&gt; 
-#&gt; Columns 43 to 48  9.5938e+00  9.8600e+00  9.7126e+00  1.8094e+01 -1.2962e+01  4.5320e+00
-#&gt;   8.8597e+00 -3.8651e+00  3.1434e+00 -1.8626e-01 -5.8093e+00  1.1049e+01
-#&gt;  -2.7338e+00 -1.1493e+01  7.7413e+00 -7.4347e+00 -2.8650e+00 -4.0432e+00
-#&gt;  -5.0291e+00 -3.8119e+00  7.6550e+00  4.3292e+00 -3.0579e-01 -3.6069e+00
-#&gt;  -5.2778e-01 -7.3304e+00  5.8848e+00 -1.2196e+01  7.4105e+00 -1.9333e+00
-#&gt;   2.8526e+00  1.9088e+01  4.3373e+00  1.6240e+01 -1.1476e+01 -6.8856e+00
-#&gt;   1.4744e+00  1.0112e+01  8.1093e+00 -3.6238e+00  1.1439e+00 -7.5707e+00
-#&gt;  -5.3667e+00 -7.7414e+00 -1.0272e+01  6.6667e-01  1.0756e+01  7.8223e+00
-#&gt;  -1.2314e+00  1.2076e+01 -2.8305e+00  7.6454e+00 -1.7534e+01  7.1417e-01
-#&gt;   7.0782e-01  2.2438e+00  1.0972e+01 -2.3305e+00  3.3615e+00  8.5989e+00
-#&gt;  -2.4608e+00  1.9173e+00  4.5344e+00 -9.2493e+00  9.1561e+00  3.1954e+00
-#&gt;   5.0468e+00  7.4412e+00  9.2711e+00 -8.5012e+00 -9.2720e+00  4.4542e-01
-#&gt;  -6.5111e+00  6.7097e+00 -4.1257e+00  2.3944e+00  2.2047e+01  7.6683e+00
-#&gt;   6.7561e+00  7.3840e+00  7.0542e+00 -1.7891e+00 -1.0972e-01 -1.3271e+01
-#&gt;  -7.1963e-01  1.1568e+01 -4.8449e+00  2.3278e+00 -1.7470e+01 -4.0480e+00
-#&gt;   4.8309e-01 -5.2807e+00  1.1232e+01  6.8060e+00 -2.8304e+00 -1.1613e+00
-#&gt;   3.3958e+00  5.9262e+00  1.4133e+01  1.1143e+01  3.2042e+00 -6.2356e+00
-#&gt;   7.5310e+00 -2.0502e+00 -2.2026e+00 -8.8068e+00 -4.4770e+00  2.5733e+00
-#&gt;   6.1012e+00  4.0045e+00  3.6754e+00  3.0534e+00 -8.3425e+00  1.6813e-01
-#&gt;  -1.0076e+01  1.3927e+00 -2.6783e+00 -9.6086e+00  3.5074e+00  1.0511e+01
-#&gt;   1.0308e+00 -4.1848e+00 -5.3140e-01  4.9027e-01 -1.4362e+00  1.7054e+00
-#&gt;   9.5634e+00  2.1233e+00 -2.3149e+00 -2.8003e+00 -1.4120e+01  1.0925e+01
-#&gt;  -8.9510e+00 -2.0107e+00  1.3626e+01  7.5999e+00 -1.0009e+01 -8.6540e+00
-#&gt;   5.8128e+00  4.3800e+00  5.1025e+00 -3.1124e-01 -9.5418e+00 -8.0206e-01
-#&gt;   4.5954e-01 -1.9820e+01 -1.0856e+01 -2.4205e+00  3.2851e+00  3.5446e+00
-#&gt;   7.4073e+00  1.0443e+01 -9.5225e+00  9.4974e+00 -3.1614e+00  8.7247e+00
-#&gt;   6.9447e+00 -3.9645e-01 -7.9139e+00  6.0300e+00  8.5722e+00 -1.7283e+00
-#&gt;  -1.0806e+01 -1.5662e+01  2.5612e+00  4.0088e+00  5.8340e+00  7.7349e+00
-#&gt;  -4.5984e+00 -2.4696e+00 -5.8806e+00  3.0326e+00  1.4204e+01 -2.3575e+00
-#&gt;   2.8538e+00 -1.9296e+00  3.6106e+00 -2.7019e+01 -9.1201e+00  1.2406e+01
-#&gt;  -1.4059e+01  6.0087e+00  7.9251e+00 -2.9546e+00 -1.3986e+00  1.0299e+00
-#&gt;  -6.0447e+00 -2.5707e+00  3.1745e+00  9.1589e+00  4.0167e+00  9.2205e-01
-#&gt;  -3.8542e+00 -1.4597e+01  1.0325e+01 -2.1651e+00  9.4333e+00 -1.0715e+01
-#&gt; 
-#&gt; Columns 49 to 54 -2.8126e+00  6.2290e-01  1.2094e+01  5.7506e+00  6.0703e+00  1.8163e-01
-#&gt;  -6.2051e+00  1.0590e+01 -1.1221e+01  4.7619e+00 -6.3597e-02  5.8108e+00
-#&gt;   7.0618e+00 -3.9845e+00  1.2555e+00  5.8426e+00  9.2383e+00  8.9532e-01
-#&gt;  -1.9856e+01 -6.1150e+00 -5.9260e+00  2.8530e+00  9.8016e-01 -2.5336e+00
-#&gt;   1.4864e+01 -6.8976e-01  1.2750e+00  2.0616e+00  3.1718e+00 -6.2053e-01
-#&gt;   4.0523e+00  1.1325e+01  1.2280e+01  3.6007e+00  3.4541e+00 -3.6653e+00
-#&gt;   8.3939e+00  1.1663e+01  1.1109e+01  8.8074e-01  5.1258e+00 -6.6330e+00
-#&gt;  -2.4700e-01 -5.0268e+00 -2.8844e+00 -7.5633e+00 -1.9285e+00 -1.2111e+00
-#&gt;  -3.1770e+00 -1.8981e+00  3.9011e-01  7.7184e-01  5.0163e+00 -5.7405e+00
-#&gt;   6.6273e+00 -1.8221e-01  1.1584e+00 -2.7757e+00 -2.0168e+00  2.6052e+00
-#&gt;  -5.7066e+00 -5.4887e+00  2.3780e+00 -6.5638e-01  7.6786e-01 -7.7979e-01
-#&gt;  -5.6415e+00  6.8836e+00  2.1560e-01  7.0334e+00  1.0236e+00 -6.6481e+00
-#&gt;  -1.0836e+01  4.5798e-01  9.5420e+00  7.7963e+00 -3.3061e-01 -1.6403e+00
-#&gt;   4.2271e+00 -1.2098e+01 -1.3656e+01  2.4545e+00  1.2756e+00  2.7649e+00
-#&gt;  -2.0405e-01  4.1072e+00 -4.2119e+00  9.9203e+00  2.4864e+00 -4.2951e+00
-#&gt;   7.1166e-02  1.0848e+01  3.1254e+00  7.9914e+00 -3.9279e+00  3.5528e+00
-#&gt;  -5.9704e+00  1.2113e+00 -1.4374e+01 -3.6522e+00 -7.1488e-01 -3.2987e+00
-#&gt;  -9.3306e+00 -2.4419e+00 -2.1639e+00 -3.6770e+00 -6.2138e-01  5.5423e-01
-#&gt;   4.0496e+00 -1.0450e+01  3.8506e+00 -3.9680e+00 -7.0082e+00 -1.1381e+01
-#&gt;   5.7722e-01 -1.5393e+01  1.1042e+01 -1.5000e+00 -3.2943e+00 -7.7883e+00
-#&gt;  -1.3542e+01  7.2040e-01 -1.4342e+01 -1.2992e-02 -1.2799e+00  5.8970e+00
-#&gt;  -1.1502e+01  1.9461e+01 -8.2887e+00  5.0860e+00 -2.7199e+00  5.8708e+00
-#&gt;  -5.4900e+00 -1.0352e+01 -9.3241e+00  5.3902e+00 -1.1108e+01 -4.9735e+00
-#&gt;   1.1056e-01  5.0094e+00 -1.0799e+00  4.5442e+00 -6.3421e-01 -1.9181e+00
-#&gt;  -5.7869e+00 -1.0112e+01  8.2043e-01  4.2532e+00  4.8572e-01 -3.0658e+00
-#&gt;   1.2075e+01  5.5917e+00  6.5248e+00 -3.1185e+00 -3.3772e-01  3.6188e+00
-#&gt;   9.3177e+00 -2.4290e+00  6.1698e+00  3.9114e+00 -8.6027e-01  6.6250e+00
-#&gt;  -1.5134e+01 -1.6484e+01 -1.0285e+01  9.0815e-01  6.7053e+00 -6.5309e-01
-#&gt;  -8.6335e+00  3.7887e+00 -6.0815e-01  7.2834e+00 -4.3596e+00  1.6165e+00
-#&gt;   7.0482e+00  1.2581e+00 -2.9573e+00 -1.2959e+00 -5.8671e+00  1.1073e+00
-#&gt;  -1.2857e+01  1.1212e+01 -6.9868e+00  1.1672e-01 -4.6736e+00  7.4065e-01
-#&gt;   1.4155e+00 -7.0236e+00 -6.6755e+00  1.3262e+01 -4.7395e+00  4.9263e-01
-#&gt;   1.0220e+01 -1.6622e+01  5.0489e+00 -5.7797e+00 -5.4130e+00  2.3046e+00
-#&gt; 
-#&gt; (6,.,.) = 
-#&gt;  Columns 1 to 8  -1.2906  13.5946  10.1609   1.2739   2.2736   2.0222  -0.3510   3.4442
-#&gt;   -5.7275  -1.5648  -8.0162   0.4344   3.3434  -8.8662   5.8548  -2.1173
-#&gt;   -2.5602  -3.4971   7.1341  12.4275  -2.3825  -1.8784  -1.5559  -8.6028
-#&gt;   -5.2301  -2.8007   0.5347  11.6950 -20.4631  -3.5238  -4.4829   6.4217
-#&gt;    6.0463 -12.9998   3.9110   5.6952   9.0395   5.3502  -2.5195   1.1746
-#&gt;   -6.3904   2.9205   7.7085   5.1703   9.8267   7.5979   9.2738   5.0525
-#&gt;    5.5847  -6.8299  11.9292  -0.2180   1.3347  11.5043  -1.4268  -6.0069
-#&gt;    0.2869  10.9916  -0.4642   1.6550  10.1435   9.0110  -5.8498  -6.5407
-#&gt;   -5.0244  -0.8937   5.7387  -3.5939 -20.8283  -1.1995   2.6885   0.3491
-#&gt;    1.9330   0.2644   4.1328  18.1709 -11.5749   5.7500  23.1029   3.4215
-#&gt;    6.1167  -2.6856  -1.9285  -4.0633  15.5307   0.2766  -2.4825  -7.5930
-#&gt;   -1.4727  -6.1985   1.0367  -9.4084   5.9970  -5.9196   8.1893   2.3675
-#&gt;   -0.8975  -0.4740   2.3347   2.8178  11.5984  -0.6683  -8.1911   2.5873
-#&gt;    6.2782  -5.5500   4.2726  -1.2471   3.3512  -4.0309  -6.6749   0.4875
-#&gt;    6.7282   6.7253   4.8038   6.0504 -10.3696   4.3334  -2.6760  -1.5284
-#&gt;   -5.7392  -2.9991  -1.2506   5.1530  -5.3925   8.0087  -2.9595   2.1494
-#&gt;    3.1805  -3.1789   3.7345  -1.2337  14.6059  11.3571  10.6334  -5.8997
-#&gt;   -7.5094  -1.0523  -0.3500  -2.5702   0.7187  -4.1282   4.0303  -7.1820
-#&gt;    8.0910  11.2153  16.4467  -2.5688  -4.1481  -5.9578  -9.3378   5.4305
-#&gt;   -2.5413   9.1377  -0.1849   0.8289  -4.1381  -8.1889  -7.9123   7.7997
-#&gt;    6.4039   9.6086   6.6445   6.9689   9.6904  -8.8979   9.8943  -1.0717
-#&gt;   -2.0200  10.0184 -16.7365  -9.6430  -1.2828  -0.5102  -8.9521  -8.7141
-#&gt;    1.0829  -0.9323  -1.9788 -11.2347   1.2045   3.7669  10.7768   6.0730
-#&gt;   -6.5144  -1.7845  -3.0716 -19.3201  -3.5435   1.3338  -0.8777  -3.1286
-#&gt;   -6.1790   8.0965  -6.8826   2.2282   4.6184  12.5645   3.0924  -3.1263
-#&gt;   -4.5024  -0.9420   1.5130  -8.1597  12.1098   2.0007   5.8567  -2.0337
-#&gt;   -0.1062  -1.1714  -0.1613 -14.2301   2.1413  -3.3113   0.8350   2.4379
-#&gt;    8.3067   7.4910  11.0588   2.2626   5.9629  -6.2164  -7.1244  -0.4086
-#&gt;    3.5105   1.5456  -4.8592   7.5274   3.0848   3.7589  -1.5261   6.4598
-#&gt;    7.7199   1.7536   2.7952   3.6789  -5.1802   6.6892  14.5219  10.6791
-#&gt;    0.7732  -0.6465  -3.0562   6.5930  -9.0988   1.7741   1.0375  -0.2780
-#&gt;   -3.2239  -0.4057   0.9958  -8.9499   0.6236   0.7190 -18.1055   4.8374
-#&gt;    0.3086  -7.5484   5.2433  -0.9087   9.3846   2.9502   5.1732  -3.6332
-#&gt; 
-#&gt; Columns 9 to 16  14.4922  -9.0520   1.7655   4.2005  -0.9748   1.0361  -3.8319  -3.0438
-#&gt;    0.3332   6.1866  -1.8103   2.5202  -4.6763   7.3212  11.9548  13.8410
-#&gt;    2.7913  -6.1743   9.6053 -13.5915   9.1463  15.6517   1.2033  -8.0341
-#&gt;    6.7896  -3.6931  -1.0360 -15.0298   2.2860   7.6720   0.8738  -1.3508
-#&gt;    0.9471   3.8266  20.7929  -9.0604  18.7446   7.6388   0.2071  -9.4922
-#&gt;    6.1561   8.5326  -1.2114   5.5687  -5.6578   1.3177  13.0493  -9.0726
-#&gt;    1.6027   9.1719   5.6655  11.2977 -10.5055 -12.8792   2.7278   1.8168
-#&gt;  -15.2426   1.5844  -3.5033   7.4899   3.4438  18.5491   8.6613  -1.9612
-#&gt;    7.7457  -3.5828 -14.3712   6.5652 -15.7928  10.0078   8.9950 -14.0851
-#&gt;    6.0489   2.3172  -9.3602   2.7904  -3.2131  13.4063  19.7302   6.5122
-#&gt;   -7.6034   1.8109   8.9422   7.0092  16.0588  -1.2942   2.1396   7.1782
-#&gt;   -0.0329 -13.2314   5.2734  -9.0207  -6.3200  15.1783   5.7580  -5.6126
-#&gt;  -13.0665  -0.2777 -12.8806 -10.6230   9.8472   0.1550  -2.8208  -4.6425
-#&gt;    8.4266   7.6153  -4.0710  -7.4449 -12.7797 -12.6976  11.2124  -1.8019
-#&gt;    2.2260  -0.8911  -8.0302  -6.1254  -6.7894   3.7435  -2.2531 -15.0217
-#&gt;    4.3711   7.2751   3.4861  -9.9310  -9.6459   5.1806  -3.0555  -1.2542
-#&gt;    7.7591  -1.4780  10.0699   6.3439 -10.2799  -1.5439 -16.2412  -3.7900
-#&gt;   -6.9606   2.6252  -1.3341   3.2982   0.2523   4.2664   3.4766  -0.9335
-#&gt;   17.7835   2.1302  -7.5523   3.5047 -18.0717  -6.1698 -13.9270  -5.4293
-#&gt;  -11.9347  -2.7808 -11.9411 -15.3471  18.3825  10.5389  -3.8798   0.3755
-#&gt;    3.5219   5.9095   0.5010  -5.5723 -11.3272 -13.9500   0.7060   7.0516
-#&gt;    2.6564 -11.4641  -5.9555  -2.0962 -14.1869  13.4808   7.7111  -4.0473
-#&gt;  -11.7709  -7.2278   3.2929  -4.9161  -5.4113   6.6190   9.6433   6.6927
-#&gt;   -1.6251  -0.1537  -0.8085  -4.9274  -2.5047  -7.8605  14.4556  12.5811
-#&gt;   10.3315  -6.0728   8.8520   6.2369  16.2882   6.1643 -18.3501  -9.8453
-#&gt;    1.9906   3.0279 -15.3214   9.9443  -2.8701   1.5651   1.0282   9.6610
-#&gt;    3.4091   0.9268   2.7886  -4.3620   4.8404 -10.7781  -2.5085   7.5129
-#&gt;    0.1259 -11.2227  14.8560  -6.3446  18.5227  -0.2072  -1.8997 -10.7735
-#&gt;   -2.5384   0.8208   1.6124   2.8615  11.5942  -3.2394 -12.9641  12.4291
-#&gt;   -2.6944   2.3566  -2.2074  -2.1300   4.6018  -9.9700   1.1242  18.0932
-#&gt;   -2.4919   4.8262   3.4302  -6.9013  -7.1076  -2.1004   8.5142   1.9163
-#&gt;    5.3395  -0.6464 -13.5380  -5.0712   0.9941  10.4185 -17.7384  -5.8762
-#&gt;  -12.3580  -1.6421   9.2891   3.8499  21.0648  -9.4984  11.6074   4.4902
-#&gt; 
-#&gt; Columns 17 to 24   5.7558  -5.1015   3.7358   3.3919  10.1574   4.8178 -21.1594 -12.2321
-#&gt;   -3.0562   0.7140  10.4205  -6.7750  -5.5420   2.6335  11.1883 -11.4260
-#&gt;    3.0956  -0.0853   5.8817   8.6862  -4.5400   7.8507   6.7873  -6.8118
-#&gt;   13.3198  -0.1248  10.6121  -0.0760 -18.0245  11.9826   0.3320  14.2314
-#&gt;   -4.0194   2.2549   2.3223  -6.7249   4.2722 -14.4571   9.4587  11.3563
-#&gt;  -17.4306   4.3258 -21.1685   8.0124   7.3428  -5.9854 -11.9992 -12.7875
-#&gt;   15.3551  11.2782  11.0832  -2.1655  -4.6535  -4.5613  -4.1144   8.5404
-#&gt;    7.6964  16.3118   2.3128  -8.6316   0.7952   4.3366  -0.4963 -19.6819
-#&gt;    8.0289  -7.0709 -13.0450   5.5670  -1.3630  -7.3526 -19.8146  -2.7441
-#&gt;    8.7356   7.6161  20.4846   4.1788   1.2975  10.3680  22.0978   6.2228
-#&gt;    5.9167  -9.3964  -5.7256  -9.7319  -4.4493 -12.1559   1.1821  -4.4289
-#&gt;    9.2950   0.7511  -0.2363 -12.1582  11.4554 -10.0148   5.8216 -10.9486
-#&gt;    9.9000  16.4000  -0.7640   1.1786   4.3074  10.8577   0.4916  -6.8029
-#&gt;   10.3147   5.5437  -7.1659  12.2463   2.2565   0.3769  20.3979   8.4227
-#&gt;   -0.3663   2.1757   5.1778  -5.3807  -1.6984   2.3133   2.5216  -2.2583
-#&gt;   -0.9128 -14.7464  15.0432  -9.6708   0.6328  -7.2687   6.0182  12.2746
-#&gt;   -3.4113   5.8405  -1.8712   9.3081   5.1289  -6.4269 -10.4528   8.3959
-#&gt;    8.7077 -19.5687   9.4994  -7.7430  -1.2161  -5.4623  -9.8941   1.4983
-#&gt;   13.8759   4.1344   9.4766  11.2207   2.2961  -8.7867   5.9747  -7.1402
-#&gt;   12.1295  12.9967   0.6639  -1.5193   0.4410  10.6945  13.6656  -3.1914
-#&gt;    5.4100   3.8496   1.6437  -3.5398   7.0846  -9.9937  15.4963   5.8231
-#&gt;    7.5987   3.5565   4.1458  -7.8678   0.6812  -4.4799  24.1990  -5.0146
-#&gt;  -10.0771  -3.8329   4.9017   7.5790  -6.6872  -0.7476   9.1992   7.2369
-#&gt;   -5.0835   5.0043   3.1980  -5.0508   3.8299  -4.9766  -9.6292  -1.7594
-#&gt;  -15.1777  -5.5674  -3.6914  11.2230  -6.3116  13.1206  -4.2019   3.9232
-#&gt;    4.7549  14.9044  -9.8537  17.3155   9.6950   7.1425   4.4975  -5.3993
-#&gt;  -16.9693   2.4203 -20.4703   8.3267  -2.2390  -6.7381  -2.9412  12.7916
-#&gt;  -11.6139  10.8966  -3.9312  -0.5212  -6.7344   3.3800 -12.3245  10.8742
-#&gt;   -5.0495 -12.9478  -1.5114 -13.5049   9.3209  -2.9167  -2.9070  -9.1915
-#&gt;   -0.3623  -6.9541   8.1159  12.0325  -9.8860  -8.7181  -2.5255   5.7182
-#&gt;    1.4827   2.1857  -3.2521   2.9576  -7.1097   4.2946   8.4858  -0.9879
-#&gt;   -0.2729   2.7021 -13.6980  -0.0209   6.9267  -1.2844   7.1979  -2.7620
-#&gt;   -6.2110 -11.8811   5.1298   3.0679  -5.8170  -4.0543  -4.4873 -10.4857
-#&gt; 
-#&gt; Columns 25 to 32   2.5510  -0.2038  14.5818  -4.6291  -0.7677  -1.7932  12.4390  -4.2010
-#&gt;   15.1813   0.3858   5.0331  -2.6030 -12.5818   8.9777   6.5527 -18.1512
-#&gt;    6.1780   5.2303   5.6359  -4.6120  15.2766  -8.3173  -5.6248  -1.8824
-#&gt;    8.1504  -1.0806  12.6728  -0.5206 -17.4869  -0.0462   8.9059   6.3680
-#&gt;  -12.1255   4.2941  12.3835  -4.6695   2.1640   9.4901  -1.9697 -12.8155
-#&gt;   -2.8595 -11.3560 -23.4308 -12.8827  -3.2424 -22.6272   4.2050   7.6287
-#&gt;  -11.3116   4.9729   6.0999   3.8416  -6.1895  -9.4986   3.3053  -7.4039
-#&gt;    3.3873  -0.7899   4.5732  21.9898   6.3325   6.0113   2.0164  15.7458
-#&gt;   10.9122   2.3831  -5.2849  -9.6523   7.8680  -2.9722  -0.3612  14.7341
-#&gt;   -0.2008  11.2113  -2.5775  -2.3837  -0.5849   0.4306  -6.9612   1.6097
-#&gt;  -14.1493  -8.8999  -1.6389  11.8062   4.1949  -5.4295 -10.4731  11.8199
-#&gt;    8.0969  -8.5132 -24.8994   4.6152  -3.4242  -2.6175  -4.3879  -4.9619
-#&gt;   -9.4342 -15.1767  -5.6920  21.9019   5.1764 -10.5333  -5.2888  16.3868
-#&gt;   15.5493  11.3486  -0.2930   4.1731  -7.7393  -3.6599  -7.6600  -5.2174
-#&gt;   -3.5141  -4.6415   0.3840  -5.3498  -2.1020  -3.9604   0.0056   3.5127
-#&gt;   -4.7510 -12.2849  -4.9326  -5.1865 -12.1735  -7.7928   4.1647  -2.0988
-#&gt;   -1.1027  -9.5895   4.5745  -5.6741   4.7831   2.0540  10.7514 -14.1080
-#&gt;   -2.9644   5.5836  -8.3494   5.9076  -1.2470  -1.0219  11.5192 -13.4809
-#&gt;   13.2887   6.7956  -0.6082  23.5026 -13.5884  -6.3495  15.0499  18.1156
-#&gt;    3.5274   6.5587   2.6680   5.6992   0.6544   7.6869 -19.7288   5.1232
-#&gt;   -0.5296  11.7520  -6.7091  -1.0494  13.8793   6.6161  -9.0327  -8.9474
-#&gt;   -3.4582   1.6826 -11.8708   2.1495  11.1554   2.5380   1.2823  -2.7771
-#&gt;   -0.4756   8.0583   0.6827 -11.9828  -2.3517  11.3830  -0.6355  -2.7412
-#&gt;  -11.4423  -5.4743 -15.1870  12.4140  -9.7539 -17.8683  -0.7848   7.7927
-#&gt;   -0.5499  -6.9762 -12.1503  10.2159  -0.7098  -0.9954  -0.4118 -11.7689
-#&gt;   -4.6858  -6.1957   0.0519  -7.1924  -2.5395   2.1236   1.3794 -15.5540
-#&gt;   -9.3657  -0.6970   5.4592 -16.0231   8.2241  -4.5828   1.4532   4.9496
-#&gt;   12.8412  -3.9305   7.5273  -3.0112  15.4585  -6.6945  15.4474   1.7287
-#&gt;  -22.4385  -0.7342 -11.1161   4.6486  -2.7083  -9.0396  14.7538  -1.0047
-#&gt;    4.5947 -21.1098 -16.8718  -6.1116  -9.9848   6.5708  -4.7937  -2.0188
-#&gt;   24.0556  -8.9986 -12.3429  17.4882  -4.6548 -13.4062   6.2575  10.7854
-#&gt;  -31.1434   1.9465   0.7855 -15.7536  10.1426  -1.6422  -8.7910  -0.0079
-#&gt;  -10.3111   0.0067  -2.4724  -6.9653  -3.8297 -21.0870  -3.7484  -4.1639
-#&gt; 
-#&gt; Columns 33 to 40 -13.3018  11.4347   4.3881 -15.3451 -14.7796 -10.7482  -9.8222  -7.6255
-#&gt;    5.7348  -2.8622  10.3386  -4.3851  -3.2253   2.3243  10.5710  -9.1831
-#&gt;   10.8954  -7.3811   7.6372  11.4209  -8.9989  -3.6543 -15.2089  -7.5075
-#&gt;   -4.4141 -10.8794  11.8271  -1.5246  -4.1511  -3.5885   0.3060   1.0262
-#&gt;    1.4067 -11.2596  -3.3783 -10.3016  -5.7747  -2.5181 -14.6493  -8.2378
-#&gt;  -10.3898  -6.8135  -0.2176  13.7231  -7.8071  -5.1213  -9.7749 -10.0052
-#&gt;  -14.7091   9.5471   3.8610  -1.7426  -9.3497  -2.6168  -0.9749   1.7457
-#&gt;  -22.4270  -0.7327   3.1410 -10.7137  -3.9862  -6.6951 -16.3995 -10.5144
-#&gt;   -3.1132   3.0083  12.4543  13.9422  -2.3628   2.5201  11.7110  -3.1082
-#&gt;  -16.7121  -8.7479   7.5455  19.0781   1.5504  -4.3607 -11.3715 -11.4387
-#&gt;    1.3740   2.8587   1.4961  -0.6484  -5.6926  -0.5601  -9.7146  -4.9405
-#&gt;    3.9314   3.1979   5.8303   0.5067   5.4296  12.0537  -3.3515 -11.3496
-#&gt;  -24.2184   7.8634  14.0017   4.3131  -0.1126  -7.2120  -9.5349   0.8077
-#&gt;   -4.7040 -15.6956   7.7416   8.2861   8.7321  22.4792   2.6020  -9.6495
-#&gt;    9.5377 -11.9823  -6.4117   8.4295   0.3117  -2.6650   7.8302  -3.7887
-#&gt;    5.5278  -2.7329  -2.2512  -7.1119   1.3326   8.0848   2.5164   0.9978
-#&gt;   -1.0899  14.8330 -14.3933  -7.3525  -7.7192  -7.0869  -3.4828 -12.8309
-#&gt;    8.4395   4.9174  -4.2364  -3.0753  21.9154   3.6303   6.2544   0.7341
-#&gt;    2.3007  -6.2655   5.5432  -5.3002  -9.3214  -8.0130   7.6070   1.5934
-#&gt;    4.3254 -11.5373  -4.2664   2.0801  -0.6847   3.1335  -5.3338   1.9036
-#&gt;   12.3775  -7.0790 -28.5314  -0.3746  18.1311   2.2420   0.0830 -12.6389
-#&gt;   -3.0356 -13.9939  14.8970   4.8902   7.3808  -2.6823  -6.3736 -12.9497
-#&gt;  -16.7200  -2.1116  -2.9996   6.8710   2.5699   5.4890   8.0162  -2.9067
-#&gt;  -13.6207  -4.2545  11.4654  14.8817  -2.2713   0.8434   7.1582   1.3200
-#&gt;    5.0770  -2.2040  12.7372  11.4328  -1.7731   5.3843  -4.7258   5.3155
-#&gt;    9.3367   7.5425  -4.8037   6.9640  18.1435   8.1916   6.8829  -1.8885
-#&gt;   13.0478   2.9280  -0.1942   1.3672  -6.5255   2.8133   1.1775   7.4638
-#&gt;   11.6807  -0.5185  -1.4964  -4.7613  -6.2701 -10.8524   3.5170  -3.0822
-#&gt;    3.3242  -5.4642   4.7088   5.5320   8.1024  -5.8204  -3.0596  10.6272
-#&gt;   -1.0657   3.4552   2.3027  -7.0692  10.1231   0.8105  -8.5921  13.5097
-#&gt;  -15.2484 -10.5065  -3.6962  -1.5426   0.5034   8.4190   1.9529 -18.8593
-#&gt;  -11.2276  11.5599   1.5902   2.1219  -1.6046   3.9939  -3.8218   6.1057
-#&gt;   -2.9041  -0.2319  20.5079  10.3067 -19.0063  -7.7289   0.2409   9.4719
-#&gt; 
-#&gt; Columns 41 to 48 -14.8274  11.1019  -2.0624 -16.6093  14.9514  -5.6329  -3.2294 -11.8248
-#&gt;  -25.6942 -11.7620  15.1268 -16.4583  -2.4520  -1.4868  -7.3511 -11.5673
-#&gt;   10.0451   9.0156  -5.6282   1.9462 -13.0216  -7.2568   5.5590  13.4736
-#&gt;    4.9235  -5.8640   5.4821  17.9450  -0.4013  -9.8630   3.4781   6.9655
-#&gt;   15.0701 -16.0830 -11.3884   5.5517  -4.9922  -9.6732  -1.6430   6.1311
-#&gt;    3.8547  13.6872   1.2339   1.6836   8.5749  -8.7089 -14.2435  11.3817
-#&gt;   12.4370   6.2683  -8.8350   5.0442   2.9360  -2.7908 -10.8230  12.0263
-#&gt;   -3.1593  -1.5564   3.6364  21.1648  -2.1498  -4.9529   5.2285   9.4477
-#&gt;   15.8922   8.4917   1.9509   1.4087   7.6780   7.7388  -8.4368 -12.4227
-#&gt;  -16.1673  13.1916 -15.0930   1.8549   9.6663 -17.2185  -1.4829   7.0265
-#&gt;   16.0285  15.2274   3.2461  -3.6231  14.0717  -8.7764  -1.0086   3.2117
-#&gt;    9.4831  -2.8242   3.4838   5.2793  -4.4744  -3.2964  -3.9987  -9.1133
-#&gt;    7.7553 -12.7563  10.4662   5.3940   4.2116  -5.8236  -9.4847  -3.2442
-#&gt;    1.3194 -12.7428   4.0818  -1.5940   1.5243   5.1842  -7.2948  -5.7164
-#&gt;    4.8879  -2.9317  -6.2709  -9.3459 -11.3165   5.0118  -0.1904  22.1940
-#&gt;  -21.0022   6.7985   4.4091 -10.0046  -6.7588   3.2733   2.6421  -4.7261
-#&gt;    3.9414   9.6563  -5.8217  10.3224   2.6729 -11.2405  -0.9543   5.5213
-#&gt;   -1.2199  -1.8814   4.7612   1.8873   8.5311  -6.8704  -0.2994 -12.2560
-#&gt;   17.0057   1.9599 -10.4429  -0.1507  -9.0125  11.3883  15.8209  -4.7667
-#&gt;   -3.3215  -1.4828  -8.9326 -14.8883  10.0406  -0.3560   2.7622  10.9950
-#&gt;  -19.2001  -0.0726  -1.9566   5.3143   7.0787  -2.4969  -4.9595 -22.0236
-#&gt;    1.4301   6.5826   6.1883   3.8234  -0.1367   5.4273   4.0576 -11.0166
-#&gt;   -7.8817   0.6544   6.0732  11.9236   7.7939   0.3150 -18.9965   1.4273
-#&gt;    2.6034 -10.2298  -4.7003  -5.3183  -3.9666   9.8251  -2.7840   2.1820
-#&gt;    8.2280  11.6911  -6.6334   7.4555   8.5682  -8.2273  -3.4682   0.9171
-#&gt;  -14.0598 -11.5994   1.5577  -7.6353  -0.4864   5.9775  -6.4676   8.4718
-#&gt;    9.0035   1.8517  12.9204   1.1901  -7.3776   9.1212  -8.3669  13.1364
-#&gt;   17.7747  10.1759   8.1650  16.2932  -3.2379  10.2901  -3.4087   0.9077
-#&gt;   26.6333  -1.2646   5.5650  27.8534  -8.3948   2.2099  15.4857   8.4266
-#&gt;    5.2051  -7.7246  10.6129  -5.9848   1.8629   1.8079  -2.9143   2.8773
-#&gt;   -4.5634  -6.9991  -0.1841  -1.9090  -3.3952 -11.1046   1.2958  -5.0358
-#&gt;   -1.5619  -4.0569   0.3958  -2.8646  -3.9297   5.8160   6.0127 -14.5869
-#&gt;   -2.1185   6.3288  14.7029   5.6887  -7.8463   9.2926  -5.1830  11.8611
-#&gt; 
-#&gt; Columns 49 to 54   7.1016 -15.8208  -2.0877   8.7288  -3.1307  -0.8803
-#&gt;   -0.4871   0.7266   4.5678   4.0105   1.2923   5.1693
-#&gt;   20.2181  11.8863  -7.4998  -6.5552  -5.5006   2.7607
-#&gt;    8.0890  -2.5741   1.3882   7.7029  -3.1784   0.5210
-#&gt;    2.2496  -4.2309   5.2862  -3.9217   4.9795  -0.7292
-#&gt;   -8.6583   6.5319   0.0370   2.8436  10.4838  -0.7236
-#&gt;   -1.1649  -4.4350  -6.3319   5.5223  -1.6165   1.1725
-#&gt;    4.8659   3.2011  -8.5719  -1.7483  -1.6685  -1.5847
-#&gt;   -2.2247  13.8979   4.1977  18.8982  10.7389   0.0871
-#&gt;    3.9782  -7.1217   0.0354  -1.4489  -5.5193   1.8505
-#&gt;   18.8135  -5.2025   0.9429  -3.0978   1.4687  -2.5012
-#&gt;   11.6686  16.0039  19.1609   9.8649  14.1246   6.5454
-#&gt;   13.3683  -8.2871  -6.7784  -8.3795  -0.2653  -3.5511
-#&gt;  -21.7449   8.3611  -4.8974   7.8489  11.9476   5.2951
-#&gt;   11.3051   4.7276  -3.7736   9.6208   4.5921   6.3180
-#&gt;   15.5787 -15.6322  -1.9335   2.3981   0.3116  -0.4815
-#&gt;  -12.1117   9.1798  -4.6968   0.7476  -1.0237   3.7553
-#&gt;   -7.8043  -0.7208   8.2035   3.4730  -4.0109   1.5793
-#&gt;    3.9935 -16.5139 -31.5667   0.2528  -0.0504  -9.1094
-#&gt;   11.5752  -5.3425   3.3273  -0.7484   2.1920   2.7529
-#&gt;    0.3867   1.0408   2.0689  -6.1926   0.9572   1.3587
-#&gt;   14.7566   4.6171   8.8172  -5.3254   3.3079  -2.3980
-#&gt;    8.7173   8.6772  12.3852  11.0251  11.5906   0.0299
-#&gt;    4.6733  -7.0466  14.9059  -4.1834  21.6208  -1.9280
-#&gt;    3.1884   6.7935  18.1165  -7.7078  -2.9576  -5.1778
-#&gt;  -18.3818   6.2766   7.1564   2.9596   0.0514   5.4900
-#&gt;  -11.5391   3.1277  -0.8124  -2.9626  -9.9937  -3.3890
-#&gt;    2.6400   7.5140 -11.0859  -9.9491  -5.3755  -7.1824
-#&gt;   -5.2797  -3.5233   2.8756  -5.8039  -2.0460  -5.4051
-#&gt;    4.4817   5.8428  14.4519   2.6131  -9.7038  -6.9624
-#&gt;    1.5933  -4.5518   0.6980  -4.8872  14.2196  -2.3431
-#&gt;   13.2308  -9.6762  -9.5056  -3.2635  -4.0156  -6.7775
-#&gt;   12.9216   5.4440   0.0763  -1.1853  -3.6075  -3.9249
-#&gt; 
-#&gt; (7,.,.) = 
-#&gt;  Columns 1 to 8   2.9533   1.0860  -8.1119  -0.0494  -5.6527  -3.9036   1.4153  14.9349
-#&gt;   -0.0426  -5.0686   0.9671   9.3674  -1.9175  -4.4150 -20.3450 -11.1734
-#&gt;   -5.7712   0.4861  -5.7229   2.3722  -0.5183   4.7114  -5.0389   9.6950
-#&gt;   -4.9070  -3.9710  -4.8358  -5.3924  -1.4841 -23.6475   3.8715  10.7274
-#&gt;   -2.5420  -0.1729   3.0972   2.3531  -9.6480  -8.7437  19.5345  -5.5293
-#&gt;    4.4810   5.3877   7.0822   7.6412  12.4621 -10.4872   4.7130   3.0768
-#&gt;    0.2558   8.5553  -1.4754   6.0410   3.9560  -4.5169  11.5759  20.3742
-#&gt;    8.0860  -2.6291  -2.9121   7.6992   3.2772  -6.0553   4.4048   6.1588
-#&gt;   -4.3041  -3.4693 -10.2610  -6.9206   8.7375   2.1002   2.5182   1.9419
-#&gt;   -4.9646  -0.1113  -1.3192  -9.4292   4.6407 -18.0276  -4.2440   6.6633
-#&gt;   -5.8289   5.9275   2.1018  -1.3827  -2.2998   1.0696   4.6979  -9.5232
-#&gt;   -2.5592 -10.3871  -4.0104  -1.7658 -10.1056 -11.3087  15.4066  -7.7634
-#&gt;    6.3138  -1.6091  -5.9848 -13.2303 -23.1200  -7.9112  10.0865  -1.6915
-#&gt;   -0.5376  -1.2372  -2.6146   1.1744   3.0030   2.3626   0.6140  -4.1495
-#&gt;   -1.0276   4.0819   1.0181 -17.9625  -1.2514  -7.6360  -2.6066   4.1145
-#&gt;  -11.0159   6.8261  14.0863   4.3507  -5.7617  -3.7358   2.9299  -8.3246
-#&gt;    2.3352  -3.0110  12.2271  18.0495  -4.5932  -7.1297   5.2973  -8.6118
-#&gt;   -0.5394  -6.9015   5.5024  -7.8668  12.8111   1.7763   9.8438  -1.7404
-#&gt;    3.3877  -4.2540  -2.2542  -6.0180  18.3600  -5.4755   3.0592 -11.9726
-#&gt;  -10.2080   7.4090   4.1472  -2.6033  -4.1521   3.2111   8.6643  11.6759
-#&gt;    1.0144  -5.1425   0.0963  -7.1641  -0.3397  19.8292  -0.3321 -16.5396
-#&gt;    4.8501   8.3368   3.3213   1.6215  -0.6207   0.4590 -15.0183 -14.6612
-#&gt;   -2.5804   3.4855  16.8857   9.2382   3.0253  -6.7524   0.1960  18.0831
-#&gt;    1.7473   0.6880 -11.5274  -8.9468   3.7359 -17.2174   1.2827   8.5942
-#&gt;   -2.4625  13.4675  13.3387  -0.6154   4.2879 -11.0969  -5.7258  21.5706
-#&gt;   -4.4280 -14.9012  -8.8041 -14.0777   4.9153  13.6668   5.2954   3.0269
-#&gt;   -1.2733  -3.2601  -1.8351  -5.1108  -3.5288   1.6957  -8.3590  -5.3381
-#&gt;    4.1251   0.9375  -3.7061  23.1854  -1.0771 -12.4512  -2.0129  -4.0775
-#&gt;    4.0202   0.9914  -3.9503  -4.0601   7.2828  -3.2260   9.1126   8.2552
-#&gt;    4.5288  13.4074   1.2214  -2.5435   6.7230   2.8578   9.0410  -2.8760
-#&gt;   -3.6678  -2.4159  -3.7118   9.2260   8.0656  -4.5218   7.3985  -8.2251
-#&gt;    5.0364  -9.3228  12.9259  -8.1170 -17.5855   3.5425  -3.0200 -10.1435
-#&gt;   -6.1906  -4.4283  -0.9003  -0.7803   9.6682   3.5322  -0.8297   8.7635
-#&gt; 
-#&gt; Columns 9 to 16   1.5017  -8.6068  -6.9181   0.8914  -5.0705  15.9220   5.7279   0.2399
-#&gt;    7.3119   1.3518  -5.9874  -0.2507   7.6702  -9.8376  -3.9085   7.4234
-#&gt;    8.6301   2.0793 -11.9055  -3.7911   2.2005  -7.8006  -5.2940  21.5218
-#&gt;    3.0038  -1.6677   1.7950  -4.5508   2.2488   5.3606  -9.1701  11.3947
-#&gt;   -5.7708   0.4336  14.7154  -3.9453   7.6350   4.7351  -8.6861  15.0580
-#&gt;   -1.3754 -17.9018  -1.1444  12.8072  -1.4358  -3.2275  -3.6352   7.4320
-#&gt;   -5.0755  -5.4128   1.8288  -5.4710  -3.5631  14.3366  15.0004  -6.0529
-#&gt;  -10.4102  -6.2087   0.9492   6.0118   7.5276  -6.7697  10.3562 -11.1823
-#&gt;  -11.1926 -13.0750   9.3369  -2.0888 -19.6530 -11.2297   0.6006 -12.6120
-#&gt;    3.0238 -16.1934  -2.7229   1.8922  13.2706   7.3099 -13.3667   1.7925
-#&gt;   12.4466  -0.6007   5.8977  -3.2004  -0.3359  -1.2151   0.8104  -3.3276
-#&gt;   -1.6860 -14.1189  11.2619  -6.9821   4.1982 -13.8986  -2.2289  16.0245
-#&gt;    7.7072   3.2524   2.7273  -0.8512   1.0577   2.9917   7.9595  -3.3827
-#&gt;    7.0726  -7.8132   0.4509  -2.2803   6.6153  -4.2307   3.3257  12.5965
-#&gt;   -7.9716 -23.9474  -3.5218   1.5840   9.3275   1.9662  13.4866  -2.4736
-#&gt;    2.7921  -3.6859  -3.9322  -4.6172   9.0890   2.9150  -0.3922   4.2977
-#&gt;   -5.9871   9.6314   9.5661  -4.8246  -8.7754  -3.9940 -18.3416   4.9380
-#&gt;   -0.0987  -0.1037  10.6268  -0.9204 -13.3450  -1.6316  10.4843   2.5481
-#&gt;  -14.0458 -11.3099   3.7968  -0.4273  -0.2426   5.3267   2.5750 -17.5287
-#&gt;   -2.2571  -2.0932 -14.2280  11.7172  17.4334   3.4308   0.5894   6.1905
-#&gt;    0.2811  15.8760   2.4301  -2.2227   7.1458  11.6980  -1.3617   1.6834
-#&gt;   -1.4061  -1.7128  -5.3377 -13.2902  -0.3802 -10.0704  11.9042  -0.1962
-#&gt;   -7.3468  -1.7626  11.2446   4.4840  10.7810 -18.4213 -13.0671  14.1763
-#&gt;    2.9852 -13.0677  -2.5682  -2.7484  -0.3983   0.4290   6.8680 -12.5134
-#&gt;   13.3903  -0.9157  -2.4934   3.4122  -3.4299   2.6442 -15.6976  18.5193
-#&gt;   -6.8927  -4.6039  -1.2142  -5.0980  -6.1670  -5.9587   2.6590   0.4891
-#&gt;    9.6014  11.4081 -11.5250  -7.0517  -0.4210   3.0345  -4.5130   0.2101
-#&gt;   -0.8494  15.2047   9.5280   1.9386 -13.3232  -3.7443 -10.3539 -14.5014
-#&gt;   12.9063   7.2197  -0.1236  -8.5376  -2.7291  -1.1630 -10.6046 -17.5495
-#&gt;   -0.0908  10.3624  11.3390   0.9138  -0.4849  13.4013  11.7459  -8.2455
-#&gt;    1.9956  -6.9150   2.7161   1.0772   2.4628   0.0884   9.3908   5.6051
-#&gt;    5.1412  20.5724   7.5165   1.3354   9.4225   1.5700   0.4186  19.6713
-#&gt;   13.9985   7.4152   2.8320  13.5761   6.1628 -12.5014   2.0404  -2.9885
-#&gt; 
-#&gt; Columns 17 to 24   9.7553 -13.1200   7.1868   4.8436   2.1946  -6.9015   1.4137   9.7216
-#&gt;   10.7145 -15.9968  -7.6058   3.1467   9.2966  14.3546  -0.1738   9.8027
-#&gt;   -9.2212  -0.5251   3.5527   5.3623 -11.8983   4.0554   4.5675  -6.3613
-#&gt;   -2.6589   8.1325   7.2018   2.1848  -4.5962   2.5252   1.0252 -14.3119
-#&gt;   -7.3349   2.4963   6.5116 -10.1706  -3.1291  -7.2340  12.6099  -0.4696
-#&gt;   -0.1394  -7.5460 -22.2137  -2.3903   2.1341 -22.2161  17.2772  28.6837
-#&gt;   -2.1571   5.4578  -1.2019 -22.6302  -2.6689   9.1606  -3.8338   2.8104
-#&gt;    4.4399  -4.1536  -0.9956   9.4338  -4.5337  -8.7534  -5.4436   5.9705
-#&gt;  -11.4283  -1.5159  -6.5968  10.1358 -15.0663  -4.5839  -3.3741   7.3909
-#&gt;    5.8492  11.3488  -2.2672  12.7561  -7.0420  -2.7220  15.0504   8.2648
-#&gt;  -18.0644   3.9038   1.3762 -20.8657 -11.8200   4.8428   3.3412 -21.4536
-#&gt;   -5.5596 -23.4892  -6.5741  11.1924 -15.3357  -2.2479   6.0471  -0.6416
-#&gt;   -5.0136   0.5910  -2.1618   6.7253  -1.5704   6.2009   1.3919   9.3597
-#&gt;   10.9548 -13.9191  12.5985  -1.2022  10.8440  -5.5861   4.4983   3.3493
-#&gt;   -1.2791   3.0089   9.8931 -13.5114  -4.6666  -1.9426   4.4158 -10.9277
-#&gt;  -12.9990   0.6456   8.3221  -4.9207  -0.7035  17.5786   7.1658   5.9181
-#&gt;   17.2244 -19.4162   2.3888  -0.8057 -11.6695   4.9724  13.7872   1.6801
-#&gt;    5.4351  -6.6086  -7.4150  -2.8655   4.8108   5.5622   6.3642 -10.0064
-#&gt;  -11.7144  11.1556   1.1453  21.3122 -11.9525  -6.5990   9.2495  -5.2196
-#&gt;  -15.3457   6.0296   4.1158  -0.5219   1.6137 -11.4727   8.4630  -2.4792
-#&gt;    2.8535   0.0787   1.0286  15.6627  13.0209  -6.1135  14.4283   9.9701
-#&gt;    2.5635  -3.7947  -1.3162   6.7904   0.7413 -17.1797 -21.7695   8.1626
-#&gt;   -3.6684  -4.9924  -2.0509  -4.8486  -2.1283  -4.5470  -1.8739   9.6315
-#&gt;  -14.5102   9.8342  -1.4152 -10.0871   9.7394  12.9369 -12.2200  -8.0341
-#&gt;  -10.4517  -4.9812   2.9651   7.1184  -1.4927   6.9153  16.0767  -8.4478
-#&gt;    4.6687   3.0401  -1.0172   2.2060   1.4107   2.0808 -11.0041  -0.6075
-#&gt;   -4.7864  -0.0843   2.4379 -12.3772   9.2019  -3.9495  -1.5036 -11.3102
-#&gt;    0.9289  -7.3573  -4.8422   5.4141   8.8234 -12.1827   6.7583  -5.4115
-#&gt;    4.5193   7.5225  -6.1402 -12.0619   3.5380   2.5877 -13.4143 -13.8412
-#&gt;    4.8879  11.3728   8.5100 -22.6038 -11.1467   9.6744  10.0143 -21.5391
-#&gt;   10.9835  -5.5687   1.3013   2.8671  -5.4365  -4.9046 -20.4971   2.6865
-#&gt;  -18.5385   4.6653  -6.3887  -0.4565   3.7783   1.8561  -6.8878   2.6661
-#&gt;  -20.1066   1.6469   2.6033 -19.8456   8.6572   3.5506   8.2641 -13.4063
-#&gt; 
-#&gt; Columns 25 to 32  -1.2997   2.5660  11.4786  21.9807  10.5563  12.5488 -16.1947   8.9865
-#&gt;   30.3387   4.3806  -2.5467   6.4973   8.2565  11.6147   1.4779   1.1958
-#&gt;   -8.6803  -5.0393  -4.5055   2.4810  -7.9344   7.4099  -4.2127  -3.3463
-#&gt;   -4.6563  23.6231 -14.3282  -0.1461   2.4661  14.0052  -8.7004  -1.3035
-#&gt;    5.4801  16.1340   1.9629  14.6541   3.6288  10.3801  -2.2284   3.0719
-#&gt;    9.1457   4.7753  26.7420   1.9238  16.1791  -2.2101  -5.3664  11.4996
-#&gt;   -2.6553  23.0783  -3.7477  -4.2019  11.1723  -4.5220   4.4127  -4.7281
-#&gt;   -2.5818   4.4406  10.7327   2.0380   2.0969   9.6335   3.9120   8.2685
-#&gt;  -33.8974 -12.8143  -6.0901  -9.0709  -7.8182   8.9614  -2.5334 -12.7631
-#&gt;    3.2786  -1.6036   7.5980  -3.9095   1.2602   2.3739   3.2521  -4.8686
-#&gt;   -7.3327  10.4040   5.5009   4.0083  16.3784  -8.6116   5.7804  10.3379
-#&gt;  -14.2673  -6.2080  -4.0674   0.1512   8.3497  -9.4116  -4.0423 -14.0406
-#&gt;  -20.3385   3.9015   2.0622   6.2539   6.8554  -7.8619  -3.0550  -4.7275
-#&gt;    2.5882 -12.9390 -22.0963  -3.0532   1.9954  -0.9987   3.1953  -8.4127
-#&gt;   -5.0772  16.6097  -9.3671  -0.4289  -0.8395  -4.9621  -8.1858  -7.3859
-#&gt;    7.6867  20.9398  -0.7564 -10.0955   4.2113   3.5465  -1.3963   8.6430
-#&gt;    1.0266   7.9831  -4.8256   2.7832  20.5387  -2.9075   1.2404   7.3119
-#&gt;    4.3127  -3.5009   1.9082   1.2073  -3.6349   0.1758   7.0910   2.4704
-#&gt;    5.2905 -13.3534  18.8257 -26.3553   2.9182 -13.3157   3.7616  -7.0972
-#&gt;   -9.0565  -9.4113  -7.3716  -6.4189  -1.4251  -0.6970 -11.1698   6.4259
-#&gt;    9.8357 -20.6985   6.5946  -6.4993   1.4075 -10.1752  -2.8046  -0.5371
-#&gt;   -7.0588 -15.2924  -4.0809  17.3121  -5.1022  14.4247  -6.8520  13.0136
-#&gt;   -5.4876 -12.7528 -31.4428  -4.2435   5.3583   0.4308  -8.7641  -7.9566
-#&gt;    2.7864   3.8519  -3.5083  -1.5176  -2.9368   9.7348  -5.4117  -6.1777
-#&gt;   -1.2136 -10.0024  -8.1429  -4.3171  -6.6987  -1.9511  -9.4266  -5.6240
-#&gt;   -7.5783  -7.4562   6.2961  -6.0867   1.4181 -16.5222  -2.1903  -8.6514
-#&gt;    9.0910  -3.3295   1.5234  -4.1993   1.2210  -0.2420   2.8981  -2.0521
-#&gt;   -5.5707 -18.4647  -0.6560  -2.7191   1.2130  17.2127  -7.8321   4.2030
-#&gt;    4.6750  -2.4481 -14.0304   6.0465  -5.7216   2.8683   3.5524  -2.6627
-#&gt;   -4.3774   4.2675  11.3429  -9.4706  -2.9024  -6.9048  -0.8443  -2.9756
-#&gt;   -6.5006   5.7395  -0.4179  -7.7384  -0.1501   9.6269  -4.5812  12.0688
-#&gt;   13.9179 -15.6514  -3.5161   5.1031  19.9115  -3.0878   4.0127 -15.4881
-#&gt;    6.5677   1.2792  -3.8230   2.6097 -13.5379  10.4018   0.3715   7.4503
-#&gt; 
-#&gt; Columns 33 to 40   5.3052  -7.0661   4.7852   8.9610 -15.2011  -3.8709  17.9290   1.6281
-#&gt;   -5.1879   5.2382  -1.0851   8.2228   4.6034  -5.9043  -1.7003 -11.1631
-#&gt;   -6.9663   6.2021   3.7609 -16.0715   0.8179   4.0172   2.3748   2.4703
-#&gt;  -18.8565  15.3300  -2.0820  -7.5073  -6.7966  15.5286 -13.4748   6.2408
-#&gt;    0.8957   3.5834  12.3402 -17.6054   6.3492   7.4437   3.7392  -8.5734
-#&gt;   -2.9738  -7.2329  -2.5599   1.7708   2.4251  -5.8812  -5.8491  -5.3014
-#&gt;    6.4677   0.8822   1.9027  -8.3738  -8.0058  12.0594  -8.5700  -5.1170
-#&gt;   18.9312  17.7423  -3.8196  10.4943  -0.9489  -7.5846  -2.0368   1.1635
-#&gt;   -2.4501   4.5884  -6.6799  -4.8542   5.8958   1.9316   7.5522   4.8995
-#&gt;   -9.1462 -12.1461   8.4455  14.3685 -16.3043  -9.3552  -8.0756  -6.7934
-#&gt;   10.4133 -12.1937  15.6228  -9.6192   2.0900  -1.8004  -6.6100  -7.5872
-#&gt;    8.8462   0.4353  11.5399  -4.4965  -6.6862  -1.5440   7.6812  -2.9164
-#&gt;    5.2504  -0.7551   9.2843  -1.7086  -3.6233   0.1112  12.3992  -0.2854
-#&gt;   -9.8543  -2.7453   2.0754   1.1477   6.3524  -7.0851   0.0442  10.8561
-#&gt;   -9.9474   9.1364  -0.2938   2.6699   1.5531  15.9298  -7.0656  -4.3737
-#&gt;   -2.8141 -23.9811   2.7360  10.6291  -3.0334  10.8106   0.6688  -9.3946
-#&gt;    4.9860   0.2085  -0.2466   1.6133  -3.4966  14.4860  -2.2270 -23.5474
-#&gt;   -2.5455  -2.8775 -15.3958  -7.0275   3.9818  12.7210 -13.5770  16.3814
-#&gt;   11.6903  -1.1662  -3.7867  17.0957  10.7837  -6.2491   7.7903  -1.4236
-#&gt;    6.9868   3.5043  10.6076  -3.3494   9.3721  -1.1851 -16.0425   7.7467
-#&gt;    6.9866  -8.2193  -5.6146  15.1631   4.8171  10.9608   0.2572  11.4145
-#&gt;   -4.2670   7.7636  13.9072  14.0630  -3.8046  -5.2689  -9.9894   1.9107
-#&gt;    9.0186  -1.3330   2.4330  20.1800 -10.8788  -3.1260  -5.8801  -9.1647
-#&gt;    5.5179  -1.6877 -19.2072 -14.4144  -5.0496  -7.9839   6.6813  12.4073
-#&gt;  -12.5223   2.0891 -12.9087  -7.6491  -1.4437  -8.5859   1.9822  11.2169
-#&gt;    9.9112 -18.6615  -7.3252  -5.5876  -1.6898  -3.7585   0.5299 -15.4517
-#&gt;    5.1917  -6.7378   0.6070  -1.0705   0.7240  -6.5471  -4.8843  -0.6816
-#&gt;   -7.0180  16.8131  -3.6724   3.8947  11.0605  -0.7513  -1.2543  -1.7039
-#&gt;    8.3646  10.8441 -21.4233 -16.3048   6.7304  -8.0358 -16.3618  14.3174
-#&gt;    3.0343  -7.0327  -5.2200   8.3443 -11.4185 -15.7410   5.5705  -2.6389
-#&gt;   -1.9251  -4.4846  10.0314   4.7699   2.6357   2.8893  -2.4603  -1.3923
-#&gt;    1.6812  -1.4077  -6.8064   0.4776  -2.6904  10.8166   4.8353 -27.0714
-#&gt;   -7.4296  -3.7325  -5.3549  -8.5688  -9.1646  -6.8415  -2.5583   6.1067
-#&gt; 
-#&gt; Columns 41 to 48   3.1174  16.3425   7.7411   4.7303   6.4072  -0.6967 -15.2469   4.0689
-#&gt;  -19.8591  20.3703 -22.3870  -0.0186  -3.8217  -4.0515 -10.3993   8.4956
-#&gt;    6.2955  -6.7682   1.2646   1.5836  -2.6305   5.3780  -4.7371  -6.3778
-#&gt;   -3.3552   3.5098  -1.0505   8.3239  -8.7252   8.8065   6.4772   8.2973
-#&gt;   -3.9051  -7.4153  -2.2978   4.8423  -6.7482   2.8870  -4.6284  12.2837
-#&gt;   -3.4785 -24.9972   3.0788   1.1094   2.2645 -26.4156   3.6031  -6.0978
-#&gt;   -5.8234   3.4496  -2.9199  -8.7993 -10.5120  -4.7851 -11.1427 -23.1279
-#&gt;  -11.7796  -0.4631 -13.1887 -11.4661   2.6153   7.1247 -10.4822 -15.9656
-#&gt;   13.4413 -11.6371  -0.9134  13.4668  -4.4373  -6.4646  -1.3270   2.8753
-#&gt;   -2.1881  -6.9783 -10.5101 -10.4630  -3.0733  -2.1742   8.1180  -6.3409
-#&gt;   -1.8502  -9.7822   2.1889  -0.1678  -5.9537  15.3674  -0.7551  -4.7234
-#&gt;   -8.0885   8.0203  -4.0589  -8.8241  -8.3500  -2.8535   9.3532  10.9975
-#&gt;  -18.7868   0.3839  21.5336   9.5365  -2.7460   8.3454   6.3590  -6.5452
-#&gt;    1.0877  11.8411  -7.7273  -3.8096  -1.4145 -16.3855  13.4120  -4.7363
-#&gt;   15.1865   5.0968  -3.6312   1.6900   9.4821   2.4396   3.0815   5.8806
-#&gt;  -10.7069   8.0684  -4.1255  13.1902  -7.9577  -1.6689  -4.5992   7.6382
-#&gt;    2.7812  -9.5274   8.5902  -4.6735  -2.9463   1.7383  -6.5527 -14.1294
-#&gt;  -17.0843  14.5295   7.6360  -1.0162   0.3424  23.1627 -11.0931   6.9220
-#&gt;   -9.3723   6.4530  -1.4624  -0.4699  -5.1367   4.7219  -9.0282   4.5032
-#&gt;   12.0693   1.0159 -23.7530  -1.8362  -2.2409  -5.2844  -4.3661  -0.5389
-#&gt;   -0.3056   3.2738   3.7991  -2.3939   8.0024  -1.8377   5.1208  10.9426
-#&gt;   -9.6241  14.9390 -16.1278  -9.0459 -16.5176  -9.3462   0.4452   4.7589
-#&gt;  -10.3488  -1.6794  -5.0653  -8.7524  -7.6349 -16.4446 -11.3896  -7.0746
-#&gt;    1.4225 -17.2559   4.2896   8.8625  10.0452  -8.2771  -0.2759   7.0084
-#&gt;   -8.0495  -7.6525   2.5955  -0.5583  15.6281 -17.6703   0.5897  11.1602
-#&gt;   27.0729   5.4696  -1.3197  -9.3038  13.9750  -2.9348   4.7927  -2.7989
-#&gt;   29.5099 -20.9963  24.6360  -6.2216  15.2439 -11.4710  16.2514   6.2927
-#&gt;   -3.5272 -19.5338   3.2136   7.2999   0.0861  -2.9485  -9.8887   0.4744
-#&gt;   -1.2962  -1.0744  14.8624  -2.6720  19.5613  23.0580  -6.7557   7.1725
-#&gt;  -17.8225  -0.3951  -7.6426  10.5787  -2.1145  -5.5434  20.5069   3.5584
-#&gt;   -2.0788   2.3581  -3.1871   7.5324 -25.1909   4.3661   7.6013  -0.0651
-#&gt;   12.2884   8.4565  20.5663   9.6368  18.6797  -2.5369  13.3668   1.8755
-#&gt;  -10.1990 -10.4719  -0.4565  10.4234   4.8150   1.9160   2.6964  -5.6770
-#&gt; 
-#&gt; Columns 49 to 54   4.7604 -17.0443   4.2362  -0.1506   5.7847   4.8275
-#&gt;   12.6656   2.2082 -15.0857  -9.7875  -1.2137   7.7689
-#&gt;   -7.9383   3.0470  -2.7657 -15.1841   7.5981   3.2548
-#&gt;   13.3431  -7.0009  -9.5811   3.9706   8.5158   0.8813
-#&gt;   -2.0129  13.4008  -1.1993  -6.5928   6.5611  -4.5572
-#&gt;  -13.2717  12.4965  -5.4569  -0.3310  10.9039   5.8270
-#&gt;    8.9575  -5.0186  -2.2719   0.3510  -4.7358  -3.2742
-#&gt;    6.5051   0.5433  -8.9381  -0.5561  -6.3295  -1.4019
-#&gt;    3.8464  -4.6707 -13.7753  -0.9268   5.7068  -1.6860
-#&gt;    3.8926  -6.4982  -3.0241   1.7773  -3.8354   2.0144
-#&gt;   -6.7829   7.4228  -0.6287  -1.7426  -6.6208  -2.8569
-#&gt;  -12.3822  14.1381 -11.0661   9.7871   2.0780  -1.8625
-#&gt;    8.6162   4.5793  -2.0453  10.8411  -8.9352  -2.9064
-#&gt;    8.2304   9.6105 -18.9576  -9.7135   8.1245   6.5796
-#&gt;    4.2747  15.7482   0.5116   0.3624  10.7449   3.9772
-#&gt;   -4.2663  -3.5648  -1.8642   7.4056   0.9312   5.9999
-#&gt;   -2.1748  -9.0366  11.5350  -4.8309  -3.1486   9.7313
-#&gt;   -3.5441  -3.7128   3.9071   1.9930  -5.4614  -4.3329
-#&gt;   -3.4657  11.1438 -16.1882   8.2909   5.3822 -11.1595
-#&gt;   -6.5959  -4.9608   1.6281   5.4256   1.1819  -7.4167
-#&gt;  -12.6978   0.9185   9.9471  -9.0970  -8.7337  -0.1304
-#&gt;  -14.7662   1.3690   5.9851  -0.7951   3.1230   6.4365
-#&gt;    7.6856   0.4292 -14.4094  -0.8849   0.1469   3.4671
-#&gt;    5.4269  22.9641 -11.5888  -0.5557  12.3558  -2.9540
-#&gt;  -17.0178   1.4444  -3.5835   1.6870   7.0000  -9.2378
-#&gt;   -3.3226  -4.0039   4.7945   6.7741 -10.1068   4.7550
-#&gt;   -3.1680 -10.0941   1.2355  -4.2952   3.2572   4.4980
-#&gt;   -1.9808 -11.6065  -7.0829  -7.6412   7.5375  -9.0860
-#&gt;   -5.1760  -1.4036  -5.5432  -5.4713   3.0101  -2.3162
-#&gt;   10.2289   1.8357  -9.1445  22.0267  -3.5414 -11.8593
-#&gt;    4.8033   6.7724  -6.2342   3.3569   0.4787   4.3952
-#&gt;   10.7536   3.2190  -5.0490  10.7763   0.7685   2.7364
-#&gt;    3.8563   5.1250  -8.5096  -8.6343   6.7572  -4.6396
-#&gt; 
-#&gt; (8,.,.) = 
-#&gt;  Columns 1 to 8   7.1508   6.7372   5.9819  -0.0601   2.6715  -5.1312  -4.7280  -6.3784
-#&gt;    7.6682  -5.1930  -3.7598   6.8804  11.7423  -9.1464  10.0633   6.0598
-#&gt;   -5.6035  -1.0263   1.6200   0.2141   4.6984  -2.8773 -12.6556   2.3026
-#&gt;   -2.9972   0.1561   7.8324   2.6244  11.8952   4.3623   3.7364 -25.3673
-#&gt;   -9.6151   1.6634  -0.2185  -7.4899   4.8237  -4.0181   4.5082  -0.4462
-#&gt;    4.0094  -2.8231   0.4799   3.3922 -11.7035 -13.0875 -10.3550   5.3582
-#&gt;    3.9708   2.3987   2.3578  -3.6164  -9.5522   6.8756 -12.5137 -12.8285
-#&gt;    0.9439   0.7691   1.1446   3.5504  -5.8556   2.2269   1.1489   3.9464
-#&gt;    1.8752  -4.9550   3.1504  -6.5187  -8.1246  12.7283 -14.8513  -1.5619
-#&gt;    0.9727  -0.8928  15.7404   4.6312   8.3961  12.8783   7.6629   6.6705
-#&gt;   -6.8592  -0.9109  -4.1087  -2.7627 -13.6369  -0.1231   2.1088  -2.5499
-#&gt;   -3.5121  -2.3167  -5.9088   3.2831  -0.5246  -4.8987   2.8872  16.5708
-#&gt;    1.6531   6.3598  -0.8847  22.2141  -2.2368  -8.2070   1.2624  -2.6903
-#&gt;   -1.7068  -4.0863  -5.5869   3.6179   9.6339  -3.1257  29.5030  -1.4866
-#&gt;   -2.4773  -5.0205   4.1964  -6.4092  -4.1407  -5.0958  -4.7861  14.8317
-#&gt;    6.5156   4.4029   6.6035   1.2587   1.1257 -16.5518  -3.6082  16.4726
-#&gt;   -1.0780   3.4886   6.4448  -3.6593   0.8148  -5.3412  -0.4133   4.7233
-#&gt;    9.4846   7.6809   6.4711   2.0481  -1.3859  -1.0715   4.2044 -20.6968
-#&gt;   -4.3761   1.6670   2.4490  -8.4354  -2.7067   9.7746   2.3280   3.9501
-#&gt;   -3.8817  -6.5747  -2.0872   2.3250 -12.6315  -5.6352   1.2802  15.7961
-#&gt;    4.2989   1.5583   4.6880   5.4209   1.1403  -0.6907  -0.5091  16.0139
-#&gt;    1.2061 -14.1669  -8.0375 -17.3101   5.6985  -0.8953  12.6394  -9.9687
-#&gt;   -5.8559  -8.4070 -11.6112  -4.7989 -24.5551 -16.0300 -10.0286   9.7208
-#&gt;    8.2044   6.9436  -3.6526  23.1866   8.5349  11.6573  -0.4488   2.0429
-#&gt;   -3.9382  -3.7899  -5.7767   5.4737   2.7066  -1.5215  -7.6963  -3.5784
-#&gt;    9.5496   8.5969  10.5426  15.4591   5.8838  14.7248   8.8217  16.1680
-#&gt;   -1.1191  -2.1120  -6.3763   1.0351  11.4906  -7.1137 -11.8626  -0.3931
-#&gt;  -10.9741  -4.9215 -13.0459 -15.8233  -3.4078 -12.5638   2.9955  -5.9202
-#&gt;   -0.1297   8.3024  12.6717  10.2556  13.5064  13.4313   0.2281  -1.7045
-#&gt;    1.6663   3.6732  -3.0221   3.8763  16.6424  -5.3196   4.7441 -17.1505
-#&gt;   -6.5405  -3.9878  -7.3507   1.7192  -9.0308  -9.1152  14.1827  -6.6667
-#&gt;    0.8203   4.1849   3.5764   1.3127   3.9395 -12.8159 -17.8305   2.2912
-#&gt;    3.0475   8.3816  -2.4021   6.5701   4.2364  -9.9423   0.9770  -9.7009
-#&gt; 
-#&gt; Columns 9 to 16 -10.0151  -9.5474  16.3357   8.2495  -1.5964   0.5695  -4.5643   9.8078
-#&gt;   30.0856   0.3242  -7.8498   8.6488   2.6957  -5.3110  -8.6937  -1.7189
-#&gt;    1.6592  -7.2534  -2.4388  12.4133   5.6499  15.6555   9.9671   1.4648
-#&gt;   11.3157  -6.9102  -4.3567  -1.6289  -1.9218   3.2060   9.5204 -16.0418
-#&gt;    7.6599  13.5402   0.1472   3.2803  -7.8039   5.7619  12.2552  -0.9779
-#&gt;  -15.4246  20.6626  18.6428  11.2379   3.3039   9.3777  -0.9136   1.9426
-#&gt;  -14.6024   3.6175   9.5540  -3.6661  10.4562  15.3631  18.0100  -3.4185
-#&gt;    1.6321  -2.9346  -6.9568  -8.9791  -3.5516   2.2035  -0.5501  -7.4288
-#&gt;   -4.0139  -5.5305 -11.6019   8.2809 -23.6624  -2.0877   3.3591  -1.3592
-#&gt;    3.6416   8.6007 -10.4185   7.1283   4.3517  13.6260   5.8551  -3.1176
-#&gt;  -11.1471 -15.0331  -2.3048  -1.3674  -2.6871   3.3351  -2.7968  -7.0620
-#&gt;   -6.1378  14.2888  -1.8011  -2.4272  -3.7110  -0.9645  -4.7425   9.3068
-#&gt;   16.9800  25.1799   9.1233  -7.2462   4.7891  -5.0251   0.7113  -9.1539
-#&gt;    9.0895   3.0683 -24.9544   6.3863   5.4586  -9.7773  -0.5664  -7.1917
-#&gt;  -10.7116  17.3930 -13.5514  11.1032  -0.2145  12.2160  -5.1363   2.3689
-#&gt;    0.3445  -2.2634   0.8781   3.2789   4.5291  12.1612   6.0505   2.4993
-#&gt;    5.3868   3.0195  -0.2482  -5.4812   8.4448   9.9567  -0.4724  -6.5618
-#&gt;   10.9629 -12.1021  13.6602  -3.9014 -15.5234  -8.2013  -9.3046  12.3432
-#&gt;  -14.5482  -5.0352 -10.1014  -4.7969  -0.9369  10.4475 -10.6851  -0.4472
-#&gt;   12.1259  -6.1691  -3.0353   4.5765  10.1662  -0.7835   2.3517 -10.8543
-#&gt;   -8.3979  -4.6937 -15.2274 -22.2141  -8.5639  -6.7124  -0.9374   7.7074
-#&gt;  -17.2386  -7.9246  -6.5068   3.9143  11.5533   3.2035 -11.3446  -2.0618
-#&gt;    9.9309 -13.9416 -10.4132 -16.5851   3.7950  10.6597   2.1974  -4.4408
-#&gt;   -0.4629  -0.9287  22.9732   1.8167   9.5406  -3.2515   1.6038   1.8185
-#&gt;   11.0190 -21.9261  22.7242 -17.9158  -7.3786  -7.2195  -0.3001  18.1530
-#&gt;    5.9330  19.3633  -2.7365   9.7048  -4.1241  10.0479  -3.0356   8.2367
-#&gt;  -10.5649   2.1628 -13.9123   9.5574  -5.5361   3.2461   1.5565   3.8338
-#&gt;   -3.5414 -14.0745  -3.8302 -14.1880  -6.9356  -9.2233  -1.5608 -11.7840
-#&gt;   -5.1279   1.8206  -1.8614   5.3786   8.2715   2.3127  -1.2312   3.2170
-#&gt;   -1.4440   1.4858   9.7767  -1.3624  -2.6655   5.8860   5.7552   9.4817
-#&gt;    1.4178 -16.0870 -17.0147  -6.7973   3.1098  -0.3210   7.8234   1.2850
-#&gt;  -10.4717   1.5837  -1.9258  -2.0323  13.3036   7.7405   7.7084  -5.0524
-#&gt;    9.6043  -7.5064   0.8189   0.4072   9.6478   1.5139  20.2461  -1.0598
-#&gt; 
-#&gt; Columns 17 to 24   8.1001  -1.0351  -6.7461 -11.0419  -8.1504 -15.6931 -19.7889  -2.3716
-#&gt;  -10.0340  -5.0651  14.4485 -11.9413   6.1078 -14.9792   2.4590 -15.2920
-#&gt;   -3.9651   6.6120  10.5014   7.5160  -9.6985   4.0468  -7.4204 -11.1921
-#&gt;  -13.8032   4.4341  -0.3191  14.4921 -10.4447  -1.8446  12.3376  -0.8948
-#&gt;   -8.0753  -1.2589  -8.4688   3.2476 -26.8242  -4.4277 -13.5342   2.0415
-#&gt;   -4.3877  -0.1280 -20.6544 -12.8968  10.8765 -10.9568 -11.3636  17.6642
-#&gt;    9.3794  -1.1094  -9.6291   3.5754  -2.1581  -8.7307  -2.9342   4.1637
-#&gt;   -6.2747 -11.1756  -8.4495 -13.4040 -13.1809 -13.8689  -7.2018 -13.4809
-#&gt;   12.8036  16.1512   5.4492  15.9207  -0.3596   0.6861  13.7002  -4.8839
-#&gt;  -10.9333   2.2218 -14.8068  13.8960  -5.6335  -0.0825   5.0305  -6.7653
-#&gt;   14.1881   0.2138  -1.2540   2.3505  -2.9398   3.0173 -16.3710   2.6998
-#&gt;    3.5745  14.2715  15.6316   2.2813  -7.2114  -1.7036  -5.6726   6.8289
-#&gt;   -1.3762 -16.8109   1.8873   5.7485  11.4382  -7.3215 -15.7174   4.9650
-#&gt;    0.9686   5.8856  -8.1113   7.1000   5.8756  -9.7769  10.2416   7.2427
-#&gt;  -10.5910  22.1120  -8.2317  -3.1513  -9.2054  -6.5088   1.6777  -1.6041
-#&gt;   -9.1672   1.9800 -16.2627  -9.7974   3.4530  13.2416  -4.8867  -3.5655
-#&gt;  -12.8684 -11.0718 -13.4428 -13.2373 -10.2448  -2.0117   7.0435   3.1549
-#&gt;   -1.6896   6.6603  15.4529   7.5885   4.8641   7.3906  -2.2209  -6.2453
-#&gt;   -2.5293  -9.9172 -13.6721  -0.2927  -6.7671   8.2823  19.9966   7.8450
-#&gt;    2.8503   4.1798   1.5580 -14.8536  -6.3334   2.6650  -4.5798  -8.2165
-#&gt;   -4.0300 -14.2928  -2.4017 -20.5895   4.7263  14.9711  -2.4875  14.4660
-#&gt;    5.1115 -13.9482  -3.6005   4.3206   9.7899  -5.5324   8.6255  -7.1614
-#&gt;    5.7668   2.7724   7.4530 -12.2066   3.6582  -2.3568  -3.9457   3.7212
-#&gt;   13.5755   5.8316   0.1492   9.8553  16.3314  15.4315  -7.3261   3.8208
-#&gt;    5.3305  12.3318   1.8238   2.7613  11.6012   8.4931  -3.4332   5.4188
-#&gt;    8.1775  12.1450  15.1162   2.3753   2.2250  -3.6509  12.8902  -2.8555
-#&gt;    7.8905  10.0615  -0.4912   6.3662  -0.6782   9.5253 -15.2438   2.5168
-#&gt;  -11.2149 -10.0954   3.3422  -2.7070  -7.0759   2.4042  -3.7015   1.2223
-#&gt;    2.3879   6.8082   0.2533  11.3374  -1.4567  14.0965   2.2258  -1.8460
-#&gt;    3.4320   9.3158  -0.6690  17.7832   9.3512  13.3587   9.3576   5.0262
-#&gt;    9.3727   1.7643   2.7273  -7.8212  -7.6490  -2.7739   7.7751  -6.1483
-#&gt;    0.2295   0.1979   2.4044   6.5403   8.0871   1.0703  -1.9011   7.0253
-#&gt;    4.3509   5.3380  -7.0635  10.6731  10.6836   0.4986  -7.3226   1.9557
-#&gt; 
-#&gt; Columns 25 to 32   6.7455   1.7868  -8.8835  -8.2019   4.5433  -1.0239  -3.1092   1.6769
-#&gt;    8.4804   5.0411  11.5685  -0.9770   2.6991  12.6585  -1.4397 -14.9938
-#&gt;  -10.9128  -5.3340  -9.6483  -6.0048   9.7009   3.1092   5.1960   1.1162
-#&gt;   11.1955  -2.2595   9.9399  -9.6134  -1.3063   9.5693   3.4519   1.7172
-#&gt;    3.1671   1.4267  -2.8816  14.6492   7.2395   1.8898   5.8684  13.9336
-#&gt;    4.4651   8.7700  -5.7401   6.6528  -5.2405  17.1295   8.9922   8.9185
-#&gt;   -3.7940 -18.7592 -11.9993 -18.3577  10.6142 -13.1228   3.9631  11.6180
-#&gt;   -1.4272  -1.2968  -1.0754   3.7228  16.9619  -9.0493  -7.8127   6.2468
-#&gt;   -7.6175   2.0480 -11.3912 -27.6616  -2.1672   1.8430   2.4369   0.5673
-#&gt;   -3.9454   5.5963   8.3102   2.1835 -15.3158  24.8253  -5.0833  10.2927
-#&gt;   -8.1090   8.9425  -9.0912   7.1353   5.2831  -4.3799  -4.0059  25.5929
-#&gt;   -4.5726  -0.3739 -18.7746   9.9125 -14.6715  15.2602  -3.1135  14.5251
-#&gt;    6.0025   3.2679  10.2790   5.0409   1.6172  -0.0848  -5.3120   4.0880
-#&gt;   10.8606  -4.5095  20.0533  -6.8515 -18.1393   7.5030  -8.9183  -7.8453
-#&gt;   -3.4917  -7.7637  -9.8252  16.4568  -2.8053   8.6339  13.7757  -0.1181
-#&gt;   14.0185   9.1554   5.6943   0.7750 -10.0743   2.1277  -2.2721  13.1413
-#&gt;   10.1655  -4.2152   1.7572   4.1830   2.1623  -8.7048  -3.9587   1.0580
-#&gt;   -6.0097  10.9930   5.1062   8.3181   0.8457  -0.1577 -18.2184  -2.3131
-#&gt;   -5.5882  -2.2534 -22.0129   4.8418 -15.4079   5.5410  -4.4979  -0.6346
-#&gt;  -14.6428   7.7315   6.3648  -5.0603   5.9393  -1.0989  11.1776   6.7368
-#&gt;    9.9365  24.6246  -1.8262  14.6768 -21.6443 -14.6404 -12.6476  -2.4409
-#&gt;    1.4070 -16.3869 -19.9686  -6.7619   4.0466   3.5202   4.3108   2.6778
-#&gt;    9.7351   1.3257  -9.6109 -21.4991  -2.8457  -9.2122  -3.7684  12.5494
-#&gt;   -1.6200  -0.4281  -3.1035  -1.5048   1.5300  16.4868  -5.0592  -1.0714
-#&gt;    8.1314  15.9266  -7.8683  -6.8566  -8.5155  -0.3033 -16.4273  -7.1421
-#&gt;  -13.8999  -5.4036   8.2061   1.4796  -2.7902   4.8665  -6.9505   0.0025
-#&gt;   -7.4138  -4.6629  -4.7055  -3.7802  -9.6058   6.1842  -2.8387  -6.5854
-#&gt;   10.8963  14.8133   0.6713  -1.0068   0.1435 -19.0033   6.1731 -15.9684
-#&gt;    3.5799  -7.0449  -4.7986   4.5287  -7.1192  -8.4011   5.7986   1.1786
-#&gt;  -13.1699 -20.9364  -9.5993   5.8298  -7.7431  23.6490   4.8607 -11.3314
-#&gt;    6.8217  -2.7287  11.7833 -12.5530  -7.4013   7.5500  -6.6971  10.5138
-#&gt;   10.9274  13.0032  -9.6783  15.5270  -2.9641   2.4258  -6.9870   2.7114
-#&gt;   12.2095  10.0677  12.3627  -0.1106   3.4960   3.6681   2.2930  -6.1216
-#&gt; 
-#&gt; Columns 33 to 40  -3.3622 -10.3849  10.2555  11.8728   5.8306 -10.4878   0.4853   8.9719
-#&gt;   18.4105  -7.9307  11.4194  -0.1296  -5.5568   3.6137   9.7678  -2.1159
-#&gt;   -3.3697   0.7608 -12.6738 -10.1236   4.4349  10.4471  -4.0326  -0.8935
-#&gt;   -0.3232  -0.7280  -0.2048   5.9241   0.5660 -11.9498  -1.1860  21.1517
-#&gt;   -2.7066   3.8992  -7.7092   2.9674  -2.1234  -1.3887   0.8307  -2.0074
-#&gt;   -0.5790  16.3288  -1.7148 -13.5067  -1.4230  -3.2069  -0.7859  12.1890
-#&gt;   -4.6540  -1.9929  -0.4003   0.7257 -10.6383 -10.1322   6.7866   7.2317
-#&gt;    3.8970  -3.4188 -11.7834  -4.6428   4.0508   2.1324   4.1699  -4.1890
-#&gt;    7.3724  16.4938  10.6386  22.3735  -2.0468   7.5446 -19.1999   2.6527
-#&gt;  -13.2517  -5.3879 -10.5804   5.9759  -2.5601  -3.3287  12.2843   6.9087
-#&gt;   -0.9873   0.0392  -3.8649   9.3947   2.5094  -8.7465 -14.7925   5.2627
-#&gt;   12.9071  17.7511   6.9966  -1.4220 -10.6707   4.9446  -6.7811  -4.1808
-#&gt;    3.2866   2.8973  -1.3446  14.3974   7.3142   4.2670   1.8701   1.2805
-#&gt;    9.1203  -7.5222  -4.2375   3.9771   1.2064   2.9481  -2.7665 -11.2163
-#&gt;  -11.7766  -6.0367 -11.9295  -6.2336  21.8730   1.4385 -13.9398   0.4111
-#&gt;   -4.0622   4.9269  -9.6263  -9.0863  15.4455  10.2114  -8.1764   5.1174
-#&gt;    7.1776  -7.6390   4.4247 -12.3476 -15.5966   7.9522   2.4543  -2.4679
-#&gt;    2.6628  -2.8408  15.8058   2.4088  -8.0436  -3.6906   9.7034  -3.1776
-#&gt;  -18.7927 -17.2439 -23.2489  19.8174  22.7149 -10.6282  -5.0924  -4.8641
-#&gt;   -8.5925   1.8419  -9.6410  -8.1455   9.6088  -5.6521   3.3799  -1.7245
-#&gt;   -3.4918 -10.8292  -2.7675 -10.0738   3.9026   9.0146   3.3701 -11.1880
-#&gt;   17.8822   4.8984   0.0398   2.5109   6.7534   4.2931  -5.9973  10.8163
-#&gt;    1.7897   5.7650   2.3038 -28.0216 -12.0954  -4.8501  -7.7992   4.6672
-#&gt;   -0.3011   3.7991  -4.8132  16.1519  13.6011   4.7395  -7.8466  -8.0761
-#&gt;   -6.7641  -1.3968  -2.4330 -26.8625 -14.1981  -3.8306   5.0209   4.4976
-#&gt;    6.8127   7.9355   5.9901  -5.3127  -5.0978   8.3256   0.7121  -8.4116
-#&gt;   -1.6115  12.8354  10.4227   0.5847  -5.0641   2.4588  -4.8166  -3.2331
-#&gt;    4.9475  -2.3680 -10.0948  10.5419   2.5680  -7.0339  -1.7302  -0.8638
-#&gt;  -13.0108  -5.0026   6.1649   5.7417  -4.4212 -12.3816  11.8004   4.8924
-#&gt;  -11.2446 -16.1600 -13.2574  -0.8420  -4.7720  -2.6097  13.6350  -3.3475
-#&gt;   17.9535   1.4541  -8.9213  -3.8055   1.3003   2.3363 -12.3381  -5.6345
-#&gt;   -9.3921  -4.6415  -0.2370 -11.6773  13.2384  -5.2224  -6.8675   5.8824
-#&gt;    5.9682   6.0117 -12.0649  -5.3713   2.1772 -21.1429   3.9779  -3.2444
-#&gt; 
-#&gt; Columns 41 to 48  -7.4203  -4.3417  -8.7223   9.8122   3.8623  -7.4629  13.9484 -23.2190
-#&gt;    3.4445  -1.3355   7.6531  10.1566  -4.0611  -8.9193  12.1846  13.4072
-#&gt;   -9.1451  -2.1885   2.3641 -12.4758 -16.5542   0.9120  -3.5647  12.2927
-#&gt;    4.6303  -5.2585   1.0651  -4.5631  -7.7577  -0.9423  -7.0766  11.9225
-#&gt;   -3.1262  -4.0675   2.1898  -8.6319  -2.6677   4.4339  -0.6671   5.0801
-#&gt;    6.2554   2.5598  -0.1234  13.0930  -1.8810  28.3096  -6.6054  -0.4741
-#&gt;  -11.8179 -12.9049   4.6068   2.6987   2.6213 -11.2886   3.4658  -5.2631
-#&gt;   -2.5020   5.5396  -3.7473   0.5592 -12.7861 -18.4386  20.1443  -9.0295
-#&gt;   -6.0943  -6.2892 -15.0037  -2.9393  -1.0958  15.7839 -17.5685  -4.1025
-#&gt;  -14.5351 -11.2419 -10.0126   8.4245  -3.8231  13.4642  -2.1626  19.3573
-#&gt;   -2.6913   1.2570   0.0191  -5.7630  -1.7038 -12.0102  -2.8262  -1.9102
-#&gt;   -9.3014  -2.7707  -5.6902   3.2790 -12.9711   6.5359   6.9999 -10.7766
-#&gt;    2.9156   8.3690  -0.5395  -6.0872  11.8689  -4.0404   2.1184  -9.6522
-#&gt;   12.3743  -1.0288  12.3481   1.4885  -0.7266   4.8815   0.7529  -0.9136
-#&gt;  -19.4519  -7.9898   7.5399   2.6351  -5.8580   6.3750   0.1136  -2.1771
-#&gt;    1.6941  -6.4103   0.5496  11.4846   7.6194   3.6779   1.9675   5.1756
-#&gt;    3.4512   6.0714   4.5734  27.0274   3.3834   8.3279  -5.7394  -1.7655
-#&gt;   -2.3163   7.9076  -5.1612  -5.3446   9.2760 -25.0731  13.2667   8.3713
-#&gt;    6.8684  -9.4366 -14.1991   8.6951  10.7399  -2.8100  -2.2233 -10.4838
-#&gt;  -14.9936  -2.4823  18.8716 -10.5186 -11.2061   2.7645   9.7941 -20.5271
-#&gt;    2.9695  10.8395 -10.4739   5.1254  -4.9681  -5.0584 -18.1055   2.5028
-#&gt;    2.7110   0.8587  -4.6111   0.3774 -11.9666   3.8982  -5.3369   6.5355
-#&gt;  -12.3133 -15.5227  -2.7682   4.9590   1.8040   0.7331  -1.1232  -6.3707
-#&gt;   -0.3749   5.4597 -11.5046   2.4737   4.4048  14.9951   5.7543 -16.8669
-#&gt;   -2.6534  -2.9687   0.4743  -3.9876 -11.8613   8.7067  11.5766 -13.5679
-#&gt;    0.9820 -10.8301   6.7273  -1.5765  -8.2368  14.5064   6.3943   7.6894
-#&gt;    3.5332  -6.1911   6.6732   4.3271  -9.1047  14.4457 -19.7375  12.9010
-#&gt;   15.6519  13.4092  -7.4260  -5.9673 -16.3715 -12.2971 -15.9744  -9.7981
-#&gt;    8.2944   9.8776 -10.1648 -10.2498  -0.8161  -5.1461   8.3299   0.3224
-#&gt;   -5.7386  -5.1824   0.2582  -9.6135  15.1354  -0.5289 -12.7296 -13.0442
-#&gt;   13.5382  -0.9888 -13.6341  -5.3329  14.3248   0.2444  -4.2781   4.5478
-#&gt;    9.2803  -0.4922   5.8904   9.9608  -0.0805  11.7786  -7.9851   1.9345
-#&gt;    2.3301  -4.4072   2.7353   0.9836   3.9349  -6.1501   1.5588 -10.2998
-#&gt; 
-#&gt; Columns 49 to 54 -20.8314  16.5843 -13.9129  -2.0479   6.8089  -3.7914
-#&gt;    6.2111   6.6074  -5.1333   2.0526   0.3237  -2.0470
-#&gt;  -14.5700   7.4540  -0.3076   7.1954  -6.8094  -4.0095
-#&gt;  -15.0809   0.6740  -0.7762   0.2321 -12.1556   2.6609
-#&gt;   14.3694  -1.3963   6.5437  10.2236  -8.9210   2.5166
-#&gt;  -12.3399 -26.4408   2.1555  -2.6613   3.1474  -2.0783
-#&gt;   -1.7603  -6.9358   7.6874   1.6465 -10.0625  -5.6285
-#&gt;   -2.7790   3.9554  -1.3829  -9.3238  -5.7191  -2.2334
-#&gt;  -11.1100  -4.3702  14.1653   4.4415   3.3352  -1.5379
-#&gt;  -14.6190  -1.4964  -1.2528  -9.3638  -8.0048  -4.8460
-#&gt;    8.4043  -8.5918  -5.1574   0.0703   1.7428  -1.1935
-#&gt;   -0.7531   1.1208  -4.4809   4.9666  14.7488  -3.2977
-#&gt;    4.3365   3.2914   4.2250  -2.9052 -11.3092  -4.1889
-#&gt;    3.3596 -16.7166   3.0995   1.4962   1.6846  -3.7894
-#&gt;   -9.7414   8.1937  13.6883  -2.5184  10.6000   3.3366
-#&gt;    2.6495   2.5624  -7.4683   2.6998   0.0631   2.7538
-#&gt;   -8.5029  -4.9118 -18.4429  -3.2778   8.0432  -8.2741
-#&gt;   -1.9020   6.6837  -5.4427   6.1116   7.2660   0.4438
-#&gt;   -7.0378  -4.7777  15.0084  -2.7419  -2.9724   3.1454
-#&gt;    4.6097   4.8506   3.8411  -4.8637 -13.3390   3.0000
-#&gt;    9.8125  -6.7690   6.7033   5.7351   3.2553  -1.0613
-#&gt;   -0.3601 -13.9705  -1.0009   4.9286  -7.7348  -2.9671
-#&gt;   -2.5662  -6.6644  -7.4420 -13.8237   5.5117   3.2655
-#&gt;   14.5174  -5.0537  21.5743  10.0272  -5.0012   3.7732
-#&gt;  -15.3295  16.5149   0.0156  -2.6484   1.1358   2.8868
-#&gt;   13.3596 -12.8918  -0.3230 -11.0566   6.0814  -0.1005
-#&gt;   -2.1543 -12.3791   9.6814  -9.1736   1.9768   0.4381
-#&gt;   -2.3409  -1.2850 -13.5981   0.2571  -8.0662  -3.2677
-#&gt;    7.2799  -3.7789  -4.2514   2.2253  -5.6518   1.1974
-#&gt;    2.7535  11.8568   1.0693  -0.8272   2.2264  -0.9253
-#&gt;   14.5961 -14.8933  -9.8182  11.7285   0.6507   0.6431
-#&gt;   -2.7609   6.4170  -0.6261   1.7213  -7.1515  -4.1353
-#&gt;   11.0345  -4.5719  -3.2490   0.7722 -13.8753   4.9701
-#&gt; 
-#&gt; (9,.,.) = 
-#&gt;  Columns 1 to 6 -3.0844e+00  3.9575e+00 -8.8483e+00  7.1485e+00  1.4732e+01  3.7237e+00
-#&gt;   1.3299e+00 -3.2750e+00 -3.2767e+00 -6.6849e+00  3.0363e+00 -1.1108e+01
-#&gt;  -5.3153e+00 -1.0945e+00 -2.6950e+00  6.9211e+00  4.3921e+00  4.0269e+00
-#&gt;  -6.4988e-01  2.1014e+00  9.9262e+00 -3.4379e+00  1.2616e+01  2.0861e+00
-#&gt;   1.9079e+00  1.3251e+00 -2.1251e+00 -5.3450e+00  3.2996e+00 -4.3239e+00
-#&gt;  -6.0243e+00 -9.6375e+00 -1.1247e+01  6.9145e+00 -4.1306e+00  2.5840e+00
-#&gt;   6.0369e+00  6.4653e-01 -2.5032e+00 -1.9317e+00  1.9454e+01  5.1159e+00
-#&gt;   6.6771e+00 -9.7476e-01  4.0785e-01 -4.2783e+00  2.9150e+00  1.3538e+01
-#&gt;  -9.8473e+00  1.0038e+00  7.7229e+00  3.0641e+00  5.7014e+00 -1.2515e+01
-#&gt;  -4.6366e+00  1.6778e+00 -6.7110e+00 -2.1135e+01 -1.1663e+01  9.1680e+00
-#&gt;   2.4572e+00  1.0856e+01 -8.8700e+00 -6.6691e+00 -9.6360e-01  1.3725e+01
-#&gt;   1.5261e+00  2.6883e-01 -3.6022e-01  6.8276e+00 -1.1354e+00 -5.7788e+00
-#&gt;   6.0398e+00 -3.6515e+00  1.9147e+00 -9.9658e+00  1.8608e+00  1.6815e+01
-#&gt;   1.0912e+01 -1.1609e+00  4.9686e+00 -8.8477e+00 -5.8313e+00 -2.2854e+00
-#&gt;   4.3442e-01  6.8882e+00  7.6787e+00 -8.1718e+00 -5.9922e+00 -2.9733e+00
-#&gt;  -2.9825e+00  2.4150e+00 -1.2465e+00 -7.3483e-01 -1.5889e+01 -1.0715e+01
-#&gt;   2.2427e+00  3.7664e+00 -2.2868e+00  4.5995e+00  1.0914e+01 -1.2551e+00
-#&gt;  -9.6294e-01  5.3982e-01  1.4011e+01  6.6027e+00  3.2266e+00 -1.1214e+01
-#&gt;  -1.2467e+00  5.1257e+00  2.9094e+00 -4.5639e+00  3.2488e+00  4.4284e+00
-#&gt;   2.6206e+00  8.2820e+00  3.8331e+00 -4.8418e+00 -1.3598e+01  9.1072e+00
-#&gt;   6.8628e-01  1.1245e+01  1.8393e-02 -2.6590e+00 -2.7642e+01 -3.6005e+00
-#&gt;   2.3190e+00 -2.0259e+00 -1.0040e+01 -6.6561e-01 -1.7592e+01  3.9268e+00
-#&gt;   7.6194e+00  5.9051e+00  1.9740e+00 -8.0803e+00 -1.1348e+01  5.4960e-01
-#&gt;  -8.1681e+00  2.1004e+00 -3.6650e+00  1.0278e+01  5.2578e+00  2.8381e+00
-#&gt;  -4.0629e+00  3.3020e+00 -6.8705e+00  6.6090e+00 -5.2204e+00  8.8572e+00
-#&gt;  -2.8817e+00 -7.1743e+00 -5.8274e+00 -1.4024e+00 -1.6453e+00 -1.2242e+01
-#&gt;  -1.8511e+00  5.2588e+00 -3.8408e+00  1.7996e+01 -2.0355e+00  4.0652e+00
-#&gt;   2.0675e+00  8.8085e+00  1.3759e+00  7.3567e-01 -9.5449e+00  3.0625e+00
-#&gt;   1.1249e+00 -2.9889e+00  1.9286e+00 -3.4670e+00  1.6181e+01  1.6632e+01
-#&gt;  -9.4529e+00  2.5716e+00  7.1519e+00  3.4283e+00 -7.8391e+00  4.5298e+00
-#&gt;   6.1931e-01  1.0553e+00  6.9422e+00 -1.0306e+01 -1.4759e+01 -7.0191e+00
-#&gt;   9.1682e+00 -4.0056e-01 -9.7737e+00  9.0546e+00 -5.7929e+00 -5.6782e+00
-#&gt;   2.3222e+00 -4.0269e+00  3.3736e+00  3.6830e+00  1.0166e+01  6.4749e+00
-#&gt; 
-#&gt; Columns 7 to 12 -5.3888e+00 -2.9333e+00  1.5363e+00  5.4292e+00 -9.1061e-01  1.2076e+01
-#&gt;   1.2150e+00  1.0525e+01  6.0796e+00  2.6328e+00 -1.1000e+01  2.9484e+00
-#&gt;   3.1673e+00 -8.9752e+00 -7.2490e-01  1.5361e+01  2.7796e+00 -7.5944e+00
-#&gt;  -1.4430e+01  9.7151e+00 -9.1587e-01  3.2778e+00  5.2619e+00 -3.7392e-01
-#&gt;   3.7892e+00 -5.1312e+00 -9.0128e+00  5.1808e-01 -3.1414e+00  2.7353e+00
-#&gt;  -6.7068e+00 -9.0294e+00  1.1638e+01  1.0029e+00  1.1856e+01 -2.6498e+00
-#&gt;  -1.1853e+01 -9.1865e+00 -9.7623e+00  7.9592e+00  1.4931e+01 -5.3246e+00
-#&gt;   1.1537e+01 -4.9769e+00  5.7990e+00 -1.7946e+01 -2.5516e+00 -6.2747e+00
-#&gt;  -1.0631e-01 -1.8550e+00 -6.2494e+00  1.0040e+01  1.2557e+01  1.6425e+01
-#&gt;   1.5632e+00  8.7546e-01 -1.0170e+01 -1.5403e+00  8.9363e-01 -3.8374e+00
-#&gt;   1.6753e+01 -1.7494e+01 -1.5118e+01  6.6152e+00 -3.2020e+00  9.7728e+00
-#&gt;   1.8719e+01  7.2675e+00  1.6485e+01  2.5752e+00 -2.4923e+00  1.7008e+01
-#&gt;   6.3191e+00 -6.5026e+00 -2.9797e+00 -1.0865e+01 -5.6393e+00  2.7777e+00
-#&gt;   2.7979e+00  8.5942e+00 -2.8034e+00 -1.0326e+01  1.2389e+01  1.6556e+01
-#&gt;   1.3892e+01  6.8219e+00  7.3136e+00  1.1366e+01  8.1391e-01  4.3374e+00
-#&gt;   4.8084e-01  4.3842e+00 -4.2161e+00  1.8518e+00 -3.7542e+00  9.6281e+00
-#&gt;  -2.2650e+00  7.0146e+00  1.1047e+01  6.1887e+00  1.8057e+01  1.4751e+00
-#&gt;  -2.4461e+01  9.5519e+00 -8.2912e-01 -1.0050e+01 -8.2641e+00  4.6827e+00
-#&gt;   7.4679e+00 -3.5030e+00 -1.4055e+00  1.9115e+01  2.4359e+01  8.1015e+00
-#&gt;   2.4131e+01  2.4365e+00 -6.8482e+00  2.9907e+00 -4.4200e+00  2.4605e+00
-#&gt;  -2.3553e+00  6.7272e+00 -1.4875e+01 -1.3162e+01  9.2889e+00  6.4767e+00
-#&gt;   2.3963e+01  1.2350e+01  8.0211e+00  1.3432e+01 -1.4131e-01  6.6445e+00
-#&gt;   1.3471e+01  7.7444e+00  7.6033e+00 -2.4399e+00  2.5683e+01 -6.2176e+00
-#&gt;   8.8612e+00 -4.4346e+00  1.4746e+01 -2.9410e+00 -6.8655e+00  6.2316e+00
-#&gt;  -3.5537e+00 -7.2698e+00 -4.7409e+00 -7.2148e+00 -5.8450e+00 -5.2691e+00
-#&gt;   1.3115e+00  1.6191e+00  4.7505e-01 -1.5181e+01 -1.1890e+00 -1.3413e+01
-#&gt;   2.0220e+00  4.9279e+00  2.4231e+00 -5.2500e+00  4.8225e+00 -1.3004e+01
-#&gt;   1.6325e+00 -8.4463e-01 -5.2737e+00 -4.4634e+00  9.0230e-01  1.5081e+01
-#&gt;  -9.6546e+00 -1.6568e+01 -2.3547e+00 -9.3497e+00 -9.6521e+00 -4.5624e+00
-#&gt;  -1.1359e+01  4.4158e+00  7.5889e+00  5.7622e-01 -7.3438e+00 -1.0305e+01
-#&gt;   3.7111e+00 -3.7338e-01  1.4702e+01 -6.3014e+00 -6.5463e+00  2.1742e+01
-#&gt;   9.0815e+00  2.7954e+00 -1.5308e+01  1.0759e+01  3.7131e+00 -1.3179e+01
-#&gt;   3.3553e+00 -8.0263e+00 -4.2763e+00  8.2564e-01 -3.5557e+00 -6.8022e-01
-#&gt; 
-#&gt; Columns 13 to 18 -7.4942e+00 -1.4010e+01 -5.3054e+00 -8.4081e+00 -3.8957e-01  7.1862e+00
-#&gt;  -7.1702e+00 -7.3959e+00  2.7816e-01  9.9267e+00 -2.1226e+01  2.2359e+01
-#&gt;   3.7178e+00  8.9062e+00 -1.9239e+00  3.9103e+00 -3.0656e+00 -2.7693e+00
-#&gt;  -6.8793e+00  6.7350e+00  7.0029e+00 -1.1243e+00  9.9269e+00 -6.0519e+00
-#&gt;  -1.9346e+01  9.3772e+00 -1.6757e+01  8.7048e+00  2.2695e+00 -9.9442e+00
-#&gt;  -2.1270e+01 -1.9596e+01 -1.2583e+01 -2.6858e+01 -2.5704e-01 -2.3715e+01
-#&gt;  -1.5152e+01 -1.1151e+01 -1.4880e+00 -1.2560e+01  1.4956e+01  1.3154e+00
-#&gt;   1.4843e+01  1.0410e+01  6.9810e+00 -2.2508e+01  5.1875e+00  9.2162e+00
-#&gt;   8.8139e+00  8.1885e+00  1.5398e+01 -6.9044e+00  1.1073e+01 -4.3035e+00
-#&gt;  -5.6897e+00  1.0101e+01 -2.1102e-01 -8.2544e+00 -7.0694e+00  1.1567e-01
-#&gt;  -5.6823e+00  4.4739e+00 -1.0758e+01 -1.8899e+00  3.8160e+00 -8.9889e+00
-#&gt;  -6.3853e+00  3.2104e+00  5.0729e+00 -2.8138e+00  2.5488e+00  3.5416e+00
-#&gt;  -1.3139e+01 -1.6489e+01  1.7487e+01  5.5499e+00  9.9309e+00 -5.3077e-01
-#&gt;   3.8411e+00  6.1458e+00  6.2721e-02  8.6752e-01  1.6274e+00  1.2679e+01
-#&gt;   3.6154e+00 -6.3606e+00 -7.1458e+00 -9.3122e+00  5.1054e+00  3.1354e+00
-#&gt;  -8.9520e+00 -7.4094e+00 -1.3353e+01  1.1036e+01  3.9082e+00 -9.9292e+00
-#&gt;  -8.0426e+00 -2.7335e+00 -1.6823e+01 -1.5870e+01 -2.4214e+00 -1.9141e+00
-#&gt;   3.9491e+00  5.3443e+00  1.4690e+01 -2.9426e+00  4.3608e+00  7.3118e+00
-#&gt;   4.9603e+00 -4.8410e+00 -8.5335e+00 -2.0889e+00  2.1733e+00 -6.1265e-01
-#&gt;   7.4755e-01  1.5469e+01  6.2350e+00 -9.7556e+00  1.9131e+01  4.6571e+00
-#&gt;   1.3613e+01 -8.7656e-01  5.4168e-01  9.9604e+00  1.6535e-01  9.2111e+00
-#&gt;   4.1108e+00  1.3246e+00 -3.6717e+00 -3.7175e+00 -1.5989e+01 -3.8839e+00
-#&gt;   1.0627e+01  1.4176e+01 -5.0590e+00 -2.0990e+01  1.3081e+01 -3.2022e+00
-#&gt;   2.4884e+00 -8.7539e+00 -1.5358e+00  9.2289e+00  6.3881e+00 -6.5302e-01
-#&gt;   8.6044e+00  5.5272e+00 -3.5769e+00 -4.3029e+00  8.1571e+00 -1.5234e+01
-#&gt;   1.0456e+00  1.2951e+01  1.2300e+01 -7.5131e+00 -8.2078e-01 -5.4459e+00
-#&gt;   1.1622e+00 -3.2786e+00 -2.4809e+00  9.2949e+00  4.5887e-01 -5.6015e+00
-#&gt;   1.0684e+01 -8.1112e-01 -7.1593e+00  6.1027e+00 -6.0294e+00 -4.8785e+00
-#&gt;  -3.0858e+00  1.3670e+01 -2.5882e+00  7.5500e+00  7.6051e+00 -9.8345e+00
-#&gt;  -1.4309e+01 -2.4152e+01 -4.3382e+00  2.0233e+01 -6.3286e+00 -1.8082e+01
-#&gt;   1.5897e-02  3.6208e+00  2.3562e+00 -1.3223e+01 -9.8380e+00 -4.8735e+00
-#&gt;   2.0443e+00 -1.5113e+01 -3.1743e+00 -4.9740e-01 -1.1086e+01  3.2641e+00
-#&gt;   1.5150e+01 -5.8453e+00 -1.1136e+01  6.0637e+00 -6.3859e+00 -2.8524e+00
-#&gt; 
-#&gt; Columns 19 to 24 -5.6893e+00 -1.9453e+01 -3.5957e+00  1.5872e+01 -1.3028e+01  5.1139e+00
-#&gt;   6.9340e+00  1.2190e+00  1.2480e+01  1.2942e+00 -1.6315e+00  1.4260e+01
-#&gt;   1.2432e+01 -4.2904e+00  2.6480e+00 -1.8259e+00 -1.5810e+00  3.2235e+00
-#&gt;   1.4535e+01 -7.8076e+00  1.7186e+00 -7.7738e+00 -7.1399e+00 -8.9696e-01
-#&gt;   8.6471e-01  5.4114e+00  1.1150e+01  5.3790e+00  7.0586e+00  1.9771e+00
-#&gt;  -9.0428e+00 -6.1166e+00 -1.1130e+01  1.3702e+01 -7.3280e+00  1.9078e+00
-#&gt;  -1.2262e+01  4.3546e+00  6.0670e+00 -3.4008e+00  8.6816e+00  7.8010e-02
-#&gt;  -2.0900e+01  4.2239e+00 -9.2654e-01  7.3594e+00  8.6544e+00  2.1153e+00
-#&gt;  -1.1320e+01  1.2950e+01 -8.8325e+00  5.0883e+00  1.2992e+01 -1.0941e+01
-#&gt;  -5.0212e+00  2.9902e+00 -3.3185e+00  5.6894e+00 -1.4010e+01 -1.0241e+01
-#&gt;  -1.1013e+01 -3.3661e-01  2.2183e+00  1.8726e+00 -1.7061e+00  5.6472e+00
-#&gt;  -8.8008e-01  4.3267e+00 -4.8622e+00  7.6512e+00 -5.9359e+00  2.2392e+01
-#&gt;  -3.8189e+00 -8.8454e+00 -2.0480e+00 -1.0577e+01 -4.6067e+00 -1.5634e-01
-#&gt;   1.4468e+00 -4.0155e+00 -7.7699e+00 -6.3497e+00 -1.2672e+01  6.8436e+00
-#&gt;   2.3073e+00  1.2243e+01 -9.7741e+00  1.7353e+01 -8.2820e-01  1.3717e+00
-#&gt;   1.4566e+01 -1.2031e+01 -2.8944e+00  1.1853e+01  5.4134e+00 -1.2361e+00
-#&gt;  -8.3577e+00  2.4359e+00 -1.3276e+01 -4.0730e+00 -1.0948e+01  1.3474e+01
-#&gt;   3.8182e-01 -6.4262e+00  3.8115e+00 -2.2974e+00 -5.7632e-01  9.6762e+00
-#&gt;  -5.3428e+00  8.6593e+00 -6.0399e+00  1.8404e+01  2.2284e+00 -9.9725e+00
-#&gt;   4.7909e+00 -2.2855e+00 -4.3346e+00 -5.4306e+00  2.7422e-01 -2.0013e+00
-#&gt;  -8.8541e+00 -6.9699e+00 -1.3400e+01  8.0493e+00  6.8156e+00 -9.7870e-01
-#&gt;  -7.3828e+00  2.8325e+00  7.9561e+00  8.8198e+00  1.1244e+00 -1.6940e+00
-#&gt;   4.4528e+00 -1.8023e+00 -6.6527e+00 -3.9291e+00 -5.5194e+00  1.8781e+00
-#&gt;   6.1969e+00 -1.2150e+01  1.4234e+01  1.0549e+01  1.6503e+01  5.4961e+00
-#&gt;   1.9726e+01 -1.9853e+01  1.1585e+01 -2.1674e+00 -1.4682e+01  5.4222e+00
-#&gt;  -7.7661e+00 -2.6476e+00 -8.8573e+00 -3.4332e+00 -9.9211e+00  8.6490e-02
-#&gt;   1.4378e+01  4.0144e+00 -9.0094e+00  2.5586e+00 -7.2253e+00 -7.1700e+00
-#&gt;  -1.1073e+00 -2.0247e+01  1.6292e+00 -1.5077e+01 -1.4288e+00 -1.5738e+00
-#&gt;  -1.2809e+01 -8.0958e-01 -3.1857e+00 -1.3108e+01  4.3087e+00 -1.0828e+01
-#&gt;   1.0087e+01 -8.5014e+00  8.2723e+00  5.4861e+00 -1.5241e+01  5.1375e+00
-#&gt;   4.1856e+00 -6.3674e+00 -6.0491e-01 -3.4339e+00  3.0145e+00  7.3578e+00
-#&gt;  -2.0565e+00  7.9100e+00 -1.1618e+01 -1.0194e+01 -1.0763e+01 -1.8640e+01
-#&gt;   6.7430e+00 -1.1374e+01  2.1704e+00 -7.5976e+00 -8.7040e+00 -7.4410e-03
-#&gt; 
-#&gt; Columns 25 to 30  1.3558e+01  3.3122e+00  2.0959e+00  1.5647e+01  1.0997e+01 -1.9436e+01
-#&gt;   2.6344e-02  7.2531e+00  1.2503e+01 -9.3091e+00  3.1167e+00 -1.1548e+01
-#&gt;  -2.7886e-01  7.5301e+00  4.8612e+00  2.8948e+00 -2.8620e+00  6.4170e+00
-#&gt;  -6.6708e+00  1.7084e+00 -1.2577e+01 -4.7185e+00 -4.7590e+00  1.1023e+01
-#&gt;  -5.3235e+00  1.5145e+00  8.6505e-01  8.3207e+00 -2.0379e+01  4.0297e+00
-#&gt;   1.5575e+01 -2.6839e+00  7.3206e+00  1.3451e+01 -5.3136e+00 -4.7260e+00
-#&gt;   6.1753e+00  1.2691e+00 -7.8798e-02  1.5560e+01 -1.3044e+00 -2.4853e+00
-#&gt;  -5.1093e+00  2.5675e+00  4.8341e-01  2.7719e+00  5.3591e+00 -2.0743e-01
-#&gt;   8.9852e+00  9.6871e+00 -6.3932e+00  3.0404e+00  7.4364e+00 -1.0637e+01
-#&gt;   4.8914e+00 -6.7086e+00  3.4064e+00  8.1951e+00  6.9123e+00 -8.8660e+00
-#&gt;  -7.4082e-01 -3.7787e+00  7.6736e-02  1.8565e+00 -6.1790e+00  1.2534e+01
-#&gt;   9.1540e+00 -4.7321e+00  5.9293e+00  1.2966e+00 -1.2415e+01  1.3284e+01
-#&gt;  -1.3991e+01  9.3310e-01 -2.2098e+00  6.6424e+00  4.0365e+00  1.3118e+01
-#&gt;  -1.1929e+01 -4.9660e+00  8.5513e+00 -3.3803e+00 -1.0103e+01 -2.6659e+00
-#&gt;   3.5112e+00 -6.5770e+00  5.2533e+00  6.0158e+00 -4.1765e-01  9.3664e+00
-#&gt;   3.5893e+00 -5.5666e+00 -8.5346e-01  1.0235e+01 -2.8308e-01 -5.9867e+00
-#&gt;   1.2652e+01 -2.2574e+00  5.1448e+00  1.3336e+00 -1.3777e+01 -2.0938e+01
-#&gt;  -9.7569e-01  7.9474e+00 -7.8825e+00 -3.6059e+00  4.1351e+00  1.9781e+00
-#&gt;   4.8112e+00 -4.8876e+00 -1.4709e+00  4.2883e+00  2.9530e+00  1.8854e+00
-#&gt;  -1.3174e-01 -6.2074e+00  4.1328e+00  6.5042e+00 -9.7857e+00  6.5055e-01
-#&gt;  -3.8156e+00 -1.3507e+01  1.6801e+00  2.0514e+00  5.6115e+00  3.8388e+00
-#&gt;  -6.6274e+00 -7.6941e+00  1.3906e+00 -8.0568e+00  2.6097e+00 -1.2881e+00
-#&gt;   6.0680e-01 -3.7622e+00  1.9647e+00  1.7416e+01 -1.8234e+01 -1.8430e+00
-#&gt;  -4.6856e+00 -4.4744e+00  3.9488e+00  9.2486e+00  5.6437e+00  1.2768e+01
-#&gt;   5.5231e+00 -5.7111e+00  9.3682e-01  5.9836e+00 -9.2493e+00  3.0670e+00
-#&gt;   9.8058e+00 -2.6402e+00  1.1336e+01  6.2685e+00 -2.6886e+00  7.6441e+00
-#&gt;   5.9363e+00 -6.5192e+00  7.8321e-01 -4.0427e-01 -1.3133e+00  3.0544e+00
-#&gt;  -6.3091e+00  1.0040e+01 -1.2751e+01 -1.1534e+01 -5.8298e+00  2.7647e+00
-#&gt;  -7.4592e+00  9.4103e+00  1.4476e-01  5.4149e+00  6.6634e+00  1.8745e+01
-#&gt;   2.3166e+00 -2.2831e+00  2.4495e+00 -1.4437e+01 -4.8693e+00  4.2169e+00
-#&gt;  -2.0170e+01 -3.3310e+00 -8.3002e+00 -5.6621e+00  2.9494e+00  1.0479e+01
-#&gt;  -1.6103e+01  2.7127e+00  1.2102e+01  1.2768e+01  1.7274e+01 -4.4694e+00
-#&gt;  -8.0425e+00  1.6162e+01 -6.0518e-02  1.0420e+01  4.4894e+00  6.6914e+00
-#&gt; 
-#&gt; Columns 31 to 36  3.6761e+00  1.0637e+01  1.4562e+01  5.9846e+00  4.0722e+00  9.0006e+00
-#&gt;  -5.1966e+00  1.5071e+01 -3.9094e+00 -8.8287e+00  1.9831e+01  1.1739e+01
-#&gt;  -1.7767e+00  5.8233e+00 -7.6486e+00  4.5091e+00  7.6294e+00 -5.1123e+00
-#&gt;  -6.5812e-01  5.4449e+00  9.8120e+00  3.2020e+00 -5.4120e+00  8.7914e+00
-#&gt;  -1.8320e+00  2.6157e+00  2.6970e+00 -2.7039e+00 -6.0996e+00 -3.1244e+00
-#&gt;  -3.5625e+00 -4.4053e+00 -2.8956e+00  1.3660e+01 -5.8036e-01  1.0343e+00
-#&gt;  -8.0378e+00 -1.0343e+01  1.2668e+01  3.7481e+00 -5.9861e+00  1.0574e+01
-#&gt;  -5.5252e+00  6.8272e+00 -8.0569e-01  4.2449e+00  7.8909e+00  1.2782e+01
-#&gt;  -5.3252e+00  7.3852e+00 -6.1989e+00  8.6960e+00  2.6933e+00 -1.5599e+01
-#&gt;   1.6819e+01 -2.1453e-01  1.8835e+01  4.1777e+00  5.2485e+00  1.8756e+01
-#&gt;   1.0849e+01 -3.0162e+00 -1.2584e+01 -1.1527e+00  1.0408e+01 -3.3177e+00
-#&gt;   6.6933e-01  6.6165e+00 -7.3807e-01  7.7241e-01  7.5175e+00  8.1878e+00
-#&gt;  -2.0470e+00  1.7072e+01  1.0649e+01 -1.3827e+01 -7.3002e+00  7.4366e+00
-#&gt;  -1.2941e+01 -1.1538e+01  7.9025e+00 -1.5590e+00  4.0351e+00  4.5150e+00
-#&gt;   1.2127e+01  9.3004e-01 -7.7179e+00  8.3346e+00 -8.8323e-01  3.2883e+00
-#&gt;   1.3323e+01  4.5492e+00  4.5777e+00  3.5815e+00 -6.6529e-01 -8.3083e-01
-#&gt;   6.3809e+00 -1.5644e+01  1.8017e+00 -5.0205e+00  4.5602e+00  1.4026e+01
-#&gt;  -1.9529e+00  7.3174e+00 -2.8223e+00  8.9153e-01 -5.3674e+00  3.2901e+00
-#&gt;  -1.2805e+01  2.5653e+00 -2.9729e-01  8.0468e+00 -4.1089e+00  1.0789e+01
-#&gt;  -8.7715e-01  1.3078e+00 -1.4629e+01  8.8644e+00  1.8758e+01 -1.1461e+01
-#&gt;   8.1886e+00  7.9778e+00  1.5112e+01 -6.0972e+00 -7.2719e+00  5.6152e+00
-#&gt;  -1.0019e+01 -2.1547e+00  6.7985e+00 -1.1222e+00  3.7693e+00 -3.7409e+00
-#&gt;  -1.3928e+01  1.2986e+00 -1.6339e+00  2.8692e+00 -1.0651e+01  1.3526e+01
-#&gt;  -6.4898e+00  6.9418e+00  1.5826e+01  1.5640e+01 -1.0684e+01 -6.8203e+00
-#&gt;   1.0347e+01 -1.3085e+01 -1.5079e+01  2.2178e+01 -1.2524e+01 -2.0429e+01
-#&gt;   1.3768e+01  6.9692e+00 -7.8130e+00  3.4595e+00  6.7786e+00  1.5439e-01
-#&gt;   1.0279e+01 -7.5763e+00 -6.7798e+00 -5.3758e-01  3.0871e+00 -5.8363e+00
-#&gt;  -6.6974e+00  9.5524e+00 -5.5794e+00  4.1938e-01  1.8463e+00  4.4021e+00
-#&gt;   3.0242e+00 -1.3227e+01  6.3768e+00 -3.2215e+00  2.1403e+00  1.8556e+00
-#&gt;   1.7823e+01  5.1552e+00  3.1660e+00  1.7945e+00 -2.3683e+01  9.8308e-01
-#&gt;  -1.4320e+01 -3.1440e-01 -9.9361e-01 -8.1371e+00  8.5137e+00 -2.5419e+00
-#&gt;   1.5456e+01 -1.1247e+01  9.6495e+00 -1.7449e+01  1.5162e+00  3.9664e+00
-#&gt;  -4.0748e+00  1.2323e+01  1.0367e+01  1.8966e+00 -6.5433e-01  9.7306e-01
-#&gt; 
-#&gt; Columns 37 to 42  6.3193e+00  4.1580e-01 -5.5026e+00 -5.2077e+00  3.8273e+00 -1.7859e+01
-#&gt;   6.0152e+00  4.8147e+00 -3.1357e+00 -1.1225e+01  4.4666e-02 -2.4823e+00
-#&gt;   1.6241e+01 -1.2788e+01 -7.8586e+00  4.2744e+00  8.6902e-01  1.9180e+00
-#&gt;   4.2309e+00 -1.2683e+00  1.1844e+00  5.9364e-01  1.2986e+00  7.0099e+00
-#&gt;   1.8031e+01 -1.3735e+01  3.0254e+00 -6.7109e+00 -8.0463e+00  4.5866e+00
-#&gt;   1.2389e+01 -1.3599e+00 -1.3221e+01 -1.2302e+01 -1.9856e+01  4.3722e+00
-#&gt;  -2.8307e+00  1.1498e+01  3.3417e+00 -2.7400e+00 -1.3726e+01  8.2211e+00
-#&gt;   1.5877e-01  1.0815e+01  2.8379e+00  4.1172e+00  1.7293e+00 -2.6715e+01
-#&gt;   5.9303e+00  1.5277e+01 -1.0294e+01  1.1066e+01  2.9799e+00  4.0886e+00
-#&gt;   9.0479e+00  9.2719e+00  1.0262e+01 -6.9908e+00  7.9677e+00 -3.2507e+00
-#&gt;   5.7520e+00  8.5322e+00  8.9157e+00  4.7481e+00 -7.1603e+00 -1.1803e+01
-#&gt;  -2.4535e+00  2.2372e+00  4.7389e-01 -2.4764e-01  6.1815e+00 -6.0736e-01
-#&gt;   2.7749e+00  9.9736e+00  8.4133e-01 -1.1915e+00  1.6670e+01  5.8009e+00
-#&gt;  -1.0435e+01  1.1925e+01  6.6147e+00 -1.0507e+01 -2.8868e+00  5.8958e+00
-#&gt;   4.8700e+00 -1.6851e+01  1.1281e+01  3.3564e+00  9.3250e+00 -8.3811e+00
-#&gt;   4.7449e+00 -6.5033e+00 -9.0380e+00 -7.3736e+00 -1.5892e+00  5.9191e-02
-#&gt;  -1.1771e+00  7.8254e-01 -4.0813e-01 -2.0650e+00 -2.8252e+01  2.5435e+00
-#&gt;  -8.5939e+00  1.0028e+00  5.0527e+00 -2.2370e+00  7.3478e+00 -1.6387e-01
-#&gt;  -3.6050e+00  1.1006e+01  2.0796e+01 -8.6269e+00  9.8430e+00  1.6179e+00
-#&gt;  -2.2736e+00 -2.3937e+00 -1.7850e+00  1.6858e+00  3.9957e+00 -1.4926e+01
-#&gt;  -6.3791e-01  8.6191e-01  1.3340e+01 -8.5667e+00 -2.6413e+00 -1.2077e+00
-#&gt;  -1.3345e+00  9.3251e+00 -1.2439e+01  2.0178e+00  3.7639e+00 -8.5700e+00
-#&gt;   4.7723e+00 -2.5637e-02 -4.7521e+00 -6.5413e+00 -1.6969e+01  7.9883e+00
-#&gt;  -1.3204e+01  4.1165e+00 -1.6554e+01  8.3988e+00 -7.9562e+00 -3.6346e+00
-#&gt;   4.0281e+00 -1.1415e+01 -1.5121e+01  1.3674e+01  9.2672e-01  7.8058e+00
-#&gt;  -6.4940e+00  3.4464e+00  4.2818e+00  1.0922e+01  1.1316e+00 -3.7089e+00
-#&gt;   5.0213e+00  9.4807e-01 -2.1677e+00  5.5849e+00  1.1946e+01  1.0366e+01
-#&gt;   1.1274e+00  5.3332e+00  1.5231e+00  1.3328e+01 -1.3047e+01  8.8516e+00
-#&gt;  -3.4887e+00 -7.2093e+00  1.3508e+01  3.4285e+00 -2.4273e+00 -2.3980e+00
-#&gt;   1.1403e+01 -1.4971e+01  2.1474e+00  2.2323e+00  5.8092e+00  5.7479e+00
-#&gt;  -3.5859e+00  1.2391e+01 -3.6762e+00  5.5437e+00 -1.0012e+01 -1.2154e+01
-#&gt;   6.0459e+00 -9.5193e+00  1.4791e+01 -2.6113e+01  7.2202e+00  6.5619e+00
-#&gt;  -3.8888e-01  8.4880e-01 -5.9651e+00 -1.3199e+01  1.5273e-01 -1.0037e+01
-#&gt; 
-#&gt; Columns 43 to 48  1.2266e+01 -2.8834e+01 -8.0280e+00 -2.0428e+00 -4.9001e+00  9.2407e-01
-#&gt;   9.0277e+00  1.7013e+00  5.1573e+00 -5.6153e+00  2.1339e+00 -2.9950e+00
-#&gt;   7.0761e+00  1.5237e-02 -1.3783e+01 -5.3301e-01  3.7376e+00  1.3496e+01
-#&gt;  -1.1369e+01 -1.5329e+01 -7.7484e+00  5.5542e+00  1.0380e+01  5.9031e+00
-#&gt;   1.7468e+00 -3.4204e-01  7.3675e+00  4.2309e+00  6.3147e+00  4.0213e+00
-#&gt;   1.1571e+01  1.5676e+01 -6.5670e+00  6.0116e+00 -9.0544e+00 -1.0544e+01
-#&gt;  -8.3220e+00 -1.5512e-01  1.5130e+01 -5.3928e+00 -4.0473e+00  1.3477e+00
-#&gt;  -1.3731e+01 -9.9624e+00  1.8703e+00 -3.4599e+00 -1.0661e+01  6.7202e+00
-#&gt;   9.7868e+00 -5.0907e+00 -1.8674e+01  2.8963e+00  2.7677e+00  1.6540e+01
-#&gt;  -2.8286e+00  2.6729e+00 -7.6072e+00  9.1315e+00 -3.8775e+00 -7.6609e+00
-#&gt;   1.5656e+00 -6.6805e+00  9.2606e+00  1.1768e+01 -1.4334e+01  2.7171e+00
-#&gt;  -7.4580e-01 -3.4155e+00 -4.1006e+00  8.6670e+00  9.1879e-01 -1.4253e+00
-#&gt;  -4.0258e+00 -1.1773e+01  4.4728e+00  8.1287e+00 -1.2450e+01  6.1224e+00
-#&gt;  -4.0809e+00  1.3555e+01 -3.5414e+00  8.0674e+00 -5.2967e-03  6.7733e-01
-#&gt;  -8.5613e-01  8.3502e+00  4.0190e-01  6.8232e+00  1.3272e+01 -1.0222e+01
-#&gt;   6.6294e+00  3.8793e+00  6.6421e+00 -3.5255e+00  1.1354e+00 -1.6485e+01
-#&gt;  -1.1456e+01 -6.4231e+00 -6.0706e+00 -1.6325e+00 -9.0896e+00 -3.1131e+00
-#&gt;   6.9775e+00 -1.0652e+01  6.8487e+00 -4.7825e+00  9.5261e+00  5.9431e+00
-#&gt;  -5.6337e+00  2.5851e+00 -1.9914e+01 -2.2390e+00 -8.6217e+00 -3.2185e+00
-#&gt;  -3.1673e+00 -7.3470e+00 -8.4587e-01  1.4544e+01  4.8756e+00 -1.0150e+01
-#&gt;  -1.8867e+01  1.3802e-01  2.3707e+00 -4.2196e+00  5.3611e+00 -7.3660e+00
-#&gt;  -1.3701e+00  4.8789e-01  8.8058e+00 -1.4194e+01  5.8139e+00 -2.0546e+00
-#&gt;  -1.4534e+01  3.2997e-04  8.3650e+00  4.9874e+00 -1.5715e+00 -2.5045e+00
-#&gt;   7.4516e+00 -1.2611e+01  3.1515e-01 -3.5712e+00 -2.8321e+00 -1.1929e+01
-#&gt;   1.7093e+01 -5.9745e+00  2.7888e+00  1.9365e+00  1.1775e+01 -1.8040e+01
-#&gt;   2.6475e+00  1.1220e+01 -6.5698e+00  4.0266e+00 -2.2043e+00  3.9965e-01
-#&gt;  -8.9387e+00  1.1510e+01 -1.3995e+01  6.5618e+00 -8.3189e+00  2.2141e+00
-#&gt;  -4.2109e+00 -1.1936e+01 -7.9612e+00  5.2782e+00  5.8308e+00  1.4404e+01
-#&gt;  -3.3679e+00 -5.2825e+00  9.1787e+00 -1.5533e+01  2.4056e+00 -6.0696e+00
-#&gt;   1.0202e+01 -1.0092e+01  1.2604e+00  6.0711e+00 -6.1118e+00 -7.1285e+00
-#&gt;   6.6391e+00 -1.0309e+01  9.7536e+00  9.0842e+00 -5.4748e+00  6.3011e+00
-#&gt;  -1.3415e+01  3.2043e+00 -8.2222e+00 -7.4362e+00  7.4519e-01  1.9304e+00
-#&gt;  -3.7220e+00 -4.5726e+00 -2.2351e+00 -1.9753e+00 -1.2845e+00 -7.2767e+00
-#&gt; 
-#&gt; Columns 49 to 54 -7.8283e+00  7.6700e+00 -6.4372e+00  6.7907e-01  7.5293e+00 -2.7664e+00
-#&gt;   6.8952e+00 -1.7029e+00 -1.9042e-01  3.6731e+00  2.5942e-01  4.6866e+00
-#&gt;  -1.9880e+00  5.6396e+00 -7.6415e+00 -3.5026e+00 -3.4724e-01  4.2378e+00
-#&gt;  -2.9653e+00  3.0887e+00  4.5493e+00 -7.4611e+00 -1.0166e+01  9.6342e+00
-#&gt;   6.3533e+00 -2.0191e+00 -3.0445e-01  6.5535e-01  6.8669e+00  2.7846e+00
-#&gt;  -3.0310e+00  1.6618e+01 -1.5621e+01 -2.1432e+00  7.1358e+00  5.5269e+00
-#&gt;   5.7609e+00 -4.8679e+00  7.3759e+00 -9.2898e-01  2.1645e+00  8.0198e-01
-#&gt;  -1.5698e+00  9.1562e-01  4.2051e+00 -5.0667e+00 -6.5677e-02 -1.8864e+00
-#&gt;  -1.6002e+01  4.7873e+00 -2.8521e+00 -6.0687e+00 -2.3322e+00  8.8416e-01
-#&gt;   1.2803e+01  2.7640e+00  4.7702e+00 -3.9992e+00 -2.7644e+00  5.7228e+00
-#&gt;   1.3082e+00  4.6939e+00 -4.8850e+00  1.8737e+00  7.4326e+00 -8.6145e+00
-#&gt;   1.6210e+00 -7.7442e+00  7.6699e-01  3.9052e+00 -7.2906e-01  8.8425e+00
-#&gt;  -3.1784e+00  1.6128e+01  7.4539e+00 -7.2732e+00 -2.3566e+00 -4.6841e-01
-#&gt;   1.8711e+01 -2.0399e+01 -1.7557e+00 -6.4544e+00  7.8731e-01 -3.3787e+00
-#&gt;  -3.0505e-01  1.3782e+00 -2.6054e-01  2.7923e+00 -9.2549e-01  1.0817e+01
-#&gt;   1.4942e+00  7.8411e+00  1.3749e+00 -8.3084e+00 -1.3907e-01  7.3425e+00
-#&gt;   3.9848e+00 -4.5565e+00 -1.3949e+01  4.5808e-01  4.6363e+00 -5.0544e-02
-#&gt;   4.7378e-01  2.4725e+00  8.0652e-01  8.6825e+00  3.1991e-01 -3.7330e+00
-#&gt;   2.5099e+00 -7.2661e+00  4.7621e+00  3.0879e+00 -5.1366e+00 -1.6244e+00
-#&gt;  -1.0469e+01  7.9192e+00  2.0401e+00 -1.2957e+01  3.4234e+00 -2.3505e+00
-#&gt;   6.6999e+00  7.5522e+00 -2.8123e+00 -3.0786e+00 -1.0235e+01 -2.7743e+00
-#&gt;  -1.6822e+01 -1.2617e+01  9.4496e-01  2.7541e+00  1.7316e+00  6.2106e+00
-#&gt;   1.0395e+01 -2.0956e+00 -1.2255e+01 -4.5416e-01 -3.9487e+00  4.3195e+00
-#&gt;   4.6914e+00  3.1649e+00  4.1611e+00  6.1286e+00  5.0255e+00  7.1065e+00
-#&gt;   7.5021e+00  6.8406e+00 -9.6136e+00  1.7223e+01 -1.3089e+01 -3.5942e+00
-#&gt;  -5.5354e+00 -5.7653e+00  4.2610e+00  2.9217e+00  7.4059e+00  1.9449e+00
-#&gt;  -3.5394e+00  8.6149e+00 -2.3641e+00 -5.7500e+00  1.5863e+00 -2.8873e+00
-#&gt;  -1.4569e+01  4.0575e+00 -9.7085e+00 -3.8323e+00 -1.4526e+01 -3.2623e+00
-#&gt;   4.0992e+00 -5.9664e+00 -5.2388e+00  7.7750e-01 -5.2460e+00  1.2358e+00
-#&gt;   6.7283e+00 -1.1768e+01  4.6034e+00  1.5952e+01  1.1168e+00 -9.1776e-01
-#&gt;  -3.8493e+00 -2.4761e+00  5.4825e-01 -8.8532e+00  1.0645e+00  9.2228e+00
-#&gt;   1.3682e+00 -4.5516e+00  8.4050e-01  9.9312e-01 -2.7629e+00  3.6249e+00
-#&gt;   1.8553e+01  3.0052e-01  3.0743e+00 -8.5578e+00  2.4675e+00 -3.0139e+00
-#&gt; 
-#&gt; (10,.,.) = 
-#&gt;  Columns 1 to 8   4.1819  -2.7002 -18.1760  -1.1440  -2.9460   6.0683  -1.4644  -8.8791
-#&gt;   -5.2517  -2.8884  -7.8021   1.1429   1.3565  18.4330  21.5672  -6.8964
-#&gt;    7.6318  -6.9620   8.9294  -8.1504  -0.3623   0.9137   4.5002  -3.5007
-#&gt;   -5.7043  -0.1398  -5.9344  14.1726   4.8862   3.5814   5.0882  -6.4967
-#&gt;   -0.8601  -6.4114  11.4836  -7.3367  -7.5688   7.5060  -5.5756  -1.5425
-#&gt;   -8.2925  -6.0803   6.6156 -13.9416   1.9437   3.9939   1.1387   7.1872
-#&gt;    2.5452   2.3789  -6.3848   1.8546   1.7084 -12.1336  -3.9954  13.7758
-#&gt;   -1.8303   3.3219   5.7164  -3.2491  -1.4877   9.1777   8.8536   5.8444
-#&gt;    8.2554   5.8091  -6.1897  -8.2495   1.8647 -10.6258  -0.8845  -2.1385
-#&gt;    1.7795   7.0254  -9.9362   6.2154  -3.4837  -0.7500  20.6577   2.2969
-#&gt;    2.0167  -1.8900   0.5460  -1.2813  -1.4822  -3.7381  -4.6472   6.2117
-#&gt;   -2.2446  -0.4886  -4.3657   6.1555 -10.2198   4.8470  -7.4376  -2.0771
-#&gt;   -5.2713  -1.6133  -1.1467  -4.1871   5.4227   9.1644  -3.4754  -5.3385
-#&gt;    0.3023  11.8181   1.3283 -12.7279  -7.5569  10.1936   6.1990  11.6441
-#&gt;   -3.3104   3.9960  -2.4172   2.2504  -7.6548 -12.2355  10.1682   7.0920
-#&gt;   -6.2455  -2.9252   1.0112  10.4484   1.2471  -0.9377 -12.7561  -5.3247
-#&gt;   -3.9030  -9.2627   1.0247   1.9701  -8.9632   0.5715   2.5766   1.9814
-#&gt;   -2.1366   5.8496  -6.1677   8.8440  -5.9129   0.9059  -8.7046  -2.5762
-#&gt;    5.2249   5.9455  -8.3770  -7.0487   1.7812  -7.4599   0.4756  18.2719
-#&gt;    2.1648  -0.6042   5.4310  -3.9076   1.8279   3.3609   8.3237   4.8889
-#&gt;   -3.8500   8.1186   3.1748   0.4045  -3.5455   1.0117  -5.7007  -7.4015
-#&gt;    2.6253  10.7632  -3.3398   4.9756 -20.4394   3.7262  11.1934  17.5135
-#&gt;   -0.7312   4.0369   6.8090   3.6341  -0.8025   1.7529  -4.8715  10.0601
-#&gt;   -1.1232  -2.8163  -2.6153   0.1292 -13.4160   3.9119  -7.7021  10.4302
-#&gt;    2.2927  -2.3197   3.5505   8.4843   7.7590   6.3685  -6.6163 -17.7623
-#&gt;    2.3017   5.6381   0.4991 -10.5783  -4.1324 -13.1374   5.4518   6.6804
-#&gt;   -2.0478  -7.5091  11.3796  -7.9909  10.7743  -3.7296  -2.3580 -15.9901
-#&gt;    4.0426  -9.7816   8.3604   2.4883   8.6378   9.9463  -3.2728  -0.6286
-#&gt;    1.2885  -0.2170  12.5709   1.1797  -8.4373  -3.5454 -11.9339  -8.7778
-#&gt;    5.4442   1.5278  -9.7229   1.7396  11.1748  -4.8533  -4.4837  -3.0608
-#&gt;    3.1077  -2.4470   7.1603   5.6823 -21.7576   0.4642  -6.7589  10.2302
-#&gt;   -4.3704   2.2297   0.9107 -17.3731   2.7938  -0.4466  -1.6011   1.0906
-#&gt;   -1.2622  -2.4540  -1.1643  -8.5674   9.3976   2.6191 -14.8639   1.3717
-#&gt; 
-#&gt; Columns 9 to 16   1.8556   0.0946   0.1899  11.7107  -5.4183  11.5789   9.7279  -2.8268
-#&gt;   -3.3962   4.7751 -14.7356  11.8734   2.1015   2.1329   2.1923   3.2486
-#&gt;    1.4306   0.3409  -9.0292  -0.4911  -3.6641  -0.5287  18.8181  -1.0346
-#&gt;   -9.8678  -5.0219  15.4837 -13.4747  -5.8500  17.1277  -0.1490   2.6720
-#&gt;   -6.8133  -7.6474   5.9233  -0.4694  -1.0901  -7.4239  10.4323   3.4986
-#&gt;   -5.5519  -0.0618  11.3379   8.0623  -3.5955   1.7862   7.5723   2.4541
-#&gt;    1.3072   0.6549   6.0913 -18.9477   1.6956   7.7507   1.1176  -0.0589
-#&gt;    6.1180   3.5580   2.0954   3.3366   4.7224  -3.3512   9.1941  -3.5992
-#&gt;   -8.3560  -4.7540   1.6382 -13.4815   3.3727  -1.9814  -0.0188  -3.9449
-#&gt;    4.9662 -12.4013  19.1299 -15.2008   8.3711   3.6924   4.5674  -2.7297
-#&gt;    0.6927  -5.4917  -5.8683  -2.1278  -9.2548  -8.4762   9.8112  -6.1467
-#&gt;    0.5953  -0.1684  -3.7758  -3.3063   3.1845 -12.9019  -5.7863  -2.8776
-#&gt;   -9.7528  18.2416  19.8239   8.8325 -16.4710 -13.3338   4.2949   7.3125
-#&gt;   -3.6096  -7.3529   4.0815  -0.1361   4.7710  -6.8037 -22.0151  11.4496
-#&gt;   13.0295 -16.0956  20.0523   0.4107  -3.8567   6.1604 -10.1262 -17.2949
-#&gt;    3.1475  -0.2741  12.1048  17.4119   4.1416  -7.9165  10.1866   7.0605
-#&gt;    7.9490   6.5653  -0.1566   6.7108   8.9105   4.9730 -11.4455   1.9475
-#&gt;   -7.0497  -1.4302  -0.2031  -0.7717  10.2553  -5.8846 -12.2485   5.1642
-#&gt;   -1.4445  -1.0975   4.7951 -10.3304   3.8726 -11.5951  18.1794  -1.8722
-#&gt;   -3.5374   1.9384   7.5930  -1.7373  -1.3728   0.1740   9.8508 -17.4885
-#&gt;    5.2514   1.7544   7.5342  21.3597   2.5470 -13.2217  -2.4640  15.8839
-#&gt;   15.8121   1.9745 -12.0076   9.3390 -10.4766 -11.5816   5.4715  15.8169
-#&gt;    2.7863   4.0846   3.5614  11.5852  13.7292  -3.9139  -1.5637  -4.1002
-#&gt;   -8.6593   5.3422  -7.6377  -6.7198   2.7386  -2.0494  -7.7664   1.0160
-#&gt;   -7.9545  11.3175   5.0165   2.6514  -4.5948   4.2351  -5.6257  -0.6661
-#&gt;    2.0352   8.5044 -11.0207   3.9361  -3.1783  -5.0761 -10.5843  -6.2035
-#&gt;   -9.2982   3.4657  -8.0456   1.1406  -5.7957  -0.0046  -2.1324   8.1229
-#&gt;  -16.6553  10.8810  -0.9489  13.3421  -8.0725   9.1832  -4.2330   7.3050
-#&gt;   -8.2410   7.6877   4.3861 -15.3545  -0.3129   0.7203   1.5005   6.7024
-#&gt;   -6.5903  -1.3819  -6.9875  -4.7477   4.7515   0.0780 -13.0780 -12.4752
-#&gt;    0.5467   0.2573 -11.7447  11.3582   0.7454 -11.0607  -4.9295   6.2202
-#&gt;    0.9685   0.8027  11.2603   1.4244 -10.0601  -9.5701  14.1312  11.6304
-#&gt;  -11.8415   2.6648   0.9239  -9.2368  -4.5917  12.4541   8.8730   4.3792
-#&gt; 
-#&gt; Columns 17 to 24  17.7241  16.4431  -4.4976   4.1706 -12.8776 -15.2067 -10.9706   4.8594
-#&gt;   -2.4854   4.0536   0.9395   0.8457  -2.1911  -0.2284   2.7090  -5.2006
-#&gt;   -1.5690   2.2409  15.2344   2.3106 -15.5675   4.6727 -18.2378  -8.5262
-#&gt;  -19.8752   9.8127 -10.5200   8.9852 -14.8782  -3.1370   4.1668 -10.8551
-#&gt;   -3.6712 -12.0466   7.5911   7.8817 -14.2461  -1.8762   6.1248  -4.2913
-#&gt;    7.4452  -0.9572  -3.7197   1.3354   6.7883 -13.2608 -13.4355  14.7262
-#&gt;   10.5368  -0.6395  15.0316   7.2175  -6.9461 -20.6147   1.1905  13.3449
-#&gt;    9.9587  17.1341 -11.5921  -1.7717 -12.4661   6.2366  -9.3802  -1.1792
-#&gt;    0.3752   8.5826 -13.2740  -0.0306  13.3570  17.4113 -12.8242   9.3084
-#&gt;  -11.8180   9.5951   6.7905  12.8367  -8.0408 -15.6782   6.5608 -18.7112
-#&gt;   22.3179  -7.4422   1.5256  11.6816   5.9146 -10.3219  -3.7901   7.5836
-#&gt;    3.8820  -7.6058   8.6871  -4.0585   8.9200  -4.1044  -1.5731  -3.6648
-#&gt;   -1.7718  -1.6068  -6.4439  11.8292  -2.6022 -16.4736 -11.8727  11.3917
-#&gt;  -17.9264  -4.7390   6.3397  -8.9043   8.8302 -16.6837   2.7382 -14.5637
-#&gt;   -1.9014   7.9378   1.7300   0.9099  -7.4772  -0.5940  -6.2970 -17.5786
-#&gt;    1.4715  -0.3907  -9.3192  10.1859  11.9984 -14.9277  12.3849  17.7064
-#&gt;    2.3705 -17.2180   4.6233   4.2500  -4.6967 -11.6569  -5.3638 -11.5411
-#&gt;   -1.3751  12.4701  -7.0619   4.3700  -0.2691  -0.2517   6.5075   2.4046
-#&gt;    9.9320  -0.5399   3.7089  -8.3076   4.6503  -6.4728 -24.8052  24.1327
-#&gt;    1.9584   7.0046   1.5319  -5.6831 -14.7582   1.4350  -3.2149  16.2855
-#&gt;   12.0401  -0.7776   3.8262  -6.7161   0.6593   2.0989  -4.3371  12.7094
-#&gt;  -19.7485   3.2476 -10.9042  14.1623   8.4786   7.8983   0.4614 -12.9178
-#&gt;   -7.0698   1.4244 -11.8563   1.1273  15.1136  10.7658   2.1623  -7.2914
-#&gt;    4.7051   3.1767  -8.4571  -4.8364  14.5448   8.8465  -1.5965   9.6834
-#&gt;   -4.4673  -7.8511   0.8354   5.9707   3.3181  12.2863  11.1134 -10.8450
-#&gt;   -7.2237   0.3425  17.3627 -14.8729   2.3539 -13.4249   3.3829   4.3209
-#&gt;    1.4075  -4.3673   1.7913  -1.9425  10.2997  -7.9903   1.3192   5.3896
-#&gt;   19.7331  -9.6629  -7.2495  -9.8141   5.0245   8.9532 -18.5641   8.1088
-#&gt;   -7.1797  -1.4011  -7.3396   2.2222  -7.3143   7.9236   2.3504 -12.2137
-#&gt;    6.8541   2.4263   0.0412   8.6535  -3.8287   4.7261   8.2146  -1.9722
-#&gt;    0.9613   2.0930  -8.1847   8.7364  11.5909   4.4428 -15.8480   2.7165
-#&gt;  -14.3310   3.1957  13.1065  11.7513  -4.6397   4.3785 -23.1525   2.6458
-#&gt;    8.8166   9.1156   0.1519  -5.9758   6.7014  -3.0737 -10.1020   1.6576
-#&gt; 
-#&gt; Columns 25 to 32  10.5292  -3.9645  12.8190   2.1494  10.7930  11.1448   9.9790  12.4497
-#&gt;   11.4912  12.2822  -0.5843  -8.5035 -10.4811   7.8215   5.3842   1.7837
-#&gt;    9.6353  14.6377   3.8013  14.2778  -5.8059  19.4480 -11.1792   9.9659
-#&gt;    1.2615 -12.5058  -1.1939  -1.1402 -15.7723   5.3684   1.1663  -5.7907
-#&gt;    2.3630  -5.0501  13.3084  18.5605   1.1618   8.6902  -2.5698  -1.1850
-#&gt;  -13.9340   4.3304   5.8433  -7.3367   4.6003  -6.4036   4.8493   5.0689
-#&gt;   -1.3893  14.5972  16.2413  14.9188  -0.5374  -7.7302  -0.7117  -2.1723
-#&gt;    1.4423   2.1201   1.2344  -0.5692  14.9963   3.6928  -3.9090   0.6049
-#&gt;    0.7095  -7.0357  -2.1930  -3.7832  -5.9221  -8.0483  -6.4077   2.4676
-#&gt;   -1.5388   0.4704  12.8991  -6.0182  14.1894  -0.9701   9.0996 -16.5178
-#&gt;  -10.3212 -10.7765   4.8461   4.5388   4.3799 -10.6147   0.8329  -6.4080
-#&gt;    7.8106   2.1017  -9.8434  -0.4331   5.9101  -3.3945  -2.3179  -2.2783
-#&gt;  -13.8799   0.6262  -4.3279   3.8890  -3.8753  -8.0789  -0.7328  -4.0891
-#&gt;   14.3206   7.7849   3.6460  -4.0085  -7.5287 -11.1466   4.8729 -11.5882
-#&gt;   -0.0881  -2.9461   2.1459  11.0899   3.5483  -0.2515   2.0340   1.7700
-#&gt;    9.2189 -13.4063  -2.4231   3.0836   5.5822   2.4684   2.2476   1.8075
-#&gt;   -2.4374   2.6486  -5.4516   1.9154   5.0113 -13.6252   9.9636 -11.3138
-#&gt;    2.4418  -4.0569  -8.3882 -19.7733  -2.0071   4.8646   2.7268   6.5430
-#&gt;   -5.4768  -4.4051  17.2406   1.0203   7.2529 -12.8022   1.9017   4.4126
-#&gt;    5.6344   7.9751   3.8124  -4.3246   6.1148  15.3043 -18.8127  10.1895
-#&gt;    1.9013   4.9514   4.0854  -3.0271  19.8358  -0.8026  14.0764  -0.8553
-#&gt;   -2.2414 -11.0523  18.3026  -1.5447   0.4390  -9.4300   0.1453  -6.5077
-#&gt;  -12.4334   6.4966 -14.2762  13.4793   6.2123   0.7264  -8.5034  -7.8488
-#&gt;    3.6735 -10.9852  12.3695  -3.4940   3.7098 -15.8378  -2.1686  -8.8639
-#&gt;   -6.1870   6.3384 -17.9945   4.0391   5.4411   3.9668 -13.1839   6.2893
-#&gt;  -12.5335  10.5731  -9.3001  -6.2328 -10.8204   4.7457   3.1881   6.8758
-#&gt;   -9.5590   4.8932  -1.7767  -4.7540  -8.1867   0.3086  12.7548 -12.7002
-#&gt;    3.6840 -12.3161  -6.0951  -1.2251   4.7612  -1.1004  -1.5123  -2.2366
-#&gt;   -3.0114  -9.8084  -9.4691   0.2194  -9.6904 -13.4815  -1.6036  -6.8736
-#&gt;  -13.0158 -10.7943  -6.4755   4.5214   0.5924  -7.7346   8.6752  10.1047
-#&gt;    7.4194 -13.7749   5.0595   5.8050   5.8419  -0.0406   9.7345  -1.6591
-#&gt;  -12.7815   0.2335   2.2811  -2.3682  -7.0829 -12.3729  -3.2147   1.8516
-#&gt;    8.9504  -2.9962   8.6639  -3.1886  -5.3588   5.4321  -9.9454  -5.3727
-#&gt; 
-#&gt; Columns 33 to 40  -2.6904  -3.1793  -3.9982   2.2110 -15.0660  -7.8316  -4.5374  -3.6815
-#&gt;    6.3429  -0.5526 -12.4770  10.9454  10.4932   2.0863   7.1767 -15.6242
-#&gt;    9.9699  -2.9062  -7.5707  -4.7522  -8.1896  -2.9347  -0.8152  -4.8127
-#&gt;  -12.2653  11.1087   0.8183  -3.0673   5.4707  -7.3395   8.2259   0.0790
-#&gt;   10.7333  11.7357  -0.5174  -2.7349 -11.8983   4.5970   4.7187   7.6155
-#&gt;    0.8824  -0.0948   2.7482 -16.1985  -3.4809 -13.0749  -5.6538   1.3078
-#&gt;   -4.6005  10.9233  -8.3767 -23.2996  -9.9544   4.9645   6.6196  -1.6382
-#&gt;   -4.8016   1.2827   2.5463  -9.7602 -11.3491  -6.9045   8.6490  -3.3517
-#&gt;   10.8844  -4.3575   4.9099  25.9703  -7.6978  -8.9655 -17.4221   1.0042
-#&gt;   -4.2847   2.7185  -2.9281  -5.9636   7.4574   0.7742  -1.9685   3.4123
-#&gt;    9.2045   5.2980  15.7517  -5.8331 -15.0311   7.7835  -9.3464  -1.9853
-#&gt;    6.4127  -4.6117  19.0878  -3.0422  -1.5535  -2.1965  -1.4020   1.2183
-#&gt;  -11.4244   7.5634   8.4587  -1.8272   3.1815  11.3702   1.6303   8.1204
-#&gt;    1.2992  -2.9209  -2.0812  11.5782  22.2623   7.6336  -5.1908   5.6859
-#&gt;   -2.1399   3.7254  -9.4623  -7.0854  -2.7738  12.2496   1.6377   2.5113
-#&gt;  -12.5850  -4.9262   3.3072  -5.5994   4.4245  -1.8885  -0.8919 -10.0518
-#&gt;   13.9544  -2.2759   4.0960 -12.4514 -14.1780  -0.7708  -2.8591   2.4331
-#&gt;   -0.4444  -4.2921   7.0387   6.5836   9.4148 -14.9592   4.8886   7.0435
-#&gt;  -19.0743  -3.3144 -13.8473  -2.4842  -1.5997  -0.7481  15.9668   0.6583
-#&gt;  -10.1261  -2.2332  -7.1975  -2.3450   9.0592  10.7116  -0.8987  -8.9685
-#&gt;  -11.9545  -8.5190   4.8090  13.1186  10.6559  -4.5238  -2.1798  -8.8657
-#&gt;   -1.9783   8.6018   3.2554 -16.5278 -14.0102  -3.3953   9.9409   3.1031
-#&gt;  -13.6221  -1.6317  17.8017 -13.5058  -0.0398 -20.7438  -2.9618   4.1430
-#&gt;    0.8359  -2.0096  -1.9416   4.9894   4.1868  -2.3571   5.3722   7.2971
-#&gt;    2.0086 -12.1338  17.6228 -10.7163   5.5852  -7.9251   1.4832 -14.7635
-#&gt;    5.7109   5.6109  -7.2849   1.9554   5.1607   2.8878   1.7395   1.9746
-#&gt;    1.1878  -8.7317  -0.0576  13.3530   1.2356   4.8184 -11.7238  -1.7304
-#&gt;   -5.7051  -4.8007  10.1910  18.0028 -12.7282 -13.6544   2.4402  -9.5052
-#&gt;   -2.6643  14.4130  10.9647  -9.3912   3.2169  -7.6773 -12.9542   0.5395
-#&gt;   -7.9634  -6.3668  -8.8339   3.0626   3.5328  -5.4092   6.0550  -2.6830
-#&gt;   -5.5458   1.8084   3.9153   6.7767   5.4337 -10.1347  -0.7966   9.2530
-#&gt;    5.2944 -15.6789  16.6287   0.8781  -8.6488  12.3779 -11.0757  -2.0095
-#&gt;   -1.9817   1.1022   3.5618  -4.4506   0.1626  -8.8249  -2.2472  -2.0069
-#&gt; 
-#&gt; Columns 41 to 48  17.5704   3.3890 -14.7766   8.8433  -8.1115  18.5641   5.5488 -15.7888
-#&gt;    1.3682  -0.3895 -13.8270 -15.9155  -4.3090  -8.9518  10.9437 -12.4455
-#&gt;   17.5254   1.6004   1.1251  -3.3617   6.1294  14.9661 -12.5143  14.6155
-#&gt;    2.5869  11.4185  -0.2229 -15.8317  16.1505  20.4406 -10.4021   4.9527
-#&gt;   -4.8332   2.2539   2.9690 -19.8020  -0.6848  10.3750  -3.9140  -7.7243
-#&gt;    9.0005  -4.9784 -14.8227  -5.6539 -22.5182   3.3849  -6.7069  -9.1133
-#&gt;  -13.7044   8.7026  15.7930 -21.8754  14.5765  -6.7201  -2.8081   2.8092
-#&gt;   -8.3891  -2.3845   8.5504  12.8548   1.4612  -3.3912 -12.1353   5.9169
-#&gt;   -0.5118   0.2147   5.5487  -9.9571   7.8578  31.2956  -7.6141   6.0582
-#&gt;   -4.1179  17.5565  -1.9947   7.3969   1.8679  -1.0288 -10.8080  -6.1185
-#&gt;  -12.9663  -2.5697  12.2865  13.0955  -2.8212 -13.4418  -3.3537  11.4065
-#&gt;    0.6306  -6.9157 -22.3353   7.5794   5.3888  -1.9895   6.8287 -15.9672
-#&gt;   -3.7175  -7.1140  -7.8213   0.2052   7.8420  23.6224  -1.2615   5.3317
-#&gt;   -2.8885   7.7248   5.9880 -12.1741  -8.5161   3.1150  20.8556  -8.3987
-#&gt;    2.4623   2.5093   8.3615  -4.6639   6.6286 -13.4688  -6.7489   9.2497
-#&gt;   -9.4874   0.9945   5.8155  -4.5071  -3.0293 -14.6485   0.9375  -9.3388
-#&gt;    5.6274  -2.6494   2.2440   6.4652  -6.9652  -8.7051  -4.4503 -11.7992
-#&gt;   -7.1215 -11.7251  -0.0189  -7.7000  20.6908  -9.6292  17.5161   0.8517
-#&gt;   -1.1748  16.3685  17.1124  -0.1526   9.9417 -15.2438  -2.5329  -3.7418
-#&gt;    9.6564   9.9983 -10.8354   6.3171   5.9041  -1.7046 -10.1057   1.9377
-#&gt;   -8.3920  -5.5874  -3.4125   9.7132  11.8479   8.2080   4.9230 -10.1816
-#&gt;    9.3801   3.2812   4.9593   2.1379 -10.3081  -8.9848 -15.6181  -5.7878
-#&gt;    1.7352  14.5038  -3.1819  -9.0198   9.3146   9.1462 -12.0767  -4.1429
-#&gt;   -7.9687   1.5227 -10.3528  -6.1670 -12.7228  -8.4186   9.4608  -7.8613
-#&gt;   -0.3986  -0.5350 -20.1001   7.9296   2.2427  14.5613  16.7416  -7.5759
-#&gt;   -4.4657   1.9143  -2.0119   9.2431  15.5990  -9.8887  -2.0631  -5.6964
-#&gt;   -4.3807   2.3498   2.3197   2.3938  -1.6953   5.2110   4.9993   2.3942
-#&gt;    8.3853 -10.0567  17.5188  -0.2648   6.5264  23.4170   0.1300   4.5117
-#&gt;  -16.0887   2.2805  16.1597  -5.0918  14.6784   0.1509  17.1091  10.7455
-#&gt;   -5.0488  -1.5862   3.4866  -7.5867  12.9261   1.0782  -9.2606 -10.5018
-#&gt;    4.2590  -8.3886  12.6921  14.9392 -12.0440  -5.4093 -14.8714   9.9903
-#&gt;   10.3134   7.1416  -2.7204  -8.9261  -3.6273  29.5756  -9.7316  12.0568
-#&gt;   -4.3794 -11.0773  10.8934  -7.9329   5.4392  -4.8761   4.0482   7.3628
-#&gt; 
-#&gt; Columns 49 to 54 -14.5902  -0.2382   4.3159  -2.6877   5.0703   2.4193
-#&gt;   -7.1263  -7.5356  -5.7498   7.8019  -0.7866  -2.3049
-#&gt;  -12.3100  -1.9089   0.3939   9.3053  -5.7823   1.2514
-#&gt;  -10.8058   5.7169   6.6433  11.4855  -1.1410  -4.5655
-#&gt;    8.8070   5.1104   8.8327  12.1930   5.0258   5.7235
-#&gt;  -25.6938  -3.5064  -2.6577 -15.0395  -2.9256  -3.4516
-#&gt;   -6.6522  -0.1651 -11.4028  -6.1774   2.9720  -3.9651
-#&gt;  -11.4142 -13.1631  14.7867   5.8335   5.6973   0.3327
-#&gt;  -24.5765   5.7319  12.5413  -2.6588   1.5041  -1.9145
-#&gt;    6.3423  -8.7484  -4.6776   1.1261  -4.7972   0.9373
-#&gt;   13.7444  -5.1163  -2.2804  -7.3522   3.7646   6.7636
-#&gt;   -6.6618  -3.2632  10.3643   0.2059  -1.5956   2.7000
-#&gt;  -20.9447   4.3667  -0.4386 -11.0839   1.1866   3.1471
-#&gt;   10.5578  -6.5905  -5.1642   6.4413   0.7218  -5.2311
-#&gt;    1.3020   9.0326   7.8362   0.2074  -1.2691  -3.3912
-#&gt;    0.0193  11.9974  -4.4291 -10.2958 -13.7476  -0.6264
-#&gt;   -7.6380   2.1646 -13.6631  -4.7001   5.4647  -4.1669
-#&gt;    5.9041   4.0855  -2.6119   2.6817   6.5380  -0.4950
-#&gt;    6.0665   3.0488   7.5467 -16.5522   1.2797  -2.5643
-#&gt;    2.4455  -9.6235   7.8516  -1.5448   2.5017   3.9083
-#&gt;   10.5918  10.5633   4.1331   3.3853  -0.5743   0.0424
-#&gt;    6.7426  -6.8340  -1.2416  -1.1304 -13.0831  -2.6119
-#&gt;   -9.2593  -1.9201  -3.0986  14.1559  -0.0596 -11.1140
-#&gt;  -11.5428   2.5954  10.6733  -4.6600  -3.8252  -3.4938
-#&gt;    1.3032  -0.2393  -5.7651   3.2347   1.5240  -3.7687
-#&gt;    4.9202  -6.3740 -17.5235   4.7132   2.0002  -0.2020
-#&gt;   11.3402   5.2820 -17.9162  -0.2594  -7.4186  -2.8355
-#&gt;  -19.4105   5.8406   7.7503  -7.5794   1.5874  -2.1943
-#&gt;    1.6365  -4.2029  -6.0806   1.8311  -1.0763   0.0894
-#&gt;   17.7446  21.0740  -0.0280 -10.0488   5.2612   4.8506
-#&gt;   -4.6811   9.2956  18.5009  -6.1713  -1.4305   0.4427
-#&gt;  -10.9019   7.2243 -14.8779  -8.3666  -7.9722   0.3536
-#&gt;   -8.8841   2.5430  -2.5944   1.4092  -9.3180  -2.0870
-#&gt; 
-#&gt; (11,.,.) = 
-#&gt;  Columns 1 to 6 -3.7849e+00 -5.0196e+00  2.1432e+00  1.7021e+00  2.1228e+00 -4.3782e+00
-#&gt;   4.1938e+00 -3.9595e+00  4.0635e+00 -5.5167e+00  5.6394e+00  4.2589e+00
-#&gt;  -2.8545e+00  1.2807e+00 -6.3746e+00 -3.4508e+00  1.5402e+01  5.1446e+00
-#&gt;   2.8870e+00  3.8449e+00  9.1714e+00  6.2121e+00  8.3932e+00  8.3983e+00
-#&gt;  -2.2077e+00  8.2990e+00 -1.5067e+00 -5.9537e+00 -1.3866e+01 -6.3206e+00
-#&gt;  -3.9909e-01  4.0141e+00 -1.2573e+01  8.3191e+00  2.9710e+00 -2.4224e+00
-#&gt;  -1.8828e+00 -2.7120e+00  4.2654e+00 -1.2382e+00 -1.4503e+00  5.6398e+00
-#&gt;   3.4461e+00 -6.0116e+00  2.6112e+00 -4.3507e+00 -6.3859e+00 -4.9195e+00
-#&gt;  -1.6851e+00 -9.8991e+00  5.0588e+00 -2.4422e+00  6.6687e+00  1.7778e+00
-#&gt;   4.5697e-01 -7.3823e-02  4.4258e+00 -4.7368e-01  1.0315e+00  3.9583e+00
-#&gt;   1.1417e+00 -1.6804e+00 -2.8100e+00 -2.1802e+00 -3.9562e+00 -1.0400e+01
-#&gt;  -5.8564e-01  1.0935e+01  2.6781e+00  4.0670e+00  8.1068e-01 -3.8475e+00
-#&gt;   4.1610e+00 -5.0571e-01  2.9245e+00 -7.9322e+00 -1.6292e+01 -3.1278e+00
-#&gt;  -1.4342e+00 -2.3585e+00  4.6606e+00  1.9605e+01  2.9582e+00  1.8324e+01
-#&gt;  -5.5404e-01  7.1800e-01 -1.4267e+01  4.0446e+00  5.6713e+00 -1.4284e+01
-#&gt;   4.1463e+00  7.7104e+00 -1.1590e+01 -4.9941e+00  6.1806e+00 -1.0090e+01
-#&gt;  -2.4521e-01  8.6493e+00  3.0875e+00  1.5020e+00  7.0473e+00 -7.6942e-01
-#&gt;  -1.9347e+00 -1.0450e+01  3.0863e+00  2.8111e+00  1.0976e+00  1.5948e+01
-#&gt;  -2.6669e+00 -1.8782e+00  2.9217e+00  1.3300e+01 -1.6403e+00 -8.4687e+00
-#&gt;   1.7010e+00  6.6925e-01 -2.5950e+00  1.3026e+00  8.2395e+00  4.8721e+00
-#&gt;   2.0192e+00  2.7153e+00  2.1160e+00 -7.7253e+00 -8.3825e+00 -3.1612e+00
-#&gt;   3.9173e+00 -6.8199e+00  1.5681e+01  8.2122e+00  2.8327e-01 -9.1162e+00
-#&gt;   1.6034e+00  3.3712e+00  1.5284e+00  1.3668e+00  5.2616e+00  4.2150e+00
-#&gt;   9.3535e+00  5.1441e-01 -5.8771e+00  6.4237e+00 -7.5995e+00 -1.9427e+01
-#&gt;   1.0015e-02  5.9925e+00 -4.5338e+00 -4.7200e+00  5.7863e+00  3.5718e+00
-#&gt;  -3.8353e-01  1.7491e+00 -1.1367e+01  1.3499e+00 -7.0512e+00  1.4846e+00
-#&gt;  -6.6765e-01  6.4164e+00 -7.2331e+00  8.6983e+00 -4.7788e+00  9.2212e+00
-#&gt;  -3.8685e+00  3.6740e+00  4.8216e+00  1.3614e+01  2.4227e-01  1.1748e+00
-#&gt;  -1.2426e+00 -6.0361e+00 -5.0742e+00 -1.0331e+01 -1.2833e+01  7.2044e+00
-#&gt;   8.5433e-01  3.0204e+00  6.8311e-01 -1.3152e+00 -1.2573e+00  1.1791e+00
-#&gt;   1.0658e+00 -1.8278e+00  2.1841e+00  1.3825e+00  5.6781e+00 -1.4014e+01
-#&gt;  -3.4659e+00 -2.0254e-02 -6.6509e+00 -2.2007e+00 -1.0925e+01 -3.6717e+00
-#&gt;   3.3764e+00  2.2750e+00 -8.4900e+00  7.7153e+00 -4.4704e+00 -1.6245e+00
-#&gt; 
-#&gt; Columns 7 to 12  6.0973e-01 -1.4816e+01 -1.6339e+01 -3.2286e+00  4.9790e+00  3.0585e+00
-#&gt;   6.8249e+00  3.8479e+00  1.2093e+00 -4.8146e+00 -1.4413e+01 -2.8921e+00
-#&gt;  -1.7814e+00  6.0191e+00 -1.1324e+01  5.8392e+00 -5.0403e+00  3.6101e+00
-#&gt;   4.6124e+00  2.8248e+00  9.4244e-01  2.1540e+00  5.7656e+00  1.0509e+01
-#&gt;   1.6468e+00  8.3349e+00  1.0383e+01  1.0796e+01 -1.1249e+01  4.1644e+00
-#&gt;  -1.0202e+01 -1.2100e+00 -1.3321e+01 -1.6383e+01 -1.6935e+01 -4.5484e+00
-#&gt;   8.5930e-01 -5.8933e+00  7.5169e+00  5.9695e+00  1.0066e+00 -6.7549e+00
-#&gt;   2.3309e+00 -4.0142e+00 -2.3207e+00 -2.2595e-01  3.0484e+00 -2.7808e+00
-#&gt;   2.0428e+00 -6.2656e+00 -7.2521e+00 -5.2489e+00  9.3829e+00  1.3080e+00
-#&gt;  -6.5883e-01 -4.0317e-01 -1.2260e+00 -1.4979e+01  2.1824e+00  2.7155e+00
-#&gt;   7.3020e+00  9.7149e-01 -1.2680e+01  9.1245e+00  1.1231e-01 -1.4746e+01
-#&gt;  -8.0618e+00 -2.8863e+00 -1.4427e+01 -2.7446e+00  3.0282e+00 -7.5802e+00
-#&gt;   7.9672e+00 -3.7802e+00 -5.4363e+00  7.7521e+00 -1.0090e+01 -7.8178e+00
-#&gt;   1.2089e+01  5.3239e+00  4.9814e+00 -5.6864e+00 -3.6484e+00 -4.6010e+00
-#&gt;  -2.8823e+00 -1.1789e+01 -1.1138e+01 -1.0029e+01  1.0326e+01 -1.0063e+01
-#&gt;  -5.3812e+00  3.2276e+00 -3.6145e+00 -3.5016e+00 -4.2648e+00 -4.8598e+00
-#&gt;  -8.2244e+00  2.2045e+00 -5.3808e+00 -9.6569e+00  1.8693e+00 -3.7419e+00
-#&gt;   4.7899e+00  4.2961e+00 -9.5148e-01  1.1050e+01  2.0492e+00 -6.1241e+00
-#&gt;  -9.6661e+00 -1.4662e+00 -1.4761e+01  4.7909e+00  9.8864e+00  5.9882e+00
-#&gt;   1.0315e+01 -3.5138e+00 -1.0110e+01  8.8079e+00  6.5133e+00 -5.1630e+00
-#&gt;  -1.4707e+01 -1.8352e+00  8.1046e+00  3.2276e+00  5.4078e+00  1.4720e+00
-#&gt;   4.0316e+00 -4.7272e+00  2.2854e+00 -1.0523e+01 -9.7445e-01 -2.0957e+00
-#&gt;  -8.9254e-01  4.7154e-01 -3.2894e+00 -4.5754e+00 -3.8137e+00 -7.3894e+00
-#&gt;  -2.3613e+00 -1.3534e+00  1.3022e+01  4.2130e+00  3.0146e+00  1.2287e+00
-#&gt;   6.0422e+00 -2.9257e+00 -8.1346e+00 -5.4069e+00 -6.5148e+00  1.1687e+01
-#&gt;  -4.2277e+00  6.4720e+00 -2.5719e+00 -5.1238e+00 -6.9550e+00 -1.9845e-01
-#&gt;  -4.8493e+00 -3.1173e+00 -3.0814e+00  4.2641e-01 -2.9877e+00  4.9414e+00
-#&gt;  -6.0841e+00 -4.0842e+00 -1.1773e+01  4.2059e-01 -8.0204e+00  2.0235e-01
-#&gt;   3.8129e+00 -5.0090e+00  8.0143e+00  7.2605e+00 -8.3539e+00  1.9997e-01
-#&gt;  -5.2110e+00 -2.1361e+00  5.1565e+00  5.2963e+00  7.4855e+00  8.4635e+00
-#&gt;  -6.7656e+00  1.0277e+01  3.9130e-01  1.8962e+00  5.5895e+00 -7.6272e+00
-#&gt;   4.1073e+00  4.8892e+00  1.0822e+00 -4.1000e+00 -1.1186e+01  1.5630e+00
-#&gt;   2.1621e+00 -1.8975e+00 -1.0544e+00  7.2115e+00 -5.1892e+00  1.8350e+00
-#&gt; 
-#&gt; Columns 13 to 18  1.2055e+01 -1.5823e+00 -4.0961e+00  4.4132e+00  1.6397e+01 -7.4848e+00
-#&gt;  -8.9138e+00 -1.2013e+00  8.9636e-01 -2.3079e+00 -9.8273e+00 -3.5474e+00
-#&gt;  -4.1584e+00  9.4283e+00  3.3601e+00  1.1121e+01 -2.0212e+00 -7.8219e+00
-#&gt;  -5.1769e+00  3.6503e+00 -1.1306e+01  4.6026e+00 -6.1186e+00  4.3157e+00
-#&gt;   7.2794e-02 -3.5959e+00 -6.4562e+00 -4.8976e+00 -8.0606e+00 -6.6448e+00
-#&gt;  -2.9705e+00 -1.6580e+00  9.4389e+00 -1.0084e+00 -1.6057e+00  5.3935e+00
-#&gt;   1.2491e+01 -4.7120e+00 -5.2667e+00  1.6226e+01  3.9891e+00 -1.4046e+01
-#&gt;   1.3069e+01 -3.1612e+00 -9.8630e+00 -2.3612e+00  1.6367e+00  2.9848e+00
-#&gt;   2.8621e+00  1.2066e+01 -8.5787e+00  1.0951e+01 -5.8907e+00  8.6545e+00
-#&gt;  -3.6769e+00  8.8309e+00 -4.3361e+00 -2.2998e+00 -5.8985e+00  1.7773e+00
-#&gt;   5.7171e-01  4.1866e+00 -3.4568e+00  2.9769e-01  1.8227e+01  2.3260e+00
-#&gt;  -4.9518e+00 -4.5572e+00 -1.9396e+00 -3.8327e+00 -4.9439e+00  1.4059e+01
-#&gt;  -1.8056e+00 -1.0121e+01 -1.8802e+01 -3.8292e+00  1.4599e+01  7.9980e+00
-#&gt;  -5.1516e+00 -6.4763e-01 -1.7462e+00 -1.9015e+01  5.6483e+00 -3.0024e-01
-#&gt;  -1.0154e+01 -8.1130e+00  3.3937e+00  6.0944e-01 -1.4344e+01  9.7953e+00
-#&gt;  -4.8702e+00  2.4856e+00  7.8282e+00  3.4010e-01 -1.4870e+01  1.3035e+00
-#&gt;  -1.1545e+00 -3.1283e+00  5.7233e+00  2.2559e+00 -1.8827e+00 -2.2008e+00
-#&gt;   2.7410e+00 -2.7028e+00  4.8085e+00 -5.9716e+00 -6.9213e+00 -6.9172e-01
-#&gt;   1.5735e+01  4.2947e+00 -3.2594e+00  7.8355e-01 -4.0335e-01 -2.7748e-01
-#&gt;  -1.2516e+01  9.3661e-01  8.7588e+00 -1.0998e+01 -1.0668e+01  3.7740e+00
-#&gt;   2.1174e+00 -1.1302e+01  2.5620e+00 -1.2488e+01 -3.4072e-02  1.3111e+01
-#&gt;   1.3338e+01 -2.0520e+00 -1.5102e+01 -2.9870e+00  5.0300e+00  6.6853e+00
-#&gt;   9.5776e-02 -1.1173e+01 -7.8260e+00 -7.9570e-01 -4.0905e+00  3.1859e+00
-#&gt;   9.8249e+00 -8.4933e+00  6.2898e+00  1.8266e+00  4.9972e-01  1.1476e+00
-#&gt;  -3.1772e+00  5.4599e+00  9.6864e+00 -9.3830e-01  1.3744e+01  4.6697e+00
-#&gt;  -7.9928e-01  9.6497e+00  1.1326e+01  5.6546e-01 -4.4016e+00  1.2161e+01
-#&gt;  -4.6892e+00 -2.7207e+00  1.5347e+01 -1.3501e+00  1.0858e+01 -1.9357e-01
-#&gt;   3.9911e+00 -2.1637e+00  3.5168e+00 -5.6583e+00  4.2209e+00  4.6337e+00
-#&gt;   9.9214e+00 -7.2976e-01  1.0678e+01  1.8799e+01  1.7413e+01 -8.4330e-01
-#&gt;   2.3533e+00 -5.9228e+00  1.3134e+01 -1.4971e+00  3.8762e+00  1.5080e+00
-#&gt;  -7.7518e-01  1.4636e+00 -7.8376e+00 -1.2756e+01 -3.8406e+00  3.6612e+00
-#&gt;   2.5242e+00  8.6385e-01 -6.2545e+00  4.3963e+00  9.6608e+00  1.5203e+01
-#&gt;  -4.8494e+00 -6.9702e+00  4.1564e+00  4.8794e+00  1.7624e+01 -7.4009e+00
-#&gt; 
-#&gt; Columns 19 to 24 -1.1275e+01 -8.8870e+00 -1.1503e+00 -1.3656e+00 -1.4318e+00 -4.2171e+00
-#&gt;   1.2225e+00  6.3358e+00  1.4911e+00 -9.2090e+00  2.2159e+00 -3.0175e+00
-#&gt;  -6.2561e+00 -6.9127e+00 -1.5702e+00 -1.1056e+01  3.5933e+00 -2.8720e+00
-#&gt;  -8.9248e+00 -5.0138e+00  1.0471e+01 -5.2612e+00  5.8908e+00  1.0312e+01
-#&gt;   9.3393e+00 -1.1216e+00 -9.9527e+00 -5.3251e+00  3.2107e-01 -2.6164e+00
-#&gt;  -1.4038e+00 -2.8797e+00  3.0678e+00  1.7291e+01 -1.7825e+01  1.5292e+00
-#&gt;  -1.1628e+00 -1.6507e+00  4.8153e+00 -1.1936e+01 -1.5914e+00 -1.2476e+01
-#&gt;   5.0162e-01 -2.5429e+00 -3.8954e+00 -1.0717e+00 -7.4392e-01 -8.1214e+00
-#&gt;  -2.0364e+00 -3.6251e+00 -1.8497e+00  4.7399e+00  1.0754e+01 -1.1310e+01
-#&gt;  -4.8160e+00  1.7677e+01 -2.3417e+00  1.8043e+00 -1.5608e+01 -3.7794e+00
-#&gt;   1.0231e+00  9.7939e-01 -4.3045e+00 -5.1458e+00  3.6919e+00  4.1636e+00
-#&gt;   3.0280e+00  6.1041e+00  9.0048e-01  4.3932e+00 -1.2203e+01  2.1182e+01
-#&gt;  -2.6326e+00 -5.6641e+00  6.3962e+00  8.7000e-01  1.0775e-01  1.2407e+01
-#&gt;   9.1635e+00  2.8067e+00  2.2553e+00  2.4540e+00  5.8933e-01  1.7804e+01
-#&gt;   3.9390e-01  1.5576e+00  2.1263e+00  3.5894e+00  7.0876e+00  1.6979e+01
-#&gt;   2.2544e-01  4.1435e+00  1.8176e+00  3.9213e+00 -1.5278e+01  2.8989e+00
-#&gt;  -4.3914e-01  1.2226e+01 -4.4174e+00  2.6144e-01 -5.7790e+00  1.1095e+01
-#&gt;  -1.0954e+00  3.1233e+00 -4.9339e+00 -5.5239e+00  1.2868e+01 -1.3096e+00
-#&gt;  -1.7473e+01 -3.2913e+00 -9.9370e+00  1.6746e+01 -1.0182e+00 -1.9162e+01
-#&gt;   6.0977e+00 -7.1840e+00  9.8135e+00 -1.5442e+01  4.1905e+00  4.2416e+00
-#&gt;   8.6695e+00 -1.1826e+00 -1.3659e+01  1.9926e+00  3.4558e+00 -8.9040e+00
-#&gt;  -7.1326e+00  3.0948e+00  5.0819e+00  8.8750e-01  8.3385e+00 -1.4594e+00
-#&gt;   1.3645e+01  5.1997e+00  3.6020e+00 -8.2307e+00  2.1137e+00  1.2046e+01
-#&gt;  -4.8727e-01 -7.2430e+00 -2.7028e+00  1.1680e+01 -7.5035e+00 -5.5648e+00
-#&gt;   7.9166e+00 -2.5712e+00  1.0126e+01 -5.6492e-01 -1.6257e+01  8.2240e+00
-#&gt;   1.1700e+01  1.9525e+00 -1.7176e+00  7.5046e+00 -9.1546e+00  6.5096e+00
-#&gt;   5.9320e-01 -5.0605e+00 -1.0223e+00  1.9240e+01 -1.0675e+01  2.9595e+00
-#&gt;   1.3353e+01 -1.2302e+01 -2.9855e+00 -1.1250e+00  5.4672e+00 -7.9218e+00
-#&gt;  -2.6932e+00  6.7082e+00 -1.1248e+01  5.8509e+00  1.2224e+01 -1.1559e+01
-#&gt;   6.9015e+00 -6.2887e+00  1.2609e+00  3.2606e+00 -1.2129e+01  2.1034e+00
-#&gt;   5.9783e+00  2.8002e+00 -1.0461e+00  2.0890e+00 -1.5872e+00  4.5608e+00
-#&gt;   2.3425e+00  9.2480e+00 -1.0278e+01  8.0220e+00 -4.5289e+00  1.0035e+01
-#&gt;   1.3217e+00 -2.8492e+00  4.2375e+00 -8.9814e+00 -8.6686e+00 -8.7894e+00
-#&gt; 
-#&gt; Columns 25 to 30  8.3360e+00 -3.7383e+00 -1.0755e+01 -4.4215e+00 -5.5599e+00  4.4972e+00
-#&gt;   9.7614e+00  3.3047e+00  8.1004e+00 -1.7579e-01 -2.6047e+00  6.5980e+00
-#&gt;  -4.3140e+00 -9.9903e+00  3.5220e+00 -1.1364e+01 -2.4828e+00 -1.2487e+01
-#&gt;   7.3264e-01  1.2492e+01  1.8758e-03 -1.5940e+01 -1.7799e+01  7.2498e-01
-#&gt;  -6.0802e+00 -2.9302e+00  5.6184e+00 -7.8101e+00 -3.4038e+00  3.7225e-01
-#&gt;  -3.1649e+00 -8.9998e+00  2.3312e+00  7.6182e+00  8.3816e+00  9.3550e+00
-#&gt;  -1.1552e+01  2.3792e-01  1.4321e+00  3.3447e+00 -1.7611e+01  8.7142e+00
-#&gt;  -8.7107e-01  8.1181e+00 -1.4570e+01  6.2605e+00  7.8271e+00 -4.8895e+00
-#&gt;  -2.9786e+00 -2.9680e+00 -2.1442e+00 -1.3693e+01  2.4202e+00 -1.5083e+00
-#&gt;  -3.8475e+00 -6.6983e-01  1.0256e+00 -1.1677e+01 -4.5688e+00  1.2044e+01
-#&gt;  -1.4389e+01 -1.1644e+01 -2.4801e+00  2.1499e+00 -5.2939e-01  3.5510e-01
-#&gt;  -2.3637e+00 -3.1054e+01 -6.8584e+00  9.6814e-02  1.5122e+00  9.6570e+00
-#&gt;   4.2619e+00  5.8534e+00 -1.5839e+01  1.4565e+00 -1.7065e+00 -9.5515e+00
-#&gt;  -8.0406e+00  5.8786e+00  7.9152e+00 -6.5251e+00 -1.3515e+01  1.7494e+01
-#&gt;  -5.5358e+00 -1.6714e+01 -1.0522e+00  5.3414e+00 -1.4394e+01  7.4968e+00
-#&gt;   5.9624e+00  7.6743e-01  4.9624e+00  9.7006e+00 -5.8005e-01 -2.2949e+00
-#&gt;   3.7649e-01 -1.2126e+01  1.4155e+01  8.3035e+00  1.8507e+00  4.5304e+00
-#&gt;   2.6175e+00  1.0547e+01 -5.2089e+00 -1.7335e+00 -1.1900e+01 -3.6790e-01
-#&gt;  -2.6824e+00 -1.0599e+01  1.6807e+01  1.3984e+01  1.0876e+01  1.8125e+01
-#&gt;  -6.5827e+00 -2.4784e+00  2.5894e-01 -8.1518e+00 -5.2204e+00  7.2081e+00
-#&gt;   9.8621e+00  4.5915e-02  2.3873e-01  1.2638e+01  1.6099e+00  3.0094e+00
-#&gt;   3.0689e+00  5.8365e+00 -1.1364e+01  3.0868e+00  1.1472e+01 -3.2644e-01
-#&gt;   4.4706e-01  6.1634e+00 -8.9733e+00 -1.2721e+01  6.9967e-01  1.8322e+01
-#&gt;  -5.6052e+00  6.5106e+00  8.6718e+00  4.4310e+00 -9.5444e-01 -6.2647e-01
-#&gt;   4.7944e+00 -5.0659e+00  5.1348e+00 -5.5745e+00  2.4925e+00 -1.3703e+00
-#&gt;  -1.4523e+01 -1.3315e+01  7.2122e-04 -1.1568e+01 -4.8835e+00  4.6232e+00
-#&gt;  -3.9652e+00  6.9381e+00 -4.8351e+00 -1.2155e+01  8.0029e+00 -8.4143e+00
-#&gt;   6.9426e+00  3.5703e+00 -9.3369e+00  3.6161e+00  2.8410e+00  4.1312e+00
-#&gt;  -6.9064e+00  9.6734e+00 -1.0496e+01 -3.5655e+00 -8.3905e+00 -1.7785e+01
-#&gt;   3.1462e-03  1.9450e+00 -5.5292e+00  2.8397e+00 -6.2833e+00  3.3583e+00
-#&gt;  -5.6672e+00  3.4510e+00 -1.5409e+00  8.8068e+00 -5.1308e+00  2.6718e+00
-#&gt;  -3.3111e+00  1.4582e+01  5.8425e+00 -1.7964e+01  6.5661e+00 -2.8826e+01
-#&gt;  -6.9143e+00  1.0431e+01 -5.4161e+00 -2.9465e+00  3.6363e+00  3.3457e+00
-#&gt; 
-#&gt; Columns 31 to 36  1.2633e-01  2.0837e+00 -1.7995e+01 -7.4558e+00 -3.4899e+00 -2.1166e+00
-#&gt;   5.1230e+00  9.6372e+00  4.8431e+00 -4.0710e-02 -1.1583e+01  2.3542e+01
-#&gt;   3.2173e+00  6.4572e+00  8.2509e+00  8.3110e+00 -1.1414e+01 -1.0241e+01
-#&gt;   1.9424e+00 -8.8026e+00  1.1571e+01  6.9345e+00  2.2727e+00  1.0056e+00
-#&gt;  -7.7589e+00 -1.0472e+01 -3.7097e+00 -1.0164e+01 -3.7912e-01 -2.1021e+01
-#&gt;   1.9517e+01  4.7494e+00  4.4957e+00 -1.0816e+00  5.4489e+00 -6.5487e+00
-#&gt;  -8.7826e+00  7.8509e-02  9.0676e+00  6.6272e+00  2.6549e+00 -1.5556e+01
-#&gt;   9.7232e-01  1.4177e+01  7.6577e+00  1.4539e+00 -1.0341e+01 -4.9364e+00
-#&gt;   5.9784e+00  5.7458e+00 -1.4109e+01 -5.6991e+00  9.1061e+00  1.4150e+01
-#&gt;   1.6477e+01 -3.1146e+00  2.9849e+01 -1.8320e-01 -2.9251e+00  4.5001e-01
-#&gt;  -5.6236e-03  1.4766e+00 -6.0240e+00  9.4568e+00 -1.5395e+00 -1.5155e+01
-#&gt;   4.9929e+00  6.7127e+00 -8.6386e+00 -4.3836e+00 -5.1465e+00 -2.8905e+00
-#&gt;  -3.2138e+00  6.1757e+00  1.0868e+01  2.0150e+01  2.3389e+00 -1.8949e+01
-#&gt;  -3.8071e+00  9.3911e+00  1.2564e+01 -9.0786e+00  4.9773e+00  9.1801e+00
-#&gt;   6.5899e+00 -1.0259e+01  2.5031e+00 -6.8856e+00  2.3481e+00 -2.8523e-01
-#&gt;   7.7059e+00 -3.3388e+00 -1.0783e+01  3.0331e+00  1.0327e+01  1.5650e+01
-#&gt;   5.5370e+00 -2.3466e+00  7.0102e+00  3.5636e+00  5.4982e+00  2.2237e-01
-#&gt;  -4.8712e+00 -8.5445e-02 -6.6527e+00  1.0074e+01 -9.5377e+00  5.8579e+00
-#&gt;   7.7541e+00 -5.7797e+00 -1.3062e+00 -1.3028e+01  1.2405e+01  7.4622e+00
-#&gt;   7.9735e+00  1.0001e+01 -2.2889e+00  2.2752e+00 -8.3279e+00 -1.2083e+01
-#&gt;  -6.8952e+00 -8.5599e+00 -8.3242e+00  5.5835e+00  2.0972e+00  5.6569e+00
-#&gt;   1.2612e+00  1.4267e+01  1.1097e+00  1.6704e+01 -3.4209e+00  7.5645e+00
-#&gt;   2.4637e+00  4.9550e+00 -2.2320e+00 -6.2892e-01  5.8074e+00  2.5013e+00
-#&gt;  -1.9164e+01 -3.9155e+00 -9.6766e+00 -1.0788e+01  1.0344e+00 -4.6280e+00
-#&gt;  -5.2133e-01 -5.1429e+00  4.8088e-01  2.4318e+00  4.7498e+00  1.5048e+01
-#&gt;   9.6296e+00  7.1187e+00  7.5106e+00 -1.4648e+01 -6.7761e+00 -9.6407e+00
-#&gt;  -6.2040e+00 -5.7022e+00  3.5299e+00 -7.0126e+00  2.2069e+01 -1.1930e+01
-#&gt;   1.0790e+01  1.6324e+01 -2.4648e+00  8.3412e+00  1.5067e+01  2.6021e+00
-#&gt;  -5.1471e+00 -6.1570e+00  1.7403e+01  9.0279e+00 -1.1493e+01 -2.4385e-01
-#&gt;  -6.8378e+00 -2.1936e+01 -8.3022e+00 -7.4845e+00  9.9043e+00  3.4503e+00
-#&gt;   3.5370e+00 -4.7399e+00 -9.0786e+00 -8.4300e+00 -1.1417e+01 -2.3562e-01
-#&gt;   1.6061e+00 -6.5874e+00  6.4658e+00 -7.2971e+00  1.1293e+01 -1.7372e+01
-#&gt;  -6.1479e+00  4.7600e+00  2.8297e+00 -4.8681e+00 -5.9525e+00 -2.5531e+00
-#&gt; 
-#&gt; Columns 37 to 42 -4.1066e+00  2.3703e+00 -5.9486e-01 -4.4666e-01  1.8248e-01 -8.1558e+00
-#&gt;   8.1020e-01  1.9982e+00  1.4639e+01  4.4896e+00  7.8410e+00  9.7412e+00
-#&gt;  -1.4223e+01 -1.3682e+01 -1.7861e+00 -6.5508e+00  8.4454e+00  7.9100e+00
-#&gt;  -1.8200e+01  1.3744e+01 -1.3529e+01  1.0235e+01  5.1733e+00 -1.4189e+01
-#&gt;  -6.4968e+00  1.8096e+01 -2.5983e+00  3.8399e+00  5.0918e+00  3.8648e+00
-#&gt;   1.0602e+01  1.2529e+01  1.3578e+01  3.7652e+00 -2.2580e+00 -5.7485e+00
-#&gt;  -7.6017e+00 -2.7962e+00  7.1716e+00 -1.3455e-01 -1.4039e+00 -4.6689e+00
-#&gt;   3.3376e+00 -8.9963e+00  1.1831e+01 -7.4716e+00 -5.3106e+00  4.3870e+00
-#&gt;   7.4744e+00 -2.4187e+00 -2.0261e+00 -6.3718e+00 -6.1785e+00 -9.6643e+00
-#&gt;  -1.2513e+01  1.6945e+01 -3.6329e+00  6.4687e+00 -5.0772e+00  8.4799e+00
-#&gt;   7.7864e+00 -1.5982e+01  9.4983e-01  4.9537e+00  3.2252e+00  7.0543e+00
-#&gt;   1.2666e+00 -4.2042e+00 -1.7502e+00 -2.3269e+00 -1.6726e+00 -9.4753e+00
-#&gt;   6.5305e+00 -8.1240e+00  9.4768e+00  4.4920e+00 -6.1574e+00 -5.3549e-01
-#&gt;   2.2090e+00  4.4112e+00 -7.0229e+00  1.0717e+01 -6.2096e+00  3.4585e+00
-#&gt;  -1.1082e+01  6.0274e+00 -2.1499e+01 -3.2891e+00  2.8027e+00 -1.1171e+01
-#&gt;  -1.3268e+01  3.0344e+01  6.3035e-01 -4.0915e-01 -1.5139e-01 -1.0354e+01
-#&gt;   6.5644e+00  7.3598e+00 -6.2678e+00 -1.0709e+00 -4.2123e+00  2.6543e+00
-#&gt;   1.8287e+01 -1.2104e+01  3.0959e+00  6.8876e+00 -9.4723e-01  1.1671e+00
-#&gt;  -4.3596e-01 -9.0169e+00  1.3417e+01 -4.2153e+00  3.0050e+00  1.0411e-01
-#&gt;  -3.4339e+00 -1.8052e+01 -1.4217e+00 -3.8098e+00  7.3061e-01 -1.7107e+01
-#&gt;   1.3988e+01  9.8941e+00  7.7778e+00 -1.4597e-01 -3.0472e+00 -5.8861e+00
-#&gt;  -1.7668e+01  2.0067e+00 -1.7894e+01 -7.7294e+00 -7.6481e+00  1.0407e+01
-#&gt;   2.3894e+00 -8.2646e+00  9.7261e-01 -2.3514e+01 -1.4048e+01 -1.6880e+01
-#&gt;  -2.4144e+00  2.2946e+00  4.3116e+00 -5.1044e+00 -1.0307e+01  5.4957e+00
-#&gt;  -1.0266e+01 -6.9912e+00  2.0645e+01 -1.2542e+01  2.4918e+00 -9.1484e+00
-#&gt;   3.8444e+00  3.6482e+00  6.9221e+00 -6.6864e+00  7.9277e-01 -6.1825e+00
-#&gt;  -7.1886e-01 -1.9885e+00 -8.9606e+00  1.2146e+01 -6.4248e-01 -6.7472e+00
-#&gt;  -5.4758e+00 -1.1466e+01 -2.0698e+00  1.4037e+01  9.3900e+00  4.5658e+00
-#&gt;   5.3529e+00 -4.9850e+00  4.7814e+00  1.0499e+01 -1.3354e+01  1.0404e+01
-#&gt;  -1.2554e+01 -6.0901e-01 -4.0208e+00 -2.2119e+01  5.2214e+00 -1.0635e-01
-#&gt;  -4.0157e+00  5.0545e-04 -4.0805e-01 -6.4190e+00 -9.8647e+00  7.2780e+00
-#&gt;  -3.5262e+00  1.7072e-01 -1.3999e+01  4.5315e-01 -1.7660e+00 -1.6332e+00
-#&gt;  -5.1083e+00 -4.2060e+00 -2.6048e+00  2.4064e+00  3.4505e+00  1.4462e+01
-#&gt; 
-#&gt; Columns 43 to 48  7.4749e+00  3.2896e+00 -6.5771e-01 -2.0536e+00 -1.5341e+00 -2.1485e+00
-#&gt;  -1.4133e+01 -5.2030e+00  1.2760e+01 -1.2155e+01  1.1957e+01 -8.8063e+00
-#&gt;   5.0999e+00 -6.1652e+00  3.1785e+00 -6.0359e+00 -4.3141e+00  2.7696e+00
-#&gt;   4.1555e+00 -1.0005e+01  7.6561e-01  1.1265e+00 -5.4895e+00  7.6430e+00
-#&gt;   4.9313e+00 -1.8493e+00  5.9208e+00 -9.9984e+00 -4.2710e+00 -1.3521e+00
-#&gt;   5.3910e+00 -4.5412e-01 -9.8172e+00  2.3860e+01 -1.8580e+01  1.1603e+01
-#&gt;   5.4036e+00  3.2782e+00  3.6406e+00 -7.9052e+00 -3.6935e+00 -5.4650e+00
-#&gt;   4.7777e-01  1.8790e+01 -7.1115e+00 -1.1741e+01  5.1975e+00 -9.0889e-01
-#&gt;  -4.3182e+00  9.0281e+00 -8.1416e+00  7.9910e+00 -9.8128e+00 -1.0565e+01
-#&gt;   5.0018e-01 -4.6825e+00  5.4171e+00 -1.3931e+01  1.5320e+00 -8.1043e+00
-#&gt;   1.3373e+01 -5.6451e+00 -6.6508e+00  1.5060e+00 -8.8349e-01  1.1201e+00
-#&gt;  -5.6232e+00  3.6242e+00 -1.0497e+00  2.5834e+00 -2.0360e+00 -7.3847e+00
-#&gt;   6.6888e+00  2.3927e+00 -3.8077e+00 -2.1639e-02 -5.8558e+00  6.8943e+00
-#&gt;  -7.8989e+00 -2.6013e+00  5.0901e+00  2.8595e+00 -5.5578e+00 -2.1903e+00
-#&gt;   9.7989e+00  5.4863e+00 -1.1755e+01  1.3804e+01  6.5772e+00 -2.0106e+00
-#&gt;   4.7886e+00  4.1859e-01  1.0380e+00  1.2049e+01  3.2355e+00 -6.1523e+00
-#&gt;  -1.0745e+00  4.7642e+00  2.3202e+00 -7.0219e+00 -1.0187e+00 -7.0501e+00
-#&gt;  -5.4901e+00 -3.2077e+00  2.0884e+01  2.2532e+00  3.0159e+00  7.5106e+00
-#&gt;  -1.1099e+01  1.3630e+01 -7.1980e+00 -1.1558e+01  1.6250e-01  7.4032e+00
-#&gt;   1.2947e+01  5.4460e+00 -1.5756e+01  9.4801e+00 -1.2139e+00  6.2564e+00
-#&gt;   1.1998e-01  5.7220e+00  1.1314e+01 -3.5625e+00  3.5268e+00  1.1134e+01
-#&gt;   1.3069e+01  1.1677e+00 -8.4681e+00 -1.1823e+01 -1.4532e+01 -5.3117e+00
-#&gt;   3.4807e+00  1.9474e+01  8.1345e-01 -4.3280e+00  3.3045e+00  8.0262e+00
-#&gt;  -4.1458e-01  2.9176e+00  3.3451e+00 -2.6143e+00  8.6070e-01  2.2524e-01
-#&gt;   4.1285e+00  9.9700e-01  9.8412e+00  3.2570e+00 -8.5901e-01  4.1803e+00
-#&gt;  -1.1954e+01  5.9839e+00 -1.7945e+01  1.3585e+01 -3.0892e+00  6.8838e-01
-#&gt;   2.6369e+00 -5.5210e+00 -1.5965e+01  1.8688e+01 -1.1416e+01  9.1712e+00
-#&gt;   1.5720e+00 -3.2047e+00  3.7386e+00 -1.0319e+01 -6.0105e+00  6.2452e-02
-#&gt;  -1.0030e+00 -3.8978e+00  8.4415e+00 -6.1983e+00  9.5013e+00  7.3072e+00
-#&gt;   1.0870e+00 -1.7976e+00  1.0758e+01  6.4537e+00 -3.5953e-01  8.7095e+00
-#&gt;  -1.2589e+01  1.6091e+00  4.8148e+00  3.5849e+00 -3.8170e+00 -3.7377e+00
-#&gt;  -1.0399e+00  1.7239e+01  3.7850e+00  8.8687e+00  1.6787e+00  1.8082e+01
-#&gt;   9.9256e+00 -5.7379e+00  6.7655e+00 -4.6335e+00  1.2040e+01  4.8663e-01
-#&gt; 
-#&gt; Columns 49 to 54  2.0588e+00 -1.1653e+01  1.0608e+01  2.7881e+00  5.6513e+00 -5.9351e-01
-#&gt;  -1.5856e+01 -1.0242e+01 -7.9768e-01  2.1046e+00 -4.7630e+00 -2.5084e+00
-#&gt;   5.8313e+00 -1.4300e+01  4.8191e+00 -3.9928e+00  3.9323e+00 -1.7959e+00
-#&gt;   7.1854e+00  7.4052e+00 -3.5649e+00  3.0255e+00 -6.5195e+00  4.0929e-02
-#&gt;   7.3524e+00 -4.1666e+00  1.4574e+01 -8.3825e+00  1.2557e+00  2.2931e+00
-#&gt;  -1.6590e+01 -4.2074e+00  6.5455e+00 -2.3427e+00 -5.9171e+00 -2.4614e+00
-#&gt;   8.4701e+00 -4.9499e+00  2.8532e+00  1.2714e+00 -1.0051e+01 -2.1670e+00
-#&gt;  -4.7380e+00 -4.1595e+00  7.7442e-01  4.5593e+00 -3.1357e+00  1.0338e+00
-#&gt;   1.7061e+01 -1.1289e+01  8.1655e+00  5.5929e+00  5.5192e+00  6.9233e-01
-#&gt;  -1.1707e+01 -4.1463e+00 -4.4399e+00 -5.5704e-01 -5.4864e+00 -4.6775e+00
-#&gt;  -1.9277e+00  4.0682e+00  1.0254e+01 -2.4243e-01 -1.0002e+00 -1.2724e+00
-#&gt;   6.7865e+00 -1.4280e+00  1.7221e+00 -4.3760e+00  5.2218e+00 -1.1768e-01
-#&gt;  -3.3110e+00 -9.4838e-01 -1.9602e+01  9.0612e+00 -1.4708e+00  4.8763e+00
-#&gt;  -3.5278e+00  4.1600e+00 -1.1134e+00  7.8087e-01 -2.6953e+00 -1.4792e+00
-#&gt;   6.3919e+00 -8.5068e+00  1.0860e+01 -2.0888e+00  4.6862e+00 -2.6934e+00
-#&gt;  -1.3631e+01  3.2825e+00  3.6291e+00  2.1424e-01 -6.6992e+00 -2.3271e+00
-#&gt;  -8.4119e+00 -8.5404e+00  4.4918e+00  2.3728e+00 -2.8177e+00  2.3407e+00
-#&gt;  -5.7681e+00  4.3214e+00 -1.0287e+01  3.5729e+00 -4.2341e+00  1.4796e+00
-#&gt;  -8.7989e+00  5.0641e+00 -4.5184e-01 -7.1411e+00  4.0992e+00 -2.9186e+00
-#&gt;  -5.6270e+00 -6.4904e+00  3.5192e+00 -4.1002e+00  5.7519e+00 -9.8820e-01
-#&gt;  -4.6634e+00 -5.7731e-01 -5.6198e+00 -2.6096e+00  5.3468e+00 -2.4138e+00
-#&gt;   1.9903e+01 -1.9132e-01  3.7509e+00 -2.5854e+00  8.5715e-01 -1.6367e+00
-#&gt;  -1.8672e+01 -6.1275e+00  6.0109e+00  6.0365e+00 -3.1170e+00  2.3840e-01
-#&gt;  -3.7971e-01 -6.9648e+00  2.7984e+00 -2.4435e+00 -4.0945e+00  3.8353e-01
-#&gt;  -1.4058e+01  5.9574e+00 -1.9671e+00 -5.1292e+00  1.5424e+00 -5.5177e-01
-#&gt;  -8.7823e+00  1.3229e+00 -7.1644e+00  1.2158e+00 -3.8932e+00  3.1650e+00
-#&gt;  -5.6919e+00  1.2080e+01  1.2179e+00  4.1779e+00 -5.4655e-02  2.0174e-01
-#&gt;  -8.4263e+00  3.1701e+00  2.4079e-01  5.1354e+00  2.8352e+00 -2.6872e-01
-#&gt;   4.9421e+00 -5.0250e+00  2.6910e+00  2.7623e+00 -6.1163e+00  1.4400e+00
-#&gt;  -1.1406e+01  2.4924e+01 -9.8252e+00 -1.6379e+00 -2.1415e+00 -1.5942e+00
-#&gt;   5.9189e+00  6.6855e+00  5.4495e+00  8.0702e+00 -6.0936e+00  1.2601e+00
-#&gt;  -7.1622e+00  7.4579e+00 -1.6247e+01 -1.7368e+00  3.5875e+00  8.7810e-01
-#&gt;  -5.6202e+00 -4.4285e+00  1.1572e+00 -4.1747e+00 -1.2560e+00 -6.9329e+00
-#&gt; 
-#&gt; (12,.,.) = 
-#&gt;  Columns 1 to 6  2.2343e+00  2.4861e-01  1.3061e+01 -1.3344e+01  1.2442e+00  1.6980e+01
-#&gt;   5.8112e-01 -1.1779e+01 -2.8015e+00 -1.2982e+01 -3.2822e+00  7.1146e-02
-#&gt;  -4.3058e+00  1.0108e+01  2.0428e+00  4.8671e+00 -7.7594e+00 -1.3086e+01
-#&gt;  -1.4351e+00 -5.7256e+00 -4.4247e+00  1.3467e+00  1.5534e+00 -1.1222e+01
-#&gt;   1.7914e+00  6.6421e+00 -2.5117e+00  2.6416e+00  4.7904e+00 -3.9372e-01
-#&gt;  -8.3136e+00 -1.5627e+00 -4.9528e+00  6.6515e+00  5.2495e+00 -1.7103e+00
-#&gt;   5.3299e+00  6.1510e+00 -7.8576e-01 -3.3475e+00 -1.0934e+01 -3.2163e+00
-#&gt;  -2.7892e+00  7.9525e+00  1.3028e+01 -3.7640e+00 -2.0863e+01 -2.6415e+00
-#&gt;  -4.3488e+00 -8.0201e+00 -5.8985e+00 -3.7523e-01  1.3658e+01 -5.0732e+00
-#&gt;  -7.9534e-01 -8.1160e+00 -4.3030e+00 -8.5787e-02 -1.4162e+01 -1.9195e+01
-#&gt;   1.7526e+00 -1.3950e+00 -3.5435e+00 -3.9843e+00 -5.4550e-01  1.4017e+00
-#&gt;  -5.9295e+00 -1.8240e+00  2.3624e+00  2.2988e+00  8.0792e+00  5.5243e+00
-#&gt;   3.5374e+00  1.2951e+00  1.0387e+01 -7.4211e+00 -1.6104e+01 -9.4376e+00
-#&gt;   4.2853e+00 -5.5056e+00 -4.3562e-01  1.1677e+00  6.9683e+00  3.3208e+00
-#&gt;   7.1005e-02  8.5128e+00  3.6922e+00  7.9138e+00  5.0410e+00  3.8249e+00
-#&gt;  -7.2560e-01 -9.5460e-01 -4.3871e+00 -4.4477e+00  5.1031e+00  5.3250e+00
-#&gt;   9.0075e-01  9.4957e+00  5.7318e+00 -5.9370e-01  7.3959e+00  8.1217e+00
-#&gt;  -6.2461e-02 -8.1013e+00 -2.5499e+00 -1.3845e+00  3.1540e+00  1.0728e+01
-#&gt;   5.4301e+00  2.6902e+00 -5.4021e+00  1.0105e+01 -1.1946e+01 -8.0718e+00
-#&gt;   1.5006e-02  5.5758e+00  4.5327e+00  3.3208e+00  3.3094e-01 -2.0927e+01
-#&gt;   7.4586e+00  4.6525e+00  5.8436e-01  1.0973e+01 -2.3610e+00  4.2508e+00
-#&gt;  -1.2983e+00 -1.1301e+01  1.2430e+01  4.0250e+00  4.1832e-01 -1.6513e+01
-#&gt;  -7.8391e+00  2.4531e+00 -1.8300e+00  1.0450e+01  1.5860e+00 -2.5208e+00
-#&gt;   5.6105e+00  5.5700e+00 -4.3786e+00 -9.1806e+00 -5.7021e+00 -7.4651e+00
-#&gt;  -9.2395e+00  6.9638e+00  6.7176e+00  4.8742e+00  1.5557e+00 -4.7856e+00
-#&gt;   1.3244e+00 -1.5630e+00 -6.8633e+00 -2.1048e+00 -4.5452e+00 -1.2455e+01
-#&gt;  -3.4415e+00 -3.6602e+00 -8.8038e+00  1.2900e+01 -1.6583e+00  3.5402e-01
-#&gt;  -5.4946e-01  4.2501e+00 -3.8394e+00 -1.1170e+00 -8.1395e-01 -3.9050e+00
-#&gt;   2.5022e+00  8.3729e+00 -2.6832e+00  2.7693e-01  3.8717e+00 -6.5552e+00
-#&gt;   7.8093e+00  1.3572e+00 -1.4310e+01  5.0189e+00  3.0458e+00  8.0139e+00
-#&gt;   1.3326e+00 -6.4450e+00  1.0170e+00  5.7269e-01  1.9765e+01 -6.6270e+00
-#&gt;   1.9113e+00  5.7325e+00  7.8303e+00 -8.4500e-01 -7.9439e+00 -6.7554e+00
-#&gt;   2.8425e+00  8.1149e+00  6.1998e-01 -6.4198e+00 -1.3863e+01  2.7923e+00
-#&gt; 
-#&gt; Columns 7 to 12  1.0106e+01 -8.0998e+00 -1.6541e+00 -1.0441e+01 -6.2678e+00  1.5084e+01
-#&gt;   4.2660e+00  5.1183e+00 -4.0667e+00 -2.3064e+01 -4.2162e+00 -1.1576e+00
-#&gt;   1.3151e+00 -4.1282e+00  1.2276e+01  2.8656e+00  1.3460e+01 -8.0012e+00
-#&gt;  -4.6559e+00 -1.3864e+01 -4.5554e+00 -3.6413e+00 -5.4804e+00 -1.7346e+01
-#&gt;   9.5870e+00  7.6032e+00  8.3130e+00  1.2399e+00  2.7644e+00  3.0823e+00
-#&gt;   7.4200e+00 -1.7400e+01 -1.1742e+01  7.4787e+00 -2.5101e+01  5.8978e+00
-#&gt;  -1.8558e+00  2.3167e+00 -5.5227e+00 -3.7291e+00  4.4580e+00  1.3574e+00
-#&gt;   4.9338e+00  1.0376e+01 -5.2100e+00  6.7813e+00 -3.7380e+00 -1.6642e+01
-#&gt;   1.8014e+01 -2.5866e+00 -5.0389e+00 -3.0290e+00  1.5388e+00 -1.0736e+00
-#&gt;  -2.3015e+01 -4.0512e+00 -2.0893e+00 -4.2346e+00 -2.2044e+01  1.4695e+00
-#&gt;   4.4195e+00 -1.5963e+00  2.3283e+00 -7.8490e-01 -1.9061e+00  9.3110e-01
-#&gt;  -1.0458e+01  2.6943e+00 -1.1518e+01  3.4626e+00  9.9623e-01  1.5833e+01
-#&gt;   5.4454e-01 -1.8906e-01 -4.6633e-01  8.7036e+00 -1.3710e+00 -1.1635e+01
-#&gt;  -3.0853e+00  1.0756e+01 -2.1256e+01 -1.3527e+01  2.0147e-01  8.2447e+00
-#&gt;  -2.6700e+00 -2.6537e+00  3.8012e-02 -4.0746e-01  4.2514e+00  1.3912e+01
-#&gt;   4.6423e+00 -1.3562e+01  3.3131e-01 -4.1649e+00 -7.3361e+00  4.3607e+00
-#&gt;  -1.1995e+01  7.7007e+00 -1.9198e+00 -1.5712e+01 -1.6685e+01  9.2774e+00
-#&gt;  -5.1702e+00  1.3754e+01  1.1154e+00 -2.0734e+00  1.8528e-02 -3.4237e-01
-#&gt;  -7.5284e+00 -9.7365e+00 -3.6080e+00  6.4859e+00 -8.1583e+00  6.2707e+00
-#&gt;   1.2302e+01  4.8155e+00  6.7645e-01 -9.2286e-01  7.5249e+00 -1.2391e+00
-#&gt;  -3.8789e+00 -7.4176e-01 -3.9320e+00 -6.7518e+00  2.9841e+00  2.0185e+01
-#&gt;   3.6928e+00 -1.8126e+01 -9.5705e+00  2.5406e+00  7.8560e+00 -1.4716e+01
-#&gt;   1.0955e+01  3.9481e+00 -1.9212e+01 -2.1933e+01 -1.2778e+01  1.1302e+01
-#&gt;   7.4397e+00 -7.9327e-01 -9.3109e-01  1.9953e+01 -1.2216e+00  1.0269e+01
-#&gt;   1.7136e+00 -1.5817e+00 -5.9169e+00 -6.5075e+00  4.2173e+00  1.1803e+01
-#&gt;   6.3738e-01  4.2163e+00 -1.4027e+00  9.7656e+00 -2.3720e+00  1.1708e+01
-#&gt;   4.1512e+00 -6.7453e+00 -7.7274e+00  3.5501e+00  8.7879e-01  4.4375e+00
-#&gt;  -3.6729e+00 -1.0202e+01 -6.3316e-01 -1.5827e+01 -5.6612e+00 -1.3232e+01
-#&gt;   3.4021e+00  1.2152e+01  4.2543e+00  1.6077e+01  7.1638e+00 -9.5322e+00
-#&gt;  -1.6027e+01 -1.2267e+01  9.8984e+00  5.8649e+00  1.5113e+01  2.3995e+01
-#&gt;   6.6292e+00 -6.8196e+00  4.2903e+00  7.1128e+00 -1.0787e+01 -1.3526e+00
-#&gt;  -7.1549e+00  1.3952e+01 -9.2232e+00 -6.3592e+00  6.6941e-01 -9.2557e+00
-#&gt;  -3.7317e+00  9.6946e+00 -6.1778e+00  9.9506e+00 -5.2459e+00 -5.7252e+00
-#&gt; 
-#&gt; Columns 13 to 18  7.9775e+00 -3.3716e+00 -1.6398e+01  1.0290e+00  1.4219e+01 -7.9817e-02
-#&gt;  -2.8099e+00  9.6442e+00  5.9975e+00 -8.3840e+00 -1.2924e+01  1.7883e+01
-#&gt;  -2.9478e+00 -4.1491e+00  6.5517e+00 -3.4311e+00 -2.5219e-01 -4.7551e+00
-#&gt;  -1.7489e+00  4.8127e+00  1.7067e-01 -1.0031e+01  4.8095e-01  1.7671e+01
-#&gt;   3.3080e+00 -5.0196e-01  1.6195e+01  2.1735e-01 -1.1563e+01 -1.8092e+01
-#&gt;   5.8877e+00  8.8385e+00  4.5792e+00 -1.3592e+01  3.6850e+00  3.2994e+00
-#&gt;  -3.6112e+00  6.8737e+00  3.0362e+00  3.9409e+00  1.3417e+01 -7.3520e+00
-#&gt;  -1.2533e+01 -5.9631e+00 -6.3714e+00  2.5638e+00  1.9757e+01 -1.3020e+00
-#&gt;   1.8081e+01 -3.9881e+00 -2.4899e+01 -1.1685e+01 -5.0189e+00  2.7581e-01
-#&gt;  -1.0677e+01 -1.7292e+00  6.1302e+00  9.3948e+00 -5.9134e+00  1.0920e+01
-#&gt;  -1.5548e+01 -9.7880e+00  1.5163e+01  3.2234e+00 -1.4654e+01 -2.7111e+00
-#&gt;   2.6025e+00  1.6772e+01 -9.5342e+00 -1.7929e+01  6.9729e+00  1.7990e+00
-#&gt;  -4.6987e+00  1.8096e+00 -4.8640e-01  1.7515e+00  1.6400e+00  8.6115e+00
-#&gt;   5.8794e+00  2.3319e+01 -1.2410e+01 -1.4471e+01 -2.5027e+00  1.3834e+01
-#&gt;   3.6492e+00  1.0097e+01  6.0911e+00 -1.6952e+01  3.7765e+00  1.2470e+00
-#&gt;   9.0154e+00 -7.9646e+00  1.0940e+01 -2.5419e+00 -1.2839e+01 -3.2985e-01
-#&gt;  -1.0842e+01  1.9563e+00  2.8683e+00  6.0006e+00 -4.8396e+00 -3.4927e+00
-#&gt;   5.7079e-01 -9.4869e+00 -7.2187e+00 -2.3264e+00  1.1500e+00  1.3042e+01
-#&gt;   9.3394e-02 -1.3834e+01 -1.0763e+01 -9.9645e+00  1.9327e+01 -3.2349e+00
-#&gt;   1.6126e+01 -7.6733e+00  7.4829e+00 -1.7533e+00 -4.0451e+00  6.5047e+00
-#&gt;  -7.6359e+00 -1.2013e+01 -7.0419e+00  7.2818e+00 -9.0434e+00  6.7979e+00
-#&gt;  -7.6891e+00 -3.2007e+00 -1.9260e+01  1.1613e+01  1.1378e+01  1.1982e+01
-#&gt;   1.8505e+01  1.5860e+01  1.0207e+01 -1.2126e+01 -3.8591e+00 -5.2286e+00
-#&gt;  -1.0642e+01  9.9338e+00 -4.5486e+00 -2.5227e+00 -4.0303e-02 -1.1448e+00
-#&gt;  -2.2505e+00  2.7698e+00  1.3077e+01 -9.0763e+00  7.3168e+00 -5.4729e-01
-#&gt;  -1.9906e+00 -1.5236e+00 -5.2221e+00 -6.9238e-01  9.1291e+00  2.5066e+00
-#&gt;  -3.4526e+00  1.7163e+00 -1.1267e+00  4.2589e+00 -4.5121e-01  1.1336e+00
-#&gt;  -8.9524e+00 -9.5551e+00 -4.5162e+00 -2.4994e+00  1.2130e+00 -2.3913e+01
-#&gt;  -4.9009e+00 -8.7625e+00  3.8590e+00 -2.1340e+00 -7.0136e+00 -7.2723e+00
-#&gt;  -4.2614e+00 -1.5789e+01  1.1984e+01  1.4028e+01  2.2274e+00 -9.6963e+00
-#&gt;   1.0833e+01  1.0684e+01  3.9278e+00 -2.4064e+00 -7.7770e+00  8.4217e+00
-#&gt;   5.8047e+00  1.9016e+01 -1.1636e+01  5.5353e+00 -4.7503e+00  1.2186e+01
-#&gt;  -5.2927e+00  1.4010e+00  1.2860e+00  6.6502e+00 -5.9610e+00 -1.4549e+01
-#&gt; 
-#&gt; Columns 19 to 24 -7.8774e+00  9.3987e+00 -3.1508e+00  3.2035e+00 -1.7237e+00 -2.2337e+00
-#&gt;   9.8753e+00 -5.1873e+00 -1.5979e-01  7.0176e+00  1.4790e+00 -2.2652e+00
-#&gt;   2.4684e+00 -1.5629e+01 -1.0845e+00  2.5782e+00  5.0271e+00 -1.5199e+01
-#&gt;   1.1172e+01  1.2462e+00  1.2406e+01  7.3422e+00 -1.3610e+00 -7.2763e+00
-#&gt;   1.1630e+01 -6.2654e+00  1.3605e+00  3.8456e+00  9.3753e+00 -9.7017e+00
-#&gt;   2.6302e+00 -4.7561e+00 -1.1254e+01 -2.3001e+01  4.0595e-01  6.5323e+00
-#&gt;  -1.6812e+01 -1.8110e+01 -3.6096e+00 -3.5465e-02 -1.4968e+01  9.3158e+00
-#&gt;  -2.4737e+01  8.3503e+00 -1.7397e+00  2.9828e+00 -8.2400e+00  4.5024e+00
-#&gt;  -3.7867e+00  8.1052e+00 -8.3192e-01  8.8176e+00  4.7793e+00 -1.0590e+00
-#&gt;  -3.2457e+00  2.4234e+00 -5.5022e+00 -5.7761e-01  5.2855e+00 -7.4730e+00
-#&gt;  -1.1944e+01 -6.8048e-01 -8.7536e+00 -1.1724e+00  3.2814e+00 -1.1310e+01
-#&gt;   2.9503e+00  1.9739e+00 -8.4449e+00 -1.2881e+01 -7.4179e+00 -2.1671e+00
-#&gt;  -7.6667e+00  9.0520e+00 -4.4530e+00  3.8394e+00 -4.5973e+00  1.5565e+01
-#&gt;   1.2824e+01 -3.3694e+00  1.1752e+01 -2.3582e+00  5.0714e+00  4.2392e+00
-#&gt;   8.0841e+00 -2.5142e+00 -1.0000e+01  1.6647e+00 -8.9167e+00 -1.6243e+01
-#&gt;   1.3602e+01  2.1327e+00 -1.9909e+01  2.4326e+00  1.2900e+01 -3.7971e+00
-#&gt;   1.8914e+01  6.4058e+00  2.3033e-01 -8.2163e+00  3.4782e+00 -1.0740e+00
-#&gt;   6.3810e+00 -4.0515e+00  1.0759e+01 -8.3499e-01  7.3965e-01 -9.1388e+00
-#&gt;  -5.8142e+00  4.5707e-02  1.1224e+00 -4.1406e+00  6.5294e+00 -6.1837e+00
-#&gt;  -9.5804e+00 -7.5517e+00 -4.6984e+00  1.7209e+00 -6.8727e+00  1.3254e-01
-#&gt;   8.7612e+00 -5.2527e+00 -6.8012e+00 -9.3108e-01  6.9843e+00  1.3341e+01
-#&gt;  -1.4546e+01  9.7236e+00 -6.3226e-01 -4.3340e+00 -5.9874e+00 -3.2339e+00
-#&gt;   3.9066e+00  2.2284e-01 -1.4156e+01 -3.8453e+00 -8.6821e+00 -1.9315e+00
-#&gt;  -1.5990e+00  7.5505e-01 -1.8868e+00  1.4217e+01  1.5455e+00 -2.3546e+00
-#&gt;  -1.0250e+01 -7.7651e+00 -7.1299e+00 -3.6324e+00  3.5405e+00 -4.7819e+00
-#&gt;   1.4602e+01  1.3240e+00  9.4274e+00 -8.6895e+00 -5.1178e+00  2.1310e+00
-#&gt;   8.2942e+00 -2.1216e+01  9.4975e+00 -7.5825e+00  1.0006e+01 -2.7521e+00
-#&gt;   1.1945e+01 -1.4659e+01  6.7279e+00 -6.1521e+00  4.0640e+00 -6.7216e+00
-#&gt;  -6.6725e+00  2.7873e+00  8.7322e+00  9.3220e+00  5.0820e+00  5.8792e+00
-#&gt;   3.6809e+00 -1.6383e+01 -1.5499e+01  1.1922e+01  3.0115e+00 -2.6662e+01
-#&gt;  -3.1499e-01  7.1257e+00 -1.5790e+01  1.0345e+00  2.9703e+00  4.5194e-01
-#&gt;  -1.3069e+00 -1.4656e+00 -2.5011e+00  8.5862e+00 -3.4828e-01  1.6991e+01
-#&gt;   2.6757e+00 -2.0333e+01  1.2204e+00 -5.4096e+00  6.6630e+00 -6.8385e+00
-#&gt; 
-#&gt; Columns 25 to 30 -1.5597e+00 -4.3700e+00 -8.6704e+00 -4.0963e+00 -1.6321e+01 -4.3423e+00
-#&gt;   2.9396e+00 -5.9848e+00  2.3327e+00  9.8268e+00  1.4889e+00  1.8608e+01
-#&gt;  -3.4125e+00  9.3013e+00  1.5506e+00  1.0031e+01 -5.2014e+00 -6.4869e+00
-#&gt;  -3.6186e+00 -5.2734e+00 -8.8278e+00  1.1643e+01 -1.0888e+01 -7.8657e+00
-#&gt;   8.9388e-01 -3.4935e+00 -1.1399e+01  8.2926e+00  1.1714e+01  1.8860e+01
-#&gt;  -2.4677e+01 -1.4618e+01  1.2835e+01  2.7846e+00  3.1225e+00 -7.5194e+00
-#&gt;  -3.2098e+00 -1.2272e+01 -7.2635e+00  2.9061e+00 -1.4480e+00 -7.6684e+00
-#&gt;   6.1910e+00  1.7474e+00 -1.5868e+00 -1.4324e+01 -3.1537e+00  2.5270e+00
-#&gt;  -1.4938e+01 -2.9654e-01 -5.9241e+00  4.5311e+00  5.1367e-01 -4.2442e+00
-#&gt;  -1.3926e+01  1.4841e+01  1.2362e+01  3.4737e+00 -3.6818e+00  6.5808e+00
-#&gt;   5.6785e+00  4.2856e+00 -4.1781e+00 -4.9512e+00  1.3393e+01 -4.2968e-01
-#&gt;  -5.5229e+00 -1.6487e+01  6.4846e-01  7.7945e-01  1.2717e+01  1.9683e+00
-#&gt;   4.8212e-01 -1.0946e+01  5.6029e+00  1.0808e+01  1.0435e+00 -4.6982e+00
-#&gt;  -8.2592e+00 -5.3752e+00 -5.2363e+00 -5.4708e-01 -7.7221e+00  1.0334e+01
-#&gt;  -5.4707e+00  2.1817e+01  6.3952e-01  2.8722e+00 -7.4404e+00 -9.4587e+00
-#&gt;  -9.4711e+00  3.9907e+00  2.3350e+00  6.3608e+00  1.4797e+01  8.6591e+00
-#&gt;  -1.5397e+01 -1.2832e+01 -1.2764e+01 -9.1527e+00 -1.3297e+00  3.4214e+00
-#&gt;   2.7281e+01 -1.0290e+01 -8.7485e-02 -3.3851e+00  6.6595e+00 -2.6885e+00
-#&gt;   7.5860e+00  4.2211e+00  1.8520e+01 -1.6380e+01 -5.6852e+00 -1.8309e+01
-#&gt;  -4.2865e+00  1.0817e+01  6.2852e+00 -7.2758e+00 -4.1168e+00  3.6913e+00
-#&gt;   2.4672e+00 -1.1057e+01  2.4086e+00 -4.3137e+00  9.9728e+00  1.6034e+01
-#&gt;  -6.7063e+00 -6.8820e+00  4.8363e-01  1.1105e+01 -1.0333e+01 -1.6367e+00
-#&gt;  -2.1280e+01 -4.1415e+00  6.0902e-01 -1.1766e+01  9.4662e+00  1.6602e+01
-#&gt;  -1.0490e+00 -9.4947e+00  8.6725e+00  3.2525e-01  4.2604e+00 -2.7379e+00
-#&gt;   5.9630e+00 -5.8807e+00  1.1127e+01 -7.1153e+00  1.0029e+01 -1.1379e+01
-#&gt;  -2.1555e+00  6.7623e+00  2.2071e+00 -1.9433e+00  1.1161e+01 -7.5301e+00
-#&gt;  -1.9755e+00  7.1774e+00 -2.1621e+00 -4.2744e+00 -1.2805e+01 -1.0321e+01
-#&gt;   1.6206e+01 -9.0574e+00 -1.3386e+01  1.4836e+00 -3.6717e+00  1.9019e+00
-#&gt;   1.8684e+01 -1.0844e+01 -3.7766e+00 -1.4817e-01 -6.8664e+00 -1.0706e+01
-#&gt;   4.5852e+00  1.4066e+01  9.4515e+00  7.7569e-01 -4.8496e+00 -1.4866e+01
-#&gt;  -2.0469e+00 -4.9087e-01  7.5441e-01 -2.6077e+00 -3.6706e-02  1.2476e+01
-#&gt;   1.8071e-01 -7.4970e+00 -3.3489e-01  6.6271e+00 -2.0375e+00 -1.4445e+01
-#&gt;  -1.8088e+00  4.1301e+00 -2.2527e+00  2.1262e+00 -3.4904e-01  6.9723e+00
-#&gt; 
-#&gt; Columns 31 to 36 -6.5040e+00  5.8752e+00  3.1383e+00  1.1837e+01  6.4853e+00  1.0323e+01
-#&gt;   4.4193e+00  2.0463e+00  5.9618e-01  7.4200e+00 -1.3114e+01 -1.4656e+00
-#&gt;  -1.0508e+01  3.8826e+00  5.1851e+00 -9.6904e+00  1.1859e+01  4.9136e+00
-#&gt;  -4.1396e+00 -7.9817e+00 -5.4383e+00 -1.7414e+01  7.0854e+00 -9.6429e+00
-#&gt;   2.2758e+00  6.2357e+00  9.3515e+00 -6.7403e+00  1.8872e+01 -5.0694e+00
-#&gt;   5.8505e+00  4.4309e+00  1.3236e+01  1.0722e+01  8.9095e+00 -2.9298e+00
-#&gt;   6.5115e+00  3.5489e+00 -9.1628e-01  2.5802e+00  7.0845e+00  1.8733e+01
-#&gt;  -3.0491e+00  3.2534e+00  1.1997e+00  8.8833e-01 -2.1584e+00  2.3641e+00
-#&gt;   1.6468e+00  4.4843e+00  3.3471e+00 -1.8669e+00  5.9386e-01  8.3609e+00
-#&gt;  -2.0089e+00 -1.9370e+01 -2.9176e+00 -5.4528e+00  5.9043e+00  8.6076e+00
-#&gt;  -9.3280e+00  6.0964e+00  3.1025e+00  1.0701e+00  1.0776e+01  9.5514e+00
-#&gt;   6.1485e+00 -3.3274e+00  8.4756e+00 -4.8317e+00 -2.7427e+00  1.5232e+00
-#&gt;   4.6530e+00  9.2008e-01 -6.9505e+00 -6.6205e+00  7.9752e+00 -1.4570e-01
-#&gt;  -2.1591e+00 -1.0310e+01 -1.2721e+01 -2.1465e+01 -1.4941e+01  5.3263e+00
-#&gt;   8.0429e-01  9.5050e+00  5.3638e+00 -5.6013e+00  2.7263e+00 -3.2744e+00
-#&gt;   2.6271e+00 -7.3372e+00  6.6152e+00  2.8711e+00  1.1847e+01 -3.4755e-01
-#&gt;  -3.4368e+00 -9.0752e+00 -1.5055e+00 -3.1626e+00 -9.8245e+00 -4.7415e-01
-#&gt;   2.9191e+00  2.4938e-01  1.8888e-01  9.8061e+00 -1.7531e+01 -6.0103e+00
-#&gt;  -4.7865e+00  1.6937e-01 -5.0390e+00  4.5317e+00  2.9974e+00  1.9773e+01
-#&gt;   2.9439e+00  4.0795e+00 -3.9380e+00 -6.3246e+00  1.0128e+00 -4.5317e+00
-#&gt;   2.6851e+00 -5.0098e+00 -6.2676e+00  6.7208e+00 -1.0663e+01  5.7691e+00
-#&gt;  -1.2311e+01 -6.5510e-01  7.9312e+00 -1.9693e+00 -6.8400e+00  1.4329e+01
-#&gt;   9.2061e+00 -5.5078e+00 -5.9201e+00 -1.1016e+01 -1.0204e+01 -9.2475e+00
-#&gt;   1.3350e+01  1.9800e+00 -9.1722e+00 -3.6948e+00  8.2696e+00  8.0762e+00
-#&gt;   4.6315e+00 -7.5339e+00  1.5947e+00 -1.1322e+01 -8.3186e+00 -6.6386e+00
-#&gt;  -5.7139e+00 -1.3925e+01 -2.3077e+00  1.1494e+00 -7.2648e+00  2.1355e+00
-#&gt;  -4.2203e+00  2.6027e+00  1.7255e+00 -5.4968e+00  3.3107e+00 -7.9746e+00
-#&gt;  -3.7495e+00  1.9044e+00 -5.6979e+00  1.2216e+00  2.1435e+00  4.0822e+00
-#&gt;  -4.3472e+00 -1.2315e+01 -5.7846e+00 -9.3757e+00  6.5540e+00 -5.8813e+00
-#&gt;  -4.1403e+00 -3.7812e+00 -4.1460e+00  3.9681e+00  1.9608e-01  3.2650e-01
-#&gt;   1.4559e+00 -1.7231e+00  1.6940e+00 -5.9361e+00  9.1474e+00 -6.8446e-01
-#&gt;  -5.8992e+00 -3.6607e-02 -4.8166e-01  4.3366e-01 -1.3624e+00  8.6887e+00
-#&gt;   5.1203e+00  4.1813e+00 -9.7441e+00 -2.3176e+00  6.0868e+00  8.1336e+00
-#&gt; 
-#&gt; Columns 37 to 42  1.1062e+01 -2.0296e+00  8.4705e+00 -3.9911e+00  8.3573e-01  1.9506e+00
-#&gt;  -1.1974e+01  5.3647e+00 -1.1954e+01 -5.3444e+00 -1.2646e+01 -1.6711e+01
-#&gt;   2.1595e+01 -4.4778e+00  1.0384e+01 -5.3072e-01 -9.7432e+00 -1.0414e+01
-#&gt;  -1.0581e+01 -4.4263e-01  4.7731e+00 -2.6030e+00  6.7749e+00 -1.6233e+00
-#&gt;  -9.3047e+00 -1.5634e+01 -1.5564e+01 -1.4220e+01 -1.4309e+01 -6.0601e-01
-#&gt;   5.9982e-01 -4.9061e+00  3.9769e+00 -6.1671e+00 -1.0324e+01  4.0731e+00
-#&gt;  -4.3588e+00 -5.6208e+00  6.1008e+00 -9.9672e+00  1.3759e+00  4.8355e+00
-#&gt;   7.6434e+00 -1.1719e+01 -4.9626e+00 -5.4398e-02 -9.2290e+00  5.2844e+00
-#&gt;   1.2625e+01 -1.1581e+01  1.8633e+01  5.7880e+00 -1.1121e+01  2.0442e+01
-#&gt;  -3.0447e+00  5.1596e-01 -1.4120e+01 -2.8625e+00  5.8997e+00  4.8108e+00
-#&gt;  -1.3048e+00  1.6969e+00 -6.9976e+00 -2.0704e+00 -1.4876e+01  1.3682e+01
-#&gt;  -1.0865e+00  3.1554e+00  1.2313e+00  8.0548e-01 -2.0513e+01  7.1541e+00
-#&gt;  -1.4557e+00  2.9544e+00  2.4083e+00  1.6820e+00  7.4094e+00  2.2584e+01
-#&gt;  -2.5237e+00  1.0195e+01 -2.6441e+00 -3.7556e+00 -3.9881e+00  2.7679e+00
-#&gt;   1.6696e+01 -7.1908e+00  2.1827e+00 -2.7923e+00 -1.9770e+01  5.8063e+00
-#&gt;  -1.1795e+01  1.0671e+01  2.6017e+00 -8.2290e+00 -1.6968e+00  7.3746e+00
-#&gt;  -1.9721e+01  3.6066e-01 -2.9877e+00 -8.9195e-01 -2.4205e+00 -1.4037e+01
-#&gt;  -1.7180e+00  2.5656e+01  1.3118e+01  2.6885e+00  2.4311e+00 -1.9627e+01
-#&gt;  -1.2209e+00 -1.7275e+01 -1.0422e+01 -1.7955e+00 -3.1315e+00  2.8352e+01
-#&gt;   5.5952e+00 -1.3472e-01 -1.9804e+00 -2.7732e+00 -3.5788e+00  1.0023e+01
-#&gt;   4.8103e-03  1.3171e+01 -7.5450e-01  8.6808e+00  7.5673e+00  1.6787e+00
-#&gt;   7.9544e+00  8.2651e+00 -1.1695e+01 -1.4135e+01 -3.2763e+00  6.9574e+00
-#&gt;  -4.4499e+00  4.4580e+00  1.5868e+01 -4.1087e+00 -8.7055e+00 -4.7099e+00
-#&gt;  -9.6114e-01  9.5251e-01  1.4457e+01  2.6207e+00 -2.6928e+00  1.6989e+01
-#&gt;   1.2193e+01  7.8603e+00  5.9201e+00 -2.7639e-01  9.1031e+00 -1.2982e+01
-#&gt;   5.6224e+00  6.9850e+00  1.0509e+01  1.2624e+01  1.2922e+01 -1.3763e-01
-#&gt;   1.1296e+00  5.3891e+00  2.9965e+00 -7.8478e+00  1.1889e+01 -2.1520e+00
-#&gt;   1.0341e+01 -1.0417e+01  7.8661e+00 -8.1170e+00 -4.0068e+00 -7.5216e+00
-#&gt;   4.2167e-01  9.3623e+00  6.6411e+00  2.1331e+01  1.4457e+01  8.5066e+00
-#&gt;  -5.5189e+00  5.7353e+00  8.0404e+00 -3.5269e+00  1.4370e+01  8.0252e-01
-#&gt;  -5.0023e+00  8.6836e+00 -1.6432e+00 -3.7779e+00 -1.8732e+01  7.3776e+00
-#&gt;  -2.2673e+00  1.3026e+01  1.6185e+01  5.9499e+00  8.0218e+00  1.3399e+01
-#&gt;   4.0683e+00  4.2663e+00  1.6034e+01  2.7645e-01  7.4202e+00 -2.4128e+00
-#&gt; 
-#&gt; Columns 43 to 48 -1.2115e+01  1.1046e+00 -1.2515e+01  2.2028e+00 -7.2036e+00 -4.0879e+00
-#&gt;   3.2179e+00  1.7676e+00 -1.1806e+00  1.7817e+01 -4.8552e+00  4.6124e+00
-#&gt;  -4.8195e+00  9.0433e+00 -4.6589e+00  7.1531e+00 -1.0476e-01 -2.9681e+00
-#&gt;  -9.5588e+00 -2.9691e+00 -3.7199e+00  8.4445e+00  5.2903e+00 -3.8193e+00
-#&gt;  -5.4232e-01 -3.8415e+00 -7.7434e+00  2.5880e+00  1.6367e+00 -1.3412e+01
-#&gt;  -8.0844e+00  3.3727e-01  5.1215e+00 -1.5609e+01 -5.1258e+00  8.3984e+00
-#&gt;  -2.3635e+00 -3.6531e+00 -5.6427e+00 -8.5791e+00  5.5740e-01 -1.5238e+01
-#&gt;  -4.3731e+00  1.1792e+00  1.4618e+01 -2.5758e+01 -1.0487e+01 -2.2543e+00
-#&gt;  -1.5963e+01 -1.3927e+01  2.1588e-01 -3.2920e+00  9.5727e+00 -5.3125e+00
-#&gt;   2.9978e+00  8.8854e+00  6.2099e+00 -4.2905e+00 -9.6987e+00  1.4991e+00
-#&gt;  -2.1656e+00 -7.8803e+00 -4.0464e+00 -1.5159e+01 -1.1318e+01 -4.2191e+00
-#&gt;  -6.2049e+00  5.2066e+00  1.2489e+01 -7.5448e-01  7.2532e+00 -5.8020e+00
-#&gt;  -5.8869e+00  1.3076e+00  1.9979e+01 -9.7389e+00 -1.3736e+01 -1.6782e+01
-#&gt;   5.1603e+00 -6.6834e+00  5.8808e+00  9.0616e+00  1.1712e+01 -6.1902e+00
-#&gt;  -6.4048e+00  7.1058e+00 -1.6456e+00  2.8912e+00  3.1004e+01 -1.2531e+01
-#&gt;   6.8006e+00  1.9610e+01 -9.1857e+00  4.4045e+00  2.9447e+00  1.6091e+01
-#&gt;   1.6478e+01  4.0959e-01 -2.3223e-02 -7.2793e+00 -2.9199e+00 -1.1647e+01
-#&gt;  -9.9995e+00 -8.1696e+00  1.4737e+00  6.5148e+00 -1.0667e+01 -3.2807e+00
-#&gt;   2.2444e+00 -2.0464e+01  3.0094e+00 -2.6361e+00 -3.3323e+00  1.1333e+01
-#&gt;  -1.0368e+01  7.5013e+00  1.0117e+01  1.3035e+01 -7.9614e+00  8.9583e+00
-#&gt;   5.1484e+00 -3.3187e+00  4.4564e+00  4.0747e+00 -1.4273e+01 -4.7209e+00
-#&gt;  -6.8290e-01  5.9370e+00  1.9618e-01  2.6352e+00  8.7978e+00  1.3552e+01
-#&gt;  -3.7305e+00  2.9977e+00  1.2201e+01  8.4139e+00  1.1424e+01 -4.4993e+00
-#&gt;   1.6751e+01 -8.0573e+00  1.2008e+00 -3.8915e+00  1.2050e+01  1.0548e+01
-#&gt;   6.2575e+00  1.6294e+01  3.5308e+00 -1.2285e+00  4.1870e+00  8.4701e+00
-#&gt;   3.4958e+00  1.4690e+01  8.2981e+00 -1.0359e+01 -1.0257e+01 -7.3372e+00
-#&gt;  -6.5664e+00  9.0567e-01  1.2777e+01 -3.5199e+00  2.0997e+00  4.1423e+00
-#&gt;   8.9526e+00 -1.3091e+01 -1.2533e+00  6.2042e+00 -7.8835e+00 -1.1464e+01
-#&gt;   9.2127e+00 -3.9388e-01  7.7766e+00 -1.4394e+01 -1.3689e+01 -1.4557e+01
-#&gt;  -3.7454e+00  3.2210e+00 -1.3385e+01  4.2240e-01  8.1938e+00 -5.3303e+00
-#&gt;   2.7591e+00 -4.6040e+00  2.7475e+00 -5.0884e+00 -1.3997e+00  4.3406e+00
-#&gt;   4.6150e+00 -9.1322e+00  2.9752e+00  1.5551e+01 -1.3454e-01 -1.5530e+01
-#&gt;   1.6372e+01  1.1288e+00 -3.9405e-01 -3.8099e+00 -6.5638e+00 -7.1677e+00
-#&gt; 
-#&gt; Columns 49 to 54  2.5190e+00  1.1175e+01  1.1601e+01 -7.7790e+00 -5.6342e+00  3.8789e-01
-#&gt;   7.7460e+00  1.1136e+01  1.7623e+00  6.0582e+00 -1.1285e+00  2.3664e+00
-#&gt;   3.6941e+00  5.5731e+00  1.9186e+00  4.5717e+00 -1.0011e+01 -3.9924e-01
-#&gt;   1.0815e+01  2.3231e+01 -4.9671e+00 -4.7148e+00  7.6496e-01  2.7236e+00
-#&gt;  -8.7334e+00  7.1061e-01  2.8245e+00  7.4412e-01  1.5410e-01  5.2671e+00
-#&gt;   9.0524e+00 -4.5650e+00  6.4491e+00  1.6855e+00  7.0050e+00 -9.4149e-02
-#&gt;   8.5457e+00  1.3220e+01  7.7125e+00 -6.8548e-01  3.0141e+00 -5.4146e+00
-#&gt;   4.9509e+00 -2.3485e+00  1.4033e+01  9.0368e+00  3.3652e+00  5.8068e-01
-#&gt;  -2.0652e+00 -5.7014e+00 -3.8886e+00 -5.5111e+00 -5.6780e+00 -3.0847e+00
-#&gt;  -2.7895e+00  3.7967e+00 -1.0079e+00  1.4597e+01  4.4697e+00  3.1462e+00
-#&gt;  -5.1495e+00  2.1150e+00  5.2226e+00 -1.7447e+00 -4.7373e+00  2.6336e+00
-#&gt;   1.9226e+00 -1.4160e+00 -4.5898e+00 -6.5440e+00 -2.6794e+00  3.3858e+00
-#&gt;  -9.6982e-01  4.6653e+00  9.2560e+00 -5.5485e-01 -1.1931e+00  1.4302e+00
-#&gt;   3.4949e+00 -6.4405e+00  9.6045e+00 -2.4561e+00  5.1457e+00 -4.2440e+00
-#&gt;   4.8166e+00 -3.9878e+00 -3.9815e+00 -1.3134e+01 -1.0717e-04  3.4807e+00
-#&gt;  -6.2559e+00 -4.7228e-01 -6.3469e+00  6.4103e-01 -3.5107e+00  3.2496e+00
-#&gt;   2.0834e+00  1.0204e+01  1.1521e+01  1.6283e+01  6.7450e+00  4.0019e-01
-#&gt;   5.8669e+00  8.3477e+00 -2.4140e-01 -5.7797e+00 -2.8879e-01 -3.9505e+00
-#&gt;  -5.4113e+00 -2.5000e+00  6.8480e+00  2.3189e+00 -2.7195e+00  3.8497e+00
-#&gt;   3.4269e+00 -3.1728e+00 -1.1029e-01 -9.0684e-01  1.3045e+00  1.5835e+00
-#&gt;  -8.3938e+00  4.9229e-02 -2.7664e+00  3.1438e+00 -3.9460e+00 -2.1360e+00
-#&gt;  -4.3670e+00 -3.5643e+00  2.9556e+00  8.9076e+00 -6.1031e+00  1.0562e+00
-#&gt;   4.2641e+00 -9.5336e-01 -3.9316e+00 -3.7728e-02  7.7775e+00 -5.3253e+00
-#&gt;   2.4582e+00 -7.1300e+00 -3.4889e-01 -3.3291e+00  2.4103e+00 -5.9407e-01
-#&gt;  -2.3643e+01 -9.1169e+00  2.0369e-01  1.8650e+00 -5.5027e-01 -2.4543e+00
-#&gt;  -2.8845e+00 -4.8286e+00  8.2757e+00 -1.1491e+00  7.9805e+00 -1.8078e+00
-#&gt;   6.6483e+00 -1.0365e+01 -8.0959e+00 -8.7907e+00  1.4690e+00 -7.9068e-01
-#&gt;  -8.7006e-01  1.8929e+00  4.0245e+00  1.9152e+00 -2.2162e+00 -2.2415e+00
-#&gt;   3.6591e-01 -5.5330e+00 -8.4923e+00  1.8120e+00  5.9263e+00  3.9024e+00
-#&gt;  -6.2291e+00  8.7485e+00 -1.9414e+00 -7.9282e+00  4.7886e-01  4.3102e+00
-#&gt;   5.4525e+00 -1.2424e+01 -7.3165e+00  2.8767e-01 -1.4393e-01  4.6123e+00
-#&gt;  -1.2733e+01  4.2735e+00 -2.6760e+00  1.1882e+01 -5.1786e+00  5.2276e+00
-#&gt;   6.1855e+00 -8.2518e+00  8.2563e+00  8.2125e-01  1.9322e+00 -2.0311e-01
-#&gt; 
-#&gt; (13,.,.) = 
-#&gt;  Columns 1 to 8   3.3697  -1.6417  -2.5881   1.4601   4.3656  -6.9630  -4.3418   0.4763
-#&gt;    1.1765  -1.5559   2.3349  -1.8196  -0.6485  10.1627  -6.8879  -7.2488
-#&gt;    0.8502   1.8956 -11.6411  -5.9180  -5.4087  14.6186 -24.2376   0.7743
-#&gt;   -1.4274   3.4590  -9.0911   8.5080  -2.2069   9.8465  12.1775   6.1672
-#&gt;    2.4107   3.6783   0.7586  -9.3225   4.0492   3.9703  -6.0808   0.2643
-#&gt;   -2.4323   1.7145   6.8121  -6.2586   8.6515  -8.2777   9.1493  -2.4481
-#&gt;    4.9859  -0.4675 -15.7742   6.5457  -1.6402   0.0901  -6.2409  -7.5844
-#&gt;   -4.2467   2.7866  -5.5029   6.2417  -0.0762  -4.2140  -9.9406   8.5973
-#&gt;   -1.1883   7.0216  -1.1023  -6.5494  -6.9241   4.7190  -8.5682   6.3939
-#&gt;   -0.1639   5.8292  -8.6185 -16.1194   3.0383   6.4299  13.7305 -25.3119
-#&gt;   -3.3431   1.2020  -3.0795  -5.2280   8.8131  -1.5852  -3.8657   6.2194
-#&gt;   -0.8473   0.6341  -5.4182  -2.6758  -2.1707  -6.6575  -0.5795   0.3148
-#&gt;   -0.2078  -1.1004  -7.5125   7.7188   4.9043 -11.1906  -0.4454   6.0457
-#&gt;    2.4375   2.4327  -6.1229  -7.1326   7.1260  -7.1742   4.7441  -3.1286
-#&gt;    0.3591  -2.6065   1.6676  -8.6926   7.5336   5.8167  -6.4093  -9.9438
-#&gt;    0.9972   8.1101  -0.7143 -10.3995  -6.7773  10.8206  11.4318  -4.4148
-#&gt;   -1.1437  -0.7181  -3.4500  -4.8707  -1.9937   5.6570  16.0217   5.5252
-#&gt;    2.4227   8.3839  -2.4364   3.3629  -5.9968 -12.7720   7.5399  -6.6289
-#&gt;   -1.8409  -6.0536   1.2139 -12.4322   8.8837  -0.7812   8.3287  -2.8039
-#&gt;    1.9115  -4.3994  -9.7979  -8.3191   7.2758   4.4275 -16.5625  -4.0027
-#&gt;    3.4311   2.6624   2.9892 -10.4464  -2.2261   1.0225   0.3791  -8.0550
-#&gt;   -4.4709  -0.7903   0.7995   3.1568  -1.2654  11.3205  -1.4539  17.6271
-#&gt;   -1.7305   9.0243 -13.2431  -1.9026 -16.2815   0.5502   5.2861   7.0291
-#&gt;   -4.2350  -1.8609   0.3718   4.0760   0.5236  -8.7421  -6.9448   7.3357
-#&gt;   -1.3744   3.5020 -12.2297  13.4129   1.6466 -11.8234  -8.9527   1.1651
-#&gt;    2.0638  -4.1548   8.5688  -6.7970   5.9547  -7.3101   3.1767 -17.7759
-#&gt;   -1.1274  -3.6897  10.8214  -4.8866  15.7748  -7.5586   4.6709   0.7997
-#&gt;   -4.9754  -8.4870  -0.9146  -1.8098  -2.3627   9.0146  -2.1974  12.3374
-#&gt;   -0.2340  -3.5367   2.8041  15.3531   0.9489 -13.3056  -0.0813  -1.9383
-#&gt;    0.3238  -4.0122  -1.6916   9.1604   8.2029  -4.6214  -4.9758 -12.1864
-#&gt;   -3.2851   6.1557   6.3449  -3.0137 -10.8634   5.8261   7.5779   5.0891
-#&gt;    7.6729  -5.9596   2.6869  -4.4836  21.3971  -9.8410  -1.4419   5.5365
-#&gt;   -2.3335   1.0808  -6.9140  -9.3190 -13.7158  -4.9634  -1.6641   9.4183
-#&gt; 
-#&gt; Columns 9 to 16   1.4272   6.9005  -8.1758  11.1291   1.9131   0.3419 -16.7107   2.3946
-#&gt;    2.9979   9.6438  -1.1152  10.2243  10.0655   2.6768   0.7035  -7.3764
-#&gt;    0.7594 -11.4686  -0.8667  -6.9460  -3.0995 -10.4431   9.9436  -4.9980
-#&gt;  -12.8362   6.3509   8.7563   1.5577  -6.4292  -1.0006   0.2837   2.4161
-#&gt;   -0.8063 -11.6076   5.4064  -1.6572   5.0197  -0.9882   6.2821 -11.3196
-#&gt;   -0.4334  -5.9995   8.1809   9.3257  -7.2008  11.0680   9.0294  11.7098
-#&gt;    1.7393 -10.8395  -3.5343  -2.6829  -6.3353   9.3118  -4.4164  -0.7906
-#&gt;    3.3009  10.0124  -0.3810   5.4467  -6.9107  -2.5141   2.8867  -7.0754
-#&gt;    9.0013   2.4283   5.4354   6.2346  -2.8392   3.9953   0.3989   7.1242
-#&gt;   -1.1859   9.7886  -9.4103   4.9508  -3.2787   7.1588   0.6914   9.1727
-#&gt;    3.9983  -4.8992  -4.3854   5.9145   8.3597   1.4387   5.1238 -11.0169
-#&gt;    6.1850 -18.2773  16.1798  -0.8564   4.5464 -12.6405  16.6850  -5.5169
-#&gt;  -14.1683  -3.7150   8.3266   3.9403 -10.1845   3.7448  14.6025 -10.5982
-#&gt;   -2.1483  -1.9087  -4.0616   4.0809  -7.5876  -1.6227   6.5654 -12.7282
-#&gt;  -12.4211  -2.9235   4.4465  13.7550  -3.7480 -11.1861  -4.5634   0.1438
-#&gt;   -4.0812  -0.1487   1.5998  -2.6823  -1.8722   3.0849   0.9512  -3.3778
-#&gt;   20.1330  -4.9240   0.1899  -1.7735  -2.1026   5.9774   1.7584  11.2253
-#&gt;    2.7877   9.6033   2.8122 -10.5991  -4.5545   7.3662   3.2882   1.9574
-#&gt;   -1.0704 -11.4845  17.3092   0.6163   7.4801  -0.4920 -11.8827   3.2849
-#&gt;  -13.0745   8.8030  -2.1587  -2.8709 -12.8417 -10.1367  -7.5423  -9.6992
-#&gt;   -3.0761   7.7702  -2.4629  -3.4838  -3.1326  -6.3012  -0.3314   0.4033
-#&gt;   -0.9980   0.8173 -13.4021   5.3338   4.5769   0.6703   7.1546  -5.3624
-#&gt;    6.4712  -7.9052  -8.2864 -10.3298  -5.9160  -9.6548  -6.2402  -3.2102
-#&gt;    8.4025 -13.5483   4.8272  -1.4405  -7.0647  -2.4812  10.6723  -6.7507
-#&gt;   10.1343 -10.1757  -5.0400  -3.4735  -3.4416  -8.9320  -2.7094  17.5644
-#&gt;    9.3466  -0.2112   6.1917 -14.8537   7.7931   7.1170   8.1069   0.8100
-#&gt;   -0.9844  10.4327   9.0464  -0.4420   0.5409  -7.9996   4.3075   0.8815
-#&gt;    4.1023  -5.1221   1.8569   2.7524   1.9987  -3.7410   1.0878   3.2000
-#&gt;    4.1913   2.2717  14.7937  -8.9496  -7.7735   1.2565  10.5878   8.3647
-#&gt;  -19.0380   2.2726  -6.1166   9.6046   5.4028  -8.7725  10.7857 -15.7318
-#&gt;   -6.8082  -8.1418   2.6750   3.2514 -13.1861   3.7538  14.1491 -20.7132
-#&gt;    2.5634  -7.6944  -1.8655  -0.1787  -5.3450   5.7111   1.7207  -2.0251
-#&gt;    2.7810  -2.1352 -14.3000 -14.9511  -4.4896   2.1630   4.6799 -10.1312
-#&gt; 
-#&gt; Columns 17 to 24   4.1233 -17.0780  -3.9538  -2.8758  -3.5483  -7.9644  13.6918   7.0413
-#&gt;   -4.7687   0.5470   1.2882  -1.0949  -0.6276   1.5143  -9.6125  -1.6341
-#&gt;  -15.2562  -1.1686 -16.4718   8.9318   7.9628   9.2988  -2.5182  -9.5424
-#&gt;   -0.6619   5.0462 -12.8017   1.7106 -15.0936  14.4036 -13.6283 -22.3025
-#&gt;   -8.0790  -0.9867   3.7640   7.9434   1.0856  11.9036  15.1968 -13.2958
-#&gt;   -4.3092  -4.7185  -0.5972   7.3328   5.9813   0.2981   7.5770   5.4590
-#&gt;    9.5222 -13.4433  -6.4562  -3.4667   3.6051  19.5683  -0.8224  11.0804
-#&gt;   -7.5286  -8.2387  -3.8117   9.0431  -1.1148 -11.5416   2.0163   2.2055
-#&gt;   -2.1770   6.3546  -7.6080  -5.0374  -6.2474  -7.7696  18.1866   6.6824
-#&gt;   -7.2299  -4.4548  -7.4621   2.6389 -14.8909  -8.9010 -13.5682  -8.0851
-#&gt;   10.6522  -5.2483   4.8124 -14.9675  13.2051   7.2541  20.9926  -3.5390
-#&gt;  -17.9549  14.9953 -11.2778 -11.2486  -0.9213   1.1407  15.7856   1.4826
-#&gt;   13.3178  -6.0633  -3.3993 -16.2574  -7.6736   4.8620  11.2303 -21.1926
-#&gt;   15.5492   3.6144   6.5018  -6.6918 -13.9382 -14.1719  -3.3112  -1.1664
-#&gt;  -12.5781   6.3902 -10.8792   6.3373   8.9104  -0.7249   0.7847  -9.6297
-#&gt;    5.4024   6.1987   0.7445   6.3244  -7.0532  13.4351  -0.4791  -7.8003
-#&gt;   -8.9566   4.4521   3.5503   0.7959  -2.4220  -4.2371 -16.6129  -3.5669
-#&gt;   -5.0858  -3.2332   4.4177  -2.7194  -5.7067   0.9085  -0.5359  23.8047
-#&gt;    6.6007   1.4694 -14.1240   5.0184   2.4091 -14.1085 -10.9890 -13.9345
-#&gt;   -4.3680  -6.0325   5.6439  -3.9697  12.0833  -1.6750  -1.3681  -2.9893
-#&gt;    3.2750   1.4166   7.3507 -13.6353 -13.9985 -15.8404   9.4659   5.0599
-#&gt;    7.0789   1.9307  -7.8050   5.8272  -6.3961 -15.0296  10.2564   8.1358
-#&gt;    2.6946  -0.3610  -6.2785  15.8479   1.6669  -1.4408  10.4534  14.8757
-#&gt;    7.3278   3.8470  -8.3954  -5.5899  -7.4549  -2.1057   4.4085  16.0832
-#&gt;   -4.0996  19.4165  -3.9743   0.4099   4.5863   8.4134  -2.0642   4.1411
-#&gt;   -7.2185  -5.7259  -2.9592  -1.7083  -2.6126 -13.4320 -22.0603   7.3150
-#&gt;   11.2710  -4.9157   6.0592  -2.3185   2.4728   6.5344   0.5997  -0.1768
-#&gt;    1.0739   4.0677  12.0173  -1.5614  -6.8807   1.5222   8.9047  -7.8059
-#&gt;    1.2063   7.1509  -8.9546   8.0253  -1.3432  -5.8292  -8.4954  -1.0355
-#&gt;    7.4239  -4.5134   1.7243  -5.2007 -10.9802  16.0628  11.1323  -8.3712
-#&gt;    4.0806  -0.9152  -2.2090   5.6934  -4.1941  -4.5412   9.3974  -0.6221
-#&gt;   15.5935   0.7072 -10.0603   5.7345   1.6642   8.9193   5.1053  -2.7519
-#&gt;    7.1138 -12.1894   0.6080   7.8461  -8.7989  22.0221   6.3918   9.6505
-#&gt; 
-#&gt; Columns 25 to 32  -2.6800  -5.7408   5.5992   2.6666   5.3303  -4.8828 -11.9063   3.5820
-#&gt;    6.6322  -2.1588  -2.1263   1.4245  18.6545  -7.0352 -17.6687  -3.6482
-#&gt;   12.9900   1.3050 -11.1928  -9.5810  -5.0537   8.9391   2.4537  -3.9121
-#&gt;   11.7591   9.5109 -13.7638  -6.9739   6.2391   7.8595 -18.5428 -13.9119
-#&gt;   -7.2973   4.5046  -8.6841   0.8250  -1.5067   9.7065   7.1418  17.4973
-#&gt;   -5.0957   4.2618   6.5113  10.0076   2.1287   1.5233  13.4246   1.1008
-#&gt;   13.6883  12.8369   2.2686   1.3899  -3.6863  -4.7827 -18.9132   0.2136
-#&gt;    0.2509  10.7896  15.7100  -2.1480   2.9210 -11.5030  -2.3106   1.9727
-#&gt;    1.2314 -13.5424 -16.2364 -15.6695  -8.0330  -4.8299  -2.7412 -10.9407
-#&gt;   -7.7083   9.8008  -1.3087  -8.5017 -15.5218  -5.4738 -14.0310  -5.9766
-#&gt;    3.9822  -3.2992   4.6238  -6.0679  -6.0140  -5.3986  -3.0122   7.2745
-#&gt;    0.5601   2.7388 -11.0853  -0.6145  -2.6784  -3.7761   5.2345 -10.7856
-#&gt;   -4.9741   3.9108  19.4934  -4.8232   9.6452   1.3108   0.9940  -2.7460
-#&gt;    1.2843  -7.5330  -7.7228  12.3964  -4.2323   1.3258  -1.2630 -16.1458
-#&gt;   -1.2016  12.5133 -14.4585   5.6997  18.9993  10.7756   5.3936   7.2677
-#&gt;   -1.4915   2.3885   3.8869  -7.1487   7.6098  -3.9885  -3.6000   1.5721
-#&gt;   -9.4018   6.0380  -8.7007  15.1696  11.1714   3.3346   8.8122  -1.4180
-#&gt;    8.4743  -7.6606   2.2928   6.4204  -2.3149   1.3092   0.0668 -23.7015
-#&gt;  -19.6935   7.9098   4.7952  -0.2914  -5.0775   5.1258 -13.0558 -19.8275
-#&gt;   14.4639  -7.6469   1.4158  -1.6512  10.6585   7.2336 -15.5307   4.4070
-#&gt;  -20.4726  -3.5960  16.8774  12.4712 -10.9533 -14.0708   0.3716  -0.7920
-#&gt;   -6.4765   1.7232  11.5085  -4.0904  -4.2319 -14.4870   3.9247  12.0280
-#&gt;    8.0161  -5.6225  -4.3256  -0.7993   5.5562  -0.6457 -10.7856  -8.2703
-#&gt;    5.5643  -2.1936 -12.2500   1.4277  -7.6654  -3.2104  -5.2612   9.9382
-#&gt;    5.8360  -5.2719 -11.2102   3.7008  -4.7814   4.4835  -3.3795 -11.0323
-#&gt;   -5.5010  -5.6203  -4.4000   7.3807  -1.5143   0.2017   1.5784  -7.5409
-#&gt;    2.7717   3.4020 -14.4807   5.5100 -13.9762  -4.2596  -3.8514   7.4326
-#&gt;  -16.6801  -2.2176  -3.4166  -3.9260  -0.4806   1.3265 -18.2089  -8.6009
-#&gt;   -0.0372   9.5898 -11.3903  -1.9474  -2.1608   0.1678  10.6816   0.2370
-#&gt;    7.6345   9.2515  17.8873   3.6420  -7.1994   5.8380  -6.9410   6.9322
-#&gt;    7.5231  -5.2615   4.9636  -4.4058  -2.2565  -5.9325   5.9708   1.2859
-#&gt;    4.2971  -4.3269  -0.0417   0.7345  21.4405   3.9511  12.8331   0.6572
-#&gt;    6.0391  -3.2032  -9.0262  -4.9702  -5.1867   6.1670  -3.9718   0.8178
-#&gt; 
-#&gt; Columns 33 to 40  10.8348  14.7557   3.3659  -0.1846 -11.8096   4.6553   4.4372   3.0805
-#&gt;   -1.4661 -23.1553   9.9883   2.0956   4.4233 -18.6846  -8.7162  -8.2316
-#&gt;   -7.0449   2.0095   3.8353  -5.8263  19.1000 -13.2257   3.6385   7.9960
-#&gt;   17.4574 -10.0265  -0.9992 -17.1924  -1.2527 -11.1004   3.5991   4.4013
-#&gt;    0.8899   2.4346  -4.6935  -5.2829   3.8203  -5.5734   3.9520  16.4752
-#&gt;    4.7666  15.8362  -2.0676  -1.8818   1.2573  20.5019 -14.3063  17.9730
-#&gt;   17.2786 -12.8405  -3.7920   5.0189   1.6979   0.9932  12.1372   0.0466
-#&gt;    3.7036   6.8519  -9.6157   6.8130  -9.7540  -3.0379   8.6012  -5.9710
-#&gt;   -5.5511  15.5132  -6.4415 -14.1951   3.1228  -9.8643  -1.0604  10.2775
-#&gt;   14.2856 -10.0348 -12.9017  -7.8481   0.7524 -14.6779 -16.8381   8.3535
-#&gt;  -10.8446   4.1617  -6.2735   5.5409   2.7467 -14.3498   7.3312   9.8202
-#&gt;   -3.1855   6.0698   9.7219  -3.7233  -9.9178  -3.9497   1.5279  18.1157
-#&gt;    2.7665   3.5974 -11.1750  -2.1840 -10.3358  -0.4430  14.4890  -2.0752
-#&gt;   -7.7241  -2.5540   8.6497   8.3299   2.8415 -20.8746 -24.1512  18.1693
-#&gt;    9.1557   9.8385  -3.6903   7.4441   0.4999  17.7083 -16.9265   3.5991
-#&gt;   23.0757  -8.6160   6.5006  -4.3907  10.3004   0.7045   1.5834 -10.7031
-#&gt;   -4.5615  -7.9168   2.6719  -0.5778   5.2735   1.1896  -2.8907  10.4492
-#&gt;   -9.2825  -7.2149  -0.4448  14.7885  -2.6618  -8.3046   4.2973  -6.7991
-#&gt;    6.1126  20.5788 -26.1840  -1.7718 -11.1458  -7.2249   2.3674  -9.6824
-#&gt;   -1.3163   5.0976   3.1525   6.4770   6.3339   0.9214  -2.7082   5.1429
-#&gt;   -3.1457  12.5447   8.5838   2.9981  -7.9624 -12.5725 -10.0365  -9.2533
-#&gt;    0.9133   5.9405  11.3644 -18.7311   4.3938  -7.6613  12.7711  -2.6682
-#&gt;   30.8365  -7.4982   0.9603  21.9556  -6.2884 -11.5021   4.4946  10.3176
-#&gt;   14.4685   0.7428 -16.6316  -8.9628  -4.4752   3.2429  -1.9568  11.5486
-#&gt;   15.3273  -3.5033  -3.1176   4.2342  -2.5407  10.6061  -6.7412   0.8499
-#&gt;   -1.9486 -13.6522   1.0336   7.3432   0.7010  12.5901  -9.1793  10.5238
-#&gt;   -7.0167  -0.1492  -8.6312   5.7787   2.9533  14.0871 -16.4314   2.4016
-#&gt;  -18.2783  16.0457   8.4394  -4.0634  -8.0765  -7.8682  -6.6131   3.6371
-#&gt;    3.8207  -4.2039 -16.9315   3.1379  -6.0499   1.3322   9.1753  -1.0800
-#&gt;   15.7705 -12.9205 -13.6020   8.6659   6.2298   7.5931  -2.9307   1.7454
-#&gt;    3.2957  16.8939  11.6730  -2.9389   8.2990 -14.7993  -2.8298   9.3996
-#&gt;    1.9768   2.8376   3.7889   3.5501 -10.1581  -5.4513   8.9801  -7.9636
-#&gt;   10.2112 -16.3559  -7.1759  -0.9930   9.6437  -1.5389  -1.1243  10.5625
-#&gt; 
-#&gt; Columns 41 to 48  -3.3578  -1.1139  -2.4762 -11.2209   9.3577  -8.8033  13.6489   4.8650
-#&gt;    6.5548   1.7133  -0.1935   5.5874 -10.3480  -6.8775   3.3956  -6.4977
-#&gt;   13.0252  -6.4013  -1.1414  11.4570  -7.0205   6.2467  -4.4751  -4.3536
-#&gt;    1.8241  28.0728 -12.6101   8.6788   2.0485   6.1478 -12.4244 -16.7476
-#&gt;   -8.6532  -8.1284   6.3043 -12.8859 -12.4685   9.6203 -18.7328 -18.8922
-#&gt;  -15.2930  -5.9777  -0.0329 -28.5118  -3.1383   2.8168   0.3067 -13.5621
-#&gt;  -14.5134  -3.2274  -0.0055   4.0627   2.8727   1.3799  -6.3114   0.0714
-#&gt;   -0.6586   5.8188  -6.9768  -4.4756   2.2888   2.8688 -13.7075  11.0588
-#&gt;   10.5744  13.1215  -2.0282  11.4851   2.1987   9.1812   3.6154  14.4257
-#&gt;    3.2649  20.1229  -6.1985 -15.9335  -3.7413  -2.8519  -9.2608 -15.2161
-#&gt;   -6.5173  -7.6655  -0.2815  -4.8623   2.1578  -1.2280   9.3485  -0.0405
-#&gt;   11.4033   7.2183  -8.9260   1.6441   0.8568  -7.4824   1.2641   3.8432
-#&gt;   -6.9809   6.6826  -9.4452  -2.2702   3.1859  -2.7236  -9.9576   1.2124
-#&gt;   -5.0526  25.2570   3.2705  -8.0824   0.9330  -8.9063  14.1930  -0.4507
-#&gt;    1.8832  -4.0172   0.1316 -12.6761  -5.1909   9.1281 -10.9429  18.3555
-#&gt;   -3.4178   0.2063   1.9687   3.5427 -13.6490   3.5390  -2.1170  -7.5645
-#&gt;   -4.9405  -9.4943  -8.6500  -7.4028  -4.2744 -15.9314  10.8900  -7.9999
-#&gt;   -5.6152   0.7457  -8.4452  15.5308  -9.3691  -2.9904  10.8867   2.9986
-#&gt;  -20.7752  -1.4896   7.0495  -8.1634   6.6833  18.6335  -6.1095  18.3737
-#&gt;   10.9329   1.9044   6.1540  -2.9243  -0.0544  -3.7527  -3.9820  10.0857
-#&gt;   -1.5593  -0.9560  16.8549  -3.6897 -10.3189  -0.4447  11.5176  -5.2639
-#&gt;    6.5579  13.6265  -3.9144  10.4601   3.4433   9.1518   6.4529  -4.3566
-#&gt;   11.3136  12.5980   7.1762  -5.4726 -20.0146  10.0288  -6.0000  -3.1094
-#&gt;   -6.4761  -7.8402   9.1078   1.4559  -1.1450   2.7579   4.0419  -2.2974
-#&gt;    3.5403  -6.0334  -3.7099   3.2879   6.2914  -9.1560   0.7688 -11.4910
-#&gt;    4.6251  -0.9901   1.4971  -9.3215  -6.6628  -6.7178   8.7838   6.6056
-#&gt;    2.6455   0.9620  13.9202 -10.0320  13.9161  -1.2996   6.1395   2.7685
-#&gt;    0.3381   3.3796  -0.1898  -3.0536   7.6887   1.7112  16.5639  -2.5761
-#&gt;    5.5124  -6.3711  -2.4787   7.4375 -13.7518   3.8744  -4.3973   2.8493
-#&gt;   -2.6076  -9.5805  -3.0601 -13.9946   4.2460  -6.1053  -2.4415  -2.1601
-#&gt;   -3.5259   8.7255  -4.6512   9.3553 -13.5940  -4.1108  14.2099 -19.3789
-#&gt;   -9.5535   7.4221  11.2101 -15.1180   6.3322  -0.1481 -11.3736  12.0167
-#&gt;   -4.7469   3.2653  -3.6165 -11.0855   5.2520  -7.3072  -6.8884  -0.6938
-#&gt; 
-#&gt; Columns 49 to 54  -9.5841  -3.1883   4.8819   0.9323   2.6546   3.5816
-#&gt;   -0.0657  -3.6876   6.0528  13.6310  12.1754   0.8895
-#&gt;    6.4921  -1.6472   0.3413  -4.9985  -5.0039  10.1445
-#&gt;   17.9599  -1.9585   2.2322   8.4005  -2.2082  -4.5209
-#&gt;    1.2629  -0.2790  15.5301   7.7452   7.6565   8.6430
-#&gt;   -8.3178  18.0239   1.7017  -4.8925   6.3538   1.4147
-#&gt;    1.9469   6.5868  -6.3767  11.1967  -4.1276  -3.9215
-#&gt;   -6.0379  11.6491   8.1192  14.2532   2.8171  -3.9183
-#&gt;   -7.5273  -5.7284  -2.2543   5.3219 -13.6635  -1.2019
-#&gt;   11.3169  16.1229   5.3380   5.9345   7.8532  -1.9193
-#&gt;   -1.1561   0.6397  -5.8041  -5.6023   3.9126   4.1689
-#&gt;  -11.1197   9.0098   0.2090  -4.2656   5.6267   2.1441
-#&gt;    0.2229  23.9966   4.7711  -3.8389  -2.0389   0.9057
-#&gt;   -0.7366  -1.9622  -0.0843   5.3703   7.7059  -5.1786
-#&gt;    7.1206  -9.2183 -13.2013   2.0726   5.0482   0.8396
-#&gt;   14.2594   7.7721   3.2311 -13.0711  -4.3741   0.9782
-#&gt;    3.7075  11.6253   2.4502   1.5064   0.6112   1.8650
-#&gt;    0.5343   4.5191  -2.2040  -3.6395   3.1832  -3.9657
-#&gt;  -10.2922   0.4036 -10.7387  -0.4683   7.3636  -4.6882
-#&gt;    5.1110   2.4479  -8.0885  -0.8889  -1.2094  -0.3844
-#&gt;   -4.1646  14.5772  16.0076  -7.5377  -3.2482  -3.9920
-#&gt;    0.1385 -18.6015   0.7036 -10.1109  -3.2682   3.0401
-#&gt;    4.0277   3.9081  -8.8889   2.1189  -7.2020  -7.7930
-#&gt;   -5.4221  -2.3047  -2.3612  -5.3930   1.0088  -1.1463
-#&gt;   10.4664  -0.2838   2.3142  -2.4206  -3.5280  -7.2229
-#&gt;   -5.9612  11.5086  -9.4708   9.8642   0.9766  -3.3587
-#&gt;   -0.9830  -5.6997   0.4194  -5.3989  -7.7813   0.5598
-#&gt;    5.4461 -11.4660  14.4418  -5.9451  -5.2957  -3.8965
-#&gt;    9.3387   3.2802  13.7992  -1.7918   2.2744  -0.3253
-#&gt;   -5.5320   6.5413   0.8090   0.4279   1.2553  -0.8095
-#&gt;   -6.7395   4.1986  -5.4247 -12.3497   4.4859  -1.4505
-#&gt;   -0.5004 -13.5893  -4.6648 -16.0753  -8.4585  -0.8199
-#&gt;   11.9082   0.3645  -0.3096  -5.3894  -1.3357  -2.1738
-#&gt; 
-#&gt; (14,.,.) = 
-#&gt;  Columns 1 to 8   0.3770  11.4530  12.3254  -8.1717   2.0064   9.2537  -6.0489  -2.5420
-#&gt;    3.3567   0.3686  -2.1060  -8.9118  -3.2569   8.7885  -6.6666  -0.7948
-#&gt;    1.4497  -6.2063  -8.3565   5.7694   0.5264   6.2934  -8.0693  -3.4899
-#&gt;   -3.0604  -2.0974   3.9263  -2.7745  -3.1499  14.5660  10.1938  -4.8976
-#&gt;   -5.2067  -7.2476  -3.7690   2.6146  -5.7421 -14.2176  -5.4898   9.6478
-#&gt;    2.7384   9.4170  -1.8324   9.9095   6.4603  -9.8938  -1.7702   5.1115
-#&gt;   -4.0225   2.1869   4.1249  -6.0901   2.7495  -9.6204   1.9513   6.8786
-#&gt;    6.3655  -0.8163  -3.4434   8.3752  -2.3094   6.4114  -4.9764   0.9664
-#&gt;   -0.0141   2.9467   9.7410  -3.6164  -2.9697   3.0740   1.8683  15.1830
-#&gt;    4.5287   7.7451   1.3771  -5.9612  18.3195   3.0365 -13.9349   0.0934
-#&gt;   -2.5749  -7.0213   3.1655   8.8867   7.6434 -14.2088   6.4947   0.1312
-#&gt;    0.2012   0.2631 -14.1806   9.5833   2.1844  -8.8791   6.7946  -6.6629
-#&gt;   -1.3827  -1.3199   4.2433  13.1405  -5.5283   5.0318   7.1572  -6.3521
-#&gt;   -1.7635  -9.2385   1.3914  -0.1005   3.9263   0.8530   2.5017  -0.9691
-#&gt;    6.6711  -7.8012  -4.7570   8.3643  -3.3958  -8.0918  -3.4167   2.7507
-#&gt;   -0.3402   0.0913   6.1923  -0.4942   9.8246 -10.3545  -3.3388  -2.1711
-#&gt;   -0.1663   2.2720  -4.4339  11.3540  13.9740  -2.5846   2.5917   8.9986
-#&gt;   -7.9547   3.4470   2.9946  -0.8519  -3.0121   5.5671   2.6166  -6.7560
-#&gt;   -2.8500  14.7417  11.9077   0.6807  -3.3666  -5.2585  -1.2208   5.3802
-#&gt;    1.9767 -10.0252 -11.7052  -0.6985   8.8981  -7.7196  -6.4009 -11.7299
-#&gt;    0.5899   8.1488  -1.2487   3.1090  11.6321  -1.5359   4.3982  -9.4264
-#&gt;    4.3843   3.0785   5.5632  -9.2855  -6.0409   0.6462   0.5483  -0.7019
-#&gt;   -1.0347  -1.7948 -18.4069   4.8440   3.1485 -12.7971   2.4637  13.6364
-#&gt;    1.7668   1.3412  -1.5615   5.8986   1.3487 -17.8216   3.9517   0.0958
-#&gt;    0.1322   0.1879 -17.0802   7.1159  13.9271  -5.1603   1.4898   8.4073
-#&gt;    1.5698   9.0832   4.5893   6.2004   6.9843  -1.5408  -6.6988  -3.6267
-#&gt;    3.2580  -1.6388  -6.9130  -4.1207   3.7113   7.9904  10.7252  -1.3340
-#&gt;   -2.4975  -2.1435   4.3989   9.1296   7.1549  11.2097   6.9807   4.6930
-#&gt;    0.9505  -3.5208   3.4347  18.2746   4.8642  15.6792   8.6931   6.0316
-#&gt;    2.9614  -2.0693  -0.3657  -4.5516   2.1412  -5.9561   0.8259   7.0303
-#&gt;    0.2068  -7.9595   0.9035  -1.1417  -6.3314  -5.8986  -2.8805  -7.1754
-#&gt;   -6.0468   1.5532   7.6197   4.5963  -9.7855  12.0498   4.3925  12.1035
-#&gt;   -1.9815  -5.6976   4.5649   0.0077   1.1741  15.6724  -8.5468  13.0375
-#&gt; 
-#&gt; Columns 9 to 16  14.8517  -7.5338   2.0898   7.6614   9.8378  -4.6831 -10.0600  -2.1377
-#&gt;    0.2623  -4.5503  -9.4165   8.9472   5.1810   1.5789  -4.1377  10.3595
-#&gt;  -11.9591 -22.1948  -3.1148   9.7955  11.0973  17.6188 -16.4940   6.3169
-#&gt;  -28.6606  -0.8674  -1.7593  -0.1612   7.4730   0.9168   9.7108   4.6269
-#&gt;  -14.9942  -7.2785  -5.0295  -3.6556  -1.4412   3.0655  -4.7327  -2.4419
-#&gt;    7.7481  20.4307   6.6899   3.0492  14.3508   6.8140 -18.2257  -9.2996
-#&gt;   -6.0070   6.3098  -6.6324   9.5148  -2.5147   7.1132   6.0873 -21.6238
-#&gt;    6.2963   4.4553   4.4620  15.4355  -2.1113  13.9785   5.0768   3.0637
-#&gt;   -0.0940 -11.8444   1.7085   7.9298 -21.2709  12.9068  -1.5473   1.5120
-#&gt;    3.5864  -1.8245  -3.1646   3.0038  17.3009   3.9642   4.0612 -17.3035
-#&gt;    6.0550  -2.8465   6.7859   5.7545  -3.3479   7.4782  -0.4281   0.6236
-#&gt;   -7.2762  -8.2545  -3.9465   1.5299   2.3039   3.3165 -10.5609  16.3569
-#&gt;    1.4526   8.4141  -3.7688  -0.0326   6.0030 -10.1505  -4.4883   0.2529
-#&gt;   -8.2809  -3.0374 -28.4246  -4.4572  15.7466 -10.8104  14.7449  -3.9600
-#&gt;  -15.1718   4.2424   9.0816  -0.5463  -4.5732  -3.3233  -6.8652   7.9014
-#&gt;    4.3162  15.7039  20.4732 -12.0980   7.2798  -3.6740  -1.9739 -12.1692
-#&gt;   -2.6370   0.7528  -9.7031   1.6575   8.0723   8.4717  -2.0160   3.2745
-#&gt;   -4.5273  -0.0800   3.9357   9.6299 -14.8624  -5.6028  -1.8641   6.1496
-#&gt;    8.7650   8.5841   8.5647   4.0912 -27.8494   6.4642  -8.0686   4.8601
-#&gt;   -5.1022  -7.4816   0.6639   0.6033   2.0572   4.6621  -1.3301  -3.6485
-#&gt;   12.1442   3.4068  -2.5187 -10.2796 -14.7906   5.9140   0.7293  -5.2769
-#&gt;    8.1061  -4.8040  -2.1649   0.6224  11.7789  16.9427   2.5081   6.6288
-#&gt;    4.9209  -7.8456 -26.9416  -0.1341   0.9170  12.5050   8.5160  16.3678
-#&gt;   -3.3597   8.2125   5.1914  -7.7349   7.0574  -6.7789  -8.3004  -8.7751
-#&gt;    5.3466  -3.3574   6.1179 -13.2464  14.9734  -9.8865 -19.7086  19.1938
-#&gt;  -11.9796   6.3637   5.0602   7.1964   1.0075   5.9998   6.7007  -9.5336
-#&gt;   -4.5889  13.0579   5.0471 -13.9253   1.0684   3.8819   6.7362   5.6673
-#&gt;   -5.1198  -4.9673  10.4607   0.9514  -6.6400   6.9641  -0.0188   8.3754
-#&gt;    2.1722  15.0798   6.2502   5.0564   9.8680  10.7211  16.0995  -4.0884
-#&gt;   -9.4826  -2.3942  14.4484  -1.4258   4.7605 -15.5945  -9.1665  17.3647
-#&gt;   -9.1650   1.6061  -2.9621 -12.2872  -5.8349  -4.7291  15.9064   3.0888
-#&gt;    8.6125  -0.0677  -1.3918 -11.1935   5.1555 -11.0749   7.4689  -3.5442
-#&gt;   -2.2461  -0.5258  -7.8684   1.0981  11.2391  -1.9608   1.9035  -7.2017
-#&gt; 
-#&gt; Columns 17 to 24  -4.9053  -2.2667  -1.0283   9.6957   0.2477 -10.0500  -4.8617   0.0063
-#&gt;  -13.0942   1.2088  -5.3956  -1.5164  15.8247   4.6247   3.8747  -0.5828
-#&gt;  -10.8099   4.3983  -8.7463  -3.5179   2.2671   5.8489   0.7554  -5.0317
-#&gt;   -1.0272   8.7758  -6.7657   4.8201   6.0469   6.8353  -1.8373  -8.9972
-#&gt;   -4.4041   5.9650   6.4497  -1.7451 -12.9368  10.2491   8.8281   8.5340
-#&gt;    9.2032 -14.1359 -18.1122  13.0184   6.0143  -9.3855   0.8192   7.0896
-#&gt;    3.8926  -9.6341   4.3712  14.9302 -10.4555   2.5968   7.1624  -5.0525
-#&gt;   -6.0445  -0.9045  10.0906  -2.3694  -1.8475  -3.1114   7.8476  -1.0478
-#&gt;   13.0187  10.4528   4.5314   4.6289  -6.1119  -3.1832 -17.6575  -9.1404
-#&gt;   15.6379  -0.9983 -15.1494  14.3846   1.5959   3.0012  -1.9903  -2.3397
-#&gt;   10.5089  11.0922 -10.5190   3.0268  -6.0590   8.4724  -7.9977   6.2165
-#&gt;    2.7381  -5.1110   5.9924  10.3621 -14.0646  -2.0999 -11.6789  -1.5407
-#&gt;    2.9158  -4.4157   1.3596   9.1929  -6.0317  -2.0577  -0.3624   8.0497
-#&gt;    0.9831 -17.8929  11.4422  15.9151  10.2596   0.3767  -3.8407  -6.7646
-#&gt;    8.4754  -5.1325  -5.9244  -4.3481  -2.2820   9.5119  -3.3970 -18.9483
-#&gt;    6.4051  -5.7837  -3.7830   2.3501  -1.5908  -1.0771  -7.2282   1.1143
-#&gt;   -4.5312  -6.2030   1.3716   0.0105  -0.7657  11.8283  14.8129   4.3418
-#&gt;  -20.4536   6.8824   3.8435  12.0614   7.8767  -3.0026   5.1493   6.4343
-#&gt;    6.8865   8.7946  -7.3705  10.7327  -6.2733  -3.3637   3.3572 -12.2461
-#&gt;    6.0896   1.9243  -4.6876  -1.3111   9.2689  -1.3074 -17.4840  -7.6172
-#&gt;   -6.5077  24.6859   1.1896   6.5526   5.2203  -8.3758  -0.7854   2.7145
-#&gt;    0.3053  -0.3795   5.7081  -9.1353 -10.9340  -7.9618  -7.4539   0.8218
-#&gt;    1.8895   0.8432   4.5600  -7.0166  -5.9432   3.2469  -1.9923  -5.0270
-#&gt;    2.7993  -9.1268  -3.3086   0.9909  -9.4538   3.3323 -20.2697   5.6602
-#&gt;  -11.5537  -6.1840  -5.3921  17.5635  -2.1432   1.7829  -1.8565   8.2781
-#&gt;    0.6469  -8.2766  10.0507 -10.9065  -8.3207  -8.7234   2.6877   1.4146
-#&gt;    3.9395 -10.7987  -2.3744  -0.4842   9.9037  -6.3236 -16.1055   1.0521
-#&gt;    0.3880  21.7167   8.6837  -1.8329   9.5305   7.4091  -7.5914  -9.3135
-#&gt;  -11.2178  -0.9932   4.0680 -18.4544   7.8578   4.7529  -9.2014   3.2285
-#&gt;   -5.4010   8.5502  -7.7846  -0.5676  -3.9020   7.2746  13.7788   4.3461
-#&gt;    7.3959   3.2083  -5.1978  -0.5509   8.3545  -5.1124 -16.1492   1.5927
-#&gt;   -5.4056  -7.4793  -1.7978  -0.9334 -20.6932   3.2113   8.4319 -12.3685
-#&gt;   -1.3231  -0.5456  -8.2329   1.9113  -5.4219   1.5084   1.4032   7.3739
-#&gt; 
-#&gt; Columns 25 to 32  -4.0028   4.8457  -5.0337  -5.2634   5.4246  -7.4923  -6.1144  10.9337
-#&gt;   -5.0902  -2.3824   0.2125   8.0339   1.0811  -8.2466  -3.7509  -5.0870
-#&gt;   -9.6053 -12.6881   6.7873   7.1377  11.8440  -9.8080   9.0595  12.2712
-#&gt;    0.4937   4.3730   8.4381  12.5765 -13.4349  -1.9148   0.0590   5.0614
-#&gt;    5.8021  -0.0977   7.5855  16.1178   5.8871   1.0429  -3.0092  12.8243
-#&gt;    2.4016  -4.3190  -6.2966   2.1748  -1.2029  -5.1658 -15.5438  -2.8358
-#&gt;   -2.3391 -11.2238   2.7683   0.0513  -6.0206  -8.7212   1.2179   9.4017
-#&gt;   -4.2383  -3.6747  -0.1034  -1.2900   2.7896   3.6094  -5.0779 -11.1294
-#&gt;   -0.4860  -1.2960  -7.0285  -3.7450   0.6067  -2.7440  -2.7454   2.7484
-#&gt;    5.7242   6.1890  -6.7480  10.1540  -5.7611  12.0798  -8.7057   7.2471
-#&gt;   -4.3361 -14.2966   9.0196  -4.6395   7.8449   8.9978   2.8720  -6.5707
-#&gt;   -2.1374  -4.8998  -0.3253  -1.8154  -4.7756   0.1213   4.8799   0.6199
-#&gt;    3.0059 -20.9024   2.4299  -4.7674  -3.9045  -1.9628  -6.2359  -4.9126
-#&gt;    4.3799   7.5832  -0.6820  13.5544 -13.8275   6.0846 -12.6547   9.0053
-#&gt;   -0.1091  13.7221  -3.1154   7.0069 -11.3168   7.5186  -4.2445   2.5850
-#&gt;    1.4133   1.5797   0.1667   4.9029 -14.3867   0.2739   0.6428   3.6051
-#&gt;    1.4212 -10.1993 -11.1234   5.9852  -9.9299  -4.6147  -6.8913  -3.7936
-#&gt;    1.9547   2.7585   2.8571  -8.3228  -8.9590   4.0832  -1.1072   3.0466
-#&gt;   11.6103  -2.7016   6.9391  -3.3514  -8.2382   7.7049 -13.1237   2.9195
-#&gt;    6.5049   0.7257  -1.6754 -13.8499   6.7940   6.9524  14.0003  -0.5845
-#&gt;   15.9858  10.4259  -1.7270   4.0562   6.4757  -2.5022  -2.3366  -8.7249
-#&gt;  -20.8702   7.3881 -10.0172  -4.7277  -2.9041   6.2343   0.9083  -6.3604
-#&gt;   11.2073 -18.9086 -10.6599  -6.8272  10.4249  -2.8861   9.5068   3.4434
-#&gt;   -3.0630  -3.0321  12.9202  -5.8672   4.3428 -16.0467   6.4028  16.5198
-#&gt;    2.1288   4.1729   1.9260  -6.8011  27.9381 -14.9567   5.8274   0.3583
-#&gt;   -0.3553  -4.5055   6.6590 -16.1631  -3.7251   4.1277   3.0556  10.5262
-#&gt;   -1.7344  -8.8326   2.0646  -6.5968  -3.3695  -2.0245  -6.8629   4.3947
-#&gt;   -3.8936  -4.9729  -9.2678   7.3137  13.4529  -1.2636   0.7179  -4.3780
-#&gt;    7.6871   2.5940  -2.2730  -2.1216  -6.6604   2.8323 -16.7128  -0.9180
-#&gt;   11.0534   0.7482  -5.2661   6.5815   3.0277  -9.2092  12.3954  17.9892
-#&gt;    1.9958   1.8362  13.7950   4.5558 -13.7289   8.9340   4.6768   2.5235
-#&gt;   15.4579  -0.8263  -0.8164   9.3055 -23.9530   6.0654   5.2620  -3.2954
-#&gt;    7.7474 -13.7643   0.0998   3.7900   0.5168   5.0000   1.4169  17.5255
-#&gt; 
-#&gt; Columns 33 to 40   3.7398  11.9164  -2.1994  17.3545   7.6919  18.2535  16.2565   3.0534
-#&gt;  -22.1746  -2.5972 -10.5328  -5.6384  17.1634   0.1492   2.6765  -8.7797
-#&gt;   15.7078  13.5311  -1.9342   4.0391  -6.3492   0.9819   9.8864  -8.1232
-#&gt;   -7.4723   1.2027   5.8385 -15.9131 -16.5342   1.8749   7.4802 -13.5046
-#&gt;  -14.5844  -6.6044   6.7171  11.3866  -1.6240   4.8207  14.8608  -5.6832
-#&gt;   -5.3270  -8.5717 -11.8968  -5.8006   8.5304   6.1275   5.4905  -1.5662
-#&gt;   20.9067  -3.2596  16.0589  -8.4640  -4.7471   0.1439  -9.1014   1.8498
-#&gt;    6.4862   0.3508   5.7172  -7.2685  16.5987  -5.4466 -11.0539   9.2751
-#&gt;    8.8171   3.6017   4.7839   6.9964 -12.3529  -3.2227   6.5608  18.1055
-#&gt;   -1.8451  -7.4598   2.2739  -2.4590 -23.3320   3.6801  -1.7517  -0.7305
-#&gt;   -2.1438  -0.6471 -15.6980   9.3079  -3.2956   3.1527   1.0967   6.6124
-#&gt;   -9.3272   5.5347   5.8071 -15.6016  23.7244  -3.1802   2.5827   2.6769
-#&gt;   -1.8480   3.5504  -9.2644 -16.1763   7.2894  13.7280  -0.3345  -6.5412
-#&gt;   -6.9716  -2.2138   7.0869 -13.8287   5.5485 -20.5346  -9.7861  -2.8131
-#&gt;    0.6799 -17.7559   2.9644  -3.0908   8.0651  -5.7967  17.4581  -7.6221
-#&gt;    1.4858  -4.2815   9.1155   3.8075   5.8086  -0.4867   3.1360  -3.1349
-#&gt;  -10.7100  -4.7337  -4.7890  -9.3010  11.4710   5.1431  -6.5741  -6.5903
-#&gt;    1.1697   2.8402   0.9476 -12.3607  13.7122  -3.1704 -12.2792  -3.6520
-#&gt;   -1.7845 -10.8223   1.3488  -4.9230  -6.2594  -8.1701   8.8945   7.6211
-#&gt;    9.5109  13.8461  -2.3755  12.3814  -1.8163  -7.5476  -0.9768  -0.7303
-#&gt;   13.7919  13.6527  15.5735  10.0841  10.3817  10.6021   9.0033   1.7628
-#&gt;   13.4732   5.4684  -8.3357 -10.4179  -7.9186 -15.1649  -8.5268  13.5443
-#&gt;    7.3305  -0.4604  10.7282  -6.5767   3.2823  -7.0912  -6.9034  -7.9658
-#&gt;    3.3321  13.0385   3.1378   1.2988 -11.9930  11.8531  -7.0118  -3.9382
-#&gt;    2.0293   9.2086  -0.8242  12.7483 -19.4353   0.8570  -3.8385   5.4355
-#&gt;  -11.7978   4.2577  -3.3972   3.6214  11.9412 -15.4736  -8.1722  -5.0488
-#&gt;   -9.0630   1.7378  -9.9009   3.4573  -3.5296   6.7400   1.7715  -6.7341
-#&gt;   -8.6042  -2.0558   0.2023  -2.2278 -11.8652   1.5105  11.7174   2.7020
-#&gt;    7.3962  -5.1905   4.9489  -4.2631   3.5248   0.8108  -4.9632  -8.9748
-#&gt;  -14.8573 -14.3859  14.9262  -2.3562 -20.1310   4.1151  13.4385 -11.9751
-#&gt;   -5.2578  13.2722   0.9739  -5.3776   3.9516   3.5788  -3.9147  -5.6340
-#&gt;    7.1877   3.0640 -10.6718  -2.7446  -0.6904   8.7616   6.3383   7.2771
-#&gt;   15.3132   2.0735   4.8753   0.2851   4.6359  -0.4220   9.4668 -14.1132
-#&gt; 
-#&gt; Columns 41 to 48  -2.7489  -8.7637   3.4835   1.0170  -0.7160  -1.7874   4.3668  18.0269
-#&gt;   11.4088  -3.2957  -4.7056   7.7861  -8.8527  -3.9654   9.5578  -0.8020
-#&gt;   -2.7851   4.7159   8.3065  -5.3754   2.2233   9.1180   4.2429   3.4977
-#&gt;   -3.0859  -0.8083 -13.5866   6.5434 -10.6219 -11.0563  -5.6027   2.0945
-#&gt;    6.7164   0.0129   3.6310   3.6835  -9.5113  12.8007  -0.6328   5.7456
-#&gt;    3.5358   2.2530   1.3245 -20.5415  -1.0149  -3.6555 -11.8244   4.6888
-#&gt;  -13.7008  -0.9236   1.7582  16.2404   3.8166 -10.3989   1.1112  -3.0338
-#&gt;    1.4287  -1.9917  -9.1448  -2.0913  10.1029  -1.3184   1.9773  10.6877
-#&gt;   -2.1908   5.9729  15.2308   7.5356  -2.5093   7.4100   4.0253   4.1294
-#&gt;    5.3162   4.4217   3.4451  -7.0947  -6.7558  10.9257  -5.3132  -9.3164
-#&gt;   17.2181   6.2738   1.8721  -4.4210  12.1152   8.9523 -14.2943   5.3058
-#&gt;   10.6419  -0.2886  10.8269  -5.1502   0.3061   0.6123   6.4105  13.0537
-#&gt;   -6.0470  -2.5747  -5.8937   1.1997   6.6017  -6.8245  -4.9771   8.0405
-#&gt;    8.6029  -4.6612  -0.0321   4.4581  -7.2621  -3.3948  -4.2746  -0.9244
-#&gt;    3.5538   9.7629  -6.2850  -0.0550   0.5231  11.7813   1.8676  -2.0707
-#&gt;   -7.6222  12.5437   7.6741  -3.4922   1.6843  -1.0553   5.5238  -7.2334
-#&gt;   -2.4571  -6.9917  -8.7695 -11.9418 -12.1425  -9.5730  -2.0288   7.2764
-#&gt;   -0.2375  -6.6255  -1.9505   2.1925   2.3345  -6.5458  12.4992   6.5844
-#&gt;    9.2619 -10.2640   2.0741   7.4089   4.0091   7.8756 -13.4588   0.8496
-#&gt;  -10.6017   9.2146   6.2082  -4.6360  -7.2165  18.5288  -2.6394  -6.6095
-#&gt;   -1.1088   4.6886  14.3204  -1.6789   2.7128  12.7741  12.5627  -7.0749
-#&gt;    3.5440  10.7971  -7.8047  -4.8617   6.3549   1.0599   7.3522  -7.8855
-#&gt;  -16.5681  12.9976  14.0391 -18.0699  -2.0621  -1.0039  -7.8445  -5.6196
-#&gt;   -3.3615  -7.7826   2.4972  -9.4898   7.0115   2.9747  -2.6037   1.7966
-#&gt;  -16.6786   0.8662  10.1673 -12.8165   6.5936   3.3037  -1.5560  -8.5763
-#&gt;    3.9353 -11.2481   4.8464 -17.0819  -7.2871   3.1291   1.1024  -1.7613
-#&gt;    4.4568  -2.9537  16.3754  -1.8688   1.7240   0.7697 -11.0913  -7.0720
-#&gt;   15.3738   2.0966   1.3261   8.6218   5.8933  -2.8206  -0.0873   7.9319
-#&gt;   -2.3210   3.2512 -13.6099  -1.0325  -2.7013 -14.6160  -2.2551  -1.8460
-#&gt;  -10.7967   1.6568  -6.5915 -11.5341  -9.9567   2.6287   4.8549 -18.6987
-#&gt;   17.4152   9.9368  -4.7196  -2.0201   7.0575   1.9245 -10.3459   5.4035
-#&gt;   -9.8628  -1.2139   0.5197  -9.0915  -9.3666 -17.6017 -11.3053 -11.6865
-#&gt;    5.1867  -0.8220  -2.7756   3.3252   5.9480  -6.7896  -3.5799   0.5004
-#&gt; 
-#&gt; Columns 49 to 54  -0.6389  -7.4733 -14.9716 -11.8120  -1.9556  -5.3680
-#&gt;   10.7929  12.4003 -14.2223  -7.3193  -3.7665   0.6283
-#&gt;    8.6197   2.0170  -6.0246  -6.1153  -0.3049  -7.7032
-#&gt;   11.1266 -22.0888   0.6137   2.3653   1.6914   6.3109
-#&gt;   -0.6324  -5.6238 -12.4644  -6.6811  -6.2189  -8.1869
-#&gt;   -2.6817 -15.2342  -6.2547  -0.4114  -9.7645  -5.8096
-#&gt;   -2.0674 -12.6191 -15.4729  -7.7481  -3.3053   3.9714
-#&gt;    4.4544  -6.5520  -3.6067  -3.6903  -1.3885  -1.8380
-#&gt;   10.1238  23.3828   6.2061  -0.9916   9.4449   1.7633
-#&gt;   -1.6249 -16.9761   3.6877  -4.3206  -9.6594  -4.2910
-#&gt;    0.2759  -0.0149   6.6960  -3.9407  -4.2733  -0.9428
-#&gt;    8.0797  -1.3322   3.3517  -1.2963  -1.9725  -2.5632
-#&gt;   -3.1325  -3.4439  -1.0952  -1.0389   5.6795  -2.8524
-#&gt;   10.0878  12.1763  -3.0694  -2.4060  -6.0147   8.5415
-#&gt;   -1.3119  -6.9195  10.8808  -0.8992  -4.8843   1.3601
-#&gt;   -4.5548 -20.3759   1.4613   8.7679   9.1140  -1.0856
-#&gt;    0.3173   3.1479  -8.8415  -2.1779 -12.6805  -1.6526
-#&gt;    6.4048   6.2706   4.1337   3.4527   3.3755  -1.7199
-#&gt;   -0.6586  -5.9617  12.3254   5.0271   8.6379   6.4460
-#&gt;    4.2861 -12.6578   9.2266   1.6816  -0.8269   4.5423
-#&gt;    9.5541  -5.7773   2.6169   3.7411   8.2549   1.6539
-#&gt;    5.7854   0.5161  -1.5401   2.0400  -0.6564  -5.9205
-#&gt;   15.7457  -0.5584  -4.0106   2.0536  -0.5748   2.0987
-#&gt;   -1.1142   8.8274  -2.3402   0.8552   8.2448  -0.9579
-#&gt;    4.9794   2.9381  -5.4608   0.4388  -2.2405   1.1353
-#&gt;   -1.2917   1.1117  -4.4265   3.8347 -12.0188  -2.6317
-#&gt;   -6.2574   2.3697  -2.6166   2.7013   3.3212  -0.2425
-#&gt;   -3.8129  19.2050  -4.5047  -5.3278  10.3334  -2.1496
-#&gt;   -0.3943  11.1524   5.1407   9.3953   3.7767   0.9381
-#&gt;  -13.8334  -2.3486   1.6269  -4.3620   4.8251  -1.9973
-#&gt;    0.3519  -7.7991  -6.3443   3.6354  -2.9999  -4.0078
-#&gt;   -1.0298   7.8492  -4.6288   1.7104   0.7321  -3.8820
-#&gt;   -2.9105   6.6443  -1.8968  -0.9760  -1.5631   4.2653
-#&gt; 
-#&gt; (15,.,.) = 
-#&gt;  Columns 1 to 8 -10.0990   9.5713   1.5318 -12.0500  10.1011  -0.1370  13.3657 -12.0867
-#&gt;   -2.0152  -2.9317   2.3648   0.7224   7.9930   5.1094  10.4382   2.3264
-#&gt;   -0.3481  -1.4523  -7.0102   3.4108  -2.3078  -5.3298 -15.2268   9.4144
-#&gt;   -2.6055  -9.8104   9.6568  12.2556 -10.5023  -2.5403  -8.1588  11.3824
-#&gt;    4.8885  -1.2973   0.0989  20.7426   3.1836   2.7362  -4.7865  21.5729
-#&gt;    4.6923   1.6641  -1.2218  21.5251   2.9839 -10.0373  15.2247   6.9074
-#&gt;    0.9719  -3.3859  -6.3710   0.5712 -14.3356   8.1015  10.3925  -7.1472
-#&gt;   -5.1478  -0.4332   7.1794 -11.7585  11.1017  -0.9514  -0.9117  -7.5852
-#&gt;    0.8523   4.0596  10.2042   8.8919  -6.7690  -3.1127   2.1410   5.3746
-#&gt;   -0.7264  -9.5159  13.2099   0.0314  -0.9318 -12.5969   2.5364  13.7676
-#&gt;    3.2831   2.4765  -0.7692   0.4533   3.5371  11.6717   4.3137  10.3082
-#&gt;    1.7459   3.0613   4.4691   3.0374   1.8916  -4.2943  -4.3574   2.6327
-#&gt;   -5.2546   0.7602   5.5456   2.5400  -7.4997   3.5122   8.7494  -3.9388
-#&gt;   -4.8197  -7.6028   1.1249   9.1811 -12.9616  -2.8928   4.7218 -12.8941
-#&gt;    7.2280  -6.4193   0.2792   0.6822   6.5073  -7.9635  -0.0429   5.5636
-#&gt;    7.6099  -0.7829  -5.8236   3.8139   2.2075  -0.5448   3.2761   4.8688
-#&gt;    3.5679   0.7753 -15.0284  -4.2804 -11.6611  -5.5340   8.6002   5.5089
-#&gt;    3.7578  -1.4981   0.1193  -5.3650  -1.1050  12.6927  -6.2565  -1.5469
-#&gt;   -0.8684  -3.1628   2.5079 -10.9548 -12.2026 -11.2409  -5.4725   6.1974
-#&gt;   -4.8493  -5.0138   4.2155  -2.2080  -8.8043  -5.2325  -5.2402  -5.7408
-#&gt;    3.4794   1.2526   1.7075  -2.2464   5.5673 -13.7139 -11.0148  -7.4443
-#&gt;   -8.2279   0.0555   3.0568 -11.7474  12.0560   3.7196  -3.5746  -5.4535
-#&gt;   -1.3951   1.4963  -0.4092 -11.0516 -22.7744   3.8872  -2.3623 -20.0583
-#&gt;   -4.2623  -9.1239  -3.2667   3.5099  -9.2813 -14.1512  11.7609  -1.7458
-#&gt;   -4.4801   2.6365   7.5302  -6.0688 -19.6205  -5.4619   2.6827 -14.5390
-#&gt;    1.4995   4.9967  -4.6511   2.8508  -1.2898   4.5070   0.4309   7.5099
-#&gt;    1.0764   1.9196   0.5463   3.8593  -3.8163   9.3600   7.4160  -3.2611
-#&gt;   -3.6800   4.9303 -10.1005  16.2065  -2.8412  -9.7141  -9.2764  13.5852
-#&gt;    2.1092  -2.0972   6.2155  -2.3583  -1.2559   1.8911 -17.7634  18.2820
-#&gt;   -0.5072  -1.4840  -1.8773 -13.4366  -9.4894  -5.1573  19.5537  -5.9230
-#&gt;   -3.0255   3.9880  -7.6719   6.5883   9.1100  -8.1304  -5.2865   6.3455
-#&gt;   -1.7676  -3.3332   2.5160  -3.1491 -18.3137  -9.7269  15.7431 -19.0633
-#&gt;    3.7121  -0.4173  -9.2082   0.8137 -20.6655  -6.5320   1.3618  -3.3141
-#&gt; 
-#&gt; Columns 9 to 16  -5.0178   2.8376 -13.4590  -1.7824  -3.2256   6.0948  10.5693  -7.3420
-#&gt;  -13.4998   9.7929   7.6963  -5.6667   5.6835  -5.7594  17.1983  -8.0135
-#&gt;   -6.6259   1.8900   0.4777  -2.9068  -1.9392   2.3020  12.7189  11.0737
-#&gt;    9.7699  -1.4176  19.3781  -8.8971   1.0619   1.5661  12.3683   6.8381
-#&gt;   -8.8433   6.7435   5.3905  -2.1816   7.6027 -13.1408   9.6943  18.9325
-#&gt;   -3.7578 -20.4967 -12.1749  13.7635  -8.2955  10.1202  -9.7523   0.7279
-#&gt;   -1.5795 -27.3397   6.2327   4.8520  -6.9096   7.8854  11.5034  19.2436
-#&gt;   21.0009  10.6960   0.3235  18.1828   1.3996   1.6413   9.7474 -12.8684
-#&gt;    3.1225   3.5732   4.3682  10.1331  -2.1940   1.4707   6.3428   7.3753
-#&gt;    5.8743  -4.9510   7.6673   3.3482   0.4906   4.1957  21.5036  -0.4971
-#&gt;    4.3325   4.0696  -7.1543   4.7685   9.2816  -8.0765  13.1303   2.2542
-#&gt;    1.3199   8.9509 -14.1791   0.3937  12.9080   2.3078  13.6441  -7.4704
-#&gt;   14.1569   5.6918  -2.8820   5.4940   5.2787  -3.9716  11.0201  -9.0025
-#&gt;   -1.4026   0.8986   0.0281  -1.1187   0.9862 -23.6242  12.1266   2.6198
-#&gt;   -5.9391  -5.4533   2.9304 -14.2286   8.5711   6.9260  -5.7363   9.8813
-#&gt;  -13.7160 -13.2488   6.5590   0.5550  -0.6141   0.2143 -11.7267  -2.9618
-#&gt;    8.3337  -8.7911  -4.3042   0.2995  -3.9446   2.9244 -13.8378  13.2256
-#&gt;    8.2129  -0.0243  -2.5634   4.2056   7.3335 -13.9953 -12.1880 -10.9778
-#&gt;  -15.5198   3.0817   4.3212   2.8397  -4.5304  -2.8506  18.1735   0.9459
-#&gt;   -0.1223  -5.3029  -0.6803   1.7909   8.2157   9.3911   3.0801 -20.6149
-#&gt;    6.4214  -5.0558   1.5302  -1.3221   6.9508 -19.6272 -11.9980  -1.2132
-#&gt;   10.1410  15.7525   0.6530 -15.2663  -6.4518  -4.8851  16.6364   9.8039
-#&gt;    2.4659  -9.5107   5.0274   3.2727 -20.7507  -2.8210   0.5230   9.5059
-#&gt;  -17.5149   7.4447  -0.9244   5.0719  -8.1361   2.1893   7.6107   2.9166
-#&gt;  -15.9416   3.8260  -6.9479   2.5022 -14.7595  12.8274 -20.2647   1.7912
-#&gt;    7.4356  -4.8601  -4.9859  13.4862  -3.5921  12.9788  -8.9852 -11.1844
-#&gt;   -9.6747   2.3929  -1.8941  -0.1248   1.6079   8.6074  -6.0711  10.0755
-#&gt;    5.3568  16.4666   6.2013  -1.3843   0.6055   2.8061 -12.3449  17.9458
-#&gt;   11.9440  -0.8353   3.4883  10.3915   7.5584  -1.4332  -4.6735  12.5461
-#&gt;  -22.4059  -4.5566   7.3376  -4.3847  -1.5582  -7.5045   7.5009  -1.9745
-#&gt;    5.6077  -5.3056 -13.0279   1.4611  -4.6591 -15.2655   4.6496 -10.6508
-#&gt;    0.5694  -4.6875  -1.8906 -10.3570 -15.9350  -3.2111  -2.0236  -6.8917
-#&gt;  -11.4327  -4.4946  11.8158  -3.7137  -5.7878  -3.2148   2.5095   5.8133
-#&gt; 
-#&gt; Columns 17 to 24  23.9548   4.4337  22.5554  10.9288  -2.2020   3.8517   1.3465  15.9809
-#&gt;   -2.5955   9.0179  -3.8016  -8.0012   1.7868  -3.1316  -5.1833 -18.7513
-#&gt;  -11.2852  13.7604   9.7052  -4.6647   9.1436   1.4140  15.3762  -5.4790
-#&gt;  -30.2964   9.0454   3.9029 -30.8797  -6.0035   6.8617  12.3477  -8.1561
-#&gt;   -7.8357  -2.4154   0.1894 -17.0568  12.7654  -1.0467  -1.8494  -2.1127
-#&gt;    9.1948   0.0944   3.3596   1.7337   7.1465  -3.6636 -13.6780   2.3150
-#&gt;    1.4789  11.8322  15.1057  13.5677   2.5585  10.9729   1.6040 -10.0423
-#&gt;   18.6681   6.5529  -2.8593   3.3970   6.8450   0.3297 -10.0932  -0.2324
-#&gt;    2.9418  -4.5070  17.7137  -1.9664   2.8364 -10.0479   6.7950  10.6355
-#&gt;   -3.8491  14.8961   0.6555 -14.6044   7.5461  19.3217  -9.9193  -8.7238
-#&gt;   13.7032 -13.1803  -1.7174  11.4773  12.4560  -9.4533   6.6293 -14.7780
-#&gt;   10.5144   6.1302  -7.4889  19.8148   5.8770  -0.9864   6.1714  -4.5893
-#&gt;   10.9235 -19.2067  -7.2074   1.9999  -0.9868   0.1538  -3.3172 -12.9561
-#&gt;   -8.1066  -9.5891  -5.8863  -4.7349   3.4509  -1.9967  -0.9951   2.1819
-#&gt;   -0.1153  -1.2667   7.1105 -10.0341 -10.6734  25.9604  11.4678   6.4583
-#&gt;   15.4342   3.8854  -0.8302  -5.8226 -13.3946   1.0479   6.4934 -15.2569
-#&gt;    7.5051   3.4195   1.0269   7.6924  13.4544  -7.2745   0.9785  -1.0435
-#&gt;   -7.2453  10.1602   2.5436 -12.7391   0.8896  -1.8076  -5.2475  -9.5786
-#&gt;    0.9025  10.1723   8.4099  17.3126  -3.8429   0.2009  18.8467  17.4271
-#&gt;    5.6315  15.4214  -6.9537 -16.5940   5.3793  19.0690  -8.0471 -13.2905
-#&gt;   11.9089   3.0548  -5.6932   7.0078  -2.3373  -3.9093   5.7294   4.5638
-#&gt;   11.4385 -25.3665   0.1245   4.7662   6.2247  -4.7778   0.2985   1.8013
-#&gt;   11.1348  11.0020   0.1509  -5.2465  10.6244   8.7351  -2.8962  -4.9217
-#&gt;    4.3799  13.4892   4.0956  -2.8105  -4.9192  -1.0366   1.2922   0.6402
-#&gt;   -8.3492  24.5169 -10.7443  -2.3853   8.8224   1.1678   9.0221   0.4602
-#&gt;   -0.2645   2.5483  -1.9621   6.9814   6.4197  15.2534 -17.7681  -2.2454
-#&gt;   -7.4653   4.4360  10.7323  -7.0816   3.3647   9.4408   9.3030   8.1634
-#&gt;   -2.1583   1.4271  -0.0035  -8.9739   5.9935  -9.9652  15.8939   2.9064
-#&gt;  -22.6930   3.8092  -0.5091  -9.1934  10.3580  13.3964  -6.4586  -0.6747
-#&gt;   -6.0896   5.1857   3.3376  -4.1952  -0.2352  -1.3639  27.2648  -4.6405
-#&gt;   13.2255  -4.4454  -0.9535  -1.1588   5.7364  -5.5923   8.2029   1.3131
-#&gt;    8.5942 -18.0943  -3.8576  14.8559  -0.2151  -0.5652   5.3782   5.7814
-#&gt;   -2.4000   7.8375   8.4794  -0.1797   8.0042  -5.4103  -2.8932  -6.7073
-#&gt; 
-#&gt; Columns 25 to 32   9.5944  -3.9121  -0.6456  -4.2796   4.8669  -3.1430 -16.6505  10.3947
-#&gt;    6.2774  -4.8735 -10.7184  -1.3617   8.1813  -6.5940   9.6497  11.8253
-#&gt;   -0.8060  -7.0873  -5.1033   3.6967  -5.9224  -1.5106  -3.2493 -22.3877
-#&gt;   -2.1715  16.7673 -14.4711  -6.9998  -2.7722  -5.2056  -5.5870  -3.6650
-#&gt;  -10.9918   9.5981  -4.8545   1.3297   1.6327   6.8057  17.8017 -10.4679
-#&gt;   -5.9841   5.0507  -1.8719  16.6006  -8.6540  18.5502   6.4155  -0.4553
-#&gt;   -1.2806  -4.6073   1.7010  -2.0524  21.2555   1.6784 -14.3942  14.7029
-#&gt;   -6.6802 -10.5134  -5.3347   1.4647   6.6026   7.7703  -8.3399  -1.4836
-#&gt;   -6.5434   0.1531   0.9292   2.4402 -17.5561   5.7259   9.4887  -2.3714
-#&gt;   -0.8271   9.9857  -0.1178  -1.8616  -7.1793   6.0239  -7.9769   7.4283
-#&gt;    7.1402   4.4461  -0.9369  -2.3136  -0.0208  10.2257  -4.8433  -9.9684
-#&gt;    5.9708   1.4622  -4.8579  -8.3934  -7.1991  14.0736  -2.2637   0.5849
-#&gt;    4.8735   8.7754  -5.3562 -14.4586  11.7871   3.8676 -26.7182   6.0868
-#&gt;   -1.8051  -0.7627   7.4101 -11.8195   9.1956   3.0181   1.0301   0.4012
-#&gt;    2.3346   5.5885  -6.8960   2.6455 -11.1043   1.1019  -3.3641   1.5160
-#&gt;    0.4052   2.8550   9.9087  -9.2560   4.5426   3.6349   1.1024   1.1567
-#&gt;   -3.4402   4.1977   2.2295   3.9754  -5.8424   2.3379   9.0788   1.3631
-#&gt;   -0.3071   4.4000   0.4199   6.6844   9.9604  -3.5819  -0.1863  -0.4231
-#&gt;  -12.1294  12.6241   4.2895  -2.8436 -20.4353  -1.1826 -21.5068  -3.8060
-#&gt;   -1.0920  -4.3573  -8.0907   1.3174  -1.0410  -4.7343   3.5406 -20.6220
-#&gt;   11.1171   5.0765  12.8293  -2.8107  -9.1112  10.5481  -5.7296 -10.1943
-#&gt;    2.2845 -14.3124  -0.5741 -18.6881   7.3470   8.4301 -16.0972  -4.9773
-#&gt;   14.1851  15.5705  -8.0232 -14.2519  -8.7095   5.3573   8.2457   3.0091
-#&gt;   -8.0929   0.0361 -15.0765   1.3317  -9.0183   0.3250  -2.7328   4.0589
-#&gt;   -0.5257   6.8122   2.5629  23.2483  -1.5479  12.1290   3.9113 -14.1497
-#&gt;   -3.3007  -0.4385   0.9678  11.9494  -6.6841  -8.8127   8.3065   2.3145
-#&gt;   -2.0953   4.7034  -6.3555   4.3592  -7.7332   2.2606  10.0530  -0.9074
-#&gt;   -8.5917   8.7902  11.5992   4.3040 -12.4374  -3.4665  -0.0276 -18.1506
-#&gt;  -13.3506   1.1399  -7.6358   3.4748  12.0200   1.2798   1.8368   6.4288
-#&gt;   -1.7819  13.8193   9.8684  -1.6199  -5.4542  -4.0736   1.5121  11.8665
-#&gt;    3.2625  10.5987   1.5617 -10.7859  -7.1992   1.7146   5.2890  -3.0862
-#&gt;    6.1506   0.8400   9.1340  -6.4293 -11.4344  13.4539 -25.8874 -12.8097
-#&gt;   -0.2576   2.2089   0.5780  -3.6492   9.1683  -5.4479 -13.5015  -8.2683
-#&gt; 
-#&gt; Columns 33 to 40   8.8396  -4.0908   3.3033   5.9178  -2.1426   4.7535  11.7253   2.1089
-#&gt;   -7.8518  -0.6864   5.4062  -0.0704   2.2593   1.2757  -3.3115  -2.8540
-#&gt;    8.0880   8.8613  -0.2046  25.4886  -0.0231  -0.6460  -3.0581   3.7206
-#&gt;    1.3567   2.0511   3.5708  -3.5456  -7.6781  -1.4215  -6.5459   3.4979
-#&gt;    8.6816   6.3005  -2.1095   7.5814 -10.5794   2.5243   2.6340   2.6379
-#&gt;    2.2035  10.3515 -11.2097  -9.3520   7.2193  16.8672  -1.1143  -6.3585
-#&gt;   -6.3557   2.2419   4.5310  -5.1427   4.8296   0.9774  17.5645  -2.1679
-#&gt;    0.7955   3.9828  19.1163   7.6492  -5.3007   4.0388  10.3170 -10.0017
-#&gt;  -10.3534   6.4167 -19.4180   3.1978   5.7691 -21.6791  -2.2751   3.2430
-#&gt;   -1.7144  -2.9233  -4.6692   2.2329  24.4980   3.3718  -0.1364   3.9223
-#&gt;   15.9999  -5.0894  -0.0592   5.6503  -1.0933 -18.6162  13.1086  -2.5467
-#&gt;   -8.7221  -2.9894   1.3795  11.1901  -4.6743  -4.0438   6.3607 -14.1750
-#&gt;    6.4703 -12.0389  15.9626 -14.4179 -11.5250  11.3063  18.6204  -6.1838
-#&gt;   -9.8206  -8.7003   8.7307   6.8307   5.9919  -0.1534 -13.9388   3.1807
-#&gt;   -5.1773  -2.5748 -13.1049  17.7876   2.0671   7.2958   1.2341  24.2162
-#&gt;    2.9031  -0.5510  -8.2014 -11.4646  -0.3466   5.5836   5.9189  -2.3536
-#&gt;   -7.3025   9.3635  -6.8624   3.7640   1.4827   4.0607  -1.9772   0.5987
-#&gt;  -12.8694  -6.1116   8.6849 -18.8294 -10.3818  -5.0519 -12.2981   6.4965
-#&gt;   22.5084   4.1215  -7.5574   0.8125   7.2067  -9.4430  14.0021   5.2206
-#&gt;    2.0273   4.4054   6.1284  13.6395  14.3388  -8.0690   6.9133  -0.9929
-#&gt;   -6.8994   3.5835   4.0496 -10.4464   2.8428  10.3485   3.0069  -3.1604
-#&gt;    2.0785   9.0722   3.1944  16.6751   0.8630   4.7099   2.2799 -12.4083
-#&gt;  -14.1698 -11.5007  -2.5575  18.4120  12.5671   0.3347  14.5842  -6.3854
-#&gt;   -0.4839   3.4926  -0.2347  -4.1269  -8.5848 -17.0848   5.6182 -10.1792
-#&gt;    6.5714  -2.6046  -5.7125   6.4672  -3.9295 -19.8987   6.2386  18.6289
-#&gt;  -18.1906  13.5562  -6.0317  -7.6967   9.0732   5.7860   3.1072  -0.3426
-#&gt;    2.8831  -2.1550  -8.5623   6.4899  -9.2266   7.8874 -10.9110  -7.0155
-#&gt;    0.4335  12.8337  11.4560  11.9060 -20.1756   0.3952  -4.7485  11.1188
-#&gt;   -0.0864  -0.7748   5.9271  -9.5802 -24.4697   3.9133  -9.2256 -10.1374
-#&gt;    9.4869 -26.5448  -4.7492   1.5490  -0.8644   4.6959   0.7020   6.8054
-#&gt;  -11.8460  -2.2999   8.3583  -4.7632   5.6372  12.6364   4.8529 -11.5818
-#&gt;    7.3621 -14.5742   5.1122  -4.9590  -6.8797   0.0120  15.1780  -1.1519
-#&gt;    9.1480  -8.6119   5.6794   1.8353  -1.2049  -3.6436 -15.5298   6.1394
-#&gt; 
-#&gt; Columns 41 to 48  -0.9185   1.0292   2.3816 -19.0016   3.2346   5.1427  -9.2602   3.5470
-#&gt;   -4.6642  -7.5193  -6.2972  -3.6340  -4.3782   3.5069  12.3738   3.8607
-#&gt;   -1.7061  17.2409   0.5289  -6.2155  -9.2250   7.7128   2.6069  -4.1813
-#&gt;  -22.3711  15.8590 -12.1630  -1.9723   3.4912   0.7936  -0.4754  -4.8637
-#&gt;  -10.7078   8.7035 -17.7855  -6.9211   9.6944  -0.4565  14.0562   7.5147
-#&gt;   15.2351   6.8293  11.9645   9.6385  16.1149   5.4100  13.9070  11.3935
-#&gt;  -18.9558   4.3913 -11.3429 -11.7447  10.8551  -1.7770 -10.4569   2.5707
-#&gt;   -5.0401  -9.8150   1.7656  -1.7277 -14.3881  -3.8345   2.5442   7.6936
-#&gt;   -3.8712   7.5262   5.4346  -3.8012  -4.1631   3.5016 -10.7002   2.8133
-#&gt;  -12.1691   9.2626 -20.9391  -0.4648   0.0210 -11.2584  -9.1199  24.2553
-#&gt;   -4.7094  -2.3166  -9.8245   9.5187  -3.5064  16.1850   3.9799   2.8132
-#&gt;    6.3814  -3.9819  -1.0964   3.3634  -8.4593  17.8737   5.7258  -2.5306
-#&gt;   13.5904  -5.1623  -4.3888  -9.1726  -5.7060  13.3960   9.3317  11.0526
-#&gt;  -15.8183   3.6202   8.2642  -3.5512   7.4538  -7.7091   6.9085 -20.2944
-#&gt;   -1.1283 -18.0089   9.1768 -19.2064  13.2384   0.6168  -3.4532   1.7254
-#&gt;    1.0596   4.8809  -7.4798   9.9955   2.8880  -0.7054  -1.5500   3.2815
-#&gt;    3.6454  -0.9967   1.9723   7.5453  10.3257   9.4845  16.9860  -0.8021
-#&gt;   -7.3195  14.6915  -0.9854  -4.1521  -0.8937 -13.6568  -3.7865 -23.4725
-#&gt;  -14.4901  -3.1384 -17.0815  -4.9651   5.3681  -3.0924 -18.9295 -15.6902
-#&gt;   -2.3697  -4.0433 -11.8967  13.1645   5.7599   3.2860  -4.1032   8.4708
-#&gt;    5.5353  -7.1651   5.3395   2.2028 -13.1871 -10.0166 -14.7682   1.3285
-#&gt;   12.3502   2.8922   8.9898  -1.6964 -13.0801  -0.3030  -7.0138  11.3802
-#&gt;   -8.0877   3.5271  -2.7746  -7.2920  11.5408  12.2708   5.6332  15.9228
-#&gt;    7.5764  11.6671  -3.1646  -0.9293  -8.9805 -10.0701   3.1878   3.2721
-#&gt;    3.0567   9.6918   0.9289   9.2551 -17.5344  -4.5718  11.4749  -8.6895
-#&gt;    9.8700  -5.0108  11.2222   2.5289  14.6404 -18.5415 -10.1208  -7.4381
-#&gt;   -5.1425  -8.1582   5.6891   1.3202   9.9968 -10.1401   9.7812  -5.5545
-#&gt;    6.0983   3.3249   4.1080   3.2006   5.4616   6.3398  14.2389  -3.0887
-#&gt;   -5.4333   9.9757  14.0214   0.1371   9.8365  -4.8653  -3.6701 -17.4995
-#&gt;   -2.9224   1.7300  -6.4158  -4.8144   3.8159 -14.3869   1.6443   3.7402
-#&gt;    5.6625  -1.9869  -0.9393   2.7615   0.5217   1.2785   0.9142   7.9043
-#&gt;   13.3689  -3.6108   0.6681 -16.7220  -0.2150   9.0452   3.9819   8.0725
-#&gt;  -11.0557  21.5327  -4.6524  -2.5301  15.3207  -1.6733  29.7789   0.5129
-#&gt; 
-#&gt; Columns 49 to 54   1.5166   8.2477   6.7136   6.3118  -4.9695  11.5162
-#&gt;    1.4363 -12.9781   0.1371   0.9093  -3.6536   4.9203
-#&gt;  -18.0463   5.2483  -0.5983   5.6387  -2.6682   1.3394
-#&gt;   -2.6935  -6.1874  -6.5942  -4.2045   6.6405  -9.5767
-#&gt;    0.8123   0.8789 -17.2362  -1.3923   9.8621  -4.7030
-#&gt;    0.9047  25.0544   4.3639   7.7567  19.5197   8.8219
-#&gt;   18.4562  11.7296   5.6156   4.0337   8.2729  -3.8409
-#&gt;    6.9517  -5.0020  -3.8453  -0.1510   1.8457   4.7716
-#&gt;   -7.2966   8.9733  -7.9880  -5.7693  -1.0765  -3.2241
-#&gt;   19.3973  -9.3490  -5.5793   2.3670   4.4611   6.6473
-#&gt;    2.0844  -0.0334  -8.0439   5.7844   1.4116   4.2696
-#&gt;   -0.1985   5.3961   6.0418  -0.2969  -0.8843  -2.1523
-#&gt;    2.0317  -4.4195  -3.6542   0.5706  -2.4710   0.7280
-#&gt;    7.4199  -3.0333  -4.4803  -2.6479   6.0699  -2.7246
-#&gt;  -11.5188  16.6455  -0.1031  -2.7591   3.5070  -0.1705
-#&gt;   -3.0831  -4.5135  12.9683   1.0926   1.7067   2.5385
-#&gt;    5.8785   6.7963   7.6884   5.5664  11.5878   2.8810
-#&gt;   13.8876  -7.7859   1.2779  -7.9931  -4.2584  -0.1300
-#&gt;   14.8720  15.5157   3.7080  11.8443  -3.9134  -2.1870
-#&gt;   -5.7535   7.5996  -4.3604   2.5150  -6.0650  -3.4270
-#&gt;    3.9493   1.7726  -3.3017  -2.0998  -8.4011   8.5350
-#&gt;    0.7961  -3.1373  15.8146  -4.7486  -1.0243   4.7894
-#&gt;    8.8234  -8.4761  -7.7498   9.6531  10.3125   0.0289
-#&gt;   11.9533   9.9974   0.7059 -16.6528   1.1749  -7.9864
-#&gt;  -10.4745  -5.0384  11.4629   2.7173   4.2650  -1.2628
-#&gt;   11.0660  -5.0848  -0.7776  -6.1537  -0.6618  -0.3784
-#&gt;   -9.9422  -1.2817  -1.6077   5.3577   3.3337   0.4067
-#&gt;  -12.0042   5.7682  -6.7125   4.8503   6.7699   2.9700
-#&gt;   -3.3479  -7.8093  -2.1469  -7.6515  11.3454  -7.3593
-#&gt;   21.3429   2.5380 -10.8956   2.5114  -3.3077  -1.5169
-#&gt;    1.3157   5.7816   2.1688  -8.4880   6.3563   1.6823
-#&gt;    4.5685  -1.2746   8.4463  10.5379 -11.6546   5.8264
-#&gt;    9.5908 -13.6034   2.6211   4.8941   5.8864   2.5896
-#&gt; 
-#&gt; (16,.,.) = 
-#&gt;  Columns 1 to 8   5.7149   2.6410   0.4331  -1.4741  -3.6267  -1.4720  -1.6433 -14.4904
-#&gt;   -4.1965  -2.3145   2.4730   7.7392  -2.5778  -1.9739   5.9185   6.2945
-#&gt;   -0.3824   2.3342   5.6595  -3.6659  13.7773  -5.9059  -3.7858   3.5306
-#&gt;   -2.4134  -1.4280  -7.1424   2.0948   2.2874  -1.0853   5.7903  10.8578
-#&gt;    3.2607   0.0906   0.4323  -3.3052  13.3146  -0.6935   5.2667  13.1755
-#&gt;    1.6097  -0.1559   1.9115   5.1168   6.6133  13.6786 -12.4811   5.9716
-#&gt;    7.3616  -5.4578   2.2418   8.3101   8.4899   7.1793   4.6639  -8.8759
-#&gt;    1.4888   3.3524   4.0891   7.2668  -7.6339  10.0442   3.4466   1.9793
-#&gt;    2.9624  -0.7599  -4.4550 -16.5763  -7.2150  -9.3511 -14.4847   3.8877
-#&gt;    5.4535   2.1113   0.6556   8.3213  11.4949   4.0772  -0.9157   6.0573
-#&gt;    1.3422  -2.3339   2.4545   2.0971   8.9639 -10.1366  -1.9312  11.8052
-#&gt;   -1.4407  -5.1037  -0.8735  -7.5622  -6.1879  -0.0690  -6.2049   6.1181
-#&gt;    1.4357  -3.5222  -1.9755   9.4500  -1.6771   4.4376   0.3270   3.4174
-#&gt;    2.5677   0.9643  -0.4635  -1.1444  -5.9775 -13.5607   9.5219  -1.5825
-#&gt;   -3.4731   4.3262   7.8018  -1.1242  -0.3613  -4.4380 -17.4636  11.9620
-#&gt;   -4.2367  -0.5903  -4.0691   4.3360   6.9549   5.8410  -7.6657   5.2381
-#&gt;    0.2624 -11.5379  -1.8299  -6.6303  17.5205   6.7721   7.8004   2.3381
-#&gt;   -3.8974   1.7662  -4.8138   9.5514  -9.4977   0.3022  -1.0301  -7.1786
-#&gt;    8.0423   3.0451  -5.4620  -1.9462 -11.0921   2.8610  -8.4385  18.6906
-#&gt;   -0.7504   8.3239   2.5793   2.7778  -8.9122   6.5091  -6.5768   5.6288
-#&gt;    4.3457  -1.8958 -10.0769  -3.2070   0.9191  15.5076   1.3231  -1.1706
-#&gt;   -3.5223   0.8839   0.7376   4.0680  -6.0472 -10.2105   2.2769   2.2336
-#&gt;    3.7377  -5.7436  -0.2260   4.7655   3.2836   9.0940   3.0380   4.9916
-#&gt;   -0.6492  -7.3590  -3.8933  -4.5155   3.0945 -17.9669   0.7992  -2.5830
-#&gt;   -4.1239   3.6299   0.7977   1.3964   0.1331  -2.0033  18.6986  -2.7015
-#&gt;    0.3150  -1.0361   4.5186  -2.9666  -1.6695   4.2238  -9.6508  -5.4337
-#&gt;   -2.3472  -2.5659   5.6535  -6.8268   0.4131 -15.7032   4.3612  -4.8776
-#&gt;    5.7511   1.5074   4.6386 -14.9877  11.4655  -3.4371   6.8665   4.7969
-#&gt;   -1.2161   2.2089  -1.8261   3.9251  -1.3776  -0.7568   0.8804  -8.0807
-#&gt;   -0.7410   0.6941   2.5636  16.1539  17.1623   4.5810 -12.6779   0.4777
-#&gt;   -0.5473   0.5947  -0.6145  -0.4223   4.8203  -2.9468  -8.0156  16.5324
-#&gt;   -2.8870  -0.4101  -8.1284   8.2239 -12.4741   9.3738   0.8917   2.3292
-#&gt;   -0.0762  -4.8836  -2.4877  -2.8065  11.9033  -6.2954  12.8217   4.6448
-#&gt; 
-#&gt; Columns 9 to 16 -10.8486  19.0040  15.5205   6.3897  12.7509  18.7076   6.7341  24.7735
-#&gt;  -12.0438  12.6606  -0.3553  -4.9212  12.0861   4.6795   6.6952   5.5794
-#&gt;   -4.6007   0.9672   8.7212  11.1086   0.4787   8.4304  -7.2332  13.4946
-#&gt;   -8.7545   2.6975  -7.5476  -0.5051 -26.9210   7.1168   7.2227   7.0064
-#&gt;    5.0392  10.5801   3.8094  10.7401   4.8611   6.8573  -2.9970  10.8328
-#&gt;    2.7897   2.6428  -3.0699  -1.1935   1.6653   3.4466  -6.4703  15.9608
-#&gt;   -4.0934 -13.0422  30.0203   9.2027  -0.0284  -8.5338  17.6166   5.7459
-#&gt;    0.6521   6.9662   3.7325  13.7420  22.0283   3.8541  -6.6730 -10.8181
-#&gt;    8.4981  -0.7829   1.4430   7.1065 -20.1671  -2.2740   4.0409   1.3867
-#&gt;    1.0489   7.8033 -10.2676   6.4569 -12.2644  27.0497 -12.3430  -4.7140
-#&gt;   -4.2990  -7.0005   9.4127  10.5111   5.6435 -16.3177 -19.4785   4.6994
-#&gt;    7.1485   0.1711  -2.3173   8.7568  13.6645  15.3494  -2.7278  -6.3752
-#&gt;  -22.2274 -13.7903   2.3464   9.1934  12.2973   1.7982  -8.8251   1.1781
-#&gt;   -1.8933 -11.6445  -5.1614   2.8326   7.9728  -9.9083   1.7724   6.6256
-#&gt;   -0.4213  -2.8628  -9.6671  12.5749  -0.2841  17.4236 -15.0692  12.7773
-#&gt;   -2.8915   4.1267   3.7853  -2.7139   6.6256  11.3734 -20.9926  -4.2265
-#&gt;    2.4341  -8.6435  -2.8338   0.5613   7.2049   6.5807  -2.2237  -2.7587
-#&gt;  -10.2696   0.0926  -3.1129 -10.9694   7.9198  -8.8719  13.5763  -9.7744
-#&gt;  -15.7203  -4.0138  12.2160  -7.0447 -18.8194 -24.3269  12.7430   9.2565
-#&gt;    1.8916   2.4190   4.8938   8.2787  25.5384  11.2692  -9.0044  -2.9264
-#&gt;    4.7253  -1.1566  -7.7629   6.2313  19.2309  12.9958  -1.3933 -23.9209
-#&gt;    4.0398   3.1965   1.0156   1.8936   0.3614  -6.0729  -5.9055  -5.6367
-#&gt;   12.6499  -8.3206  -7.9043  -2.4800  19.1821   5.0694  -7.8344 -22.8667
-#&gt;    1.9533  11.7377  11.7026  -3.2078   4.5977  -9.8745  -2.6903  -0.1726
-#&gt;    0.5964   2.2094 -20.7317  -7.7429   8.8774  -9.4002  -8.3394  -5.2582
-#&gt;   -7.7434   2.7373  -0.4027  -2.3704  12.3224  -3.0172   3.5563 -13.3000
-#&gt;   -6.1673   2.1377  -9.6263  -7.3032  -8.1092   4.8288   0.9269   6.6680
-#&gt;    7.1307 -11.4082  -1.8733  -3.4725   0.7833 -11.1207   1.6904  -3.3928
-#&gt;  -13.8459  -8.0944  -4.5687  -1.3178 -12.7660 -14.1142   2.3761  -1.5851
-#&gt;   -4.4088  -4.7370 -13.4434 -11.3929 -10.0721   0.3293  14.4283  -5.7123
-#&gt;    1.6628  -4.8135   4.1967  -3.6642   2.6072   8.6424  -5.3487  -5.5875
-#&gt;  -15.1982 -16.2075 -11.1081   2.1837 -15.1037   2.5881 -16.0889  19.7138
-#&gt;   -1.3740  -0.5834  11.2036  -2.6599   8.2420 -12.5969 -12.3959   1.4875
-#&gt; 
-#&gt; Columns 17 to 24   2.0507   2.6598  -8.2677  -0.3819  -9.7047 -13.0155   0.8416  11.0928
-#&gt;    9.5006 -16.9117 -12.5434  -9.1153   0.4889  -1.8486   0.6586   7.6950
-#&gt;   15.1938   3.1147   0.5621   1.7471 -14.8611  -2.8270  -0.4675  11.0833
-#&gt;  -21.2756 -17.3433  -3.7241   2.3021   1.9242  17.5036   2.0321  11.3387
-#&gt;    4.0239  -4.6242   1.3642   0.7304  -4.2136   6.3788  -5.1951   1.0344
-#&gt;   -0.0384  -2.6888  -6.1012   6.7180 -12.0463 -12.4807  -5.0917  -9.4530
-#&gt;    3.7491  -1.2887  10.0114   0.4980 -19.6182 -11.8975  -6.1498  -9.0285
-#&gt;    3.5560   4.8208   2.8959  -9.5669  -5.5017   1.9822   8.5510  -2.1044
-#&gt;   -2.2116   2.3504   2.2195  14.9792  -9.9029  -1.9384  -0.5256   3.0722
-#&gt;  -22.9213  21.1300  -9.5968  13.4872 -10.8303   0.4232 -12.2590  -4.7347
-#&gt;   19.0331   2.7374   4.2416   4.2413  -0.5785  -2.3002  -2.3245  -8.5098
-#&gt;    3.4308  -4.5625   2.0586   6.0886   5.3807  -1.9928  -3.4943   0.4703
-#&gt;   -1.6577 -15.8600   8.2561   7.0638   1.7736 -16.2779   7.8722  -3.1787
-#&gt;   -0.3687 -16.4942   6.5590  -1.1787   3.5540 -14.2099  12.5565   4.2178
-#&gt;   13.0011   0.9513   4.4265  10.9985  -0.1402   4.5048 -13.1231   7.3450
-#&gt;    7.5692  -4.8010 -20.2641  10.6312   6.1431   9.6376  -9.9602  -9.9894
-#&gt;   11.8609   6.2776  -2.6604 -13.9893  -0.5079  -3.7566  -5.1813   4.9594
-#&gt;   -5.7975  -2.5242   2.6223 -15.3010   8.8385  15.4189  13.6916   2.9766
-#&gt;   15.4826  14.5025  -1.2235   9.1774  -3.8551  -1.9364 -10.3641  -6.1060
-#&gt;   -4.3016   0.8259  -4.8363   0.1938  -5.1897  -5.0624  -3.6538  14.0120
-#&gt;   -5.6653  12.6322  -1.3424   7.4415  10.4254   5.4954   5.7740   5.2493
-#&gt;   10.3612 -11.3761  -4.5315  20.6901  -4.6147  -4.6538  -6.1524  -4.4536
-#&gt;    1.1449  -1.2692   4.8713  10.5046   4.0514   1.0056   1.6884  -9.5293
-#&gt;   -4.5487  -7.4370   0.9980   0.2414  -9.4703   0.0699   6.6126   1.3606
-#&gt;   -7.5593  -4.8113   8.1871  15.3340  16.2282   0.7743 -10.5867  -0.0072
-#&gt;   -7.5182   2.6224  -0.3788  -3.3806  -4.6903  -6.0372   5.0801 -11.2094
-#&gt;   -0.7491  -4.1805   6.0432  10.6777   0.9205 -10.8444  -2.9452  -1.8480
-#&gt;   10.1078  -2.1582  -1.2274  -9.9255  15.0571  17.5290  -3.8803   3.5488
-#&gt;   -7.4319   5.0795  13.8641  -1.4426   1.3595   0.4998  -3.2554  -2.9333
-#&gt;   -3.0166  -4.6155  21.9036  -3.6327   0.2526   4.0147  -3.2307   2.0131
-#&gt;    2.7417  -0.6201   4.8743   2.8442   3.4621   6.0141   0.4053 -16.9837
-#&gt;    0.8861   1.6744  -1.4065  14.6046   1.5714 -24.4373   0.3676  -3.4827
-#&gt;    4.0054   0.5725   8.7190  -8.5844  -1.3806  -6.2709  13.3831  -4.2304
-#&gt; 
-#&gt; Columns 25 to 32  -4.1254   9.3742 -11.9406  19.7302   6.2971   2.7894  13.8389   6.6408
-#&gt;   -1.9426  -1.7817 -12.3767   1.1051   3.3208  -1.9366   2.5308 -11.9070
-#&gt;    8.8551  -2.2768  -3.4851  -2.1931   4.0168   3.8313   7.3140  11.3613
-#&gt;    5.8380   1.4407 -14.6187  -6.7719 -12.8975 -12.0475  -1.8910  -8.9767
-#&gt;    1.2515  -0.0015  11.6763   6.6943   3.9679  -4.1267 -11.1167   6.0731
-#&gt;   -7.4304  -0.4224  -1.8240  -6.7562   3.3597   3.2047  14.7926   3.0720
-#&gt;   18.2255  -5.5199  12.2591  12.3209 -10.4560  -6.3152   0.1159 -13.3165
-#&gt;    3.0514  -6.1623   1.4615  -2.6432  -3.0623 -16.2089   3.5304  -2.6365
-#&gt;    1.8521   3.1686   1.3424   0.4103   7.6215  -2.8771   5.7098 -15.4748
-#&gt;    7.0573   8.4287 -15.0557  -2.5131 -13.4608  -2.3850   1.0915 -15.5075
-#&gt;   -7.8992   0.6289  -4.1602   5.5693  -4.8073  -8.5766   4.1574  -1.1716
-#&gt;   -7.6856   4.9891   0.0264   8.6593  12.1591 -10.1124   4.3597  10.4212
-#&gt;   -2.5367  -9.9828 -11.1830   3.8636  -6.6999  -9.8037  14.4445  18.1645
-#&gt;    3.0433   8.5416  -6.2895  -0.2634   7.1224 -22.0405  -3.1111   5.2978
-#&gt;    2.5170  -4.6569  -3.9122 -10.7506  13.1106  -1.4986  13.1161  14.7422
-#&gt;    1.7151   5.4397  -4.4998   0.5734  -3.2140   8.3545  -6.4990 -22.1587
-#&gt;    1.0871  -0.3733   6.6648  -1.9736  -3.1345   9.0372  -7.9000   3.6416
-#&gt;   -5.7346  -5.5697  -7.7569  -6.2873   4.6999  -4.1704   1.8441  -8.9156
-#&gt;    1.9548   6.3252   3.0557  -9.0061   5.7293  -4.4918  10.9426  -2.5873
-#&gt;   15.6885  -2.0274 -10.3964   4.3669   5.7090 -10.1337  -2.0274  -5.9175
-#&gt;  -10.9127   8.2897   1.2689  -2.7881   5.2909  -7.6923  -7.8837   2.5822
-#&gt;  -12.6344  11.2309  -6.9455   4.0729  -0.8880 -14.4394 -11.9550   5.4840
-#&gt;   11.6747 -18.4813  -0.9224  14.7047 -13.1532 -16.9584 -14.5604  -3.2588
-#&gt;    7.1224   7.6081   7.8043   6.9435  -4.3951  19.4718   0.1496  -0.0237
-#&gt;    3.6916  -3.1680 -14.2709  12.8420   5.4446   3.6627 -16.5807  15.2178
-#&gt;    5.9516  -3.0243  14.8711  -3.7670   6.9118   9.2722   8.1439   7.4384
-#&gt;   -5.9544  -2.2944   6.3805  -9.6320  12.2830   6.9966  -4.4074   0.8192
-#&gt;  -18.1687 -16.2847 -10.2525  -7.6339  -7.2074   9.0036 -15.0035   4.3509
-#&gt;    1.0724  -6.5366  11.6397  -5.2278  -2.8765   4.4298   5.9241   6.1974
-#&gt;    3.5181   1.3551  -1.8297  -4.7495  -8.6474   3.3282  16.5622  -4.5502
-#&gt;   -8.1147  12.4193   6.6285   7.0230  -7.9623  11.4584  -7.5197 -14.5441
-#&gt;    4.9385  -5.5366 -15.0028   8.5349   0.1998 -13.5030   7.5491  10.4168
-#&gt;    6.9734  -9.1558   2.4214   2.2550  -6.5530   0.7602   9.4171  -5.7679
-#&gt; 
-#&gt; Columns 33 to 40 -16.2546 -23.7417 -17.0193  -1.3142   2.8238   7.1652  -5.3280  -1.3377
-#&gt;   14.1420  -1.5059  22.5524 -21.2378  13.9141   8.4371   9.6989   5.8117
-#&gt;   -7.3609  -3.2967 -12.9083 -11.0725   4.1133  11.0140   9.3464   7.2597
-#&gt;  -10.0718  16.3289  -1.2712  -3.5266   5.6203  -5.0587  -0.1206 -14.1769
-#&gt;   -5.1636   5.6576  10.7469  20.5651   8.4264  10.4495  -3.8374   5.2713
-#&gt;   15.6951  14.4955 -11.6119   9.4762  -3.1124  -2.3191   2.1196   3.5569
-#&gt;  -10.7368 -10.5437 -12.2213  10.9230  -4.4011  -1.6173   0.0974  -0.5739
-#&gt;  -14.4136 -15.9359 -10.0872  -3.8252  16.9249  -4.7211   7.9440   1.1553
-#&gt;   -3.7608  -0.2942 -17.8180  -3.6190   8.0015 -14.3660   1.6582 -10.8504
-#&gt;   21.1495  16.3938   9.4025   1.9902  -3.1828  10.3133  -0.6980 -12.5243
-#&gt;    3.1599  -1.0148 -10.7063   4.5622  -6.2987 -11.1163   8.4843   1.3144
-#&gt;    3.5440  13.5956   5.0636 -11.6090   9.5306  -3.8764  -3.9018   7.6265
-#&gt;    5.8936   0.2157  -4.4326  12.8327   8.0861 -25.1949 -14.3138 -12.4501
-#&gt;    5.9666  15.9317   3.8190  -4.7217  11.1876   5.2509 -10.9786  -0.5133
-#&gt;    0.6909  18.5410   4.4026  -8.1224   8.0649  18.7591  18.1166 -18.0383
-#&gt;    9.0911  14.8643  11.4708   4.9207  -8.1068   7.9500   4.9009  -6.7330
-#&gt;    9.7842  -4.6255  20.4290  -4.4621   1.0605  -4.9756   0.1691  15.5058
-#&gt;   -2.8511 -16.5230 -13.0380  -6.8763  -5.0870   5.3321  -3.4553  10.2239
-#&gt;   -4.1570 -19.2809 -12.6503   4.1567   6.9719   5.7000  -1.0137   5.4218
-#&gt;    3.9844  23.7523 -20.5564   2.7967  13.0207   1.8161   4.7416  -9.0043
-#&gt;    0.8970 -10.7307   9.0940  10.2688   1.0261   2.0990  -3.9108   3.1225
-#&gt;   13.4368 -10.2608   8.3270  -0.6738   3.3290  -3.8392  10.7277   0.5851
-#&gt;   -2.8274  21.8511  17.5014   7.3997   2.9204  -2.4399  -3.8114  -6.8817
-#&gt;  -12.0053   9.8954 -13.6067  10.2075  -0.5364   4.7130  -2.0404   2.1682
-#&gt;    7.2322   1.0930  -6.5927   1.4633  -1.6856  11.1873 -12.6404  -4.7624
-#&gt;   21.7513   1.6566  -7.0826  -9.0371 -14.3815 -19.6564  -6.0599   8.9391
-#&gt;   -3.6946  -2.1719 -14.3045   3.9593 -11.4235  -0.6905 -10.8632  -6.2279
-#&gt;   -7.9132   4.1077  -7.4057   3.9989   2.7405  10.1765 -11.8403   0.2625
-#&gt;  -12.8951  -8.4539  -3.7227  -8.7636 -19.2086 -20.4250 -23.3362  -6.3952
-#&gt;    9.0696  -1.4293  -5.9031  -7.8074 -13.0599   0.1288  -5.4286  -6.5128
-#&gt;    3.3542  21.9397   1.2557   0.1547  18.7915  -2.2035  12.2932   4.3940
-#&gt;   19.6359  -5.6027   3.4627  -4.9635   4.0945 -17.2492  -8.7759  -7.4648
-#&gt;  -14.4994  13.8843  -3.1236   9.1751  -7.8411  -6.0328  -1.2764   5.8990
-#&gt; 
-#&gt; Columns 41 to 48  12.4684   1.5413  -0.9921  -2.4225  15.1364  11.2646   9.6565  18.3759
-#&gt;   -2.2483  -9.2219  -9.1508   8.3533  -7.1090  -0.2778  -0.5484   4.5380
-#&gt;    6.2917  -1.6471  -0.3049  -6.6333  -9.6016  11.6996  -4.1160   8.6289
-#&gt;    6.0482   2.3693  -8.5061   6.0234  -1.0929   8.3549  -6.7385  -3.8282
-#&gt;    3.0921  -0.1369   2.3729  -4.0608 -12.5768   6.9210  -2.2395   0.3691
-#&gt;   -3.9794  -9.2462  -3.3190  -5.6941   5.3892  -5.3677   0.8627   1.9602
-#&gt;    5.0847   7.7314  -8.0740  -0.1435  12.0844  -1.2089  18.1352  -6.2714
-#&gt;    4.8262   2.1688   6.4618  -0.6966  14.3354  -0.1032   7.7291   7.0555
-#&gt;    3.2609  14.0137  -2.2330 -13.2423  -4.2445   1.9784  -8.1714  -6.3727
-#&gt;   -4.8374  -6.5268  12.9251  15.1205  -8.1213  -1.3245 -10.2242 -14.8910
-#&gt;   -6.9816   8.6871  -3.6590 -14.3809  -9.3692   2.6268  -1.2327   8.6051
-#&gt;    1.6206  13.5033  -5.4795  -2.0907  -5.7077 -14.7999  -5.7586   4.2999
-#&gt;    8.7203   3.3506 -14.3992  -5.7758   3.1561 -16.5495   5.3469   3.9362
-#&gt;   -4.2642   8.1815   4.7881   2.3056  -6.4157 -12.4372   8.1750 -13.3807
-#&gt;    9.0167  12.7056  11.6007 -11.0262   4.2971  -3.2547   1.4945   4.9691
-#&gt;  -16.8074  -6.4873   3.3881  13.5651   3.2480   2.2724  -7.9075  -0.5044
-#&gt;    6.9120   5.2717   2.6873  10.3306  -8.6791   4.5743  11.1047  -7.8470
-#&gt;    3.4473  -9.3260 -13.5324  -2.1988   3.6893   9.5879  -2.7626  -3.1742
-#&gt;   -1.0179  15.3903  16.5882 -15.5050  10.5391  16.1257  -4.2574  -0.6911
-#&gt;    0.2554  10.1870  11.1322  -0.8859   7.5715  -6.3537  -6.4601   8.0480
-#&gt;    1.9401  -4.3159  13.1100   7.9316  -0.6930  12.7543  -0.0876   2.2636
-#&gt;    1.8901  -1.4288   1.4151  12.0978  -8.4133   1.3448 -13.1126  -4.5250
-#&gt;   14.9244  15.4586  22.7820  14.1672  -0.7533  -3.9422  -0.6175  -6.9593
-#&gt;    2.2502   8.3303  -2.0124  -6.4444  -1.9069  -9.1721  -9.1885   0.4218
-#&gt;   -1.2529   4.6876   3.5248   0.3786   6.1484  -2.2346 -12.1829  25.1356
-#&gt;   -3.8372 -10.7091  -7.9372   2.7616  -1.8327  -8.7472   3.2606 -12.9457
-#&gt;   -0.8361  -8.9647  -5.8202   0.8926   5.8276   0.1345  -3.0728   7.0463
-#&gt;   -6.0486  10.4944  -4.7435  -7.4233  -1.6023   2.5923   0.8755   7.7830
-#&gt;    6.3221 -10.5719 -15.1768  -0.5792  -4.6111   5.8933   1.8873   1.3806
-#&gt;    4.9719   2.2166  -5.4620  -3.4779   8.0047 -12.0724   4.7335  -3.8603
-#&gt;   -5.3593  -5.7105   7.5636  13.7879  -7.3514   4.2796  -1.3703 -14.9948
-#&gt;    1.2680   6.3434   4.6993  -5.8200   6.6031  -3.4956   5.4723   8.7672
-#&gt;   -8.4542   3.9480  -2.9003  -7.2767  -2.1211 -11.5426  -1.4058  -4.2086
-#&gt; 
-#&gt; Columns 49 to 54   6.9345   6.8547   3.0552  -3.7366  15.4697   0.1524
-#&gt;    6.2922  -0.7933  -6.4809   2.7974  -5.4409   0.0367
-#&gt;   -3.3257  -0.0392   2.6232  -1.5331  10.6490  -2.2028
-#&gt;    4.5382  -9.6036  -9.4649   6.2032  -3.8240   1.4407
-#&gt;   13.5199   6.7690   2.6443  -3.3702   5.4460  -3.5104
-#&gt;  -16.6280   2.0464   1.1608  -5.5702  -3.6791  -5.3750
-#&gt;    6.2361  -2.5801  -0.8141   5.3224  -2.5808  -0.4248
-#&gt;    6.5722   4.9813  13.0259   3.5532  -0.2108  -0.3481
-#&gt;   -8.5610  -4.7566   0.1911 -19.4216   8.7068   1.1852
-#&gt;   16.1358  11.3908   0.7024  -5.9319  -4.2298  -3.7900
-#&gt;    0.7456  -2.5285   2.6793   0.9100   4.2093  -1.8958
-#&gt;  -14.6449  -7.2198   2.0372  -3.0012   0.7265  -2.1829
-#&gt;    4.9887  -4.5485  -7.1482   0.7946  -0.2220   1.1991
-#&gt;    7.3561  -6.5854   0.4942  -9.0523 -14.6778  -0.6867
-#&gt;    1.8704  -5.6577  14.4754   0.7415   2.1388  -1.6480
-#&gt;    1.0187   7.2594  -7.2277   2.4263  -2.5897  -0.5953
-#&gt;    5.8552  -3.0989  -4.6741   2.1087  -6.3685  -5.4360
-#&gt;   -4.7954  -3.0337  -2.6234   9.1441   4.1686   1.3206
-#&gt;    0.5679   9.4099   5.8011   0.1202  -0.3942  -0.2886
-#&gt;   -2.6996  -0.6632   0.7895  -1.5725  -0.1924   0.0885
-#&gt;   14.5838  10.1875   0.2721   3.2883   1.1273   2.9354
-#&gt;   -8.6839   4.6683   4.7676  -4.0084  -3.8531  -2.5571
-#&gt;   -2.7880   3.9429   3.5918   2.5719  -6.0650  -0.8717
-#&gt;  -10.4243  -2.6173  -0.4466  -6.1775  -4.4496  -1.5418
-#&gt;  -13.9890   3.4637 -13.8115   7.9119   2.7131   0.7817
-#&gt;   -2.7265  -7.1634   4.9050   0.6368  -4.2845   0.3284
-#&gt;    6.9923 -13.7115   2.1192  -0.0309   3.4392  -0.0764
-#&gt;   -2.5682   6.7601 -11.6196  -0.8245   5.3279   2.1712
-#&gt;   -1.3624  -9.8982  -3.0243   3.6376  -3.2003  -0.3407
-#&gt;   -1.5863  12.3002 -15.6227   9.8221  10.8348   1.3270
-#&gt;   -0.2349  16.1233   7.4516   5.6809  -5.4817  -1.3903
-#&gt;    5.5133   7.0013  -9.5816   2.0696   2.0649  -0.7826
-#&gt;    4.8998  -2.3120  -3.3847  -3.6179  -0.9100  -0.9852
-#&gt; 
-#&gt; (17,.,.) = 
-#&gt;  Columns 1 to 8   6.1443   6.9194  -1.0562  -4.3711   4.4654   0.5805  -9.1360 -14.1596
-#&gt;    2.9841   6.6422  -2.7177  -1.1271   0.7274  -5.4302   2.3300  -5.4333
-#&gt;    1.9327  -5.6299  -6.9753  -3.4866   7.1639   4.7113  -8.2368   9.1127
-#&gt;    4.4792  -8.6570  -7.9390 -12.0866  -1.9326   4.4371 -24.3383   4.0757
-#&gt;   -0.5831  -6.2944  -0.5927  -0.3604  11.4115   2.1684  -7.3916   3.0542
-#&gt;    4.4370  -0.6599   3.2872   9.1564   2.6958   6.7784  -1.4288  -2.8477
-#&gt;   -2.1455   4.1315 -10.8879   1.0214   7.5736   2.6349 -12.0602 -16.0574
-#&gt;    1.5223   4.4186  -2.2786  -1.9884   0.6818 -11.9779  -7.7711   0.4184
-#&gt;   -0.6403   9.8892  -0.0395  -1.9764  -3.8331  12.8968  -8.1312  12.7658
-#&gt;    4.0269   4.3379  -2.1609 -17.5246   8.5467   2.9727  -6.4218  -1.9910
-#&gt;   -3.8391   2.7261  -0.3629   0.9506   2.2703   8.2623  13.7309  -7.8259
-#&gt;    2.4645  -2.5920 -12.7258   0.0863  -2.0993  -6.0942   0.4621  13.7128
-#&gt;    2.4760   1.2588  -9.4273   8.8638   4.6503   1.9964   4.9189  -3.0370
-#&gt;   -0.5484  10.3669  -6.2906  -7.2585 -16.6554  -2.5892  -7.9406  -4.5755
-#&gt;   -0.7397  -8.5692   4.6649  -3.7718   0.4087   4.4101 -11.1031  -4.4849
-#&gt;    0.1002  -2.6376   2.6019  12.8042  -1.8125   2.6265   8.1862   2.4251
-#&gt;   -1.0834  -3.4857  -4.6050   9.3387  -5.0314  -9.9726  -9.3422  -8.8175
-#&gt;   -1.5390   0.1313   9.5713  -3.0821  -2.9337  -3.8771   3.7128   4.3213
-#&gt;   -7.7632   6.8190   6.7207 -13.4798  14.6956   3.4435  -2.8333 -21.4868
-#&gt;    3.2687  -4.6805  -4.7812  -3.1980   1.6897 -13.9128  -9.4960   3.1243
-#&gt;   -1.6829   3.7035  14.6709  -2.2077  -3.2603  -1.1370  10.1571   1.2648
-#&gt;   -1.0883  11.0762   1.3416 -12.7511 -21.1933   2.9799   1.6736  15.2179
-#&gt;    4.4480   2.8421  -5.4820  -4.7026  -5.0694 -11.4562 -23.3585   7.1575
-#&gt;   -2.9082  -1.0902  -8.4298  -1.1375  -2.0630  -7.2376   8.0495  15.6140
-#&gt;    7.1673 -10.4637  -7.5146   0.0550  10.0286  -4.0913  -6.3031  20.5785
-#&gt;   -5.4081  -0.1117   2.7705  12.1973  -8.7742  -0.2077   8.2314  11.1816
-#&gt;    1.1246  -5.8824  -3.4459   2.8865   3.2704   4.4080  -1.6483   5.7580
-#&gt;   -0.6552   3.7956  -3.3537  -1.6918   3.8882   4.0538   3.1555  -8.2176
-#&gt;   -2.3748  -6.2723   5.2245   4.1894  10.9072  -3.6395  -0.2955  -1.0144
-#&gt;   -3.5075  -5.5204  -6.1432   0.8693   9.1727 -10.3702   6.5089   3.1676
-#&gt;   -2.1095   6.3571  -2.2335  10.9228 -13.9975  -2.3345  22.6371  -9.1804
-#&gt;   -0.1568  -0.9048   5.5829  -2.9037  13.8580  -8.1319  -1.9552   3.5291
-#&gt;   -3.8153  -0.3087  -7.4113  -8.1043   7.0150   4.7633  16.0928   3.2886
-#&gt; 
-#&gt; Columns 9 to 16  -3.1810  -3.2627   1.0612   4.0951   1.5249   5.4468  -0.3204   7.8181
-#&gt;   -4.6727  -5.7859   0.5801   3.7623   0.3317  -1.4330   1.5571  -0.2559
-#&gt;   -7.3271   5.7157  -5.1694  16.8466  -2.7004   2.6971  -3.5376 -10.4199
-#&gt;   -6.5211  11.6065  -8.2419   5.3917  -1.8777  -7.2023   5.5177   7.2373
-#&gt;    6.9915 -10.4649  -8.8021  -0.0459  -0.1659  -1.2228  -1.3253   8.1643
-#&gt;    3.4482   6.1002   3.7333  -9.9588  11.2046  11.5108  -6.2545  -9.3857
-#&gt;    8.2876   1.7361 -12.5369   2.4079  16.4401   5.7171   2.8861  13.5347
-#&gt;    3.4647 -11.3662 -21.3331   7.0156   6.9562   6.3768 -10.0717   0.1385
-#&gt;   15.1363  16.1886  -0.6350  11.5681  -6.6108  -8.0296   2.9866  -5.0846
-#&gt;    3.5970  18.8378 -11.3572  -4.2139   0.3005   7.6319  18.2322  -4.4625
-#&gt;   -0.8092  -5.0381   4.6445   1.8130  -9.0166   8.6400   7.3903 -17.6204
-#&gt;   14.0589  -4.6007  10.7953  -7.4705 -12.3759   9.3497  -3.1502  -5.5020
-#&gt;   -6.2404  -2.1810  -2.7716   0.7559   2.4455  11.5034   4.8724  -9.3390
-#&gt;    1.5732  -4.2609  14.5878 -21.0616   2.1890  -5.8857  -4.9004   8.9875
-#&gt;    1.0933  -3.6500   3.7066   5.4885 -13.8248  -0.8643 -16.2240   7.1034
-#&gt;  -17.7197   5.6153  12.2955  10.4517  -3.1934   4.9312   5.7033  13.4370
-#&gt;    1.3269 -10.6556  -6.0935   6.4030   2.2019  -2.4704  -3.2745  -7.5166
-#&gt;   -6.9070  -1.5901  -3.8122  -5.1475  -3.1792   1.8839  -6.9131   7.7553
-#&gt;    9.1986  -4.2436  -2.6430   4.5851 -18.2240 -13.6614   2.8320  -6.5608
-#&gt;   -3.7126  -0.7393  12.5292   5.0482  -8.4647  -1.1843  15.3833 -12.8543
-#&gt;  -13.4071  -6.8954  -6.4824  -6.8973  -8.1641   7.5570  20.1569  13.7861
-#&gt;    7.7510  10.6749  -6.2694   0.7353   1.9931   3.6837  -7.3125   1.7338
-#&gt;   -0.2942   6.2285   5.4998  -8.2894  14.3482  12.4478  -3.6528  -4.6218
-#&gt;   13.3558  -4.2591  18.4636  -8.0938   5.3546   8.5646  -4.5114   3.6857
-#&gt;   -1.7329 -15.8077  12.7673 -10.2201   9.5951   8.8423 -11.7141  -9.4775
-#&gt;   19.4649  -6.8292   9.3490  -7.6591   8.2599  -1.1958  -1.2143   9.2001
-#&gt;   -9.5506  -3.6073  14.2369  -3.8460  -1.9205   5.9566  -4.4946  -9.4718
-#&gt;    2.9816  -0.1607  -3.5225  14.3550  -7.2591 -11.0014  -2.8438   3.6171
-#&gt;    3.2333  -0.8371   2.9425   2.9499   2.0615   9.2706  -8.6544  -7.0034
-#&gt;  -16.4620   2.1289   0.1354  -8.4689  -1.2499   4.8122   2.5817  13.4833
-#&gt;   -0.5567   1.8226  -2.1932 -11.4949  -8.9891   7.0014  11.6330   0.0430
-#&gt;  -15.2057  -1.4595 -11.9441  16.5283  16.2629  -5.4902  20.6133  -0.9546
-#&gt;   -7.3588  -4.2087  -0.1877   4.6742  10.1569   4.0011  -0.8341   8.5255
-#&gt; 
-#&gt; Columns 17 to 24   4.7876  17.2720   3.3702  -1.0435 -10.8395  -8.1406  -7.5049  -5.5539
-#&gt;   -9.9691  -1.1988  -0.3827   8.6837 -16.2213 -13.0124  -2.5975  -7.0430
-#&gt;    4.5661  17.4118   7.9166   2.5864 -10.3547  -1.7683   9.1435  10.5543
-#&gt;  -23.2334  10.3631 -25.5210 -12.3129  -4.8079   9.2390  -4.7016  -1.0820
-#&gt;   -2.2913  -9.7322  -9.6260   5.5965  -2.5656  -0.5326  -6.9418 -10.7495
-#&gt;   -3.2222   8.0219   5.9834 -14.8087  -6.2356  -2.7075 -13.7779 -38.2064
-#&gt;    1.0871   0.2784   4.6760  -6.9322  15.0898 -14.0151  -7.9154 -12.2438
-#&gt;   20.5265 -13.0739  -4.6356 -14.1243   1.4672  -5.4715  -0.2433  19.5680
-#&gt;  -12.5312  -2.4462  12.4738 -14.6028  17.5852   3.3055   0.7279   2.9435
-#&gt;   -9.0759   7.4424 -13.4984  -3.4864  10.9933  23.4347 -12.4106  -6.8116
-#&gt;    0.2945  -2.3298  -7.9317  14.3155  -0.0490  -8.9765  -2.0218   7.3739
-#&gt;  -13.2542   5.0205  -2.1731   1.3804   1.0738  -4.6849 -13.5458   2.3084
-#&gt;    7.1460  -7.8515   2.2562  -6.2295  11.3502  -1.0303  -5.2992  15.8323
-#&gt;  -15.0881   0.1767   6.4603   2.2481  10.8668  -1.9336  -3.0059  -3.9025
-#&gt;   -3.4863   0.9929  17.7379  -0.4459   4.1941  -0.3456   7.3584  -9.6679
-#&gt;   -7.0207   0.9989 -17.1677  12.6743  -3.4119   5.4517   4.1034  -7.5619
-#&gt;   -6.6729  -7.0634  -8.5295  -6.7227   7.5573 -20.6982 -17.9593  -6.2486
-#&gt;   -0.9564  16.8376 -17.7000   8.7197  -8.0258   3.5434   3.8272   8.6623
-#&gt;    4.7000  -2.5202  -4.5670  -4.7674  -4.1558  19.1030   0.3822  -3.8329
-#&gt;   -4.2104  18.9013  -0.6283  -5.7242  -0.6763   4.5063   4.3415  -4.9077
-#&gt;   -6.0676  -5.2026  -9.6267  16.3435  -8.3280  16.4242  10.7939   5.0139
-#&gt;   -6.8391   1.9071 -11.6442  21.1259 -11.9573  -2.9056  -0.0309  -0.9524
-#&gt;  -15.3838 -14.4158  -5.6303  10.7407  18.0120  -5.8079   3.7584  -4.3958
-#&gt;   12.5217  -0.7652  -7.5056   1.5211 -10.9556   9.6789   1.6204  -0.5635
-#&gt;    8.5732  20.9318 -13.5805   8.2473  -2.1256  13.2180  -0.0080   2.3703
-#&gt;   12.6771  -0.0184  15.6403   1.7141  19.6619 -13.3052  17.5606  -7.7566
-#&gt;    2.2863   0.1965   7.5713  -2.2330   0.8285 -10.7955   3.7508  -3.1906
-#&gt;  -12.9624  -5.9707  -4.7057  -9.0526 -14.0223  20.6805   0.6815   6.7069
-#&gt;   11.4943  -6.2175   7.6624   8.1907   3.7794   3.9137  16.4800   9.7840
-#&gt;   -6.1348   4.4264   4.3409  11.0234  -1.3618   5.7269 -19.5008  -0.8807
-#&gt;    3.5661  -2.3396  -6.4453   4.2679   7.8474  10.3957  -0.1799   1.4265
-#&gt;    3.1961  -9.3156  25.0814   8.3016   9.0736  -3.5149  -4.4687   4.5316
-#&gt;   -2.1196   1.2412  -5.3532  -1.9141   1.0356   2.0371   3.8051   3.0251
-#&gt; 
-#&gt; Columns 25 to 32 -12.7784  -3.1151  -4.1067 -17.6687 -13.4600   6.3249  -9.3811  -7.3388
-#&gt;   -0.7619 -11.6666  12.7568  -7.8023 -13.0570   0.8027  15.5756  -0.4485
-#&gt;    0.7670  -3.6165   1.0185   6.6977   0.9419   0.6155  -6.7409   9.6993
-#&gt;    5.1670   0.1816  -5.8841  10.4241   8.7812  -0.6064  17.1802   5.0143
-#&gt;   -4.9780  -2.7439 -14.4820  -3.7913  -4.8708  -2.0714  -3.2589  12.7400
-#&gt;  -11.4598  -1.2145 -10.0119 -10.2576  -8.2680 -11.5806  -3.2187  -2.6170
-#&gt;   -2.6593   3.2811 -11.8429   6.7609  -2.9523 -20.6242   1.8380   5.0370
-#&gt;   -0.6210 -23.1475  -2.7063  -5.0878   3.5943  -1.2283  -3.5178  -7.9973
-#&gt;    3.6137   9.2104   9.5769  -2.2702  11.1843  -0.4034   1.9130  11.4164
-#&gt;    7.6460  -0.3827 -20.7806  -0.2912  -2.5550   2.5483  -0.9074   3.8046
-#&gt;  -10.1698  -1.4984  -7.6352   1.1972   3.4435   4.5729   2.1873   2.6382
-#&gt;    2.0410  -4.2316   1.8286   4.2103  -2.9651   6.1363   9.6191  -2.9329
-#&gt;   -2.6176   1.7511  -8.1758  -4.8099  -9.2014   3.0600   4.1247  -6.5042
-#&gt;   -0.7695  -2.7357   6.5961   0.2550  -8.4879   5.1099   7.8306  -3.8784
-#&gt;    7.1554  -8.0673  -4.7431   4.5292  12.5880   0.7548  17.6900  -0.6163
-#&gt;   -9.0725   9.8556   5.5272  -7.2525  -4.6794  -9.3108  12.0713   6.8277
-#&gt;  -15.2457 -11.3464  -0.8299  -5.3889  -7.3931 -14.3264  -2.2502   4.6353
-#&gt;   10.7940   4.0557  14.0390  -9.8524   2.6743  -4.7332   5.4225  -1.7788
-#&gt;    5.1810  -9.9234   5.5246   1.2374   7.2331  -0.2130  10.8272  -0.3448
-#&gt;    4.7325   4.7821  -2.9912   4.3711  -2.0756  12.5127  -0.9461  -8.1639
-#&gt;    8.8977   8.8578  -9.6155  -7.2892  -4.1856   4.3503   5.4361  -3.7193
-#&gt;   -3.5420 -11.7217   5.6519  -1.5706   1.9119   4.2155   4.5332 -15.1637
-#&gt;    9.5145   6.2907  -9.3909   7.1549   4.9775   3.4711   7.4276   7.5582
-#&gt;   -4.8574   7.1038   5.1234   8.3762  -4.2527  -6.3355   6.4191   1.9055
-#&gt;   10.1519  12.8104   4.5860   0.7293   0.0193  -1.5512  -3.6323  -3.6445
-#&gt;    9.6038   3.8469   3.8026  -2.4492  -5.6320  -7.9020  -0.5373  -3.5500
-#&gt;   -3.2464   6.0656  -0.9350   3.5552   3.2878   8.9093   4.0319  -3.2805
-#&gt;   -0.9716  -4.9932   1.2699  -2.3636   9.6565   2.8870  -2.7157   6.6711
-#&gt;   -4.0200   2.8403  11.0463   5.4527  13.8217 -11.7949  -6.8832 -15.9980
-#&gt;   20.2676  10.8881  -7.9728 -13.7948   4.5471  -4.2364   4.6383  12.1830
-#&gt;  -13.1566  -7.2088   3.0935  -5.6241  -1.0032  -1.1888   5.7101   9.9611
-#&gt;  -16.2755  11.5573   6.1171  -1.1188  -9.2500   7.4123   6.8200  -2.3877
-#&gt;    0.3761   1.2744   1.5478   4.8301   3.0607  -1.7451  -8.4018   5.3828
-#&gt; 
-#&gt; Columns 33 to 40  -2.6798  10.7408   5.4904  -4.9847  -6.8072   5.9453  -7.2387   9.5456
-#&gt;   -1.6163  -9.3078   7.8399 -18.4102   7.3172  -3.7820 -10.7607   6.2403
-#&gt;  -10.8727   3.4745  -0.8042  18.6595   2.5602   7.4274  -2.0725  -3.4180
-#&gt;   -4.3281  11.4767   6.3640  10.9480  -6.1661 -18.5870  -4.1403  10.0234
-#&gt;   -2.4974   1.1080  -6.0992  10.2662  -2.3050  13.6306  16.0703   9.8415
-#&gt;   -3.2326   1.2055  -2.1907  -1.9525  -3.4973   8.5366 -14.1278  -2.0794
-#&gt;    1.5418  12.4918  11.6216   2.1373   6.5673   4.2851  -1.3019  15.2588
-#&gt;    6.4475   4.5054  -8.5098  -8.8409  -0.7175  -3.4654  10.5672   2.5838
-#&gt;    5.9579   0.1376   4.9462   1.3211   9.5206  -8.4694   0.2855  -4.4919
-#&gt;    5.8723   8.9215   9.1594  -7.8355   5.6397   2.0405  -0.8956  -3.4817
-#&gt;   -0.3586   0.7275  -0.6278   6.3590  13.4949   3.0733   3.7876   0.0623
-#&gt;   12.0649   0.3028   0.3616  -4.0420  10.4038  -0.6742   2.6561  23.8164
-#&gt;   -9.3511  -1.2811   1.8103  -9.9004   8.7360  -3.9421  -0.2291  15.3043
-#&gt;   -2.3802  -7.0159  11.3137 -13.9367   6.5566  -6.2087 -11.2825   7.4017
-#&gt;    5.6427  -5.7506  -0.9135   1.0892  -2.6045 -13.5775   4.7687   0.4022
-#&gt;  -10.2733 -16.6287  -0.3390  -2.6910   0.1103   0.9711   2.0200 -10.0394
-#&gt;    0.4027  -8.1790  -1.2274  -1.8224   6.4739   0.3178 -10.8276   5.8462
-#&gt;    5.6329   0.4834   1.0717   2.2724  -4.3132  -6.7506   1.2790   9.4726
-#&gt;   -6.6045  10.1189   6.9280   1.8852  -2.8820  -9.5778 -18.9174  -7.5127
-#&gt;   -4.6235  11.4260 -15.3983   4.8237   0.5430   3.1613  17.0547  -7.7714
-#&gt;    0.9165 -15.7357  -2.9726 -16.5025  -4.2553   8.5064   4.5877   6.8933
-#&gt;   -1.0045  -1.5503  11.9903 -16.7153   1.7860  -8.8464  -1.3527  -5.7166
-#&gt;    1.9460   4.4425 -13.1039   0.3578   8.0725   8.0274   9.0462 -14.6979
-#&gt;   -1.4275   7.5097   6.2892   0.8070  -3.7205   0.7155 -12.4836   7.3437
-#&gt;   -3.1733   9.4923 -12.6845  17.5650  -1.7871   8.4914  -5.2970  -6.5126
-#&gt;    5.3399   6.7639   7.6913  -3.0564   8.4512   2.6952   8.8310  -0.6163
-#&gt;   -4.9734   1.6869   3.8372  11.4282 -10.7808  -0.1684   8.9313  -4.8800
-#&gt;   -8.7450  -6.8279  -6.4785  21.0190 -10.2943  21.6078 -29.3649   4.0950
-#&gt;   -2.7746  -2.4019   1.7970   8.5955  -9.3389  -1.3879  13.5859  -5.6466
-#&gt;   11.1821  -5.2228  13.2041   1.8326  -6.4913   5.4415 -16.9786  -5.4153
-#&gt;   -2.8319  -4.7344   9.0530  -7.0999   4.7814  -6.2008  -4.4767  28.7513
-#&gt;  -19.1089  -8.6855  13.1355  -9.2310   6.0743  -9.0933  11.9112 -10.9186
-#&gt;   -9.4442   0.6115  -4.7476   4.9595   2.3222  31.3597  -0.4587  -3.3852
-#&gt; 
-#&gt; Columns 41 to 48  13.2335  18.4072   3.8452  -6.9916  16.0191  -1.4387   7.3699  14.8535
-#&gt;   -4.2993  10.6820   7.0353   1.3130  -8.7711  10.1553   4.1372  -0.5599
-#&gt;    3.2517  12.1267 -15.7250   0.9289 -12.1262  -4.4340  -5.4977  10.2717
-#&gt;    2.3578  -1.1384 -13.6929   1.9523  -9.0392  16.9055   5.2991 -21.7345
-#&gt;   20.1297   9.7540  -4.4430  17.1155 -10.7662   3.5038   7.8592   3.0781
-#&gt;    8.2474  -7.9042   2.5285   4.0943  11.3423  -2.9282  -3.7371  -5.7120
-#&gt;    4.6633  -6.2643  -2.6094 -16.2379   1.1435   4.4389  17.2909   8.6930
-#&gt;   -0.7847 -14.8175  14.3593  -0.9707  13.6857   8.9951  -1.7974   5.3307
-#&gt;   -2.7323  13.8403  -4.1151  -8.8232  -5.2754   3.0921 -12.9723  -7.8679
-#&gt;   12.0113 -14.4334  -7.5176  -4.4632   3.3497   4.9875 -13.5565  -1.4509
-#&gt;    2.6535   2.2753   3.7532   1.5156   2.9532 -11.4222  15.0870  12.4004
-#&gt;   -0.8589   2.4802  -6.1565  -4.2074  -2.5356 -13.0278  -3.9227 -18.4011
-#&gt;    6.0597 -10.3485   1.7872  -6.2897   0.9800   3.4572  17.1559  -0.8167
-#&gt;   -3.0977  -4.0392   5.5219  -7.0214 -15.3351  -7.9001   5.0116 -29.1513
-#&gt;   24.2113  14.1920   3.8048   5.9459  -8.5433 -11.8677  -0.5119   5.3183
-#&gt;   -1.5524  -1.9231 -10.8412  14.6299   6.4453  -4.2846  -3.5581   6.6648
-#&gt;  -17.1757  -4.2006  -2.9053  -7.5145  11.6744 -18.1325   5.4189  14.1761
-#&gt;  -13.5461   6.5545  18.4231   9.6028   6.9905   7.2333  -4.9760  -7.3707
-#&gt;    4.2127  -3.3927   8.6950 -22.2378  -4.6125  -7.6642   4.6457  22.9384
-#&gt;    1.9780 -16.4488  -4.6691  -0.0215   3.3830   1.5566 -12.8181 -11.4244
-#&gt;    4.7052   4.0998   3.6235  -2.1365  11.1460  -6.0001 -11.2796  -2.0067
-#&gt;   -9.5723   6.5852  -8.2466   3.4642 -19.0371  -1.4680 -10.6882  -6.0754
-#&gt;    1.5706  -2.1467  -8.1451   4.2954  -5.1098  -9.6729  -8.2277 -16.7589
-#&gt;    4.9486  -7.8326  -9.1857   4.9922  -6.8312   2.1194  13.4255  -7.9282
-#&gt;   -2.9973   4.8711 -14.2213  12.5082  -8.9272 -13.5373 -10.6331  -1.7991
-#&gt;  -20.9875 -15.0079  -0.1355  -4.0733   3.4722  -1.1602  -1.9319 -10.5716
-#&gt;   10.5513  -6.3437 -10.0204  -5.1811  -7.3454   3.2951  13.2194 -10.6875
-#&gt;   -9.0411   9.6437  -0.7267  -8.0317  -3.7791 -14.4714   3.3345   0.9388
-#&gt;  -12.0010 -11.0004 -18.0084  11.7813  -4.8742  17.4474  13.8801   2.0438
-#&gt;   31.7265 -13.4833  13.5332  -2.5016  -7.9255   4.0851   1.6553  21.2569
-#&gt;   -1.0274  -0.3764   3.9221   0.6137   4.0974  -6.4963  -6.7259 -11.1689
-#&gt;    7.8031   0.8675   5.7429   2.3580  -2.4164   2.3665   5.2437  22.7314
-#&gt;   -4.2878 -10.5383  -9.9768   1.3139  -9.4152  -6.3573   9.6883   5.9264
-#&gt; 
-#&gt; Columns 49 to 54  -0.2671   9.7701   6.0322   4.6811   2.9762   2.2722
-#&gt;  -14.4196  -1.0555  10.7776  -8.0051   1.9966  -1.7877
-#&gt;    3.2285 -12.2791   6.1325  -8.0227   6.4722   2.3322
-#&gt;   -9.7100 -12.2404  12.3004  -3.0280  -5.8205  -3.4416
-#&gt;    1.8904   1.2133   7.7791   2.6255   1.2190   0.2403
-#&gt;   13.7775  11.0335 -12.7964  -3.6531  -5.7224  -0.2613
-#&gt;    2.1140   0.6198   8.4620   2.8346 -13.2978  -4.7245
-#&gt;    6.4016   6.4218   5.0143  -7.5094 -10.5561  -1.5737
-#&gt;    5.6732 -21.9615   9.2310   6.7673  -3.2778   0.9200
-#&gt;   10.3036  -3.3432  -9.5236   6.6128  -3.7361  -1.9285
-#&gt;   -3.4186  -3.4549  -3.8103   2.1477   0.9592  -0.1993
-#&gt;   -4.8919  10.2478   8.8289   1.0471   5.5893  -2.8752
-#&gt;    1.7449   1.9779  -4.1833   2.3643  -0.7714  -1.5880
-#&gt;   -7.5838  -4.1429  -6.4595  16.2080  -8.8949  -4.2480
-#&gt;   -7.4641  -0.9506  -3.1015   8.1307  11.6222   0.3097
-#&gt;   13.7682   8.3304   1.3823 -18.3744   4.5722   3.6273
-#&gt;  -11.2872   3.8188   0.5564 -11.4159  -6.8905  -0.0975
-#&gt;   11.2198  -1.0655   6.9872  -7.0629  -3.6281  -0.3584
-#&gt;  -10.5131  -2.6208  -5.8299   0.5071   9.3178   3.8768
-#&gt;    3.3297   8.2219 -13.3294  -0.0814   1.1981  -1.7593
-#&gt;    7.4771  22.3231   3.6667  -4.9007   1.9783  -1.0990
-#&gt;   11.3080 -15.7802   1.4606 -12.4734   2.3892   1.7499
-#&gt;    0.4649  -9.0525   7.7570  -7.6014 -11.8574   0.6533
-#&gt;    5.2104   1.1293   6.9246   6.9781  -3.4111   0.2557
-#&gt;    6.8321  -5.3332   0.8903  -1.9140  -0.1111   0.7168
-#&gt;   11.2020  13.3858  -6.2562  15.5736  -7.2203  -3.6565
-#&gt;   -2.5946  10.7949  -9.5117  13.1883   0.9728   2.0467
-#&gt;  -16.0799 -13.1516   0.1700 -12.3154   1.6330   4.2724
-#&gt;    1.1213   8.2665  14.9941  -4.0737  -4.9093   1.5110
-#&gt;    4.0347  -4.5001  -0.1521   7.3414   6.3084  -0.2847
-#&gt;    3.4297   8.9231  13.8903  -1.4625  -3.8002   0.3066
-#&gt;  -15.8880  12.6141   4.3672  -4.6317  -0.2330   2.2429
-#&gt;   10.0463   3.7442  -3.5000   0.3014  -3.8686  -2.9811
-#&gt; 
-#&gt; (18,.,.) = 
-#&gt;  Columns 1 to 8   1.6420   1.2879   5.3164  -7.8571  12.7048   5.8433  11.3036   3.7826
-#&gt;   -2.5109   0.0431  -1.9696   4.5404 -18.1270  -4.8284   9.7173   6.8896
-#&gt;    3.3734  -1.0133  -1.5317  13.5069   8.2392  -2.2201  18.2343   8.3940
-#&gt;   -0.6494   1.1275  -0.7992   6.7130   6.8398  -0.1860   3.1122  -4.9259
-#&gt;   -0.1896   1.4126  -7.5902   8.6638  -6.4634   2.9422   6.8588  -8.1542
-#&gt;   -2.5423   1.3982   8.7002  -5.7975  -3.0473 -11.9556  13.9886   8.3534
-#&gt;    0.0925  -1.9056   4.1929   9.6189   3.0107  18.1900  -4.0006  -0.3840
-#&gt;   -1.9065 -12.0112   0.1455  -5.1230  -5.6154  -0.0627  -6.9732   7.9400
-#&gt;    3.1135   9.0919  -4.3371 -19.9986   6.5665 -11.0074  -8.8907  -3.8055
-#&gt;    7.1416   3.7126  -1.0907  10.3849  11.9761  -1.6818   9.6157   1.0954
-#&gt;    1.6181  -7.9607  -6.3046  11.3903 -19.3071   0.1217   6.8742   7.1179
-#&gt;    3.1030   0.0477  -3.0999  -4.6140  -9.5560  -5.0644  -0.4219   9.1221
-#&gt;   -0.3628  -5.0311   7.3550   5.5133  -4.0872  -4.3923   8.5265  11.5909
-#&gt;   -2.2950   5.0372   6.1929   0.2985  -9.3952   2.1639  -2.5889   0.1801
-#&gt;    0.5811  -7.8022  -0.1743  -1.9489  -9.0799  -2.7091  12.2969  -0.8607
-#&gt;    2.0249   1.1647  -2.1831  13.7438  -3.9637  -1.8503   8.7329  -7.0915
-#&gt;   -3.8079  -6.8342  -6.9091  -2.8699  -1.6679 -16.3355   2.1067  -1.9638
-#&gt;   -8.4592   8.9154   0.8565   0.4704  -4.0822   7.5768 -11.9635  12.3542
-#&gt;    3.6177   3.4600 -10.9865   1.7910  -2.5461  10.9428 -11.2756  -9.3878
-#&gt;    2.6046  -3.8795  -1.0403   8.5378   3.9353   3.5416  -1.2939  14.9872
-#&gt;    2.5174   1.1112  -1.4658   5.9015   6.6519  -5.6944  -8.8193  -4.0332
-#&gt;   -1.5362   0.8535  -5.1453  -6.1017  -5.8288 -13.3209 -13.5367  -0.0891
-#&gt;    5.5770  -5.0353  -3.9072   9.3719 -15.7994 -14.6314   3.9531  14.4180
-#&gt;    4.4054  -8.4694  10.0511   8.5610 -10.1093   4.8425   1.0703  -1.3042
-#&gt;    6.2056  -1.5487  -4.2585   6.9884   1.3044  -2.5759   5.4127   9.3555
-#&gt;    2.2354   6.1777   8.6935   0.8330   7.0591   9.0817   4.4968  -2.9316
-#&gt;   -0.3657  -2.1088   1.9124  -2.1532   0.5189  -4.1221  10.8716   4.1775
-#&gt;   -0.4629  -0.4061  -5.8569  -5.7979   8.3717  -0.3196  -4.8337  -5.8717
-#&gt;   -6.2881  -4.1826   0.7540   6.1380  -4.3152  -7.9107   6.6223   2.5199
-#&gt;    8.2005   6.7751  -7.6609  12.9753  -3.5936  10.3625  -2.7662   0.3874
-#&gt;    2.6145  -1.6590   1.1084   6.4919 -10.4815  -7.4845   5.0912  -3.3561
-#&gt;   -5.6482   6.7101   0.6589  -2.6008   9.2927  -2.4136  25.8413   4.2583
-#&gt;   -1.5522  -0.6731   3.7094  13.6084  -3.4717  14.0455  18.0751   9.0139
-#&gt; 
-#&gt; Columns 9 to 16   5.3804 -12.1351 -10.3660   0.5842   8.7693  14.6643   1.6818  -4.7866
-#&gt;   -6.3455   3.6485  -5.2393   8.7314 -12.4487   4.7045   7.5412  -7.6969
-#&gt;    8.4230 -14.6234  11.1057 -14.9826   4.4700  -5.2711  19.1685  -4.3796
-#&gt;   17.0186  -6.2625   7.5988  -5.7124   6.4860   5.1624   2.9079  -1.0623
-#&gt;    5.3565  -3.7169  29.1445   0.5628  -6.2952 -15.5886  -2.4912   1.7751
-#&gt;    4.5112 -12.6647  -5.2051  -5.0987  -2.6762 -12.0308 -17.8213   2.3569
-#&gt;    9.6305   7.1402  -9.4125   4.1921   5.6178  -4.1310  -7.4195  20.3406
-#&gt;  -23.4526   6.1727   4.8096   6.1797  -0.8832  13.5739  10.5601   1.5159
-#&gt;   11.5793   3.3354  -4.7421  -2.3175   7.3162  -5.4094   5.8834  10.9601
-#&gt;   21.4171   3.9900   5.2486  17.4438   4.1869 -10.7578   9.4085  15.2720
-#&gt;    3.6663  10.5914   9.0197 -12.5738   3.7574  -8.8465  -1.0220   3.9162
-#&gt;    8.9307  -4.4353   2.4044   3.1607  -5.1845  -4.3000  -9.6301 -17.7922
-#&gt;    4.1468  -5.9985  -0.0510  -0.9246   8.0400  -0.8469 -19.3457  -0.0925
-#&gt;    2.5744  -1.3962  -4.2692   6.4907 -18.3116   3.7004 -19.8901   2.7946
-#&gt;   -0.8892  -3.9821  -2.5982   1.1824  -8.8158  -0.4670 -10.6899   7.9036
-#&gt;  -11.7663  -8.2280  10.0735  12.8687  -3.1658  -1.1699   5.9914   4.3675
-#&gt;   -2.5636 -13.9672 -10.1241   6.0991 -17.4340 -10.4532  -0.0374   6.1176
-#&gt;   -7.6453   9.5692  -7.7252 -12.5117   6.0954  -0.0058   2.8967 -21.3386
-#&gt;  -13.3735   5.3355 -13.9365  10.3804   8.2995 -10.9733  33.1979  27.0988
-#&gt;  -12.7816 -11.8679  11.4570  -3.9373   5.9092   7.1102  -6.8729  -6.7231
-#&gt;   -6.3768   3.6131   2.9764  -1.4754  15.5975   8.9042  16.6951  13.3923
-#&gt;   -0.5956   8.4284   9.3541   6.6690   3.1478  15.4808  10.6831  -8.6146
-#&gt;   -2.4373 -18.7960  18.9351  15.9599  11.4891  -7.5023 -15.6213  -6.1754
-#&gt;    3.8959  -1.9634   2.8737   4.6599   4.4457  -1.1856   4.8629 -12.7711
-#&gt;    6.0529 -14.6800   2.7329  -1.2511  -7.1787  -5.7072   6.1981 -25.1700
-#&gt;    4.8224   1.9774   8.1258   1.0748 -27.5141  -2.1715  -2.2420   0.7899
-#&gt;   11.7260 -18.2582   6.0380  -6.0932  -3.7677   3.0523   4.9454 -11.8659
-#&gt;   -0.3142  -1.3074  -2.0408  -8.9408  15.0563  -0.6220  19.6902   1.4908
-#&gt;   -6.0120   9.0143 -10.1494 -11.0386  -5.0090  -5.1131  -9.2287  -8.1093
-#&gt;   14.9981  18.3641  -4.4207  -3.9998 -10.9331 -15.5465   0.6011  -2.4611
-#&gt;    5.7311   2.3118   7.1591  16.2519  -0.1902   6.0938  -4.1115   0.3160
-#&gt;   16.1710  -7.7254   5.3234  -6.6723   0.1106   3.8326   3.5976  -9.7607
-#&gt;   -1.7634  -1.1571   2.4107  -5.2687  10.5207  -9.7934  -5.3621  -4.9413
-#&gt; 
-#&gt; Columns 17 to 24  12.7919  13.8965   7.2914   7.1090   2.6619 -11.1852  20.6129  -0.3397
-#&gt;   -6.1425   4.7137   0.3191  12.4660 -12.7929 -10.1171   1.9036  -9.9026
-#&gt;   -2.9329   5.4450   8.1227  13.2214   2.8128 -23.6955  13.6789 -10.9361
-#&gt;  -21.1594   3.2425  14.4772 -10.3611  21.0172  -5.6483   9.4607   6.3364
-#&gt;   -8.1113   2.5801  -8.5287  -0.6597 -14.0022  -0.5884   3.7643 -12.5078
-#&gt;   15.2535  -7.8894  21.2285 -13.4478 -13.0378  -9.4753   6.7139  -6.2283
-#&gt;  -18.2535   2.6662   2.6795  -2.7393   4.5147   2.3993 -10.3161   9.5086
-#&gt;    9.5280 -14.5483   4.6656   3.5450 -12.7685   5.6188  -4.8503  11.8668
-#&gt;   -8.0276  17.9217  -7.9395  -7.7207  20.0763 -10.4341   0.3116   6.6646
-#&gt;  -12.0671   3.4923   3.1873  -8.6960  13.8439 -15.9396  -5.4830   9.7459
-#&gt;    6.6190  -1.9811  -8.0694   9.4840 -16.2822   2.9730   1.0627   3.1966
-#&gt;   20.4468  -0.6983   4.6333   5.5524 -14.3997  -1.8202  -4.0220 -12.3155
-#&gt;   11.8901  -5.3156   4.3961  -4.3514 -17.1259  10.1295  -3.1806  -3.7633
-#&gt;  -22.2255   5.1331  19.4986   9.8792   1.8949  14.8938 -12.4817   3.8372
-#&gt;   -2.5785  -4.3861   9.4344  -4.7644   2.7722 -10.0445  -4.9544   2.5280
-#&gt;   10.1179   4.8086  -4.7714   6.3682  -4.8846  -8.6565  14.4215  -2.6060
-#&gt;    2.7266  -6.6072   7.6766   8.2032  -2.2043   1.3790   2.5085  -2.5679
-#&gt;   -9.4860  13.6252   0.5499   4.3637  -0.8586   3.5431   2.8470   5.7581
-#&gt;    8.7090   8.6967 -25.1840 -12.8700  -0.1018   8.5919   1.0733  10.8271
-#&gt;   -6.4616   3.3153   5.3792   7.9816  -1.5986  -3.9117  -6.9535   3.2914
-#&gt;   19.0527  -3.1730   3.6325   3.6042   9.1775   6.4150   2.8763   3.6961
-#&gt;    7.6531   1.3698  -9.0558  13.6710  -0.5876 -10.8151  11.8866 -19.2419
-#&gt;   -8.6815 -17.6992  12.5567  12.1497   6.0738   3.7060  -3.1910  -0.9991
-#&gt;    4.7524  -5.6268   2.4945   1.2878  -8.5590  -1.7245   2.4662 -12.2543
-#&gt;   20.1751   1.5843  12.8122   2.2665  -0.8076  -7.3859  17.5358 -16.9012
-#&gt;   -4.6932   9.3886   0.5833  -5.7472  -2.2443  -9.8603 -19.1601   3.3404
-#&gt;    0.8012  -0.8414   2.3527  -1.0912   8.3260   2.9054   2.3910   3.4724
-#&gt;   22.5107   3.1786  12.0413   1.5933   7.6991  -9.4353  13.6481  -2.4569
-#&gt;   -3.8804 -11.3571  13.3741   1.0029  -6.2457  12.8943   0.5813   3.3167
-#&gt;   -0.2584  -1.0563 -17.1628 -12.4293   1.2945  11.4786  -6.6795   6.9568
-#&gt;   -6.6891 -13.8328   2.5390  -0.3918  -2.6157  -3.9123   7.7240  -1.6569
-#&gt;   -9.8826  -1.3782   1.5560  13.3592   0.2965  17.9120   7.0649 -11.1058
-#&gt;   -5.5006   9.0927   8.0763   2.2972   0.9185   5.0729  -2.5752   2.4860
-#&gt; 
-#&gt; Columns 25 to 32   2.6157  12.1656  -3.4293  11.2619  29.0579   9.0954  -7.2592   2.0037
-#&gt;    0.8193  -0.0767   3.5949 -10.6917  -5.6788  11.6697  -5.4333  -6.3205
-#&gt;   -3.6810   1.5525  -5.6771  -0.1137   4.2578   9.2257  -7.5001   1.5330
-#&gt;   -0.0569  -4.2848   9.9154   6.3289  -5.2351   7.7988   4.8216  -2.9912
-#&gt;   -3.8000   6.5655  -2.0004   1.7156 -17.9381  -4.2242   3.4018  -1.0467
-#&gt;   -9.4344  -9.8031 -12.0416   8.2862  13.2678  -0.7302   0.4335  -8.4655
-#&gt;   -7.8939   3.8292   9.9277   0.7376   1.4882   4.2665  10.4770  -8.0951
-#&gt;   10.8728   9.1898   1.6196  -2.5027   1.6465  18.7318  -5.1867   4.6003
-#&gt;  -11.2066  -9.4481  -5.4285  -4.1906  -7.5989  -2.8678 -16.7686  -2.0709
-#&gt;  -17.2278 -10.1598  10.4174  -3.8264  12.3131  -2.5120  18.8831  -7.7288
-#&gt;    0.3052   5.7338  -7.8968  -1.5548  -9.5453   4.5017  -5.5146  12.2713
-#&gt;   -9.3960   3.2968 -12.8012   4.0913 -15.1407  -0.3459 -14.7093  -7.6451
-#&gt;   -1.7817   4.8734   5.0182   4.4158  -6.2907  18.4289 -16.1908  10.3766
-#&gt;    0.4862   5.4331  -6.9836   3.9721  -4.2630   9.7002   2.3095 -14.5059
-#&gt;   -4.1786  -5.7285   2.5321   6.2680  -4.6553  -2.1756   6.8678   2.6513
-#&gt;   -8.6665  -6.0416  -0.5409   0.5216   4.5455  -1.9902   0.3366   0.8028
-#&gt;  -10.9652   6.0440  -2.8763  -5.2005  15.1445   1.7218  19.8993  -3.4327
-#&gt;   10.6043  -3.2512 -10.1111   4.6157  -4.0006  -2.1343  -1.4302  10.6029
-#&gt;   -0.7327  -0.6984  -2.8468  22.2596   8.2914   2.4505  16.9699  -3.4296
-#&gt;    2.0949   2.8143   5.6126  -0.4120  -5.6877   3.9536 -10.9128   0.8354
-#&gt;   16.6468   7.1970  -5.7594   1.3074   7.8698   0.9892   6.8667  -4.1368
-#&gt;   -2.2990   3.1578  -5.4826  -1.8124   2.9626   8.2702 -14.0820  -6.5329
-#&gt;   -5.5275  -8.0167  -3.2093  -3.3525  -9.3943  13.0748  -7.8546  -4.9310
-#&gt;   -1.6238  -7.5335  -0.1831   3.1611  -7.2427  -1.5588  -5.7129  -0.7068
-#&gt;    8.0404  -3.7601   4.9198  -5.5980  -1.6015 -12.4899  -1.4713  10.5558
-#&gt;   -7.2427   2.0596  -5.3566  -7.9559  -4.5101  -5.5048  18.3094   4.4071
-#&gt;   -1.3398   2.4388   6.1397   1.8313   3.2439  -6.4362   6.1027   1.0680
-#&gt;    9.4973  -1.7636   0.4137   8.9540  -8.8187   0.1385  -0.4692  -5.5934
-#&gt;   14.0594  -1.4364   3.2645  -2.2201  -1.2013  -4.2073  11.8217   9.4594
-#&gt;    7.2463  -7.3954  14.7617  11.0248 -11.8653  -7.9344  -2.0461  17.9101
-#&gt;   -4.8893   0.5295 -10.6414   2.5047  -1.8547   8.1770 -11.2773   3.5899
-#&gt;    6.6188   5.2276  17.5914 -12.3284  13.5262  15.2202   2.1484  -6.2322
-#&gt;   -3.2337  -6.4030   2.5477   0.6266  -1.7585   4.5958  -3.8904  -2.0269
-#&gt; 
-#&gt; Columns 33 to 40   0.0029  -3.3509 -12.6342   0.0077 -13.8816  19.4735   6.4888  -4.3073
-#&gt;    1.5395   0.2632  -5.4625   0.9212  -9.7440   5.6657  -5.8855  -1.8879
-#&gt;    5.9266   0.1231  -6.3651   0.3660  -1.8050 -17.6215  -0.0248 -11.7501
-#&gt;  -10.3363  -1.2273   6.9968  -4.5623  24.2431  -8.3985   7.2473 -31.0217
-#&gt;    1.2550   9.8227   3.1240 -18.6046 -13.7359  -3.0107  -1.1276  -2.6916
-#&gt;   -8.9907  -9.6516  14.6502   8.2522   3.6585  -7.0430   2.7615   4.5177
-#&gt;  -26.4023  -3.6227   6.8321  -8.3929 -11.2319   2.2254  -2.6124  -8.0238
-#&gt;    6.9105   7.0635  -1.4849   2.4072 -19.7923   6.6321   3.8779   2.5356
-#&gt;    6.3276  -8.5924  -5.0837  13.8570  12.6960  10.3987  11.6815   2.7757
-#&gt;    2.8557  -1.6411  -8.9849 -14.0671 -11.5407  -7.7375   5.4139 -15.6535
-#&gt;    0.7287  -7.0148  -4.2302  21.0221 -13.1672  11.1729  -5.9700  -4.3374
-#&gt;   10.4731 -10.1309   3.3727  20.1838  10.8264   5.5776 -10.2286 -10.2985
-#&gt;    3.3903  -8.1233  -0.6413   4.7653 -13.4528   3.2653   1.5537  -0.2125
-#&gt;   -4.8917 -23.8506   8.8801  -4.8572   4.5650   8.1567   8.8407  10.9621
-#&gt;    2.9578   9.7805  -9.9187   2.7422   9.3902   0.2009 -16.6340  10.2006
-#&gt;   -3.8805  -6.7501   7.9872   2.7867  -0.0815  -7.9427  -3.5837  -0.0629
-#&gt;    1.6030 -13.3641  11.1395  -1.3434  18.6885   3.3939   9.9179  -1.1933
-#&gt;  -18.0162  -8.5544  -0.6633  11.2242  -0.5546 -10.2144  -3.5952  -0.8228
-#&gt;   -0.0304   3.3971 -13.0760  12.1379 -18.7442   6.0984  11.3735  13.1533
-#&gt;   -1.3424   7.4699 -16.1672  15.2030  -1.1397   1.4846  -9.6994   0.7061
-#&gt;  -17.6452   7.4832 -12.2968  20.7037  -5.9765   6.0838  -3.7101  14.0650
-#&gt;    9.8086  17.2381  14.2677   5.9241 -12.9382  15.8107   0.3432  13.0134
-#&gt;   -7.9321  19.8009   6.0164 -11.8849  21.6689   7.9013  -0.1456 -14.9485
-#&gt;    3.3430   3.6445  10.6610  -2.1489 -17.0001  -7.2675   6.2436  -3.5449
-#&gt;    3.4334  -2.1735  13.1118 -14.9150   6.0767 -11.2966   4.3785 -12.3123
-#&gt;   -2.0132  -6.5839  11.9638 -18.3315  -3.5849  -8.2416  -3.7682   6.0796
-#&gt;   -0.0251 -11.1150   7.8212   1.7854   7.5301  -4.5684   0.8110   4.1804
-#&gt;    8.2421  -1.7128  -1.8108  12.8005  17.7682   4.3181   8.1897  -3.9628
-#&gt;    0.6283   2.0559  12.3124  -3.8224  -3.3201 -12.3931   6.2987   3.5806
-#&gt;   -2.0786   6.6837  -3.2043   3.0021 -12.1649   0.8647 -19.2996 -11.3476
-#&gt;    3.8575 -17.6842  13.0417  20.2883  -6.0133 -10.4405  -0.8185   3.9150
-#&gt;    6.9854  10.4905   7.3135  -9.4315  -7.8653  -0.3362  -3.7238   3.9443
-#&gt;    7.3930  -1.5779   0.2267  -8.8062   2.0794  -3.5313  10.0632 -14.0367
-#&gt; 
-#&gt; Columns 41 to 48   4.0510  -1.2702 -10.7254  -0.1518   6.2255 -16.2182   0.0292  13.3147
-#&gt;   -2.1117   7.7726   4.7524  -6.3753   9.8414  -0.9707  -1.7454   8.6750
-#&gt;   -0.9473  -6.6488  25.7680 -21.5618  -1.7513   8.2230  -4.7134  -9.3620
-#&gt;    2.9891  -9.4650  26.0017 -24.0466  -3.2031  16.5410  -1.8369  21.9893
-#&gt;   -1.7402   8.2179  11.1601  -4.2580   2.8483  13.6395   5.4053   2.7420
-#&gt;    8.2215   6.4315 -10.5551   6.0965 -29.5574   9.0842   4.3430  -8.8682
-#&gt;    4.0511  -4.8678  -7.8091  -7.2168  -3.2019   0.7052   2.3832 -13.7279
-#&gt;    3.7785   7.8156  -3.6508  -6.4915   9.9509  10.5353   0.7613  -1.9243
-#&gt;   -3.4082  -3.2314   7.5694  -9.4198   1.2483 -10.8518  -0.4544   5.1223
-#&gt;    2.7928  -4.2954  13.6518  -4.1961 -32.9449   8.2825  10.6235  -6.5547
-#&gt;   -2.1706   0.5347  -4.1825   9.5515  -1.3363  -0.9141  -8.9849  -2.0354
-#&gt;   11.3627  -0.6651  -7.9567   1.5583 -23.5124  15.5929   0.6318   0.4863
-#&gt;    9.5106  -0.9316  -1.4384  -7.9514   3.3732   2.6686  -4.9989  -3.0646
-#&gt;    1.5673  -3.4351   3.2348   5.1354 -16.7270   2.7156 -10.4553 -14.2158
-#&gt;   -2.6128   9.3098   6.3065  -3.1190  -4.8262   8.8057  -9.8614  -5.6171
-#&gt;   19.3820  -7.3091  -1.5507  -3.3476   1.9809  10.8549   2.1270  -3.7262
-#&gt;    7.5950   5.9518 -14.4187   1.6114  -7.7032   7.1640  17.7226  -8.4070
-#&gt;    0.6154   5.7930 -13.4261   5.2674   9.2733   3.7338  -7.8101   1.1300
-#&gt;   -8.1761  12.1908 -21.7284  13.0551 -16.3943  -5.5680 -10.0639   3.2579
-#&gt;    1.8726   4.2444   4.9620  -6.5421  -9.1609  10.7864  -0.0778 -10.7919
-#&gt;    9.7009  -2.5378 -15.6215   8.3960  -7.2251  -3.9734   8.8126  -4.3367
-#&gt;    2.8564 -32.7612  16.8721   0.2182  11.6929  -9.1474  -5.9281   3.8051
-#&gt;    1.7788   1.4127  -1.8833  -6.9912 -12.5033  18.4125   3.6896 -15.0041
-#&gt;   -5.9166  -7.0237  -7.5080  -8.2371   7.0100  13.8009 -11.6932  -1.6627
-#&gt;    4.9167  -2.6230   0.3647  -3.4979 -20.9022  16.2120  -6.7058 -14.1063
-#&gt;   -1.9939  -0.7363 -17.2596  14.2294 -19.4004   8.7278  -0.1109  -9.6073
-#&gt;   -4.8946  -0.0505  -6.9824  15.6053  -5.0343  -2.1242  -2.9937   0.5938
-#&gt;   -4.5974  10.0005   5.7879 -12.7138   4.0694 -22.5153  -8.6040  19.5947
-#&gt;   -7.7176   9.4326  -0.3602  -1.8544   9.7682   2.2847 -11.9673  -4.2708
-#&gt;  -16.3190  -5.7646  -6.2865  -0.0257   2.4428  12.3580 -13.9446   2.4780
-#&gt;    8.4663  -8.2000   9.8663 -13.7863  -1.1844  13.4650  -4.9049   5.9373
-#&gt;    2.4027   0.5915 -21.0702  -1.0956   6.1958 -18.1889   5.0352 -18.4239
-#&gt;   -9.9235  -7.0443  -2.4528 -12.1563   3.5734  -3.8522  -9.3329  -9.5187
-#&gt; 
-#&gt; Columns 49 to 54   2.4848   8.9600  -6.6746 -12.3711  -0.5049   2.8786
-#&gt;   10.6893  -5.4225   9.1650 -13.3324 -13.8708   3.7911
-#&gt;  -16.2479  27.7164   0.7434 -11.4350  -8.6765   0.6951
-#&gt;   -0.2513  -5.2872  -8.1689  -6.9968  -0.3131   2.7240
-#&gt;  -20.2858  15.1985  -7.0279  -9.8774  -2.8880   7.5356
-#&gt;   -5.0441  -5.9110  -3.6796  10.4714  -2.4352  -5.0220
-#&gt;   -2.9365   3.6362  10.7610  -0.0537   1.7815  -1.1395
-#&gt;    1.9543 -10.6093  -5.5753  -3.9769  -3.0863   1.6045
-#&gt;    3.8588  11.3072   2.7108  -5.5991   4.0178   0.1168
-#&gt;  -22.2331 -13.1465  -2.4980   5.7941  -6.1919  -8.3823
-#&gt;   -0.9479   7.0255   0.0698   1.3584   0.6481   0.1080
-#&gt;    4.3669   5.3655  -2.4001   3.0138 -11.6708  10.0307
-#&gt;    5.1871  -7.0168   0.4818  -0.0774  -0.6100   2.0541
-#&gt;   17.7132   6.4588  10.1605 -10.7837  -6.7728  -7.9383
-#&gt;   -7.6739   6.9783  -1.2624  -0.2311   1.1638   3.9497
-#&gt;  -17.5456  -9.7086   2.0341   9.9349  -3.0648  -0.5089
-#&gt;   -3.1795   4.3665  17.0024  -8.8965  -1.5841  -8.8056
-#&gt;    2.2149  15.9212  -4.4968   0.0624  -3.1900   2.9269
-#&gt;   11.9618  -2.4289 -17.1797  17.8757  -0.4401   7.4848
-#&gt;    0.8993   9.7873  -2.5482  -0.0111  -6.4800  -1.8389
-#&gt;  -12.8026  -5.3223 -10.4207  -7.1540  -3.7932  -0.5174
-#&gt;    3.0748  -8.7669   3.6482  -0.7108   2.5877   2.6914
-#&gt;   -1.5469  -5.7033   1.4735   4.2395   1.8257  -3.1212
-#&gt;   20.0404  -0.7424  -2.4644   9.7535   1.2855   5.3932
-#&gt;   -4.9952   6.6275  -9.0983  11.9380  -6.4644  -2.4656
-#&gt;   14.4117 -11.4367   6.1705  -2.5055  -4.0090   1.6790
-#&gt;    1.7852   2.5159   5.6664   7.5982  13.7174  -5.9481
-#&gt;   -0.8873  -0.4220 -12.1559  -7.4173  -7.3533  -0.6071
-#&gt;    3.2437   1.4743  -7.6123  13.1501   8.1817  -3.4226
-#&gt;    3.7093 -13.1205  -7.3952   9.8814   4.7580  10.3219
-#&gt;    3.5487 -23.3564   5.8674   6.2407   0.5246   4.0238
-#&gt;    8.0665  28.6886   2.2886  -3.1141  11.1435  -2.9868
-#&gt;    4.3882   5.3014  -3.7234   8.4248  -0.0245  -8.0384
-#&gt; 
-#&gt; (19,.,.) = 
-#&gt;  Columns 1 to 8   2.2563  11.1571   5.1803   2.0074  20.4880   1.9959 -11.7897  -3.3816
-#&gt;    1.6023   4.9350  -9.8570   2.5549   3.4270  -6.4630  12.5979  -3.4586
-#&gt;   -3.7042  -7.2734  10.1350  -2.1168   4.5737   4.7926  -6.3151  13.1638
-#&gt;    6.8103  -6.0863   3.9821  23.3459 -23.1046   3.2218  -2.8127  -2.0219
-#&gt;   10.0323  -1.5816  -0.3515  -3.7162  -1.0843  -8.9585  -9.5895  -1.1016
-#&gt;    4.5691   0.3556   6.5799  11.4361  -5.5251  -0.7265  -4.4567   7.4877
-#&gt;    3.7387  -5.0253   6.2016  12.1069   1.3906  -3.1792   5.3994  -8.8015
-#&gt;   -6.3128  19.6994   0.8121  -5.3457   2.7312  -7.0969   0.8557  -5.9668
-#&gt;   -0.6554 -17.8413  -3.6626   7.7413  -3.0338   1.9055   2.3498   4.4129
-#&gt;    2.2045 -17.2951  14.3557   5.4896 -13.9269  13.9771   0.1688  13.7065
-#&gt;    1.4103 -10.9335   6.4542  -7.1951   0.4260   6.0540  -8.6265 -10.6153
-#&gt;   -2.4367  -3.0208   1.7582  -3.9473  11.5795  -9.8707   8.2718   3.9334
-#&gt;    2.6910  13.7177 -12.4472  -0.7891  -4.1189  -4.9260   1.9796   6.3284
-#&gt;   -2.2626   0.8278 -16.8472   8.7072  -1.5437  -2.5874  18.4442 -12.6984
-#&gt;    0.2770  -5.7886  -1.6831  -6.2280   0.3892 -10.7264   1.6883   0.5731
-#&gt;    5.9888   2.8220   5.1822 -10.9491   2.9278  -1.2400   6.9684  -3.0588
-#&gt;    2.0853   6.4697   9.1610  14.2869   3.9100   7.3812  -5.6622  -2.6049
-#&gt;   -2.8214  -2.8717   3.4763  -4.1231   4.3919  -7.9700  14.0622 -10.4217
-#&gt;    0.0360  -7.2489   7.1088  -5.2261   7.6209  -6.4602   3.4370  -3.3627
-#&gt;    3.0146  -8.4109   7.3443   6.8336 -16.3648   7.2043   7.3347  -7.1156
-#&gt;   11.6654  -2.6083  -1.8932 -11.5787  -7.3925 -12.9529  -8.5728   6.7741
-#&gt;   -7.3200  10.8262  -1.1320 -12.8965  17.0946  -4.7953   1.3419  -5.9471
-#&gt;    0.9198  -4.3271  -5.4991   3.7542 -11.3688   7.0314  -0.1410  -5.9312
-#&gt;    5.9632   8.4848   2.1999   4.6207   0.4360   1.6097  17.6682   7.0791
-#&gt;   -3.5695  -2.5270  -0.5916  -8.6880  -8.8335  14.8022   4.9419  -2.7857
-#&gt;   -5.3153  -5.2006   1.6115   9.8521  -2.7637  13.6420  20.2294   2.9587
-#&gt;    0.1992  -6.7249   2.8040   3.9956  -7.6735  16.6353   0.2961  -0.4600
-#&gt;   -0.9324   4.6584  -9.8605  -3.0216   3.8279 -18.3609 -14.6428   2.9325
-#&gt;   -2.3640   2.2242  -7.8729  -2.3704  11.0849  -3.0016  12.9536   4.4597
-#&gt;    0.6707  -3.5127  -1.1921   7.2875   5.6279  -5.3606  16.7637  -0.5393
-#&gt;   -6.0561   6.4866  -2.1347   2.0663  -9.1948  -4.9570   4.9678  -2.9562
-#&gt;    1.7940   8.1519  -9.7820  -8.1634   8.2479   7.6515  -7.6138  20.4446
-#&gt;    1.0375   8.2653  -6.3321   4.4135   7.8574   2.1843  10.2095  -3.0581
-#&gt; 
-#&gt; Columns 9 to 16  -6.7446 -19.2839  -5.2996  10.6448  -2.2137  -9.8432  16.7385   1.8806
-#&gt;    2.7721  -9.6319  15.9549  -3.9562  -2.7533  -1.4063  17.1808  -3.8886
-#&gt;    0.8954   3.4040  -4.2748  -7.6926  -1.9848  17.2263   0.3864  -4.7782
-#&gt;   13.6218   3.0880  -9.0650  13.7402  14.2595  14.3139   8.1423 -10.8839
-#&gt;   -2.8056  -1.5642 -10.9384   2.4840 -14.6773  15.3640 -11.8735   8.1836
-#&gt;  -12.2327   8.0373  -9.9830  -7.0531  -2.4499  -2.4429  -9.1589   7.2055
-#&gt;    6.7662   1.5342   1.1651   4.3661  -5.1278   7.6165   2.6917  -7.9761
-#&gt;   -4.9940 -12.7029  -0.5018   4.1232   7.7914  -3.3340   3.7179 -15.3366
-#&gt;   -2.8687  11.0463 -19.0638   0.3108   8.7410  -5.9274  -4.5910  10.8263
-#&gt;    2.7317   2.1383 -13.6276  31.3021   5.1940   6.0216  -4.0457  -5.5339
-#&gt;   -8.8773   1.3894   6.0943   8.2040 -22.9392   6.0407  -5.8266   9.3254
-#&gt;   -5.3709   6.6576  -0.3668   0.5234  -0.9117   8.5379  -2.6543   5.2700
-#&gt;   -4.0529  -2.2297  -3.6299   8.0911   7.2778  -1.6554  15.2207  -8.6982
-#&gt;    2.9273  10.0753   1.0093   2.4645   6.0291   2.2682   1.5076   3.4788
-#&gt;  -16.8549   8.1650  -5.1286  -7.7697  -3.0778  13.3012  -7.2841   0.8980
-#&gt;   -4.7963  -5.0447  11.0234  19.7087 -13.5973  -1.4497  -6.0398   3.9615
-#&gt;  -11.6929 -12.9310   3.0861   3.8349  -0.8498   0.3676   7.5970 -15.8111
-#&gt;   10.5982  -8.7752  -4.8810   3.8208  -2.7940   0.2052   2.5400   8.1477
-#&gt;   -3.8651  11.3115 -15.3378  -0.9253   7.8896  -0.5798  -9.4853  21.2292
-#&gt;    1.5519   2.7537   7.5006   5.2048   3.9256   3.6954 -14.5668  -1.0811
-#&gt;   -6.4954 -15.8022   2.8659   9.0508  11.4639  -2.6907  -8.2854  -1.4667
-#&gt;    7.8183  -1.5043   9.2190  -3.2909  -3.6400  -7.3343   4.0588   3.0736
-#&gt;    1.6113  -7.8219   7.8065  20.6163  -7.6171 -11.7764   8.7982  -8.1107
-#&gt;    4.7483   9.2704  -0.8942   2.4555   7.9658   2.8396 -11.1332   6.9196
-#&gt;    9.2414   3.9240   7.5839 -10.3013  -8.0429  -0.9105   7.5698   5.5207
-#&gt;    9.1179   9.3893  -2.6667  -0.5141   6.6394   2.4894  -3.3466 -12.0105
-#&gt;   -7.6842   7.3149  -2.5730   4.5251  -6.0077   4.9602   5.9953   2.1281
-#&gt;  -10.2351  -0.4251   2.1078  -6.7500   6.6732  -0.6379  -4.8256   7.8820
-#&gt;    7.6989   5.7581  -0.8032  -1.8760  -1.4233   4.0420  -9.7446 -10.8131
-#&gt;   13.1536  -3.2820  -5.9773   0.4688  -7.2200  -1.9802   6.8597   7.1834
-#&gt;   -6.9597  -6.3075 -10.8629   6.5933   5.1967  -2.3144 -13.2033  -6.3124
-#&gt;  -15.1643  11.3891   1.7059  -1.9721   6.4254  -8.3010  13.8574  -8.0904
-#&gt;   -0.6400   0.8845   9.1436  12.9765  -0.8867  -5.4252  -2.4219   2.5684
-#&gt; 
-#&gt; Columns 17 to 24 -11.6251  -3.3042  -9.5897 -19.7662 -11.3639   2.9764  -7.6847   5.3094
-#&gt;    5.5535  12.3224  -0.4621  -1.7751  -6.2082  -2.8243  -3.3639   4.4086
-#&gt;    4.9465  -6.1456  13.8044  -2.3682   0.1131  -1.0469  -0.6537 -18.7963
-#&gt;   -0.2615   3.3738  12.8766  -0.7729  13.0538  -3.7030  21.3555  -0.0547
-#&gt;   -5.4725  -2.8746   3.7941  -7.9571 -14.6536  -3.4342  -0.7045   1.1588
-#&gt;    3.2876  -3.8150   2.2217  -7.6296  -6.1589   9.3530  -2.3499  17.2963
-#&gt;   -8.1793   7.5804  12.0472 -14.9795   2.5250   4.4984  18.0541   8.3617
-#&gt;  -10.3372   2.5190 -17.6749  -9.9759  -7.6969  -6.4078  13.4594  11.9478
-#&gt;   -5.4648  -6.1133  -3.1051  15.8897  -0.9202   8.7116   5.8395 -20.7721
-#&gt;   -4.9633   7.0661   1.5125  -9.6190  -6.7895   3.5231  -0.0329  23.3810
-#&gt;  -15.8677  12.4208   2.0475   6.4192  -9.7269  -0.6899 -15.3368  17.5331
-#&gt;   -7.8009  13.4144  -4.8032  15.5606 -10.1968   9.3907 -12.3619   0.2271
-#&gt;  -17.6470   3.5627  12.0824  -4.9767  -6.5277   0.2517   1.5120  17.2585
-#&gt;   -7.6810   2.1964  14.6920  -5.9527  10.4770  -6.3552   7.7612   9.4945
-#&gt;    0.4429  -3.4509  -2.8073   0.4457  -8.6056  -3.4702 -23.0327   0.9529
-#&gt;    0.8179   4.0997   2.5110 -15.3957   3.0363  10.0195  -9.8834  -8.2598
-#&gt;    0.9310 -15.8169  -2.7237  -3.8845 -11.1570   3.2370  -4.0259  -0.8774
-#&gt;   -5.0513  18.1145  -1.9949  18.7507  16.7231   7.7012  -4.9110  -4.4177
-#&gt;  -32.6358   0.2473  -4.0207   2.8901 -18.4278  11.5049 -11.9611   8.3352
-#&gt;    3.0942   8.3002  -8.3140  -5.4769   9.5909  -5.1340   1.8389   9.0214
-#&gt;  -16.4604 -16.0312 -13.7154   5.3215 -13.6139  12.0238 -12.3383  -9.2225
-#&gt;   -5.7482   1.7096  -0.1163  14.3944   0.9409 -12.6108  -5.2439  -6.4736
-#&gt;   -2.8593  -0.6078   5.9682  -8.9257   6.8423   6.6952   1.2522  -8.5496
-#&gt;    0.1467   9.9529   4.9536   4.2114  14.9164   8.9636 -11.2608  -6.2331
-#&gt;    7.4674  -1.1952  -6.9978  -3.2490  11.2160  -6.2838  -1.1349   5.1366
-#&gt;   11.8175   3.2338   2.4789  -7.7572   9.2989  -6.1385  -4.1347   4.6263
-#&gt;    3.8211  -2.9451  12.8165 -13.0431   2.9364  -0.6053  -3.3417   0.7684
-#&gt;   -5.6006 -21.6590   0.9646  13.6898  -1.2653 -14.3905   4.7676  -5.7793
-#&gt;    3.3701  -2.2316  -6.6199  -1.5730   0.6153   2.5474  14.7909  14.1248
-#&gt;    1.4418   6.3772   0.3450   6.5393   9.4165  15.8391  -5.4509  -7.6689
-#&gt;    9.1360  -3.4296   4.2381   0.1421  -6.0637 -10.6114 -10.5209  -1.9886
-#&gt;    8.9036 -12.5969  22.4951  -0.5840 -12.5121  15.3569 -18.5965   9.6492
-#&gt;    1.6620  -3.6564  29.1041 -15.0301  24.3040   2.8217   5.5570  -3.0949
-#&gt; 
-#&gt; Columns 25 to 32   0.6754  -5.4351  -1.7427  -2.1690 -12.6507  -7.9582  -2.0196  -5.9914
-#&gt;    6.2957  24.2517  -0.6646   5.8407   4.2708   0.6678   0.8753   7.4855
-#&gt;    9.4350  -1.2963  -3.6902  -5.5026   2.7794  -3.1165  -1.7660   1.7011
-#&gt;   10.9539 -17.5292   4.3171  -8.1743 -14.3399   9.7072  -7.5103   9.4845
-#&gt;   -3.8901   7.8685  11.0553  -7.6921  -0.4697  11.2485  -3.0967   7.2322
-#&gt;    5.9094   3.8987   8.5515  24.0678  -3.1706   2.4028   0.1868  -4.1452
-#&gt;   -4.8616   5.1777  23.9718 -11.5407   7.5438   3.0655 -15.7713  -8.2458
-#&gt;   -0.8644   5.6632  -3.6068  -3.0043  14.2817   7.5471   5.6906  -4.2427
-#&gt;   -7.6225 -10.5315  -8.6553  -2.2209 -15.1343  -2.7813  14.5287 -10.1209
-#&gt;    8.1794  -1.0438  17.5802   1.3836   6.7183  10.8328  -2.5115   2.8458
-#&gt;    2.9136 -10.5741  12.6177   0.9100   2.0254   3.3510  -2.8068 -13.4172
-#&gt;  -13.1170   1.6400 -13.0929 -10.3412   5.5702 -11.1293   2.2765   3.6344
-#&gt;    1.1890  -1.9082  -2.3892  -8.1190   2.6411  -3.1444  -6.9475  -6.7670
-#&gt;  -14.5015  11.7778  -2.8347 -13.3463  -5.1831   4.0238   0.1387   2.6846
-#&gt;   11.0696 -13.9293  -6.1347   7.4526  -5.8511  -3.2405  -0.3918  13.2905
-#&gt;   17.0813  -5.0977   6.4885  12.9523   8.3253 -10.0022  -6.1836  14.6672
-#&gt;   -9.5167  -0.1452   8.0347   9.8185  -4.8826   7.0202 -13.3114  -7.3593
-#&gt;   -3.5866  -2.1113   6.1158  -4.5314   0.5352   3.9646  -8.2554   1.0600
-#&gt;   -6.3913  -3.4983  -2.0942   2.1771   1.5844  -3.6043   4.8755  -9.8164
-#&gt;   11.0548 -20.8779 -16.4557   5.0648  -3.3323 -10.0792  18.6829  -5.9163
-#&gt;   -2.9553  -3.4663   5.8928 -17.9741   7.4210  14.4276  12.8937   3.8324
-#&gt;    6.7334  12.8358  -6.2878  -5.9928  12.9341   1.9918  -0.3133   0.8225
-#&gt;   -8.5431  -4.5959   5.7668  -6.6540   6.5829   6.1331   1.0447  -7.4261
-#&gt;   -5.5379   0.1180  -2.7121  -5.2564  -3.0441  -9.8881   4.7905  -2.0531
-#&gt;    1.8131  -4.7013  -3.3520   6.5667   3.2757   5.0640   9.5182   2.7596
-#&gt;   -7.9812   6.5552 -21.8246  15.2338  -1.6433  -0.1308   4.5212  -3.7304
-#&gt;    1.0232   1.4512  -3.1897  -7.0585 -15.2776   3.4033 -12.1728  -0.5707
-#&gt;    6.9714  -8.0632  -3.9023  -1.7853  -7.5021   9.3818   9.8124  -7.2047
-#&gt;   -8.3226  -3.2363   5.5813  -9.9950  -1.7240  17.5094  -6.7417  -5.7870
-#&gt;    9.7854  15.8732  13.8283  -0.1509  -6.0653   9.1286  -3.0200  -1.7873
-#&gt;   -1.2083  -4.4204  -5.7270  11.8766  -5.3162  -0.7704   9.3954  -1.7334
-#&gt;   -2.2619  11.5894 -10.0027  -5.0707   4.5968  -5.3793 -13.2128  12.2694
-#&gt;   -9.4825  -1.5247  -0.3769   3.1203   5.3710  -5.7538  -8.5316  -1.2781
-#&gt; 
-#&gt; Columns 33 to 40  -9.2212  17.9095   8.4176  -3.7203  -6.5719  -1.9328  -3.5606   4.5551
-#&gt;   -1.8634   6.8070   5.5377   7.7612  -0.3723  -8.5310 -11.2221  12.8836
-#&gt;   -0.5835 -20.5723   6.2568 -13.6192   6.2027   2.5340   8.3277  -1.0690
-#&gt;    9.4813 -13.0169  -6.5955  -9.6504   3.9302 -10.4059   0.3368  11.0317
-#&gt;   13.2242  -6.7998  -7.4696  -4.5022   7.5813  -2.4320  18.8617   6.3256
-#&gt;    5.4512   5.7200   4.0642  -1.2367  -0.7577  13.4109   1.9488   2.7067
-#&gt;   17.0755  -5.8967  -4.9994   2.3772  -3.3988   7.8152  -7.2953  -7.5021
-#&gt;   -8.0343  -4.6180   2.3566   9.2143   5.1217  18.9750   7.0116   0.8720
-#&gt;    8.8868 -12.8316  32.2617   1.5173 -29.2014  -9.1697   1.7793 -11.5384
-#&gt;   -8.6272  -4.4338 -21.9003   4.1937 -13.0200   8.8821   0.1906  13.0268
-#&gt;   11.8491  11.2913  -2.7882 -20.0160  22.0333  16.8525  -3.0531  -2.2457
-#&gt;    3.9485  -1.9158   2.2538 -21.1789  -3.8281   0.7445   2.8729   5.1457
-#&gt;    4.8669  -7.2527  -3.9915 -13.5313  13.7822  12.5903   8.1406 -10.5367
-#&gt;   -2.3059   2.7685  -3.8104  11.5816 -24.4048 -18.5444  -8.2961   3.2680
-#&gt;  -16.0640 -19.7496 -16.1403  -6.2305  15.5044   0.6679   0.0522  19.7908
-#&gt;   11.9814   5.5630  -0.8154 -19.5417   9.2743   8.0217 -15.5096  15.4365
-#&gt;    4.4094  14.3800   3.7023  -2.6637  -6.9884  15.1600 -10.9028   8.2918
-#&gt;    8.1691  17.8184   3.8099   3.3481 -12.3396  -3.6603  -0.4540  -1.5425
-#&gt;  -15.0172 -12.5406   2.6438   1.7852  10.4317  16.6561 -11.3111 -20.8243
-#&gt;  -12.4438 -10.9364   3.7333  -6.6735  13.5679  -1.7383  -2.9847   6.8149
-#&gt;   -1.8302  17.7674   5.9496   1.7236 -20.1008   2.4569   4.4831  -5.7613
-#&gt;   -0.7342  -9.7959  -7.8026  -6.3387  -2.7167  13.1393   7.8315   9.4000
-#&gt;    9.4051   9.6574  14.3628 -27.0737 -19.5745 -15.6395  -3.8150   2.3063
-#&gt;   -9.8362   7.6502   0.7495   5.7295  -0.4509 -10.2346   3.5372  -6.7736
-#&gt;  -10.6270  14.9071   3.1708 -15.4192   2.3833 -13.9320  -9.1408   3.5938
-#&gt;   -9.1457 -11.3525  -5.3337   4.7483  -4.7272  -3.0666   0.1602   2.4069
-#&gt;   -8.1658   3.7447  -3.9244   9.3006  -1.0673  -7.4670  -3.5135  -1.1845
-#&gt;   -3.0652   9.2276  17.3474  -4.8320   0.7358  -4.0601  13.5146 -17.2049
-#&gt;  -12.7639   1.8174  -5.2838  10.5123  11.9389   1.5286   5.6593   0.2460
-#&gt;  -12.6807   2.0698 -15.8524  -7.2091  -7.4212   0.1610  13.4838   2.1601
-#&gt;    7.2512  -1.3859   0.5661 -15.6594  -3.3170  -2.9406   0.2242  -3.9722
-#&gt;  -25.7610  -6.7176  10.3714  12.6513 -11.1680  15.6171 -19.9599   1.9016
-#&gt;   -2.6267  20.8037  -2.0124   4.1648   1.2791   6.2882  -3.8924   3.8411
-#&gt; 
-#&gt; Columns 41 to 48  12.6592 -29.1091   4.9542   6.5500  -6.5881  -9.1306  -3.5637   6.9740
-#&gt;   12.5785   4.9390  -7.1512   9.5374  19.1770  -2.1619  -8.8319  -6.1387
-#&gt;    1.6069   0.0530  -7.3822   0.7928   4.1775  -0.2300 -11.3976  15.9961
-#&gt;   -0.9627   5.4817 -15.4045  10.0725   5.5603 -11.5714  12.0004   1.3725
-#&gt;   -2.4268  17.9863  10.2603   6.4608  12.7709   1.9074  -2.4812   2.6753
-#&gt;    6.0541   0.7232  12.2480   5.2808   1.4006  10.6693  12.6639  12.8870
-#&gt;   -6.6617 -14.0500 -14.0936  -3.0179 -11.9357   5.9934   3.5963  -6.5327
-#&gt;   -1.3355  -2.3099  -5.0284   8.5370   3.2054 -19.0103  -6.5679   6.2471
-#&gt;   10.6122  11.9086  -0.1262   6.2764  -3.3073   0.9882   0.3496  13.5524
-#&gt;    1.8818   3.3550 -19.4137  -0.5076   8.5231   5.5520   7.0628  -4.7189
-#&gt;    2.0409 -11.5721   1.2794   4.2566  -1.2600  11.6436   8.4207   9.9688
-#&gt;    7.2530  -0.5176   9.8714  18.3778   1.0340   4.8684  10.6200   8.9634
-#&gt;   -1.2232  -8.9204  -2.1755  -1.1896  -3.3770  -2.9910   0.3296  -5.7852
-#&gt;   -7.5940   5.8494 -15.8490 -10.0028   6.6867   3.4989   2.2816  -7.2253
-#&gt;    6.5804  -0.6134  -2.1385  15.5190   8.1329 -11.0707  11.2124  -2.0956
-#&gt;   20.2437  -4.8852  -6.5738   2.1046  -1.0111  -4.7278   1.7603   7.4301
-#&gt;    4.8448   1.4679   3.6642  -7.8998 -14.3289   8.8894   7.9565   2.9185
-#&gt;   -2.0559   3.1594   5.7790 -13.0144 -14.4777  -5.6680 -10.7241  -4.0730
-#&gt;   -2.1987  -0.4621 -29.1733  -3.3608 -10.2317  -5.5618  -2.7433  16.9024
-#&gt;   -2.5571 -18.0090  -6.6635  17.0925   7.7247  -9.7994  -3.9254   9.3806
-#&gt;   -2.6549  11.5325   1.9270 -18.3873  -4.6849   3.4927  -5.3377   8.0902
-#&gt;   -6.9130   0.0039   0.7236 -23.0587  10.3118  17.0714  10.3968   9.7206
-#&gt;    7.9891  -6.5689 -13.5171   4.7222   1.5046  -7.6745  -7.4960  19.0716
-#&gt;   18.9985   2.7868   8.8201   3.8623   4.7100  -1.8755   1.1543   0.4049
-#&gt;   -1.9884 -16.9054   6.6718   6.4880   5.4618 -16.1783  -5.3956   5.4232
-#&gt;    2.3665   6.1933  11.9676   5.5544  -6.5978   9.0037  -4.2192  -6.4146
-#&gt;   -8.1628 -11.2495  -7.0179  -6.8217  -1.2933   6.0335  -4.5630  -6.4349
-#&gt;   -9.8572 -11.5498  -4.4926  -3.8380   0.7976  -1.0803   8.9509  24.5363
-#&gt;  -16.4516  13.6568  10.2520 -10.9182   7.4908 -13.2014  -1.2110 -18.3038
-#&gt;   -0.4914   4.0439  -2.5239   7.8569  -3.8332  -2.2241   4.7996 -14.7188
-#&gt;   10.2891   6.7213  -2.7195  -8.7093   0.4224   8.2590  14.4842   5.9226
-#&gt;   -2.3089 -15.3783  -6.7664  -7.4489   5.3329  -1.4130  -3.1034  -6.9561
-#&gt;    0.3502  -1.1984 -10.8877  -5.4941   5.8245  -7.2559   2.3815   5.3173
-#&gt; 
-#&gt; Columns 49 to 54   1.2747   5.2395  13.4698  14.2738  12.2964  -1.5817
-#&gt;    1.6910 -12.1397 -12.7877 -10.0018   7.3331   0.6355
-#&gt;    0.7433  -5.8941  -6.2776   4.1104   2.8945   0.8537
-#&gt;  -12.5226  -6.5539 -10.7138   7.6140  -1.7271  -7.8163
-#&gt;    3.8362  -6.7615   2.8636  -6.1179  -3.3340  -9.7257
-#&gt;   -1.7788   4.3488  22.8753   1.8028   5.4304  -1.8112
-#&gt;   -1.4235   3.3501   5.2342   4.2336 -14.7682  -7.2064
-#&gt;   10.1970   3.3946  -1.0360  -1.1210  -4.4042  -4.1440
-#&gt;   11.1277   0.9043  12.8983  13.0145   5.9844  -1.5443
-#&gt;   -4.2875  -5.3223   3.4411  -5.3851  -1.3105   0.6720
-#&gt;   -6.5519  12.1854   0.0411  -1.8376   0.8744  -3.2010
-#&gt;   -0.1420   7.3235   7.9048   7.8784   4.8304 -10.4523
-#&gt;   -3.6911   4.2254   5.7063   8.9996  -4.4160   0.4081
-#&gt;    8.5811  -7.6967  -5.5423  -5.7741  13.3399   1.0483
-#&gt;    7.6063  -7.0702   3.5762  -6.5361  15.6316  -0.6609
-#&gt;   -9.8814   1.9607  -0.4300  -0.0419   2.0097   9.4041
-#&gt;   13.4482  10.9558  14.7211   0.1087  -0.7896   2.0498
-#&gt;  -16.6676  -4.7743  -7.8918   1.7374  -4.2081  -5.8012
-#&gt;   -8.2581  -6.0684   8.2776 -10.0404   9.0940  -0.6857
-#&gt;   -3.9399   6.9471  -3.0039  -4.1868   0.1530  -1.3818
-#&gt;    5.2030   3.0198   0.3190   4.3111  -2.7122   9.6921
-#&gt;    8.5626   9.0417  -1.1888   6.9106  -3.1359   5.2059
-#&gt;   11.7007   7.5871  -0.9854  -0.6513   0.4028  -3.6121
-#&gt;   -9.2621   4.3883  -7.0322  -1.0245  -1.7525  -3.5827
-#&gt;  -20.7991  20.7254 -10.1940   0.9200   5.2781  -3.8300
-#&gt;    1.3659   0.4175  12.2263 -13.9632   9.0599  -8.4849
-#&gt;   -4.6873   1.5768   7.5582  -0.5245  10.7458   5.4537
-#&gt;   13.1642  -0.6127  -1.5006  20.3494   3.8420   6.7404
-#&gt;   -3.1472 -10.0608   7.6488   4.2702  -4.5538  -2.1601
-#&gt;  -27.4567   4.2973 -14.8549  -6.8283 -10.6547  -6.2378
-#&gt;    4.6752  -3.8682   5.5952   1.8841  -0.3845  -1.9298
-#&gt;    5.4148 -13.7245   6.6207   5.3980  -1.0152   7.5767
-#&gt;    4.4342   1.4319   0.4299  -7.3339  -1.4901  -0.7209
-#&gt; 
-#&gt; (20,.,.) = 
-#&gt;  Columns 1 to 8  -0.1621  -1.1462   5.5624  -3.2040 -15.1401   7.7637   2.6860   5.4238
-#&gt;    0.8396  -3.5203   9.1231  15.4953  11.7898  -1.6306  -9.3022   1.3270
-#&gt;   -1.3684  -3.0398 -10.3844   3.6498  -5.7445 -19.0700  -0.8349  10.7056
-#&gt;   -0.0485   5.6198   3.5489   9.6158   1.7576  -4.0273 -13.8786 -16.4744
-#&gt;   -2.5919  -8.6451 -13.4473  -2.8219   6.3921  -3.4349   3.5949  -5.9716
-#&gt;   -4.9905   4.9731  10.2412   1.4691  -4.3466   7.6242  16.1750   6.4483
-#&gt;   -1.5315  -0.1067   0.8477 -12.7635   2.7935  15.6729  -8.6513  -7.0986
-#&gt;    4.7082  -2.4865  -1.2656   4.2381   1.8470  -8.7176  -0.1376  -1.4295
-#&gt;   -4.5697   1.0897   4.0721 -16.2454   0.7888 -14.9746  -1.8295  -3.1963
-#&gt;    0.1823   1.8564   6.2780   3.6687   7.3347  -1.5156  -4.9225 -16.4798
-#&gt;   -1.4463   1.9402  -1.1858  -8.6737 -12.8343  -5.3619   6.5962  -7.5694
-#&gt;   -2.6235   4.2898  -3.5294   4.9846 -10.0827 -30.8690  -0.3993  -3.9279
-#&gt;    0.2410  -3.7194   5.1257   8.5350  -3.8493   3.5762  -2.3895 -23.1393
-#&gt;    1.4401   2.6178  11.1458  10.0722  10.3905  -0.3786  -1.1106 -14.5141
-#&gt;    1.5678  -0.4691  -5.4716 -15.0261  -3.4612 -16.8536  10.2880   0.9811
-#&gt;   -5.6570   0.1170   5.6482   8.2850  -9.1239   2.9178  14.5901  -0.7696
-#&gt;   -1.4326   3.0316   5.5369  10.9070  17.6759   0.0452   7.9348   3.0246
-#&gt;   -1.1936   2.5122   7.0014   1.9653   0.1634  -1.7728  -1.8913   9.4284
-#&gt;    1.6027   5.4183  -3.9848 -12.1217  10.1409  13.7953  21.0640   2.8829
-#&gt;   -1.6356  -3.6014   0.0734  10.9810 -12.7295 -14.2390  -3.9708   6.6003
-#&gt;   -2.8815  -3.3364   9.8267  14.1771   5.8635  -0.1086  -6.7500  11.7749
-#&gt;    7.1709   8.3964   1.4076 -13.0953  -0.5085   4.6746 -12.6335   1.4956
-#&gt;   -0.1749   3.9424   3.2652  16.9313 -10.2269 -11.3709 -13.7632  -4.8729
-#&gt;    2.5850  -0.2872  -5.2173  -7.3208 -12.8036   9.8185   4.2883  -4.7945
-#&gt;    2.4509  10.4656  -9.4248   8.9418  -7.5186   1.9619  -2.0981   9.3907
-#&gt;   -2.6993  -4.5462  -2.2453  -4.4655  -2.1707  -3.4057  -9.1211   7.5554
-#&gt;    2.3860   0.4197  -4.8111  -3.5332  -5.9333  -0.6345  -5.2828 -10.2688
-#&gt;    1.8418   5.4025   3.4547   8.0757  -2.6860  -1.5656  -6.3146   9.1767
-#&gt;    3.1711  -3.4898  -6.5597   0.5113   0.2847  -9.3321   5.6376  -4.6058
-#&gt;    8.5215   6.1679  -2.6810  -5.5771  -6.6683  12.2519   2.2518 -16.5943
-#&gt;   -0.2038  -2.1986   2.9182   1.5978  -9.3678  -4.8177   3.7600  -1.1258
-#&gt;   -2.5421  -3.1019  -1.8657   3.2041   3.9845  -5.3289   8.3623  -6.0053
-#&gt;   -2.2026  -4.4154   1.7093   1.2705 -11.6828   5.0229   7.7633  -4.8920
-#&gt; 
-#&gt; Columns 9 to 16  -9.6379  -8.8353  -2.0164  -3.9967 -14.9024 -13.2522   5.6158  -8.8205
-#&gt;   -1.7650   3.1118  -1.6893  16.0329  11.6731   0.8990   9.0909  -8.5504
-#&gt;   -4.1310  -3.4250   2.2022  -3.6775  -2.3736  -1.0813  -7.3223   2.0234
-#&gt;   -7.7183   5.4651  -2.1186  -0.2175   2.3976  -5.4979  -1.3650   7.4051
-#&gt;    0.3214  -4.2397  -1.5178   7.8210  -2.7930  -4.5397 -10.7320  11.6219
-#&gt;    3.3722   8.3514  12.6042  -6.2435   9.9449   6.8076  -7.1994   7.9202
-#&gt;   -0.7087  -3.7693  -5.8845   2.2852   4.7547   7.6629  -0.9728  -5.5279
-#&gt;    0.5844 -13.7592  -3.8658  -4.3545   1.7405  10.1863   3.6145   7.0898
-#&gt;    1.1853   1.7078  11.3930  -4.2465 -10.0186  -9.8318   6.1848   2.0908
-#&gt;   14.1360   3.3278   8.2308  20.9610  -2.2449   2.7065 -13.6995  12.0065
-#&gt;    8.1762   5.8692  -1.0152  -1.7100  -3.0871   7.8233  -0.6612  -0.3667
-#&gt;    0.7398   2.9508  14.0272 -14.8148  -6.7409   8.5475  -8.1818   7.2170
-#&gt;    3.1297  11.1152  16.2606 -10.3462   4.2060  -4.3647  -0.5266  -2.5029
-#&gt;    6.4961  -8.4803  -3.9518   3.8498  10.7788  -2.2223  -5.9765   1.3549
-#&gt;   -5.1063   4.6187  -2.1797 -22.5261  -7.0005   6.2757  -9.0270  25.0982
-#&gt;   -0.1420   1.3720   7.4795   9.6153   4.3483   7.4302  -9.9186  -5.4024
-#&gt;    3.4592  -8.3573 -17.2632  -6.2594   4.1314  10.3204   7.1232   6.3911
-#&gt;   -5.9489   2.3011  -0.3504  15.1394   0.2342   6.0624   4.9731  -7.1409
-#&gt;   -6.4556  -3.4254 -15.0925  -5.4326  -7.9049   5.4858   1.3068  17.6305
-#&gt;  -11.9726  -0.1709  -2.0448  -8.8095  -3.7666  -1.9334  -3.4856   8.0408
-#&gt;   -5.5254  -2.6180  -5.0557  11.0289  -9.9996  10.3354  -9.6572  -0.4148
-#&gt;  -10.8925 -12.7567   0.0669   4.5049  -2.1159   7.2722  -7.0065 -12.0945
-#&gt;    0.5809  -2.2126  -3.6979 -12.0575  -1.5567  -1.7114   6.3236  -0.1161
-#&gt;   -9.8552   5.4610   0.4954   8.4186  16.3263   8.1864  10.1633  -7.8072
-#&gt;   -5.9334  17.6178  12.9692   3.4399  -1.6990  -7.9805  -3.4114  -7.6213
-#&gt;   19.0645  -4.7203  11.0164  -0.9063   4.8046  -8.6888  11.7567  -3.8675
-#&gt;   11.7528   2.8265  -1.7707  -4.1104 -10.3084  -4.3146   2.8402  -6.7274
-#&gt;  -16.2602  11.0432  -2.8828   0.1409  -2.7798  -4.6135   4.9228  -6.8063
-#&gt;   13.7149  12.8582  -4.4341   4.5278  10.9037   6.1661  -4.7417  14.1037
-#&gt;   -0.3279  21.5061  -1.1329   3.3908 -11.6120  -4.9290  10.7559  11.6238
-#&gt;    4.9558  -5.5311  -8.7086 -15.1959   8.1782  -3.1170 -10.8156 -11.8758
-#&gt;    9.2265  -7.8700   8.2888  -2.7848  -6.6362  -3.8733   6.7170  -9.3142
-#&gt;    7.6552  11.7999   4.7896   7.7007   8.5952   2.0664  11.5924  -1.0443
-#&gt; 
-#&gt; Columns 17 to 24  -0.5321  11.0464   0.0856   0.5655  14.5991  12.9319   0.4836   7.6641
-#&gt;   -6.7579  -5.3101  19.7148   3.8677 -14.5008  -9.3260  -5.3839   3.1493
-#&gt;  -11.6755  -0.6071   2.5071  10.7974  -6.7518   1.3822   3.6109   4.1592
-#&gt;   -2.6031  -5.6818  18.6989  -2.4348  -9.8791   6.2369 -15.8756   9.3603
-#&gt;   -8.1665   6.7317  12.1462  16.9517 -15.2266  -5.7444   1.5100   6.8900
-#&gt;   14.4074   8.8029  -5.4545  15.4322   6.9543   0.2443   2.1472  21.9676
-#&gt;   -1.3419   6.2277  11.9838  -7.4835   2.0090   2.4590   2.9050  11.5184
-#&gt;   -4.5372   1.9943  13.6520  -3.9550 -19.4712   0.5986  25.6229   7.6501
-#&gt;  -12.0105  14.3773   0.4855  -3.1355  12.9980  17.6869  -7.5678 -20.4467
-#&gt;    5.4924  -7.7566 -11.8491   7.2381   3.8651 -11.9726 -13.3154  30.7087
-#&gt;    4.9715   3.6618  -5.9609   6.0388  10.1822  -0.1636   2.9248  21.1720
-#&gt;    4.4321  13.4352   5.0641  11.5861  -9.4310  -1.9816  -6.3792  13.6577
-#&gt;    5.9904  -4.2367   2.1718   3.8697  -2.1954 -12.3323   0.6447  11.7598
-#&gt;  -12.3906  -5.8361  15.9661  -4.0532 -19.7730  -7.9416 -13.6731 -14.6051
-#&gt;  -11.5482  -6.3060   0.4562  10.2639  -7.1850   2.3008   8.4813   4.6316
-#&gt;   16.3523  -1.7841 -12.6560  -0.8742  14.8849  -1.3381  -6.6948  20.4880
-#&gt;   15.8185   0.9278   5.1805  13.5886  -8.4595 -12.5742  -5.5707   6.3068
-#&gt;   -1.7460  -6.1560  -0.1155  -1.3805 -11.9903   5.2742  -2.6962   1.2403
-#&gt;  -20.6755   8.6058 -19.3932 -14.9239  14.5644  24.0100  -0.9756  -3.3321
-#&gt;    2.8825   1.0881  -2.3569  -6.5990  -1.0636  -9.5998  -2.6571   2.1721
-#&gt;   -0.2178 -13.1473 -11.7695 -11.7659  11.5251   6.0879   0.9299   4.1882
-#&gt;    9.5220  -7.2763   9.1356  -7.0332   5.8918  -4.9225  -2.3877  -2.8981
-#&gt;   -3.8195 -11.3067  17.5316  14.3856  -2.0751 -10.7862 -10.6612   0.7596
-#&gt;    4.8557  -3.7417   2.7421  -3.9924  15.1623   5.9950  -3.4114  -5.8673
-#&gt;   12.6678 -10.7893   5.1291   5.0086 -12.7731  -7.5149   1.4797   4.7836
-#&gt;    6.8793  15.9510  -0.8191 -11.1323 -21.8725  -1.1746   9.8490  -1.9807
-#&gt;   -6.6938  11.1216  -5.1915   9.9398   6.9210   0.1600   7.1468  -3.6307
-#&gt;   -9.6642   2.1299  -1.9093   4.7243   2.9268   8.8768  -1.7267  -6.9847
-#&gt;    5.6409   0.9974  22.4437   3.0573 -12.4774   7.3325  14.7486  -7.2137
-#&gt;   11.1331  -3.1796 -14.4943  -6.3280   4.9811  12.2918  -0.0304  -3.3065
-#&gt;   12.5455 -14.9207  -2.9549  -0.1376   7.9348  -4.7665 -13.3366   7.2516
-#&gt;  -17.0092   3.3592  12.2121   0.9171   9.6731   1.8164  10.6970  -3.2334
-#&gt;   -6.6542   7.6337   4.5601   4.0772   9.9508  10.3802  -0.0720  10.9570
-#&gt; 
-#&gt; Columns 25 to 32  14.7814   2.0855 -11.5443  -8.8636  -3.2921  -8.8975  -2.2202   2.0542
-#&gt;   -4.3575  14.1877  21.3428   7.5668  -0.0204   0.6640  -1.0708 -12.3276
-#&gt;   -5.2300   6.1240   7.4403   2.9730   2.8976   1.5309  -4.3767  -3.9842
-#&gt;   10.1208   5.2596  -2.1734  -1.7602   5.0769 -13.1904   3.5458   7.7316
-#&gt;    2.8890   4.8204  -0.0606  -1.4020   6.3128  -0.8638  10.7315   4.2823
-#&gt;   12.1047  -4.3720   4.1811  11.6347   9.3276   9.7779 -12.5684  12.7005
-#&gt;    8.1034  -5.7556   1.0095   7.1351  17.4726   3.1717   7.3035   7.1441
-#&gt;   -1.0227  13.4178  12.7260  -4.6267 -11.1069   3.4204  -7.7886   0.5724
-#&gt;   -1.9399   3.8624  -1.8930  -7.9153  -3.2695  -4.7378  16.7488  10.2630
-#&gt;   17.3373  11.5215   4.4709   2.6323  -5.6626 -22.0692 -10.8594  -5.0825
-#&gt;    8.9432  -0.7131  11.7734  -3.8360  12.8653   9.0661   5.6882 -10.2663
-#&gt;    3.6119   5.3201  -1.0089   7.1640   6.3922   5.7196   4.8207  -6.0330
-#&gt;   16.9539  -9.5322  -8.6009 -10.1206  11.5182   2.3165 -12.3131  -6.9663
-#&gt;   -7.6772   5.6404  11.2296  -6.5533  -3.4842  -1.0550 -11.3338  -2.7795
-#&gt;   -3.0589   2.6489   0.0237   3.8240  -3.4378  -0.5842  12.3299   5.6363
-#&gt;    1.8475  -3.6678  -7.2582   2.2023   7.2895  -5.9783  -3.0408  -9.8344
-#&gt;   -3.9609 -16.0397  -2.3379   9.4380  -2.0241   6.7547   4.8268  -3.5794
-#&gt;  -10.1694  -6.8895  -1.8487  10.7999  11.4416  -8.0720  12.5670  -3.0657
-#&gt;   -5.8882  -6.5137 -14.9760  -9.7027 -10.1098  -4.3701   2.7771  -3.2866
-#&gt;    4.8151  -0.7031  13.0597   0.4731   2.4569   5.2606  -7.8424  -6.6531
-#&gt;    4.7037  -5.5518 -13.4641 -14.5033  -9.6714  -9.2366 -10.1225 -18.6002
-#&gt;   -4.9474  23.4492   7.2622  -3.3553   0.5726   4.0995  11.6019  -6.0000
-#&gt;    7.2762   6.6382  14.5240   2.3849   1.0163   9.1811   4.0564   7.0639
-#&gt;   -7.9470   1.7570  -2.2795  -8.6340 -10.8866   4.2379   2.3539   4.4703
-#&gt;   -1.2421  11.0018   1.2535 -10.1757 -10.0624   4.5975 -10.5913  -5.5541
-#&gt;   -6.7632  -9.0438   3.3362   3.3541  -7.8718  -7.5242 -13.5980   5.6186
-#&gt;   -0.8035  -7.1194   3.7788   0.8013  -3.8723  13.9522  -7.8633   2.2792
-#&gt;   13.8338  21.8256   3.2970  -3.9498 -10.6964   2.5834  -0.7092  -9.7314
-#&gt;   -6.8580  -4.3072 -12.3198  -4.7211 -11.5913   0.8492  -7.2394 -14.1186
-#&gt;   11.9156   4.7210  -0.4337  -4.2159   6.3054  -5.5326   2.0532  23.0029
-#&gt;    4.6552  -2.2351   4.9680  -2.8215   2.8679   3.2595  -1.1829   7.9291
-#&gt;    3.0608  -3.2694  -4.6975  -0.3494  -3.4043   1.4205   2.0749  -7.1137
-#&gt;    0.9281   0.6515   3.0646  -1.4669   3.3878   3.2400  -1.6345  -0.2212
-#&gt; 
-#&gt; Columns 33 to 40   9.4695  -4.2720   5.6363  -3.0229   1.0772   4.3404   2.7501   8.8858
-#&gt;   -0.6602  -6.7124   0.2431   9.1193 -10.9290  -5.7383   0.3658  -0.0696
-#&gt;    4.7950  -5.0814  -4.8913 -10.7810  17.6417  -3.0259  10.1834   9.6151
-#&gt;  -12.9637   1.7315   1.6938   0.9733   4.8371  -3.4247   5.4597  -3.4403
-#&gt;   11.6400  -6.0170   0.6291   4.2209   8.5032 -11.3803   3.6310  -2.7111
-#&gt;   18.9459  -0.5712  -1.3361  -2.9936  -7.7226   4.8759   3.8769  -4.6880
-#&gt;   -6.3998   2.6967   1.3923  18.0601   2.7739   5.9172  -8.6638 -17.0653
-#&gt;   -0.1278   1.9916  -3.3368   4.4702  -4.1882  -0.3656   0.7066 -15.3845
-#&gt;    7.9409   5.3684  -7.0261  -2.6445  -0.9552  -8.4829  -3.8520  -2.4927
-#&gt;   -6.1056 -10.0198   1.6371   9.4007   2.8853   0.1508 -15.6866  -5.7417
-#&gt;   16.0239   3.5886   2.7665   3.9043 -11.8213  -0.2662  -9.6202   2.5200
-#&gt;   22.2557 -10.0216  14.6203  -1.1390 -17.5313   8.1871   0.2831   6.0888
-#&gt;    1.0272   0.8753   7.7316  15.5033 -12.1568  -7.3910   1.0686 -14.9231
-#&gt;  -10.8576 -14.7991   0.4624  17.4074  -3.3230  12.4914  -1.6869  -6.4875
-#&gt;    2.1269  -1.6013 -18.9873  -4.1625   1.2891   0.9499  10.2098  -8.4856
-#&gt;    5.0698  -2.1100   4.8530  -5.0091  -0.4301   4.8984   1.7890   1.5331
-#&gt;   -0.8267  -1.6853  -1.6022  -2.2362  -6.0568  11.0617   0.2674   8.6695
-#&gt;   -0.0252   9.0893  -7.1012   0.4103  -7.2297   3.1142   4.9596  -2.1848
-#&gt;   -3.7477  -5.8867 -15.8268   7.3226   8.2175   2.2897   1.7719  -8.0674
-#&gt;   -5.4004  -0.0574   4.5258   0.9831   2.4997   3.1957   0.6281   5.7712
-#&gt;   -4.2081   5.1848  -1.3553  12.6855   7.1704  -0.9142  -4.2758  -1.6022
-#&gt;    0.9877  -2.9343  -1.7928  -5.7885  14.6740   1.5706  -5.1352   1.3063
-#&gt;    4.1812   1.9999  -0.0702  -3.6045 -12.3558  -4.5112  13.6995  10.0074
-#&gt;   19.6984 -15.9481  -2.8311  15.3651   4.1220  -0.2127   5.8846  -8.1123
-#&gt;    4.0772  -9.8475  13.4586  -2.3518  -0.8136   2.3596   2.0469   3.7419
-#&gt;   -9.5606   2.4306  10.7676 -10.6189  -7.2922   4.6915  -7.9171  -0.7868
-#&gt;   -3.5749   1.9010   0.1177   1.1493  -5.8188  -1.8577   6.5739   0.6710
-#&gt;   -7.3241   4.7530  -3.0966   2.7295  13.0151 -16.4036   2.9880  -4.5060
-#&gt;   12.3952   3.8545  -4.4082   4.3266  -0.9524 -16.2677  -0.3679  -8.6198
-#&gt;   -1.7516  -3.5273   2.3961  16.9297  -8.4036  -5.4464  -2.6408   2.6277
-#&gt;    8.5223  -5.4636   8.0825  -4.6614   0.9806   6.6712   1.4723   6.5970
-#&gt;   -8.9293   8.8014   4.4584   9.0321  -5.9880   0.2031   1.6938   2.3761
-#&gt;    2.8712   1.0475  -3.0858   9.3976   3.4289  -2.6453   8.2908  -2.6215
-#&gt; 
-#&gt; Columns 41 to 48   1.0282  13.6111  -7.0692  -6.5561  -6.1215   4.2179 -15.8656   1.0944
-#&gt;   -6.8862  -1.9006  -0.4087   1.5739  -1.7935  -7.1098 -11.9831  -3.2336
-#&gt;   -8.8803   7.4065  10.8403  -8.0686  -6.7777   1.9696  -6.3820  10.2839
-#&gt;    6.9485   3.3956   3.3277   3.0010  -5.4892   9.3176   2.4230  -8.8501
-#&gt;   -1.8685  -6.0866  -7.0440   1.3316   0.4566 -10.7386 -12.7538   2.4884
-#&gt;    1.0826   3.4354  -2.3901  -2.5510   5.8950 -18.2303  -2.8210 -15.3365
-#&gt;   12.4550  -8.2105  -6.3106   9.6198   2.0183 -11.0000   7.1958 -12.3621
-#&gt;   -6.6099  -6.4649   5.2101  -5.0436  -0.9363   1.8431   7.5087  17.3076
-#&gt;   11.8163  -9.2981  12.9937  -7.0190   0.4832   7.5841  13.7286 -11.5256
-#&gt;    4.6048  -7.5720  -0.0066  -1.8987   7.8794  12.4534  -2.0041 -15.7481
-#&gt;   -6.0992   1.4299   0.5921   7.1506   3.3198   2.5087  -5.8267   5.3067
-#&gt;   -7.0652  -5.6709   8.6364   1.6091   8.9214  -6.8446  -8.9418   2.2526
-#&gt;   -4.1896   4.0452  -8.5550   5.1426   8.1493  -5.5370  -0.8277  20.2468
-#&gt;    0.6847  -4.9379   5.2159  13.2771   9.5256  -5.6407   5.5542  -9.0216
-#&gt;    1.2668 -10.6842   7.2327  -6.9216 -10.3167  -6.8236  11.4308  -6.7491
-#&gt;  -16.9219   8.2406 -14.5830   4.0330  -4.7996  -2.9334 -18.2929 -10.6340
-#&gt;    6.6933 -19.0212   1.3064   5.3452  19.1179  -4.2772   6.7768   6.4978
-#&gt;   -2.2510  -1.6161  -3.3499  12.3447  -8.1975  -5.6954   0.0561   4.9685
-#&gt;   -2.0489   4.4903  12.4049 -11.0199  -7.8862   3.5034  22.8815  -1.3020
-#&gt;  -11.0295   7.3003   9.1650   0.5886  -5.0501  16.9966   3.6578  -1.9543
-#&gt;  -12.0994  -8.8277  -8.1078   9.0297   0.4771 -14.6433 -13.8196   8.1879
-#&gt;   -8.8032   8.7606  12.5391   3.2308  -3.6012  13.6578  -6.5386  11.2676
-#&gt;    1.1408 -15.7426  -9.8566  11.0532  18.8391  -5.4709  -7.6888  -3.2865
-#&gt;    9.8008  15.5037  -2.0344  -2.3890   6.5953   4.7175 -13.4495   5.5906
-#&gt;   -8.6769   9.2517  -4.9664 -12.0861 -10.7764  16.0101 -19.5229  -1.9862
-#&gt;    4.7044   0.4214  14.4001   0.7839   2.9285  -1.7922  27.9028  -4.0251
-#&gt;    4.2268   2.6263   4.2395  -9.2513  -2.2608  -0.8726   6.1136  -7.9764
-#&gt;   -8.6987   3.2674   1.5147   1.2253   1.7308   8.3466   3.8393   8.1229
-#&gt;    3.5640   1.9986  -0.7368  -4.6264  -1.0220   6.6309  12.8355  23.6585
-#&gt;    9.5552  13.7640 -17.8714  -0.0075   1.6017   6.0655 -11.4160  -1.1558
-#&gt;   -8.3849   9.1110  -1.7346   7.3672  14.7431  -1.6898 -14.6558   5.7885
-#&gt;    5.6095   2.8757  10.7389   9.7179  -6.1931 -11.3480   3.8011  14.7688
-#&gt;    9.2174   6.8806  -3.9769   1.5860   5.7901   7.8094  -5.1846  -2.5478
-#&gt; 
-#&gt; Columns 49 to 54   2.4065 -13.3140   0.6704  -2.4768 -12.8549  -1.7246
-#&gt;    0.2845 -18.0647  12.4616  16.8168  -3.2826  -5.1261
-#&gt;    3.2000 -17.7489 -12.3529   4.3966   3.3123  -0.0723
-#&gt;    5.7643   6.9179  -1.1015   0.8025   1.3063  -1.2388
-#&gt;   -2.1739  -6.4094  -4.2746  -3.4872   1.8942   5.8524
-#&gt;   -0.2877   8.1071  -7.1430  -2.0073  -0.5958  -0.6888
-#&gt;   -2.2713   1.2798 -17.8757  -5.1527   4.8490   2.7732
-#&gt;  -15.9087 -18.2601  -2.4891  -6.5220  -4.8303   1.9908
-#&gt;    6.6733  13.3821   3.4212  -9.9961  -7.5872  -0.9345
-#&gt;   13.2082  -5.6533  -6.0926   4.7916  -0.9141  -4.4621
-#&gt;   -5.1544 -13.5698  -5.2283 -10.9659  -3.0882   5.9742
-#&gt;   12.7492  -2.8567  11.5335  -9.9027  -8.9687  -0.3478
-#&gt;   12.4571  13.0040  -9.6064 -15.1820   0.5775   4.7391
-#&gt;    8.7508   7.6705  18.0860  12.4494  -0.2345  -5.2390
-#&gt;    1.8744  -3.1752  -9.6094   7.4359  -3.3472  -4.7954
-#&gt;   10.9020   9.8056   1.9755  -6.1540   1.8149  -2.7576
-#&gt;    9.7128 -10.5345   5.5649  -0.0743   1.4130  -2.4817
-#&gt;   -0.7271  -1.5640  11.7662   1.4352  -6.7959  -1.4346
-#&gt;    2.9927  -5.6591  -1.1774  -1.0120  -0.9716   9.8057
-#&gt;   -7.2276   4.0850  -0.8801   5.2085  -4.5947   4.2419
-#&gt;    6.9749  18.1678  -2.7030 -12.8955  -7.5763  -2.7017
-#&gt;   -1.0681 -17.7503  -5.8964  -6.9143   6.0632  -0.1159
-#&gt;    1.6390   6.9608  -4.7435   4.3092  -2.7255  -7.1128
-#&gt;    3.2927   4.4731   4.8954  -4.1874   1.2480   2.9463
-#&gt;    8.6057  15.0453   2.8538   2.7735 -10.3994  -0.4426
-#&gt;    4.0538  -6.3148   3.5170   7.2334  -4.1120  -6.4150
-#&gt;   -0.8619  11.2716  -4.3405  -1.9576   5.3423  -2.5746
-#&gt;  -11.5102   4.1975   6.4558 -12.8352  -8.9418   0.9216
-#&gt;   -6.5220   5.2047   4.1922  -4.8530   7.3112  -0.0121
-#&gt;   11.3263   9.6909 -11.5103  -4.5442  -2.5944   4.6614
-#&gt;   -5.3980  -7.5770   7.9005  -7.3473  -2.6545  -0.8043
-#&gt;   -1.5698   1.8889  -9.2245  -2.3570   1.1759   0.8253
-#&gt;    1.0218   1.3002  -6.2019   3.4486   0.1029   2.7112
-#&gt; [ CPUFloatType{20,33,54} ]</div><div class='input'># }
-</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></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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,1,.,.) = 
-#&gt;   3.1282  4.7949 -5.5622 -1.0866  3.7899
-#&gt;   3.4914  4.8807  4.3209  0.8437  7.1147
-#&gt;  -5.0276  7.5935  2.4507  6.2129  4.9112
-#&gt;   2.9302 -4.0742  1.0907  0.0252  4.4256
-#&gt;   4.1733  6.6849 -0.1333  0.7716  1.2488
-#&gt; 
-#&gt; (1,2,.,.) = 
-#&gt;    4.0475   8.1682   9.4413  -3.4628  -2.3695
-#&gt;   -3.0556  -4.0963   5.6845   2.0032   0.2438
-#&gt;    1.7169  -2.3353  -2.5287  -5.3750  -4.0894
-#&gt;    1.7329  17.4464  -1.9850  -1.2224  -1.0126
-#&gt;   -8.3888   0.5081  -5.4379  -7.7908   1.4902
-#&gt; 
-#&gt; (1,3,.,.) = 
-#&gt;  -4.6889  1.3331 -3.8890 -4.2812 -1.6408
-#&gt;  -5.7474  6.4888 -0.3864 -0.5556  3.3423
-#&gt;  -0.5830 -4.7014  0.4339 -4.4822 -0.9338
-#&gt;  -3.2573 -5.3475 -6.7339 -4.1705  5.4993
-#&gt;   1.4175  5.6303 -1.1562  5.8984  3.9368
-#&gt; 
-#&gt; (1,4,.,.) = 
-#&gt;   2.7974 -1.8220  1.8960 -2.4363  9.3931
-#&gt;  -0.0791  9.0332  2.4753  6.5632 -1.9094
-#&gt;  -0.4198 -4.7226  4.5077 -6.0814  0.9503
-#&gt;  -0.6672  3.4472 -9.0451  1.0115 -4.7566
-#&gt;   6.7951  6.0656  9.2166  3.3023  1.2087
-#&gt; 
-#&gt; (1,5,.,.) = 
-#&gt;   4.9513 -5.1344  0.4485  2.9806  0.6510
-#&gt;   2.6860 -0.6071  5.0654  6.0352 -0.5143
-#&gt;   3.0599  2.8382 -1.2406 -3.1389 -6.3846
-#&gt;  -5.3770  3.7280 -12.7695 -4.8459  3.4087
-#&gt;   1.6425 -3.5262 -2.5308  5.2363  0.8194
-#&gt; 
-#&gt; (1,6,.,.) = 
-#&gt;    1.6877  11.4338   1.1768  -0.3375  -1.5256
-#&gt;   -3.2510  -1.9791   0.4848 -15.0722   2.7618
-#&gt;    0.3499   4.9010  -0.0095  -6.5474  -4.1558
-#&gt;    3.7509   8.8759   8.5394   2.6775  -2.9372
-#&gt;   -6.3247   2.4670  -8.6403  -6.7153  -1.6774
-#&gt; 
-#&gt; (1,7,.,.) = 
-#&gt;   4.5894  2.9587  3.1108  0.5861 -1.6432
-#&gt;   0.2160 -2.0561  2.0616  0.3782  0.4569
-#&gt;  -0.6198 -5.6256 -4.3481 -0.5680 -6.8947
-#&gt;  -1.1064  3.6694  3.1163 -0.8316  5.3151
-#&gt;  -2.6586 -10.2829 -6.7049  1.4970  0.0378
-#&gt; 
-#&gt; (1,8,.,.) = 
-#&gt;  -0.6657 -4.0047 -1.7929 -2.8312 -5.8341
-#&gt;   0.8324  4.9875 -2.9101  4.2271 -0.9717
-#&gt;  -2.1073  2.4393  0.2561 -7.6416 -2.0861
-#&gt;  -0.1300  5.5534 -0.9693  0.1627 -0.2768
-#&gt;   6.7889 -0.3533  3.2501  3.3338 -0.3276
-#&gt; [ CPUFloatType{1,8,5,5} ]</div><div class='input'># }
-</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_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"></pre>
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diff --git a/docs/reference/torch_cos.html b/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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6755
-#&gt; -0.8212
-#&gt; -0.0338
-#&gt;  0.3401
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_cos</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.7804
-#&gt;  0.6813
-#&gt;  0.9994
-#&gt;  0.9427
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.5781
-#&gt; -0.2866
-#&gt; -1.7790
-#&gt; -0.1226
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_cosh</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.1718
-#&gt;  1.0414
-#&gt;  3.0465
-#&gt;  1.0075
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_cosine_similarity.html b/docs/reference/torch_cosine_similarity.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_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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0628
-#&gt;  0.0734
-#&gt;  0.0535
-#&gt; -0.0163
-#&gt; -0.0151
-#&gt; -0.0383
-#&gt; -0.0902
-#&gt;  0.0108
-#&gt; -0.0247
-#&gt;  0.0816
-#&gt;  0.0586
-#&gt;  0.0277
-#&gt; -0.0927
-#&gt;  0.0636
-#&gt;  0.0423
-#&gt;  0.0609
-#&gt;  0.1381
-#&gt; -0.0185
-#&gt;  0.0668
-#&gt; -0.0083
-#&gt; -0.0827
-#&gt; -0.0799
-#&gt;  0.0255
-#&gt; -0.0536
-#&gt;  0.0417
-#&gt;  0.1178
-#&gt; -0.0586
-#&gt; -0.0301
-#&gt; -0.2182
-#&gt; -0.0238
-#&gt;  0.0960
-#&gt; -0.1743
-#&gt;  0.0430
-#&gt; -0.0019
-#&gt; -0.0712
-#&gt;  0.1294
-#&gt; -0.0705
-#&gt; -0.0441
-#&gt; -0.0381
-#&gt; -0.0269
-#&gt;  0.0380
-#&gt;  0.2009
-#&gt;  0.0309
-#&gt; -0.0537
-#&gt;  0.0422
-#&gt; -0.0888
-#&gt; -0.0909
-#&gt; -0.0396
-#&gt; -0.0815
-#&gt;  0.0297
-#&gt; -0.0226
-#&gt;  0.0781
-#&gt; -0.1015
-#&gt; -0.0516
-#&gt;  0.1183
-#&gt;  0.1247
-#&gt; -0.0117
-#&gt;  0.0998
-#&gt;  0.0107
-#&gt; -0.1497
-#&gt; -0.0889
-#&gt;  0.0906
-#&gt; -0.0145
-#&gt; -0.1604
-#&gt; -0.0323
-#&gt;  0.0500
-#&gt; -0.1800
-#&gt;  0.0532
-#&gt;  0.0932
-#&gt;  0.0290
-#&gt;  0.0148
-#&gt; -0.0677
-#&gt;  0.0150
-#&gt;  0.1278
-#&gt;  0.0463
-#&gt; -0.0320
-#&gt;  0.0187
-#&gt; -0.0964
-#&gt;  0.0039
-#&gt; -0.0098
-#&gt; -0.0187
-#&gt; -0.1616
-#&gt; -0.0879
-#&gt; -0.0506
-#&gt;  0.0167
-#&gt; -0.0330
-#&gt; -0.0717
-#&gt;  0.1178
-#&gt; -0.0280
-#&gt;  0.0411
-#&gt; -0.1074
-#&gt; -0.0523
-#&gt; -0.1518
-#&gt; -0.0476
-#&gt; -0.0382
-#&gt;  0.0293
-#&gt;  0.0484
-#&gt; -0.0200
-#&gt; -0.1260
-#&gt;  0.0981
-#&gt; [ CPUFloatType{100} ]</div><div class='input'># }
-</div></pre>
-  </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>
-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.1643 -0.5561  0.7230
-#&gt; -0.0220  2.0844 -0.3671
-#&gt; -1.4814 -0.9811 -1.6675
-#&gt;  0.4909  0.6285 -0.6379
-#&gt; [ CPUFloatType{4,3} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.5896 -1.4300 -0.5647
-#&gt; -1.4339 -1.2857  0.3288
-#&gt; -0.9875  0.1254  0.1272
-#&gt;  0.6789 -0.6983 -0.5560
-#&gt; [ CPUFloatType{4,3} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.3479 -1.8068  0.7810
-#&gt;  0.2135  0.5336  3.0171
-#&gt;  0.0842  1.8351 -1.1546
-#&gt; -0.7949 -0.1601 -0.7695
-#&gt; [ CPUFloatType{4,3} ]</div><div class='input'><span class='fu'>torch_cross</span>(<span class='no'>a</span>, <span class='no'>b</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.3479 -1.8068  0.7810
-#&gt;  0.2135  0.5336  3.0171
-#&gt;  0.0842  1.8351 -1.1546
-#&gt; -0.7949 -0.1601 -0.7695
-#&gt; [ CPUFloatType{4,3} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_cummax.html b/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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.4011
-#&gt;  0.0282
-#&gt;  1.4709
-#&gt; -0.3322
-#&gt; -1.1082
-#&gt; -0.6219
-#&gt; -1.1143
-#&gt;  0.0945
-#&gt; -0.5687
-#&gt; -0.1941
-#&gt; [ CPUFloatType{10} ]</div><div class='input'><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><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  0.4011
-#&gt;  0.4011
-#&gt;  1.4709
-#&gt;  1.4709
-#&gt;  1.4709
-#&gt;  1.4709
-#&gt;  1.4709
-#&gt;  1.4709
-#&gt;  1.4709
-#&gt;  1.4709
-#&gt; [ CPUFloatType{10} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  0
-#&gt;  0
-#&gt;  2
-#&gt;  2
-#&gt;  2
-#&gt;  2
-#&gt;  2
-#&gt;  2
-#&gt;  2
-#&gt;  2
-#&gt; [ CPULongType{10} ]
-#&gt; </div><div class='input'># }
-</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/docs/reference/torch_cummin.html b/docs/reference/torch_cummin.html
deleted file mode 100644
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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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.1300
-#&gt; -0.0916
-#&gt;  1.2476
-#&gt;  1.1859
-#&gt; -0.8123
-#&gt; -1.0110
-#&gt;  0.5914
-#&gt;  1.0707
-#&gt;  1.3137
-#&gt; -0.0139
-#&gt; [ CPUFloatType{10} ]</div><div class='input'><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><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; -1.1300
-#&gt; -1.1300
-#&gt; -1.1300
-#&gt; -1.1300
-#&gt; -1.1300
-#&gt; -1.1300
-#&gt; -1.1300
-#&gt; -1.1300
-#&gt; -1.1300
-#&gt; -1.1300
-#&gt; [ CPUFloatType{10} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  0
-#&gt;  0
-#&gt;  0
-#&gt;  0
-#&gt;  0
-#&gt;  0
-#&gt;  0
-#&gt;  0
-#&gt;  0
-#&gt;  0
-#&gt; [ CPULongType{10} ]
-#&gt; </div><div class='input'># }
-</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/docs/reference/torch_cumprod.html b/docs/reference/torch_cumprod.html
deleted file mode 100644
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-
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-
-<div class="row">
-  <div class="col-md-9 contents">
-    <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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.1828
-#&gt; -0.2114
-#&gt;  0.2918
-#&gt;  0.5649
-#&gt; -0.9559
-#&gt; -0.9795
-#&gt; -0.7152
-#&gt; -0.2064
-#&gt;  0.5134
-#&gt;  0.5777
-#&gt; [ CPUFloatType{10} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.1828
-#&gt;  0.2500
-#&gt;  0.0729
-#&gt;  0.0412
-#&gt; -0.0394
-#&gt;  0.0386
-#&gt; -0.0276
-#&gt;  0.0057
-#&gt;  0.0029
-#&gt;  0.0017
-#&gt; [ CPUFloatType{10} ]</div><div class='input'># }
-</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/docs/reference/torch_cumsum.html b/docs/reference/torch_cumsum.html
deleted file mode 100644
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.2718
-#&gt;  0.1169
-#&gt;  0.3449
-#&gt; -1.6346
-#&gt; -0.0393
-#&gt; -0.0197
-#&gt;  0.0704
-#&gt;  0.5245
-#&gt; -1.4307
-#&gt; -0.8103
-#&gt; [ CPUFloatType{10} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.2718
-#&gt;  0.3887
-#&gt;  0.7335
-#&gt; -0.9010
-#&gt; -0.9403
-#&gt; -0.9600
-#&gt; -0.8896
-#&gt; -0.3651
-#&gt; -1.7958
-#&gt; -2.6060
-#&gt; [ CPUFloatType{10} ]</div><div class='input'># }
-</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/docs/reference/torch_det.html b/docs/reference/torch_det.html
deleted file mode 100644
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-  <div class="col-md-9 contents">
-    <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></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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.485994
-#&gt; [ CPUFloatType{} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -0.0024 -1.0185
-#&gt;   0.3236 -0.2787
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   0.4021 -0.3008
-#&gt;  -0.8884  0.5782
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -0.3821  1.3521
-#&gt;  -0.3608  0.3485
-#&gt; [ CPUFloatType{3,2,2} ]</div><div class='input'><span class='no'>A</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;  0.3302
-#&gt; -0.0347
-#&gt;  0.3547
-#&gt; [ CPUFloatType{3} ]</div><div class='input'># }
-</div></pre>
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-    <div class="hidden name"><code>torch_device.Rd</code></div>
-    </div>
-
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-    <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='co'># \dontrun{</span>
-
-<span class='co'># Via string</span>
-<span class='fu'>torch_device</span>(<span class='st'>"cuda:1"</span>)</div><div class='output co'>#&gt; torch_device(type='cuda', index=1)</div><div class='input'><span class='fu'>torch_device</span>(<span class='st'>"cpu"</span>)</div><div class='output co'>#&gt; torch_device(type='cpu')</div><div class='input'><span class='fu'>torch_device</span>(<span class='st'>"cuda"</span>) <span class='co'># current cuda device</span></div><div class='output co'>#&gt; torch_device(type='cuda')</div><div class='input'>
-<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>)</div><div class='output co'>#&gt; torch_device(type='cuda', index=0)</div><div class='input'><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><div class='input'>
-# }
-</div></pre>
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-    <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>
-
-
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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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-    </nav>
-  </div>
-</div>
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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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -2.3038  0.0000  0.0000
-#&gt;   0.0000  2.0129  0.0000
-#&gt;   0.0000  0.0000 -1.6884
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   0.8534  0.0000  0.0000
-#&gt;   0.0000 -0.5520  0.0000
-#&gt;   0.0000  0.0000  2.2299
-#&gt; [ CPUFloatType{2,3,3} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   0.0000 -2.3038  0.0000  0.0000
-#&gt;   0.0000  0.8534  0.0000  0.0000
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   0.0000  0.0000  2.0129  0.0000
-#&gt;   0.0000  0.0000 -0.5520  0.0000
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;   0.0000  0.0000  0.0000 -1.6884
-#&gt;   0.0000  0.0000  0.0000  2.2299
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;   0  0  0  0
-#&gt;   0  0  0  0
-#&gt; [ CPUFloatType{4,2,4} ]</div><div class='input'># }
-</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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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.2456
-#&gt; -1.0479
-#&gt;  0.2374
-#&gt; [ CPUFloatType{3} ]</div><div class='input'><span class='fu'>torch_diagflat</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.2456  0.0000  0.0000
-#&gt;  0.0000 -1.0479  0.0000
-#&gt;  0.0000  0.0000  0.2374
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><span class='fu'>torch_diagflat</span>(<span class='no'>a</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000  1.2456  0.0000  0.0000
-#&gt;  0.0000  0.0000 -1.0479  0.0000
-#&gt;  0.0000  0.0000  0.0000  0.2374
-#&gt;  0.0000  0.0000  0.0000  0.0000
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5628 -0.2248
-#&gt;  0.2077 -2.6745
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'><span class='fu'>torch_diagflat</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5628  0.0000  0.0000  0.0000
-#&gt;  0.0000 -0.2248  0.0000  0.0000
-#&gt;  0.0000  0.0000  0.2077  0.0000
-#&gt;  0.0000  0.0000  0.0000 -2.6745
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'># }
-</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.4461  1.6781  0.7912
-#&gt; -2.0005 -0.9287 -0.1604
-#&gt;  0.6617 -0.6939  1.5567
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.4461
-#&gt; -0.9287
-#&gt;  1.5567
-#&gt; [ CPUFloatType{3} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.6781
-#&gt; -0.1604
-#&gt; [ CPUFloatType{2} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   0.3670
-#&gt;  -1.7063
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   0.6795
-#&gt;   1.1359
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -0.5056
-#&gt;  -0.1993
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;   0.5929
-#&gt;  -0.6632
-#&gt; [ CPUFloatType{4,2,1} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_digamma.html b/docs/reference/torch_digamma.html
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-
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.5772
-#&gt; -1.9635
-#&gt; [ CPUFloatType{2} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_dist.html b/docs/reference/torch_dist.html
deleted file mode 100644
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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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.3528
-#&gt; -0.2518
-#&gt; -0.8406
-#&gt; -0.3756
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5376
-#&gt; -1.6162
-#&gt; -0.8764
-#&gt;  0.2081
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_dist</span>(<span class='no'>x</span>, <span class='no'>y</span>, <span class='fl'>3.5</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 1.38438
-#&gt; [ CPUFloatType{} ]</div><div class='input'><span class='fu'>torch_dist</span>(<span class='no'>x</span>, <span class='no'>y</span>, <span class='fl'>3</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 1.40023
-#&gt; [ CPUFloatType{} ]</div><div class='input'><span class='fu'>torch_dist</span>(<span class='no'>x</span>, <span class='no'>y</span>, <span class='fl'>0</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 4
-#&gt; [ CPUFloatType{} ]</div><div class='input'><span class='fu'>torch_dist</span>(<span class='no'>x</span>, <span class='no'>y</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 2.16879
-#&gt; [ CPUFloatType{} ]</div><div class='input'># }
-</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/docs/reference/torch_div.html b/docs/reference/torch_div.html
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.2469
-#&gt; -0.8301
-#&gt;  0.6777
-#&gt;  0.4991
-#&gt;  2.3110
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='fu'>torch_div</span>(<span class='no'>a</span>, <span class='fl'>0.5</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -2.4938
-#&gt; -1.6601
-#&gt;  1.3554
-#&gt;  0.9983
-#&gt;  4.6219
-#&gt; [ CPUFloatType{5} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.1627  0.0346  0.2195  0.4264
-#&gt; -1.3478 -2.4998 -1.5522  0.7143
-#&gt;  0.2875 -1.3762  0.8071 -0.8691
-#&gt;  0.7893 -1.7013 -0.2038 -0.3908
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.2924
-#&gt;  1.8341
-#&gt;  0.0283
-#&gt; -0.6063
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_div</span>(<span class='no'>a</span>, <span class='no'>b</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;   0.5566   0.0188   7.7430  -0.7033
-#&gt;   4.6100  -1.3630 -54.7623  -1.1781
-#&gt;  -0.9833  -0.7503  28.4751   1.4334
-#&gt;  -2.6995  -0.9276  -7.1916   0.6445
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'># }
-</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>
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diff --git a/docs/reference/torch_dot.html b/docs/reference/torch_dot.html
deleted file mode 100644
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-  <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></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='co'># \dontrun{</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><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_dtype.html b/docs/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></small>
-    <div class="hidden name"><code>torch_eig.Rd</code></div>
-    </div>
-
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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>
-
-    
-
-
-<p>Computes the eigenvalues and eigenvectors of a real square matrix.</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>
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-    </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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.6481 -0.3038  0.7547  0.3279
-#&gt; -0.7964  0.3732 -0.9273 -0.4029
-#&gt;  0.2729 -0.1279  0.3178  0.1381
-#&gt; -0.4882  0.2288 -0.5684 -0.2470
-#&gt;  2.6219 -1.2288  3.0530  1.3266
-#&gt; [ CPUFloatType{5,4} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  4.7791 -6.3600  2.0599
-#&gt;  1.2294  0.6515 -3.1033
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   0.7724  0.1715  0.3778  0.5154
-#&gt;  -0.7152  3.1174  0.5206 -3.0151
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -2.1745  4.5514  0.2279  2.8187
-#&gt;  -0.4776  1.4331 -1.1076  0.3273
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;   1.0775  0.4867 -2.7086 -0.8871
-#&gt;  -1.4834 -2.0710 -1.2663  0.2878
-#&gt; [ CPUFloatType{3,2,4} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.7209
-#&gt;  0.7370
-#&gt;  0.0642
-#&gt; [ CPUFloatType{3} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.1323  2.2000 -0.2178
-#&gt;  1.1550 -0.8415 -1.0067
-#&gt;  2.1543 -0.0441 -1.3181
-#&gt; -0.0296 -2.3285  0.7952
-#&gt; [ CPUFloatType{4,3} ]</div><div class='input'><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><div class='output co'>#&gt; [1] 2 3 5 4</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_empty.html b/docs/reference/torch_empty.html
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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='co'># \dontrun{</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;  0.0000e+00  1.0842e-19 -2.0454e-24
-#&gt;  8.5920e+09  8.4078e-45  4.9045e-44
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_empty_like.html b/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>
-
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-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.4057e+14  1.4057e+14  1.4057e+14
-#&gt;  0.0000e+00  8.5899e+09  1.4057e+14
-#&gt; [ CPULongType{2,3} ]</div><div class='input'># }
-</div></pre>
-  </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>
-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000e+00 -4.2887e-24  2.2101e-10
-#&gt;  1.0842e-19  2.0005e+00  4.5592e+30
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'><span class='no'>a</span>$<span class='fu'>stride</span>(<span class='fl'>1</span>)</div><div class='output co'>#&gt; [1] 1</div><div class='input'><span class='no'>a</span>$<span class='fu'>size</span>(<span class='fl'>1</span>)</div><div class='output co'>#&gt; [1] 2</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <p>Eq</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 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>
-
-    
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-<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>
-
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-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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><div class='input'># }
-</div></pre>
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-<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><div class='input'># }
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-    <h1>Erf</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</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>
-
-    <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='co'># \dontrun{</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>)))</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000
-#&gt; -0.8427
-#&gt;  1.0000
-#&gt; [ CPUFloatType{3} ]</div><div class='input'># }
-</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_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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.0000e+00
-#&gt;  1.8427e+00
-#&gt;  2.8026e-45
-#&gt; [ CPUFloatType{3} ]</div><div class='input'># }
-</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/docs/reference/torch_erfinv.html b/docs/reference/torch_erfinv.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_erfinv.Rd</code></div>
-    </div>
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-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000
-#&gt;  0.4769
-#&gt;    -inf
-#&gt; [ CPUFloatType{3} ]</div><div class='input'># }
-</div></pre>
-  </div>
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diff --git a/docs/reference/torch_exp.html b/docs/reference/torch_exp.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_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='co'># \dontrun{</span>
-
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-#&gt;  1
-#&gt;  2
-#&gt; [ CPUFloatType{2} ]</div><div class='input'># }
-</div></pre>
-  </div>
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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='co'># \dontrun{</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><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_eye.html b/docs/reference/torch_eye.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_eye.Rd</code></div>
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-
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-    <p>Eye</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>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='co'># \dontrun{</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><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_fft.html b/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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   1.6091  1.1177
-#&gt;   1.7387  0.5245
-#&gt;   1.3022 -7.5020
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -2.2896  1.5947
-#&gt;   2.3894 -3.7256
-#&gt;   2.4421 -6.0534
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -2.6684  0.2216
-#&gt;   4.6351  2.1387
-#&gt;   1.0104  2.1655
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;   0.6484  1.5756
-#&gt;   3.6644  0.9869
-#&gt;   0.8331  1.2714
-#&gt; [ CPUFloatType{4,3,2} ]</div><div class='input'><span class='co'># batched 1D FFT</span>
-<span class='fu'>torch_fft</span>(<span class='no'>x</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -0.6751  1.1274
-#&gt;   3.1069 -0.0189
-#&gt;   1.3969 -2.5296
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   1.0646 -0.5105
-#&gt;   0.4540 -0.7223
-#&gt;   1.9041 -2.0146
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;   0.1455 -0.4577
-#&gt;   0.0800  1.3505
-#&gt;  -0.2407 -0.1386
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;   1.0741  0.9585
-#&gt;  -1.9022 -0.0848
-#&gt;  -1.7582 -2.8191
-#&gt; [ CPUFloatType{4,3,2} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,1,1,.,.) = 
-#&gt;  -5.4725 -1.0036
-#&gt;  -0.4072 -9.9106
-#&gt;  -6.2423 -6.4070
-#&gt;  -1.9919 -0.2080
-#&gt;  -1.5095 -4.7941
-#&gt; 
-#&gt; (2,1,1,.,.) = 
-#&gt;   -1.3392   3.2072
-#&gt;   -2.8398   7.8139
-#&gt;    0.8047  -3.0656
-#&gt;   10.5509  -0.0158
-#&gt;    0.4441   1.4712
-#&gt; 
-#&gt; (3,1,1,.,.) = 
-#&gt;   5.8889 -0.8239
-#&gt;   0.6557 -4.5634
-#&gt;   3.6018 -5.1202
-#&gt;   6.7747 -1.8286
-#&gt;  -6.4471  3.7626
-#&gt; 
-#&gt; (1,2,1,.,.) = 
-#&gt;  -0.2606  1.6037
-#&gt;   1.2933  2.7796
-#&gt;  -1.2262 -8.3113
-#&gt;  -2.9285 -0.8387
-#&gt;  -8.8958  7.9346
-#&gt; 
-#&gt; (2,2,1,.,.) = 
-#&gt;   -5.8665  -5.8427
-#&gt;    3.5695  -0.4600
-#&gt;   10.6353  -4.9917
-#&gt;   -1.8417  -2.2605
-#&gt;    4.0205   3.3620
-#&gt; 
-#&gt; (3,2,1,.,.) = 
-#&gt;  -5.3450  5.1190
-#&gt;  -7.1003  3.1183
-#&gt;   2.1035 -0.7317
-#&gt;  -1.7222  6.8806
-#&gt;   1.1505  7.5300
-#&gt; 
-#&gt; (1,3,1,.,.) = 
-#&gt;  -9.1081  7.4756
-#&gt;   5.1849 -1.2153
-#&gt;  -1.6891 -2.1615
-#&gt;  -4.9926  0.1991
-#&gt;   5.4721 -2.1878
-#&gt; 
-#&gt; (2,3,1,.,.) = 
-#&gt;  -4.9459  0.0120
-#&gt;  -5.7250 -0.8370
-#&gt;   0.7157  1.3166
-#&gt;  -9.2864  4.1198
-#&gt;  -2.5485  6.5091
-#&gt; 
-#&gt; (3,3,1,.,.) = 
-#&gt;   2.9661 -4.1025
-#&gt;   5.0720  3.2858
-#&gt;  -1.6322 -9.3722
-#&gt;  -0.3661 -5.5639
-#&gt;   6.5081 -2.4129
-#&gt; 
-#&gt; (1,1,2,.,.) = 
-#&gt;   -2.3401   1.2643
-#&gt;    5.3967  -7.4990
-#&gt;    0.1779   3.4217
-#&gt;    6.0804   6.1065
-#&gt;   10.1270  -3.5843
-#&gt; 
-#&gt; (2,1,2,.,.) = 
-#&gt;   -9.0572  -1.9909
-#&gt;    4.2537  -0.0385
-#&gt;    5.9323  -1.1033
-#&gt;   -1.4235  11.0225
-#&gt;    0.3750   3.9369
-#&gt; 
-#&gt; (3,1,2,.,.) = 
-#&gt;   7.3848 -1.2410
-#&gt;  -1.4446  1.3323
-#&gt;  -1.4867  6.3887
-#&gt;  -1.3989  4.3918
-#&gt;  -1.6242  0.4001
-#&gt; 
-#&gt; (1,2,2,.,.) = 
-#&gt;   2.2515 -11.7348
-#&gt;  -2.9592 -0.4199
-#&gt;   6.0865 -2.5877
-#&gt;  -3.9066  0.8276
-#&gt;  -6.4707 -0.1932
-#&gt; 
-#&gt; (2,2,2,.,.) = 
-#&gt;   -3.5320 -15.9640
-#&gt;   -3.2020  -4.1973
-#&gt;    3.5907  -1.9048
-#&gt;    4.8187  10.5827
-#&gt;    2.0354   3.6088
-#&gt; 
-#&gt; (3,2,2,.,.) = 
-#&gt;  -1.5620  2.4318
-#&gt;  -4.4488 -3.6563
-#&gt;   1.4412  6.2894
-#&gt;   3.7142 -4.4966
-#&gt;  -1.9539  0.9322
-#&gt; 
-#&gt; (1,3,2,.,.) = 
-#&gt;   -2.5739  11.5350
-#&gt;   -5.6638  -1.9311
-#&gt;   -7.1538   1.8841
-#&gt;    9.9282  -5.0425
-#&gt;    4.0550 -13.0364
-#&gt; 
-#&gt; (2,3,2,.,.) = 
-#&gt;  -4.4776 -3.1815
-#&gt;   5.2017  7.8643
-#&gt;  -1.9960 -3.8433
-#&gt;   1.8730 -3.0428
-#&gt;   0.4528  1.8324
-#&gt; 
-#&gt; (3,3,2,.,.) = 
-#&gt;  -1.6393  3.0116
-#&gt;  -7.4910  4.6567
-#&gt;   3.9892 -1.2984
-#&gt;   2.3925  1.7542
-#&gt;  -0.4821  2.7867
-#&gt; 
-#&gt; (1,1,3,.,.) = 
-#&gt;  -1.9416  6.9300
-#&gt;  -5.9122  4.7269
-#&gt;  -6.1427 -3.6378
-#&gt;   4.8333 -7.6424
-#&gt;  -0.8626 -7.3429
-#&gt; 
-#&gt; (2,1,3,.,.) = 
-#&gt;   0.7169  7.3508
-#&gt;  -2.2415  0.6318
-#&gt;   8.5924 -3.5120
-#&gt;  -1.8660 -2.9021
-#&gt;   2.5242  6.2449
-#&gt; 
-#&gt; (3,1,3,.,.) = 
-#&gt;   3.2523 -9.4747
-#&gt;   6.7858  2.1141
-#&gt;  -3.1627 -8.3128
-#&gt;   8.1738  1.7333
-#&gt;  -12.5794  7.6793
-#&gt; 
-#&gt; (1,2,3,.,.) = 
-#&gt;   2.1388 -0.8053
-#&gt;  -7.5468 -4.9134
-#&gt;  -1.4956  0.7191
-#&gt;   4.5485  1.3213
-#&gt;   1.9775  3.2742
-#&gt; 
-#&gt; (2,2,3,.,.) = 
-#&gt;    2.7705  -3.2113
-#&gt;   -7.1385  10.5362
-#&gt;    2.1267  -3.4305
-#&gt;   -3.4051  -2.5411
-#&gt;   -2.7076   1.7485
-#&gt; 
-#&gt; (3,2,3,.,.) = 
-#&gt;    3.3394  10.9207
-#&gt;   -0.5970  -0.5034
-#&gt;    8.1799   3.0040
-#&gt;   -0.9163  -3.5497
-#&gt;   -3.1114  -5.1809
-#&gt; 
-#&gt; (1,3,3,.,.) = 
-#&gt;  -4.1027 -2.0095
-#&gt;   2.7722 -4.7394
-#&gt;  -3.5039 -1.9563
-#&gt;  -4.3085  8.7966
-#&gt;   2.7449 -4.6416
-#&gt; 
-#&gt; (2,3,3,.,.) = 
-#&gt;  -8.7689 -5.5072
-#&gt;   0.0556 -7.0627
-#&gt;   6.2366  2.7693
-#&gt;  -0.9913  0.2007
-#&gt;   2.4020  2.7007
-#&gt; 
-#&gt; (3,3,3,.,.) = 
-#&gt;    0.1972  -1.1722
-#&gt;    7.4460   3.4332
-#&gt;   -4.7450 -11.9086
-#&gt;   12.5079 -12.2708
-#&gt;   -2.9972  -0.8924
-#&gt; 
-#&gt; (1,1,4,.,.) = 
-#&gt;  -2.6641 -4.3296
-#&gt;   3.0735  1.2215
-#&gt;   0.2079 -4.6184
-#&gt;  -2.3720 -1.2896
-#&gt;  -3.9109 -9.9345
-#&gt; 
-#&gt; (2,1,4,.,.) = 
-#&gt;  -5.0131 -2.2123
-#&gt;   2.7328 -6.4654
-#&gt;  -3.3791 -1.4795
-#&gt;  -3.0048  4.2683
-#&gt;   8.6069  3.8774
-#&gt; 
-#&gt; (3,1,4,.,.) = 
-#&gt;   0.5565 -3.4152
-#&gt;   3.1825 -1.2100
-#&gt;  -5.6883 -4.5265
-#&gt;  -2.6019  6.2445
-#&gt;  -1.2380  0.0733
-#&gt; 
-#&gt; (1,2,4,.,.) = 
-#&gt;  -0.0619 -1.4112
-#&gt;   3.0482 -12.0317
-#&gt;   2.3142 -5.9550
-#&gt;   1.6332 -4.9998
-#&gt;   3.3110  3.2442
-#&gt; 
-#&gt; (2,2,4,.,.) = 
-#&gt;   5.7054 -4.5310
-#&gt;   4.0814  0.5770
-#&gt;  -7.4352  1.0886
-#&gt;  -4.4217 -0.5822
-#&gt;  -2.2274 -2.5406
-#&gt; 
-#&gt; (3,2,4,.,.) = 
-#&gt;   -6.0243   1.6214
-#&gt;    0.9687   2.5785
-#&gt;   -9.2330  12.6507
-#&gt;    0.1245  -3.6675
-#&gt;    2.2133  -0.1646
-#&gt; 
-#&gt; (1,3,4,.,.) = 
-#&gt;  -9.8819  5.0379
-#&gt;  -15.9787 -0.0044
-#&gt;  -3.3972 -4.1292
-#&gt;  -0.2631  3.1780
-#&gt;  -8.3382  2.3260
-#&gt; 
-#&gt; (2,3,4,.,.) = 
-#&gt;   2.6493  5.7085
-#&gt;  -0.8552 -6.9286
-#&gt;   8.8915  1.1790
-#&gt;  -8.0308 -2.3641
-#&gt;  -5.5359 -0.2864
-#&gt; 
-#&gt; (3,3,4,.,.) = 
-#&gt;   6.0509 -4.4619
-#&gt;   2.5174  1.0450
-#&gt;   3.2386 -2.9879
-#&gt;  -4.2332 -11.7932
-#&gt;   8.3415 -11.6524
-#&gt; 
-#&gt; (1,1,5,.,.) = 
-#&gt;  -2.6344  8.4594
-#&gt;  -9.3032 -0.0826
-#&gt;  -0.7539  0.4355
-#&gt;  -5.7352  6.4314
-#&gt;   1.3710  5.0478
-#&gt; 
-#&gt; (2,1,5,.,.) = 
-#&gt;   5.7266 -5.1236
-#&gt;  -3.1017 -0.2088
-#&gt;  -6.6610  5.8354
-#&gt;   3.1587  1.7131
-#&gt;   3.4059  0.6908
-#&gt; 
-#&gt; (3,1,5,.,.) = 
-#&gt;   12.7228   0.6750
-#&gt;   -4.4296   8.4994
-#&gt;    2.5491   1.5131
-#&gt;   -1.2545  -5.1292
-#&gt;    8.7953   3.5627
-#&gt; 
-#&gt; (1,2,5,.,.) = 
-#&gt;  -2.5561 -2.6800
-#&gt;   3.0704  6.9316
-#&gt;  -4.2936  0.3370
-#&gt;  -3.2507  9.0391
-#&gt;   7.8262 -2.8746
-#&gt; 
-#&gt; (2,2,5,.,.) = 
-#&gt;  -4.7915 -2.9852
-#&gt;   0.4093 -1.2396
-#&gt;   1.8851  0.8607
-#&gt;  -3.2552 -4.0731
-#&gt;   7.0195 -3.8280
-#&gt; 
-#&gt; (3,2,5,.,.) = 
-#&gt;  -6.9953 -12.4542
-#&gt;   7.8834  1.2176
-#&gt;   9.6941 -9.1636
-#&gt;  -15.3205  2.1126
-#&gt;   0.3537 -10.4525
-#&gt; 
-#&gt; (1,3,5,.,.) = 
-#&gt;  -10.7714   0.7571
-#&gt;   -0.7565  -0.4820
-#&gt;    5.6134  -8.9826
-#&gt;   -1.1373  -4.2921
-#&gt;    4.1096  -1.6137
-#&gt; 
-#&gt; (2,3,5,.,.) = 
-#&gt;   3.5382  6.4175
-#&gt;  -2.8167 -1.5735
-#&gt;   8.0241 -4.9140
-#&gt;   1.6143  7.2625
-#&gt;  -2.5749  4.8191
-#&gt; 
-#&gt; (3,3,5,.,.) = 
-#&gt;   9.8500 -0.4376
-#&gt;  -4.1255 -2.8707
-#&gt;  -4.6457 -0.7456
-#&gt;  -4.6735  2.8975
-#&gt;   2.7895  9.0651
-#&gt; [ CPUFloatType{3,3,5,5,2} ]</div><div class='input'>
-# }
-</div></pre>
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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></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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt; [ CPUFloatType{2} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  2
-#&gt; [ CPUFloatType{1,2} ]</div><div class='input'># }
-</div></pre>
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deleted file mode 100644
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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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   0  1
-#&gt;   2  3
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   4  5
-#&gt;   6  7
-#&gt; [ CPUFloatType{2,2,2} ]</div><div class='input'><span class='fu'>torch_flip</span>(<span class='no'>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><div class='input'># }
-</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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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
-</div>
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diff --git a/docs/reference/torch_floor.html b/docs/reference/torch_floor.html
deleted file mode 100644
index 049e2d127..000000000
--- a/docs/reference/torch_floor.html
+++ /dev/null
@@ -1,234 +0,0 @@
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.4440
-#&gt;  0.1195
-#&gt;  2.0850
-#&gt;  0.6923
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_floor</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  0
-#&gt;  2
-#&gt;  0
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
-</div>
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-</html>
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diff --git a/docs/reference/torch_floor_divide.html b/docs/reference/torch_floor_divide.html
deleted file mode 100644
index 5c92aad1e..000000000
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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>
-
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-    <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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2
-#&gt;  1
-#&gt; [ CPUFloatType{2} ]</div><div class='input'><span class='fu'>torch_floor_divide</span>(<span class='no'>a</span>, <span class='fl'>1.4</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2
-#&gt;  2
-#&gt; [ CPUFloatType{2} ]</div><div class='input'># }
-</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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1
-#&gt; -0
-#&gt; -1
-#&gt;  1
-#&gt;  0
-#&gt;  1
-#&gt; [ CPUFloatType{6} ]</div><div class='input'><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><div class='input'># }
-</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/docs/reference/torch_frac.html b/docs/reference/torch_frac.html
deleted file mode 100644
index 208d4c8a2..000000000
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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>
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-    <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='co'># \dontrun{</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='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><div class='input'># }
-</div></pre>
-  </div>
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diff --git a/docs/reference/torch_full.html b/docs/reference/torch_full.html
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-    <h1>Full</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</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>
-
-
-    <h2 class="hasAnchor" 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='co'># \dontrun{</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><div class='input'># }
-</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="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> 1.5.1.</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>
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-    <table class="ref-arguments">
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-    <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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-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  1
-#&gt;  4  3
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'># }
-</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_ge.Rd</code></div>
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-    <p>Ge</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 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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  1
-#&gt;  0  1
-#&gt; [ CPUBoolType{2,2} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_generator.html b/docs/reference/torch_generator.html
deleted file mode 100644
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-    <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'># \dontrun{</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>()</div><div class='output co'>#&gt; <span class='message'>Loading required package: bit64</span></div><div class='output co'>#&gt; <span class='message'>Loading required package: bit</span></div><div class='output co'>#&gt; <span class='message'>Attaching package bit</span></div><div class='output co'>#&gt; <span class='message'>package:bit (c) 2008-2012 Jens Oehlschlaegel (GPL-2)</span></div><div class='output co'>#&gt; <span class='message'>creators: bit bitwhich</span></div><div class='output co'>#&gt; <span class='message'>coercion: as.logical as.integer as.bit as.bitwhich which</span></div><div class='output co'>#&gt; <span class='message'>operator: ! &amp; | xor != ==</span></div><div class='output co'>#&gt; <span class='message'>querying: print length any all min max range sum summary</span></div><div class='output co'>#&gt; <span class='message'>bit access: length&lt;- [ [&lt;- [[ [[&lt;-</span></div><div class='output co'>#&gt; <span class='message'>for more help type ?bit</span></div><div class='output co'>#&gt; <span class='message'></span>
-#&gt; <span class='message'>Attaching package: ‘bit’</span></div><div class='output co'>#&gt; <span class='message'>The following object is masked from ‘package:base’:</span>
-#&gt; <span class='message'></span>
-#&gt; <span class='message'>    xor</span></div><div class='output co'>#&gt; <span class='message'>Attaching package bit64</span></div><div class='output co'>#&gt; <span class='message'>package:bit64 (c) 2011-2012 Jens Oehlschlaegel</span></div><div class='output co'>#&gt; <span class='message'>creators: integer64 seq :</span></div><div class='output co'>#&gt; <span class='message'>coercion: as.integer64 as.vector as.logical as.integer as.double as.character as.bin</span></div><div class='output co'>#&gt; <span class='message'>logical operator: ! &amp; | xor != == &lt; &lt;= &gt;= &gt;</span></div><div class='output co'>#&gt; <span class='message'>arithmetic operator: + - * / %/% %% ^</span></div><div class='output co'>#&gt; <span class='message'>math: sign abs sqrt log log2 log10</span></div><div class='output co'>#&gt; <span class='message'>math: floor ceiling trunc round</span></div><div class='output co'>#&gt; <span class='message'>querying: is.integer64 is.vector [is.atomic} [length] format print str</span></div><div class='output co'>#&gt; <span class='message'>values: is.na is.nan is.finite is.infinite</span></div><div class='output co'>#&gt; <span class='message'>aggregation: any all min max range sum prod</span></div><div class='output co'>#&gt; <span class='message'>cumulation: diff cummin cummax cumsum cumprod</span></div><div class='output co'>#&gt; <span class='message'>access: length&lt;- [ [&lt;- [[ [[&lt;-</span></div><div class='output co'>#&gt; <span class='message'>combine: c rep cbind rbind as.data.frame</span></div><div class='output co'>#&gt; <span class='message'>WARNING don't use as subscripts</span></div><div class='output co'>#&gt; <span class='message'>WARNING semantics differ from integer</span></div><div class='output co'>#&gt; <span class='message'>for more help type ?bit64</span></div><div class='output co'>#&gt; <span class='message'></span>
-#&gt; <span class='message'>Attaching package: ‘bit64’</span></div><div class='output co'>#&gt; <span class='message'>The following object is masked from ‘package:bit’:</span>
-#&gt; <span class='message'></span>
-#&gt; <span class='message'>    still.identical</span></div><div class='output co'>#&gt; <span class='message'>The following objects are masked from ‘package:base’:</span>
-#&gt; <span class='message'></span>
-#&gt; <span class='message'>    :, %in%, is.double, match, order, rank</span></div><div class='output co'>#&gt; integer64
-#&gt; [1] 67280421310721</div><div class='input'><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><div class='output co'>#&gt; integer64
-#&gt; [1] 1234567</div><div class='input'>
-
-# }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_geqrf.html b/docs/reference/torch_geqrf.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_geqrf.Rd</code></div>
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-    <p>Geqrf</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 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/docs/reference/torch_ger.html b/docs/reference/torch_ger.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_ger.Rd</code></div>
-    </div>
-
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-    <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='co'># \dontrun{</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><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><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <th>input</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
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-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
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-      <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="gt-input-other-out-none-gt-tensor-"><a class="anchor" href="#gt-input-other-out-none-gt-tensor-"></a>gt(input, other, out=None) -&gt; Tensor </h2>
-
-    
-
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-<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>
-
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-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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='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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  1
-#&gt;  0  0
-#&gt; [ CPUBoolType{2,2} ]</div><div class='input'># }
-</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>
-
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-    <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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-  <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_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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diff --git a/docs/reference/torch_histc.html b/docs/reference/torch_histc.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_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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  2
-#&gt;  1
-#&gt;  0
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <div class="page-header">
-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -0.4097 -0.4074
-#&gt;   0.4656  0.3306
-#&gt;  -0.5886 -2.1190
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   0.3168 -1.2703
-#&gt;  -0.7706 -0.4545
-#&gt;   0.5461  1.3650
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -0.0778 -0.2976
-#&gt;  -0.1324  0.2705
-#&gt;   1.3860 -0.0941
-#&gt; [ CPUFloatType{3,3,2} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -0.4097 -0.4074
-#&gt;   0.4656  0.3306
-#&gt;  -0.5886 -2.1190
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   0.3168 -1.2703
-#&gt;  -0.7706 -0.4545
-#&gt;   0.5461  1.3650
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -0.0778 -0.2976
-#&gt;  -0.1324  0.2705
-#&gt;   1.3860 -0.0941
-#&gt; [ CPUFloatType{3,3,2} ]</div><div class='input'># }
-</div></pre>
-  </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>
-    <div class="hidden name"><code>torch_imag.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Imag</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="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>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"></pre>
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6090  0.2050 -0.2528  0.6190
-#&gt;  0.3118  1.3114 -1.0626 -1.2048
-#&gt;  0.3125 -0.2527  0.4871 -0.3394
-#&gt; [ CPUFloatType{3,4} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6090  0.2050 -0.2528  0.6190
-#&gt;  0.3125 -0.2527  0.4871 -0.3394
-#&gt; [ CPUFloatType{2,4} ]</div><div class='input'><span class='fu'>torch_index_select</span>(<span class='no'>x</span>, <span class='fl'>2</span>, <span class='no'>indices</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6090 -0.2528
-#&gt;  0.3118 -1.0626
-#&gt;  0.3125  0.4871
-#&gt; [ CPUFloatType{3,2} ]</div><div class='input'># }
-</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_inverse.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>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 tensor of size \((*, n, n)\) where <code>*</code> is zero or more                    batch dimensions</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>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"></pre>
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diff --git a/docs/reference/torch_irfft.html b/docs/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></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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -2.6442  0.0000
-#&gt;   3.2951 -1.0012
-#&gt;  -10.0001  0.0000
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   1.3956 -4.9153
-#&gt;   3.1204  7.3054
-#&gt;  -1.9820  2.2872
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;   3.3926  0.0000
-#&gt;   5.2495  2.0603
-#&gt;  -4.0185  0.0000
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;   1.3956  4.9153
-#&gt;   4.1712  1.2315
-#&gt;  -1.9820 -2.2872
-#&gt; [ CPUFloatType{4,3,2} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   6.1729  0.0000
-#&gt;   2.7828 -0.8086
-#&gt;   0.0001 -0.7514
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -0.3550  1.3322
-#&gt;  -1.3598  3.0191
-#&gt;   1.5158 -3.2844
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;   5.1938  0.0000
-#&gt;  -2.5545  1.6405
-#&gt;   7.3076  5.2401
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  -0.3550 -1.3322
-#&gt;   0.9130 -2.5817
-#&gt;  -0.4966 -2.0859
-#&gt; [ CPUFloatType{4,3,2} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.3437 -0.2165  0.6494  0.9663 -0.0787
-#&gt; -0.7215  1.0848 -0.3526 -0.1508 -0.2811
-#&gt;  1.3002 -0.5011  1.6580 -0.1709  0.7330
-#&gt;  0.4254  1.6956 -1.7164 -0.2147  0.7211
-#&gt; [ CPUFloatType{4,5} ]</div><div class='input'># }
-</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_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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-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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='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><div class='input'># }
-</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></small>
-    <div class="hidden name"><code>torch_isinf.Rd</code></div>
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-
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-    <p>Isinf</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>tensor</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>
-
-    
-
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-<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'># \dontrun{</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='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><div class='input'># }
-</div></pre>
-  </div>
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-    </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) 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'># \dontrun{</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><div class='input'># }
-</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>
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-    <p>Kthvalue</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>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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt;  4
-#&gt;  5
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='fu'>torch_kthvalue</span>(<span class='no'>x</span>, <span class='fl'>4</span>)</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; 4
-#&gt; [ CPUFloatType{} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; 3
-#&gt; [ CPULongType{} ]
-#&gt; </div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  2  3
-#&gt;  4  5  6
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'><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><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><div class='input'># }
-</div></pre>
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deleted file mode 100644
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-    <p>Creates the corresponding layout</p>
-    </div>
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-
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diff --git a/docs/reference/torch_le.html b/docs/reference/torch_le.html
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-    <h1>Le</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</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>
-
-    <div class="ref-description">
-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  0
-#&gt;  1  1
-#&gt; [ CPUBoolType{2,2} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_lerp.html b/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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt;  4
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='no'>end</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  10
-#&gt;  10
-#&gt;  10
-#&gt;  10
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_lerp</span>(<span class='no'>start</span>, <span class='no'>end</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><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  5.5000
-#&gt;  6.0000
-#&gt;  6.5000
-#&gt;  7.0000
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_lgamma.html b/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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5724
-#&gt;  0.0000
-#&gt; -0.1208
-#&gt; [ CPUFloatType{3} ]</div><div class='input'># }
-</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/docs/reference/torch_linspace.html b/docs/reference/torch_linspace.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_linspace.Rd</code></div>
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-    <p>Linspace</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>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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;   3.0000
-#&gt;   4.7500
-#&gt;   6.5000
-#&gt;   8.2500
-#&gt;  10.0000
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -10
-#&gt;  -5
-#&gt;   0
-#&gt;   5
-#&gt;  10
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -10
-#&gt;  -5
-#&gt;   0
-#&gt;   5
-#&gt;  10
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; -10
-#&gt; [ CPUFloatType{1} ]</div><div class='input'># }
-</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>
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-    <pre class="usage"><span class='fu'>torch_load</span>(<span class='no'>path</span>)</pre>
-
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-    <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>
-
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-
-    <div class='dont-index'><p>Other torch_save: 
-<code><a href='torch_save.html'>torch_save</a>()</code></p></div>
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-    <div class="page-header">
-    <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></small>
-    <div class="hidden name"><code>torch_log.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.6609
-#&gt; -1.5017
-#&gt;  0.7439
-#&gt; -0.6239
-#&gt;  1.2395
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='fu'>torch_log</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5074
-#&gt;     nan
-#&gt; -0.2958
-#&gt;     nan
-#&gt;  0.2147
-#&gt; [ CPUFloatType{5} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_log10.html b/docs/reference/torch_log10.html
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.6619
-#&gt;  0.0908
-#&gt;  0.8331
-#&gt;  0.1240
-#&gt;  0.1908
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='fu'>torch_log10</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.1792
-#&gt; -1.0419
-#&gt; -0.0793
-#&gt; -0.9067
-#&gt; -0.7195
-#&gt; [ CPUFloatType{5} ]</div><div class='input'># }
-</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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-
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.7431
-#&gt; -0.9335
-#&gt;  0.2461
-#&gt;  0.9212
-#&gt; -0.0972
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='fu'>torch_log1p</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.3591
-#&gt; -2.7110
-#&gt;  0.2200
-#&gt;  0.6530
-#&gt; -0.1023
-#&gt; [ CPUFloatType{5} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_log2.html b/docs/reference/torch_log2.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_log2.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5313
-#&gt;  0.0120
-#&gt;  0.0760
-#&gt;  0.6910
-#&gt;  0.9385
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='fu'>torch_log2</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.9125
-#&gt; -6.3856
-#&gt; -3.7179
-#&gt; -0.5332
-#&gt; -0.0915
-#&gt; [ CPUFloatType{5} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_logdet.html b/docs/reference/torch_logdet.html
deleted file mode 100644
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-    <div class="page-header">
-    <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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.779207
-#&gt; [ CPUFloatType{} ]</div><div class='input'><span class='fu'>torch_logdet</span>(<span class='no'>A</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; nan
-#&gt; [ CPUFloatType{} ]</div><div class='input'><span class='no'>A</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.1953 -0.9165 -0.4366
-#&gt;  0.0674  0.1296 -1.5398
-#&gt; -0.7582 -1.0511 -0.3637
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><span class='no'>A</span>$<span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.779207
-#&gt; [ CPUFloatType{} ]</div><div class='input'><span class='no'>A</span>$<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><div class='output co'>#&gt; torch_tensor 
-#&gt; nan
-#&gt; [ CPUFloatType{} ]</div><div class='input'># }
-</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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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</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"></pre>
-  </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>
-    <div class="hidden name"><code>torch_logical_not.Rd</code></div>
-    </div>
-
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-    <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='co'># \dontrun{</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>)))</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  1
-#&gt; [ CPUBoolType{2} ]</div><div class='input'><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>()))</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  0
-#&gt;  0
-#&gt; [ CPUBoolType{3} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  0
-#&gt;  0
-#&gt; [ CPUBoolType{3} ]</div><div class='input'># }
-</div></pre>
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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_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"></pre>
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diff --git a/docs/reference/torch_logical_xor.html b/docs/reference/torch_logical_xor.html
deleted file mode 100644
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--- a/docs/reference/torch_logical_xor.html
+++ /dev/null
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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></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='co'># \dontrun{</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>)))</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  0
-#&gt;  1
-#&gt; [ CPUBoolType{3} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  1
-#&gt;  0
-#&gt;  0
-#&gt; [ CPUBoolType{4} ]</div><div class='input'><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>$<span class='fu'>to</span>(<span class='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()))</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  1
-#&gt;  0
-#&gt;  0
-#&gt; [ CPUBoolType{4} ]</div><div class='input'><span class='fu'>torch_logical_xor</span>(<span class='no'>a</span>$<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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  1
-#&gt;  0
-#&gt;  0
-#&gt; [ CPUBoolType{4} ]</div><div class='input'># }
-</div></pre>
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-    <div class="hidden name"><code>torch_logspace.Rd</code></div>
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-    <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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.0000e-10
-#&gt;  1.0000e-05
-#&gt;  1.0000e+00
-#&gt;  1.0000e+05
-#&gt;  1.0000e+10
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.2589
-#&gt;  2.1135
-#&gt;  3.5481
-#&gt;  5.9566
-#&gt; 10.0000
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.2589
-#&gt; [ CPUFloatType{1} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  4
-#&gt; [ CPUFloatType{1} ]</div><div class='input'># }
-</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>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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.9705
-#&gt;  0.9976
-#&gt;  1.5809
-#&gt; [ CPUFloatType{3} ]</div><div class='input'># }
-</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>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='co'># \dontrun{</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><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><div class='input'># }
-</div></pre>
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-      <a href="../articles/examples/mnist-dcgan.html">mnist-dcgan</a>
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-    <h1>Lt</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</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>
-
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-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  0
-#&gt;  1  0
-#&gt; [ CPUBoolType{2,2} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-is set to True." />
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-    <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='co'># \dontrun{</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><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   1.5274 -0.8578 -1.5278
-#&gt;  -0.5446  0.3682 -0.1468
-#&gt;   0.6892  0.4103  1.5956
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -1.3121 -0.1896 -2.3469
-#&gt;  -0.7969  1.0315 -0.9561
-#&gt;   0.7149 -0.0635  1.4525
-#&gt; [ CPUFloatType{2,3,3} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  3  2  3
-#&gt;  3  2  3
-#&gt; [ CPUIntType{2,3} ]
-#&gt; </div><div class='input'>
-# }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_lu_solve.html b/docs/reference/torch_lu_solve.html
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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></small>
-    <div class="hidden name"><code>torch_lu_solve.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; 7.02886e-08
-#&gt; [ CPUFloatType{} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_masked_select.html b/docs/reference/torch_masked_select.html
deleted file mode 100644
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.1475 -0.7929 -0.3681  1.0487
-#&gt; -1.1304  1.8525 -0.8021  0.2346
-#&gt;  1.8222  0.7533  0.4753  0.3604
-#&gt; [ CPUFloatType{3,4} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  0  0  1
-#&gt;  0  1  0  0
-#&gt;  1  1  0  0
-#&gt; [ CPUBoolType{3,4} ]</div><div class='input'><span class='fu'>torch_masked_select</span>(<span class='no'>x</span>, <span class='no'>mask</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.0487
-#&gt;  1.8525
-#&gt;  1.8222
-#&gt;  0.7533
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_matmul.html b/docs/reference/torch_matmul.html
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-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -2.01064
-#&gt; [ CPUFloatType{} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.8498
-#&gt; -0.1725
-#&gt; -0.2413
-#&gt; [ CPUFloatType{3} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3051  0.8335  0.7817
-#&gt; -1.7290 -0.1315 -1.0013
-#&gt; -2.2799 -2.0186 -1.7212
-#&gt; -2.0462  2.6530  2.2152
-#&gt;  1.0726 -1.8564 -0.0805
-#&gt;  2.5083  0.1464 -1.9015
-#&gt;  0.1432  1.3745  0.8548
-#&gt; -0.5524  0.7222  0.1316
-#&gt; -1.5975  0.3861 -2.5685
-#&gt; -3.1016  2.3700  0.1975
-#&gt; [ CPUFloatType{10,3} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   2.7383  3.7776  1.1984 -0.0462  5.9024
-#&gt;   0.1247 -2.9988  0.8401 -1.2394 -2.9948
-#&gt;   3.0933  3.5992  3.5134 -3.3567  6.0899
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -0.9786 -0.3967 -0.3440  0.3824 -1.2635
-#&gt;   0.4944  0.7149  0.5734 -1.1592  0.6260
-#&gt;   1.3268  0.8352  1.9060 -3.7034  0.1600
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;   0.4321  0.9564  1.6369 -0.1843 -0.3877
-#&gt;  -1.4475 -2.3336 -2.1501 -1.6377  3.0016
-#&gt;  -1.9856  0.2134  1.8436  1.6812  1.5892
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;   0.7306 -0.1213  0.6505 -2.7818 -0.1171
-#&gt;  -1.4915  3.2909  0.5003  2.5146  1.3735
-#&gt;   0.0837 -0.1117  0.8951  3.6226  1.1665
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  -1.9671  0.1609  2.2518  0.7587  2.6455
-#&gt;  -0.5965 -1.3687 -2.4585 -1.6623 -3.6542
-#&gt;   0.0899  1.6690  0.2659 -2.6994  2.8543
-#&gt; 
-#&gt; (6,.,.) = 
-#&gt;  -4.5552  0.7339 -2.3152 -5.9254 -1.6420
-#&gt;  -0.8628  0.0829 -0.6564  0.9928 -0.0438
-#&gt;  -4.1770  0.7927 -2.3129 -4.7196 -0.5457
-#&gt; 
-#&gt; (7,.,.) = 
-#&gt;   1.3550 -0.9541 -1.7768  2.5931  1.6567
-#&gt;   4.4144  1.4763  1.1692 -2.8514 -1.1695
-#&gt;   4.2236  0.0365 -0.3523  1.0364  0.0855
-#&gt; 
-#&gt; (8,.,.) = 
-#&gt;  -3.3315  1.4609 -1.3792 -2.9510 -0.7443
-#&gt;   1.3569 -2.2547  0.7054 -2.1291  5.2801
-#&gt;  -0.0102  1.3483 -0.3866  2.5474 -3.7250
-#&gt; 
-#&gt; (9,.,.) = 
-#&gt;   0.8332  2.8945 -1.7215  1.0412  1.7159
-#&gt;  -1.0112 -5.9806  1.3106  3.5058 -2.5003
-#&gt;   0.3979  0.5793 -0.9296 -2.5669 -3.1548
-#&gt; 
-#&gt; (10,.,.) = 
-#&gt;   1.3155 -0.3605  0.2988 -0.0092 -0.7717
-#&gt;   0.6723  1.2307 -1.8691  0.1663 -1.7657
-#&gt;  -1.2338  0.4575  0.0627  1.7217  2.0104
-#&gt; [ CPUFloatType{10,3,5} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  -5.1244  2.2971 -1.6275  2.7803 -4.4830
-#&gt;   1.8873 -3.6560  1.6412  0.0507  0.1216
-#&gt;  -0.5433  1.9666 -1.4803 -0.5111  0.0638
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   0.0601 -4.0015  2.7077  1.7992 -1.7779
-#&gt;  -1.6433  1.4184 -1.3365  0.5739 -1.9782
-#&gt;  -3.4216  6.9532 -3.3050 -0.2836  0.0183
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -1.8677 -0.3711  0.3040  1.6625 -2.7921
-#&gt;   3.2992 -0.5994 -1.6079 -2.2177 -1.6740
-#&gt;  -3.7891  4.2637  2.9312  2.2233  0.8338
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  -2.1277  3.2238  0.0312  0.5080  1.3242
-#&gt;   2.2424 -0.8814  0.2856 -1.2700  1.1283
-#&gt;  -1.0811  2.6808 -0.7240 -0.2231  1.0720
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  -3.4333  2.0806 -0.9037  1.4697 -0.1231
-#&gt;   2.3334 -3.4847  1.1505 -0.3664 -0.2367
-#&gt;   1.2297 -2.4053  2.6418  0.6376 -1.1939
-#&gt; 
-#&gt; (6,.,.) = 
-#&gt;  -2.2066  0.7770  1.6664  1.7391 -0.3200
-#&gt;  -2.7109 -1.6160 -1.9334  1.4483  0.3787
-#&gt;  -1.2449 -0.9002 -1.0765  0.8913 -1.9713
-#&gt; 
-#&gt; (7,.,.) = 
-#&gt;   0.2789 -1.0134 -0.5889  0.0808 -1.8249
-#&gt;   0.6334 -0.8853  2.2413  0.6094 -1.5061
-#&gt;  -0.1358  0.5410  0.6620  0.1202  0.3539
-#&gt; 
-#&gt; (8,.,.) = 
-#&gt;   0.5331  0.8902  1.7452 -0.4616  3.8193
-#&gt;   0.4330  2.0916 -0.6593 -0.9295  0.1894
-#&gt;   0.8105 -0.3216  0.7608 -0.4534  2.0104
-#&gt; 
-#&gt; (9,.,.) = 
-#&gt;   2.2947  1.8882  2.8289 -1.2676  2.0636
-#&gt;  -0.6974 -2.0088 -1.8360  0.1831  1.3406
-#&gt;   4.7011 -3.7311  0.0230 -1.9564 -1.7259
-#&gt; 
-#&gt; (10,.,.) = 
-#&gt;   2.7739 -3.8386 -1.8082 -1.4422 -0.0495
-#&gt;   1.3869 -3.8928  0.8670  0.1969  0.0855
-#&gt;  -3.0783  1.1996 -2.8824  0.9550 -0.9141
-#&gt; [ CPUFloatType{10,3,5} ]</div><div class='input'># }
-</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>
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-    </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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   0.0871  0.0197
-#&gt;   0.3185  0.4297
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -0.7818 -0.6402
-#&gt;   0.2400 -0.4367
-#&gt; [ CPUFloatType{2,2,2} ]</div><div class='input'><span class='fu'>torch_matrix_power</span>(<span class='no'>a</span>, <span class='fl'>3</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;  0.01 *
-#&gt;   0.4446  0.4644
-#&gt;    7.5134  8.5264
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -0.1705 -0.6335
-#&gt;   0.2375  0.1711
-#&gt; [ CPUFloatType{2,2,2} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_matrix_rank.html b/docs/reference/torch_matrix_rank.html
deleted file mode 100644
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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></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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; 10
-#&gt; [ CPULongType{} ]</div><div class='input'># }
-</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/docs/reference/torch_max.html b/docs/reference/torch_max.html
deleted file mode 100644
index 00d5f5b8b..000000000
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+++ /dev/null
@@ -1,315 +0,0 @@
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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>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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.7404  0.4095  0.0815
-#&gt; [ CPUFloatType{1,3} ]</div><div class='input'><span class='fu'>torch_max</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 0.409483
-#&gt; [ CPUFloatType{} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.4374  0.4921  0.5690 -0.5727
-#&gt; -0.7344  1.4273  0.3648  1.0731
-#&gt;  0.4967  0.2687  0.5694 -0.4233
-#&gt;  0.2623  0.0037 -1.2246 -0.3742
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  0.4967
-#&gt;  1.4273
-#&gt;  0.5694
-#&gt;  1.0731
-#&gt; [ CPUFloatType{4} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  3
-#&gt;  2
-#&gt;  3
-#&gt;  2
-#&gt; [ CPULongType{4} ]
-#&gt; </div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.0640
-#&gt; -0.2356
-#&gt; -0.5395
-#&gt;  1.8484
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.4095
-#&gt; -0.6778
-#&gt; -0.0908
-#&gt; -0.2021
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.4095
-#&gt; -0.2356
-#&gt; -0.0908
-#&gt;  1.8484
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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>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_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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.0395  1.7826  1.2161
-#&gt; [ CPUFloatType{1,3} ]</div><div class='input'><span class='fu'>torch_mean</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 0.986383
-#&gt; [ CPUFloatType{} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0216 -1.8383 -0.8037 -1.2803
-#&gt;  1.6988 -1.2344 -0.5559  0.7407
-#&gt; -1.5668  0.8250 -0.0814  0.6922
-#&gt; -0.7400 -0.0428  0.7179 -0.2121
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><span class='fu'>torch_mean</span>(<span class='no'>a</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.1466
-#&gt; -0.5726
-#&gt; -0.1808
-#&gt; -0.0149
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_mean</span>(<span class='no'>a</span>, <span class='fl'>1</span>, <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.1466 -0.5726 -0.1808 -0.0149
-#&gt; [ CPUFloatType{1,4} ]</div><div class='input'># }
-</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/docs/reference/torch_median.html b/docs/reference/torch_median.html
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-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.1294  0.8996  0.0937
-#&gt; [ CPUFloatType{1,3} ]</div><div class='input'><span class='fu'>torch_median</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 0.0937234
-#&gt; [ CPUFloatType{} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.2865 -0.8226  0.6805  0.3636 -0.6890
-#&gt;  0.8853  0.3427  0.8220 -2.2562 -1.8976
-#&gt; -1.3180  0.3580  1.1346 -0.5496 -0.2493
-#&gt; -1.1359  0.0354 -0.3702 -0.0126  1.0450
-#&gt; [ CPUFloatType{4,5} ]</div><div class='input'><span class='fu'>torch_median</span>(<span class='no'>a</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; -1.1359
-#&gt;  0.0354
-#&gt;  0.6805
-#&gt; -0.5496
-#&gt; -0.6890
-#&gt; [ CPUFloatType{5} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  3
-#&gt;  3
-#&gt;  0
-#&gt;  2
-#&gt;  0
-#&gt; [ CPULongType{5} ]
-#&gt; </div><div class='input'># }
-</div></pre>
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-    <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>()
-
-<span class='fu'>torch_channels_last_format</span>()</pre>
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-    <li class="dropdown-header">Vision</li>
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-
-<div class="row">
-  <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></small>
-    <div class="hidden name"><code>torch_meshgrid.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Meshgrid</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>(list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be</p></td>
-    </tr>
-    <tr>
-      <th>treated</th>
-      <td><p>(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'># \dontrun{</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'>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/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>))
-<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><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><div class='input'># }
-</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/docs/reference/torch_min.html b/docs/reference/torch_min.html
deleted file mode 100644
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+++ /dev/null
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.0189  1.0439  1.3884
-#&gt; [ CPUFloatType{1,3} ]</div><div class='input'><span class='fu'>torch_min</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.01891
-#&gt; [ CPUFloatType{} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.9733  2.4571  1.7912 -1.4290
-#&gt;  0.5607 -0.5847 -0.4779 -0.7823
-#&gt; -0.7391  0.6672 -0.9647  0.1703
-#&gt; -0.5473 -0.2047 -0.1148  1.4254
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; -0.7391
-#&gt; -0.5847
-#&gt; -0.9647
-#&gt; -1.4290
-#&gt; [ CPUFloatType{4} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  3
-#&gt;  2
-#&gt;  3
-#&gt;  1
-#&gt; [ CPULongType{4} ]
-#&gt; </div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.2877
-#&gt; -1.1317
-#&gt; -1.0846
-#&gt;  0.0735
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.2118
-#&gt; -0.7290
-#&gt; -0.8948
-#&gt;  0.5896
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.2118
-#&gt; -1.1317
-#&gt; -1.0846
-#&gt;  0.0735
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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/docs/reference/torch_mm.html b/docs/reference/torch_mm.html
deleted file mode 100644
index d2edb9063..000000000
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+++ /dev/null
@@ -1,234 +0,0 @@
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-<!DOCTYPE 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>
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-
-    <div class="ref-description">
-    <p>Mm</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 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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; -4.2557 -0.6099 -0.8782
-#&gt; -3.0933  0.3112 -0.9391
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'># }
-</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/docs/reference/torch_mode.html b/docs/reference/torch_mode.html
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-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  18
-#&gt;   0
-#&gt;   7
-#&gt;   4
-#&gt;  19
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='fu'>torch_mode</span>(<span class='no'>a</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; 0
-#&gt; [ CPUFloatType{} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; 1
-#&gt; [ CPULongType{} ]
-#&gt; </div><div class='input'># }
-</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/docs/reference/torch_mul.html b/docs/reference/torch_mul.html
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-    <div class="hidden name"><code>torch_mul.Rd</code></div>
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-
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-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.7353
-#&gt;  0.3087
-#&gt;  0.8232
-#&gt; [ CPUFloatType{3} ]</div><div class='input'><span class='fu'>torch_mul</span>(<span class='no'>a</span>, <span class='fl'>100</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  73.5282
-#&gt;  30.8688
-#&gt;  82.3200
-#&gt; [ CPUFloatType{3} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.1683
-#&gt;  0.6845
-#&gt;  1.4773
-#&gt;  1.1179
-#&gt; [ CPUFloatType{4,1} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.4203  0.6324 -0.8087 -0.5061
-#&gt; [ CPUFloatType{1,4} ]</div><div class='input'><span class='fu'>torch_mul</span>(<span class='no'>a</span>, <span class='no'>b</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.2390  0.1064 -0.1361 -0.0852
-#&gt; -0.9722  0.4329 -0.5535 -0.3464
-#&gt; -2.0981  0.9343 -1.1946 -0.7476
-#&gt; -1.5877  0.7070 -0.9040 -0.5657
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt; [ CPULongType{2} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  1
-#&gt;  2
-#&gt; [ CPULongType{4} ]</div><div class='input'># }
-</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/docs/reference/torch_mv.html b/docs/reference/torch_mv.html
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-    <div class="hidden name"><code>torch_mv.Rd</code></div>
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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) 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>
-
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-
-    <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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.9277
-#&gt;  1.8568
-#&gt; [ CPUFloatType{2} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_mvlgamma.html b/docs/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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.2019  1.8425  1.1256
-#&gt;  1.9082  1.8734  1.5464
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'><span class='fu'>torch_mvlgamma</span>(<span class='no'>a</span>, <span class='fl'>2</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.7450  0.3997  0.8720
-#&gt;  0.4162  0.4065  0.4292
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'># }
-</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>
-</div>
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diff --git a/docs/reference/torch_narrow.html b/docs/reference/torch_narrow.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_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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  2  3
-#&gt;  4  5  6
-#&gt; [ CPUIntType{2,3} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  2  3
-#&gt;  5  6
-#&gt;  8  9
-#&gt; [ CPUIntType{3,2} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_ne.html b/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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  1
-#&gt;  1  0
-#&gt; [ CPUBoolType{2,2} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_neg.html b/docs/reference/torch_neg.html
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-    <div class="hidden name"><code>torch_neg.Rd</code></div>
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-
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-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.3160
-#&gt; -0.4731
-#&gt;  0.1641
-#&gt;  0.6355
-#&gt;  0.2480
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='fu'>torch_neg</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3160
-#&gt;  0.4731
-#&gt; -0.1641
-#&gt; -0.6355
-#&gt; -0.2480
-#&gt; [ CPUFloatType{5} ]</div><div class='input'># }
-</div></pre>
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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></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='co'># \dontrun{</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><div class='input'># }
-</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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diff --git a/docs/reference/torch_norm.html b/docs/reference/torch_norm.html
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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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 14.2829
-#&gt; [ CPUFloatType{} ]</div><div class='input'><span class='fu'>torch_norm</span>(<span class='no'>b</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 14.2829
-#&gt; [ CPUFloatType{} ]</div><div class='input'><span class='fu'>torch_norm</span>(<span class='no'>a</span>, <span class='fl'>Inf</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 8
-#&gt; [ CPUFloatType{} ]</div><div class='input'><span class='fu'>torch_norm</span>(<span class='no'>b</span>, <span class='fl'>Inf</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 8
-#&gt; [ CPUFloatType{} ]</div><div class='input'>
-# }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_normal.html b/docs/reference/torch_normal.html
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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></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"></pre>
-  </div>
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diff --git a/docs/reference/torch_ones.html b/docs/reference/torch_ones.html
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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='co'># \dontrun{</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>))</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  1  1
-#&gt;  1  1  1
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'><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><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_ones_like.html b/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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  1  1
-#&gt;  1  1  1
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_orgqr.html b/docs/reference/torch_orgqr.html
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diff --git a/docs/reference/torch_ormqr.html b/docs/reference/torch_ormqr.html
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-      <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>
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-      <th>input3</th>
-      <td><p>(Tensor) the matrix to be multiplied.</p></td>
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-    <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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-    <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></small>
-    <div class="hidden name"><code>torch_pdist.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <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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-    <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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0625  0.0470 -0.6356  1.0166 -0.2998
-#&gt;  0.2736 -0.5027  2.6768  0.3714  0.5533
-#&gt; -0.5951  1.2603  0.2886  0.6099 -1.3339
-#&gt; [ CPUFloatType{3,5} ]</div><div class='input'><span class='fu'>torch_pinverse</span>(<span class='no'>input</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.1974  0.0598 -0.1754
-#&gt; -0.2446 -0.0864  0.3409
-#&gt; -0.1949  0.3106  0.1489
-#&gt;  0.8721  0.2108 -0.0062
-#&gt;  0.0375  0.0552 -0.3200
-#&gt; [ CPUFloatType{5,3} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   1.0000e+00  5.2154e-08 -1.0431e-07
-#&gt;   2.9802e-08  1.0000e+00  3.7253e-08
-#&gt;   2.9802e-08 -6.7055e-08  1.0000e+00
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   1.0000e+00  2.3283e-08  1.9372e-07
-#&gt;   2.9802e-08  1.0000e+00 -5.9605e-07
-#&gt;   2.9802e-08  1.5087e-07  1.0000e+00
-#&gt; [ CPUFloatType{2,3,3} ]</div><div class='input'># }
-</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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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</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>
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-    <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='co'># \dontrun{</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><div class='output co'>#&gt; [1]  1  1 12 12</div><div class='input'># }
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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 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='co'># \dontrun{</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  4  0  4
-#&gt;  6  0  4  2
-#&gt;  1  0  1  0
-#&gt;  1  3  3  4
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_polygamma.html b/docs/reference/torch_polygamma.html
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-      <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"></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_pow.html b/docs/reference/torch_pow.html
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6638
-#&gt;  0.2351
-#&gt; -0.1040
-#&gt;  0.7775
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_pow</span>(<span class='no'>a</span>, <span class='fl'>2</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.4406
-#&gt;  0.0553
-#&gt;  0.0108
-#&gt;  0.6045
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt;  4
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='no'>exp</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt;  4
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_pow</span>(<span class='no'>a</span>, <span class='no'>exp</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;    1
-#&gt;    4
-#&gt;   27
-#&gt;  256
-#&gt; [ CPUFloatType{4} ]</div><div class='input'>
-
-<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><div class='output co'>#&gt; torch_tensor 
-#&gt;   2
-#&gt;   4
-#&gt;   8
-#&gt;  16
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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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>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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0090  0.8878  1.0236
-#&gt; [ CPUFloatType{1,3} ]</div><div class='input'><span class='fu'>torch_prod</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 0.00817587
-#&gt; [ CPUFloatType{} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.1330  0.8404
-#&gt;  2.0557  0.2876
-#&gt;  2.0148  1.2245
-#&gt;  0.4052  0.2208
-#&gt; [ CPUFloatType{4,2} ]</div><div class='input'><span class='fu'>torch_prod</span>(<span class='no'>a</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.9012
-#&gt;  0.0653
-#&gt; [ CPUFloatType{2} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_promote_types.html b/docs/reference/torch_promote_types.html
deleted file mode 100644
index d368948a8..000000000
--- a/docs/reference/torch_promote_types.html
+++ /dev/null
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-    <h1>Promote_types</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</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>
-
-    <div class="ref-description">
-    <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='co'># \dontrun{</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>())</div><div class='output co'>#&gt; torch_Float</div><div class='input'><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><div class='input'># }
-</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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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
-</div>
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diff --git a/docs/reference/torch_qr.html b/docs/reference/torch_qr.html
deleted file mode 100644
index ed7e5d585..000000000
--- a/docs/reference/torch_qr.html
+++ /dev/null
@@ -1,247 +0,0 @@
-<!-- Generated by pkgdown: do not edit by hand -->
-<!DOCTYPE html>
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-    <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='co'># \dontrun{</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>)$<span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span>()</div><div class='output co'>#&gt; torch_tensor 
-#&gt;   12  -51    4
-#&gt;    6  167  -68
-#&gt;   -4   24  -41
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='no'>q</span>$<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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  0  0
-#&gt;  0  1 -0
-#&gt;  0 -0  1
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'># }
-</div></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/qscheme.R'><code>R/qscheme.R</code></a></small>
-    <div class="hidden name"><code>torch_qscheme.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Creates the corresponding Scheme object</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_per_channel_affine</span>()
-
-<span class='fu'>torch_per_tensor_affine</span>()
-
-<span class='fu'>torch_per_channel_symmetric</span>()
-
-<span class='fu'>torch_per_tensor_symmetric</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_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='co'># \dontrun{</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>())</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1  0
-#&gt;  1  2
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;    0   10
-#&gt;  100  200
-#&gt; [ CPUByteType{2,2} ]</div><div class='input'># }
-</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_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='co'># \dontrun{</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>())</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1
-#&gt;  0
-#&gt;  1
-#&gt;  2
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;   0
-#&gt;  10
-#&gt;  20
-#&gt;  30
-#&gt; [ CPUByteType{4} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_rand.html b/docs/reference/torch_rand.html
deleted file mode 100644
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-    <p>Rand</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>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='co'># \dontrun{</span>
-
-<span class='fu'>torch_rand</span>(<span class='fl'>4</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.8391
-#&gt;  0.5766
-#&gt;  0.5790
-#&gt;  0.0523
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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;  0.9712  0.5669  0.1881
-#&gt;  0.4962  0.3052  0.8577
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'># }
-</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>
-
-    <div class="ref-description">
-    <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>
-
-    <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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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_randint.html b/docs/reference/torch_randint.html
deleted file mode 100644
index 497200d06..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_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>
-
-    
-
-
-<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='co'># \dontrun{</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>))</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  4
-#&gt;  3
-#&gt;  3
-#&gt; [ CPUFloatType{3} ]</div><div class='input'><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>))</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  7
-#&gt;  8  6
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'><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;  8  8
-#&gt;  3  5
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'># }
-</div></pre>
-  </div>
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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>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_randn.html b/docs/reference/torch_randn.html
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-    <p>Randn</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>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='co'># \dontrun{</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>))</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.5578
-#&gt; -1.6968
-#&gt; -0.0944
-#&gt; -0.7900
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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; -2.1279  1.0919  2.2659
-#&gt;  0.1722  0.1719 -1.1738
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'># }
-</div></pre>
-  </div>
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-    <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/docs/reference/torch_randperm.html b/docs/reference/torch_randperm.html
deleted file mode 100644
index d61a9b411..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>
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-
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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='co'># \dontrun{</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;  1
-#&gt;  3
-#&gt; [ CPULongType{4} ]</div><div class='input'># }
-</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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-    </nav>
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diff --git a/docs/reference/torch_range.html b/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='co'># \dontrun{</span>
-
-<span class='fu'>torch_range</span>(<span class='fl'>1</span>, <span class='fl'>4</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
-#&gt;  2
-#&gt;  3
-#&gt; [ CPUFloatType{3} ]</div><div class='input'><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; torch_tensor 
-#&gt;  1.0000
-#&gt;  1.5000
-#&gt;  2.0000
-#&gt;  2.5000
-#&gt;  3.0000
-#&gt;  3.5000
-#&gt; [ CPUFloatType{6} ]</div><div class='input'># }
-</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>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></small>
-    <div class="hidden name"><code>torch_real.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Real</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="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>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"></pre>
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-    <div class="hidden name"><code>torch_reciprocal.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Reciprocal</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="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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6585
-#&gt;  0.2569
-#&gt;  1.4761
-#&gt; -0.0839
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_reciprocal</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.5185
-#&gt;  3.8925
-#&gt;  0.6775
-#&gt; -11.9170
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
-  </div>
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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-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
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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 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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  0
-#&gt;  1
-#&gt;  1
-#&gt;  0
-#&gt;  1
-#&gt; [ CPUFloatType{6} ]</div><div class='input'><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><div class='input'># }
-</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_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='co'># \dontrun{</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>,]$<span class='fu'>fill_</span>(<span class='fl'>2</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2
-#&gt;  2
-#&gt;  2
-#&gt; [ CPUFloatType{3} ]</div><div class='input'><span class='no'>x</span>[<span class='fl'>3</span>,]$<span class='fu'>fill_</span>(<span class='fl'>3</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  3
-#&gt;  3
-#&gt;  3
-#&gt; [ CPUFloatType{3} ]</div><div class='input'><span class='no'>x</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  1  1
-#&gt;  2  2  2
-#&gt;  3  3  3
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><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><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><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_repeat_interleave.html b/docs/reference/torch_repeat_interleave.html
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-    <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"></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_reshape.html b/docs/reference/torch_reshape.html
deleted file mode 100644
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <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='co'># \dontrun{</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>))</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  1
-#&gt;  2  3
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_result_type.html b/docs/reference/torch_result_type.html
deleted file mode 100644
index a1f275a04..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_result_type.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Result_type</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>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'># \dontrun{</span>
-
-<span class='fu'>torch_result_type</span>(<span class='kw'>tensor</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='kw'>dtype</span><span class='kw'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int</a></span>()), <span class='fl'>1.0</span>)</div><div class='output co'>#&gt; torch_Float</div><div class='input'># torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_uint8()), torch_tensor(1))
-# }
-</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/docs/reference/torch_rfft.html b/docs/reference/torch_rfft.html
deleted file mode 100644
index ffc88d168..000000000
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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 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'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   4.9202  0.0000
-#&gt;   2.9093  2.7036
-#&gt;   1.8227 -4.8232
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -2.0160  4.4730
-#&gt;   1.8297 -3.0301
-#&gt;   0.4639  0.6830
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -6.8659  1.3907
-#&gt;  -5.6917 -4.3527
-#&gt;   2.7115  0.4562
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  -6.8659 -1.3907
-#&gt;  -3.4394 -1.4155
-#&gt;   4.7454 -1.6532
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  -2.0160 -4.4730
-#&gt;  -1.0448  5.6595
-#&gt;   5.7855 -3.2515
-#&gt; [ CPUFloatType{5,3,2} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   4.9202  0.0000
-#&gt;   2.9093  2.7036
-#&gt;   1.8227 -4.8232
-#&gt;   1.8227  4.8232
-#&gt;   2.9093 -2.7036
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -2.0160  4.4730
-#&gt;   1.8297 -3.0301
-#&gt;   0.4639  0.6830
-#&gt;   5.7855  3.2515
-#&gt;  -1.0448 -5.6595
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -6.8659  1.3907
-#&gt;  -5.6917 -4.3527
-#&gt;   2.7115  0.4562
-#&gt;   4.7454  1.6532
-#&gt;  -3.4394  1.4155
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  -6.8659 -1.3907
-#&gt;  -3.4394 -1.4155
-#&gt;   4.7454 -1.6532
-#&gt;   2.7115 -0.4562
-#&gt;  -5.6917  4.3527
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;  -2.0160 -4.4730
-#&gt;  -1.0448  5.6595
-#&gt;   5.7855 -3.2515
-#&gt;   0.4639 -0.6830
-#&gt;   1.8297  3.0301
-#&gt; [ CPUFloatType{5,5,2} ]</div><div class='input'># }
-</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/docs/reference/torch_roll.html b/docs/reference/torch_roll.html
deleted file mode 100644
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-    <div class="page-header">
-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  2
-#&gt;  3  4
-#&gt;  5  6
-#&gt;  7  8
-#&gt; [ CPUFloatType{4,2} ]</div><div class='input'><span class='fu'>torch_roll</span>(<span class='no'>x</span>, <span class='fl'>1</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  7  8
-#&gt;  1  2
-#&gt;  3  4
-#&gt;  5  6
-#&gt; [ CPUFloatType{4,2} ]</div><div class='input'><span class='fu'>torch_roll</span>(<span class='no'>x</span>, -<span class='fl'>1</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  3  4
-#&gt;  5  6
-#&gt;  7  8
-#&gt;  1  2
-#&gt; [ CPUFloatType{4,2} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  6  5
-#&gt;  8  7
-#&gt;  2  1
-#&gt;  4  3
-#&gt; [ CPUFloatType{4,2} ]</div><div class='input'># }
-</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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diff --git a/docs/reference/torch_rot90.html b/docs/reference/torch_rot90.html
deleted file mode 100644
index 09927b2d5..000000000
--- a/docs/reference/torch_rot90.html
+++ /dev/null
@@ -1,248 +0,0 @@
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-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  1
-#&gt;  2  3
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  3
-#&gt;  0  2
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   0  1
-#&gt;   2  3
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   4  5
-#&gt;   6  7
-#&gt; [ CPUFloatType{2,2,2} ]</div><div class='input'><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><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><div class='input'># }
-</div></pre>
-  </div>
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deleted file mode 100644
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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></small>
-    <div class="hidden name"><code>torch_round.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Round</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="round-input-out-none-gt-tensor-"><a class="anchor" href="#round-input-out-none-gt-tensor-"></a>round(input, out=None) -&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'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.8911
-#&gt; -0.2131
-#&gt; -0.4903
-#&gt; -0.0724
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_round</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt; -0
-#&gt; -0
-#&gt; -0
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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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diff --git a/docs/reference/torch_rrelu_.html b/docs/reference/torch_rrelu_.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>
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-    </div>
-
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-    <p>Rsqrt</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="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>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5826
-#&gt; -0.3344
-#&gt; -0.2779
-#&gt; -1.1775
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_rsqrt</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.3101
-#&gt;     nan
-#&gt;     nan
-#&gt;     nan
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
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-    <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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-    <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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-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2.1600
-#&gt; -1.3253
-#&gt; -0.1559
-#&gt;  0.1856
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_sigmoid</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.8966
-#&gt;  0.2099
-#&gt;  0.4611
-#&gt;  0.5463
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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_sign.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Sign</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="sign-input-out-none-gt-tensor-"><a class="anchor" href="#sign-input-out-none-gt-tensor-"></a>sign(input, out=None) -&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'># \dontrun{</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'>0.7</span>, -<span class='fl'>1.2</span>, <span class='fl'>0.</span>, <span class='fl'>2.3</span>))
-<span class='no'>a</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.7000
-#&gt; -1.2000
-#&gt;  0.0000
-#&gt;  2.3000
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_sign</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt; -1
-#&gt;  0
-#&gt;  1
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_sin.html b/docs/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></small>
-    <div class="hidden name"><code>torch_sin.Rd</code></div>
-    </div>
-
-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.5115
-#&gt;  0.7241
-#&gt; -0.6876
-#&gt; -0.3453
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_sin</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.4895
-#&gt;  0.6625
-#&gt; -0.6347
-#&gt; -0.3384
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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/docs/reference/torch_sinh.html b/docs/reference/torch_sinh.html
deleted file mode 100644
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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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.7504
-#&gt; -0.3593
-#&gt; -0.6244
-#&gt; -1.7192
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_sinh</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.8228
-#&gt; -0.3671
-#&gt; -0.6658
-#&gt; -2.7003
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0461 -1.3909  0.9825
-#&gt;  0.5340  0.3877 -0.2309
-#&gt;  0.3683  1.6290  0.3208
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><span class='fu'><a href='torch_det.html'>torch_det</a></span>(<span class='no'>A</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 1.094
-#&gt; [ CPUFloatType{} ]</div><div class='input'><span class='fu'><a href='torch_logdet.html'>torch_logdet</a></span>(<span class='no'>A</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 0.0898445
-#&gt; [ CPUFloatType{} ]</div><div class='input'><span class='fu'>torch_slogdet</span>(<span class='no'>A</span>)</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; 1
-#&gt; [ CPUFloatType{} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; 0.0898445
-#&gt; [ CPUFloatType{} ]
-#&gt; </div><div class='input'># }
-</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/docs/reference/torch_solve.html b/docs/reference/torch_solve.html
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-
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-  <div class="col-md-9 contents">
-    <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></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='co'># \dontrun{</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>))</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 7.09771e-06
-#&gt; [ CPUFloatType{} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; 6.14486e-06
-#&gt; [ CPUFloatType{} ]</div><div class='input'># }
-</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/docs/reference/torch_sort.html b/docs/reference/torch_sort.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_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='co'># \dontrun{</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></div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; -0.9253 -0.3520 -0.0946  1.1472
-#&gt; -0.6827  0.1674  0.7136  1.4204
-#&gt; -0.4754 -0.0864  0.5561  1.1917
-#&gt; [ CPUFloatType{3,4} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  3  0  1  2
-#&gt;  0  1  2  3
-#&gt;  2  0  3  1
-#&gt; [ CPULongType{3,4} ]
-#&gt; </div><div class='input'><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><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; -0.6827 -0.0946 -0.4754 -0.9253
-#&gt; -0.3520  0.1674  0.7136  0.5561
-#&gt; -0.0864  1.1917  1.1472  1.4204
-#&gt; [ CPUFloatType{3,4} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  1  0  2  0
-#&gt;  0  1  1  2
-#&gt;  2  2  0  1
-#&gt; [ CPULongType{3,4} ]
-#&gt; </div><div class='input'># }
-</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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diff --git a/docs/reference/torch_sparse_coo_tensor.html b/docs/reference/torch_sparse_coo_tensor.html
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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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; [ SparseCPUFloatType{}
-#&gt; indices:
-#&gt;  0  1  1
-#&gt;  2  0  2
-#&gt; [ CPULongType{2,3} ]
-#&gt; values:
-#&gt;  3
-#&gt;  4
-#&gt;  5
-#&gt; [ CPUFloatType{3} ]
-#&gt; size:
-#&gt; [2, 3]
-#&gt; ]</div><div class='input'><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>))</div><div class='output co'>#&gt; torch_tensor 
-#&gt; [ SparseCPUFloatType{}
-#&gt; indices:
-#&gt;  0  1  1
-#&gt;  2  0  2
-#&gt; [ CPULongType{2,3} ]
-#&gt; values:
-#&gt;  3
-#&gt;  4
-#&gt;  5
-#&gt; [ CPUFloatType{3} ]
-#&gt; size:
-#&gt; [2, 4]
-#&gt; ]</div><div class='input'>
-<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>)
-)
-<span class='co'># }</span></div></pre>
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-    <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></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
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.4471
-#&gt;  1.6376
-#&gt;  0.0918
-#&gt;  0.6598
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_sqrt</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.6686
-#&gt;  1.2797
-#&gt;  0.3031
-#&gt;  0.8123
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
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-  <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>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></small>
-    <div class="hidden name"><code>torch_square.Rd</code></div>
-    </div>
-
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-    <p>Square</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="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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3828
-#&gt;  0.0233
-#&gt; -1.1883
-#&gt;  1.0369
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_square</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.1466
-#&gt;  0.0005
-#&gt;  1.4121
-#&gt;  1.0752
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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>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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,1,1,.,.) = 
-#&gt;   0  0
-#&gt; 
-#&gt; (2,1,1,.,.) = 
-#&gt;   0  0
-#&gt; 
-#&gt; (1,1,2,.,.) = 
-#&gt;   0  0
-#&gt; 
-#&gt; (2,1,2,.,.) = 
-#&gt;   0  0
-#&gt; [ CPUFloatType{2,1,2,1,2} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,.,.) = 
-#&gt;   0  0
-#&gt;   0  0
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   0  0
-#&gt;   0  0
-#&gt; [ CPUFloatType{2,2,2} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; (1,1,1,.,.) = 
-#&gt;   0  0
-#&gt; 
-#&gt; (2,1,1,.,.) = 
-#&gt;   0  0
-#&gt; 
-#&gt; (1,1,2,.,.) = 
-#&gt;   0  0
-#&gt; 
-#&gt; (2,1,2,.,.) = 
-#&gt;   0  0
-#&gt; [ CPUFloatType{2,1,2,1,2} ]</div><div class='input'><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><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><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_stack.html b/docs/reference/torch_stack.html
deleted file mode 100644
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-    <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>
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3162  0.4255  0.1976
-#&gt; [ CPUFloatType{1,3} ]</div><div class='input'><span class='fu'>torch_std</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 0.379947
-#&gt; [ CPUFloatType{} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.2036 -2.0630 -2.1182 -0.6214
-#&gt; -1.6360  0.5014 -0.1266 -1.7918
-#&gt;  0.2972 -0.5018  1.3086  1.4842
-#&gt;  0.6903 -0.5105  0.5911  0.7067
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.2400
-#&gt;  1.0589
-#&gt;  1.4759
-#&gt;  1.4476
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_std_mean.html b/docs/reference/torch_std_mean.html
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-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.8958  2.0764  0.0589
-#&gt; [ CPUFloatType{1,3} ]</div><div class='input'><span class='fu'>torch_std_mean</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; 1.51748
-#&gt; [ CPUFloatType{} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; 0.413156
-#&gt; [ CPUFloatType{} ]
-#&gt; </div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.2699 -0.5021 -0.4712  1.0471
-#&gt; -2.2339  0.6823 -0.9388  0.5461
-#&gt; -0.4050  0.9288  0.8127  1.5763
-#&gt;  1.1732 -0.2562  0.5626  0.8124
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><span class='fu'>torch_std_mean</span>(<span class='no'>a</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  1.4429
-#&gt;  0.6986
-#&gt;  0.8327
-#&gt;  0.4380
-#&gt; [ CPUFloatType{4} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; -0.2989
-#&gt;  0.2132
-#&gt; -0.0087
-#&gt;  0.9955
-#&gt; [ CPUFloatType{4} ]
-#&gt; </div><div class='input'># }
-</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_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>
-
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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>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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6977 -0.5155 -1.9107
-#&gt; [ CPUFloatType{1,3} ]</div><div class='input'><span class='fu'>torch_sum</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -3.12391
-#&gt; [ CPUFloatType{} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.4190  0.0463 -0.7716  0.4229
-#&gt; -1.2665  0.4791 -0.5515 -1.0623
-#&gt;  1.1148 -1.2247  0.0682  1.1490
-#&gt; -0.7653 -1.3195  0.4248 -0.6928
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><span class='fu'>torch_sum</span>(<span class='no'>a</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.3360
-#&gt; -2.0188
-#&gt; -0.8302
-#&gt; -0.1831
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;   435
-#&gt;  1335
-#&gt;  2235
-#&gt;  3135
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.7888 -0.7696  0.3986
-#&gt; -0.7711  0.2121 -1.5684
-#&gt;  0.3715 -0.0456 -1.0608
-#&gt;  1.1539 -0.8844 -0.7384
-#&gt; -1.3783 -0.2028  1.6554
-#&gt; [ CPUFloatType{5,3} ]</div><div class='input'><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>$<span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()))</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 8.51333e-07
-#&gt; [ CPUFloatType{} ]</div><div class='input'><span class='no'>a_big</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>7</span>, <span class='fl'>5</span>, <span class='fl'>3</span>))
-<span class='no'>out</span> <span class='kw'>=</span> <span class='fu'>torch_svd</span>(<span class='no'>a_big</span>)
-<span class='no'>u</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>1</span>]]
-<span class='no'>s</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>2</span>]]
-<span class='no'>v</span> <span class='kw'>=</span> <span class='no'>out</span><span class='kw'>[[</span><span class='fl'>3</span>]]
-<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='no'>a_big</span>, <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='no'>u</span>, <span class='fu'><a href='torch_diag_embed.html'>torch_diag_embed</a></span>(<span class='no'>s</span>)), <span class='no'>v</span>$<span class='fu'>transpose</span>(-<span class='fl'>2</span>, -<span class='fl'>1</span>)))</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 2.71036e-06
-#&gt; [ CPUFloatType{} ]</div><div class='input'># }
-</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/docs/reference/torch_symeig.html b/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='co'># \dontrun{</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>$<span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()  <span class='co'># To make a symmetric</span>
-<span class='no'>a</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2.1703 -0.5663 -0.5122 -0.2134 -0.0549
-#&gt; -0.5663  0.9832 -0.9685  0.1017  0.9142
-#&gt; -0.5122 -0.9685 -0.8703  0.7874 -0.6067
-#&gt; -0.2134  0.1017  0.7874  2.8112 -0.1549
-#&gt; -0.0549  0.9142 -0.6067 -0.1549 -0.4494
-#&gt; [ CPUFloatType{5,5} ]</div><div class='input'><span class='no'>o</span> <span class='kw'>=</span> <span class='fu'>torch_symeig</span>(<span class='no'>a</span>, <span class='kw'>eigenvectors</span><span class='kw'>=</span><span class='fl'>TRUE</span>)
-<span class='no'>e</span> <span class='kw'>=</span> <span class='no'>o</span><span class='kw'>[[</span><span class='fl'>1</span>]]
-<span class='no'>v</span> <span class='kw'>=</span> <span class='no'>o</span><span class='kw'>[[</span><span class='fl'>2</span>]]
-<span class='no'>e</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.5747
-#&gt; -0.9141
-#&gt;  1.6122
-#&gt;  2.4168
-#&gt;  3.1047
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='no'>v</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.1632 -0.0736  0.4932  0.7860 -0.3270
-#&gt;  0.3015 -0.4689  0.6486 -0.5181 -0.0109
-#&gt;  0.8988 -0.0414 -0.3445  0.1244  0.2372
-#&gt; -0.1524  0.0538  0.3046  0.2248  0.9114
-#&gt;  0.2266  0.8776  0.3530 -0.2188 -0.0780
-#&gt; [ CPUFloatType{5,5} ]</div><div class='input'><span class='no'>a_big</span> <span class='kw'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
-<span class='no'>a_big</span> <span class='kw'>=</span> <span class='no'>a_big</span> + <span class='no'>a_big</span>$<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><div class='output co'>#&gt; [1] TRUE</div><div class='input'># }
-</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_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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.4813  0.4524 -0.0294
-#&gt;  2.6447 -0.7806  0.5434
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'><span class='fu'>torch_t</span>(<span class='no'>x</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.4813  2.6447
-#&gt;  0.4524 -0.7806
-#&gt; -0.0294  0.5434
-#&gt; [ CPUFloatType{3,2} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.0126
-#&gt; -0.5420
-#&gt;  0.5410
-#&gt; [ CPUFloatType{3} ]</div><div class='input'><span class='fu'>torch_t</span>(<span class='no'>x</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.0126
-#&gt; -0.5420
-#&gt;  0.5410
-#&gt; [ CPUFloatType{3} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.9746 -0.2671  0.6073
-#&gt; -0.1863  0.6615  1.5133
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'><span class='fu'>torch_t</span>(<span class='no'>x</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.9746 -0.1863
-#&gt; -0.2671  0.6615
-#&gt;  0.6073  1.5133
-#&gt; [ CPUFloatType{3,2} ]</div><div class='input'># }
-</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></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='co'># \dontrun{</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'>0</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><div class='output co'>#&gt; torch_tensor 
-#&gt;  8
-#&gt;  3
-#&gt;  7
-#&gt; [ CPUFloatType{3} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-</div>
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diff --git a/docs/reference/torch_tan.html b/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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -1.1158
-#&gt;  0.0583
-#&gt;  0.2334
-#&gt; -0.9159
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_tan</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -2.0440
-#&gt;  0.0583
-#&gt;  0.2377
-#&gt; -1.3021
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_tanh.html b/docs/reference/torch_tanh.html
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-    <h1>Tanh</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.5525
-#&gt; -0.3071
-#&gt;  0.8392
-#&gt; -0.6511
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_tanh</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.5024
-#&gt; -0.2978
-#&gt;  0.6854
-#&gt; -0.5724
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</div></pre>
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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>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='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>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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>))</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt;  4
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt;  4
-#&gt; [ CPUIntType{4} ]</div><div class='input'>
-# }
-</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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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/docs/reference/torch_tensordot.html b/docs/reference/torch_tensordot.html
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-      <a href="../articles/examples/mnist-cnn.html">mnist-cnn</a>
-    </li>
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-      <a href="../articles/examples/mnist-dcgan.html">mnist-dcgan</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../reference/index.html">Reference</a>
-</li>
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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></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt;  4
-#&gt;  5
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='fu'>torch_topk</span>(<span class='no'>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><div class='input'># }
-</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/docs/reference/torch_trace.html b/docs/reference/torch_trace.html
deleted file mode 100644
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-    <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></small>
-    <div class="hidden name"><code>torch_trace.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Trace</p>
-    </div>
-
-
-
-    <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'># \dontrun{</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'>10.</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='no'>x</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  2  3
-#&gt;  4  5  6
-#&gt;  7  8  9
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><span class='fu'>torch_trace</span>(<span class='no'>x</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 15
-#&gt; [ CPUFloatType{} ]</div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_transpose.html b/docs/reference/torch_transpose.html
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-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.3114 -0.1716  0.4504
-#&gt; -0.4723 -1.0927  2.1773
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'><span class='fu'>torch_transpose</span>(<span class='no'>x</span>, <span class='fl'>1</span>, <span class='fl'>2</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.3114 -0.4723
-#&gt; -0.1716 -1.0927
-#&gt;  0.4504  2.1773
-#&gt; [ CPUFloatType{3,2} ]</div><div class='input'># }
-</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_trapz.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0190  1.0024  1.9078
-#&gt; -0.0511 -0.7302  0.0112
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  2.4765
-#&gt; -0.7502
-#&gt; [ CPUFloatType{2} ]</div><div class='input'>
-# }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/docs/reference/torch_triangular_solve.html b/docs/reference/torch_triangular_solve.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_triangular_solve.Rd</code></div>
-    </div>
-
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-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3460  0.1356
-#&gt;  0.0000  1.5035
-#&gt; [ CPUFloatType{2,2} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.4014 -0.1958  0.0379
-#&gt; -1.3143 -0.0766 -0.3524
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'><span class='fu'>torch_triangular_solve</span>(<span class='no'>b</span>, <span class='no'>A</span>)</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  0.8174  0.5459 -0.2014
-#&gt; -0.8742 -0.0509 -0.2344
-#&gt; [ CPUFloatType{2,3} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; -0.3460  0.1356
-#&gt;  0.0000  1.5035
-#&gt; [ CPUFloatType{2,2} ]
-#&gt; </div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6849  0.8749 -0.7531
-#&gt; -0.5497 -1.0238  0.3323
-#&gt; -0.0980  0.0908 -0.4867
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><span class='fu'>torch_tril</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.6849  0.0000  0.0000
-#&gt; -0.5497 -1.0238  0.0000
-#&gt; -0.0980  0.0908 -0.4867
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.7116 -0.9359 -1.5487  1.6909  0.9290 -1.8224
-#&gt;  2.1791 -0.8098  1.4367 -0.5204  1.0782 -0.4998
-#&gt;  1.3149 -1.0202 -0.4302 -0.5773  0.0928 -1.0440
-#&gt; -1.7950  0.6438 -0.7581  0.0569 -1.0737  1.3707
-#&gt; [ CPUFloatType{4,6} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.7116 -0.9359  0.0000  0.0000  0.0000  0.0000
-#&gt;  2.1791 -0.8098  1.4367  0.0000  0.0000  0.0000
-#&gt;  1.3149 -1.0202 -0.4302 -0.5773  0.0000  0.0000
-#&gt; -1.7950  0.6438 -0.7581  0.0569 -1.0737  0.0000
-#&gt; [ CPUFloatType{4,6} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
-#&gt;  2.1791  0.0000  0.0000  0.0000  0.0000  0.0000
-#&gt;  1.3149 -1.0202  0.0000  0.0000  0.0000  0.0000
-#&gt; -1.7950  0.6438 -0.7581  0.0000  0.0000  0.0000
-#&gt; [ CPUFloatType{4,6} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_tril_indices.html b/docs/reference/torch_tril_indices.html
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-    <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"></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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3956 -0.1868  0.0874
-#&gt; -0.3459 -0.2626 -0.7964
-#&gt;  0.5357 -0.4833 -0.2087
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><span class='fu'>torch_triu</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3956 -0.1868  0.0874
-#&gt;  0.0000 -0.2626 -0.7964
-#&gt;  0.0000  0.0000 -0.2087
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 0.01 *
-#&gt;  0.0000 -18.6760  8.7416
-#&gt;   0.0000  0.0000 -79.6368
-#&gt;   0.0000  0.0000  0.0000
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.3956 -0.1868  0.0874
-#&gt; -0.3459 -0.2626 -0.7964
-#&gt;  0.0000 -0.4833 -0.2087
-#&gt; [ CPUFloatType{3,3} ]</div><div class='input'><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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.1247  0.3568  1.5481  0.9310  0.2551 -1.8148
-#&gt;  0.7493  0.8313 -0.6427  0.3658 -0.2912  0.3553
-#&gt;  0.9661  2.0171  0.9854 -0.1047 -1.6832 -0.0952
-#&gt;  0.0011  0.5442  0.5278 -0.5429  0.4507 -0.8038
-#&gt; [ CPUFloatType{4,6} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0000  0.3568  1.5481  0.9310  0.2551 -1.8148
-#&gt;  0.0000  0.0000 -0.6427  0.3658 -0.2912  0.3553
-#&gt;  0.0000  0.0000  0.0000 -0.1047 -1.6832 -0.0952
-#&gt;  0.0000  0.0000  0.0000  0.0000  0.4507 -0.8038
-#&gt; [ CPUFloatType{4,6} ]</div><div class='input'><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><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.1247  0.3568  1.5481  0.9310  0.2551 -1.8148
-#&gt;  0.7493  0.8313 -0.6427  0.3658 -0.2912  0.3553
-#&gt;  0.0000  2.0171  0.9854 -0.1047 -1.6832 -0.0952
-#&gt;  0.0000  0.0000  0.5278 -0.5429  0.4507 -0.8038
-#&gt; [ CPUFloatType{4,6} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/torch_triu_indices.html b/docs/reference/torch_triu_indices.html
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-    <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"></pre>
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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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.6667
-#&gt;  1.5000
-#&gt; [ CPUFloatType{2} ]</div><div class='input'><span class='fu'>torch_true_divide</span>(<span class='no'>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><div class='input'># }
-</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/docs/reference/torch_trunc.html b/docs/reference/torch_trunc.html
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-    <div class="hidden name"><code>torch_trunc.Rd</code></div>
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-    <p>Trunc</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="trunc-input-out-none-gt-tensor-"><a class="anchor" href="#trunc-input-out-none-gt-tensor-"></a>trunc(input, out=None) -&gt; Tensor </h2>
-
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-the elements of <code>input</code>.</p>
-
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-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.4632
-#&gt; -1.3494
-#&gt;  0.0517
-#&gt; -1.1300
-#&gt; [ CPUFloatType{4} ]</div><div class='input'><span class='fu'>torch_trunc</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0
-#&gt; -1
-#&gt;  0
-#&gt; -1
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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_unbind.Rd</code></div>
-    </div>
-
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-    <p>Unbind</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 unbind</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension to remove</p></td>
-    </tr>
-    </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>
-
-    
-
-
-<p>Removes a tensor dimension.</p>
-<p>Returns a tuple of all slices along a given dimension, already without it.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'># \dontrun{</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><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt; [ CPUIntType{3} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt;  4
-#&gt;  5
-#&gt;  6
-#&gt; [ CPUIntType{3} ]
-#&gt; 
-#&gt; [[3]]
-#&gt; torch_tensor 
-#&gt;  7
-#&gt;  8
-#&gt;  9
-#&gt; [ CPUIntType{3} ]
-#&gt; </div><div class='input'># }
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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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='co'># \dontrun{</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></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; [ CPULongType{0} ]
-#&gt; </div><div class='input'><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>)</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;  0
-#&gt;  0
-#&gt;  1
-#&gt;  1
-#&gt;  2
-#&gt;  3
-#&gt;  3
-#&gt;  4
-#&gt; [ CPULongType{8} ]
-#&gt; 
-#&gt; [[3]]
-#&gt; torch_tensor 
-#&gt; [ CPULongType{0} ]
-#&gt; </div><div class='input'><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><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><div class='input'># }
-</div></pre>
-  </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>
-    <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='co'># \dontrun{</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>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1  2  3  4
-#&gt; [ CPUFloatType{1,4} ]</div><div class='input'><span class='fu'>torch_unsqueeze</span>(<span class='no'>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><div class='input'># }
-</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_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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.9097 -1.4605 -0.9481
-#&gt; [ CPUFloatType{1,3} ]</div><div class='input'><span class='fu'>torch_var</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt; 1.55533
-#&gt; [ CPUFloatType{} ]</div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.9322 -0.2661 -1.1364 -0.1596
-#&gt; -0.8385  0.0366 -0.8830 -0.5310
-#&gt;  1.6003  0.1409 -0.4186  2.4136
-#&gt; -0.7193 -0.5766  0.0958 -0.3928
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><span class='fu'>torch_var</span>(<span class='no'>a</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.4709
-#&gt;  0.1046
-#&gt;  0.2947
-#&gt;  1.9483
-#&gt; [ CPUFloatType{4} ]</div><div class='input'># }
-</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/docs/reference/torch_var_mean.html b/docs/reference/torch_var_mean.html
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+++ /dev/null
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-    <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='co'># \dontrun{</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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  1.4242 -0.2759  0.7106
-#&gt; [ CPUFloatType{1,3} ]</div><div class='input'><span class='fu'>torch_var_mean</span>(<span class='no'>a</span>)</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt; 0.728761
-#&gt; [ CPUFloatType{} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; 0.61961
-#&gt; [ CPUFloatType{} ]
-#&gt; </div><div class='input'>
-
-<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></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0.0532 -0.3304 -0.5824  1.8389
-#&gt; -2.0310 -0.6095 -0.1087  0.3034
-#&gt; -1.3414  1.7987 -0.3098  0.8658
-#&gt;  0.0799 -0.7031 -0.5875 -1.0066
-#&gt; [ CPUFloatType{4,4} ]</div><div class='input'><span class='fu'>torch_var_mean</span>(<span class='no'>a</span>, <span class='fl'>1</span>)</div><div class='output co'>#&gt; [[1]]
-#&gt; torch_tensor 
-#&gt;  1.1034
-#&gt;  1.4015
-#&gt;  0.0538
-#&gt;  1.4116
-#&gt; [ CPUFloatType{4} ]
-#&gt; 
-#&gt; [[2]]
-#&gt; torch_tensor 
-#&gt; -0.8098
-#&gt;  0.0389
-#&gt; -0.3971
-#&gt;  0.5004
-#&gt; [ CPUFloatType{4} ]
-#&gt; </div><div class='input'># }
-</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/docs/reference/torch_where.html b/docs/reference/torch_where.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_where.Rd</code></div>
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-    <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>
-
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diff --git a/docs/reference/torch_zeros.html b/docs/reference/torch_zeros.html
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-    <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='co'># \dontrun{</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>))</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  0  0
-#&gt;  0  0  0
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'><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><div class='input'># }
-</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>
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diff --git a/docs/reference/torch_zeros_like.html b/docs/reference/torch_zeros_like.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_zeros_like.Rd</code></div>
-    </div>
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-    <p>Zeros_like</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 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='co'># \dontrun{</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_zeros_like</span>(<span class='no'>input</span>)</div><div class='output co'>#&gt; torch_tensor 
-#&gt;  0  0  0
-#&gt;  0  0  0
-#&gt; [ CPUFloatType{2,3} ]</div><div class='input'># }
-</div></pre>
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diff --git a/docs/reference/utils_dataset.html b/docs/reference/utils_dataset.html
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-<meta property="og:description" content="All datasets that represent a map from keys to data samples should subclass
-it. All subclasses should overwrite get_item, supporting fetching a
-data sample for a given key. Subclasses could also optionally overwrite
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-~torch.utils.data.Sampler implementations and the default options
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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>utils_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'>utils_dataset</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>public methods for the dataset class</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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-  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
-    <h2>Contents</h2>
-    <ul class="nav nav-pills nav-stacked">
-      <li><a href="#arguments">Arguments</a></li>
-      <li><a href="#note">Note</a></li>
-    </ul>
-
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/docs/reference/utils_dataset_tensor.html b/docs/reference/utils_dataset_tensor.html
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diff --git a/docs/reference/vision_make_grid.html b/docs/reference/vision_make_grid.html
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/vision_utils.R'><code>R/vision_utils.R</code></a></small>
-    <div class="hidden name"><code>vision_make_grid.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Arranges a batch of (image) tensors in a grid, with optional padding between images.
-Expects a 4d mini-batch tensor of shape (B x C x H x W).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>vision_make_grid</span>(
-  <span class='no'>tensor</span>,
-  <span class='kw'>scale</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>num_rows</span> <span class='kw'>=</span> <span class='fl'>8</span>,
-  <span class='kw'>padding</span> <span class='kw'>=</span> <span class='fl'>2</span>,
-  <span class='kw'>pad_value</span> <span class='kw'>=</span> <span class='fl'>0</span>
-)</pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>tensor to arrange in grid</p></td>
-    </tr>
-    <tr>
-      <th>scale</th>
-      <td><p>whether to normalize (min-max-scale) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>num_rows</th>
-      <td><p>number of rows making up the grid (default 8)</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>amount of padding between batch images (default 2)</p></td>
-    </tr>
-    <tr>
-      <th>pad_value</th>
-      <td><p>pixel value to use for padding</p></td>
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diff --git a/docs/reference/with_enable_grad.html b/docs/reference/with_enable_grad.html
deleted file mode 100644
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-    <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'># \dontrun{</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>({
-    <span class='no'>y</span> <span class='kw'>=</span> <span class='no'>x</span> * <span class='fl'>2</span>
-  })
-})
-<span class='no'>y</span>$<span class='fu'>backward</span>()
-<span class='no'>x</span>$<span class='no'>grad</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt;  2
-#&gt; [ CPUFloatType{1} ]</div><div class='input'>
-# }
-</div></pre>
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diff --git a/docs/reference/with_no_grad.html b/docs/reference/with_no_grad.html
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-<meta property="og:title" content="Temporarily modify gradient recording. — with_no_grad" />
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-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.1</span>
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-    <li class="dropdown-header">Tensors</li>
-    <li>
-      <a href="../articles/tensor-creation.html">Creating tensors</a>
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-    <li class="dropdown-header">Datasets</li>
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-    </li>
-    <li class="dropdown-header">Autograd</li>
-    <li>
-      <a href="../articles/using-autograd.html">Using autograd</a>
-    </li>
-    <li>
-      <a href="../articles/extending-autograd.html">Extending autograd</a>
-    </li>
-  </ul>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Examples
-     
-    <span class="caret"></span>
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-    <li class="dropdown-header">Vision</li>
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-      <a href="../articles/examples/mnist-mlp.html">mnist-mlp</a>
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-      <a href="../articles/examples/mnist-cnn.html">mnist-cnn</a>
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-      <a href="../articles/examples/mnist-dcgan.html">mnist-dcgan</a>
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-  <a href="../reference/index.html">Reference</a>
-</li>
-      </ul>
-      <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="https://github.com/mlverse/torch/">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-      
-    </div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header>
-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <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='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='co'># \dontrun{</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>$<span class='fu'>sub_</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>as.numeric</a></span>(<span class='fl'>1</span>:<span class='fl'>5</span>)))
-})</div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.1943
-#&gt; -1.1859
-#&gt; -2.5961
-#&gt; -3.7816
-#&gt; -4.5816
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='no'>x</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt; -0.1943
-#&gt; -1.1859
-#&gt; -2.5961
-#&gt; -3.7816
-#&gt; -4.5816
-#&gt; [ CPUFloatType{5} ]</div><div class='input'><span class='no'>x</span>$<span class='no'>grad</span></div><div class='output co'>#&gt; torch_tensor 
-#&gt; [ Tensor (undefined) ]</div><div class='input'>
-# }
-</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>
-
-
-      <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> 1.5.1.</p>
-</div>
-
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-   </div>
-
-  
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-
-  </body>
-</html>
-
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-- 
GitLab


From 9ff9682a496a1fbb412c95e6cb08b62009a44a1e Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 25 Aug 2020 17:21:51 -0300
Subject: [PATCH 062/157] add auto development mode

---
 _pkgdown.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 4cc2b9c81..57dcd7d4b 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -2,6 +2,9 @@ destination: docs
 template:
   params:
     bootswatch: united
+    
+development:
+  mode: auto
 
 navbar:
  structure:
-- 
GitLab


From 5432fc07bda054197f745531cbe2f157d9d187cf Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 25 Aug 2020 17:46:45 -0300
Subject: [PATCH 063/157] add some badges

---
 README.Rmd |   3 ++
 README.md  | 155 +++++++++++++++++++++++++++--------------------------
 2 files changed, 81 insertions(+), 77 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index a91c01a6d..5591abd56 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -17,6 +17,9 @@ knitr::opts_chunk$set(
 
 [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
 ![R build status](https://github.com/mlverse/torch/workflows/Test/badge.svg)
+[![CRAN status](https://www.r-pkg.org/badges/version/torch)](https://CRAN.R-project.org/package=torch)
+
+[![](https://cranlogs.r-pkg.org/badges/torch)](https://cran.r-project.org/package=torch)
 
 ## Installation
 
diff --git a/README.md b/README.md
index d8eedd868..2b74085bf 100644
--- a/README.md
+++ b/README.md
@@ -1,71 +1,72 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# torch <a href='https://mlverse.github.io/torch'><img src='man/figures/torch.png' align="right" height="139" /></a>
+torch <a href='https://mlverse.github.io/torch'><img src='man/figures/torch.png' align="right" height="139" /></a>
+==================================================================================================================
 
 [![Lifecycle:
 experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
 ![R build
 status](https://github.com/mlverse/torch/workflows/Test/badge.svg)
+[![CRAN
+status](https://www.r-pkg.org/badges/version/torch)](https://CRAN.R-project.org/package=torch)
 
-## Installation
+[![](https://cranlogs.r-pkg.org/badges/torch)](https://cran.r-project.org/package=torch)
+
+Installation
+------------
 
 Run:
 
-``` r
-remotes::install_github("mlverse/torch")
-```
+    remotes::install_github("mlverse/torch")
 
 At the first package load additional software will be installed.
 
-## Example
+Example
+-------
 
 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.
 
-``` r
-library(torch)
-x <- array(runif(8), dim = c(2, 2, 2))
-y <- torch_tensor(x, dtype = torch_float64())
-y
-#> torch_tensor 
-#> (1,.,.) = 
-#>   0.8687  0.0157
-#>   0.4237  0.8971
-#> 
-#> (2,.,.) = 
-#>   0.4021  0.5509
-#>   0.3374  0.9034
-#> [ CPUDoubleType{2,2,2} ]
-identical(x, as_array(y))
-#> [1] TRUE
-```
+    library(torch)
+    x <- array(runif(8), dim = c(2, 2, 2))
+    y <- torch_tensor(x, dtype = torch_float64())
+    y
+    #> torch_tensor 
+    #> (1,.,.) = 
+    #>   0.1292  0.9074
+    #>   0.8109  0.5279
+    #> 
+    #> (2,.,.) = 
+    #>   0.0765  0.7386
+    #>   0.0176  0.6603
+    #> [ CPUDoubleType{2,2,2} ]
+    identical(x, as_array(y))
+    #> [1] TRUE
 
 ### Simple Autograd Example
 
 In the following snippet we let torch, using the autograd feature,
 calculate the derivatives:
 
-``` r
-x <- torch_tensor(1, requires_grad = TRUE)
-w <- torch_tensor(2, requires_grad = TRUE)
-b <- torch_tensor(3, requires_grad = TRUE)
-y <- w * x + b
-y$backward()
-x$grad
-#> torch_tensor 
-#>  2
-#> [ CPUFloatType{1} ]
-w$grad
-#> torch_tensor 
-#>  1
-#> [ CPUFloatType{1} ]
-b$grad
-#> torch_tensor 
-#>  1
-#> [ CPUFloatType{1} ]
-```
+    x <- torch_tensor(1, requires_grad = TRUE)
+    w <- torch_tensor(2, requires_grad = TRUE)
+    b <- torch_tensor(3, requires_grad = TRUE)
+    y <- w * x + b
+    y$backward()
+    x$grad
+    #> torch_tensor 
+    #>  2
+    #> [ CPUFloatType{1} ]
+    w$grad
+    #> torch_tensor 
+    #>  1
+    #> [ CPUFloatType{1} ]
+    b$grad
+    #> torch_tensor 
+    #>  1
+    #> [ CPUFloatType{1} ]
 
 ### Linear Regression
 
@@ -75,41 +76,41 @@ scratch using torch’s Autograd.
 **Note** all methods that end with `_` (eg. `sub_`), will modify the
 tensors in place.
 
-``` r
-x <- torch_randn(100, 2)
-y <- 0.1 + 0.5*x[,1] - 0.7*x[,2]
-
-w <- torch_randn(2, 1, requires_grad = TRUE)
-b <- torch_zeros(1, requires_grad = TRUE)
-
-lr <- 0.5
-for (i in 1:100) {
-  y_hat <- torch_mm(x, w) + b
-  loss <- torch_mean((y - y_hat$squeeze(1))^2)
-  
-  loss$backward()
-  
-  with_no_grad({
-    w$sub_(w$grad*lr)
-    b$sub_(b$grad*lr)   
-    
-    w$grad$zero_()
-    b$grad$zero_()
-  })
-}
-print(w)
-#> torch_tensor 
-#>  0.5000
-#> -0.7000
-#> [ CPUFloatType{2,1} ]
-print(b) 
-#> torch_tensor 
-#> 0.01 *
-#> 10.0000
-#> [ CPUFloatType{1} ]
-```
-
-## Contributing
+    x <- torch_randn(100, 2)
+    y <- 0.1 + 0.5*x[,1] - 0.7*x[,2]
+
+    w <- torch_randn(2, 1, requires_grad = TRUE)
+    b <- torch_zeros(1, requires_grad = TRUE)
+
+    lr <- 0.5
+    for (i in 1:100) {
+      y_hat <- torch_mm(x, w) + b
+      loss <- torch_mean((y - y_hat$squeeze(1))^2)
+      
+      loss$backward()
+      
+      with_no_grad({
+        w$sub_(w$grad*lr)
+        b$sub_(b$grad*lr)   
+        
+        w$grad$zero_()
+        b$grad$zero_()
+      })
+    }
+    print(w)
+    #> torch_tensor 
+    #> 1e-10 *
+    #> -2.6495
+    #>  -9.1423
+    #> [ CPUFloatType{2,1} ]
+    print(b) 
+    #> torch_tensor 
+    #> 0.01 *
+    #>  4.2967
+    #> [ CPUFloatType{1} ]
+
+Contributing
+------------
 
 No matter your current skills it’s possible to contribute to `torch`
 development. See the contributing guide for more information.
-- 
GitLab


From fce3defabf982cf49ee661e727d51d1fc73db803 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 25 Aug 2020 17:47:30 -0300
Subject: [PATCH 064/157] fix position

---
 README.Rmd |  1 -
 README.md  | 17 ++++++++---------
 2 files changed, 8 insertions(+), 10 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 5591abd56..c2a6c7a36 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -18,7 +18,6 @@ knitr::opts_chunk$set(
 [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
 ![R build status](https://github.com/mlverse/torch/workflows/Test/badge.svg)
 [![CRAN status](https://www.r-pkg.org/badges/version/torch)](https://CRAN.R-project.org/package=torch)
-
 [![](https://cranlogs.r-pkg.org/badges/torch)](https://cran.r-project.org/package=torch)
 
 ## Installation
diff --git a/README.md b/README.md
index 2b74085bf..b66a3e339 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,6 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h
 status](https://github.com/mlverse/torch/workflows/Test/badge.svg)
 [![CRAN
 status](https://www.r-pkg.org/badges/version/torch)](https://CRAN.R-project.org/package=torch)
-
 [![](https://cranlogs.r-pkg.org/badges/torch)](https://cran.r-project.org/package=torch)
 
 Installation
@@ -35,12 +34,12 @@ torch Tensor to an R object.
     y
     #> torch_tensor 
     #> (1,.,.) = 
-    #>   0.1292  0.9074
-    #>   0.8109  0.5279
+    #>   0.8270  0.1017
+    #>   0.1992  0.7632
     #> 
     #> (2,.,.) = 
-    #>   0.0765  0.7386
-    #>   0.0176  0.6603
+    #>   0.2694  0.9735
+    #>   0.1965  0.7447
     #> [ CPUDoubleType{2,2,2} ]
     identical(x, as_array(y))
     #> [1] TRUE
@@ -99,14 +98,14 @@ tensors in place.
     }
     print(w)
     #> torch_tensor 
-    #> 1e-10 *
-    #> -2.6495
-    #>  -9.1423
+    #> 1e-09 *
+    #>  7.8984
+    #>   8.7077
     #> [ CPUFloatType{2,1} ]
     print(b) 
     #> torch_tensor 
     #> 0.01 *
-    #>  4.2967
+    #>  9.9867
     #> [ CPUFloatType{1} ]
 
 Contributing
-- 
GitLab


From 148991e10b4730aac798ab488b347cf6e4149620 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 26 Aug 2020 14:58:41 -0300
Subject: [PATCH 065/157] start adding getting started vignettes

---
 .github/workflows/pkgdown.yaml              |   1 +
 _pkgdown.yml                                |   7 +-
 vignettes/getting-started/what-is-torch.Rmd | 171 ++++++++++++++++++++
 3 files changed, 178 insertions(+), 1 deletion(-)
 create mode 100644 vignettes/getting-started/what-is-torch.Rmd

diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index 0e658c182..d610ff6f7 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -9,6 +9,7 @@ jobs:
     runs-on: macOS-latest
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      TORCH_TEST: 1
     steps:
       - uses: actions/checkout@v2
 
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 57dcd7d4b..26abdfcbd 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -8,9 +8,14 @@ development:
 
 navbar:
  structure:
-  left:  [home, articles, examples, reference, news]
+  left:  [home, getting-started, articles, examples, reference, news]
   right: [github]
  components:
+  getting-started:
+    text: Getting started
+    menu:
+      - text: What's torch?
+        href: articles/getting-started/what-is-torch.html
   articles:
     text: Articles
     menu:
diff --git a/vignettes/getting-started/what-is-torch.Rmd b/vignettes/getting-started/what-is-torch.Rmd
new file mode 100644
index 000000000..f4ba5b66e
--- /dev/null
+++ b/vignettes/getting-started/what-is-torch.Rmd
@@ -0,0 +1,171 @@
+---
+title: What is torch?
+type: docs
+---
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py). All credits goes to [Soumith Chintala](http://soumith.ch/).
+
+```{r setup}
+library(torch)
+```
+
+It’s a scientific computing package targeted at two sets of audiences:
+
+- An array library to use the power of GPUs
+- a deep learning research platform that provides maximum flexibility and speed
+
+## Getting started
+
+### Tensors
+
+Tensors are similar to R arrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.
+
+> Note: An uninitialized matrix is declared, but does not contain definite known values before it is used. When an uninitialized matrix is created, whatever values were in the allocated memory at the time will appear as the initial values.
+
+Construct a 5x3 matrix, uninitialized:
+
+```{r}
+x <- torch_empty(5, 3)
+x
+```
+
+Construct a randomly initialized matrix:
+
+```{r}
+x <- torch_rand(5, 3)
+x
+```
+
+Construct a matrix filled zeros and of dtype long:
+
+```{r}
+x <- torch_zeros(5, 3, dtype = torch_long())
+x
+```
+
+Construct a tensor directly from data:
+
+```{r}
+x <- torch_tensor(c(5.5, 3))
+x
+```
+
+or create a tensor based on an existing tensor. These methods will reuse properties of the input tensor, e.g. dtype, unless new values are provided by user
+
+```{r}
+x <- x$new_ones(5, 3, dtype = torch_double())   # new_* methods take in sizes
+x
+
+x <- torch_randn_like(x, dtype = torch_float()) # override dtype!
+x                                               # result has the same size
+```
+
+Get its size:
+
+```{r}
+x$size
+```
+
+### Operations
+
+There are multiple syntaxes for operations. In the following example, we will take a look at the addition operation.
+
+Addition: syntax 1
+
+```{r}
+y <- torch_rand(5, 3)
+x + y
+```
+
+Addition: syntax 2
+
+```{r}
+torch_add(x, y)
+```
+
+Addition: in-place
+
+```{r}
+y$add_(x)
+y
+```
+
+> Note: Any operation that mutates a tensor in-place is post-fixed with an `_`. For example: `x$copy_(y)`, `x$t_()`, will change x.
+
+You can use standard R-like indexing with all bells and whistles! See more about indexing with `vignette("indexing")`.
+
+```{r}
+x[, 1]
+```
+
+Resizing: If you want to resize/reshape tensor, you can use `torch_view`:
+
+```{r}
+x <- torch_randn(4, 4)
+y <- x$view(16)
+z <- x$view(size = c(-1, 8))  # the size -1 is inferred from other dimensions
+x$size()
+y$size()
+z$size()
+```
+If you have a one element tensor, use `$item()` to get the value as an R number
+
+```{r}
+x <- torch_randn(1)
+x
+x$item()
+```
+
+You can find a complete list of operations in the reference page.
+
+
+## R bridge
+
+Converting a Torch Tensor to an R array and vice versa is a breeze.
+
+### Converting a torch tensor into an R array
+
+```{r}
+a <- torch_ones(5)
+a
+```
+
+```{r}
+b <- as_array(a)
+b
+```
+
+### Converting R arrays to torch tensors
+
+```{r}
+a <- rep(1, 5)
+a
+b <- torch_tensor(a)
+b
+```
+
+Currently supported types are numerics and boolean types.
+
+## CUDA tensors
+
+Tensors can be moved onto any device using the `$to` method.
+
+```{r}
+if (cuda_is_available()) {
+  device <- torch_device("cuda")
+  y <- torch_ones_like(x, device = device)  # directly create a tensor on GPU
+  x <- x$to(device)                       # or just use strings ``.to("cuda")``
+  z <- x + y
+  print(z)
+  print(z$to(device = "cpu", torch_double())) # `$to` can also change dtype together!
+}
+```
+
+
-- 
GitLab


From 0f934fbb8a1012d87092d6d4f63507cedee789ec Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 26 Aug 2020 16:37:01 -0300
Subject: [PATCH 066/157] increase timeout limit to 15 minutes for CRAN

---
 tools/renamelib.R | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/tools/renamelib.R b/tools/renamelib.R
index 9d225c750..ce11e0b11 100644
--- a/tools/renamelib.R
+++ b/tools/renamelib.R
@@ -3,13 +3,12 @@ libs_path <- getwd()
 # wait, because of it's built in parallel this script might run before
 # finishing.
 start <- Sys.time()
-timeout <- 180
+timeout <- 15*60
 
 while(length(dir(libs_path, pattern = "torch\\.")) == 0) {
   Sys.sleep(4)
-  cat("Waiting for compilation...\n")
   if (as.numeric(Sys.time() - start) > timeout)
-    break
+    break 
 }
 
 for (lib in dir(libs_path, pattern = "torch\\.")) {
-- 
GitLab


From c127f0c411ddb50359f92b068c27121798e73182 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 27 Aug 2020 10:26:30 -0300
Subject: [PATCH 067/157] action that merge docs and landing page websites

---
 .github/workflows/website.yaml | 30 ++++++++++++++++++++++++++++++
 .gitignore                     |  1 +
 2 files changed, 31 insertions(+)
 create mode 100644 .github/workflows/website.yaml

diff --git a/.github/workflows/website.yaml b/.github/workflows/website.yaml
new file mode 100644
index 000000000..cf322d87e
--- /dev/null
+++ b/.github/workflows/website.yaml
@@ -0,0 +1,30 @@
+on:
+  push:
+    branches: 
+      - gh-pages
+      - blogdown
+
+name: Merge websites
+
+jobs:
+  pkgdown:
+    runs-on: macOS-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      TORCH_TEST: 1
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          ref: 'blogdown'
+      - name: Deploy package
+        run: |
+          git config --local user.email "actions@github.com"
+          git config --local user.name "GitHub Actions"
+          mkdir static/docs
+          cd static/docs
+          git clone -b gh-pages https://github.com/mlverse/torch.git .
+          rm -r .git/
+          cd ../..
+          git add -A
+          git commit -m "Update site"
+          git push --force origin HEAD:website
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index ccef4c394..8f55aaa05 100644
--- a/.gitignore
+++ b/.gitignore
@@ -18,3 +18,4 @@ lantern/.idea*
 lantern/cmake-build*
 check/
 docs/
+public
-- 
GitLab


From f6782a5a8b04bd8821dd6bc615f8c878912643b6 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 27 Aug 2020 10:51:31 -0300
Subject: [PATCH 068/157] add other building options

---
 .github/workflows/website.yaml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/website.yaml b/.github/workflows/website.yaml
index cf322d87e..1b8dc2cdd 100644
--- a/.github/workflows/website.yaml
+++ b/.github/workflows/website.yaml
@@ -3,6 +3,8 @@ on:
     branches: 
       - gh-pages
       - blogdown
+    page_build:
+    workflow_dispatch:
 
 name: Merge websites
 
-- 
GitLab


From 285a7748e83b1562a3346b387774dcb8ea455591 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 27 Aug 2020 10:53:10 -0300
Subject: [PATCH 069/157] correctly ident

---
 .github/workflows/website.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/website.yaml b/.github/workflows/website.yaml
index 1b8dc2cdd..5b9bf3ace 100644
--- a/.github/workflows/website.yaml
+++ b/.github/workflows/website.yaml
@@ -3,8 +3,8 @@ on:
     branches: 
       - gh-pages
       - blogdown
-    page_build:
-    workflow_dispatch:
+  page_build:
+  workflow_dispatch:
 
 name: Merge websites
 
-- 
GitLab


From 95b29543223eef7f5614bcd9625f9badb26ebbc8 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 27 Aug 2020 11:39:14 -0300
Subject: [PATCH 070/157] trigger merge sites when pkgdown finishes run

---
 .github/workflows/website.yaml | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/website.yaml b/.github/workflows/website.yaml
index 5b9bf3ace..4eaacd377 100644
--- a/.github/workflows/website.yaml
+++ b/.github/workflows/website.yaml
@@ -1,10 +1,13 @@
 on:
   push:
-    branches: 
-      - gh-pages
+    branches:
       - blogdown
-  page_build:
   workflow_dispatch:
+  workflow_run:
+    workflows: ["pkgdown"]
+    branches: ["master"]
+    types: 
+      - completed
 
 name: Merge websites
 
-- 
GitLab


From d6b4d1a64309f17eb6c5b47fb2162559161f3d25 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 27 Aug 2020 17:49:15 -0300
Subject: [PATCH 071/157] add autograd getting started document

---
 _pkgdown.yml                           |   2 +
 vignettes/getting-started/autograd.Rmd | 181 +++++++++++++++++++++++++
 2 files changed, 183 insertions(+)
 create mode 100644 vignettes/getting-started/autograd.Rmd

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 26abdfcbd..6f0f140e0 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -16,6 +16,8 @@ navbar:
     menu:
       - text: What's torch?
         href: articles/getting-started/what-is-torch.html
+      - text: 'Autograd: automatic differentiation'
+        href: articles/getting-started/autograd.html
   articles:
     text: Articles
     menu:
diff --git a/vignettes/getting-started/autograd.Rmd b/vignettes/getting-started/autograd.Rmd
new file mode 100644
index 000000000..2947f012b
--- /dev/null
+++ b/vignettes/getting-started/autograd.Rmd
@@ -0,0 +1,181 @@
+---
+title: 'Autograd: automatic differentiation'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py). All credits goes to [Soumith Chintala](http://soumith.ch/).
+
+```{r setup}
+library(torch)
+```
+
+Central to all neural networks in torch is the autograd functionality. Let's first briefly visit this, and we will then go to training our first neural network.
+
+Autograd provides automatic differentiation for all operations on Tensors. It is a define-by-run framework, which means that your backprop is defined by how your code is run, and that every single iteration can be different.
+
+Let us see this in more simple terms with some examples.
+
+## Tensor
+
+`torch_tensor` is the central class of the package. If you set its attribute `$requires_grad` as `TRUE`, it starts to track all operations on it. When you finish your computation you can call `$backward()` and have all the gradients computed automatically. The gradient for this tensor will be accumulated into `$grad` attribute.
+
+To stop a tensor from tracking history, you can call `$detach()` to detach it from the computation history, and to prevent future computation from being tracked.
+
+To prevent tracking history (and using memory), you can also wrap the code block in `with_no_grad({<code>})`. This can be particularly helpful when evaluating a model because the model may have trainable parameters with `requires_grad=TRUE`, but for which we don't need the gradients.
+
+There's one more class which is very important for autograd implementation - a `autograd_function`.
+
+Tensor and Function are interconnected and build up an acyclic graph, that encodes a complete history of computation. Each tensor has a `$grad_fn` attribute that references an `autograd_function` that has created the Tensor (except for Tensors created by the user - their `grad_fn` is `NULL`).
+
+If you want to compute the derivatives, you can call `$backward()` on a Tensor. If Tensor is a scalar (i.e. it holds a one element data), you don't need to specify any arguments to `backward()`, however if it has more elements, you need to specify a gradient argument that is a tensor of matching shape.
+
+Create a tensor and set `requires_grad=TRUE` to track computation with it:
+
+```{r}
+x <- torch_ones(2, 2, requires_grad = TRUE)
+x
+```
+
+Do a tensor operation:
+
+```{r}
+y <- x + 2
+y
+```
+
+`y` was created as a result of an operation, so it has a `grad_fn`.
+
+```{r}
+y$grad_fn
+```
+
+Do more operations on `y`
+
+```{r}
+z <- y * y * 3
+z
+out <- z$mean()
+out
+```
+
+`$requires_grad_( ... )` changes an existing Tensor's `requires_grad` flag in-place. The input flag defaults to `FALSE` if not given.
+
+```{r}
+a <- torch_randn(2, 2)
+a <- (a * 3) / (a - 1)
+a$requires_grad
+a$requires_grad_(TRUE)
+a$requires_grad
+b <- (a * a)$sum()
+b$grad_fn
+```
+
+## Gradients
+
+Let's backprop now. Because out contains a single scalar, `out$backward()` is equivalent to `out$backward(torch.tensor(1.))`.
+
+```{r}
+out$backward()
+```
+
+Print gradients d(out)/dx
+
+```{r}
+x$grad
+```
+
+You should have got a matrix of `4.5`. Let's call the `out` *Tensor* $o$.
+
+We have that $o = \frac{1}{4}\sum_i z_i$, $z_i = 3(x_i+2)^2$ and $z_i\bigr\rvert_{x_i=1} = 27$. Therefore, $\frac{\partial o}{\partial x_i} = \frac{3}{2}(x_i+2)$, hence $\frac{\partial o}{\partial x_i}\bigr\rvert_{x_i=1} = \frac{9}{2} = 4.5$.
+
+Mathematically, if you have a vector valued function $\vec{y}=f(\vec{x})$,
+then the gradient of $\vec{y}$ with respect to $\vec{x}$
+is a Jacobian matrix:
+
+$$
+  J=\left(\begin{array}{ccc}
+   \frac{\partial y_{1}}{\partial x_{1}} & \cdots & \frac{\partial y_{1}}{\partial x_{n}}\\
+   \vdots & \ddots & \vdots\\
+   \frac{\partial y_{m}}{\partial x_{1}} & \cdots & \frac{\partial y_{m}}{\partial x_{n}}
+   \end{array}\right)
+$$
+
+Generally speaking, `autograd` is an engine for computing
+vector-Jacobian product. That is, given any vector
+$v=\left(\begin{array}{cccc} v_{1} & v_{2} & \cdots & v_{m}\end{array}\right)^{T}$,
+compute the product $v^{T}\cdot J$. If $v$ happens to be
+the gradient of a scalar function $l=g\left(\vec{y}\right)$,
+that is,
+$v=\left(\begin{array}{ccc}\frac{\partial l}{\partial y_{1}} & \cdots & \frac{\partial l}{\partial y_{m}}\end{array}\right)^{T}$,
+then by the chain rule, the vector-Jacobian product would be the
+gradient of $l$ with respect to $\vec{x}$:
+
+$$
+  J^{T}\cdot v=\left(\begin{array}{ccc}
+   \frac{\partial y_{1}}{\partial x_{1}} & \cdots & \frac{\partial y_{m}}{\partial x_{1}}\\
+   \vdots & \ddots & \vdots\\
+   \frac{\partial y_{1}}{\partial x_{n}} & \cdots & \frac{\partial y_{m}}{\partial x_{n}}
+   \end{array}\right)\left(\begin{array}{c}
+   \frac{\partial l}{\partial y_{1}}\\
+   \vdots\\
+   \frac{\partial l}{\partial y_{m}}
+   \end{array}\right)=\left(\begin{array}{c}
+   \frac{\partial l}{\partial x_{1}}\\
+   \vdots\\
+   \frac{\partial l}{\partial x_{n}}
+   \end{array}\right)
+$$
+
+(Note that $v^{T}\cdot J$ gives a row vector which can be
+treated as a column vector by taking $J^{T}\cdot v$.)
+
+This characteristic of vector-Jacobian product makes it very
+convenient to feed external gradients into a model that has
+non-scalar output.
+
+Now let's take a look at an example of vector-Jacobian product:
+
+```{r}
+x <- torch_randn(3, requires_grad=TRUE)
+y <- 100 * x
+y
+```
+
+Now in this case y is no longer a scalar. `autograd` could not compute the full Jacobian directly, but if we just want the vector-Jacobian product, simply pass the vector to backward as argument:
+
+```{r}
+v <- torch_tensor(c(0.1, 1.0, 0.0001))
+y$backward(v)
+
+x$grad
+```
+
+You can also stop autograd from tracking history on Tensors with `$requires_grad=TRUE` either by wrapping the code block in with `with_no_grad()`:
+
+```{r}
+x$requires_grad
+(x ** 2)$requires_grad
+
+with_no_grad({
+  print((x ** 2)$requires_grad)
+})
+```
+
+```{r}
+x$requires_grad
+y <- x$detach()
+y$requires_grad
+x$eq(y)$all()
+```
+
+Read Later:
+
+Document about `help(autograd_function)`, `vignette("using-autograd")`, `vignette("extending-autograd")`.
-- 
GitLab


From 33ee04a344a1cbd0b78153656e8fab4f87c15cb7 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 27 Aug 2020 18:49:04 -0300
Subject: [PATCH 072/157] add another chapter in the docs

---
 _pkgdown.yml                                  |   2 +
 vignettes/getting-started/assets/mnist.png    | Bin 0 -> 42703 bytes
 vignettes/getting-started/neural-networks.Rmd | 216 ++++++++++++++++++
 3 files changed, 218 insertions(+)
 create mode 100644 vignettes/getting-started/assets/mnist.png
 create mode 100644 vignettes/getting-started/neural-networks.Rmd

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 6f0f140e0..4681f18b9 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -18,6 +18,8 @@ navbar:
         href: articles/getting-started/what-is-torch.html
       - text: 'Autograd: automatic differentiation'
         href: articles/getting-started/autograd.html
+      - text: Neural networks
+        href: articles/getting-started/neural-networks.html
   articles:
     text: Articles
     menu:
diff --git a/vignettes/getting-started/assets/mnist.png b/vignettes/getting-started/assets/mnist.png
new file mode 100644
index 0000000000000000000000000000000000000000..53c876a89d53ccb3ae4fb5167460e84248ad3672
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diff --git a/vignettes/getting-started/neural-networks.Rmd b/vignettes/getting-started/neural-networks.Rmd
new file mode 100644
index 000000000..40214cb0f
--- /dev/null
+++ b/vignettes/getting-started/neural-networks.Rmd
@@ -0,0 +1,216 @@
+---
+title: Neural networks
+type: docs
+---
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py). All credits goes to [Soumith Chintala](http://soumith.ch/).
+
+```{r setup}
+library(torch)
+```
+
+Neural networks can be constructed using the `nn` functionality.
+
+Now that you had a glimpse of `autograd`, `nn` depends on `autograd` to define models and differentiate them. An nn.Module contains layers, and a method `forward(input)` that returns the output.
+
+For example, look at this network that classifies digit images:
+
+![Convnet for mnist classification](assets/mnist.png)
+
+It is a simple feed-forward network. It takes the input, feeds it through several layers one after the other, and then finally gives the output.
+
+A typical training procedure for a neural network is as follows:
+
+- Define the neural network that has some learnable parameters (or weights)
+- Iterate over a dataset of inputs
+- Process input through the network
+- Compute the loss (how far is the output from being correct)
+- Propagate gradients back into the network’s parameters
+- Update the weights of the network, typically using a simple update rule: `weight = weight - learning_rate * gradient`.
+
+## Define the network
+
+Let's define this network:
+
+```{r}
+Net <- nn_module(
+  initialize = function() {
+    self$conv1 = nn_conv2d(1, 6, 3)
+    self$conv2 = nn_conv2d(6, 16, 3)
+    # an affine operation: y = Wx + b
+    self$fc1 = nn_linear(16 * 6 * 6, 120)  # 6*6 from image dimension
+    self$fc2 = nn_linear(120, 84)
+    self$fc3 = nn_linear(84, 10)
+  },
+  forward = function(x) {
+    x %>% 
+      
+      self$conv1() %>% 
+      nnf_relu() %>% 
+      nnf_max_pool2d(c(2,2)) %>% 
+      
+      self$conv2() %>% 
+      nnf_relu() %>% 
+      nnf_max_pool2d(c(2,2)) %>% 
+      
+      torch_flatten(start_dim = 2) %>% 
+      
+      self$fc1() %>% 
+      nnf_relu() %>% 
+      
+      self$fc2() %>% 
+      nnf_relu() %>% 
+      
+      self$fc3()
+  }
+)
+
+net <- Net()
+```
+
+You just have to define the `forward` function, and the `backward` function (where gradients are computed) is automatically defined for you using `autograd.` You can use any of the Tensor operations in the `forward` function.
+
+The learnable parameters of a model are returned by `net$parameters`.
+
+```{r}
+str(net$parameters)
+```
+
+Let’s try a random 32x32 input. Note: expected input size of this net (LeNet) is 32x32. To use this net on the MNIST dataset, please resize the images from the dataset to 32x32.
+
+```{r}
+input <- torch_randn(1, 1, 32, 32)
+out <- net(input)
+out
+```
+
+Zero the gradient buffers of all parameters and backprops with random gradients:
+
+```{r}
+net$zero_grad()
+out$backward(torch_randn(1, 10))
+```
+
+> **Note**: `nn` only supports mini-batches. The entire torch.nn package only supports inputs that are a mini-batch of samples, and not a single sample. For example, `nn_conv2d` will take in a 4D Tensor of nSamples x nChannels x Height x Width.
+If you have a single sample, just use `input$unsqueeze(1)` to add a fake batch dimension.
+
+Before proceeding further, let’s recap all the classes you’ve seen so far.
+
+### Recap
+
+- `torch_tensor` - A multi-dimensional array with support for autograd operations like `backward()`. Also holds the gradient w.r.t. the tensor.
+
+- `nn_module` - Neural network module. Convenient way of encapsulating parameters, with helpers for moving them to GPU, exporting, loading, etc.
+
+- `nn_parameter` - A kind of Tensor, that is automatically registered as a parameter when assigned as an attribute to a Module.
+
+- `autograd_function` - 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.
+
+### At this point, we covered
+
+- Defining a neural network
+- Processing inputs and calling backward
+
+### Still left
+
+- Computing the loss
+- Updating the weights of the network
+
+## Loss function
+
+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.
+
+There are several different loss functions under the nn package . A simple loss is: `nnf_mse_loss` which computes the mean-squared error between the input and the target.
+
+For example:
+
+```{r}
+output <- net(input)
+target <- torch_randn(10)  # a dummy target, for example
+target <- target$view(c(1, -1))  # make it the same shape as output
+
+loss <- nnf_mse_loss(output, target)
+loss
+```
+
+Now, if you follow loss in the backward direction, using its `$grad_fn` attribute, you will see a graph of computations that looks like this:
+
+```
+input -> conv2d -> relu -> maxpool2d -> conv2d -> relu -> maxpool2d
+      -> view -> linear -> relu -> linear -> relu -> linear
+      -> MSELoss
+      -> loss
+```
+
+So, when we call `loss$backward()`, the whole graph is differentiated w.r.t. the loss, and all Tensors in the graph that has requires_grad=True will have their `#grad` Tensor accumulated with the gradient.
+
+For illustration, let us follow a few steps backward:
+
+```{r}
+loss$grad_fn
+loss$grad_fn$next_functions[[1]]
+loss$grad_fn$next_functions[[1]]$next_functions[[1]]
+```
+
+## Backprop
+
+To backpropagate the error all we have to do is to `loss$backward()`. You need to clear the existing gradients though, else gradients will be accumulated to existing gradients.
+
+Now we shall call `loss$backward()`, and have a look at conv1’s bias gradients before and after the backward.
+
+```{r}
+net$zero_grad()     # zeroes the gradient buffers of all parameters
+
+# conv1.bias.grad before backward
+net$conv1$bias$grad
+
+loss$backward()
+
+# conv1.bias.grad after backward
+net$conv1$bias$grad
+```
+
+Now, we have seen how to use loss functions.
+
+## Update the weights
+
+The simplest update rule used in practice is the Stochastic Gradient Descent (SGD):
+
+$$weight = weight - learning_rate * gradient$$ 
+
+We can implement this using simple R code:
+
+```{r}
+learning_rate <- 0.01
+for (f in net$parameters) {
+  with_no_grad({
+    f$sub_(f$grad * learning_rate)
+  })
+}
+```
+
+> **Note:** Weight updates here is wraped around `with_no_grad` as we don't the updates to be tracked by the autograd engine. 
+
+However, as you use neural networks, you want to use various different update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc. 
+
+```{r}
+# create your optimizer
+optimizer <- optim_sgd(net$parameters, lr = 0.01)
+
+# in your training loop:
+optimizer$zero_grad()   # zero the gradient buffers
+output <- net(input)
+loss <- nnf_mse_loss(output, target)
+loss$backward()
+optimizer$step()    # Does the update
+```
+
+> **Note:** Observe how gradient buffers had to be manually set to zero using `optimizer$zero_grad()`. This is because gradients are accumulated as explained in the Backprop section.
+
-- 
GitLab


From 1c4bb9fb45a39a39515fa88704bf8081037d2da2 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 28 Aug 2020 11:49:39 -0300
Subject: [PATCH 073/157] add begginer guide

---
 _pkgdown.yml                                  |  18 +++
 .../control-flow-and-weight-sharing.Rmd       | 121 ++++++++++++++++++
 vignettes/getting-started/custom-nn.Rmd       | 103 +++++++++++++++
 .../new-autograd-functions.Rmd                | 112 ++++++++++++++++
 vignettes/getting-started/nn.Rmd              |  97 ++++++++++++++
 vignettes/getting-started/optim.Rmd           |  97 ++++++++++++++
 .../getting-started/tensors-and-autograd.Rmd  |  91 +++++++++++++
 vignettes/getting-started/tensors.Rmd         |  76 +++++++++++
 vignettes/getting-started/warmup.Rmd          |  70 ++++++++++
 9 files changed, 785 insertions(+)
 create mode 100644 vignettes/getting-started/control-flow-and-weight-sharing.Rmd
 create mode 100644 vignettes/getting-started/custom-nn.Rmd
 create mode 100644 vignettes/getting-started/new-autograd-functions.Rmd
 create mode 100644 vignettes/getting-started/nn.Rmd
 create mode 100644 vignettes/getting-started/optim.Rmd
 create mode 100644 vignettes/getting-started/tensors-and-autograd.Rmd
 create mode 100644 vignettes/getting-started/tensors.Rmd
 create mode 100644 vignettes/getting-started/warmup.Rmd

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 4681f18b9..e7f94e391 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -14,6 +14,24 @@ navbar:
   getting-started:
     text: Getting started
     menu:
+      - text: Beginers guide
+      - text: Warm-up
+        href: articles/getting-started/warmup.html
+      - text: Tensors
+        href: articles/getting-started/tensors.html  
+      - text: Tensors and autograd
+        href: articles/getting-started/tensors-and-autograd.html
+      - text: Defining new autograd functions
+        href: articles/getting-started/new-autograd-functions.html
+      - text: 'nn: neural networks with torch'
+        href: articles/getting-started/nn.html
+      - text: 'optim: optimizers in torch'
+        href: articles/getting-started/optim.html
+      - text: Custom nn modules
+        href: articles/getting-started/custom-nn.html
+      - text: Control flow & Weight sharing
+        href: articles/getting-started/control-flow-and-weight-sharing.html
+      - text: Torch Mechanics
       - text: What's torch?
         href: articles/getting-started/what-is-torch.html
       - text: 'Autograd: automatic differentiation'
diff --git a/vignettes/getting-started/control-flow-and-weight-sharing.Rmd b/vignettes/getting-started/control-flow-and-weight-sharing.Rmd
new file mode 100644
index 000000000..66564b68e
--- /dev/null
+++ b/vignettes/getting-started/control-flow-and-weight-sharing.Rmd
@@ -0,0 +1,121 @@
+---
+title: 'Control flow & Weight sharing'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+As an example of dynamic graphs and weight sharing, we implement a very strange model: a fully-connected ReLU network that on each forward pass chooses a random number between 1 and 4 and uses that many hidden layers, reusing the same weights multiple times to compute the innermost hidden layers.
+
+For this model we can use normal R flow control to implement the loop, and we can implement weight sharing among the innermost layers by simply reusing the same Module multiple times when defining the forward pass.
+
+We can easily implement this model using `nn_module`:
+
+```{r}
+dynamic_net <- nn_module(
+   "dynamic_net",
+   # In the constructor we construct three nn_linear instances that we will use
+   # in the forward pass.
+   initialize = function(D_in, H, D_out) {
+      self$input_linear <- nn_linear(D_in, H)
+      self$middle_linear <- nn_linear(H, H)
+      self$output_linear <- nn_linear(H, D_out)
+   },
+   # For the forward pass of the model, we randomly choose either 0, 1, 2, or 3
+   # and reuse the middle_linear Module that many times to compute hidden layer
+   # representations.
+   # 
+   # Since each forward pass builds a dynamic computation graph, we can use normal
+   # R control-flow operators like loops or conditional statements when
+   # defining the forward pass of the model.
+   # 
+   # Here we also see that it is perfectly safe to reuse the same Module many
+   # times when defining a computational graph. This is a big improvement from Lua
+   # Torch, where each Module could be used only once.
+   forward = function(x) {
+      h_relu <- self$input_linear(x)$clamp(min = 0)
+      for (i in seq_len(sample.int(4, size = 1))) {
+         h_relu <- self$middle_linear(h_relu)$clamp(min=0)
+      }
+      y_pred <- self$output_linear(h_relu)
+      y_pred
+   }
+)
+
+
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+# Setting requires_grad=FALSE (the default) indicates that we do not need to 
+# compute gradients with respect to these Tensors during the backward pass.
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Construct our model by instantiating the class defined above
+model <- dynamic_net(D_in, H, D_out)
+
+# The nn package also contains definitions of popular loss functions; in this
+# case we will use Mean Squared Error (MSE) as our loss function.
+loss_fn <- nnf_mse_loss
+
+# Use the optim package to define an Optimizer that will update the weights of
+# the model for us. Here we will use Adam; the optim package contains many other
+# optimization algorithms. The first argument to the Adam constructor tells the
+# optimizer which Tensors it should update.
+learning_rate <- 1e-4
+optimizer <- optim_sgd(model$parameters, lr=learning_rate, momentum = 0.9)
+
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y by passing x to the model. Module objects
+   # can be called like functions. When doing so you pass a Tensor of input
+   # data to the Module and it produces a Tensor of output data.
+   y_pred <- model(x)
+   
+   # Compute and print loss. We pass Tensors containing the predicted and true
+   # values of y, and the loss function returns a Tensor containing the
+   # loss.
+   loss <- loss_fn(y_pred, y)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
+   
+   # Before the backward pass, use the optimizer object to zero all of the
+   # gradients for the variables it will update (which are the learnable
+   # weights of the model). This is because by default, gradients are
+   # accumulated in buffers( i.e, not overwritten) whenever $backward()
+   # is called. Checkout docs of `autograd_backward` for more details.
+   optimizer$zero_grad()
+
+   # Backward pass: compute gradient of the loss with respect to model
+   # parameters
+   loss$backward()
+
+   # Calling the step function on an Optimizer makes an update to its
+   # parameters
+   optimizer$step()
+}
+```
+
+
diff --git a/vignettes/getting-started/custom-nn.Rmd b/vignettes/getting-started/custom-nn.Rmd
new file mode 100644
index 000000000..58c20e326
--- /dev/null
+++ b/vignettes/getting-started/custom-nn.Rmd
@@ -0,0 +1,103 @@
+---
+title: 'Custom nn modules'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+Sometimes you will want to specify models that are more complex than a sequence of existing Modules; for these cases you can define your own Modules by using `nn_module` function and defining a forward which receives input Tensors and produces output Tensors using other modules or other autograd operations on Tensors.
+
+In this example we implement our two-layer network as a custom Module subclass:
+
+```{r}
+two_layer_net <- nn_module(
+   "two_layer_net",
+   initialize = function(D_in, H, D_out) {
+      self$linear1 <- nn_linear(D_in, H)
+      self$linear2 <- nn_linear(H, D_out)
+   },
+   forward = function(x) {
+      x %>% 
+         self$linear1() %>% 
+         nnf_relu() %>% 
+         self$linear2()
+   }
+)
+
+
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+# Setting requires_grad=FALSE (the default) indicates that we do not need to 
+# compute gradients with respect to these Tensors during the backward pass.
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Construct our model by instantiating the class defined above
+model <- two_layer_net(D_in, H, D_out)
+
+# The nn package also contains definitions of popular loss functions; in this
+# case we will use Mean Squared Error (MSE) as our loss function.
+loss_fn <- nnf_mse_loss
+
+# Use the optim package to define an Optimizer that will update the weights of
+# the model for us. Here we will use Adam; the optim package contains many other
+# optimization algorithms. The first argument to the Adam constructor tells the
+# optimizer which Tensors it should update.
+learning_rate <- 1e-4
+optimizer <- optim_sgd(model$parameters, lr=learning_rate)
+
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y by passing x to the model. Module objects
+   # can be called like functions. When doing so you pass a Tensor of input
+   # data to the Module and it produces a Tensor of output data.
+   y_pred <- model(x)
+   
+   # Compute and print loss. We pass Tensors containing the predicted and true
+   # values of y, and the loss function returns a Tensor containing the
+   # loss.
+   loss <- loss_fn(y_pred, y)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
+   
+   # Before the backward pass, use the optimizer object to zero all of the
+   # gradients for the variables it will update (which are the learnable
+   # weights of the model). This is because by default, gradients are
+   # accumulated in buffers( i.e, not overwritten) whenever $backward()
+   # is called. Checkout docs of `autograd_backward` for more details.
+   optimizer$zero_grad()
+
+   # Backward pass: compute gradient of the loss with respect to model
+   # parameters
+   loss$backward()
+
+   # Calling the step function on an Optimizer makes an update to its
+   # parameters
+   optimizer$step()
+}
+```
+
+In the [next example](control-flow-and-weight-sharing.html) we will about dynamic graphs in torch.
diff --git a/vignettes/getting-started/new-autograd-functions.Rmd b/vignettes/getting-started/new-autograd-functions.Rmd
new file mode 100644
index 000000000..1682afa2c
--- /dev/null
+++ b/vignettes/getting-started/new-autograd-functions.Rmd
@@ -0,0 +1,112 @@
+---
+title: 'Defining new autograd functions'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+Under the hood, each primitive autograd operator is really two functions that operate on Tensors. The forward function computes output Tensors from input Tensors. The backward function receives the gradient of the output Tensors with respect to some scalar value, and computes the gradient of the input Tensors with respect to that same scalar value.
+
+In torch we can easily define our own autograd operator by defining a subclass of `autograd_function` and implementing the forward and backward functions. We can then use our new autograd operator by constructing an instance and calling it like a function, passing Tensors containing input data.
+
+In this example we define our own custom autograd function for performing the ReLU nonlinearity, and use it to implement our two-layer network:
+
+```{r}
+# We can implement our own custom autograd Functions by subclassing
+# autograd_functioon and implementing the forward and backward passes
+# which operate on Tensors.
+my_relu <- autograd_function(
+   # In the forward pass we receive a Tensor containing the input and return
+   # a Tensor containing the output. ctx is a context object that can be used
+   # to stash information for backward computation. You can cache arbitrary
+   # objects for use in the backward pass using the ctx$save_for_backward method.
+   forward = function(ctx, input) {
+      ctx$save_for_backward(input = input)
+      input$clamp(min = 0)
+   },
+   # In the backward pass we receive a Tensor containing the gradient of the loss
+   # with respect to the output, and we need to compute the gradient of the loss
+   # with respect to the input.
+   backward = function(ctx, grad_output) {
+      v <- ctx$saved_variables
+      grad_input <- grad_output$clone()
+      grad_input[v$input < 0] <- 0
+      list(input = grad_input)
+   }
+)
+
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+# Setting requires_grad=FALSE (the default) indicates that we do not need to 
+# compute gradients with respect to these Tensors during the backward pass.
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Randomly initialize weights
+# Setting requires_grad=TRUE indicates that we want to compute gradients with
+# respect to these Tensors during the backward pass.
+w1 <- torch_randn(D_in, H, device=device, requires_grad = TRUE)
+w2 <- torch_randn(H, D_out, device=device, requires_grad = TRUE)
+
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y using operations on Tensors; these
+   # are exactly the same operations we used to compute the forward pass using
+   # Tensors, but we do not need to keep references to intermediate values since
+   # we are not implementing the backward pass by hand.
+   y_pred <- my_relu(x$mm(w1))$mm(w2)
+   
+   # Compute and print loss using operations on Tensors.
+   # Now loss is a Tensor of shape (1,)
+   loss <- (y_pred - y)$pow(2)$sum()
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
+   
+   # Use autograd to compute the backward pass. This call will compute the
+   # gradient of loss with respect to all Tensors with requires_grad=True.
+   # After this call w1$grad and w2$grad will be Tensors holding the gradient
+   # of the loss with respect to w1 and w2 respectively.
+   loss$backward()
+   
+   # Manually update weights using gradient descent. Wrap in `with_no_grad`
+   # because weights have requires_grad=TRUE, but we don't need to track this
+   # in autograd.
+   # You can also use optim_sgd to achieve this.
+   with_no_grad({
+      
+      # operations suffixed with an `_` operates on in-place on the tensor.
+      w1$sub_(learning_rate * w1$grad)
+      w2$sub_(learning_rate * w2$grad)
+      
+      # Manually zero the gradients after updating weights
+      w1$grad$zero_()
+      w2$grad$zero_()
+   })
+}
+```
+
+In the [next example](nn.html) we will learn how to use the neural networks abstractions in torch.
diff --git a/vignettes/getting-started/nn.Rmd b/vignettes/getting-started/nn.Rmd
new file mode 100644
index 000000000..142398bed
--- /dev/null
+++ b/vignettes/getting-started/nn.Rmd
@@ -0,0 +1,97 @@
+---
+title: 'nn: neural networks with torch'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+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.
+
+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.
+
+In TensorFlow, packages like Keras, TensorFlow-Slim, and TFLearn provide higher-level abstractions over raw computational graphs that are useful for building neural networks.
+
+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.
+
+In this example we use nn to implement our two-layer network:
+
+```{r}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+# Setting requires_grad=FALSE (the default) indicates that we do not need to 
+# compute gradients with respect to these Tensors during the backward pass.
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Use the nn package to define our model as a sequence of layers. nn_sequential
+# is a Module which contains other Modules, and applies them in sequence to
+# produce its output. Each Linear Module computes output from input using a
+# linear function, and holds internal Tensors for its weight and bias.
+model <- nn_sequential(
+    nn_linear(D_in, H),
+    nn_relu(),
+    nn_linear(H, D_out)
+)
+
+# The nn package also contains definitions of popular loss functions; in this
+# case we will use Mean Squared Error (MSE) as our loss function.
+loss_fn <- nnf_mse_loss
+
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y by passing x to the model. Module objects
+   # can be called like functions. When doing so you pass a Tensor of input
+   # data to the Module and it produces a Tensor of output data.
+   y_pred <- model(x)
+   
+   # Compute and print loss. We pass Tensors containing the predicted and true
+   # values of y, and the loss function returns a Tensor containing the
+   # loss.
+   loss <- loss_fn(y_pred, y)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
+   
+   # Zero the gradients before running the backward pass.
+   model$zero_grad()
+
+   # Backward pass: compute gradient of the loss with respect to all the learnable
+   # parameters of the model. Internally, the parameters of each Module are stored
+   # in Tensors with requires_grad=TRUE, so this call will compute gradients for
+   # all learnable parameters in the model.
+   loss$backward()
+   
+   # Update the weights using gradient descent. Each parameter is a Tensor, so
+   # we can access its gradients like we did before.
+   with_no_grad({
+      for (param in model$parameters) {
+         param$sub_(learning_rate * param$grad)
+      }
+   })
+}
+```
+
+In the [next example](optim.html) we will learn how to use optimizers implemented in torch.
diff --git a/vignettes/getting-started/optim.Rmd b/vignettes/getting-started/optim.Rmd
new file mode 100644
index 000000000..b12f197e2
--- /dev/null
+++ b/vignettes/getting-started/optim.Rmd
@@ -0,0 +1,97 @@
+---
+title: 'optim: optimizers in torch'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+Up to this point we have updated the weights of our models by manually mutating the Tensors holding learnable parameters (with `with_no_grad` 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.
+
+The optim package in torch abstracts the idea of an optimization algorithm and provides implementations of commonly used optimization algorithms.
+
+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 `optim`:
+
+```{r}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+# Setting requires_grad=FALSE (the default) indicates that we do not need to 
+# compute gradients with respect to these Tensors during the backward pass.
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Use the nn package to define our model as a sequence of layers. nn_sequential
+# is a Module which contains other Modules, and applies them in sequence to
+# produce its output. Each Linear Module computes output from input using a
+# linear function, and holds internal Tensors for its weight and bias.
+model <- nn_sequential(
+    nn_linear(D_in, H),
+    nn_relu(),
+    nn_linear(H, D_out)
+)
+
+# The nn package also contains definitions of popular loss functions; in this
+# case we will use Mean Squared Error (MSE) as our loss function.
+loss_fn <- nnf_mse_loss
+
+# Use the optim package to define an Optimizer that will update the weights of
+# the model for us. Here we will use Adam; the optim package contains many other
+# optimization algorithms. The first argument to the Adam constructor tells the
+# optimizer which Tensors it should update.
+learning_rate <- 1e-4
+optimizer <- optim_adam(model$parameters, lr=learning_rate)
+
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y by passing x to the model. Module objects
+   # can be called like functions. When doing so you pass a Tensor of input
+   # data to the Module and it produces a Tensor of output data.
+   y_pred <- model(x)
+   
+   # Compute and print loss. We pass Tensors containing the predicted and true
+   # values of y, and the loss function returns a Tensor containing the
+   # loss.
+   loss <- loss_fn(y_pred, y)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
+   
+   # Before the backward pass, use the optimizer object to zero all of the
+   # gradients for the variables it will update (which are the learnable
+   # weights of the model). This is because by default, gradients are
+   # accumulated in buffers( i.e, not overwritten) whenever $backward()
+   # is called. Checkout docs of `autograd_backward` for more details.
+   optimizer$zero_grad()
+
+   # Backward pass: compute gradient of the loss with respect to model
+   # parameters
+   loss$backward()
+
+   # Calling the step function on an Optimizer makes an update to its
+   # parameters
+   optimizer$step()
+}
+```
+
+In the [next example](custom-nn.html) we will learn how to create custom `nn_modules`.
diff --git a/vignettes/getting-started/tensors-and-autograd.Rmd b/vignettes/getting-started/tensors-and-autograd.Rmd
new file mode 100644
index 000000000..3fede0011
--- /dev/null
+++ b/vignettes/getting-started/tensors-and-autograd.Rmd
@@ -0,0 +1,91 @@
+---
+title: 'Tensors and autograd'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+In the previous examples, we had to manually implement both the forward and backward passes of our neural network. Manually implementing the backward pass is not a big deal for a small two-layer network, but can quickly get very hairy for large complex networks.
+
+Thankfully, we can use automatic differentiation to automate the computation of backward passes in neural networks. The autograd feature in torch provides exactly this functionality. When using autograd, the forward pass of your network will define a computational graph; nodes in the graph will be Tensors, and edges will be functions that produce output Tensors from input Tensors. Backpropagating through this graph then allows you to easily compute gradients.
+
+This sounds complicated, it’s pretty simple to use in practice. Each Tensor represents a node in a computational graph. If x is a Tensor that has `x$requires_grad=TRUE` then `x$grad` is another Tensor holding the gradient of x with respect to some scalar value.
+
+Here we use torch Tensors and autograd to implement our two-layer network; now we no longer need to manually implement the backward pass through the network:
+
+```{r}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+# Setting requires_grad=FALSE (the default) indicates that we do not need to 
+# compute gradients with respect to these Tensors during the backward pass.
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Randomly initialize weights
+# Setting requires_grad=TRUE indicates that we want to compute gradients with
+# respect to these Tensors during the backward pass.
+w1 <- torch_randn(D_in, H, device=device, requires_grad = TRUE)
+w2 <- torch_randn(H, D_out, device=device, requires_grad = TRUE)
+
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y using operations on Tensors; these
+   # are exactly the same operations we used to compute the forward pass using
+   # Tensors, but we do not need to keep references to intermediate values since
+   # we are not implementing the backward pass by hand.
+   y_pred <- x$mm(w1)$clamp(min=0)$mm(w2)
+   
+   # Compute and print loss using operations on Tensors.
+   # Now loss is a Tensor of shape (1,)
+   loss <- (y_pred - y)$pow(2)$sum()
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
+   
+   # Use autograd to compute the backward pass. This call will compute the
+   # gradient of loss with respect to all Tensors with requires_grad=True.
+   # After this call w1$grad and w2$grad will be Tensors holding the gradient
+   # of the loss with respect to w1 and w2 respectively.
+   loss$backward()
+   
+   # Manually update weights using gradient descent. Wrap in `with_no_grad`
+   # because weights have requires_grad=TRUE, but we don't need to track this
+   # in autograd.
+   # You can also use optim_sgd to achieve this.
+   with_no_grad({
+      
+      # operations suffixed with an `_` operates on in-place on the tensor.
+      w1$sub_(learning_rate * w1$grad)
+      w2$sub_(learning_rate * w2$grad)
+      
+      # Manually zero the gradients after updating weights
+      w1$grad$zero_()
+      w2$grad$zero_()
+   })
+}
+```
+
+In the [next example](new-autograd-functions.html) we will learn how to create new autograd functions.
diff --git a/vignettes/getting-started/tensors.Rmd b/vignettes/getting-started/tensors.Rmd
new file mode 100644
index 000000000..087300840
--- /dev/null
+++ b/vignettes/getting-started/tensors.Rmd
@@ -0,0 +1,76 @@
+---
+title: 'torch Tensors'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+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.
+
+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.
+
+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.
+
+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:
+
+```{r}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Randomly initialize weights
+w1 <- torch_randn(D_in, H, device=device)
+w2 <- torch_randn(H, D_out, device=device)
+
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y
+   h <- x$mm(w1)
+   h_relu <- h$clamp(min=0)
+   y_pred <- h_relu$mm(w2)
+   
+   # Compute and print loss
+   loss <- as.numeric((y_pred - y)$pow(2)$sum())
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", loss, "\n")
+   
+   # Backprop to compute gradients of w1 and w2 with respect to loss
+   grad_y_pred <- 2.0 * (y_pred - y)
+   grad_w2 <- h_relu$t()$mm(grad_y_pred)
+   grad_h_relu <- grad_y_pred$mm(w2$t())
+   grad_h <- grad_h_relu$clone()
+   grad_h[h < 0] <- 0
+   grad_w1 <- x$t()$mm(grad_h)
+   
+   # Update weights using gradient descent
+   w1 <- w1 - learning_rate * grad_w1
+   w2 <- w2 - learning_rate * grad_w2
+}
+```
+
+In the [next example](tensors-and-autograd.html) we will use autograd instead of computing the gradients manually.
diff --git a/vignettes/getting-started/warmup.Rmd b/vignettes/getting-started/warmup.Rmd
new file mode 100644
index 000000000..35165ecf6
--- /dev/null
+++ b/vignettes/getting-started/warmup.Rmd
@@ -0,0 +1,70 @@
+---
+title: 'Warm-up'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+A fully-connected ReLU network with one hidden layer and no biases, trained to predict y from x using Euclidean error.
+
+This implementation uses pure R to manually compute the forward pass, loss, and backward pass.
+
+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.
+
+```{r}
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+x <- array(rnorm(N*D_in), dim = c(N, D_in))
+y <- array(rnorm(N*D_out), dim = c(N, D_out))
+
+# Randomly initialize weights
+w1 <- array(rnorm(D_in*H), dim = c(D_in, H))
+w2 <- array(rnorm(H*D_out), dim = c(H, D_out))
+
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y
+   h <- x %*% w1
+   h_relu <- ifelse(h < 0, 0, h)
+   y_pred <- h_relu %*% w2
+   
+   # Compute and print loss
+   loss <- sum((y_pred - y)^2)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", loss, "\n")
+   
+   # Backprop to compute gradients of w1 and w2 with respect to loss
+   grad_y_pred <- 2 * (y_pred - y)
+   grad_w2 <- t(h_relu) %*% grad_y_pred
+   grad_h_relu <- grad_y_pred %*% t(w2)
+   grad_h <- grad_h_relu
+   grad_h[h < 0] <- 0
+   grad_w1 <- t(x) %*% grad_h
+   
+   # Update weights
+   w1 <- w1 - learning_rate * grad_w1
+   w2 <- w2 - learning_rate * grad_w2
+}
+```
+
+In the [next example](tensors.html) we will replace the R array for a torch Tensor.
+
+
-- 
GitLab


From 13bc5ed849230e59d4ffbc5e6411abbdce583c43 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 28 Aug 2020 17:50:33 -0300
Subject: [PATCH 074/157] multiple fixes for CRAN release

---
 .Rbuildignore          |  3 +-
 DESCRIPTION            |  2 +-
 R/nn-dropout.R         |  4 +--
 R/nn-pooling.R         | 77 ++++++++++++++++++++----------------------
 R/nn.R                 |  1 +
 README.Rmd             |  2 +-
 man/nn_avg_pool1d.Rd   |  6 ++--
 man/nn_avg_pool2d.Rd   |  8 ++---
 man/nn_avg_pool3d.Rd   | 22 ++++++------
 man/nn_dropout2d.Rd    |  2 +-
 man/nn_dropout3d.Rd    |  2 +-
 man/nn_lp_pool1d.Rd    |  2 +-
 man/nn_lp_pool2d.Rd    |  4 +--
 man/nn_max_pool2d.Rd   |  2 +-
 man/nn_max_pool3d.Rd   | 21 ++++++------
 man/nn_max_unpool1d.Rd |  2 +-
 man/nn_max_unpool2d.Rd |  4 +--
 man/nn_max_unpool3d.Rd |  6 ++--
 man/nn_module.Rd       |  9 ++++-
 19 files changed, 91 insertions(+), 88 deletions(-)

diff --git a/.Rbuildignore b/.Rbuildignore
index 11acc5d37..185f6f24c 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -17,5 +17,6 @@ tools/torchgen/.Rbuildignore
 ^LICENSE\.md$
 ^.*torchpkg.dll$
 ^deps.*
+^public/.*
 # uncomment below for CRAN submission
-# ^inst/deps/.*
+^inst/deps/.*
diff --git a/DESCRIPTION b/DESCRIPTION
index 26c20cccd..e1ecf6b93 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -14,7 +14,7 @@ Description: Provides functionality to define and train neural networks similar
     using the 'libtorch' library. Also supports low-level tensor operations and 
     'GPU' acceleration.
 License: MIT + file LICENSE
-URL: http://mlverse.github.io/torch, https://github.com/mlverse/torch
+URL: https://torch.mlverse.org/docs, https://github.com/mlverse/torch
 BugReports: https://github.com/mlverse/torch/issues
 Encoding: UTF-8
 LazyData: true
diff --git a/R/nn-dropout.R b/R/nn-dropout.R
index b09eda328..702afc920 100644
--- a/R/nn-dropout.R
+++ b/R/nn-dropout.R
@@ -62,7 +62,7 @@ nn_dropout <- nn_module(
 #' Usually the input comes from [nn_conv2d] modules.
 #' 
 #' As described in the paper
-#' [Efficient Object Localization Using Convolutional Networks](http://arxiv.org/abs/1411.4280) ,
+#' [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280) ,
 #' 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
@@ -104,7 +104,7 @@ nn_dropout2d <- nn_module(
 #' Usually the input comes from [nn_conv2d] modules.
 #' 
 #' As described in the paper
-#' [Efficient Object Localization Using Convolutional Networks](http://arxiv.org/abs/1411.4280) ,
+#' [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280) ,
 #' 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
diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index 6743a8815..df86c8d3a 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -81,7 +81,7 @@ nn_max_pool1d <- nn_module(
 #' can be precisely described as:
 #'
 #' \deqn{
-#'   \begin{array}{ll}
+#' \begin{array}{ll}
 #' out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
 #' & \mbox{input}(N_i, C_j, \mbox{stride[0]} \times h + m,
 #'                \mbox{stride[1]} \times w + n)
@@ -147,11 +147,10 @@ nn_max_pool2d <- nn_module(
 #' can be precisely described as:
 #' 
 #' \deqn{
-#'   \begin{aligned}
-#' \text{out}(N_i, C_j, d, h, w) ={} & \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
-#' & \text{input}(N_i, C_j, \text{stride[0]} \times d + k,
-#'                \text{stride[1]} \times h + m, \text{stride[2]} \times w + n)
-#' \end{aligned}
+#' \begin{array}{ll}
+#' \mbox{out}(N_i, C_j, d, h, w) = & \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
+#'  & \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}
 #' }
 #' 
 #' If `padding` is non-zero, then the input is implicitly zero-padded on both sides
@@ -174,18 +173,18 @@ nn_max_pool2d <- nn_module(
 #' - Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})}
 #' - Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
 #' \deqn{
-#'   D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{dilation}[0] \times
-#'     (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor
+#'   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
 #' }
 #' 
 #' \deqn{
-#'   H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{dilation}[1] \times
-#'     (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 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
 #' }
 #' 
 #' \deqn{
-#'   W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{dilation}[2] \times
-#'     (\text{kernel\_size}[2] - 1) - 1}{\text{stride}[2]} + 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
 #' }
 #' 
 #' @examples
@@ -235,7 +234,7 @@ nn_max_pool3d <- nn_module(
 #' - Input: \eqn{(N, C, H_{in})}
 #' - Output: \eqn{(N, C, H_{out})}, where
 #' \deqn{
-#'   H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{kernel\_size}[0]
+#'   H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{kernel\_size}[0]
 #' }
 #' or as given by `output_size` in the call operator
 #' 
@@ -299,10 +298,10 @@ nn_max_unpool1d <- nn_module(
 #' - Input: \eqn{(N, C, H_{in}, W_{in})}
 #' - Output: \eqn{(N, C, H_{out}, W_{out})}, where
 #' \deqn{
-#'   H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
+#'   H_{out} = (H_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
 #' }
 #' \deqn{
-#'   W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
+#'   W_{out} = (W_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
 #' }
 #' or as given by `output_size` in the call operator
 #' 
@@ -364,13 +363,13 @@ nn_max_unpool2d <- nn_module(
 #' - Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
 #' 
 #' \deqn{
-#'   D_{out} = (D_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
+#'   D_{out} = (D_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
 #' }
 #' \deqn{
-#'   H_{out} = (H_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
+#'   H_{out} = (H_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
 #' }
 #' \deqn{
-#'   W_{out} = (W_{in} - 1) \times \text{stride[2]} - 2 \times \text{padding[2]} + \text{kernel\_size[2]}
+#'   W_{out} = (W_{in} - 1) \times \mbox{stride[2]} - 2 \times \mbox{padding[2]} + \mbox{kernel\_size[2]}
 #' }
 #' 
 #' or as given by `output_size` in the call operator
@@ -410,8 +409,8 @@ nn_max_unpool3d <- nn_module(
 #' can be precisely described as:
 #'   
 #' \deqn{
-#'   \text{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1}
-#' \text{input}(N_i, C_j, \text{stride} \times l + m)
+#'   \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)
 #' }
 #' 
 #' If `padding` is non-zero, then the input is implicitly zero-padded on both sides
@@ -432,7 +431,7 @@ nn_max_unpool3d <- nn_module(
 #' 
 #' \deqn{
 #'   L_{out} = \left\lfloor \frac{L_{in} +
-#'       2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor
+#'       2 \times \mbox{padding} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
 #' }
 #' 
 #' @examples
@@ -500,12 +499,12 @@ nn_avg_pool1d <- nn_module(
 #' - Output: \eqn{(N, C, H_{out}, W_{out})}, where
 #' 
 #' \deqn{
-#'   H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \text{padding}[0] -
-#'       \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
+#'   H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] -
+#'       \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
 #' }
 #' \deqn{
-#'   W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \text{padding}[1] -
-#'       \text{kernel\_size}[1]}{\text{stride}[1]} + 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
 #' }
 #' 
 #' @examples
@@ -551,12 +550,10 @@ nn_avg_pool2d <- nn_module(
 #' can be precisely described as:
 #'   
 #' \deqn{
-#'   \begin{aligned}
-#' \text{out}(N_i, C_j, d, h, w) ={} & \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
-#' & \frac{\text{input}(N_i, C_j, \text{stride}[0] \times d + k,
-#'                      \text{stride}[1] \times h + m, \text{stride}[2] \times w + n)}
-#' {kD \times kH \times kW}
-#' \end{aligned}
+#' \begin{array}{ll}
+#' \mbox{out}(N_i, C_j, d, h, w) = & \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
+#' & \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}
 #' }
 #' 
 #' If `padding` is non-zero, then the input is implicitly zero-padded on all three sides
@@ -580,16 +577,16 @@ nn_avg_pool2d <- nn_module(
 #' - Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
 #' 
 #' \deqn{
-#'   D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] -
-#'       \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
+#'   D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] -
+#'       \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
 #' }
 #' \deqn{
-#'   H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] -
-#'       \text{kernel\_size}[1]}{\text{stride}[1]} + 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
 #' }
 #' \deqn{
-#'   W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] -
-#'       \text{kernel\_size}[2]}{\text{stride}[2]} + 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
 #' }
 #' 
 #' @examples
@@ -802,7 +799,7 @@ lp_pool_nd <- nn_module(
 #' - Output: \eqn{(N, C, L_{out})}, where
 #' 
 #' \deqn{
-#'   L_{out} = \left\lfloor\frac{L_{in} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor
+#'   L_{out} = \left\lfloor\frac{L_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
 #' }
 #' 
 #' @examples
@@ -854,10 +851,10 @@ nn_lp_pool1d <- nn_module(
 #' - Output: \eqn{(N, C, H_{out}, W_{out})}, where
 #' 
 #' \deqn{
-#'   H_{out} = \left\lfloor\frac{H_{in} - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
+#'   H_{out} = \left\lfloor\frac{H_{in} - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
 #' }
 #' \deqn{
-#'   W_{out} = \left\lfloor\frac{W_{in} - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor
+#'   W_{out} = \left\lfloor\frac{W_{in} - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
 #' }
 #' 
 #' @examples
diff --git a/R/nn.R b/R/nn.R
index 7833142f0..fccc22cfe 100644
--- a/R/nn.R
+++ b/R/nn.R
@@ -259,6 +259,7 @@ is_nn_module <- function(x) {
 #' @param classname an optional name for the module
 #' @param inherit an optional module to inherit from
 #' @param ... methods implementation 
+#' @param parent_env passed to [R6::R6Class()].
 #' 
 #' @examples 
 #' model <- nn_module(
diff --git a/README.Rmd b/README.Rmd
index c2a6c7a36..fdd1d8531 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -13,7 +13,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-# torch <a href='https://mlverse.github.io/torch'><img src='man/figures/torch.png' align="right" height="139" /></a>
+# torch <a href='https://torch.mlverse.org'><img src='man/figures/torch.png' align="right" height="139" /></a>
 
 [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
 ![R build status](https://github.com/mlverse/torch/workflows/Test/badge.svg)
diff --git a/man/nn_avg_pool1d.Rd b/man/nn_avg_pool1d.Rd
index 70e9880c0..de708603f 100644
--- a/man/nn_avg_pool1d.Rd
+++ b/man/nn_avg_pool1d.Rd
@@ -30,8 +30,8 @@ output \eqn{(N, C, L_{out})} and \code{kernel_size} \eqn{k}
 can be precisely described as:
 
 \deqn{
-  \text{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1}
-\text{input}(N_i, C_j, \text{stride} \times l + m)
+  \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)
 }
 }
 \details{
@@ -50,7 +50,7 @@ an \code{int} or a one-element tuple.
 
 \deqn{
   L_{out} = \left\lfloor \frac{L_{in} +
-      2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor
+      2 \times \mbox{padding} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
 }
 }
 
diff --git a/man/nn_avg_pool2d.Rd b/man/nn_avg_pool2d.Rd
index cc59cff42..cb0e4af18 100644
--- a/man/nn_avg_pool2d.Rd
+++ b/man/nn_avg_pool2d.Rd
@@ -56,12 +56,12 @@ and the second \code{int} for the width dimension
 }
 
 \deqn{
-  H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \text{padding}[0] -
-      \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
+  H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] -
+      \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
 }
 \deqn{
-  W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \text{padding}[1] -
-      \text{kernel\_size}[1]}{\text{stride}[1]} + 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
 }
 }
 
diff --git a/man/nn_avg_pool3d.Rd b/man/nn_avg_pool3d.Rd
index 1205df5d9..ddcd87358 100644
--- a/man/nn_avg_pool3d.Rd
+++ b/man/nn_avg_pool3d.Rd
@@ -33,12 +33,10 @@ output \eqn{(N, C, D_{out}, H_{out}, W_{out})} and \code{kernel_size} \eqn{(kD,
 can be precisely described as:
 
 \deqn{
-  \begin{aligned}
-\text{out}(N_i, C_j, d, h, w) ={} & \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
-& \frac{\text{input}(N_i, C_j, \text{stride}[0] \times d + k,
-                     \text{stride}[1] \times h + m, \text{stride}[2] \times w + n)}
-{kD \times kH \times kW}
-\end{aligned}
+\begin{array}{ll}
+\mbox{out}(N_i, C_j, d, h, w) = & \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
+& \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}
 }
 }
 \details{
@@ -60,16 +58,16 @@ the second \code{int} for the height dimension and the third \code{int} for the
 }
 
 \deqn{
-  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] -
-      \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
+  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] -
+      \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
 }
 \deqn{
-  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] -
-      \text{kernel\_size}[1]}{\text{stride}[1]} + 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
 }
 \deqn{
-  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] -
-      \text{kernel\_size}[2]}{\text{stride}[2]} + 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
 }
 }
 
diff --git a/man/nn_dropout2d.Rd b/man/nn_dropout2d.Rd
index b7ab40124..166feebd0 100644
--- a/man/nn_dropout2d.Rd
+++ b/man/nn_dropout2d.Rd
@@ -23,7 +23,7 @@ probability \code{p} using samples from a Bernoulli distribution.
 Usually the input comes from \link{nn_conv2d} modules.
 
 As described in the paper
-\href{http://arxiv.org/abs/1411.4280}{Efficient Object Localization Using Convolutional Networks} ,
+\href{https://arxiv.org/abs/1411.4280}{Efficient Object Localization Using Convolutional Networks} ,
 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
diff --git a/man/nn_dropout3d.Rd b/man/nn_dropout3d.Rd
index e51ced1c9..fd5203124 100644
--- a/man/nn_dropout3d.Rd
+++ b/man/nn_dropout3d.Rd
@@ -23,7 +23,7 @@ probability \code{p} using samples from a Bernoulli distribution.
 Usually the input comes from \link{nn_conv2d} modules.
 
 As described in the paper
-\href{http://arxiv.org/abs/1411.4280}{Efficient Object Localization Using Convolutional Networks} ,
+\href{https://arxiv.org/abs/1411.4280}{Efficient Object Localization Using Convolutional Networks} ,
 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
diff --git a/man/nn_lp_pool1d.Rd b/man/nn_lp_pool1d.Rd
index e73ad8c3a..476ff1849 100644
--- a/man/nn_lp_pool1d.Rd
+++ b/man/nn_lp_pool1d.Rd
@@ -42,7 +42,7 @@ not defined. This implementation will set the gradient to zero in this case.
 }
 
 \deqn{
-  L_{out} = \left\lfloor\frac{L_{in} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor
+  L_{out} = \left\lfloor\frac{L_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
 }
 }
 
diff --git a/man/nn_lp_pool2d.Rd b/man/nn_lp_pool2d.Rd
index 460d672ef..26ca7ebc0 100644
--- a/man/nn_lp_pool2d.Rd
+++ b/man/nn_lp_pool2d.Rd
@@ -49,10 +49,10 @@ not defined. This implementation will set the gradient to zero in this case.
 }
 
 \deqn{
-  H_{out} = \left\lfloor\frac{H_{in} - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
+  H_{out} = \left\lfloor\frac{H_{in} - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
 }
 \deqn{
-  W_{out} = \left\lfloor\frac{W_{in} - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor
+  W_{out} = \left\lfloor\frac{W_{in} - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
 }
 }
 
diff --git a/man/nn_max_pool2d.Rd b/man/nn_max_pool2d.Rd
index 260d1de22..91bcedd01 100644
--- a/man/nn_max_pool2d.Rd
+++ b/man/nn_max_pool2d.Rd
@@ -37,7 +37,7 @@ output \eqn{(N, C, H_{out}, W_{out})} and \code{kernel_size} \eqn{(kH, kW)}
 can be precisely described as:
 
 \deqn{
-  \begin{array}{ll}
+\begin{array}{ll}
 out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
 & \mbox{input}(N_i, C_j, \mbox{stride[0]} \times h + m,
                \mbox{stride[1]} \times w + n)
diff --git a/man/nn_max_pool3d.Rd b/man/nn_max_pool3d.Rd
index 00689f915..18397a2cc 100644
--- a/man/nn_max_pool3d.Rd
+++ b/man/nn_max_pool3d.Rd
@@ -35,11 +35,10 @@ can be precisely described as:
 }
 \details{
 \deqn{
-  \begin{aligned}
-\text{out}(N_i, C_j, d, h, w) ={} & \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
-& \text{input}(N_i, C_j, \text{stride[0]} \times d + k,
-               \text{stride[1]} \times h + m, \text{stride[2]} \times w + n)
-\end{aligned}
+\begin{array}{ll}
+\mbox{out}(N_i, C_j, d, h, w) = & \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
+ & \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}
 }
 
 If \code{padding} is non-zero, then the input is implicitly zero-padded on both sides
@@ -58,19 +57,19 @@ the second \code{int} for the height dimension and the third \code{int} for the
 \item Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})}
 \item Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
 \deqn{
-  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{dilation}[0] \times
-    (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor
+  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
 }
 }
 
 \deqn{
-  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{dilation}[1] \times
-    (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 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
 }
 
 \deqn{
-  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{dilation}[2] \times
-    (\text{kernel\_size}[2] - 1) - 1}{\text{stride}[2]} + 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
 }
 }
 
diff --git a/man/nn_max_unpool1d.Rd b/man/nn_max_unpool1d.Rd
index 8c76febd9..e485b0e3d 100644
--- a/man/nn_max_unpool1d.Rd
+++ b/man/nn_max_unpool1d.Rd
@@ -42,7 +42,7 @@ See the Inputs and Example below.
 \item Input: \eqn{(N, C, H_{in})}
 \item Output: \eqn{(N, C, H_{out})}, where
 \deqn{
-  H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{kernel\_size}[0]
+  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} in the call operator
 }
diff --git a/man/nn_max_unpool2d.Rd b/man/nn_max_unpool2d.Rd
index 562ea2494..a0d018ff6 100644
--- a/man/nn_max_unpool2d.Rd
+++ b/man/nn_max_unpool2d.Rd
@@ -42,10 +42,10 @@ See the Inputs and Example below.
 \item Input: \eqn{(N, C, H_{in}, W_{in})}
 \item Output: \eqn{(N, C, H_{out}, W_{out})}, where
 \deqn{
-  H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
+  H_{out} = (H_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
 }
 \deqn{
-  W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
+  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} in the call operator
 }
diff --git a/man/nn_max_unpool3d.Rd b/man/nn_max_unpool3d.Rd
index b53d193a0..5228bf168 100644
--- a/man/nn_max_unpool3d.Rd
+++ b/man/nn_max_unpool3d.Rd
@@ -44,13 +44,13 @@ See the Inputs section below.
 }
 
 \deqn{
-  D_{out} = (D_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
+  D_{out} = (D_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
 }
 \deqn{
-  H_{out} = (H_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
+  H_{out} = (H_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
 }
 \deqn{
-  W_{out} = (W_{in} - 1) \times \text{stride[2]} - 2 \times \text{padding[2]} + \text{kernel\_size[2]}
+  W_{out} = (W_{in} - 1) \times \mbox{stride[2]} - 2 \times \mbox{padding[2]} + \mbox{kernel\_size[2]}
 }
 
 or as given by \code{output_size} in the call operator
diff --git a/man/nn_module.Rd b/man/nn_module.Rd
index ecceaf7cb..3fbf27d81 100644
--- a/man/nn_module.Rd
+++ b/man/nn_module.Rd
@@ -4,7 +4,12 @@
 \alias{nn_module}
 \title{Base class for all neural network modules.}
 \usage{
-nn_module(classname = NULL, inherit = nn_Module, ...)
+nn_module(
+  classname = NULL,
+  inherit = nn_Module,
+  ...,
+  parent_env = parent.frame()
+)
 }
 \arguments{
 \item{classname}{an optional name for the module}
@@ -12,6 +17,8 @@ nn_module(classname = NULL, inherit = nn_Module, ...)
 \item{inherit}{an optional module to inherit from}
 
 \item{...}{methods implementation}
+
+\item{parent_env}{passed to \code{\link[R6:R6Class]{R6::R6Class()}}.}
 }
 \description{
 Your models should also subclass this class.
-- 
GitLab


From 77e91e1dabf0e7505f2a1cf64bb9288e8d28e02a Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 28 Aug 2020 17:51:00 -0300
Subject: [PATCH 075/157] Increment version number

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index e1ecf6b93..ab568d8b7 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: torch
 Type: Package
 Title: Tensors and Neural Networks with 'GPU' Acceleration
-Version: 0.0.1.9000
+Version: 0.0.2
 Authors@R: c(
     person("Daniel", "Falbel", email = "daniel@rstudio.com", role = c("aut", "cre", "cph")),
     person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cph")),
-- 
GitLab


From 7dc53b4656b492312b7975543585565ff13acd04 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 28 Aug 2020 17:56:48 -0300
Subject: [PATCH 076/157] add cran comments

---
 .Rbuildignore    |  1 +
 cran-comments.md | 21 +++++++++++++++++++++
 2 files changed, 22 insertions(+)
 create mode 100644 cran-comments.md

diff --git a/.Rbuildignore b/.Rbuildignore
index 185f6f24c..3a281fe9f 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -20,3 +20,4 @@ tools/torchgen/.Rbuildignore
 ^public/.*
 # uncomment below for CRAN submission
 ^inst/deps/.*
+^cran-comments\.md$
diff --git a/cran-comments.md b/cran-comments.md
new file mode 100644
index 000000000..f38dfbaff
--- /dev/null
+++ b/cran-comments.md
@@ -0,0 +1,21 @@
+## Test environments
+
+* local R installation, R 4.0.2
+* local mac OS install, R 4.0.0
+* ubuntu 16.04 (on github actions), R 4.0.0
+* mac OS 10.15.4 (on github actions) R 4.0.0
+* Microsoft Windows Server 2019 10.0.17763 (on github actions) R 4.0.0
+* win-builder (devel)
+
+## R CMD check results
+
+0 errors | 0 warnings | 1 note
+
+installed size is 23.1Mb
+sub-directories of 1Mb or more:
+    R      3.1Mb
+    help   2.6Mb
+    libs  17.2Mb
+
+* This is a new release.
+* Fixed issue with parallel make on Fedora.
\ No newline at end of file
-- 
GitLab


From 4819254a664d8f20450e11114a22e09c11faffb5 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 28 Aug 2020 17:59:15 -0300
Subject: [PATCH 077/157] submit to cran

---
 .Rbuildignore | 1 +
 CRAN-RELEASE  | 2 ++
 2 files changed, 3 insertions(+)
 create mode 100644 CRAN-RELEASE

diff --git a/.Rbuildignore b/.Rbuildignore
index 3a281fe9f..4a4877249 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -21,3 +21,4 @@ tools/torchgen/.Rbuildignore
 # uncomment below for CRAN submission
 ^inst/deps/.*
 ^cran-comments\.md$
+^CRAN-RELEASE$
diff --git a/CRAN-RELEASE b/CRAN-RELEASE
new file mode 100644
index 000000000..b5f8a27b2
--- /dev/null
+++ b/CRAN-RELEASE
@@ -0,0 +1,2 @@
+This package was submitted to CRAN on 2020-08-28.
+Once it is accepted, delete this file and tag the release (commit 7dc53b4656).
-- 
GitLab


From 48fb7bcb23656b0cead2fd560eb140e8852e6277 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 28 Aug 2020 19:52:59 -0300
Subject: [PATCH 078/157] rebuild readme

---
 README.md | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index b66a3e339..a5798e8ee 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-torch <a href='https://mlverse.github.io/torch'><img src='man/figures/torch.png' align="right" height="139" /></a>
-==================================================================================================================
+torch <a href='https://torch.mlverse.org'><img src='man/figures/torch.png' align="right" height="139" /></a>
+============================================================================================================
 
 [![Lifecycle:
 experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
@@ -34,12 +34,12 @@ torch Tensor to an R object.
     y
     #> torch_tensor 
     #> (1,.,.) = 
-    #>   0.8270  0.1017
-    #>   0.1992  0.7632
+    #>   0.5406  0.8648
+    #>   0.3097  0.9715
     #> 
     #> (2,.,.) = 
-    #>   0.2694  0.9735
-    #>   0.1965  0.7447
+    #>   0.1309  0.8992
+    #>   0.4849  0.1902
     #> [ CPUDoubleType{2,2,2} ]
     identical(x, as_array(y))
     #> [1] TRUE
@@ -99,13 +99,13 @@ tensors in place.
     print(w)
     #> torch_tensor 
     #> 1e-09 *
-    #>  7.8984
-    #>   8.7077
+    #>  5.2672
+    #>  -6.7969
     #> [ CPUFloatType{2,1} ]
     print(b) 
     #> torch_tensor 
     #> 0.01 *
-    #>  9.9867
+    #> -9.6802
     #> [ CPUFloatType{1} ]
 
 Contributing
-- 
GitLab


From 07323ca02b46e122a80ecae42803a5421768c139 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 28 Aug 2020 23:53:14 -0300
Subject: [PATCH 079/157] move examples to torchvision

---
 _pkgdown.yml                       |   9 +-
 vignettes/examples/mnist-alexnet.R |  89 ------------------
 vignettes/examples/mnist-cnn.R     |  65 -------------
 vignettes/examples/mnist-cnn.Rmd   |   9 --
 vignettes/examples/mnist-dcgan.R   | 145 -----------------------------
 vignettes/examples/mnist-dcgan.Rmd |   9 --
 vignettes/examples/mnist-mlp.R     |  53 -----------
 vignettes/examples/mnist-mlp.Rmd   |   9 --
 8 files changed, 1 insertion(+), 387 deletions(-)
 delete mode 100644 vignettes/examples/mnist-alexnet.R
 delete mode 100644 vignettes/examples/mnist-cnn.R
 delete mode 100644 vignettes/examples/mnist-cnn.Rmd
 delete mode 100644 vignettes/examples/mnist-dcgan.R
 delete mode 100644 vignettes/examples/mnist-dcgan.Rmd
 delete mode 100644 vignettes/examples/mnist-mlp.R
 delete mode 100644 vignettes/examples/mnist-mlp.Rmd

diff --git a/_pkgdown.yml b/_pkgdown.yml
index e7f94e391..f59ceab58 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -56,11 +56,4 @@ navbar:
        href: articles/extending-autograd.html
   examples:
     text: Examples
-    menu:
-    - text: Vision
-    - text: mnist-mlp
-      href: articles/examples/mnist-mlp.html
-    - text: mnist-cnn
-      href: articles/examples/mnist-cnn.html
-    - text: mnist-dcgan
-      href: articles/examples/mnist-dcgan.html
+  
diff --git a/vignettes/examples/mnist-alexnet.R b/vignettes/examples/mnist-alexnet.R
deleted file mode 100644
index e47586176..000000000
--- a/vignettes/examples/mnist-alexnet.R
+++ /dev/null
@@ -1,89 +0,0 @@
-dir <- "~/Downloads/tiny-imagenet"
-
-ds <- tiny_imagenet_dataset(
-  dir, 
-  download = TRUE, 
-  transform = function(x) {
-    x <- magick::image_resize(x, "224x224")
-    x <- as.integer(magick::image_data(x, "rgb"))  
-    x <- torch_tensor(x)
-    x <- x/256
-    x <- x$permute(c(3, 1, 2))
-  },
-  target_transform = function(x) {
-    x <- torch_tensor(x, dtype = torch_long())
-    x$squeeze(1)
-  }
-)
-
-dl <- dataloader(ds, batch_size = 128, shuffle = TRUE)
-
-net <- nn_module(
-  "Net",
-  initialize = function(num_classes = 1000) {
-    self$features <- nn_sequential(
-      nn_conv2d(3, 64, kernel_size = 11, stride = 4, padding = 2),
-      nn_relu(),
-      nn_max_pool2d(kernel_size = 3, stride = 2),
-      nn_conv2d(64, 192, kernel_size = 5, padding = 2),
-      nn_relu(),
-      nn_max_pool2d(kernel_size = 3, stride = 2),
-      nn_conv2d(192, 384, kernel_size = 3, padding = 1),
-      nn_relu(),
-      nn_conv2d(384, 256, kernel_size = 3, padding = 1),
-      nn_relu(),
-      nn_max_pool2d(kernel_size = 3, stride = 2)
-    )
-    self$avgpool <- nn_max_pool2d(c(6,6))
-    self$classifier <- nn_sequential(
-      nn_dropout(),
-      nn_linear(256, 4096),
-      nn_relu(),
-      nn_dropout(),
-      nn_linear(4096, 4096),
-      nn_relu(),
-      nn_linear(4096, num_classes)
-    )
-  },
-  forward = function(x) {
-    x <- self$features(x)
-    x <- self$avgpool(x)
-    x <- torch_flatten(x, start_dim = 2)
-    x <- self$classifier(x)
-  }
-)
-
-if (cuda_is_available()) {
-  device <- torch_device("cuda")
-} else {
-  device <- torch_device("cpu")
-}
-  
-model <- net(num_classes = 200)
-model$to(device = device)
-optimizer <- optim_adam(model$parameters)
-loss_fun <- nn_cross_entropy_loss()
-
-epochs <- 10
-
-for (epoch in 1:50) {
-  
-  pb <- progress::progress_bar$new(
-    total = length(dl), 
-    format = "[:bar] :eta Loss: :loss"
-  )
-  l <- c()
-  
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]]$to(device = device))
-    loss <- loss_fun(output, b[[2]]$to(device = device))
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-    pb$tick(tokens = list(loss = mean(l)))
-  }
-  
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
-
diff --git a/vignettes/examples/mnist-cnn.R b/vignettes/examples/mnist-cnn.R
deleted file mode 100644
index 4482c395b..000000000
--- a/vignettes/examples/mnist-cnn.R
+++ /dev/null
@@ -1,65 +0,0 @@
-dir <- "~/Downloads/mnist"
-
-ds <- mnist_dataset(
-  dir, 
-  download = TRUE, 
-  transform = function(x) {
-    x <- x$to(dtype = torch_float())/256
-    x[newaxis,..]
-  }
-)
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-net <- nn_module(
-  "Net",
-  initialize = function() {
-    self$conv1 <- nn_conv2d(1, 32, 3, 1)
-    self$conv2 <- nn_conv2d(32, 64, 3, 1)
-    self$dropout1 <- nn_dropout2d(0.25)
-    self$dropout2 <- nn_dropout2d(0.5)
-    self$fc1 <- nn_linear(9216, 128)
-    self$fc2 <- nn_linear(128, 10)
-  },
-  forward = function(x) {
-    x <- self$conv1(x)
-    x <- nnf_relu(x)
-    x <- self$conv2(x)
-    x <- nnf_relu(x)
-    x <- nnf_max_pool2d(x, 2)
-    x <- self$dropout1(x)
-    x <- torch_flatten(x, start_dim = 2)
-    x <- self$fc1(x)
-    x <- nnf_relu(x)
-    x <- self$dropout2(x)
-    x <- self$fc2(x)
-    output <- nnf_log_softmax(x, dim=1)
-    output
-  }
-)
-
-model <- net()
-optimizer <- optim_sgd(model$parameters, lr = 0.01)
-
-epochs <- 10
-
-for (epoch in 1:10) {
-  
-  pb <- progress::progress_bar$new(
-    total = length(dl), 
-    format = "[:bar] :eta Loss: :loss"
-  )
-  l <- c()
-  
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]])
-    loss <- nnf_nll_loss(output, b[[2]])
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-    pb$tick(tokens = list(loss = mean(l)))
-  }
-  
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
-
diff --git a/vignettes/examples/mnist-cnn.Rmd b/vignettes/examples/mnist-cnn.Rmd
deleted file mode 100644
index 2d2010e0d..000000000
--- a/vignettes/examples/mnist-cnn.Rmd
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: mnist-cnn
-type: docs
----
-
-```{r, echo = FALSE}
-knitr::opts_chunk$set(eval = FALSE)
-knitr::spin_child(paste0(rmarkdown::metadata$title, ".R"))
-```
\ No newline at end of file
diff --git a/vignettes/examples/mnist-dcgan.R b/vignettes/examples/mnist-dcgan.R
deleted file mode 100644
index ee16299e8..000000000
--- a/vignettes/examples/mnist-dcgan.R
+++ /dev/null
@@ -1,145 +0,0 @@
-library(torch)
-
-dir <- "~/Downloads/mnist"
-
-ds <- mnist_dataset(
-  dir, 
-  download = TRUE, 
-  transform = function(x) {
-    x <- x$to(dtype = torch_float())/256
-    x <- 2*(x - 0.5)
-    x[newaxis,..]
-  }
-)
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-generator <- nn_module(
-  "generator", 
-  initialize = function(latent_dim, out_channels) {
-    self$main <- nn_sequential(
-      nn_conv_transpose2d(latent_dim, 512, kernel_size = 4, 
-                          stride = 1, padding = 0, bias = FALSE),
-      nn_batch_norm2d(512),
-      nn_relu(),
-      nn_conv_transpose2d(512, 256, kernel_size = 4, 
-                          stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(256),
-      nn_relu(),
-      nn_conv_transpose2d(256, 128, kernel_size = 4, 
-                          stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(128),
-      nn_relu(),
-      nn_conv_transpose2d(128, out_channels, kernel_size = 4, 
-                          stride = 2, padding = 3, bias = FALSE),
-      nn_tanh()
-    )
-  },
-  forward = function(input) {
-    self$main(input)
-  }
-)
-
-discriminator <- nn_module(
-  "discriminator",
-  initialize = function(in_channels) {
-    self$main <- nn_sequential(
-      nn_conv2d(in_channels, 16, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_leaky_relu(0.2, inplace = TRUE),
-      nn_conv2d(16, 32, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(32),
-      nn_leaky_relu(0.2, inplace = TRUE),
-      nn_conv2d(32, 64, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(64),
-      nn_leaky_relu(0.2, inplace = TRUE),
-      nn_conv2d(64, 128, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_leaky_relu(0.2, inplace = TRUE)
-    )
-    self$linear <- nn_linear(128, 1)
-    self$sigmoid <- nn_sigmoid()
-  },
-  forward = function(input) {
-    x <- self$main(input)
-    x <- torch_flatten(x, start_dim = 2)
-    x <- self$linear(x)
-    self$sigmoid(x)
-  }
-)
-
-plot_gen <- function(noise) {
-  img <- G(noise)
-  img <- img$cpu()
-  img <- img[1,1,,,newaxis]/2 + 0.5
-  img <- torch_stack(list(img, img, img), dim = 2)[..,1]
-  img <- as.raster(as_array(img))
-  plot(img)
-}
-
-device <- torch_device(ifelse(cuda_is_available(),  "cuda", "cpu"))
-
-G <- generator(latent_dim = 100, out_channels = 1)
-D <- discriminator(in_channels = 1)
-
-init_weights <- function(m) {
-  if (grepl("conv", m$.classes[[1]])) {
-    nn_init_normal_(m$weight$data(), 0.0, 0.02)
-  } else if (grepl("batch_norm", m$.classes[[1]])) {
-    nn_init_normal_(m$weight$data(), 1.0, 0.02)
-    nn_init_constant_(m$bias$data(), 0)
-  } 
-}
-
-G[[1]]$apply(init_weights)
-D[[1]]$apply(init_weights)
-
-G$to(device = device)
-D$to(device = device)
-
-G_optimizer <- optim_adam(G$parameters, lr = 2 * 1e-4, betas = c(0.5, 0.999))
-D_optimizer <- optim_adam(D$parameters, lr = 2 * 1e-4, betas = c(0.5, 0.999))
-
-fixed_noise <- torch_randn(1, 100, 1, 1, device = device)
-
-loss <- nn_bce_loss()
-
-for (epoch in 1:10) {
-  
-  pb <- progress::progress_bar$new(
-    total = length(dl), 
-    format = "[:bar] :eta Loss D: :lossd Loss G: :lossg"
-  )
-  lossg <- c()
-  lossd <- c()
-  
-  for (b in enumerate(dl)) {
-    
-    y_real <- torch_ones(32, device = device)
-    y_fake <- torch_zeros(32, device = device)
-    
-    noise <- torch_randn(32, 100, 1, 1, device = device)
-    fake <- G(noise)
-    
-    img <- b[[1]]$to(device = device)
-    
-    # train the discriminator ---
-    D_loss <- loss(D(img), y_real) + loss(D(fake$detach()), y_fake)
-    
-    D_optimizer$zero_grad()
-    D_loss$backward()
-    D_optimizer$step()
-    
-    # train the generator ---
-    
-    G_loss <- loss(D(fake), y_real)
-    
-    G_optimizer$zero_grad()
-    G_loss$backward()
-    G_optimizer$step()
-    
-    lossd <- c(lossd, D_loss$item())
-    lossg <- c(lossg, G_loss$item())
-    pb$tick(tokens = list(lossd = mean(lossd), lossg = mean(lossg)))
-  }
-  plot_gen(fixed_noise)
-  
-  cat(sprintf("Epoch %d - Loss D: %3f Loss G: %3f\n", epoch, mean(lossd), mean(lossg)))
-}
diff --git a/vignettes/examples/mnist-dcgan.Rmd b/vignettes/examples/mnist-dcgan.Rmd
deleted file mode 100644
index db79ca61d..000000000
--- a/vignettes/examples/mnist-dcgan.Rmd
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: mnist-dcgan
-type: docs
----
-
-```{r, echo = FALSE}
-knitr::opts_chunk$set(eval = FALSE)
-knitr::spin_child(paste0(rmarkdown::metadata$title, ".R"))
-```
\ No newline at end of file
diff --git a/vignettes/examples/mnist-mlp.R b/vignettes/examples/mnist-mlp.R
deleted file mode 100644
index 1a1fc0b17..000000000
--- a/vignettes/examples/mnist-mlp.R
+++ /dev/null
@@ -1,53 +0,0 @@
-dir <- "~/Downloads/mnist"
-
-ds <- mnist_dataset(
-  dir, 
-  download = TRUE, 
-  transform = function(x) {
-    x$to(dtype = torch_float())/256
-  }
-)
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-net <- nn_module(
-  "Net",
-  initialize = function() {
-    self$fc1 <- nn_linear(784, 128)
-    self$fc2 <- nn_linear(128, 10)
-  },
-  forward = function(x) {
-    x %>% 
-      torch_flatten(start_dim = 2) %>% 
-      self$fc1() %>% 
-      nnf_relu() %>% 
-      self$fc2() %>% 
-      nnf_log_softmax(dim = 1)
-  }
-)
-
-model <- net()
-optimizer <- optim_sgd(model$parameters, lr = 0.01)
-
-epochs <- 10
-
-for (epoch in 1:10) {
-  
-  pb <- progress::progress_bar$new(
-    total = length(dl), 
-    format = "[:bar] :eta Loss: :loss"
-  )
-  l <- c()
-  
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]])
-    loss <- nnf_nll_loss(output, b[[2]])
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-    pb$tick(tokens = list(loss = mean(l)))
-  }
-  
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
-
diff --git a/vignettes/examples/mnist-mlp.Rmd b/vignettes/examples/mnist-mlp.Rmd
deleted file mode 100644
index 06cd5402b..000000000
--- a/vignettes/examples/mnist-mlp.Rmd
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: mnist-mlp
-type: docs
----
-
-```{r, echo = FALSE}
-knitr::opts_chunk$set(eval = FALSE)
-knitr::spin_child(paste0(rmarkdown::metadata$title, ".R"))
-```
\ No newline at end of file
-- 
GitLab


From 0445cfbb42948ac1bd0828849f7368680e909ef6 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Sat, 29 Aug 2020 12:34:10 -0300
Subject: [PATCH 080/157] re-submission after URL failed

---
 CRAN-RELEASE     | 4 ++--
 cran-comments.md | 3 ++-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/CRAN-RELEASE b/CRAN-RELEASE
index b5f8a27b2..97218a231 100644
--- a/CRAN-RELEASE
+++ b/CRAN-RELEASE
@@ -1,2 +1,2 @@
-This package was submitted to CRAN on 2020-08-28.
-Once it is accepted, delete this file and tag the release (commit 7dc53b4656).
+This package was submitted to CRAN on 2020-08-29.
+Once it is accepted, delete this file and tag the release (commit 07323ca02b).
diff --git a/cran-comments.md b/cran-comments.md
index f38dfbaff..843a871c2 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -18,4 +18,5 @@ sub-directories of 1Mb or more:
     libs  17.2Mb
 
 * This is a new release.
-* Fixed issue with parallel make on Fedora.
\ No newline at end of file
+* Fixed issue with parallel make on Fedora.
+* Fixed README.md URL redirect.
\ No newline at end of file
-- 
GitLab


From 4afe673491668b04977f5f467e6b105a8d14cd56 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 31 Aug 2020 09:45:22 -0300
Subject: [PATCH 081/157] Add NEWS.md

---
 CRAN-RELEASE | 2 --
 1 file changed, 2 deletions(-)
 delete mode 100644 CRAN-RELEASE

diff --git a/CRAN-RELEASE b/CRAN-RELEASE
deleted file mode 100644
index 97218a231..000000000
--- a/CRAN-RELEASE
+++ /dev/null
@@ -1,2 +0,0 @@
-This package was submitted to CRAN on 2020-08-29.
-Once it is accepted, delete this file and tag the release (commit 07323ca02b).
-- 
GitLab


From daf720a17de9db5d77029f61e66f63a8aea8792f Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 31 Aug 2020 09:45:37 -0300
Subject: [PATCH 082/157] add news.md

---
 NEWS.md | 4 ++++
 1 file changed, 4 insertions(+)
 create mode 100644 NEWS.md

diff --git a/NEWS.md b/NEWS.md
new file mode 100644
index 000000000..2f8c1c74d
--- /dev/null
+++ b/NEWS.md
@@ -0,0 +1,4 @@
+# torch 0.0.2
+
+* Added a `NEWS.md` file to track changes to the package.
+* Auto install when loading the package for the first time.
-- 
GitLab


From 335220299690589fbed1f5cce508f83e7725a507 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 31 Aug 2020 09:49:04 -0300
Subject: [PATCH 083/157] Increment version number

---
 DESCRIPTION | 2 +-
 NEWS.md     | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index ab568d8b7..ebd6bf738 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: torch
 Type: Package
 Title: Tensors and Neural Networks with 'GPU' Acceleration
-Version: 0.0.2
+Version: 0.0.2.9000
 Authors@R: c(
     person("Daniel", "Falbel", email = "daniel@rstudio.com", role = c("aut", "cre", "cph")),
     person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cph")),
diff --git a/NEWS.md b/NEWS.md
index 2f8c1c74d..e5f2294fe 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,5 @@
+# torch (development version)
+
 # torch 0.0.2
 
 * Added a `NEWS.md` file to track changes to the package.
-- 
GitLab


From 926a30f2fe72c36fc4c896f8bb3415c92abd7cf7 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 31 Aug 2020 17:18:46 -0300
Subject: [PATCH 084/157] make dtype and device active fields

---
 R/tensor.R | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/R/tensor.R b/R/tensor.R
index cfb4761b8..77c72e4a5 100644
--- a/R/tensor.R
+++ b/R/tensor.R
@@ -45,12 +45,6 @@ Tensor <- R7Class(
       cpp_torch_tensor_print(self$ptr)
       invisible(self)
     },
-    dtype = function() {
-      torch_dtype$new(ptr = cpp_torch_tensor_dtype(self$ptr))
-    },
-    device = function() {
-      Device$new(ptr = cpp_tensor_device(self$ptr))
-    },
     dim = function() {
       length(self$size())
     },
@@ -120,6 +114,12 @@ Tensor <- R7Class(
   active = list(
     shape = function() {
       self$size()
+    },
+    dtype = function() {
+      torch_dtype$new(ptr = cpp_torch_tensor_dtype(self$ptr))
+    },
+    device = function() {
+      Device$new(ptr = cpp_tensor_device(self$ptr))
     }
   )
 )
-- 
GitLab


From 6b51074c3b2bb6bb68ac29af9a4aa698d17bdde7 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 31 Aug 2020 17:21:08 -0300
Subject: [PATCH 085/157] fix () occurences

---
 R/codegen-utils.R            |  2 +-
 R/nn-rnn.R                   |  2 +-
 R/nnf-pooling.R              |  4 ++--
 R/tensor.R                   |  8 ++++----
 tests/testthat/test-dtype.R  |  6 +++---
 tests/testthat/test-nn.R     | 14 +++++++-------
 tests/testthat/test-tensor.R |  6 +++---
 7 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/R/codegen-utils.R b/R/codegen-utils.R
index 8d91ecec4..523794a22 100644
--- a/R/codegen-utils.R
+++ b/R/codegen-utils.R
@@ -28,7 +28,7 @@ as_1_based_tensor <- function(x) {
       runtime_error("Indices/Index start at 1 and got a 0.")  
   }
   
-  x - (x > 0)$to(dtype = x$dtype())
+  x - (x > 0)$to(dtype = x$dtype)
 }
 
 argument_to_torch_type <- function(obj, expected_types, arg_name) {
diff --git a/R/nn-rnn.R b/R/nn-rnn.R
index 9c47af147..559a4c137 100644
--- a/R/nn-rnn.R
+++ b/R/nn-rnn.R
@@ -145,7 +145,7 @@ nn_rnn_base <- nn_module(
       num_directions <- ifelse(self$bidirectional, 2, 1)
       hx <- torch_zeros(self$num_layers * num_directions,
                         max_batch_size, self$hidden_size,
-                        dtype=input$dtype(), device=input$device())
+                        dtype=input$dtype, device=input$device())
       
     } else {
       hx <- self$permute_hidden(hx, sorted_indices)  
diff --git a/R/nnf-pooling.R b/R/nnf-pooling.R
index 7e29d1dad..d22a93d9f 100644
--- a/R/nnf-pooling.R
+++ b/R/nnf-pooling.R
@@ -375,7 +375,7 @@ nnf_fractional_max_pool2d <- function(input, kernel_size, output_size=NULL,
   
   if (is.null(random_samples)) {
     random_samples <- torch_rand(input$size(1), input$size(2), 2, 
-                                 dtype = input$dtype(), device = input$device())
+                                 dtype = input$dtype, device = input$device())
   }
   
   res <- torch_fractional_max_pool2d(self = input, kernel_size = kernel_size, 
@@ -428,7 +428,7 @@ nnf_fractional_max_pool3d <- function(input, kernel_size, output_size = NULL, ou
   }
   
   if (is.null(random_samples)) {
-    random_samples <- torch_rand(input$size(1), input$size(2), 3, dtype = input$dtype(), 
+    random_samples <- torch_rand(input$size(1), input$size(2), 3, dtype = input$dtype, 
                                  device = input$device())
   }
   
diff --git a/R/tensor.R b/R/tensor.R
index 77c72e4a5..a7160038c 100644
--- a/R/tensor.R
+++ b/R/tensor.R
@@ -75,7 +75,7 @@ Tensor <- R7Class(
       args$memory_format <- memory_format
       
       if (is.null(args$dtype) && is.null(args$other))
-        args$dtype <- self$dtype()
+        args$dtype <- self$dtype
       
       do.call(private$`_to`, args)
     },
@@ -167,7 +167,7 @@ as_array_impl <- function(x) {
     out <- aperm(array(a$vec, dim = rev(a$dim)), seq(length(a$dim), 1))
   }
   
-  if (x$dtype() == torch_long() && !inherits(out, "integer64"))
+  if (x$dtype == torch_long() && !inherits(out, "integer64"))
     class(out) <- c(class(out), "integer64")
   
   out
@@ -184,7 +184,7 @@ as_array.torch_tensor <- function(x) {
     x <- x$dequantize()
   
   # auto convert to int32 if long.
-  if (x$dtype() == torch_long())
+  if (x$dtype == torch_long())
     x <- x$to(dtype = torch_int32())
   
   out <- as_array_impl(x)
@@ -205,4 +205,4 @@ is_undefined_tensor <- function(x) {
 as.integer64.torch_tensor <- function(x, keep.names = FALSE, ...) {
   x <- x$to(dtype = torch_long())
   as_array_impl(x)
-}
\ No newline at end of file
+}
diff --git a/tests/testthat/test-dtype.R b/tests/testthat/test-dtype.R
index d6d7df982..396a182c0 100644
--- a/tests/testthat/test-dtype.R
+++ b/tests/testthat/test-dtype.R
@@ -26,14 +26,14 @@ test_that("Can compare dtypes", {
 test_that("Default dtype", {
   
   x <- torch_randn(10)
-  expect_true(x$dtype() == torch_float())
+  expect_true(x$dtype == torch_float())
   expect_true(torch_get_default_dtype() == torch_float())
   
   torch_set_default_dtype(torch_float64())
   expect_true(torch_get_default_dtype() == torch_float64())
   x <- torch_randn(10)
-  expect_true(x$dtype() == torch_float64())
+  expect_true(x$dtype == torch_float64())
   
   torch_set_default_dtype(torch_float())
   
-})
\ No newline at end of file
+})
diff --git a/tests/testthat/test-nn.R b/tests/testthat/test-nn.R
index c76799009..c97520461 100644
--- a/tests/testthat/test-nn.R
+++ b/tests/testthat/test-nn.R
@@ -117,8 +117,8 @@ test_that("to", {
   net <- nn_linear(10, 10)
   net$to(dtype = torch_double())
   
-  expect_true(net$weight$dtype() == torch_double())
-  expect_true(net$bias$dtype() == torch_double())
+  expect_true(net$weight$dtype == torch_double())
+  expect_true(net$bias$dtype == torch_double())
   
   
   Net <- nn_module(
@@ -140,11 +140,11 @@ test_that("to", {
   
   net$to(dtype = torch_double())
   
-  expect_true(net$linear$weight$dtype() == torch_double())
-  expect_true(net$linear$bias$dtype() == torch_double())
-  expect_true(net$norm$running_mean$dtype() == torch_double())
-  expect_true(net$norm$running_var$dtype() == torch_double())
-  expect_true(net$linear$weight$grad$dtype() == torch_double())
+  expect_true(net$linear$weight$dtype == torch_double())
+  expect_true(net$linear$bias$dtype == torch_double())
+  expect_true(net$norm$running_mean$dtype == torch_double())
+  expect_true(net$norm$running_var$dtype == torch_double())
+  expect_true(net$linear$weight$grad$dtype == torch_double())
   
   skip_if_cuda_not_available()
   net$cuda()
diff --git a/tests/testthat/test-tensor.R b/tests/testthat/test-tensor.R
index 6b34bb69a..ba5cd9f20 100644
--- a/tests/testthat/test-tensor.R
+++ b/tests/testthat/test-tensor.R
@@ -11,10 +11,10 @@ test_that("Can create a tensor", {
   expect_equal(dim(x), 0)
   
   x <- torch_tensor(1)
-  expect_true(x$dtype() == torch_float32())
+  expect_true(x$dtype == torch_float32())
   
   x <- torch_tensor(1, dtype = torch_double())
-  expect_true(x$dtype() == torch_double())
+  expect_true(x$dtype == torch_double())
   
   device <- x$device()
   expect_equal(device$type, "cpu")
@@ -104,7 +104,7 @@ test_that("Pass only device argument to `to`", {
   
   y <- torch_tensor(1, dtype = torch_long())
   k <- x$to(other = y)
-  expect_true(k$dtype() == torch_long())
+  expect_true(k$dtype == torch_long())
 })
 
 test_that("cuda and cpu methods", {
-- 
GitLab


From a57b00c0e181b91dbb820fe8ca5329608b16b41e Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 31 Aug 2020 17:21:56 -0300
Subject: [PATCH 086/157] fix () occurences

---
 R/nn-rnn.R                   | 2 +-
 R/nnf-pooling.R              | 4 ++--
 R/tensor.R                   | 2 +-
 tests/testthat/test-cuda.R   | 4 ++--
 tests/testthat/test-device.R | 8 ++++----
 tests/testthat/test-nn.R     | 8 ++++----
 tests/testthat/test-tensor.R | 8 ++++----
 7 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/R/nn-rnn.R b/R/nn-rnn.R
index 559a4c137..744f4bc4c 100644
--- a/R/nn-rnn.R
+++ b/R/nn-rnn.R
@@ -145,7 +145,7 @@ nn_rnn_base <- nn_module(
       num_directions <- ifelse(self$bidirectional, 2, 1)
       hx <- torch_zeros(self$num_layers * num_directions,
                         max_batch_size, self$hidden_size,
-                        dtype=input$dtype, device=input$device())
+                        dtype=input$dtype, device=input$device)
       
     } else {
       hx <- self$permute_hidden(hx, sorted_indices)  
diff --git a/R/nnf-pooling.R b/R/nnf-pooling.R
index d22a93d9f..cec080578 100644
--- a/R/nnf-pooling.R
+++ b/R/nnf-pooling.R
@@ -375,7 +375,7 @@ nnf_fractional_max_pool2d <- function(input, kernel_size, output_size=NULL,
   
   if (is.null(random_samples)) {
     random_samples <- torch_rand(input$size(1), input$size(2), 2, 
-                                 dtype = input$dtype, device = input$device())
+                                 dtype = input$dtype, device = input$device)
   }
   
   res <- torch_fractional_max_pool2d(self = input, kernel_size = kernel_size, 
@@ -429,7 +429,7 @@ nnf_fractional_max_pool3d <- function(input, kernel_size, output_size = NULL, ou
   
   if (is.null(random_samples)) {
     random_samples <- torch_rand(input$size(1), input$size(2), 3, dtype = input$dtype, 
-                                 device = input$device())
+                                 device = input$device)
   }
   
   res <- torch_fractional_max_pool3d(
diff --git a/R/tensor.R b/R/tensor.R
index a7160038c..28eef18f9 100644
--- a/R/tensor.R
+++ b/R/tensor.R
@@ -176,7 +176,7 @@ as_array_impl <- function(x) {
 #' @export
 as_array.torch_tensor <- function(x) {
   
-  if (x$device()$type == "cuda")
+  if (x$device$type == "cuda")
     runtime_error("Can't convert cuda tensor to R. Convert to cpu tensor before.")
   
   # dequantize before converting
diff --git a/tests/testthat/test-cuda.R b/tests/testthat/test-cuda.R
index f348ea1ae..552f4aee5 100644
--- a/tests/testthat/test-cuda.R
+++ b/tests/testthat/test-cuda.R
@@ -13,6 +13,6 @@ test_that("cuda tensors", {
   
   x <- torch_randn(10, 10, device = torch_device("cuda"))
   
-  expect_equal(x$device()$type, "cuda")
-  expect_equal(x$device()$index, 0)
+  expect_equal(x$device$type, "cuda")
+  expect_equal(x$device$index, 0)
 })
diff --git a/tests/testthat/test-device.R b/tests/testthat/test-device.R
index bc141ae46..0faa3d3f6 100644
--- a/tests/testthat/test-device.R
+++ b/tests/testthat/test-device.R
@@ -20,19 +20,19 @@ test_that("Can create devices", {
   skip_if_cuda_not_available()
   
   x <- torch_tensor(1, device = torch_device("cuda:0"))
-  expect_equal(x$device()$type, "cuda")
+  expect_equal(x$device$type, "cuda")
 })
 
 test_that("use string to define the device", {
   
   x <- torch_randn(10, 10, device = "cpu")
-  expect_equal(x$device()$type, "cpu")
+  expect_equal(x$device$type, "cpu")
   
   x <- torch_tensor(1, device = "cpu")
-  expect_equal(x$device()$type, "cpu")
+  expect_equal(x$device$type, "cpu")
   
   skip_if_cuda_not_available()
   
   x <- torch_tensor(1, device = "cuda")
-  expect_equal(x$device()$type, "cuda")
+  expect_equal(x$device$type, "cuda")
 })
diff --git a/tests/testthat/test-nn.R b/tests/testthat/test-nn.R
index c97520461..3f10906a2 100644
--- a/tests/testthat/test-nn.R
+++ b/tests/testthat/test-nn.R
@@ -148,12 +148,12 @@ test_that("to", {
   
   skip_if_cuda_not_available()
   net$cuda()
-  expect_equal(net$linear$weight$device()$type, "cuda")
-  expect_equal(net$linear$bias$device()$type, "cuda")
+  expect_equal(net$linear$weight$device$type, "cuda")
+  expect_equal(net$linear$bias$device$type, "cuda")
   
   net$cpu()
-  expect_equal(net$linear$weight$device()$type, "cpu")
-  expect_equal(net$linear$bias$device()$type,"cpu")
+  expect_equal(net$linear$weight$device$type, "cpu")
+  expect_equal(net$linear$bias$device$type,"cpu")
   
 })
 
diff --git a/tests/testthat/test-tensor.R b/tests/testthat/test-tensor.R
index ba5cd9f20..5ef99b67b 100644
--- a/tests/testthat/test-tensor.R
+++ b/tests/testthat/test-tensor.R
@@ -16,7 +16,7 @@ test_that("Can create a tensor", {
   x <- torch_tensor(1, dtype = torch_double())
   expect_true(x$dtype == torch_double())
   
-  device <- x$device()
+  device <- x$device
   expect_equal(device$type, "cpu")
 })
 
@@ -113,15 +113,15 @@ test_that("cuda and cpu methods", {
   x <- torch_tensor(1)
   y <- x$cuda()
   
-  expect_true(y$device()$type == "cuda")
+  expect_true(y$device$type == "cuda")
   
   # calling twice dont error
   y$cuda()
-  expect_true(y$device()$type == "cuda")
+  expect_true(y$device$type == "cuda")
   
   k <- y$cpu()
   
-  expect_true(k$device()$type == "cpu")
+  expect_true(k$device$type == "cpu")
   
 })
 
-- 
GitLab


From de5d4a16aeaa65497b46cd4cecbdf17931bca2e4 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 31 Aug 2020 17:33:26 -0300
Subject: [PATCH 087/157] add some other methods

---
 R/tensor.R                   |  6 ++++++
 tests/testthat/test-tensor.R | 17 +++++++++++++++++
 2 files changed, 23 insertions(+)

diff --git a/R/tensor.R b/R/tensor.R
index 28eef18f9..806a8b5e7 100644
--- a/R/tensor.R
+++ b/R/tensor.R
@@ -120,6 +120,12 @@ Tensor <- R7Class(
     },
     device = function() {
       Device$new(ptr = cpp_tensor_device(self$ptr))
+    },
+    is_cuda = function() {
+      self$device$type == "cuda"
+    },
+    ndim = function() {
+      self$dim()
     }
   )
 )
diff --git a/tests/testthat/test-tensor.R b/tests/testthat/test-tensor.R
index 5ef99b67b..cd882b17d 100644
--- a/tests/testthat/test-tensor.R
+++ b/tests/testthat/test-tensor.R
@@ -140,3 +140,20 @@ test_that("is_contiguous", {
   expect_true(!x$is_contiguous())
   
 })
+
+test_that("is_cuda", {
+  x <- torch_randn(10, 10)
+  expect_true(!x$is_cuda)
+  
+  skip_if_cuda_not_available()
+  
+  x <- torch_randn(10, 10, device = torch_device("cuda"))
+  expect_true(X$is_cuda)
+})
+
+test_that("ndim", {
+  
+  x <- torch_randn(10, 10)
+  expect_equal(x$ndim, 2)
+  
+})
-- 
GitLab


From 61c9a33fd9706fdfd3ee55c5f2321e11f4255685 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 1 Sep 2020 14:35:02 -0300
Subject: [PATCH 088/157] export is_undefined_tensor

---
 .Rbuildignore              |  1 +
 NAMESPACE                  |  1 +
 R/tensor.R                 |  5 +++++
 man/is_undefined_tensor.Rd | 14 ++++++++++++++
 4 files changed, 21 insertions(+)
 create mode 100644 man/is_undefined_tensor.Rd

diff --git a/.Rbuildignore b/.Rbuildignore
index 4a4877249..991bd9585 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -22,3 +22,4 @@ tools/torchgen/.Rbuildignore
 ^inst/deps/.*
 ^cran-comments\.md$
 ^CRAN-RELEASE$
+
diff --git a/NAMESPACE b/NAMESPACE
index 20b428e29..d357068e9 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -100,6 +100,7 @@ export(is_torch_dtype)
 export(is_torch_layout)
 export(is_torch_memory_format)
 export(is_torch_qscheme)
+export(is_undefined_tensor)
 export(load_state_dict)
 export(nn_adaptive_avg_pool1d)
 export(nn_adaptive_avg_pool2d)
diff --git a/R/tensor.R b/R/tensor.R
index 806a8b5e7..104719986 100644
--- a/R/tensor.R
+++ b/R/tensor.R
@@ -202,6 +202,11 @@ is_torch_tensor <- function(x) {
   inherits(x, "torch_tensor")
 }
 
+#' Checks if a tensor is undefined
+#'
+#' @param x tensor to check
+#'
+#' @export
 is_undefined_tensor <- function(x) {
   cpp_tensor_is_undefined(x$ptr)
 }
diff --git a/man/is_undefined_tensor.Rd b/man/is_undefined_tensor.Rd
new file mode 100644
index 000000000..d4be33620
--- /dev/null
+++ b/man/is_undefined_tensor.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tensor.R
+\name{is_undefined_tensor}
+\alias{is_undefined_tensor}
+\title{Checks if a tensor is undefined}
+\usage{
+is_undefined_tensor(x)
+}
+\arguments{
+\item{x}{tensor to check}
+}
+\description{
+Checks if a tensor is undefined
+}
-- 
GitLab


From 8f3cbc09bb63ae509a50c428fd87927816d3422a Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 1 Sep 2020 15:23:49 -0300
Subject: [PATCH 089/157] reorganize the reference in the docs

---
 NAMESPACE              |  1 +
 R/device.R             |  8 +++++
 R/dtype.R              |  4 +++
 R/qscheme.R            |  2 +-
 R/reduction.R          |  4 +++
 R/save.R               |  5 +++
 R/type-info.R          |  4 ++-
 _pkgdown.yml           | 76 +++++++++++++++++++++++++++++++++++++++++-
 man/default_dtype.Rd   |  1 +
 man/is_torch_device.Rd | 15 +++++++++
 man/is_torch_dtype.Rd  |  1 +
 man/load_state_dict.Rd |  1 +
 man/torch_device.Rd    |  1 +
 man/torch_dtype.Rd     |  1 +
 man/torch_finfo.Rd     |  1 +
 man/torch_iinfo.Rd     |  1 +
 man/torch_load.Rd      |  1 +
 man/torch_qscheme.Rd   |  1 +
 man/torch_reduction.Rd |  1 +
 man/torch_save.Rd      |  1 +
 20 files changed, 127 insertions(+), 3 deletions(-)
 create mode 100644 man/is_torch_device.Rd

diff --git a/NAMESPACE b/NAMESPACE
index d357068e9..bd6842fb0 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -96,6 +96,7 @@ export(dataset)
 export(enumerate)
 export(install_torch)
 export(is_dataloader)
+export(is_torch_device)
 export(is_torch_dtype)
 export(is_torch_layout)
 export(is_torch_memory_format)
diff --git a/R/device.R b/R/device.R
index 0489296a1..f3faf0504 100644
--- a/R/device.R
+++ b/R/device.R
@@ -62,6 +62,8 @@ Device <- R6::R6Class(
 #' 
 #' A `torch_device` can be constructed via a string or via a string and device ordinal
 #' 
+#' @concept tensor-attributtes
+#' 
 #' @examples
 #' 
 #' # Via string
@@ -78,6 +80,12 @@ torch_device <- function(type, index = NULL) {
   Device$new(type, index)
 }
 
+#' Checks if object is a device
+#' 
+#' @param x object to check
+#' @concept tensor-attributes
+#'
+#' @export
 is_torch_device <- function(x) {
   inherits(x, "torch_device")
 }
diff --git a/R/dtype.R b/R/dtype.R
index 60524760c..a23a0f114 100644
--- a/R/dtype.R
+++ b/R/dtype.R
@@ -54,6 +54,7 @@ dtype_from_string <- function(str) {
 #' 
 #' @name torch_dtype
 #' @rdname torch_dtype
+#' @concept tensor-attributes
 #'
 NULL
 
@@ -130,6 +131,7 @@ torch_qint32 <- function() torch_dtype$new(cpp_torch_qint32())
 #' Check if object is a torch data type
 #' 
 #' @param x object to check.
+#' @concept tensor-attributes
 #' 
 #' @export
 is_torch_dtype <- function(x) {
@@ -142,6 +144,7 @@ is_torch_dtype <- function(x) {
 #'   `torch_float()`.
 #' 
 #' @rdname default_dtype
+#' @concept tensor-attributes
 #'
 #' @export
 torch_set_default_dtype <- function(d) {
@@ -149,6 +152,7 @@ torch_set_default_dtype <- function(d) {
 }
 
 #' @rdname default_dtype
+#' @concept tensor-attributes
 #' @export
 torch_get_default_dtype <- function() {
   torch_dtype$new(cpp_get_default_dtype())
diff --git a/R/qscheme.R b/R/qscheme.R
index 1361bf4c5..0fb46e76b 100644
--- a/R/qscheme.R
+++ b/R/qscheme.R
@@ -15,7 +15,7 @@ QScheme <- R6::R6Class(
 #' 
 #' @rdname torch_qscheme
 #' @name torch_qscheme
-#' 
+#' @concept tensor-attributes
 NULL
 
 #' @rdname torch_qscheme
diff --git a/R/reduction.R b/R/reduction.R
index a1f9f4aaa..958ae8f57 100644
--- a/R/reduction.R
+++ b/R/reduction.R
@@ -2,17 +2,21 @@
 #' 
 #' @name torch_reduction
 #' @rdname torch_reduction
+#' @concept tensor-attributes
 #'
 NULL
 
 #' @rdname torch_reduction
+#' @concept tensor-attributes
 #' @export
 torch_reduction_sum <- function() cpp_torch_reduction_sum()
 
+#' @concept tensor-attributes
 #' @rdname torch_reduction
 #' @export
 torch_reduction_mean <- function() cpp_torch_reduction_mean()
 
+#' @concept tensor-attributes
 #' @rdname torch_reduction
 #' @export
 torch_reduction_none <- function() cpp_torch_reduction_none()
diff --git a/R/save.R b/R/save.R
index ff498cd2e..52fc4248c 100644
--- a/R/save.R
+++ b/R/save.R
@@ -8,12 +8,14 @@
 #' @param ... not currently used.
 #'
 #' @family torch_save
+#' @concept serialization
 #'
 #' @export
 torch_save <- function(obj, path, ...) {
   UseMethod("torch_save")
 }
 
+#' @concept serialization
 #' @export
 torch_save.torch_tensor <- function(obj, path, ...) {
   values <- cpp_tensor_save(obj$ptr)
@@ -29,6 +31,7 @@ tensor_to_raw_vector <- function(x) {
   r
 }
 
+#' @concept serialization
 #' @export
 torch_save.nn_module <- function(obj, path, ...) {
  state_dict <- obj$state_dict()
@@ -43,6 +46,7 @@ torch_save.nn_module <- function(obj, path, ...) {
 #' @family torch_save
 #' 
 #' @export
+#' @concept serialization
 torch_load <- function(path) {
   r <- readRDS(path)
   if (r$type == "tensor")
@@ -81,6 +85,7 @@ torch_load_module <- function(obj) {
 #' @return a named list of tensors.
 #'
 #' @export
+#' @concept serialization
 load_state_dict <- function(path) {
   path <- normalizePath(path)
   o <- cpp_load_state_dict(path)
diff --git a/R/type-info.R b/R/type-info.R
index 1b6d347f7..9348e34cd 100644
--- a/R/type-info.R
+++ b/R/type-info.R
@@ -4,7 +4,8 @@
 #' type.
 #' 
 #' @param dtype dtype to get information from.
-#'
+#' @concept tensor-attributes
+#' 
 #' @export
 torch_iinfo <- function(dtype) {
   
@@ -44,6 +45,7 @@ torch_iinfo <- function(dtype) {
 #' floating point torch.dtype
 #' 
 #' @param dtype dtype to check information
+#' @concept tensor-attributes
 #'
 #' @export
 torch_finfo <- function(dtype) {
diff --git a/_pkgdown.yml b/_pkgdown.yml
index f59ceab58..45db66241 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -56,4 +56,78 @@ navbar:
        href: articles/extending-autograd.html
   examples:
     text: Examples
-  
+    
+   
+reference:  
+  - title: Tensor creation utilities
+    contents: 
+      - torch_empty
+      - torch_arange
+      - torch_eye
+      - torch_full
+      - torch_linspace
+      - torch_logspace
+      - torch_ones
+      - torch_rand
+      - torch_randint
+      - torch_randn
+      - torch_randperm
+      - torch_zeros
+      - matches("torch_.*_like")
+      - as_array
+  - title: Tensor attributes
+    contents:
+      - has_concept("tensor-attributes")
+      - is_torch_layout
+      - is_torch_memory_format
+      - is_torch_qscheme
+      - is_undefined_tensor
+  - title: Serialization
+    contents:
+      - has_concept("serialization")
+  - title: Mathematical operations on tensors
+    contents:
+      - starts_with("torch_")
+      - -torch_empty
+      - -torch_arange
+      - -torch_eye
+      - -torch_full
+      - -torch_linspace
+      - -torch_logspace
+      - -torch_ones
+      - -torch_rand
+      - -torch_randint
+      - -torch_randn
+      - -torch_randperm
+      - -torch_zeros
+      - -matches("torch_.*_like")
+      - -has_concept("tensor-attributes")
+      - -has_concept("serialization")
+  - title: Neural network modules
+    contents:
+      - starts_with("nn_")
+  - title: Neural networks functional module
+    contents:
+      - starts_with("nnf_")
+  - title: Optimizers
+    contents:
+      - starts_with("optim_")
+  - title: Datasets
+    contents: 
+      - starts_with("dataset")
+      - starts_with("dataloader")
+      - starts_with("enumerate")
+      - tensor_dataset
+      - is_dataloader
+  - title: Autograd
+    contents:
+      - starts_with("autograd_")
+      - with_no_grad
+      - with_enable_grad
+      - AutogradContext
+  - title: Cuda utilities
+    contents:
+      - starts_with("cuda_")
+  - title: Installation
+    contents:
+      - install_torch
diff --git a/man/default_dtype.Rd b/man/default_dtype.Rd
index a18596ee1..38c1a2846 100644
--- a/man/default_dtype.Rd
+++ b/man/default_dtype.Rd
@@ -16,3 +16,4 @@ torch_get_default_dtype()
 \description{
 Gets and sets the default floating point dtype.
 }
+\concept{tensor-attributes}
diff --git a/man/is_torch_device.Rd b/man/is_torch_device.Rd
new file mode 100644
index 000000000..bc4a64ccf
--- /dev/null
+++ b/man/is_torch_device.Rd
@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/device.R
+\name{is_torch_device}
+\alias{is_torch_device}
+\title{Checks if object is a device}
+\usage{
+is_torch_device(x)
+}
+\arguments{
+\item{x}{object to check}
+}
+\description{
+Checks if object is a device
+}
+\concept{tensor-attributes}
diff --git a/man/is_torch_dtype.Rd b/man/is_torch_dtype.Rd
index 42dd780d6..633bb12c4 100644
--- a/man/is_torch_dtype.Rd
+++ b/man/is_torch_dtype.Rd
@@ -12,3 +12,4 @@ is_torch_dtype(x)
 \description{
 Check if object is a torch data type
 }
+\concept{tensor-attributes}
diff --git a/man/load_state_dict.Rd b/man/load_state_dict.Rd
index 4fc5ae4f1..c6574d7d4 100644
--- a/man/load_state_dict.Rd
+++ b/man/load_state_dict.Rd
@@ -22,3 +22,4 @@ classes from the tensors in the dict.
 The above might change with development of \href{https://github.com/pytorch/pytorch/issues/37213}{this}
 in pytorch's C++ api.
 }
+\concept{serialization}
diff --git a/man/torch_device.Rd b/man/torch_device.Rd
index 8df2e3b91..e97293d2d 100644
--- a/man/torch_device.Rd
+++ b/man/torch_device.Rd
@@ -35,3 +35,4 @@ torch_device("cpu", 0)
 
 }
 }
+\concept{tensor-attributtes}
diff --git a/man/torch_dtype.Rd b/man/torch_dtype.Rd
index 0267873dd..3ea81c1ed 100644
--- a/man/torch_dtype.Rd
+++ b/man/torch_dtype.Rd
@@ -61,3 +61,4 @@ torch_qint32()
 \description{
 Returns the correspondent data type.
 }
+\concept{tensor-attributes}
diff --git a/man/torch_finfo.Rd b/man/torch_finfo.Rd
index 9b004bfc0..3fcd9ea76 100644
--- a/man/torch_finfo.Rd
+++ b/man/torch_finfo.Rd
@@ -13,3 +13,4 @@ torch_finfo(dtype)
 A list that represents the numerical properties of a
 floating point torch.dtype
 }
+\concept{tensor-attributes}
diff --git a/man/torch_iinfo.Rd b/man/torch_iinfo.Rd
index 93dca0e10..e2dd40dd5 100644
--- a/man/torch_iinfo.Rd
+++ b/man/torch_iinfo.Rd
@@ -13,3 +13,4 @@ torch_iinfo(dtype)
 A list that represents the numerical properties of a integer
 type.
 }
+\concept{tensor-attributes}
diff --git a/man/torch_load.Rd b/man/torch_load.Rd
index 68df05261..de6f5405e 100644
--- a/man/torch_load.Rd
+++ b/man/torch_load.Rd
@@ -16,4 +16,5 @@ Loads a saved object
 Other torch_save: 
 \code{\link{torch_save}()}
 }
+\concept{serialization}
 \concept{torch_save}
diff --git a/man/torch_qscheme.Rd b/man/torch_qscheme.Rd
index fa68e2876..2258be80b 100644
--- a/man/torch_qscheme.Rd
+++ b/man/torch_qscheme.Rd
@@ -19,3 +19,4 @@ torch_per_tensor_symmetric()
 \description{
 Creates the corresponding Scheme object
 }
+\concept{tensor-attributes}
diff --git a/man/torch_reduction.Rd b/man/torch_reduction.Rd
index 65c7f9e28..ead83db58 100644
--- a/man/torch_reduction.Rd
+++ b/man/torch_reduction.Rd
@@ -16,3 +16,4 @@ torch_reduction_none()
 \description{
 Creates the reduction objet
 }
+\concept{tensor-attributes}
diff --git a/man/torch_save.Rd b/man/torch_save.Rd
index 44c306599..8736b4f09 100644
--- a/man/torch_save.Rd
+++ b/man/torch_save.Rd
@@ -21,4 +21,5 @@ term storage.
 Other torch_save: 
 \code{\link{torch_load}()}
 }
+\concept{serialization}
 \concept{torch_save}
-- 
GitLab


From c6237a48ee939be5f9fd2df5b813a765f3c64a4d Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 1 Sep 2020 16:11:08 -0300
Subject: [PATCH 090/157] try to make examples run

---
 .github/workflows/pkgdown.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index d610ff6f7..3bf6dbc08 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -31,4 +31,4 @@ jobs:
         run: |
           git config --local user.email "actions@github.com"
           git config --local user.name "GitHub Actions"
-          Rscript -e 'pkgdown::deploy_to_branch(new_process = FALSE)'
\ No newline at end of file
+          Rscript -e 'Sys.setenv(TORCH_TEST = 1);pkgdown::deploy_to_branch(new_process = FALSE)'
\ No newline at end of file
-- 
GitLab


From 285f91b0199803e0518a8254e88204a1a4dfc842 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 1 Sep 2020 16:40:35 -0300
Subject: [PATCH 091/157] compare with strings

---
 vignettes/extending-autograd.Rmd                              | 2 +-
 vignettes/getting-started/autograd.Rmd                        | 2 +-
 vignettes/getting-started/control-flow-and-weight-sharing.Rmd | 2 +-
 vignettes/getting-started/custom-nn.Rmd                       | 2 +-
 vignettes/getting-started/neural-networks.Rmd                 | 2 +-
 vignettes/getting-started/new-autograd-functions.Rmd          | 2 +-
 vignettes/getting-started/nn.Rmd                              | 2 +-
 vignettes/getting-started/optim.Rmd                           | 2 +-
 vignettes/getting-started/tensors-and-autograd.Rmd            | 2 +-
 vignettes/getting-started/tensors.Rmd                         | 2 +-
 vignettes/getting-started/warmup.Rmd                          | 2 +-
 vignettes/getting-started/what-is-torch.Rmd                   | 2 +-
 vignettes/indexing.Rmd                                        | 2 +-
 vignettes/loading-data.Rmd                                    | 2 +-
 vignettes/tensor-creation.Rmd                                 | 2 +-
 vignettes/using-autograd.Rmd                                  | 4 ++--
 16 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/vignettes/extending-autograd.Rmd b/vignettes/extending-autograd.Rmd
index 424d84758..97f7f60be 100644
--- a/vignettes/extending-autograd.Rmd
+++ b/vignettes/extending-autograd.Rmd
@@ -11,7 +11,7 @@ vignette: >
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/getting-started/autograd.Rmd b/vignettes/getting-started/autograd.Rmd
index 2947f012b..a67c814ee 100644
--- a/vignettes/getting-started/autograd.Rmd
+++ b/vignettes/getting-started/autograd.Rmd
@@ -7,7 +7,7 @@ type: docs
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/getting-started/control-flow-and-weight-sharing.Rmd b/vignettes/getting-started/control-flow-and-weight-sharing.Rmd
index 66564b68e..8f23d9648 100644
--- a/vignettes/getting-started/control-flow-and-weight-sharing.Rmd
+++ b/vignettes/getting-started/control-flow-and-weight-sharing.Rmd
@@ -7,7 +7,7 @@ type: docs
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/getting-started/custom-nn.Rmd b/vignettes/getting-started/custom-nn.Rmd
index 58c20e326..752ae0aed 100644
--- a/vignettes/getting-started/custom-nn.Rmd
+++ b/vignettes/getting-started/custom-nn.Rmd
@@ -7,7 +7,7 @@ type: docs
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/getting-started/neural-networks.Rmd b/vignettes/getting-started/neural-networks.Rmd
index 40214cb0f..c6298ea6d 100644
--- a/vignettes/getting-started/neural-networks.Rmd
+++ b/vignettes/getting-started/neural-networks.Rmd
@@ -6,7 +6,7 @@ type: docs
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/getting-started/new-autograd-functions.Rmd b/vignettes/getting-started/new-autograd-functions.Rmd
index 1682afa2c..c4dbbcaed 100644
--- a/vignettes/getting-started/new-autograd-functions.Rmd
+++ b/vignettes/getting-started/new-autograd-functions.Rmd
@@ -7,7 +7,7 @@ type: docs
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/getting-started/nn.Rmd b/vignettes/getting-started/nn.Rmd
index 142398bed..4a6659be4 100644
--- a/vignettes/getting-started/nn.Rmd
+++ b/vignettes/getting-started/nn.Rmd
@@ -7,7 +7,7 @@ type: docs
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/getting-started/optim.Rmd b/vignettes/getting-started/optim.Rmd
index b12f197e2..4c90996c8 100644
--- a/vignettes/getting-started/optim.Rmd
+++ b/vignettes/getting-started/optim.Rmd
@@ -7,7 +7,7 @@ type: docs
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/getting-started/tensors-and-autograd.Rmd b/vignettes/getting-started/tensors-and-autograd.Rmd
index 3fede0011..62a12e2ba 100644
--- a/vignettes/getting-started/tensors-and-autograd.Rmd
+++ b/vignettes/getting-started/tensors-and-autograd.Rmd
@@ -7,7 +7,7 @@ type: docs
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/getting-started/tensors.Rmd b/vignettes/getting-started/tensors.Rmd
index 087300840..367ed5d99 100644
--- a/vignettes/getting-started/tensors.Rmd
+++ b/vignettes/getting-started/tensors.Rmd
@@ -7,7 +7,7 @@ type: docs
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/getting-started/warmup.Rmd b/vignettes/getting-started/warmup.Rmd
index 35165ecf6..f0b2abc34 100644
--- a/vignettes/getting-started/warmup.Rmd
+++ b/vignettes/getting-started/warmup.Rmd
@@ -7,7 +7,7 @@ type: docs
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/getting-started/what-is-torch.Rmd b/vignettes/getting-started/what-is-torch.Rmd
index f4ba5b66e..6ea41e0d5 100644
--- a/vignettes/getting-started/what-is-torch.Rmd
+++ b/vignettes/getting-started/what-is-torch.Rmd
@@ -6,7 +6,7 @@ type: docs
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/indexing.Rmd b/vignettes/indexing.Rmd
index d11d71fc3..2bf47d2e5 100644
--- a/vignettes/indexing.Rmd
+++ b/vignettes/indexing.Rmd
@@ -11,7 +11,7 @@ vignette: >
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/loading-data.Rmd b/vignettes/loading-data.Rmd
index bb4e6e9d0..65f1d724f 100644
--- a/vignettes/loading-data.Rmd
+++ b/vignettes/loading-data.Rmd
@@ -11,7 +11,7 @@ vignette: >
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/tensor-creation.Rmd b/vignettes/tensor-creation.Rmd
index 7f043babb..2586814e5 100644
--- a/vignettes/tensor-creation.Rmd
+++ b/vignettes/tensor-creation.Rmd
@@ -11,7 +11,7 @@ vignette: >
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/using-autograd.Rmd b/vignettes/using-autograd.Rmd
index 279e58acb..6f7f79b7f 100644
--- a/vignettes/using-autograd.Rmd
+++ b/vignettes/using-autograd.Rmd
@@ -11,7 +11,7 @@ vignette: >
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
@@ -166,4 +166,4 @@ for (t in 1:200) {
  
 ```
 
-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.
\ No newline at end of file
+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.
-- 
GitLab


From 1c444178f794b39c7725fe6c4c519c43b847c389 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 1 Sep 2020 16:56:58 -0300
Subject: [PATCH 092/157] et torch install so torch is installed when building
 pkgdown website

---
 .github/workflows/pkgdown.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index 3bf6dbc08..5f50d55b4 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -10,6 +10,7 @@ jobs:
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       TORCH_TEST: 1
+      TORCH_INSTALL: 1
     steps:
       - uses: actions/checkout@v2
 
-- 
GitLab


From 111c2c908d0de12c1f9ec67601358deadc8f6519 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 1 Sep 2020 17:15:20 -0300
Subject: [PATCH 093/157] import the pipe for vignettes

---
 DESCRIPTION    |  5 +++--
 NAMESPACE      |  3 +++
 R/dtype.R      |  5 ++++-
 R/tensor.R     | 12 ++++++++++++
 R/utils-pipe.R | 11 +++++++++++
 man/pipe.Rd    | 12 ++++++++++++
 6 files changed, 45 insertions(+), 3 deletions(-)
 create mode 100644 R/utils-pipe.R
 create mode 100644 man/pipe.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index ebd6bf738..505b789e2 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -29,7 +29,8 @@ Imports:
     methods,
     utils,
     stats,
-    bit64
+    bit64,
+    magrittr
 RoxygenNote: 7.1.1
 Roxygen: list(markdown = TRUE)
 Suggests:
@@ -37,7 +38,6 @@ Suggests:
     covr,
     knitr,
     rmarkdown,
-    magrittr,
     glue
 VignetteBuilder: knitr
 Collate: 
@@ -115,6 +115,7 @@ Collate:
     'utils-data-enum.R'
     'utils-data-fetcher.R'
     'utils-data-sampler.R'
+    'utils-pipe.R'
     'utils.R'
     'variable_list.R'
     'with-indices.R'
diff --git a/NAMESPACE b/NAMESPACE
index bd6842fb0..4c39b9219 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -75,12 +75,14 @@ S3method(sign,torch_tensor)
 S3method(sin,torch_tensor)
 S3method(sinh,torch_tensor)
 S3method(sqrt,torch_tensor)
+S3method(str,torch_tensor)
 S3method(sum,torch_tensor)
 S3method(tan,torch_tensor)
 S3method(tanh,torch_tensor)
 S3method(torch_save,nn_module)
 S3method(torch_save,torch_tensor)
 S3method(trunc,torch_tensor)
+export("%>%")
 export(as_array)
 export(autograd_backward)
 export(autograd_function)
@@ -573,6 +575,7 @@ export(with_enable_grad)
 export(with_no_grad)
 importFrom(Rcpp,sourceCpp)
 importFrom(bit64,as.integer64)
+importFrom(magrittr,"%>%")
 importFrom(rlang,":=")
 importFrom(rlang,env_bind)
 importFrom(rlang,env_get)
diff --git a/R/dtype.R b/R/dtype.R
index a23a0f114..53a3c9552 100644
--- a/R/dtype.R
+++ b/R/dtype.R
@@ -6,7 +6,10 @@ torch_dtype <- R6::R6Class(
       self$ptr <- ptr
     },
     print = function() {
-      cat("torch_", cpp_dtype_to_string(self$ptr), sep = "")
+      cat("torch_", self$.type(), sep = "")
+    },
+    .type = function() {
+      cpp_dtype_to_string(self$ptr)
     }
   ),
   active = list(
diff --git a/R/tensor.R b/R/tensor.R
index 104719986..a623cda95 100644
--- a/R/tensor.R
+++ b/R/tensor.R
@@ -217,3 +217,15 @@ as.integer64.torch_tensor <- function(x, keep.names = FALSE, ...) {
   x <- x$to(dtype = torch_long())
   as_array_impl(x)
 }
+
+#' @export
+str.torch_tensor <- function(object, ...) {
+  dtype <- object$dtype$.type()
+  
+  dims <- dim(object)
+  dims <- paste(paste0("1:", dims), collapse = ", ")
+  
+  out <- paste0(dtype, " [", dims, "]")
+  cat(out)
+  cat("\n")
+}
diff --git a/R/utils-pipe.R b/R/utils-pipe.R
new file mode 100644
index 000000000..e79f3d808
--- /dev/null
+++ b/R/utils-pipe.R
@@ -0,0 +1,11 @@
+#' Pipe operator
+#'
+#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
+#'
+#' @name %>%
+#' @rdname pipe
+#' @keywords internal
+#' @export
+#' @importFrom magrittr %>%
+#' @usage lhs \%>\% rhs
+NULL
diff --git a/man/pipe.Rd b/man/pipe.Rd
new file mode 100644
index 000000000..0eec75261
--- /dev/null
+++ b/man/pipe.Rd
@@ -0,0 +1,12 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-pipe.R
+\name{\%>\%}
+\alias{\%>\%}
+\title{Pipe operator}
+\usage{
+lhs \%>\% rhs
+}
+\description{
+See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
+}
+\keyword{internal}
-- 
GitLab


From 1e9525240358ccfe45195212d78bc40faf42a854 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 1 Sep 2020 17:34:41 -0300
Subject: [PATCH 094/157] remove new_ methods reference as they are not
 implemented

---
 vignettes/getting-started/what-is-torch.Rmd | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/vignettes/getting-started/what-is-torch.Rmd b/vignettes/getting-started/what-is-torch.Rmd
index 6ea41e0d5..cbb88f438 100644
--- a/vignettes/getting-started/what-is-torch.Rmd
+++ b/vignettes/getting-started/what-is-torch.Rmd
@@ -60,9 +60,6 @@ x
 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
 
 ```{r}
-x <- x$new_ones(5, 3, dtype = torch_double())   # new_* methods take in sizes
-x
-
 x <- torch_randn_like(x, dtype = torch_float()) # override dtype!
 x                                               # result has the same size
 ```
-- 
GitLab


From d269efae0f72b2da1905b59eb9976395719d2fd2 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 1 Sep 2020 17:48:57 -0300
Subject: [PATCH 095/157] more fixes to the vignette

---
 vignettes/getting-started/what-is-torch.Rmd | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/vignettes/getting-started/what-is-torch.Rmd b/vignettes/getting-started/what-is-torch.Rmd
index cbb88f438..5a8d5f4e7 100644
--- a/vignettes/getting-started/what-is-torch.Rmd
+++ b/vignettes/getting-started/what-is-torch.Rmd
@@ -67,7 +67,7 @@ x                                               # result has the same size
 Get its size:
 
 ```{r}
-x$size
+x$size()
 ```
 
 ### Operations
@@ -77,6 +77,7 @@ There are multiple syntaxes for operations. In the following example, we will ta
 Addition: syntax 1
 
 ```{r}
+x <- torch_rand(5, 3)
 y <- torch_rand(5, 3)
 x + y
 ```
-- 
GitLab


From 71f02e15c787c01ac727c299ebc725a53b90e6d6 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 1 Sep 2020 17:53:16 -0300
Subject: [PATCH 096/157] fix loading-data vignette

---
 vignettes/loading-data.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/loading-data.Rmd b/vignettes/loading-data.Rmd
index 65f1d724f..7a2aa90aa 100644
--- a/vignettes/loading-data.Rmd
+++ b/vignettes/loading-data.Rmd
@@ -154,7 +154,7 @@ one-hot-encode or embed it.)
 net <- nn_module(
   "PenguinNet",
   initialize = function() {
-    self$fc1 <- nn_linear(6, 32)
+    self$fc1 <- nn_linear(7, 32)
     self$fc2 <- nn_linear(32, 3)
   },
   forward = function(x) {
-- 
GitLab


From 4a83dd3b3e5283a38687478ff73447e833d710dd Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 1 Sep 2020 19:18:38 -0300
Subject: [PATCH 097/157] suggest palmerpenguins for the vignette

---
 DESCRIPTION | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 505b789e2..bbbb00511 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -38,7 +38,8 @@ Suggests:
     covr,
     knitr,
     rmarkdown,
-    glue
+    glue,
+    palmerpenguins
 VignetteBuilder: knitr
 Collate: 
     'R7.R'
-- 
GitLab


From 8287f8ea1898af2a2b9e7b78461fd5b30c9388da Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 2 Sep 2020 22:02:48 -0300
Subject: [PATCH 098/157] add the rdname to make sure there'sthe usage section.

---
 tools/torchgen/R/r.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tools/torchgen/R/r.R b/tools/torchgen/R/r.R
index 32e569302..4e9de0fa8 100644
--- a/tools/torchgen/R/r.R
+++ b/tools/torchgen/R/r.R
@@ -81,6 +81,7 @@ r_namespace <- function(decls) {
 
   glue::glue("
 
+#' @rdname {r_namespace_name(decls)}
 {r_namespace_name(decls)} <- function({r_namespace_signature(decls)}) {{
   {r_namespace_body(decls)}
 }}
-- 
GitLab


From ea1d62bacf4b99d39e9400063797f7f3211187e7 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 2 Sep 2020 22:03:12 -0300
Subject: [PATCH 099/157] regenerate adding the rdname tag

---
 R/creation-ops.R  |  21 +
 R/gen-namespace.R | 968 ++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 989 insertions(+)

diff --git a/R/creation-ops.R b/R/creation-ops.R
index 5c1c78594..39faeceb4 100644
--- a/R/creation-ops.R
+++ b/R/creation-ops.R
@@ -18,6 +18,7 @@ resolve_size <- function(...) {
   size
 }
 
+#' @rdname torch_ones
 torch_ones <- function(..., names = NULL, dtype = NULL, layout = torch_strided(), 
   device=NULL, requires_grad = FALSE) {
   args <- list(
@@ -33,6 +34,7 @@ torch_ones <- function(..., names = NULL, dtype = NULL, layout = torch_strided()
   do.call(.torch_ones, args)
 }
 
+#' @rdname torch_ones_like
 torch_ones_like <- function(input, dtype = NULL, layout = torch_strided(), 
                             device=NULL, requires_grad = FALSE, 
                             memory_format = torch_preserve_format()) {
@@ -49,6 +51,7 @@ torch_ones_like <- function(input, dtype = NULL, layout = torch_strided(),
   do.call(.torch_ones_like, args)
 }
 
+#' @rdname torch_rand
 torch_rand <- function(..., names = NULL, dtype = NULL, layout = torch_strided(), 
                        device=NULL, requires_grad = FALSE) {
   args <- list(
@@ -64,6 +67,7 @@ torch_rand <- function(..., names = NULL, dtype = NULL, layout = torch_strided()
   do.call(.torch_rand, args)
 }
 
+#' @rdname torch_rand_like
 torch_rand_like <- function(input, dtype = NULL, layout = torch_strided(), 
                             device=NULL, requires_grad = FALSE, 
                             memory_format = torch_preserve_format()) {
@@ -80,6 +84,7 @@ torch_rand_like <- function(input, dtype = NULL, layout = torch_strided(),
   do.call(.torch_rand_like, args)
 }
 
+#' @rdname torch_randint
 torch_randint <- function(low, high, size, generator = NULL, dtype = NULL, layout = torch_strided(), 
                           device=NULL, requires_grad = FALSE, 
                           memory_format = torch_preserve_format()) {
@@ -100,6 +105,7 @@ torch_randint <- function(low, high, size, generator = NULL, dtype = NULL, layou
   do.call(.torch_randint, args)
 }
 
+#' @rdname torch_randint_like
 torch_randint_like <- function(input, low, high, dtype = NULL, 
                                layout = torch_strided(), 
                                device=NULL, requires_grad = FALSE) {
@@ -118,6 +124,7 @@ torch_randint_like <- function(input, low, high, dtype = NULL,
   do.call(.torch_randint_like, args)
 }
 
+#' @rdname torch_randn
 torch_randn <- function(..., names = NULL, dtype = NULL, layout = torch_strided(), 
                        device=NULL, requires_grad = FALSE) {
   args <- list(
@@ -133,6 +140,7 @@ torch_randn <- function(..., names = NULL, dtype = NULL, layout = torch_strided(
   do.call(.torch_randn, args)
 }
 
+#' @rdname torch_rand_like
 torch_randn_like <- function(input, dtype = NULL, layout = torch_strided(), 
                             device=NULL, requires_grad = FALSE, 
                             memory_format = torch_preserve_format()) {
@@ -149,6 +157,7 @@ torch_randn_like <- function(input, dtype = NULL, layout = torch_strided(),
   do.call(.torch_randn_like, args)
 }
 
+#' @rdname torch_randperm
 torch_randperm <- function(n, dtype = torch_int64(), layout = torch_strided(), 
                            device=NULL, requires_grad = FALSE) {
   args <- list(
@@ -163,6 +172,7 @@ torch_randperm <- function(n, dtype = torch_int64(), layout = torch_strided(),
   do.call(.torch_randperm, args)
 }
 
+#' @rdname torch_zeros
 torch_zeros <- function(..., names = NULL, dtype = NULL, layout = torch_strided(), 
                        device=NULL, requires_grad = FALSE) {
   args <- list(
@@ -178,6 +188,7 @@ torch_zeros <- function(..., names = NULL, dtype = NULL, layout = torch_strided(
   do.call(.torch_zeros, args)
 }
 
+#' @rdname torch_zeros_like
 torch_zeros_like <- function(input, dtype = NULL, layout = torch_strided(), 
                             device=NULL, requires_grad = FALSE, 
                             memory_format = torch_preserve_format()) {
@@ -194,6 +205,7 @@ torch_zeros_like <- function(input, dtype = NULL, layout = torch_strided(),
   do.call(.torch_zeros_like, args)
 }
 
+#' @rdname torch_empty
 torch_empty <- function(..., names = NULL, dtype = NULL, layout = torch_strided(), 
                         device=NULL, requires_grad = FALSE) {
   args <- list(
@@ -209,6 +221,7 @@ torch_empty <- function(..., names = NULL, dtype = NULL, layout = torch_strided(
   do.call(.torch_empty, args)
 }
 
+#' @rdname torch_empty_like
 torch_empty_like <- function(input, dtype = NULL, layout = torch_strided(), 
                              device=NULL, requires_grad = FALSE, 
                              memory_format = torch_preserve_format()) {
@@ -225,6 +238,7 @@ torch_empty_like <- function(input, dtype = NULL, layout = torch_strided(),
   do.call(.torch_empty_like, args)
 }
 
+#' @rdname torch_arange
 torch_arange <- function(start, end, step = 1, dtype = NULL, layout = torch_strided(), 
                          device=NULL, requires_grad = FALSE) {
   args <- list(
@@ -241,6 +255,7 @@ torch_arange <- function(start, end, step = 1, dtype = NULL, layout = torch_stri
   do.call(.torch_arange, args)
 }
 
+#' @rdname torch_range
 torch_range <- function(start, end, step = 1, dtype = NULL, layout = torch_strided(), 
                         device=NULL, requires_grad = FALSE) {
   warning("This function is deprecated in favor of torch_arange.")
@@ -248,6 +263,7 @@ torch_range <- function(start, end, step = 1, dtype = NULL, layout = torch_strid
                device=device, requires_grad = requires_grad)
 }
 
+#' @rdname torch_linspace
 torch_linspace <- function(start, end, steps=100, dtype = NULL, layout = torch_strided(), 
                            device=NULL, requires_grad = FALSE) {
   args <- list(
@@ -264,6 +280,7 @@ torch_linspace <- function(start, end, steps=100, dtype = NULL, layout = torch_s
   do.call(.torch_linspace, args)
 }
 
+#' @rdname torch_linspace
 torch_logspace <- function(start, end, steps=100, base=10, dtype = NULL, layout = torch_strided(), 
                            device=NULL, requires_grad = FALSE) {
   args <- list(
@@ -281,6 +298,7 @@ torch_logspace <- function(start, end, steps=100, base=10, dtype = NULL, layout
   do.call(.torch_logspace, args)
 }
 
+#' @rdname torch_eye
 torch_eye <- function(n, m=n, dtype = NULL, layout = torch_strided(), 
                       device=NULL, requires_grad = FALSE) {
   args <- list(
@@ -296,6 +314,7 @@ torch_eye <- function(n, m=n, dtype = NULL, layout = torch_strided(),
   do.call(.torch_eye, args)
 }
 
+#' @rdname torch_empty_strided
 torch_empty_strided <- function(size, stride, dtype = NULL, layout = torch_strided(), 
                                 device=NULL, requires_grad = FALSE, pin_memory = FALSE) {
   args <- list(
@@ -312,6 +331,7 @@ torch_empty_strided <- function(size, stride, dtype = NULL, layout = torch_strid
   do.call(.torch_empty_strided, args)
 }
 
+#' @rdname torch_full
 torch_full <- function(size, fill_value, names = NULL, dtype = NULL, layout = torch_strided(), 
                        device=NULL, requires_grad = FALSE) {
   args <- list(
@@ -328,6 +348,7 @@ torch_full <- function(size, fill_value, names = NULL, dtype = NULL, layout = to
   do.call(.torch_full, args)
 }
 
+#' @rdname torch_full_like
 torch_full_like <- function(input, fill_value, dtype = NULL, layout = torch_strided(), 
                              device=NULL, requires_grad = FALSE, 
                              memory_format = torch_preserve_format()) {
diff --git a/R/gen-namespace.R b/R/gen-namespace.R
index e8016f107..f9224a52b 100644
--- a/R/gen-namespace.R
+++ b/R/gen-namespace.R
@@ -1,5 +1,6 @@
 # This file is autogenerated. Do not modify it by hand.
 
+#' @rdname torch___and__
 torch___and__ <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -16,6 +17,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch___lshift__
 torch___lshift__ <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -32,6 +34,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch___or__
 torch___or__ <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -48,6 +51,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch___rshift__
 torch___rshift__ <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -64,6 +68,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch___xor__
 torch___xor__ <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -80,6 +85,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__adaptive_avg_pool2d
 torch__adaptive_avg_pool2d <- function(self, output_size) {
   args <- mget(x = c("self", "output_size"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef")
@@ -96,6 +102,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__adaptive_avg_pool2d_backward
 torch__adaptive_avg_pool2d_backward <- function(grad_output, self) {
   args <- mget(x = c("grad_output", "self"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor")
@@ -112,6 +119,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__addr
 torch__addr <- function(self, vec1, vec2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("self", "vec1", "vec2", "beta", "alpha"))
 expected_types <- list(self = "Tensor", vec1 = "Tensor", vec2 = "Tensor", beta = "Scalar", 
@@ -129,6 +137,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__addr_
 torch__addr_ <- function(self, vec1, vec2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("self", "vec1", "vec2", "beta", "alpha"))
 expected_types <- list(self = "Tensor", vec1 = "Tensor", vec2 = "Tensor", beta = "Scalar", 
@@ -146,6 +155,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__addr_out
 torch__addr_out <- function(out, self, vec1, vec2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("out", "self", "vec1", "vec2", "beta", "alpha"))
 expected_types <- list(out = "Tensor", self = "Tensor", vec1 = "Tensor", vec2 = "Tensor", 
@@ -163,6 +173,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__amp_non_finite_check_and_unscale_
 torch__amp_non_finite_check_and_unscale_ <- function(self, found_inf, inv_scale) {
   args <- mget(x = c("self", "found_inf", "inv_scale"))
 expected_types <- list(self = "Tensor", found_inf = "Tensor", inv_scale = "Tensor")
@@ -179,6 +190,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__amp_update_scale
 torch__amp_update_scale <- function(growth_tracker, current_scale, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval) {
   args <- mget(x = c("growth_tracker", "current_scale", "found_inf", "scale_growth_factor", "scale_backoff_factor", "growth_interval"))
 expected_types <- list(growth_tracker = "Tensor", current_scale = "Tensor", found_inf = "Tensor", 
@@ -198,6 +210,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__baddbmm_mkl_
 torch__baddbmm_mkl_ <- function(self, batch1, batch2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("self", "batch1", "batch2", "beta", "alpha"))
 expected_types <- list(self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", beta = "Scalar", 
@@ -215,6 +228,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__batch_norm_impl_index
 torch__batch_norm_impl_index <- function(input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled) {
   args <- mget(x = c("input", "weight", "bias", "running_mean", "running_var", "training", "momentum", "eps", "cudnn_enabled"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", running_mean = "Tensor", 
@@ -234,6 +248,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__batch_norm_impl_index_backward
 torch__batch_norm_impl_index_backward <- function(impl_index, input, grad_output, weight, running_mean, running_var, save_mean, save_var_transform, train, eps, output_mask, reservedSpace) {
   args <- mget(x = c("impl_index", "input", "grad_output", "weight", "running_mean", "running_var", "save_mean", "save_var_transform", "train", "eps", "output_mask", "reservedSpace"))
 expected_types <- list(impl_index = "int64_t", input = "Tensor", grad_output = "Tensor", 
@@ -255,6 +270,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cast_Byte
 torch__cast_Byte <- function(self, non_blocking = FALSE) {
   args <- mget(x = c("self", "non_blocking"))
 expected_types <- list(self = "Tensor", non_blocking = "bool")
@@ -271,6 +287,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cast_Char
 torch__cast_Char <- function(self, non_blocking = FALSE) {
   args <- mget(x = c("self", "non_blocking"))
 expected_types <- list(self = "Tensor", non_blocking = "bool")
@@ -287,6 +304,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cast_Double
 torch__cast_Double <- function(self, non_blocking = FALSE) {
   args <- mget(x = c("self", "non_blocking"))
 expected_types <- list(self = "Tensor", non_blocking = "bool")
@@ -303,6 +321,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cast_Float
 torch__cast_Float <- function(self, non_blocking = FALSE) {
   args <- mget(x = c("self", "non_blocking"))
 expected_types <- list(self = "Tensor", non_blocking = "bool")
@@ -319,6 +338,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cast_Half
 torch__cast_Half <- function(self, non_blocking = FALSE) {
   args <- mget(x = c("self", "non_blocking"))
 expected_types <- list(self = "Tensor", non_blocking = "bool")
@@ -335,6 +355,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cast_Int
 torch__cast_Int <- function(self, non_blocking = FALSE) {
   args <- mget(x = c("self", "non_blocking"))
 expected_types <- list(self = "Tensor", non_blocking = "bool")
@@ -351,6 +372,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cast_Long
 torch__cast_Long <- function(self, non_blocking = FALSE) {
   args <- mget(x = c("self", "non_blocking"))
 expected_types <- list(self = "Tensor", non_blocking = "bool")
@@ -367,6 +389,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cast_Short
 torch__cast_Short <- function(self, non_blocking = FALSE) {
   args <- mget(x = c("self", "non_blocking"))
 expected_types <- list(self = "Tensor", non_blocking = "bool")
@@ -383,6 +406,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cat
 torch__cat <- function(tensors, dim = 1L) {
   args <- mget(x = c("tensors", "dim"))
 expected_types <- list(tensors = "TensorList", dim = "int64_t")
@@ -399,6 +423,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cat_out
 torch__cat_out <- function(out, tensors, dim = 1L) {
   args <- mget(x = c("out", "tensors", "dim"))
 expected_types <- list(out = "Tensor", tensors = "TensorList", dim = "int64_t")
@@ -415,6 +440,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cdist_backward
 torch__cdist_backward <- function(grad, x1, x2, p, cdist) {
   args <- mget(x = c("grad", "x1", "x2", "p", "cdist"))
 expected_types <- list(grad = "Tensor", x1 = "Tensor", x2 = "Tensor", p = "double", 
@@ -432,6 +458,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cdist_forward
 torch__cdist_forward <- function(x1, x2, p, compute_mode) {
   args <- mget(x = c("x1", "x2", "p", "compute_mode"))
 expected_types <- list(x1 = "Tensor", x2 = "Tensor", p = "double", compute_mode = "int64_t")
@@ -448,6 +475,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cholesky_helper
 torch__cholesky_helper <- function(self, upper) {
   args <- mget(x = c("self", "upper"))
 expected_types <- list(self = "Tensor", upper = "bool")
@@ -464,6 +492,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cholesky_solve_helper
 torch__cholesky_solve_helper <- function(self, A, upper) {
   args <- mget(x = c("self", "A", "upper"))
 expected_types <- list(self = "Tensor", A = "Tensor", upper = "bool")
@@ -480,6 +509,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__convolution
 torch__convolution <- function(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled) {
   args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "transposed", "output_padding", "groups", "benchmark", "deterministic", "cudnn_enabled"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", 
@@ -501,6 +531,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__convolution_double_backward
 torch__convolution_double_backward <- function(ggI, ggW, ggb, gO, weight, self, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, output_mask) {
   args <- mget(x = c("ggI", "ggW", "ggb", "gO", "weight", "self", "stride", "padding", "dilation", "transposed", "output_padding", "groups", "benchmark", "deterministic", "cudnn_enabled", "output_mask"))
 expected_types <- list(ggI = "Tensor", ggW = "Tensor", ggb = "Tensor", gO = "Tensor", 
@@ -523,6 +554,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__convolution_nogroup
 torch__convolution_nogroup <- function(input, weight, bias, stride, padding, dilation, transposed, output_padding) {
   args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "transposed", "output_padding"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", 
@@ -542,6 +574,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__copy_from
 torch__copy_from <- function(self, dst, non_blocking = FALSE) {
   args <- mget(x = c("self", "dst", "non_blocking"))
 expected_types <- list(self = "Tensor", dst = "Tensor", non_blocking = "bool")
@@ -558,6 +591,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__ctc_loss
 torch__ctc_loss <- function(log_probs, targets, input_lengths, target_lengths, blank = 0L, zero_infinity = FALSE) {
   args <- mget(x = c("log_probs", "targets", "input_lengths", "target_lengths", "blank", "zero_infinity"))
 expected_types <- list(log_probs = "Tensor", targets = "Tensor", input_lengths = "IntArrayRef", 
@@ -575,6 +609,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__ctc_loss_backward
 torch__ctc_loss_backward <- function(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity = FALSE) {
   args <- mget(x = c("grad", "log_probs", "targets", "input_lengths", "target_lengths", "neg_log_likelihood", "log_alpha", "blank", "zero_infinity"))
 expected_types <- list(grad = "Tensor", log_probs = "Tensor", targets = "Tensor", 
@@ -595,6 +630,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cudnn_ctc_loss
 torch__cudnn_ctc_loss <- function(log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity) {
   args <- mget(x = c("log_probs", "targets", "input_lengths", "target_lengths", "blank", "deterministic", "zero_infinity"))
 expected_types <- list(log_probs = "Tensor", targets = "Tensor", input_lengths = "IntArrayRef", 
@@ -614,6 +650,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cudnn_init_dropout_state
 torch__cudnn_init_dropout_state <- function(dropout, train, dropout_seed, options) {
   args <- mget(x = c("dropout", "train", "dropout_seed", "options"))
 expected_types <- list(dropout = "double", train = "bool", dropout_seed = "int64_t", 
@@ -631,6 +668,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cudnn_rnn
 torch__cudnn_rnn <- function(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state) {
   args <- mget(x = c("input", "weight", "weight_stride0", "weight_buf", "hx", "cx", "mode", "hidden_size", "num_layers", "batch_first", "dropout", "train", "bidirectional", "batch_sizes", "dropout_state"))
 expected_types <- list(input = "Tensor", weight = "TensorList", weight_stride0 = "int64_t", 
@@ -653,6 +691,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cudnn_rnn_backward
 torch__cudnn_rnn_backward <- function(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask) {
   args <- mget(x = c("input", "weight", "weight_stride0", "weight_buf", "hx", "cx", "output", "grad_output", "grad_hy", "grad_cy", "mode", "hidden_size", "num_layers", "batch_first", "dropout", "train", "bidirectional", "batch_sizes", "dropout_state", "reserve", "output_mask"))
 expected_types <- list(input = "Tensor", weight = "TensorList", weight_stride0 = "int64_t", 
@@ -678,6 +717,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cudnn_rnn_flatten_weight
 torch__cudnn_rnn_flatten_weight <- function(weight_arr, weight_stride0, input_size, mode, hidden_size, num_layers, batch_first, bidirectional) {
   args <- mget(x = c("weight_arr", "weight_stride0", "input_size", "mode", "hidden_size", "num_layers", "batch_first", "bidirectional"))
 expected_types <- list(weight_arr = "TensorList", weight_stride0 = "int64_t", input_size = "int64_t", 
@@ -697,6 +737,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cufft_clear_plan_cache
 torch__cufft_clear_plan_cache <- function(device_index) {
   args <- mget(x = c("device_index"))
 expected_types <- list(device_index = "int64_t")
@@ -713,6 +754,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cufft_get_plan_cache_max_size
 torch__cufft_get_plan_cache_max_size <- function(device_index) {
   args <- mget(x = c("device_index"))
 expected_types <- list(device_index = "int64_t")
@@ -729,6 +771,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cufft_get_plan_cache_size
 torch__cufft_get_plan_cache_size <- function(device_index) {
   args <- mget(x = c("device_index"))
 expected_types <- list(device_index = "int64_t")
@@ -745,6 +788,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cufft_set_plan_cache_max_size
 torch__cufft_set_plan_cache_max_size <- function(device_index, max_size) {
   args <- mget(x = c("device_index", "max_size"))
 expected_types <- list(device_index = "int64_t", max_size = "int64_t")
@@ -761,6 +805,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cummax_helper
 torch__cummax_helper <- function(self, values, indices, dim) {
   args <- mget(x = c("self", "values", "indices", "dim"))
 expected_types <- list(self = "Tensor", values = "Tensor", indices = "Tensor", 
@@ -778,6 +823,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cummin_helper
 torch__cummin_helper <- function(self, values, indices, dim) {
   args <- mget(x = c("self", "values", "indices", "dim"))
 expected_types <- list(self = "Tensor", values = "Tensor", indices = "Tensor", 
@@ -795,6 +841,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cumprod
 torch__cumprod <- function(self, dim) {
   args <- mget(x = c("self", "dim"))
 expected_types <- list(self = "Tensor", dim = "int64_t")
@@ -811,6 +858,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cumprod_out
 torch__cumprod_out <- function(out, self, dim) {
   args <- mget(x = c("out", "self", "dim"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = "int64_t")
@@ -827,6 +875,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cumsum
 torch__cumsum <- function(self, dim) {
   args <- mget(x = c("self", "dim"))
 expected_types <- list(self = "Tensor", dim = "int64_t")
@@ -843,6 +892,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__cumsum_out
 torch__cumsum_out <- function(out, self, dim) {
   args <- mget(x = c("out", "self", "dim"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = "int64_t")
@@ -859,6 +909,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__debug_has_internal_overlap
 torch__debug_has_internal_overlap <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -875,6 +926,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__dim_arange
 torch__dim_arange <- function(like, dim) {
   args <- mget(x = c("like", "dim"))
 expected_types <- list(like = "Tensor", dim = "int64_t")
@@ -891,6 +943,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__dirichlet_grad
 torch__dirichlet_grad <- function(x, alpha, total) {
   args <- mget(x = c("x", "alpha", "total"))
 expected_types <- list(x = "Tensor", alpha = "Tensor", total = "Tensor")
@@ -907,6 +960,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__embedding_bag
 torch__embedding_bag <- function(weight, indices, offsets, scale_grad_by_freq = FALSE, mode = 0L, sparse = FALSE, per_sample_weights = list(), include_last_offset = FALSE) {
   args <- mget(x = c("weight", "indices", "offsets", "scale_grad_by_freq", "mode", "sparse", "per_sample_weights", "include_last_offset"))
 expected_types <- list(weight = "Tensor", indices = "Tensor", offsets = "Tensor", 
@@ -925,6 +979,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__embedding_bag_backward
 torch__embedding_bag_backward <- function(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights) {
   args <- mget(x = c("grad", "indices", "offsets", "offset2bag", "bag_size", "maximum_indices", "num_weights", "scale_grad_by_freq", "mode", "sparse", "per_sample_weights"))
 expected_types <- list(grad = "Tensor", indices = "Tensor", offsets = "Tensor", 
@@ -946,6 +1001,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__embedding_bag_dense_backward
 torch__embedding_bag_dense_backward <- function(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights) {
   args <- mget(x = c("grad", "indices", "offsets", "offset2bag", "bag_size", "maximum_indices", "num_weights", "scale_grad_by_freq", "mode", "per_sample_weights"))
 expected_types <- list(grad = "Tensor", indices = "Tensor", offsets = "Tensor", 
@@ -967,6 +1023,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__embedding_bag_per_sample_weights_backward
 torch__embedding_bag_per_sample_weights_backward <- function(grad, weight, indices, offsets, offset2bag, mode) {
   args <- mget(x = c("grad", "weight", "indices", "offsets", "offset2bag", "mode"))
 expected_types <- list(grad = "Tensor", weight = "Tensor", indices = "Tensor", 
@@ -985,6 +1042,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__embedding_bag_sparse_backward
 torch__embedding_bag_sparse_backward <- function(grad, indices, offsets, offset2bag, bag_size, num_weights, scale_grad_by_freq, mode, per_sample_weights) {
   args <- mget(x = c("grad", "indices", "offsets", "offset2bag", "bag_size", "num_weights", "scale_grad_by_freq", "mode", "per_sample_weights"))
 expected_types <- list(grad = "Tensor", indices = "Tensor", offsets = "Tensor", 
@@ -1004,6 +1062,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__empty_affine_quantized
 torch__empty_affine_quantized <- function(size, options = list(), scale = 1L, zero_point = 0L, memory_format = torch_contiguous_format()) {
   args <- mget(x = c("size", "options", "scale", "zero_point", "memory_format"))
 expected_types <- list(size = "IntArrayRef", options = "TensorOptions", scale = "double", 
@@ -1021,6 +1080,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__empty_per_channel_affine_quantized
 torch__empty_per_channel_affine_quantized <- function(size, scales, zero_points, axis, options = list(), memory_format = torch_contiguous_format()) {
   args <- mget(x = c("size", "scales", "zero_points", "axis", "options", "memory_format"))
 expected_types <- list(size = "IntArrayRef", scales = "Tensor", zero_points = "Tensor", 
@@ -1038,6 +1098,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__fft_with_size
 torch__fft_with_size <- function(self, signal_ndim, complex_input, complex_output, inverse, checked_signal_sizes, normalized, onesided, output_sizes) {
   args <- mget(x = c("self", "signal_ndim", "complex_input", "complex_output", "inverse", "checked_signal_sizes", "normalized", "onesided", "output_sizes"))
 expected_types <- list(self = "Tensor", signal_ndim = "int64_t", complex_input = "bool", 
@@ -1058,6 +1119,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__fused_dropout
 torch__fused_dropout <- function(self, p, generator = NULL) {
   args <- mget(x = c("self", "p", "generator"))
 expected_types <- list(self = "Tensor", p = "double", generator = "Generator *")
@@ -1074,6 +1136,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__gather_sparse_backward
 torch__gather_sparse_backward <- function(self, dim, index, grad) {
   args <- mget(x = c("self", "dim", "index", "grad"))
 expected_types <- list(self = "Tensor", dim = "int64_t", index = "Tensor", grad = "Tensor")
@@ -1090,6 +1153,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__has_compatible_shallow_copy_type
 torch__has_compatible_shallow_copy_type <- function(self, from) {
   args <- mget(x = c("self", "from"))
 expected_types <- list(self = "Tensor", from = "Tensor")
@@ -1106,6 +1170,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__index_copy_
 torch__index_copy_ <- function(self, dim, index, source) {
   args <- mget(x = c("self", "dim", "index", "source"))
 expected_types <- list(self = "Tensor", dim = "int64_t", index = "Tensor", source = "Tensor")
@@ -1122,6 +1187,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__index_put_impl_
 torch__index_put_impl_ <- function(self, indices, values, accumulate = FALSE, unsafe = FALSE) {
   args <- mget(x = c("self", "indices", "values", "accumulate", "unsafe"))
 expected_types <- list(self = "Tensor", indices = "TensorList", values = "Tensor", 
@@ -1139,6 +1205,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__inverse_helper
 torch__inverse_helper <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -1155,6 +1222,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__local_scalar_dense
 torch__local_scalar_dense <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -1171,6 +1239,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__log_softmax
 torch__log_softmax <- function(self, dim, half_to_float) {
   args <- mget(x = c("self", "dim", "half_to_float"))
 expected_types <- list(self = "Tensor", dim = "int64_t", half_to_float = "bool")
@@ -1187,6 +1256,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__log_softmax_backward_data
 torch__log_softmax_backward_data <- function(grad_output, output, dim, self) {
   args <- mget(x = c("grad_output", "output", "dim", "self"))
 expected_types <- list(grad_output = "Tensor", output = "Tensor", dim = "int64_t", 
@@ -1204,6 +1274,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__lu_solve_helper
 torch__lu_solve_helper <- function(self, LU_data, LU_pivots) {
   args <- mget(x = c("self", "LU_data", "LU_pivots"))
 expected_types <- list(self = "Tensor", LU_data = "Tensor", LU_pivots = "Tensor")
@@ -1220,6 +1291,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__lu_with_info
 torch__lu_with_info <- function(self, pivot = TRUE, check_errors = TRUE) {
   args <- mget(x = c("self", "pivot", "check_errors"))
 expected_types <- list(self = "Tensor", pivot = "bool", check_errors = "bool")
@@ -1236,6 +1308,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__make_per_channel_quantized_tensor
 torch__make_per_channel_quantized_tensor <- function(self, scale, zero_point, axis) {
   args <- mget(x = c("self", "scale", "zero_point", "axis"))
 expected_types <- list(self = "Tensor", scale = "Tensor", zero_point = "Tensor", 
@@ -1253,6 +1326,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__make_per_tensor_quantized_tensor
 torch__make_per_tensor_quantized_tensor <- function(self, scale, zero_point) {
   args <- mget(x = c("self", "scale", "zero_point"))
 expected_types <- list(self = "Tensor", scale = "double", zero_point = "int64_t")
@@ -1269,6 +1343,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__masked_scale
 torch__masked_scale <- function(self, mask, scale) {
   args <- mget(x = c("self", "mask", "scale"))
 expected_types <- list(self = "Tensor", mask = "Tensor", scale = "double")
@@ -1285,6 +1360,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__max
 torch__max <- function(self, dim, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = "int64_t", keepdim = "bool")
@@ -1301,6 +1377,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__max_out
 torch__max_out <- function(max, max_indices, self, dim, keepdim = FALSE) {
   args <- mget(x = c("max", "max_indices", "self", "dim", "keepdim"))
 expected_types <- list(max = "Tensor", max_indices = "Tensor", self = "Tensor", 
@@ -1318,6 +1395,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__min
 torch__min <- function(self, dim, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = "int64_t", keepdim = "bool")
@@ -1334,6 +1412,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__min_out
 torch__min_out <- function(min, min_indices, self, dim, keepdim = FALSE) {
   args <- mget(x = c("min", "min_indices", "self", "dim", "keepdim"))
 expected_types <- list(min = "Tensor", min_indices = "Tensor", self = "Tensor", 
@@ -1351,6 +1430,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__mkldnn_reshape
 torch__mkldnn_reshape <- function(self, shape) {
   args <- mget(x = c("self", "shape"))
 expected_types <- list(self = "Tensor", shape = "IntArrayRef")
@@ -1367,6 +1447,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__mkldnn_transpose
 torch__mkldnn_transpose <- function(self, dim0, dim1) {
   args <- mget(x = c("self", "dim0", "dim1"))
 expected_types <- list(self = "Tensor", dim0 = "int64_t", dim1 = "int64_t")
@@ -1383,6 +1464,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__mkldnn_transpose_
 torch__mkldnn_transpose_ <- function(self, dim0, dim1) {
   args <- mget(x = c("self", "dim0", "dim1"))
 expected_types <- list(self = "Tensor", dim0 = "int64_t", dim1 = "int64_t")
@@ -1399,6 +1481,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__mode
 torch__mode <- function(self, dim = -1L, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = "int64_t", keepdim = "bool")
@@ -1415,6 +1498,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__mode_out
 torch__mode_out <- function(values, indices, self, dim = -1L, keepdim = FALSE) {
   args <- mget(x = c("values", "indices", "self", "dim", "keepdim"))
 expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", 
@@ -1432,6 +1516,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__multinomial_alias_draw
 torch__multinomial_alias_draw <- function(J, q, num_samples, generator = NULL) {
   args <- mget(x = c("J", "q", "num_samples", "generator"))
 expected_types <- list(J = "Tensor", q = "Tensor", num_samples = "int64_t", generator = "Generator *")
@@ -1448,6 +1533,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__multinomial_alias_setup
 torch__multinomial_alias_setup <- function(probs) {
   args <- mget(x = c("probs"))
 expected_types <- list(probs = "Tensor")
@@ -1464,6 +1550,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__nnpack_available
 torch__nnpack_available <- function() {
   args <- list()
 expected_types <- list()
@@ -1480,6 +1567,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__nnpack_spatial_convolution
 torch__nnpack_spatial_convolution <- function(input, weight, bias, padding, stride = 1L) {
   args <- mget(x = c("input", "weight", "bias", "padding", "stride"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", 
@@ -1497,6 +1585,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__nnpack_spatial_convolution_backward
 torch__nnpack_spatial_convolution_backward <- function(input, grad_output, weight, padding, output_mask) {
   args <- mget(x = c("input", "grad_output", "weight", "padding", "output_mask"))
 expected_types <- list(input = "Tensor", grad_output = "Tensor", weight = "Tensor", 
@@ -1514,6 +1603,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__nnpack_spatial_convolution_backward_input
 torch__nnpack_spatial_convolution_backward_input <- function(input, grad_output, weight, padding) {
   args <- mget(x = c("input", "grad_output", "weight", "padding"))
 expected_types <- list(input = "Tensor", grad_output = "Tensor", weight = "Tensor", 
@@ -1531,6 +1621,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__nnpack_spatial_convolution_backward_weight
 torch__nnpack_spatial_convolution_backward_weight <- function(input, weightsize, grad_output, padding) {
   args <- mget(x = c("input", "weightsize", "grad_output", "padding"))
 expected_types <- list(input = "Tensor", weightsize = "IntArrayRef", grad_output = "Tensor", 
@@ -1548,6 +1639,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__pack_padded_sequence
 torch__pack_padded_sequence <- function(input, lengths, batch_first) {
   args <- mget(x = c("input", "lengths", "batch_first"))
 expected_types <- list(input = "Tensor", lengths = "Tensor", batch_first = "bool")
@@ -1564,6 +1656,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__pack_padded_sequence_backward
 torch__pack_padded_sequence_backward <- function(grad, input_size, batch_sizes, batch_first) {
   args <- mget(x = c("grad", "input_size", "batch_sizes", "batch_first"))
 expected_types <- list(grad = "Tensor", input_size = "IntArrayRef", batch_sizes = "Tensor", 
@@ -1581,6 +1674,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__pad_packed_sequence
 torch__pad_packed_sequence <- function(data, batch_sizes, batch_first, padding_value, total_length) {
   args <- mget(x = c("data", "batch_sizes", "batch_first", "padding_value", "total_length"))
 expected_types <- list(data = "Tensor", batch_sizes = "Tensor", batch_first = "bool", 
@@ -1599,6 +1693,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__pdist_backward
 torch__pdist_backward <- function(grad, self, p, pdist) {
   args <- mget(x = c("grad", "self", "p", "pdist"))
 expected_types <- list(grad = "Tensor", self = "Tensor", p = "double", pdist = "Tensor")
@@ -1615,6 +1710,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__pdist_forward
 torch__pdist_forward <- function(self, p = 2L) {
   args <- mget(x = c("self", "p"))
 expected_types <- list(self = "Tensor", p = "double")
@@ -1631,6 +1727,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__qr_helper
 torch__qr_helper <- function(self, some) {
   args <- mget(x = c("self", "some"))
 expected_types <- list(self = "Tensor", some = "bool")
@@ -1647,6 +1744,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__reshape_from_tensor
 torch__reshape_from_tensor <- function(self, shape) {
   args <- mget(x = c("self", "shape"))
 expected_types <- list(self = "Tensor", shape = "Tensor")
@@ -1663,6 +1761,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__s_where
 torch__s_where <- function(condition, self, other) {
   args <- mget(x = c("condition", "self", "other"))
 expected_types <- list(condition = "Tensor", self = "Tensor", other = "Tensor")
@@ -1679,6 +1778,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__sample_dirichlet
 torch__sample_dirichlet <- function(self, generator = NULL) {
   args <- mget(x = c("self", "generator"))
 expected_types <- list(self = "Tensor", generator = "Generator *")
@@ -1695,6 +1795,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__shape_as_tensor
 torch__shape_as_tensor <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -1711,6 +1812,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__sobol_engine_draw
 torch__sobol_engine_draw <- function(quasi, n, sobolstate, dimension, num_generated, dtype) {
   args <- mget(x = c("quasi", "n", "sobolstate", "dimension", "num_generated", "dtype"))
 expected_types <- list(quasi = "Tensor", n = "int64_t", sobolstate = "Tensor", 
@@ -1729,6 +1831,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__sobol_engine_ff_
 torch__sobol_engine_ff_ <- function(self, n, sobolstate, dimension, num_generated) {
   args <- mget(x = c("self", "n", "sobolstate", "dimension", "num_generated"))
 expected_types <- list(self = "Tensor", n = "int64_t", sobolstate = "Tensor", dimension = "int64_t", 
@@ -1746,6 +1849,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__sobol_engine_initialize_state_
 torch__sobol_engine_initialize_state_ <- function(self, dimension) {
   args <- mget(x = c("self", "dimension"))
 expected_types <- list(self = "Tensor", dimension = "int64_t")
@@ -1762,6 +1866,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__sobol_engine_scramble_
 torch__sobol_engine_scramble_ <- function(self, ltm, dimension) {
   args <- mget(x = c("self", "ltm", "dimension"))
 expected_types <- list(self = "Tensor", ltm = "Tensor", dimension = "int64_t")
@@ -1778,6 +1883,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__softmax
 torch__softmax <- function(self, dim, half_to_float) {
   args <- mget(x = c("self", "dim", "half_to_float"))
 expected_types <- list(self = "Tensor", dim = "int64_t", half_to_float = "bool")
@@ -1794,6 +1900,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__softmax_backward_data
 torch__softmax_backward_data <- function(grad_output, output, dim, self) {
   args <- mget(x = c("grad_output", "output", "dim", "self"))
 expected_types <- list(grad_output = "Tensor", output = "Tensor", dim = "int64_t", 
@@ -1811,6 +1918,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__solve_helper
 torch__solve_helper <- function(self, A) {
   args <- mget(x = c("self", "A"))
 expected_types <- list(self = "Tensor", A = "Tensor")
@@ -1827,6 +1935,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__sparse_addmm
 torch__sparse_addmm <- function(self, sparse, dense, beta = 1L, alpha = 1L) {
   args <- mget(x = c("self", "sparse", "dense", "beta", "alpha"))
 expected_types <- list(self = "Tensor", sparse = "Tensor", dense = "Tensor", beta = "Scalar", 
@@ -1844,6 +1953,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__sparse_coo_tensor_unsafe
 torch__sparse_coo_tensor_unsafe <- function(indices, values, size, options = list()) {
   args <- mget(x = c("indices", "values", "size", "options"))
 expected_types <- list(indices = "Tensor", values = "Tensor", size = "IntArrayRef", 
@@ -1861,6 +1971,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__sparse_coo_tensor_with_dims
 torch__sparse_coo_tensor_with_dims <- function(sparse_dim, dense_dim, size, options) {
   args <- mget(x = c("sparse_dim", "dense_dim", "size", "options"))
 expected_types <- list(sparse_dim = "int64_t", dense_dim = "int64_t", size = "IntArrayRef", 
@@ -1878,6 +1989,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__sparse_coo_tensor_with_dims_and_tensors
 torch__sparse_coo_tensor_with_dims_and_tensors <- function(sparse_dim, dense_dim, size, indices, values, options) {
   args <- mget(x = c("sparse_dim", "dense_dim", "size", "indices", "values", "options"))
 expected_types <- list(sparse_dim = "int64_t", dense_dim = "int64_t", size = "IntArrayRef", 
@@ -1896,6 +2008,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__sparse_mm
 torch__sparse_mm <- function(sparse, dense) {
   args <- mget(x = c("sparse", "dense"))
 expected_types <- list(sparse = "Tensor", dense = "Tensor")
@@ -1912,6 +2025,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__sparse_sum
 torch__sparse_sum <- function(self, dim, dtype) {
   args <- mget(x = c("self", "dim", "dtype"))
 expected_types <- list(self = "Tensor", dim = "IntArrayRef", dtype = "ScalarType")
@@ -1928,6 +2042,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__sparse_sum_backward
 torch__sparse_sum_backward <- function(grad, self, dim) {
   args <- mget(x = c("grad", "self", "dim"))
 expected_types <- list(grad = "Tensor", self = "Tensor", dim = "IntArrayRef")
@@ -1944,6 +2059,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__standard_gamma
 torch__standard_gamma <- function(self, generator = NULL) {
   args <- mget(x = c("self", "generator"))
 expected_types <- list(self = "Tensor", generator = "Generator *")
@@ -1960,6 +2076,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__standard_gamma_grad
 torch__standard_gamma_grad <- function(self, output) {
   args <- mget(x = c("self", "output"))
 expected_types <- list(self = "Tensor", output = "Tensor")
@@ -1976,6 +2093,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__std
 torch__std <- function(self, unbiased = TRUE) {
   args <- mget(x = c("self", "unbiased"))
 expected_types <- list(self = "Tensor", unbiased = "bool")
@@ -1992,6 +2110,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__svd_helper
 torch__svd_helper <- function(self, some, compute_uv) {
   args <- mget(x = c("self", "some", "compute_uv"))
 expected_types <- list(self = "Tensor", some = "bool", compute_uv = "bool")
@@ -2008,6 +2127,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__symeig_helper
 torch__symeig_helper <- function(self, eigenvectors, upper) {
   args <- mget(x = c("self", "eigenvectors", "upper"))
 expected_types <- list(self = "Tensor", eigenvectors = "bool", upper = "bool")
@@ -2024,6 +2144,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__thnn_differentiable_gru_cell_backward
 torch__thnn_differentiable_gru_cell_backward <- function(grad_hy, input_gates, hidden_gates, hx, input_bias, hidden_bias) {
   args <- mget(x = c("grad_hy", "input_gates", "hidden_gates", "hx", "input_bias", "hidden_bias"))
 expected_types <- list(grad_hy = "Tensor", input_gates = "Tensor", hidden_gates = "Tensor", 
@@ -2042,6 +2163,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__thnn_differentiable_lstm_cell_backward
 torch__thnn_differentiable_lstm_cell_backward <- function(grad_hy, grad_cy, input_gates, hidden_gates, input_bias, hidden_bias, cx, cy) {
   args <- mget(x = c("grad_hy", "grad_cy", "input_gates", "hidden_gates", "input_bias", "hidden_bias", "cx", "cy"))
 expected_types <- list(grad_hy = "Tensor", grad_cy = "Tensor", input_gates = "Tensor", 
@@ -2061,6 +2183,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__thnn_fused_gru_cell
 torch__thnn_fused_gru_cell <- function(input_gates, hidden_gates, hx, input_bias = list(), hidden_bias = list()) {
   args <- mget(x = c("input_gates", "hidden_gates", "hx", "input_bias", "hidden_bias"))
 expected_types <- list(input_gates = "Tensor", hidden_gates = "Tensor", hx = "Tensor", 
@@ -2078,6 +2201,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__thnn_fused_gru_cell_backward
 torch__thnn_fused_gru_cell_backward <- function(grad_hy, workspace, has_bias) {
   args <- mget(x = c("grad_hy", "workspace", "has_bias"))
 expected_types <- list(grad_hy = "Tensor", workspace = "Tensor", has_bias = "bool")
@@ -2094,6 +2218,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__thnn_fused_lstm_cell
 torch__thnn_fused_lstm_cell <- function(input_gates, hidden_gates, cx, input_bias = list(), hidden_bias = list()) {
   args <- mget(x = c("input_gates", "hidden_gates", "cx", "input_bias", "hidden_bias"))
 expected_types <- list(input_gates = "Tensor", hidden_gates = "Tensor", cx = "Tensor", 
@@ -2111,6 +2236,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__thnn_fused_lstm_cell_backward
 torch__thnn_fused_lstm_cell_backward <- function(grad_hy, grad_cy, cx, cy, workspace, has_bias) {
   args <- mget(x = c("grad_hy", "grad_cy", "cx", "cy", "workspace", "has_bias"))
 expected_types <- list(grad_hy = "Tensor", grad_cy = "Tensor", cx = "Tensor", cy = "Tensor", 
@@ -2128,6 +2254,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__triangular_solve_helper
 torch__triangular_solve_helper <- function(self, A, upper, transpose, unitriangular) {
   args <- mget(x = c("self", "A", "upper", "transpose", "unitriangular"))
 expected_types <- list(self = "Tensor", A = "Tensor", upper = "bool", transpose = "bool", 
@@ -2145,6 +2272,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__trilinear
 torch__trilinear <- function(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim = 1L) {
   args <- mget(x = c("i1", "i2", "i3", "expand1", "expand2", "expand3", "sumdim", "unroll_dim"))
 expected_types <- list(i1 = "Tensor", i2 = "Tensor", i3 = "Tensor", expand1 = "IntArrayRef", 
@@ -2164,6 +2292,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__unique
 torch__unique <- function(self, sorted = TRUE, return_inverse = FALSE) {
   args <- mget(x = c("self", "sorted", "return_inverse"))
 expected_types <- list(self = "Tensor", sorted = "bool", return_inverse = "bool")
@@ -2180,6 +2309,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__unique2
 torch__unique2 <- function(self, sorted = TRUE, return_inverse = FALSE, return_counts = FALSE) {
   args <- mget(x = c("self", "sorted", "return_inverse", "return_counts"))
 expected_types <- list(self = "Tensor", sorted = "bool", return_inverse = "bool", 
@@ -2197,6 +2327,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__unsafe_view
 torch__unsafe_view <- function(self, size) {
   args <- mget(x = c("self", "size"))
 expected_types <- list(self = "Tensor", size = "IntArrayRef")
@@ -2213,6 +2344,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__use_cudnn_ctc_loss
 torch__use_cudnn_ctc_loss <- function(log_probs, targets, input_lengths, target_lengths, blank) {
   args <- mget(x = c("log_probs", "targets", "input_lengths", "target_lengths", "blank"))
 expected_types <- list(log_probs = "Tensor", targets = "Tensor", input_lengths = "IntArrayRef", 
@@ -2231,6 +2363,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__use_cudnn_rnn_flatten_weight
 torch__use_cudnn_rnn_flatten_weight <- function() {
   args <- list()
 expected_types <- list()
@@ -2247,6 +2380,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__var
 torch__var <- function(self, unbiased = TRUE) {
   args <- mget(x = c("self", "unbiased"))
 expected_types <- list(self = "Tensor", unbiased = "bool")
@@ -2263,6 +2397,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__weight_norm
 torch__weight_norm <- function(v, g, dim = 1L) {
   args <- mget(x = c("v", "g", "dim"))
 expected_types <- list(v = "Tensor", g = "Tensor", dim = "int64_t")
@@ -2279,6 +2414,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__weight_norm_cuda_interface
 torch__weight_norm_cuda_interface <- function(v, g, dim = 1L) {
   args <- mget(x = c("v", "g", "dim"))
 expected_types <- list(v = "Tensor", g = "Tensor", dim = "int64_t")
@@ -2295,6 +2431,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__weight_norm_cuda_interface_backward
 torch__weight_norm_cuda_interface_backward <- function(grad_w, saved_v, saved_g, saved_norms, dim) {
   args <- mget(x = c("grad_w", "saved_v", "saved_g", "saved_norms", "dim"))
 expected_types <- list(grad_w = "Tensor", saved_v = "Tensor", saved_g = "Tensor", 
@@ -2312,6 +2449,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch__weight_norm_differentiable_backward
 torch__weight_norm_differentiable_backward <- function(grad_w, saved_v, saved_g, saved_norms, dim) {
   args <- mget(x = c("grad_w", "saved_v", "saved_g", "saved_norms", "dim"))
 expected_types <- list(grad_w = "Tensor", saved_v = "Tensor", saved_g = "Tensor", 
@@ -2329,6 +2467,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_abs
 torch_abs <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -2345,6 +2484,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_abs_
 torch_abs_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -2361,6 +2501,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_abs_out
 torch_abs_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -2377,6 +2518,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_acos
 torch_acos <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -2393,6 +2535,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_acos_
 torch_acos_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -2409,6 +2552,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_acos_out
 torch_acos_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -2425,6 +2569,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_avg_pool1d
 torch_adaptive_avg_pool1d <- function(self, output_size) {
   args <- mget(x = c("self", "output_size"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef")
@@ -2441,6 +2586,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_avg_pool2d
 torch_adaptive_avg_pool2d <- function(self, output_size) {
   args <- mget(x = c("self", "output_size"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef")
@@ -2457,6 +2603,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_avg_pool2d_out
 torch_adaptive_avg_pool2d_out <- function(out, self, output_size) {
   args <- mget(x = c("out", "self", "output_size"))
 expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef")
@@ -2473,6 +2620,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_avg_pool3d
 torch_adaptive_avg_pool3d <- function(self, output_size) {
   args <- mget(x = c("self", "output_size"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef")
@@ -2489,6 +2637,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_avg_pool3d_backward
 torch_adaptive_avg_pool3d_backward <- function(grad_output, self) {
   args <- mget(x = c("grad_output", "self"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor")
@@ -2505,6 +2654,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_avg_pool3d_backward_out
 torch_adaptive_avg_pool3d_backward_out <- function(grad_input, grad_output, self) {
   args <- mget(x = c("grad_input", "grad_output", "self"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor")
@@ -2521,6 +2671,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_avg_pool3d_out
 torch_adaptive_avg_pool3d_out <- function(out, self, output_size) {
   args <- mget(x = c("out", "self", "output_size"))
 expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef")
@@ -2537,6 +2688,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_max_pool1d
 torch_adaptive_max_pool1d <- function(self, output_size) {
   args <- mget(x = c("self", "output_size"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef")
@@ -2553,6 +2705,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_max_pool2d
 torch_adaptive_max_pool2d <- function(self, output_size) {
   args <- mget(x = c("self", "output_size"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef")
@@ -2569,6 +2722,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_max_pool2d_backward
 torch_adaptive_max_pool2d_backward <- function(grad_output, self, indices) {
   args <- mget(x = c("grad_output", "self", "indices"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", indices = "Tensor")
@@ -2585,6 +2739,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_max_pool2d_backward_out
 torch_adaptive_max_pool2d_backward_out <- function(grad_input, grad_output, self, indices) {
   args <- mget(x = c("grad_input", "grad_output", "self", "indices"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -2602,6 +2757,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_max_pool2d_out
 torch_adaptive_max_pool2d_out <- function(out, indices, self, output_size) {
   args <- mget(x = c("out", "indices", "self", "output_size"))
 expected_types <- list(out = "Tensor", indices = "Tensor", self = "Tensor", output_size = "IntArrayRef")
@@ -2618,6 +2774,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_max_pool3d
 torch_adaptive_max_pool3d <- function(self, output_size) {
   args <- mget(x = c("self", "output_size"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef")
@@ -2634,6 +2791,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_max_pool3d_backward
 torch_adaptive_max_pool3d_backward <- function(grad_output, self, indices) {
   args <- mget(x = c("grad_output", "self", "indices"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", indices = "Tensor")
@@ -2650,6 +2808,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_max_pool3d_backward_out
 torch_adaptive_max_pool3d_backward_out <- function(grad_input, grad_output, self, indices) {
   args <- mget(x = c("grad_input", "grad_output", "self", "indices"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -2667,6 +2826,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_adaptive_max_pool3d_out
 torch_adaptive_max_pool3d_out <- function(out, indices, self, output_size) {
   args <- mget(x = c("out", "indices", "self", "output_size"))
 expected_types <- list(out = "Tensor", indices = "Tensor", self = "Tensor", output_size = "IntArrayRef")
@@ -2683,6 +2843,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_add
 torch_add <- function(self, other, alpha = 1L) {
   args <- mget(x = c("self", "other", "alpha"))
 expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), alpha = "Scalar")
@@ -2699,6 +2860,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_add_out
 torch_add_out <- function(out, self, other, alpha = 1L) {
   args <- mget(x = c("out", "self", "other", "alpha"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor", alpha = "Scalar")
@@ -2715,6 +2877,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addbmm
 torch_addbmm <- function(self, batch1, batch2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("self", "batch1", "batch2", "beta", "alpha"))
 expected_types <- list(self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", beta = "Scalar", 
@@ -2732,6 +2895,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addbmm_out
 torch_addbmm_out <- function(out, self, batch1, batch2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("out", "self", "batch1", "batch2", "beta", "alpha"))
 expected_types <- list(out = "Tensor", self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", 
@@ -2749,6 +2913,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addcdiv
 torch_addcdiv <- function(self, tensor1, tensor2, value = 1L) {
   args <- mget(x = c("self", "tensor1", "tensor2", "value"))
 expected_types <- list(self = "Tensor", tensor1 = "Tensor", tensor2 = "Tensor", 
@@ -2766,6 +2931,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addcdiv_out
 torch_addcdiv_out <- function(out, self, tensor1, tensor2, value = 1L) {
   args <- mget(x = c("out", "self", "tensor1", "tensor2", "value"))
 expected_types <- list(out = "Tensor", self = "Tensor", tensor1 = "Tensor", tensor2 = "Tensor", 
@@ -2783,6 +2949,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addcmul
 torch_addcmul <- function(self, tensor1, tensor2, value = 1L) {
   args <- mget(x = c("self", "tensor1", "tensor2", "value"))
 expected_types <- list(self = "Tensor", tensor1 = "Tensor", tensor2 = "Tensor", 
@@ -2800,6 +2967,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addcmul_out
 torch_addcmul_out <- function(out, self, tensor1, tensor2, value = 1L) {
   args <- mget(x = c("out", "self", "tensor1", "tensor2", "value"))
 expected_types <- list(out = "Tensor", self = "Tensor", tensor1 = "Tensor", tensor2 = "Tensor", 
@@ -2817,6 +2985,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addmm
 torch_addmm <- function(self, mat1, mat2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("self", "mat1", "mat2", "beta", "alpha"))
 expected_types <- list(self = "Tensor", mat1 = "Tensor", mat2 = "Tensor", beta = "Scalar", 
@@ -2834,6 +3003,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addmm_out
 torch_addmm_out <- function(out, self, mat1, mat2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("out", "self", "mat1", "mat2", "beta", "alpha"))
 expected_types <- list(out = "Tensor", self = "Tensor", mat1 = "Tensor", mat2 = "Tensor", 
@@ -2851,6 +3021,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addmv
 torch_addmv <- function(self, mat, vec, beta = 1L, alpha = 1L) {
   args <- mget(x = c("self", "mat", "vec", "beta", "alpha"))
 expected_types <- list(self = "Tensor", mat = "Tensor", vec = "Tensor", beta = "Scalar", 
@@ -2868,6 +3039,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addmv_
 torch_addmv_ <- function(self, mat, vec, beta = 1L, alpha = 1L) {
   args <- mget(x = c("self", "mat", "vec", "beta", "alpha"))
 expected_types <- list(self = "Tensor", mat = "Tensor", vec = "Tensor", beta = "Scalar", 
@@ -2885,6 +3057,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addmv_out
 torch_addmv_out <- function(out, self, mat, vec, beta = 1L, alpha = 1L) {
   args <- mget(x = c("out", "self", "mat", "vec", "beta", "alpha"))
 expected_types <- list(out = "Tensor", self = "Tensor", mat = "Tensor", vec = "Tensor", 
@@ -2902,6 +3075,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addr
 torch_addr <- function(self, vec1, vec2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("self", "vec1", "vec2", "beta", "alpha"))
 expected_types <- list(self = "Tensor", vec1 = "Tensor", vec2 = "Tensor", beta = "Scalar", 
@@ -2919,6 +3093,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_addr_out
 torch_addr_out <- function(out, self, vec1, vec2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("out", "self", "vec1", "vec2", "beta", "alpha"))
 expected_types <- list(out = "Tensor", self = "Tensor", vec1 = "Tensor", vec2 = "Tensor", 
@@ -2936,6 +3111,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_affine_grid_generator
 torch_affine_grid_generator <- function(theta, size, align_corners) {
   args <- mget(x = c("theta", "size", "align_corners"))
 expected_types <- list(theta = "Tensor", size = "IntArrayRef", align_corners = "bool")
@@ -2952,6 +3128,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_affine_grid_generator_backward
 torch_affine_grid_generator_backward <- function(grad, size, align_corners) {
   args <- mget(x = c("grad", "size", "align_corners"))
 expected_types <- list(grad = "Tensor", size = "IntArrayRef", align_corners = "bool")
@@ -2968,6 +3145,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_alias
 torch_alias <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -2984,6 +3162,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_align_tensors
 torch_align_tensors <- function(tensors) {
   args <- mget(x = c("tensors"))
 expected_types <- list(tensors = "TensorList")
@@ -3000,6 +3179,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_all
 torch_all <- function(self, dim, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool")
@@ -3016,6 +3196,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_all_out
 torch_all_out <- function(out, self, dim, keepdim = FALSE) {
   args <- mget(x = c("out", "self", "dim", "keepdim"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname"
@@ -3033,6 +3214,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_allclose
 torch_allclose <- function(self, other, rtol = 0.000010, atol = 0.000000, equal_nan = FALSE) {
   args <- mget(x = c("self", "other", "rtol", "atol", "equal_nan"))
 expected_types <- list(self = "Tensor", other = "Tensor", rtol = "double", atol = "double", 
@@ -3050,6 +3232,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_alpha_dropout
 torch_alpha_dropout <- function(input, p, train) {
   args <- mget(x = c("input", "p", "train"))
 expected_types <- list(input = "Tensor", p = "double", train = "bool")
@@ -3066,6 +3249,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_alpha_dropout_
 torch_alpha_dropout_ <- function(self, p, train) {
   args <- mget(x = c("self", "p", "train"))
 expected_types <- list(self = "Tensor", p = "double", train = "bool")
@@ -3082,6 +3266,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_angle
 torch_angle <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -3098,6 +3283,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_angle_out
 torch_angle_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -3114,6 +3300,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_any
 torch_any <- function(self, dim, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool")
@@ -3130,6 +3317,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_any_out
 torch_any_out <- function(out, self, dim, keepdim = FALSE) {
   args <- mget(x = c("out", "self", "dim", "keepdim"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname"
@@ -3147,6 +3335,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_arange
 .torch_arange <- function(start, end, step, options = list()) {
   args <- mget(x = c("start", "end", "step", "options"))
 expected_types <- list(start = "Scalar", end = "Scalar", step = "Scalar", options = "TensorOptions")
@@ -3163,6 +3352,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_arange_out
 torch_arange_out <- function(out, start, end, step = 1L) {
   args <- mget(x = c("out", "start", "end", "step"))
 expected_types <- list(out = "Tensor", start = "Scalar", end = "Scalar", step = "Scalar")
@@ -3179,6 +3369,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_argmax
 torch_argmax <- function(self, dim = NULL, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = "int64_t", keepdim = "bool")
@@ -3195,6 +3386,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_argmin
 torch_argmin <- function(self, dim = NULL, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = "int64_t", keepdim = "bool")
@@ -3211,6 +3403,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_argsort
 torch_argsort <- function(self, dim = -1L, descending = FALSE) {
   args <- mget(x = c("self", "dim", "descending"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), descending = "bool")
@@ -3227,6 +3420,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_as_strided
 torch_as_strided <- function(self, size, stride, storage_offset = NULL) {
   args <- mget(x = c("self", "size", "stride", "storage_offset"))
 expected_types <- list(self = "Tensor", size = "IntArrayRef", stride = "IntArrayRef", 
@@ -3244,6 +3438,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_as_strided_
 torch_as_strided_ <- function(self, size, stride, storage_offset = NULL) {
   args <- mget(x = c("self", "size", "stride", "storage_offset"))
 expected_types <- list(self = "Tensor", size = "IntArrayRef", stride = "IntArrayRef", 
@@ -3261,6 +3456,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_asin
 torch_asin <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -3277,6 +3473,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_asin_
 torch_asin_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -3293,6 +3490,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_asin_out
 torch_asin_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -3309,6 +3507,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_atan
 torch_atan <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -3325,6 +3524,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_atan_
 torch_atan_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -3341,6 +3541,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_atan_out
 torch_atan_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -3357,6 +3558,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_atan2
 torch_atan2 <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = "Tensor")
@@ -3373,6 +3575,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_atan2_out
 torch_atan2_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor")
@@ -3389,6 +3592,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_avg_pool1d
 torch_avg_pool1d <- function(self, kernel_size, stride = list(), padding = 0L, ceil_mode = FALSE, count_include_pad = TRUE) {
   args <- mget(x = c("self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", 
@@ -3406,6 +3610,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_avg_pool2d
 torch_avg_pool2d <- function(self, kernel_size, stride = list(), padding = 0L, ceil_mode = FALSE, count_include_pad = TRUE, divisor_override = NULL) {
   args <- mget(x = c("self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", 
@@ -3424,6 +3629,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_avg_pool2d_backward
 torch_avg_pool2d_backward <- function(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) {
   args <- mget(x = c("grad_output", "self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", 
@@ -3443,6 +3649,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_avg_pool2d_backward_out
 torch_avg_pool2d_backward_out <- function(grad_input, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) {
   args <- mget(x = c("grad_input", "grad_output", "self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -3463,6 +3670,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_avg_pool2d_out
 torch_avg_pool2d_out <- function(out, self, kernel_size, stride = list(), padding = 0L, ceil_mode = FALSE, count_include_pad = TRUE, divisor_override = NULL) {
   args <- mget(x = c("out", "self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override"))
 expected_types <- list(out = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", 
@@ -3481,6 +3689,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_avg_pool3d
 torch_avg_pool3d <- function(self, kernel_size, stride = list(), padding = 0L, ceil_mode = FALSE, count_include_pad = TRUE, divisor_override = NULL) {
   args <- mget(x = c("self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", 
@@ -3499,6 +3708,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_avg_pool3d_backward
 torch_avg_pool3d_backward <- function(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) {
   args <- mget(x = c("grad_output", "self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", 
@@ -3518,6 +3728,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_avg_pool3d_backward_out
 torch_avg_pool3d_backward_out <- function(grad_input, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) {
   args <- mget(x = c("grad_input", "grad_output", "self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -3538,6 +3749,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_avg_pool3d_out
 torch_avg_pool3d_out <- function(out, self, kernel_size, stride = list(), padding = 0L, ceil_mode = FALSE, count_include_pad = TRUE, divisor_override = NULL) {
   args <- mget(x = c("out", "self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override"))
 expected_types <- list(out = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", 
@@ -3556,6 +3768,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_baddbmm
 torch_baddbmm <- function(self, batch1, batch2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("self", "batch1", "batch2", "beta", "alpha"))
 expected_types <- list(self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", beta = "Scalar", 
@@ -3573,6 +3786,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_baddbmm_out
 torch_baddbmm_out <- function(out, self, batch1, batch2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("out", "self", "batch1", "batch2", "beta", "alpha"))
 expected_types <- list(out = "Tensor", self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", 
@@ -3590,6 +3804,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bartlett_window
 torch_bartlett_window <- function(window_length, periodic, options = list()) {
   args <- mget(x = c("window_length", "periodic", "options"))
 expected_types <- list(window_length = "int64_t", periodic = "bool", options = "TensorOptions")
@@ -3606,6 +3821,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_batch_norm
 torch_batch_norm <- function(input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled) {
   args <- mget(x = c("input", "weight", "bias", "running_mean", "running_var", "training", "momentum", "eps", "cudnn_enabled"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", running_mean = "Tensor", 
@@ -3625,6 +3841,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_batch_norm_backward_elemt
 torch_batch_norm_backward_elemt <- function(grad_out, input, mean, invstd, weight, mean_dy, mean_dy_xmu) {
   args <- mget(x = c("grad_out", "input", "mean", "invstd", "weight", "mean_dy", "mean_dy_xmu"))
 expected_types <- list(grad_out = "Tensor", input = "Tensor", mean = "Tensor", 
@@ -3644,6 +3861,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_batch_norm_backward_reduce
 torch_batch_norm_backward_reduce <- function(grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g) {
   args <- mget(x = c("grad_out", "input", "mean", "invstd", "weight", "input_g", "weight_g", "bias_g"))
 expected_types <- list(grad_out = "Tensor", input = "Tensor", mean = "Tensor", 
@@ -3663,6 +3881,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_batch_norm_elemt
 torch_batch_norm_elemt <- function(input, weight, bias, mean, invstd, eps) {
   args <- mget(x = c("input", "weight", "bias", "mean", "invstd", "eps"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", mean = "Tensor", 
@@ -3680,6 +3899,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_batch_norm_elemt_out
 torch_batch_norm_elemt_out <- function(out, input, weight, bias, mean, invstd, eps) {
   args <- mget(x = c("out", "input", "weight", "bias", "mean", "invstd", "eps"))
 expected_types <- list(out = "Tensor", input = "Tensor", weight = "Tensor", bias = "Tensor", 
@@ -3697,6 +3917,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_batch_norm_gather_stats
 torch_batch_norm_gather_stats <- function(input, mean, invstd, running_mean, running_var, momentum, eps, count) {
   args <- mget(x = c("input", "mean", "invstd", "running_mean", "running_var", "momentum", "eps", "count"))
 expected_types <- list(input = "Tensor", mean = "Tensor", invstd = "Tensor", running_mean = "Tensor", 
@@ -3716,6 +3937,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_batch_norm_gather_stats_with_counts
 torch_batch_norm_gather_stats_with_counts <- function(input, mean, invstd, running_mean, running_var, momentum, eps, counts) {
   args <- mget(x = c("input", "mean", "invstd", "running_mean", "running_var", "momentum", "eps", "counts"))
 expected_types <- list(input = "Tensor", mean = "Tensor", invstd = "Tensor", running_mean = "Tensor", 
@@ -3735,6 +3957,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_batch_norm_stats
 torch_batch_norm_stats <- function(input, eps) {
   args <- mget(x = c("input", "eps"))
 expected_types <- list(input = "Tensor", eps = "double")
@@ -3751,6 +3974,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_batch_norm_update_stats
 torch_batch_norm_update_stats <- function(input, running_mean, running_var, momentum) {
   args <- mget(x = c("input", "running_mean", "running_var", "momentum"))
 expected_types <- list(input = "Tensor", running_mean = "Tensor", running_var = "Tensor", 
@@ -3768,6 +3992,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bernoulli
 torch_bernoulli <- function(self, p, generator = NULL) {
   args <- mget(x = c("self", "p", "generator"))
 expected_types <- list(self = "Tensor", p = "double", generator = "Generator *")
@@ -3784,6 +4009,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bernoulli_out
 torch_bernoulli_out <- function(out, self, generator = NULL) {
   args <- mget(x = c("out", "self", "generator"))
 expected_types <- list(out = "Tensor", self = "Tensor", generator = "Generator *")
@@ -3800,6 +4026,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bilinear
 torch_bilinear <- function(input1, input2, weight, bias) {
   args <- mget(x = c("input1", "input2", "weight", "bias"))
 expected_types <- list(input1 = "Tensor", input2 = "Tensor", weight = "Tensor", 
@@ -3817,6 +4044,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_binary_cross_entropy
 torch_binary_cross_entropy <- function(self, target, weight = list(), reduction = torch_reduction_mean()) {
   args <- mget(x = c("self", "target", "weight", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", weight = "Tensor", reduction = "int64_t")
@@ -3833,6 +4061,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_binary_cross_entropy_backward
 torch_binary_cross_entropy_backward <- function(grad_output, self, target, weight = list(), reduction = torch_reduction_mean()) {
   args <- mget(x = c("grad_output", "self", "target", "weight", "reduction"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", 
@@ -3850,6 +4079,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_binary_cross_entropy_backward_out
 torch_binary_cross_entropy_backward_out <- function(grad_input, grad_output, self, target, weight = list(), reduction = torch_reduction_mean()) {
   args <- mget(x = c("grad_input", "grad_output", "self", "target", "weight", "reduction"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -3867,6 +4097,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_binary_cross_entropy_out
 torch_binary_cross_entropy_out <- function(out, self, target, weight = list(), reduction = torch_reduction_mean()) {
   args <- mget(x = c("out", "self", "target", "weight", "reduction"))
 expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", weight = "Tensor", 
@@ -3884,6 +4115,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_binary_cross_entropy_with_logits
 torch_binary_cross_entropy_with_logits <- function(self, target, weight = list(), pos_weight = list(), reduction = torch_reduction_mean()) {
   args <- mget(x = c("self", "target", "weight", "pos_weight", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", weight = "Tensor", pos_weight = "Tensor", 
@@ -3901,6 +4133,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_binary_cross_entropy_with_logits_backward
 torch_binary_cross_entropy_with_logits_backward <- function(grad_output, self, target, weight = list(), pos_weight = list(), reduction = torch_reduction_mean()) {
   args <- mget(x = c("grad_output", "self", "target", "weight", "pos_weight", "reduction"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", 
@@ -3918,6 +4151,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bincount
 torch_bincount <- function(self, weights = list(), minlength = 0L) {
   args <- mget(x = c("self", "weights", "minlength"))
 expected_types <- list(self = "Tensor", weights = "Tensor", minlength = "int64_t")
@@ -3934,6 +4168,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bitwise_and
 torch_bitwise_and <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -3950,6 +4185,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bitwise_and_out
 torch_bitwise_and_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = c("Tensor", "Scalar"
@@ -3967,6 +4203,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bitwise_not
 torch_bitwise_not <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -3983,6 +4220,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bitwise_not_out
 torch_bitwise_not_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -3999,6 +4237,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bitwise_or
 torch_bitwise_or <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -4015,6 +4254,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bitwise_or_out
 torch_bitwise_or_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = c("Tensor", "Scalar"
@@ -4032,6 +4272,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bitwise_xor
 torch_bitwise_xor <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -4048,6 +4289,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bitwise_xor_out
 torch_bitwise_xor_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = c("Tensor", "Scalar"
@@ -4065,6 +4307,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_blackman_window
 torch_blackman_window <- function(window_length, periodic, options = list()) {
   args <- mget(x = c("window_length", "periodic", "options"))
 expected_types <- list(window_length = "int64_t", periodic = "bool", options = "TensorOptions")
@@ -4081,6 +4324,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bmm
 torch_bmm <- function(self, mat2) {
   args <- mget(x = c("self", "mat2"))
 expected_types <- list(self = "Tensor", mat2 = "Tensor")
@@ -4097,6 +4341,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_bmm_out
 torch_bmm_out <- function(out, self, mat2) {
   args <- mget(x = c("out", "self", "mat2"))
 expected_types <- list(out = "Tensor", self = "Tensor", mat2 = "Tensor")
@@ -4113,6 +4358,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_broadcast_tensors
 torch_broadcast_tensors <- function(tensors) {
   args <- mget(x = c("tensors"))
 expected_types <- list(tensors = "TensorList")
@@ -4129,6 +4375,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_can_cast
 torch_can_cast <- function(from, to) {
   args <- mget(x = c("from", "to"))
 expected_types <- list(from = "ScalarType", to = "ScalarType")
@@ -4145,6 +4392,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cartesian_prod
 torch_cartesian_prod <- function(tensors) {
   args <- mget(x = c("tensors"))
 expected_types <- list(tensors = "TensorList")
@@ -4161,6 +4409,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cat
 torch_cat <- function(tensors, dim = 1L) {
   args <- mget(x = c("tensors", "dim"))
 expected_types <- list(tensors = "TensorList", dim = c("int64_t", "Dimname"))
@@ -4177,6 +4426,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cat_out
 torch_cat_out <- function(out, tensors, dim = 1L) {
   args <- mget(x = c("out", "tensors", "dim"))
 expected_types <- list(out = "Tensor", tensors = "TensorList", dim = c("int64_t", 
@@ -4194,6 +4444,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cdist
 torch_cdist <- function(x1, x2, p = 2L, compute_mode = NULL) {
   args <- mget(x = c("x1", "x2", "p", "compute_mode"))
 expected_types <- list(x1 = "Tensor", x2 = "Tensor", p = "double", compute_mode = "int64_t")
@@ -4210,6 +4461,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ceil
 torch_ceil <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -4226,6 +4478,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ceil_
 torch_ceil_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -4242,6 +4495,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ceil_out
 torch_ceil_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -4258,6 +4512,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_celu
 torch_celu <- function(self, alpha = 1.000000) {
   args <- mget(x = c("self", "alpha"))
 expected_types <- list(self = "Tensor", alpha = "Scalar")
@@ -4274,6 +4529,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_celu_
 torch_celu_ <- function(self, alpha = 1.000000) {
   args <- mget(x = c("self", "alpha"))
 expected_types <- list(self = "Tensor", alpha = "Scalar")
@@ -4290,6 +4546,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_chain_matmul
 torch_chain_matmul <- function(matrices) {
   args <- mget(x = c("matrices"))
 expected_types <- list(matrices = "TensorList")
@@ -4306,6 +4563,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cholesky
 torch_cholesky <- function(self, upper = FALSE) {
   args <- mget(x = c("self", "upper"))
 expected_types <- list(self = "Tensor", upper = "bool")
@@ -4322,6 +4580,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cholesky_inverse
 torch_cholesky_inverse <- function(self, upper = FALSE) {
   args <- mget(x = c("self", "upper"))
 expected_types <- list(self = "Tensor", upper = "bool")
@@ -4338,6 +4597,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cholesky_inverse_out
 torch_cholesky_inverse_out <- function(out, self, upper = FALSE) {
   args <- mget(x = c("out", "self", "upper"))
 expected_types <- list(out = "Tensor", self = "Tensor", upper = "bool")
@@ -4354,6 +4614,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cholesky_out
 torch_cholesky_out <- function(out, self, upper = FALSE) {
   args <- mget(x = c("out", "self", "upper"))
 expected_types <- list(out = "Tensor", self = "Tensor", upper = "bool")
@@ -4370,6 +4631,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cholesky_solve
 torch_cholesky_solve <- function(self, input2, upper = FALSE) {
   args <- mget(x = c("self", "input2", "upper"))
 expected_types <- list(self = "Tensor", input2 = "Tensor", upper = "bool")
@@ -4386,6 +4648,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cholesky_solve_out
 torch_cholesky_solve_out <- function(out, self, input2, upper = FALSE) {
   args <- mget(x = c("out", "self", "input2", "upper"))
 expected_types <- list(out = "Tensor", self = "Tensor", input2 = "Tensor", upper = "bool")
@@ -4402,6 +4665,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_chunk
 torch_chunk <- function(self, chunks, dim = 1L) {
   args <- mget(x = c("self", "chunks", "dim"))
 expected_types <- list(self = "Tensor", chunks = "int64_t", dim = "int64_t")
@@ -4418,6 +4682,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_clamp
 torch_clamp <- function(self, min = NULL, max = NULL) {
   args <- mget(x = c("self", "min", "max"))
 expected_types <- list(self = "Tensor", min = "Scalar", max = "Scalar")
@@ -4434,6 +4699,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_clamp_
 torch_clamp_ <- function(self, min = NULL, max = NULL) {
   args <- mget(x = c("self", "min", "max"))
 expected_types <- list(self = "Tensor", min = "Scalar", max = "Scalar")
@@ -4450,6 +4716,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_clamp_max
 torch_clamp_max <- function(self, max) {
   args <- mget(x = c("self", "max"))
 expected_types <- list(self = "Tensor", max = "Scalar")
@@ -4466,6 +4733,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_clamp_max_
 torch_clamp_max_ <- function(self, max) {
   args <- mget(x = c("self", "max"))
 expected_types <- list(self = "Tensor", max = "Scalar")
@@ -4482,6 +4750,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_clamp_max_out
 torch_clamp_max_out <- function(out, self, max) {
   args <- mget(x = c("out", "self", "max"))
 expected_types <- list(out = "Tensor", self = "Tensor", max = "Scalar")
@@ -4498,6 +4767,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_clamp_min
 torch_clamp_min <- function(self, min) {
   args <- mget(x = c("self", "min"))
 expected_types <- list(self = "Tensor", min = "Scalar")
@@ -4514,6 +4784,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_clamp_min_
 torch_clamp_min_ <- function(self, min) {
   args <- mget(x = c("self", "min"))
 expected_types <- list(self = "Tensor", min = "Scalar")
@@ -4530,6 +4801,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_clamp_min_out
 torch_clamp_min_out <- function(out, self, min) {
   args <- mget(x = c("out", "self", "min"))
 expected_types <- list(out = "Tensor", self = "Tensor", min = "Scalar")
@@ -4546,6 +4818,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_clamp_out
 torch_clamp_out <- function(out, self, min = NULL, max = NULL) {
   args <- mget(x = c("out", "self", "min", "max"))
 expected_types <- list(out = "Tensor", self = "Tensor", min = "Scalar", max = "Scalar")
@@ -4562,6 +4835,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_clone
 torch_clone <- function(self, memory_format = NULL) {
   args <- mget(x = c("self", "memory_format"))
 expected_types <- list(self = "Tensor", memory_format = "MemoryFormat")
@@ -4578,6 +4852,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_col2im
 torch_col2im <- function(self, output_size, kernel_size, dilation, padding, stride) {
   args <- mget(x = c("self", "output_size", "kernel_size", "dilation", "padding", "stride"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef", kernel_size = "IntArrayRef", 
@@ -4596,6 +4871,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_col2im_backward
 torch_col2im_backward <- function(grad_output, kernel_size, dilation, padding, stride) {
   args <- mget(x = c("grad_output", "kernel_size", "dilation", "padding", "stride"))
 expected_types <- list(grad_output = "Tensor", kernel_size = "IntArrayRef", dilation = "IntArrayRef", 
@@ -4614,6 +4890,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_col2im_backward_out
 torch_col2im_backward_out <- function(grad_input, grad_output, kernel_size, dilation, padding, stride) {
   args <- mget(x = c("grad_input", "grad_output", "kernel_size", "dilation", "padding", "stride"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", kernel_size = "IntArrayRef", 
@@ -4632,6 +4909,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_col2im_out
 torch_col2im_out <- function(out, self, output_size, kernel_size, dilation, padding, stride) {
   args <- mget(x = c("out", "self", "output_size", "kernel_size", "dilation", "padding", "stride"))
 expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", 
@@ -4651,6 +4929,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_combinations
 torch_combinations <- function(self, r = 2L, with_replacement = FALSE) {
   args <- mget(x = c("self", "r", "with_replacement"))
 expected_types <- list(self = "Tensor", r = "int64_t", with_replacement = "bool")
@@ -4667,6 +4946,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_conj
 torch_conj <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -4683,6 +4963,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_conj_out
 torch_conj_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -4699,6 +4980,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_constant_pad_nd
 torch_constant_pad_nd <- function(self, pad, value = 0L) {
   args <- mget(x = c("self", "pad", "value"))
 expected_types <- list(self = "Tensor", pad = "IntArrayRef", value = "Scalar")
@@ -4715,6 +4997,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_conv_tbc
 torch_conv_tbc <- function(self, weight, bias, pad = 0L) {
   args <- mget(x = c("self", "weight", "bias", "pad"))
 expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", pad = "int64_t")
@@ -4731,6 +5014,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_conv_tbc_backward
 torch_conv_tbc_backward <- function(self, input, weight, bias, pad) {
   args <- mget(x = c("self", "input", "weight", "bias", "pad"))
 expected_types <- list(self = "Tensor", input = "Tensor", weight = "Tensor", bias = "Tensor", 
@@ -4748,6 +5032,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_conv_transpose1d
 torch_conv_transpose1d <- function(input, weight, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, groups = 1L, dilation = 1L) {
   args <- mget(x = c("input", "weight", "bias", "stride", "padding", "output_padding", "groups", "dilation"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", 
@@ -4766,6 +5051,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_conv_transpose2d
 torch_conv_transpose2d <- function(input, weight, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, groups = 1L, dilation = 1L) {
   args <- mget(x = c("input", "weight", "bias", "stride", "padding", "output_padding", "groups", "dilation"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", 
@@ -4784,6 +5070,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_conv_transpose3d
 torch_conv_transpose3d <- function(input, weight, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, groups = 1L, dilation = 1L) {
   args <- mget(x = c("input", "weight", "bias", "stride", "padding", "output_padding", "groups", "dilation"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", 
@@ -4802,6 +5089,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_conv1d
 torch_conv1d <- function(input, weight, bias = list(), stride = 1L, padding = 0L, dilation = 1L, groups = 1L) {
   args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "groups"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", 
@@ -4819,6 +5107,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_conv2d
 torch_conv2d <- function(input, weight, bias = list(), stride = 1L, padding = 0L, dilation = 1L, groups = 1L) {
   args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "groups"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", 
@@ -4836,6 +5125,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_conv3d
 torch_conv3d <- function(input, weight, bias = list(), stride = 1L, padding = 0L, dilation = 1L, groups = 1L) {
   args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "groups"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", 
@@ -4853,6 +5143,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_convolution
 torch_convolution <- function(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups) {
   args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "transposed", "output_padding", "groups"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", 
@@ -4872,6 +5163,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_convolution_backward_overrideable
 torch_convolution_backward_overrideable <- function(grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask) {
   args <- mget(x = c("grad_output", "input", "weight", "stride", "padding", "dilation", "transposed", "output_padding", "groups", "output_mask"))
 expected_types <- list(grad_output = "Tensor", input = "Tensor", weight = "Tensor", 
@@ -4892,6 +5184,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_convolution_overrideable
 torch_convolution_overrideable <- function(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups) {
   args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "transposed", "output_padding", "groups"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", 
@@ -4911,6 +5204,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_copy_sparse_to_sparse_
 torch_copy_sparse_to_sparse_ <- function(self, src, non_blocking = FALSE) {
   args <- mget(x = c("self", "src", "non_blocking"))
 expected_types <- list(self = "Tensor", src = "Tensor", non_blocking = "bool")
@@ -4927,6 +5221,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cos
 torch_cos <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -4943,6 +5238,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cos_
 torch_cos_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -4959,6 +5255,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cos_out
 torch_cos_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -4975,6 +5272,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cosh
 torch_cosh <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -4991,6 +5289,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cosh_
 torch_cosh_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -5007,6 +5306,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cosh_out
 torch_cosh_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -5023,6 +5323,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cosine_embedding_loss
 torch_cosine_embedding_loss <- function(input1, input2, target, margin = 0.000000, reduction = torch_reduction_mean()) {
   args <- mget(x = c("input1", "input2", "target", "margin", "reduction"))
 expected_types <- list(input1 = "Tensor", input2 = "Tensor", target = "Tensor", 
@@ -5040,6 +5341,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cosine_similarity
 torch_cosine_similarity <- function(x1, x2, dim = 2L, eps = 0.000000) {
   args <- mget(x = c("x1", "x2", "dim", "eps"))
 expected_types <- list(x1 = "Tensor", x2 = "Tensor", dim = "int64_t", eps = "double")
@@ -5056,6 +5358,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cross
 torch_cross <- function(self, other, dim = NULL) {
   args <- mget(x = c("self", "other", "dim"))
 expected_types <- list(self = "Tensor", other = "Tensor", dim = "int64_t")
@@ -5072,6 +5375,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cross_out
 torch_cross_out <- function(out, self, other, dim = NULL) {
   args <- mget(x = c("out", "self", "other", "dim"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor", dim = "int64_t")
@@ -5088,6 +5392,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ctc_loss
 torch_ctc_loss <- function(log_probs, targets, input_lengths, target_lengths, blank = 0L, reduction = torch_reduction_mean(), zero_infinity = FALSE) {
   args <- mget(x = c("log_probs", "targets", "input_lengths", "target_lengths", "blank", "reduction", "zero_infinity"))
 expected_types <- list(log_probs = "Tensor", targets = "Tensor", input_lengths = c("IntArrayRef", 
@@ -5106,6 +5411,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_affine_grid_generator
 torch_cudnn_affine_grid_generator <- function(theta, False, C, H, W) {
   args <- mget(x = c("theta", "False", "C", "H", "W"))
 expected_types <- list(theta = "Tensor", False = "int64_t", C = "int64_t", H = "int64_t", 
@@ -5123,6 +5429,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_affine_grid_generator_backward
 torch_cudnn_affine_grid_generator_backward <- function(grad, False, C, H, W) {
   args <- mget(x = c("grad", "False", "C", "H", "W"))
 expected_types <- list(grad = "Tensor", False = "int64_t", C = "int64_t", H = "int64_t", 
@@ -5140,6 +5447,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_batch_norm
 torch_cudnn_batch_norm <- function(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon) {
   args <- mget(x = c("input", "weight", "bias", "running_mean", "running_var", "training", "exponential_average_factor", "epsilon"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", running_mean = "Tensor", 
@@ -5159,6 +5467,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_batch_norm_backward
 torch_cudnn_batch_norm_backward <- function(input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, reserveSpace) {
   args <- mget(x = c("input", "grad_output", "weight", "running_mean", "running_var", "save_mean", "save_var", "epsilon", "reserveSpace"))
 expected_types <- list(input = "Tensor", grad_output = "Tensor", weight = "Tensor", 
@@ -5178,6 +5487,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_convolution
 torch_cudnn_convolution <- function(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("self", "weight", "bias", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", 
@@ -5197,6 +5507,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_convolution_backward
 torch_cudnn_convolution_backward <- function(self, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic, output_mask) {
   args <- mget(x = c("self", "grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic", "output_mask"))
 expected_types <- list(self = "Tensor", grad_output = "Tensor", weight = "Tensor", 
@@ -5217,6 +5528,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_convolution_backward_input
 torch_cudnn_convolution_backward_input <- function(self_size, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("self_size", "grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(self_size = "IntArrayRef", grad_output = "Tensor", weight = "Tensor", 
@@ -5236,6 +5548,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_convolution_backward_weight
 torch_cudnn_convolution_backward_weight <- function(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("weight_size", "grad_output", "self", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(weight_size = "IntArrayRef", grad_output = "Tensor", self = "Tensor", 
@@ -5255,6 +5568,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_convolution_transpose
 torch_cudnn_convolution_transpose <- function(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("self", "weight", "bias", "padding", "output_padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", 
@@ -5274,6 +5588,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_convolution_transpose_backward
 torch_cudnn_convolution_transpose_backward <- function(self, grad_output, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, output_mask) {
   args <- mget(x = c("self", "grad_output", "weight", "padding", "output_padding", "stride", "dilation", "groups", "benchmark", "deterministic", "output_mask"))
 expected_types <- list(self = "Tensor", grad_output = "Tensor", weight = "Tensor", 
@@ -5295,6 +5610,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_convolution_transpose_backward_input
 torch_cudnn_convolution_transpose_backward_input <- function(grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(grad_output = "Tensor", weight = "Tensor", padding = "IntArrayRef", 
@@ -5314,6 +5630,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_convolution_transpose_backward_weight
 torch_cudnn_convolution_transpose_backward_weight <- function(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("weight_size", "grad_output", "self", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(weight_size = "IntArrayRef", grad_output = "Tensor", self = "Tensor", 
@@ -5333,6 +5650,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_grid_sampler
 torch_cudnn_grid_sampler <- function(self, grid) {
   args <- mget(x = c("self", "grid"))
 expected_types <- list(self = "Tensor", grid = "Tensor")
@@ -5349,6 +5667,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_grid_sampler_backward
 torch_cudnn_grid_sampler_backward <- function(self, grid, grad_output) {
   args <- mget(x = c("self", "grid", "grad_output"))
 expected_types <- list(self = "Tensor", grid = "Tensor", grad_output = "Tensor")
@@ -5365,6 +5684,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cudnn_is_acceptable
 torch_cudnn_is_acceptable <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -5381,6 +5701,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cummax
 torch_cummax <- function(self, dim) {
   args <- mget(x = c("self", "dim"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
@@ -5397,6 +5718,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cummax_out
 torch_cummax_out <- function(values, indices, self, dim) {
   args <- mget(x = c("values", "indices", "self", "dim"))
 expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", 
@@ -5414,6 +5736,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cummin
 torch_cummin <- function(self, dim) {
   args <- mget(x = c("self", "dim"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
@@ -5430,6 +5753,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cummin_out
 torch_cummin_out <- function(values, indices, self, dim) {
   args <- mget(x = c("values", "indices", "self", "dim"))
 expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", 
@@ -5447,6 +5771,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cumprod
 torch_cumprod <- function(self, dim, dtype = NULL) {
   args <- mget(x = c("self", "dim", "dtype"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType")
@@ -5463,6 +5788,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cumprod_out
 torch_cumprod_out <- function(out, self, dim, dtype = NULL) {
   args <- mget(x = c("out", "self", "dim", "dtype"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname"
@@ -5480,6 +5806,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cumsum
 torch_cumsum <- function(self, dim, dtype = NULL) {
   args <- mget(x = c("self", "dim", "dtype"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType")
@@ -5496,6 +5823,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_cumsum_out
 torch_cumsum_out <- function(out, self, dim, dtype = NULL) {
   args <- mget(x = c("out", "self", "dim", "dtype"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname"
@@ -5513,6 +5841,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_dequantize
 torch_dequantize <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -5529,6 +5858,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_det
 torch_det <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -5545,6 +5875,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_detach
 torch_detach <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -5561,6 +5892,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_detach_
 torch_detach_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -5577,6 +5909,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_diag
 torch_diag <- function(self, diagonal = 0L) {
   args <- mget(x = c("self", "diagonal"))
 expected_types <- list(self = "Tensor", diagonal = "int64_t")
@@ -5593,6 +5926,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_diag_embed
 torch_diag_embed <- function(self, offset = 0L, dim1 = -2L, dim2 = -1L) {
   args <- mget(x = c("self", "offset", "dim1", "dim2"))
 expected_types <- list(self = "Tensor", offset = "int64_t", dim1 = "int64_t", dim2 = "int64_t")
@@ -5609,6 +5943,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_diag_out
 torch_diag_out <- function(out, self, diagonal = 0L) {
   args <- mget(x = c("out", "self", "diagonal"))
 expected_types <- list(out = "Tensor", self = "Tensor", diagonal = "int64_t")
@@ -5625,6 +5960,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_diagflat
 torch_diagflat <- function(self, offset = 0L) {
   args <- mget(x = c("self", "offset"))
 expected_types <- list(self = "Tensor", offset = "int64_t")
@@ -5641,6 +5977,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_diagonal
 torch_diagonal <- function(self, outdim, dim1 = 1L, dim2 = 2L, offset = 0L) {
   args <- mget(x = c("self", "outdim", "dim1", "dim2", "offset"))
 expected_types <- list(self = "Tensor", outdim = "Dimname", dim1 = c("int64_t", 
@@ -5658,6 +5995,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_digamma
 torch_digamma <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -5674,6 +6012,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_digamma_out
 torch_digamma_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -5690,6 +6029,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_dist
 torch_dist <- function(self, other, p = 2L) {
   args <- mget(x = c("self", "other", "p"))
 expected_types <- list(self = "Tensor", other = "Tensor", p = "Scalar")
@@ -5706,6 +6046,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_div
 torch_div <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
@@ -5722,6 +6063,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_div_out
 torch_div_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor")
@@ -5738,6 +6080,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_dot
 torch_dot <- function(self, tensor) {
   args <- mget(x = c("self", "tensor"))
 expected_types <- list(self = "Tensor", tensor = "Tensor")
@@ -5754,6 +6097,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_dot_out
 torch_dot_out <- function(out, self, tensor) {
   args <- mget(x = c("out", "self", "tensor"))
 expected_types <- list(out = "Tensor", self = "Tensor", tensor = "Tensor")
@@ -5770,6 +6114,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_dropout
 torch_dropout <- function(input, p, train) {
   args <- mget(x = c("input", "p", "train"))
 expected_types <- list(input = "Tensor", p = "double", train = "bool")
@@ -5786,6 +6131,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_dropout_
 torch_dropout_ <- function(self, p, train) {
   args <- mget(x = c("self", "p", "train"))
 expected_types <- list(self = "Tensor", p = "double", train = "bool")
@@ -5802,6 +6148,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_eig
 torch_eig <- function(self, eigenvectors = FALSE) {
   args <- mget(x = c("self", "eigenvectors"))
 expected_types <- list(self = "Tensor", eigenvectors = "bool")
@@ -5818,6 +6165,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_eig_out
 torch_eig_out <- function(e, v, self, eigenvectors = FALSE) {
   args <- mget(x = c("e", "v", "self", "eigenvectors"))
 expected_types <- list(e = "Tensor", v = "Tensor", self = "Tensor", eigenvectors = "bool")
@@ -5834,6 +6182,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_einsum
 torch_einsum <- function(equation, tensors) {
   args <- mget(x = c("equation", "tensors"))
 expected_types <- list(equation = "std::string", tensors = "TensorList")
@@ -5850,6 +6199,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_elu
 torch_elu <- function(self, alpha = 1L, scale = 1L, input_scale = 1L) {
   args <- mget(x = c("self", "alpha", "scale", "input_scale"))
 expected_types <- list(self = "Tensor", alpha = "Scalar", scale = "Scalar", input_scale = "Scalar")
@@ -5866,6 +6216,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_elu_
 torch_elu_ <- function(self, alpha = 1L, scale = 1L, input_scale = 1L) {
   args <- mget(x = c("self", "alpha", "scale", "input_scale"))
 expected_types <- list(self = "Tensor", alpha = "Scalar", scale = "Scalar", input_scale = "Scalar")
@@ -5882,6 +6233,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_elu_backward
 torch_elu_backward <- function(grad_output, alpha, scale, input_scale, output) {
   args <- mget(x = c("grad_output", "alpha", "scale", "input_scale", "output"))
 expected_types <- list(grad_output = "Tensor", alpha = "Scalar", scale = "Scalar", 
@@ -5899,6 +6251,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_elu_backward_out
 torch_elu_backward_out <- function(grad_input, grad_output, alpha, scale, input_scale, output) {
   args <- mget(x = c("grad_input", "grad_output", "alpha", "scale", "input_scale", "output"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", alpha = "Scalar", 
@@ -5917,6 +6270,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_elu_out
 torch_elu_out <- function(out, self, alpha = 1L, scale = 1L, input_scale = 1L) {
   args <- mget(x = c("out", "self", "alpha", "scale", "input_scale"))
 expected_types <- list(out = "Tensor", self = "Tensor", alpha = "Scalar", scale = "Scalar", 
@@ -5934,6 +6288,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_embedding
 torch_embedding <- function(weight, indices, padding_idx = -1L, scale_grad_by_freq = FALSE, sparse = FALSE) {
   args <- mget(x = c("weight", "indices", "padding_idx", "scale_grad_by_freq", "sparse"))
 expected_types <- list(weight = "Tensor", indices = "Tensor", padding_idx = "int64_t", 
@@ -5951,6 +6306,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_embedding_backward
 torch_embedding_backward <- function(grad, indices, num_weights, padding_idx, scale_grad_by_freq, sparse) {
   args <- mget(x = c("grad", "indices", "num_weights", "padding_idx", "scale_grad_by_freq", "sparse"))
 expected_types <- list(grad = "Tensor", indices = "Tensor", num_weights = "int64_t", 
@@ -5969,6 +6325,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_embedding_bag
 torch_embedding_bag <- function(weight, indices, offsets, scale_grad_by_freq = FALSE, mode = 0L, sparse = FALSE, per_sample_weights = list(), include_last_offset = FALSE) {
   args <- mget(x = c("weight", "indices", "offsets", "scale_grad_by_freq", "mode", "sparse", "per_sample_weights", "include_last_offset"))
 expected_types <- list(weight = "Tensor", indices = "Tensor", offsets = "Tensor", 
@@ -5987,6 +6344,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_embedding_dense_backward
 torch_embedding_dense_backward <- function(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq) {
   args <- mget(x = c("grad_output", "indices", "num_weights", "padding_idx", "scale_grad_by_freq"))
 expected_types <- list(grad_output = "Tensor", indices = "Tensor", num_weights = "int64_t", 
@@ -6005,6 +6363,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_embedding_renorm_
 torch_embedding_renorm_ <- function(self, indices, max_norm, norm_type) {
   args <- mget(x = c("self", "indices", "max_norm", "norm_type"))
 expected_types <- list(self = "Tensor", indices = "Tensor", max_norm = "double", 
@@ -6022,6 +6381,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_embedding_sparse_backward
 torch_embedding_sparse_backward <- function(grad, indices, num_weights, padding_idx, scale_grad_by_freq) {
   args <- mget(x = c("grad", "indices", "num_weights", "padding_idx", "scale_grad_by_freq"))
 expected_types <- list(grad = "Tensor", indices = "Tensor", num_weights = "int64_t", 
@@ -6040,6 +6400,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_empty
 .torch_empty <- function(size, names, options = list(), memory_format = NULL) {
   args <- mget(x = c("size", "names", "options", "memory_format"))
 expected_types <- list(size = "IntArrayRef", names = "DimnameList", options = "TensorOptions", 
@@ -6057,6 +6418,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_empty_like
 .torch_empty_like <- function(self, options = list(), memory_format = NULL) {
   args <- mget(x = c("self", "options", "memory_format"))
 expected_types <- list(self = "Tensor", options = "TensorOptions", memory_format = "MemoryFormat")
@@ -6073,6 +6435,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_empty_out
 torch_empty_out <- function(out, size, memory_format = NULL) {
   args <- mget(x = c("out", "size", "memory_format"))
 expected_types <- list(out = "Tensor", size = "IntArrayRef", memory_format = "MemoryFormat")
@@ -6089,6 +6452,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_empty_strided
 .torch_empty_strided <- function(size, stride, options = list()) {
   args <- mget(x = c("size", "stride", "options"))
 expected_types <- list(size = "IntArrayRef", stride = "IntArrayRef", options = "TensorOptions")
@@ -6105,6 +6469,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_eq
 torch_eq <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -6121,6 +6486,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_eq_out
 torch_eq_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor"
@@ -6138,6 +6504,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_equal
 torch_equal <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = "Tensor")
@@ -6154,6 +6521,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_erf
 torch_erf <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6170,6 +6538,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_erf_
 torch_erf_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6186,6 +6555,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_erf_out
 torch_erf_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -6202,6 +6572,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_erfc
 torch_erfc <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6218,6 +6589,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_erfc_
 torch_erfc_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6234,6 +6606,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_erfc_out
 torch_erfc_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -6250,6 +6623,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_erfinv
 torch_erfinv <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6266,6 +6640,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_erfinv_out
 torch_erfinv_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -6282,6 +6657,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_exp
 torch_exp <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6298,6 +6674,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_exp_
 torch_exp_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6314,6 +6691,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_exp_out
 torch_exp_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -6330,6 +6708,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_expm1
 torch_expm1 <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6346,6 +6725,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_expm1_
 torch_expm1_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6362,6 +6742,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_expm1_out
 torch_expm1_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -6378,6 +6759,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_eye
 .torch_eye <- function(n, m, options = list()) {
   args <- mget(x = c("n", "m", "options"))
 expected_types <- list(n = "int64_t", m = "int64_t", options = "TensorOptions")
@@ -6394,6 +6776,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_eye_out
 torch_eye_out <- function(out, n, m) {
   args <- mget(x = c("out", "n", "m"))
 expected_types <- list(out = "Tensor", n = "int64_t", m = "int64_t")
@@ -6410,6 +6793,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fake_quantize_per_channel_affine
 torch_fake_quantize_per_channel_affine <- function(self, scale, zero_point, axis, quant_min, quant_max) {
   args <- mget(x = c("self", "scale", "zero_point", "axis", "quant_min", "quant_max"))
 expected_types <- list(self = "Tensor", scale = "Tensor", zero_point = "Tensor", 
@@ -6428,6 +6812,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fake_quantize_per_channel_affine_backward
 torch_fake_quantize_per_channel_affine_backward <- function(grad, self, scale, zero_point, axis, quant_min, quant_max) {
   args <- mget(x = c("grad", "self", "scale", "zero_point", "axis", "quant_min", "quant_max"))
 expected_types <- list(grad = "Tensor", self = "Tensor", scale = "Tensor", zero_point = "Tensor", 
@@ -6446,6 +6831,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fake_quantize_per_tensor_affine
 torch_fake_quantize_per_tensor_affine <- function(self, scale, zero_point, quant_min, quant_max) {
   args <- mget(x = c("self", "scale", "zero_point", "quant_min", "quant_max"))
 expected_types <- list(self = "Tensor", scale = "double", zero_point = "int64_t", 
@@ -6463,6 +6849,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fake_quantize_per_tensor_affine_backward
 torch_fake_quantize_per_tensor_affine_backward <- function(grad, self, scale, zero_point, quant_min, quant_max) {
   args <- mget(x = c("grad", "self", "scale", "zero_point", "quant_min", "quant_max"))
 expected_types <- list(grad = "Tensor", self = "Tensor", scale = "double", zero_point = "int64_t", 
@@ -6481,6 +6868,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fbgemm_linear_fp16_weight
 torch_fbgemm_linear_fp16_weight <- function(input, packed_weight, bias) {
   args <- mget(x = c("input", "packed_weight", "bias"))
 expected_types <- list(input = "Tensor", packed_weight = "Tensor", bias = "Tensor")
@@ -6497,6 +6885,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fbgemm_linear_fp16_weight_fp32_activation
 torch_fbgemm_linear_fp16_weight_fp32_activation <- function(input, packed_weight, bias) {
   args <- mget(x = c("input", "packed_weight", "bias"))
 expected_types <- list(input = "Tensor", packed_weight = "Tensor", bias = "Tensor")
@@ -6513,6 +6902,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fbgemm_linear_int8_weight
 torch_fbgemm_linear_int8_weight <- function(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias) {
   args <- mget(x = c("input", "weight", "packed", "col_offsets", "weight_scale", "weight_zero_point", "bias"))
 expected_types <- list(input = "Tensor", weight = "Tensor", packed = "Tensor", 
@@ -6532,6 +6922,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fbgemm_linear_int8_weight_fp32_activation
 torch_fbgemm_linear_int8_weight_fp32_activation <- function(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias) {
   args <- mget(x = c("input", "weight", "packed", "col_offsets", "weight_scale", "weight_zero_point", "bias"))
 expected_types <- list(input = "Tensor", weight = "Tensor", packed = "Tensor", 
@@ -6551,6 +6942,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fbgemm_linear_quantize_weight
 torch_fbgemm_linear_quantize_weight <- function(input) {
   args <- mget(x = c("input"))
 expected_types <- list(input = "Tensor")
@@ -6567,6 +6959,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fbgemm_pack_gemm_matrix_fp16
 torch_fbgemm_pack_gemm_matrix_fp16 <- function(input) {
   args <- mget(x = c("input"))
 expected_types <- list(input = "Tensor")
@@ -6583,6 +6976,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fbgemm_pack_quantized_matrix
 torch_fbgemm_pack_quantized_matrix <- function(input, K, False) {
   args <- mget(x = c("input", "K", "False"))
 expected_types <- list(input = "Tensor", K = "int64_t", False = "int64_t")
@@ -6599,6 +6993,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_feature_alpha_dropout
 torch_feature_alpha_dropout <- function(input, p, train) {
   args <- mget(x = c("input", "p", "train"))
 expected_types <- list(input = "Tensor", p = "double", train = "bool")
@@ -6615,6 +7010,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_feature_alpha_dropout_
 torch_feature_alpha_dropout_ <- function(self, p, train) {
   args <- mget(x = c("self", "p", "train"))
 expected_types <- list(self = "Tensor", p = "double", train = "bool")
@@ -6631,6 +7027,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_feature_dropout
 torch_feature_dropout <- function(input, p, train) {
   args <- mget(x = c("input", "p", "train"))
 expected_types <- list(input = "Tensor", p = "double", train = "bool")
@@ -6647,6 +7044,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_feature_dropout_
 torch_feature_dropout_ <- function(self, p, train) {
   args <- mget(x = c("self", "p", "train"))
 expected_types <- list(self = "Tensor", p = "double", train = "bool")
@@ -6663,6 +7061,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fft
 torch_fft <- function(self, signal_ndim, normalized = FALSE) {
   args <- mget(x = c("self", "signal_ndim", "normalized"))
 expected_types <- list(self = "Tensor", signal_ndim = "int64_t", normalized = "bool")
@@ -6679,6 +7078,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fill_
 torch_fill_ <- function(self, value) {
   args <- mget(x = c("self", "value"))
 expected_types <- list(self = "Tensor", value = c("Scalar", "Tensor"))
@@ -6695,6 +7095,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_flatten
 torch_flatten <- function(self, dims, start_dim = 1L, end_dim = -1L, out_dim) {
   args <- mget(x = c("self", "dims", "start_dim", "end_dim", "out_dim"))
 expected_types <- list(self = "Tensor", dims = "DimnameList", start_dim = c("int64_t", 
@@ -6712,6 +7113,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_flip
 torch_flip <- function(self, dims) {
   args <- mget(x = c("self", "dims"))
 expected_types <- list(self = "Tensor", dims = "IntArrayRef")
@@ -6728,6 +7130,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_floor
 torch_floor <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6744,6 +7147,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_floor_
 torch_floor_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6760,6 +7164,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_floor_divide
 torch_floor_divide <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
@@ -6776,6 +7181,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_floor_divide_out
 torch_floor_divide_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor")
@@ -6792,6 +7198,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_floor_out
 torch_floor_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -6808,6 +7215,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fmod
 torch_fmod <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -6824,6 +7232,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fmod_out
 torch_fmod_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor"
@@ -6841,6 +7250,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_frac
 torch_frac <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6857,6 +7267,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_frac_
 torch_frac_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -6873,6 +7284,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_frac_out
 torch_frac_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -6889,6 +7301,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fractional_max_pool2d
 torch_fractional_max_pool2d <- function(self, kernel_size, output_size, random_samples) {
   args <- mget(x = c("self", "kernel_size", "output_size", "random_samples"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", output_size = "IntArrayRef", 
@@ -6906,6 +7319,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fractional_max_pool2d_backward
 torch_fractional_max_pool2d_backward <- function(grad_output, self, kernel_size, output_size, indices) {
   args <- mget(x = c("grad_output", "self", "kernel_size", "output_size", "indices"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", 
@@ -6924,6 +7338,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fractional_max_pool2d_backward_out
 torch_fractional_max_pool2d_backward_out <- function(grad_input, grad_output, self, kernel_size, output_size, indices) {
   args <- mget(x = c("grad_input", "grad_output", "self", "kernel_size", "output_size", "indices"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -6943,6 +7358,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fractional_max_pool2d_out
 torch_fractional_max_pool2d_out <- function(output, indices, self, kernel_size, output_size, random_samples) {
   args <- mget(x = c("output", "indices", "self", "kernel_size", "output_size", "random_samples"))
 expected_types <- list(output = "Tensor", indices = "Tensor", self = "Tensor", 
@@ -6962,6 +7378,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fractional_max_pool3d
 torch_fractional_max_pool3d <- function(self, kernel_size, output_size, random_samples) {
   args <- mget(x = c("self", "kernel_size", "output_size", "random_samples"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", output_size = "IntArrayRef", 
@@ -6979,6 +7396,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fractional_max_pool3d_backward
 torch_fractional_max_pool3d_backward <- function(grad_output, self, kernel_size, output_size, indices) {
   args <- mget(x = c("grad_output", "self", "kernel_size", "output_size", "indices"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", 
@@ -6997,6 +7415,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fractional_max_pool3d_backward_out
 torch_fractional_max_pool3d_backward_out <- function(grad_input, grad_output, self, kernel_size, output_size, indices) {
   args <- mget(x = c("grad_input", "grad_output", "self", "kernel_size", "output_size", "indices"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -7016,6 +7435,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_fractional_max_pool3d_out
 torch_fractional_max_pool3d_out <- function(output, indices, self, kernel_size, output_size, random_samples) {
   args <- mget(x = c("output", "indices", "self", "kernel_size", "output_size", "random_samples"))
 expected_types <- list(output = "Tensor", indices = "Tensor", self = "Tensor", 
@@ -7035,6 +7455,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_frobenius_norm
 torch_frobenius_norm <- function(self, dim, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = "IntArrayRef", keepdim = "bool")
@@ -7051,6 +7472,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_frobenius_norm_out
 torch_frobenius_norm_out <- function(out, self, dim, keepdim = FALSE) {
   args <- mget(x = c("out", "self", "dim", "keepdim"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = "IntArrayRef", keepdim = "bool")
@@ -7067,6 +7489,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_from_file
 torch_from_file <- function(filename, shared = NULL, size = 0L, options = list()) {
   args <- mget(x = c("filename", "shared", "size", "options"))
 expected_types <- list(filename = "std::string", shared = "bool", size = "int64_t", 
@@ -7084,6 +7507,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_full
 .torch_full <- function(size, fill_value, names, options = list()) {
   args <- mget(x = c("size", "fill_value", "names", "options"))
 expected_types <- list(size = "IntArrayRef", fill_value = "Scalar", names = "DimnameList", 
@@ -7101,6 +7525,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_full_like
 .torch_full_like <- function(self, fill_value, options = list(), memory_format = NULL) {
   args <- mget(x = c("self", "fill_value", "options", "memory_format"))
 expected_types <- list(self = "Tensor", fill_value = "Scalar", options = "TensorOptions", 
@@ -7118,6 +7543,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_full_out
 torch_full_out <- function(out, size, fill_value) {
   args <- mget(x = c("out", "size", "fill_value"))
 expected_types <- list(out = "Tensor", size = "IntArrayRef", fill_value = "Scalar")
@@ -7134,6 +7560,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_gather
 torch_gather <- function(self, dim, index, sparse_grad = FALSE) {
   args <- mget(x = c("self", "dim", "index", "sparse_grad"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor", 
@@ -7151,6 +7578,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_gather_out
 torch_gather_out <- function(out, self, dim, index, sparse_grad = FALSE) {
   args <- mget(x = c("out", "self", "dim", "index", "sparse_grad"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname"
@@ -7168,6 +7596,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ge
 torch_ge <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -7184,6 +7613,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ge_out
 torch_ge_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor"
@@ -7201,6 +7631,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_gelu
 torch_gelu <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -7217,6 +7648,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_gelu_backward
 torch_gelu_backward <- function(grad, self) {
   args <- mget(x = c("grad", "self"))
 expected_types <- list(grad = "Tensor", self = "Tensor")
@@ -7233,6 +7665,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_geqrf
 torch_geqrf <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -7249,6 +7682,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_geqrf_out
 torch_geqrf_out <- function(a, tau, self) {
   args <- mget(x = c("a", "tau", "self"))
 expected_types <- list(a = "Tensor", tau = "Tensor", self = "Tensor")
@@ -7265,6 +7699,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ger
 torch_ger <- function(self, vec2) {
   args <- mget(x = c("self", "vec2"))
 expected_types <- list(self = "Tensor", vec2 = "Tensor")
@@ -7281,6 +7716,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ger_out
 torch_ger_out <- function(out, self, vec2) {
   args <- mget(x = c("out", "self", "vec2"))
 expected_types <- list(out = "Tensor", self = "Tensor", vec2 = "Tensor")
@@ -7297,6 +7733,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_glu
 torch_glu <- function(self, dim = -1L) {
   args <- mget(x = c("self", "dim"))
 expected_types <- list(self = "Tensor", dim = "int64_t")
@@ -7313,6 +7750,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_glu_backward
 torch_glu_backward <- function(grad_output, self, dim) {
   args <- mget(x = c("grad_output", "self", "dim"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", dim = "int64_t")
@@ -7329,6 +7767,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_glu_backward_out
 torch_glu_backward_out <- function(grad_input, grad_output, self, dim) {
   args <- mget(x = c("grad_input", "grad_output", "self", "dim"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -7346,6 +7785,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_glu_out
 torch_glu_out <- function(out, self, dim = -1L) {
   args <- mget(x = c("out", "self", "dim"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = "int64_t")
@@ -7362,6 +7802,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_grid_sampler
 torch_grid_sampler <- function(input, grid, interpolation_mode, padding_mode, align_corners) {
   args <- mget(x = c("input", "grid", "interpolation_mode", "padding_mode", "align_corners"))
 expected_types <- list(input = "Tensor", grid = "Tensor", interpolation_mode = "int64_t", 
@@ -7380,6 +7821,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_grid_sampler_2d
 torch_grid_sampler_2d <- function(input, grid, interpolation_mode, padding_mode, align_corners) {
   args <- mget(x = c("input", "grid", "interpolation_mode", "padding_mode", "align_corners"))
 expected_types <- list(input = "Tensor", grid = "Tensor", interpolation_mode = "int64_t", 
@@ -7398,6 +7840,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_grid_sampler_2d_backward
 torch_grid_sampler_2d_backward <- function(grad_output, input, grid, interpolation_mode, padding_mode, align_corners) {
   args <- mget(x = c("grad_output", "input", "grid", "interpolation_mode", "padding_mode", "align_corners"))
 expected_types <- list(grad_output = "Tensor", input = "Tensor", grid = "Tensor", 
@@ -7417,6 +7860,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_grid_sampler_3d
 torch_grid_sampler_3d <- function(input, grid, interpolation_mode, padding_mode, align_corners) {
   args <- mget(x = c("input", "grid", "interpolation_mode", "padding_mode", "align_corners"))
 expected_types <- list(input = "Tensor", grid = "Tensor", interpolation_mode = "int64_t", 
@@ -7435,6 +7879,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_grid_sampler_3d_backward
 torch_grid_sampler_3d_backward <- function(grad_output, input, grid, interpolation_mode, padding_mode, align_corners) {
   args <- mget(x = c("grad_output", "input", "grid", "interpolation_mode", "padding_mode", "align_corners"))
 expected_types <- list(grad_output = "Tensor", input = "Tensor", grid = "Tensor", 
@@ -7454,6 +7899,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_group_norm
 torch_group_norm <- function(input, num_groups, weight = list(), bias = list(), eps = 0.000010, cudnn_enabled = TRUE) {
   args <- mget(x = c("input", "num_groups", "weight", "bias", "eps", "cudnn_enabled"))
 expected_types <- list(input = "Tensor", num_groups = "int64_t", weight = "Tensor", 
@@ -7471,6 +7917,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_gru
 torch_gru <- function(data, input, batch_sizes, hx, params, has_biases, num_layers, dropout, train, batch_first, bidirectional) {
   args <- mget(x = c("data", "input", "batch_sizes", "hx", "params", "has_biases", "num_layers", "dropout", "train", "batch_first", "bidirectional"))
 expected_types <- list(data = "Tensor", input = "Tensor", batch_sizes = "Tensor", 
@@ -7492,6 +7939,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_gru_cell
 torch_gru_cell <- function(input, hx, w_ih, w_hh, b_ih = list(), b_hh = list()) {
   args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh"))
 expected_types <- list(input = "Tensor", hx = "Tensor", w_ih = "Tensor", w_hh = "Tensor", 
@@ -7509,6 +7957,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_gt
 torch_gt <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -7525,6 +7974,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_gt_out
 torch_gt_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor"
@@ -7542,6 +7992,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hamming_window
 torch_hamming_window <- function(window_length, periodic, alpha, beta, options = list()) {
   args <- mget(x = c("window_length", "periodic", "alpha", "beta", "options"))
 expected_types <- list(window_length = "int64_t", periodic = "bool", alpha = "double", 
@@ -7559,6 +8010,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hann_window
 torch_hann_window <- function(window_length, periodic, options = list()) {
   args <- mget(x = c("window_length", "periodic", "options"))
 expected_types <- list(window_length = "int64_t", periodic = "bool", options = "TensorOptions")
@@ -7575,6 +8027,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hardshrink
 torch_hardshrink <- function(self, lambd = 0.500000) {
   args <- mget(x = c("self", "lambd"))
 expected_types <- list(self = "Tensor", lambd = "Scalar")
@@ -7591,6 +8044,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hardshrink_backward
 torch_hardshrink_backward <- function(grad_out, self, lambd) {
   args <- mget(x = c("grad_out", "self", "lambd"))
 expected_types <- list(grad_out = "Tensor", self = "Tensor", lambd = "Scalar")
@@ -7607,6 +8061,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hardsigmoid
 torch_hardsigmoid <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -7623,6 +8078,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hardsigmoid_
 torch_hardsigmoid_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -7639,6 +8095,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hardsigmoid_backward
 torch_hardsigmoid_backward <- function(grad_output, self) {
   args <- mget(x = c("grad_output", "self"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor")
@@ -7655,6 +8112,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hardsigmoid_out
 torch_hardsigmoid_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -7671,6 +8129,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hardtanh
 torch_hardtanh <- function(self, min_val = -1L, max_val = 1L) {
   args <- mget(x = c("self", "min_val", "max_val"))
 expected_types <- list(self = "Tensor", min_val = "Scalar", max_val = "Scalar")
@@ -7687,6 +8146,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hardtanh_
 torch_hardtanh_ <- function(self, min_val = -1L, max_val = 1L) {
   args <- mget(x = c("self", "min_val", "max_val"))
 expected_types <- list(self = "Tensor", min_val = "Scalar", max_val = "Scalar")
@@ -7703,6 +8163,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hardtanh_backward
 torch_hardtanh_backward <- function(grad_output, self, min_val, max_val) {
   args <- mget(x = c("grad_output", "self", "min_val", "max_val"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", min_val = "Scalar", 
@@ -7720,6 +8181,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hardtanh_backward_out
 torch_hardtanh_backward_out <- function(grad_input, grad_output, self, min_val, max_val) {
   args <- mget(x = c("grad_input", "grad_output", "self", "min_val", "max_val"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -7737,6 +8199,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hardtanh_out
 torch_hardtanh_out <- function(out, self, min_val = -1L, max_val = 1L) {
   args <- mget(x = c("out", "self", "min_val", "max_val"))
 expected_types <- list(out = "Tensor", self = "Tensor", min_val = "Scalar", max_val = "Scalar")
@@ -7753,6 +8216,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hinge_embedding_loss
 torch_hinge_embedding_loss <- function(self, target, margin = 1.000000, reduction = torch_reduction_mean()) {
   args <- mget(x = c("self", "target", "margin", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", margin = "double", reduction = "int64_t")
@@ -7769,6 +8233,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_histc
 torch_histc <- function(self, bins = 100L, min = 0L, max = 0L) {
   args <- mget(x = c("self", "bins", "min", "max"))
 expected_types <- list(self = "Tensor", bins = "int64_t", min = "Scalar", max = "Scalar")
@@ -7785,6 +8250,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_histc_out
 torch_histc_out <- function(out, self, bins = 100L, min = 0L, max = 0L) {
   args <- mget(x = c("out", "self", "bins", "min", "max"))
 expected_types <- list(out = "Tensor", self = "Tensor", bins = "int64_t", min = "Scalar", 
@@ -7802,6 +8268,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hspmm
 torch_hspmm <- function(mat1, mat2) {
   args <- mget(x = c("mat1", "mat2"))
 expected_types <- list(mat1 = "Tensor", mat2 = "Tensor")
@@ -7818,6 +8285,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_hspmm_out
 torch_hspmm_out <- function(out, mat1, mat2) {
   args <- mget(x = c("out", "mat1", "mat2"))
 expected_types <- list(out = "Tensor", mat1 = "Tensor", mat2 = "Tensor")
@@ -7834,6 +8302,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ifft
 torch_ifft <- function(self, signal_ndim, normalized = FALSE) {
   args <- mget(x = c("self", "signal_ndim", "normalized"))
 expected_types <- list(self = "Tensor", signal_ndim = "int64_t", normalized = "bool")
@@ -7850,6 +8319,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_im2col
 torch_im2col <- function(self, kernel_size, dilation, padding, stride) {
   args <- mget(x = c("self", "kernel_size", "dilation", "padding", "stride"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", dilation = "IntArrayRef", 
@@ -7867,6 +8337,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_im2col_backward
 torch_im2col_backward <- function(grad_output, input_size, kernel_size, dilation, padding, stride) {
   args <- mget(x = c("grad_output", "input_size", "kernel_size", "dilation", "padding", "stride"))
 expected_types <- list(grad_output = "Tensor", input_size = "IntArrayRef", kernel_size = "IntArrayRef", 
@@ -7885,6 +8356,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_im2col_backward_out
 torch_im2col_backward_out <- function(grad_input, grad_output, input_size, kernel_size, dilation, padding, stride) {
   args <- mget(x = c("grad_input", "grad_output", "input_size", "kernel_size", "dilation", "padding", "stride"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", input_size = "IntArrayRef", 
@@ -7904,6 +8376,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_im2col_out
 torch_im2col_out <- function(out, self, kernel_size, dilation, padding, stride) {
   args <- mget(x = c("out", "self", "kernel_size", "dilation", "padding", "stride"))
 expected_types <- list(out = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", 
@@ -7922,6 +8395,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_imag
 torch_imag <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -7938,6 +8412,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_index
 torch_index <- function(self, indices) {
   args <- mget(x = c("self", "indices"))
 expected_types <- list(self = "Tensor", indices = "TensorList")
@@ -7954,6 +8429,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_index_add
 torch_index_add <- function(self, dim, index, source) {
   args <- mget(x = c("self", "dim", "index", "source"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor", 
@@ -7971,6 +8447,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_index_copy
 torch_index_copy <- function(self, dim, index, source) {
   args <- mget(x = c("self", "dim", "index", "source"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor", 
@@ -7988,6 +8465,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_index_fill
 torch_index_fill <- function(self, dim, index, value) {
   args <- mget(x = c("self", "dim", "index", "value"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor", 
@@ -8005,6 +8483,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_index_put
 torch_index_put <- function(self, indices, values, accumulate = FALSE) {
   args <- mget(x = c("self", "indices", "values", "accumulate"))
 expected_types <- list(self = "Tensor", indices = "TensorList", values = "Tensor", 
@@ -8022,6 +8501,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_index_put_
 torch_index_put_ <- function(self, indices, values, accumulate = FALSE) {
   args <- mget(x = c("self", "indices", "values", "accumulate"))
 expected_types <- list(self = "Tensor", indices = "TensorList", values = "Tensor", 
@@ -8039,6 +8519,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_index_select
 torch_index_select <- function(self, dim, index) {
   args <- mget(x = c("self", "dim", "index"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor")
@@ -8055,6 +8536,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_index_select_out
 torch_index_select_out <- function(out, self, dim, index) {
   args <- mget(x = c("out", "self", "dim", "index"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname"
@@ -8072,6 +8554,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_instance_norm
 torch_instance_norm <- function(input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, cudnn_enabled) {
   args <- mget(x = c("input", "weight", "bias", "running_mean", "running_var", "use_input_stats", "momentum", "eps", "cudnn_enabled"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", running_mean = "Tensor", 
@@ -8091,6 +8574,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_int_repr
 torch_int_repr <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8107,6 +8591,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_inverse
 torch_inverse <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8123,6 +8608,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_inverse_out
 torch_inverse_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -8139,6 +8625,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_irfft
 torch_irfft <- function(self, signal_ndim, normalized = FALSE, onesided = TRUE, signal_sizes = list()) {
   args <- mget(x = c("self", "signal_ndim", "normalized", "onesided", "signal_sizes"))
 expected_types <- list(self = "Tensor", signal_ndim = "int64_t", normalized = "bool", 
@@ -8156,6 +8643,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_is_complex
 torch_is_complex <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8172,6 +8660,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_is_distributed
 torch_is_distributed <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8188,6 +8677,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_is_floating_point
 torch_is_floating_point <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8204,6 +8694,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_is_nonzero
 torch_is_nonzero <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8220,6 +8711,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_is_same_size
 torch_is_same_size <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = "Tensor")
@@ -8236,6 +8728,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_is_signed
 torch_is_signed <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8252,6 +8745,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_isclose
 torch_isclose <- function(self, other, rtol = 0.000010, atol = 0.000000, equal_nan = FALSE) {
   args <- mget(x = c("self", "other", "rtol", "atol", "equal_nan"))
 expected_types <- list(self = "Tensor", other = "Tensor", rtol = "double", atol = "double", 
@@ -8269,6 +8763,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_isfinite
 torch_isfinite <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8285,6 +8780,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_isinf
 torch_isinf <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8301,6 +8797,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_isnan
 torch_isnan <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8317,6 +8814,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_kl_div
 torch_kl_div <- function(self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("self", "target", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t")
@@ -8333,6 +8831,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_kl_div_backward
 torch_kl_div_backward <- function(grad_output, self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("grad_output", "self", "target", "reduction"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", 
@@ -8350,6 +8849,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_kthvalue
 torch_kthvalue <- function(self, k, dim = -1L, keepdim = FALSE) {
   args <- mget(x = c("self", "k", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", k = "int64_t", dim = c("int64_t", "Dimname"
@@ -8367,6 +8867,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_kthvalue_out
 torch_kthvalue_out <- function(values, indices, self, k, dim = -1L, keepdim = FALSE) {
   args <- mget(x = c("values", "indices", "self", "k", "dim", "keepdim"))
 expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", 
@@ -8384,6 +8885,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_l1_loss
 torch_l1_loss <- function(self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("self", "target", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t")
@@ -8400,6 +8902,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_l1_loss_backward
 torch_l1_loss_backward <- function(grad_output, self, target, reduction) {
   args <- mget(x = c("grad_output", "self", "target", "reduction"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", 
@@ -8417,6 +8920,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_l1_loss_backward_out
 torch_l1_loss_backward_out <- function(grad_input, grad_output, self, target, reduction) {
   args <- mget(x = c("grad_input", "grad_output", "self", "target", "reduction"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -8434,6 +8938,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_l1_loss_out
 torch_l1_loss_out <- function(out, self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("out", "self", "target", "reduction"))
 expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", reduction = "int64_t")
@@ -8450,6 +8955,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_layer_norm
 torch_layer_norm <- function(input, normalized_shape, weight = list(), bias = list(), eps = 0.000010, cudnn_enable = TRUE) {
   args <- mget(x = c("input", "normalized_shape", "weight", "bias", "eps", "cudnn_enable"))
 expected_types <- list(input = "Tensor", normalized_shape = "IntArrayRef", weight = "Tensor", 
@@ -8467,6 +8973,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_le
 torch_le <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -8483,6 +8990,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_le_out
 torch_le_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor"
@@ -8500,6 +9008,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_leaky_relu
 torch_leaky_relu <- function(self, negative_slope = 0.010000) {
   args <- mget(x = c("self", "negative_slope"))
 expected_types <- list(self = "Tensor", negative_slope = "Scalar")
@@ -8516,6 +9025,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_leaky_relu_
 torch_leaky_relu_ <- function(self, negative_slope = 0.010000) {
   args <- mget(x = c("self", "negative_slope"))
 expected_types <- list(self = "Tensor", negative_slope = "Scalar")
@@ -8532,6 +9042,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_leaky_relu_backward
 torch_leaky_relu_backward <- function(grad_output, self, negative_slope, self_is_result) {
   args <- mget(x = c("grad_output", "self", "negative_slope", "self_is_result"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", negative_slope = "Scalar", 
@@ -8549,6 +9060,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_leaky_relu_out
 torch_leaky_relu_out <- function(out, self, negative_slope = 0.010000) {
   args <- mget(x = c("out", "self", "negative_slope"))
 expected_types <- list(out = "Tensor", self = "Tensor", negative_slope = "Scalar")
@@ -8565,6 +9077,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_lerp
 torch_lerp <- function(self, end, weight) {
   args <- mget(x = c("self", "end", "weight"))
 expected_types <- list(self = "Tensor", end = "Tensor", weight = c("Scalar", "Tensor"
@@ -8582,6 +9095,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_lerp_out
 torch_lerp_out <- function(out, self, end, weight) {
   args <- mget(x = c("out", "self", "end", "weight"))
 expected_types <- list(out = "Tensor", self = "Tensor", end = "Tensor", weight = c("Scalar", 
@@ -8599,6 +9113,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_lgamma
 torch_lgamma <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8615,6 +9130,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_lgamma_out
 torch_lgamma_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -8631,6 +9147,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_linear
 torch_linear <- function(input, weight, bias = list()) {
   args <- mget(x = c("input", "weight", "bias"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor")
@@ -8647,6 +9164,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_linspace
 .torch_linspace <- function(start, end, steps = 100L, options = list()) {
   args <- mget(x = c("start", "end", "steps", "options"))
 expected_types <- list(start = "Scalar", end = "Scalar", steps = "int64_t", options = "TensorOptions")
@@ -8663,6 +9181,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_linspace_out
 torch_linspace_out <- function(out, start, end, steps = 100L) {
   args <- mget(x = c("out", "start", "end", "steps"))
 expected_types <- list(out = "Tensor", start = "Scalar", end = "Scalar", steps = "int64_t")
@@ -8679,6 +9198,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log
 torch_log <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8695,6 +9215,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log_
 torch_log_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8711,6 +9232,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log_out
 torch_log_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -8727,6 +9249,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log_sigmoid
 torch_log_sigmoid <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8743,6 +9266,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log_sigmoid_backward
 torch_log_sigmoid_backward <- function(grad_output, self, buffer) {
   args <- mget(x = c("grad_output", "self", "buffer"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", buffer = "Tensor")
@@ -8759,6 +9283,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log_sigmoid_backward_out
 torch_log_sigmoid_backward_out <- function(grad_input, grad_output, self, buffer) {
   args <- mget(x = c("grad_input", "grad_output", "self", "buffer"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -8776,6 +9301,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log_sigmoid_forward
 torch_log_sigmoid_forward <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8792,6 +9318,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log_sigmoid_forward_out
 torch_log_sigmoid_forward_out <- function(output, buffer, self) {
   args <- mget(x = c("output", "buffer", "self"))
 expected_types <- list(output = "Tensor", buffer = "Tensor", self = "Tensor")
@@ -8808,6 +9335,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log_sigmoid_out
 torch_log_sigmoid_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -8824,6 +9352,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log_softmax
 torch_log_softmax <- function(self, dim, dtype = NULL) {
   args <- mget(x = c("self", "dim", "dtype"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType")
@@ -8840,6 +9369,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log10
 torch_log10 <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8856,6 +9386,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log10_
 torch_log10_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8872,6 +9403,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log10_out
 torch_log10_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -8888,6 +9420,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log1p
 torch_log1p <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8904,6 +9437,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log1p_
 torch_log1p_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8920,6 +9454,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log1p_out
 torch_log1p_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -8936,6 +9471,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log2
 torch_log2 <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8952,6 +9488,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log2_
 torch_log2_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -8968,6 +9505,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_log2_out
 torch_log2_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -8984,6 +9522,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_logdet
 torch_logdet <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -9000,6 +9539,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_logical_and
 torch_logical_and <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = "Tensor")
@@ -9016,6 +9556,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_logical_and_out
 torch_logical_and_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor")
@@ -9032,6 +9573,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_logical_not
 .torch_logical_not <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -9048,6 +9590,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_logical_not_out
 torch_logical_not_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -9064,6 +9607,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_logical_or
 torch_logical_or <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = "Tensor")
@@ -9080,6 +9624,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_logical_or_out
 torch_logical_or_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor")
@@ -9096,6 +9641,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_logical_xor
 torch_logical_xor <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = "Tensor")
@@ -9112,6 +9658,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_logical_xor_out
 torch_logical_xor_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor")
@@ -9128,6 +9675,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_logspace
 .torch_logspace <- function(start, end, steps = 100L, base = 10.000000, options = list()) {
   args <- mget(x = c("start", "end", "steps", "base", "options"))
 expected_types <- list(start = "Scalar", end = "Scalar", steps = "int64_t", base = "double", 
@@ -9145,6 +9693,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_logspace_out
 torch_logspace_out <- function(out, start, end, steps = 100L, base = 10.000000) {
   args <- mget(x = c("out", "start", "end", "steps", "base"))
 expected_types <- list(out = "Tensor", start = "Scalar", end = "Scalar", steps = "int64_t", 
@@ -9162,6 +9711,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_logsumexp
 torch_logsumexp <- function(self, dim, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), 
@@ -9179,6 +9729,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_logsumexp_out
 torch_logsumexp_out <- function(out, self, dim, keepdim = FALSE) {
   args <- mget(x = c("out", "self", "dim", "keepdim"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = c("IntArrayRef", 
@@ -9196,6 +9747,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_lstm
 torch_lstm <- function(data, input, batch_sizes, hx, params, has_biases, num_layers, dropout, train, batch_first, bidirectional) {
   args <- mget(x = c("data", "input", "batch_sizes", "hx", "params", "has_biases", "num_layers", "dropout", "train", "batch_first", "bidirectional"))
 expected_types <- list(data = "Tensor", input = "Tensor", batch_sizes = "Tensor", 
@@ -9217,6 +9769,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_lstm_cell
 torch_lstm_cell <- function(input, hx, w_ih, w_hh, b_ih = list(), b_hh = list()) {
   args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh"))
 expected_types <- list(input = "Tensor", hx = "TensorList", w_ih = "Tensor", w_hh = "Tensor", 
@@ -9234,6 +9787,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_lstsq
 torch_lstsq <- function(self, A) {
   args <- mget(x = c("self", "A"))
 expected_types <- list(self = "Tensor", A = "Tensor")
@@ -9250,6 +9804,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_lstsq_out
 torch_lstsq_out <- function(X, qr, self, A) {
   args <- mget(x = c("X", "qr", "self", "A"))
 expected_types <- list(X = "Tensor", qr = "Tensor", self = "Tensor", A = "Tensor")
@@ -9266,6 +9821,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_lt
 torch_lt <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -9282,6 +9838,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_lt_out
 torch_lt_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor"
@@ -9299,6 +9856,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_lu_solve
 torch_lu_solve <- function(self, LU_data, LU_pivots) {
   args <- mget(x = c("self", "LU_data", "LU_pivots"))
 expected_types <- list(self = "Tensor", LU_data = "Tensor", LU_pivots = "Tensor")
@@ -9315,6 +9873,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_lu_solve_out
 torch_lu_solve_out <- function(out, self, LU_data, LU_pivots) {
   args <- mget(x = c("out", "self", "LU_data", "LU_pivots"))
 expected_types <- list(out = "Tensor", self = "Tensor", LU_data = "Tensor", LU_pivots = "Tensor")
@@ -9331,6 +9890,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_margin_ranking_loss
 torch_margin_ranking_loss <- function(input1, input2, target, margin = 0.000000, reduction = torch_reduction_mean()) {
   args <- mget(x = c("input1", "input2", "target", "margin", "reduction"))
 expected_types <- list(input1 = "Tensor", input2 = "Tensor", target = "Tensor", 
@@ -9348,6 +9908,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_masked_fill
 torch_masked_fill <- function(self, mask, value) {
   args <- mget(x = c("self", "mask", "value"))
 expected_types <- list(self = "Tensor", mask = "Tensor", value = c("Scalar", "Tensor"
@@ -9365,6 +9926,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_masked_scatter
 torch_masked_scatter <- function(self, mask, source) {
   args <- mget(x = c("self", "mask", "source"))
 expected_types <- list(self = "Tensor", mask = "Tensor", source = "Tensor")
@@ -9381,6 +9943,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_masked_select
 torch_masked_select <- function(self, mask) {
   args <- mget(x = c("self", "mask"))
 expected_types <- list(self = "Tensor", mask = "Tensor")
@@ -9397,6 +9960,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_masked_select_out
 torch_masked_select_out <- function(out, self, mask) {
   args <- mget(x = c("out", "self", "mask"))
 expected_types <- list(out = "Tensor", self = "Tensor", mask = "Tensor")
@@ -9413,6 +9977,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_matmul
 torch_matmul <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = "Tensor")
@@ -9429,6 +9994,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_matmul_out
 torch_matmul_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor")
@@ -9445,6 +10011,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_matrix_power
 torch_matrix_power <- function(self, n) {
   args <- mget(x = c("self", "n"))
 expected_types <- list(self = "Tensor", n = "int64_t")
@@ -9461,6 +10028,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_matrix_rank
 torch_matrix_rank <- function(self, tol, symmetric = FALSE) {
   args <- mget(x = c("self", "tol", "symmetric"))
 expected_types <- list(self = "Tensor", tol = "double", symmetric = "bool")
@@ -9477,6 +10045,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_max
 .torch_max <- function(self, dim, other, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "other", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), other = "Tensor", 
@@ -9494,6 +10063,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_max_out
 .torch_max_out <- function(max, out, max_values, other, self, dim, keepdim = FALSE) {
   args <- mget(x = c("max", "out", "max_values", "other", "self", "dim", "keepdim"))
 expected_types <- list(max = "Tensor", out = "Tensor", max_values = "Tensor", other = "Tensor", 
@@ -9511,6 +10081,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_pool1d
 torch_max_pool1d <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) {
   args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", 
@@ -9528,6 +10099,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_max_pool1d_with_indices
 .torch_max_pool1d_with_indices <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) {
   args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", 
@@ -9545,6 +10117,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_pool2d
 torch_max_pool2d <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) {
   args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", 
@@ -9562,6 +10135,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_max_pool2d_with_indices
 .torch_max_pool2d_with_indices <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) {
   args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", 
@@ -9579,6 +10153,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_pool2d_with_indices_backward
 torch_max_pool2d_with_indices_backward <- function(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices) {
   args <- mget(x = c("grad_output", "self", "kernel_size", "stride", "padding", "dilation", "ceil_mode", "indices"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", 
@@ -9598,6 +10173,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_pool2d_with_indices_backward_out
 torch_max_pool2d_with_indices_backward_out <- function(grad_input, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices) {
   args <- mget(x = c("grad_input", "grad_output", "self", "kernel_size", "stride", "padding", "dilation", "ceil_mode", "indices"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -9617,6 +10193,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_max_pool2d_with_indices_out
 .torch_max_pool2d_with_indices_out <- function(out, indices, self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) {
   args <- mget(x = c("out", "indices", "self", "kernel_size", "stride", "padding", "dilation", "ceil_mode"))
 expected_types <- list(out = "Tensor", indices = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", 
@@ -9635,6 +10212,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_pool3d
 torch_max_pool3d <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) {
   args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", 
@@ -9652,6 +10230,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_max_pool3d_with_indices
 .torch_max_pool3d_with_indices <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) {
   args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", 
@@ -9669,6 +10248,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_pool3d_with_indices_backward
 torch_max_pool3d_with_indices_backward <- function(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices) {
   args <- mget(x = c("grad_output", "self", "kernel_size", "stride", "padding", "dilation", "ceil_mode", "indices"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", 
@@ -9688,6 +10268,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_pool3d_with_indices_backward_out
 torch_max_pool3d_with_indices_backward_out <- function(grad_input, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices) {
   args <- mget(x = c("grad_input", "grad_output", "self", "kernel_size", "stride", "padding", "dilation", "ceil_mode", "indices"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -9707,6 +10288,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_max_pool3d_with_indices_out
 .torch_max_pool3d_with_indices_out <- function(out, indices, self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) {
   args <- mget(x = c("out", "indices", "self", "kernel_size", "stride", "padding", "dilation", "ceil_mode"))
 expected_types <- list(out = "Tensor", indices = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", 
@@ -9725,6 +10307,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_unpool2d
 torch_max_unpool2d <- function(self, indices, output_size) {
   args <- mget(x = c("self", "indices", "output_size"))
 expected_types <- list(self = "Tensor", indices = "Tensor", output_size = "IntArrayRef")
@@ -9741,6 +10324,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_unpool2d_backward
 torch_max_unpool2d_backward <- function(grad_output, self, indices, output_size) {
   args <- mget(x = c("grad_output", "self", "indices", "output_size"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", indices = "Tensor", 
@@ -9758,6 +10342,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_unpool2d_backward_out
 torch_max_unpool2d_backward_out <- function(grad_input, grad_output, self, indices, output_size) {
   args <- mget(x = c("grad_input", "grad_output", "self", "indices", "output_size"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -9776,6 +10361,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_unpool2d_out
 torch_max_unpool2d_out <- function(out, self, indices, output_size) {
   args <- mget(x = c("out", "self", "indices", "output_size"))
 expected_types <- list(out = "Tensor", self = "Tensor", indices = "Tensor", output_size = "IntArrayRef")
@@ -9792,6 +10378,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_unpool3d
 torch_max_unpool3d <- function(self, indices, output_size, stride, padding) {
   args <- mget(x = c("self", "indices", "output_size", "stride", "padding"))
 expected_types <- list(self = "Tensor", indices = "Tensor", output_size = "IntArrayRef", 
@@ -9809,6 +10396,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_unpool3d_backward
 torch_max_unpool3d_backward <- function(grad_output, self, indices, output_size, stride, padding) {
   args <- mget(x = c("grad_output", "self", "indices", "output_size", "stride", "padding"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", indices = "Tensor", 
@@ -9827,6 +10415,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_unpool3d_backward_out
 torch_max_unpool3d_backward_out <- function(grad_input, grad_output, self, indices, output_size, stride, padding) {
   args <- mget(x = c("grad_input", "grad_output", "self", "indices", "output_size", "stride", "padding"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -9846,6 +10435,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_unpool3d_out
 torch_max_unpool3d_out <- function(out, self, indices, output_size, stride, padding) {
   args <- mget(x = c("out", "self", "indices", "output_size", "stride", "padding"))
 expected_types <- list(out = "Tensor", self = "Tensor", indices = "Tensor", output_size = "IntArrayRef", 
@@ -9864,6 +10454,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_max_values
 torch_max_values <- function(self, dim, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), 
@@ -9881,6 +10472,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mean
 torch_mean <- function(self, dim, keepdim = FALSE, dtype = NULL) {
   args <- mget(x = c("self", "dim", "keepdim", "dtype"))
 expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), 
@@ -9898,6 +10490,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mean_out
 torch_mean_out <- function(out, self, dim, keepdim = FALSE, dtype = NULL) {
   args <- mget(x = c("out", "self", "dim", "keepdim", "dtype"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = c("IntArrayRef", 
@@ -9915,6 +10508,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_median
 torch_median <- function(self, dim, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool")
@@ -9931,6 +10525,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_median_out
 torch_median_out <- function(values, indices, self, dim, keepdim = FALSE) {
   args <- mget(x = c("values", "indices", "self", "dim", "keepdim"))
 expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", 
@@ -9948,6 +10543,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_meshgrid
 torch_meshgrid <- function(tensors) {
   args <- mget(x = c("tensors"))
 expected_types <- list(tensors = "TensorList")
@@ -9964,6 +10560,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_min
 .torch_min <- function(self, dim, other, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "other", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), other = "Tensor", 
@@ -9981,6 +10578,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_min_out
 .torch_min_out <- function(min, out, min_indices, other, self, dim, keepdim = FALSE) {
   args <- mget(x = c("min", "out", "min_indices", "other", "self", "dim", "keepdim"))
 expected_types <- list(min = "Tensor", out = "Tensor", min_indices = "Tensor", 
@@ -9999,6 +10597,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_min_values
 torch_min_values <- function(self, dim, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), 
@@ -10016,6 +10615,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_batch_norm
 torch_miopen_batch_norm <- function(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon) {
   args <- mget(x = c("input", "weight", "bias", "running_mean", "running_var", "training", "exponential_average_factor", "epsilon"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", running_mean = "Tensor", 
@@ -10035,6 +10635,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_batch_norm_backward
 torch_miopen_batch_norm_backward <- function(input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon) {
   args <- mget(x = c("input", "grad_output", "weight", "running_mean", "running_var", "save_mean", "save_var", "epsilon"))
 expected_types <- list(input = "Tensor", grad_output = "Tensor", weight = "Tensor", 
@@ -10054,6 +10655,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_convolution
 torch_miopen_convolution <- function(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("self", "weight", "bias", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", 
@@ -10073,6 +10675,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_convolution_backward
 torch_miopen_convolution_backward <- function(self, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic, output_mask) {
   args <- mget(x = c("self", "grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic", "output_mask"))
 expected_types <- list(self = "Tensor", grad_output = "Tensor", weight = "Tensor", 
@@ -10093,6 +10696,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_convolution_backward_bias
 torch_miopen_convolution_backward_bias <- function(grad_output) {
   args <- mget(x = c("grad_output"))
 expected_types <- list(grad_output = "Tensor")
@@ -10109,6 +10713,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_convolution_backward_input
 torch_miopen_convolution_backward_input <- function(self_size, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("self_size", "grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(self_size = "IntArrayRef", grad_output = "Tensor", weight = "Tensor", 
@@ -10128,6 +10733,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_convolution_backward_weight
 torch_miopen_convolution_backward_weight <- function(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("weight_size", "grad_output", "self", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(weight_size = "IntArrayRef", grad_output = "Tensor", self = "Tensor", 
@@ -10147,6 +10753,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_convolution_transpose
 torch_miopen_convolution_transpose <- function(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("self", "weight", "bias", "padding", "output_padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", 
@@ -10166,6 +10773,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_convolution_transpose_backward
 torch_miopen_convolution_transpose_backward <- function(self, grad_output, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, output_mask) {
   args <- mget(x = c("self", "grad_output", "weight", "padding", "output_padding", "stride", "dilation", "groups", "benchmark", "deterministic", "output_mask"))
 expected_types <- list(self = "Tensor", grad_output = "Tensor", weight = "Tensor", 
@@ -10187,6 +10795,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_convolution_transpose_backward_input
 torch_miopen_convolution_transpose_backward_input <- function(grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(grad_output = "Tensor", weight = "Tensor", padding = "IntArrayRef", 
@@ -10206,6 +10815,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_convolution_transpose_backward_weight
 torch_miopen_convolution_transpose_backward_weight <- function(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("weight_size", "grad_output", "self", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(weight_size = "IntArrayRef", grad_output = "Tensor", self = "Tensor", 
@@ -10225,6 +10835,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_depthwise_convolution
 torch_miopen_depthwise_convolution <- function(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("self", "weight", "bias", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", 
@@ -10244,6 +10855,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_depthwise_convolution_backward
 torch_miopen_depthwise_convolution_backward <- function(self, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic, output_mask) {
   args <- mget(x = c("self", "grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic", "output_mask"))
 expected_types <- list(self = "Tensor", grad_output = "Tensor", weight = "Tensor", 
@@ -10264,6 +10876,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_depthwise_convolution_backward_input
 torch_miopen_depthwise_convolution_backward_input <- function(self_size, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("self_size", "grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(self_size = "IntArrayRef", grad_output = "Tensor", weight = "Tensor", 
@@ -10283,6 +10896,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_depthwise_convolution_backward_weight
 torch_miopen_depthwise_convolution_backward_weight <- function(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic) {
   args <- mget(x = c("weight_size", "grad_output", "self", "padding", "stride", "dilation", "groups", "benchmark", "deterministic"))
 expected_types <- list(weight_size = "IntArrayRef", grad_output = "Tensor", self = "Tensor", 
@@ -10302,6 +10916,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_rnn
 torch_miopen_rnn <- function(input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state) {
   args <- mget(x = c("input", "weight", "weight_stride0", "hx", "cx", "mode", "hidden_size", "num_layers", "batch_first", "dropout", "train", "bidirectional", "batch_sizes", "dropout_state"))
 expected_types <- list(input = "Tensor", weight = "TensorList", weight_stride0 = "int64_t", 
@@ -10324,6 +10939,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_miopen_rnn_backward
 torch_miopen_rnn_backward <- function(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask) {
   args <- mget(x = c("input", "weight", "weight_stride0", "weight_buf", "hx", "cx", "output", "grad_output", "grad_hy", "grad_cy", "mode", "hidden_size", "num_layers", "batch_first", "dropout", "train", "bidirectional", "batch_sizes", "dropout_state", "reserve", "output_mask"))
 expected_types <- list(input = "Tensor", weight = "TensorList", weight_stride0 = "int64_t", 
@@ -10349,6 +10965,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mkldnn_adaptive_avg_pool2d
 torch_mkldnn_adaptive_avg_pool2d <- function(self, output_size) {
   args <- mget(x = c("self", "output_size"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef")
@@ -10365,6 +10982,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mkldnn_convolution
 torch_mkldnn_convolution <- function(self, weight, bias, padding, stride, dilation, groups) {
   args <- mget(x = c("self", "weight", "bias", "padding", "stride", "dilation", "groups"))
 expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", 
@@ -10383,6 +11001,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mkldnn_convolution_backward
 torch_mkldnn_convolution_backward <- function(self, grad_output, weight, padding, stride, dilation, groups, output_mask) {
   args <- mget(x = c("self", "grad_output", "weight", "padding", "stride", "dilation", "groups", "output_mask"))
 expected_types <- list(self = "Tensor", grad_output = "Tensor", weight = "Tensor", 
@@ -10402,6 +11021,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mkldnn_convolution_backward_input
 torch_mkldnn_convolution_backward_input <- function(self_size, grad_output, weight, padding, stride, dilation, groups, bias_defined) {
   args <- mget(x = c("self_size", "grad_output", "weight", "padding", "stride", "dilation", "groups", "bias_defined"))
 expected_types <- list(self_size = "IntArrayRef", grad_output = "Tensor", weight = "Tensor", 
@@ -10421,6 +11041,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mkldnn_convolution_backward_weights
 torch_mkldnn_convolution_backward_weights <- function(weight_size, grad_output, self, padding, stride, dilation, groups, bias_defined) {
   args <- mget(x = c("weight_size", "grad_output", "self", "padding", "stride", "dilation", "groups", "bias_defined"))
 expected_types <- list(weight_size = "IntArrayRef", grad_output = "Tensor", self = "Tensor", 
@@ -10440,6 +11061,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mkldnn_linear
 torch_mkldnn_linear <- function(input, weight, bias = list()) {
   args <- mget(x = c("input", "weight", "bias"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor")
@@ -10456,6 +11078,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mkldnn_max_pool2d
 torch_mkldnn_max_pool2d <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) {
   args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", 
@@ -10473,6 +11096,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mkldnn_reorder_conv2d_weight
 torch_mkldnn_reorder_conv2d_weight <- function(self, padding = 0L, stride = 1L, dilation = 1L, groups = 1L) {
   args <- mget(x = c("self", "padding", "stride", "dilation", "groups"))
 expected_types <- list(self = "Tensor", padding = "IntArrayRef", stride = "IntArrayRef", 
@@ -10490,6 +11114,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mm
 torch_mm <- function(self, mat2) {
   args <- mget(x = c("self", "mat2"))
 expected_types <- list(self = "Tensor", mat2 = "Tensor")
@@ -10506,6 +11131,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mm_out
 torch_mm_out <- function(out, self, mat2) {
   args <- mget(x = c("out", "self", "mat2"))
 expected_types <- list(out = "Tensor", self = "Tensor", mat2 = "Tensor")
@@ -10522,6 +11148,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mode
 torch_mode <- function(self, dim = -1L, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool")
@@ -10538,6 +11165,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mode_out
 torch_mode_out <- function(values, indices, self, dim = -1L, keepdim = FALSE) {
   args <- mget(x = c("values", "indices", "self", "dim", "keepdim"))
 expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", 
@@ -10555,6 +11183,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mse_loss
 torch_mse_loss <- function(self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("self", "target", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t")
@@ -10571,6 +11200,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mse_loss_backward
 torch_mse_loss_backward <- function(grad_output, self, target, reduction) {
   args <- mget(x = c("grad_output", "self", "target", "reduction"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", 
@@ -10588,6 +11218,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mse_loss_backward_out
 torch_mse_loss_backward_out <- function(grad_input, grad_output, self, target, reduction) {
   args <- mget(x = c("grad_input", "grad_output", "self", "target", "reduction"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -10605,6 +11236,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mse_loss_out
 torch_mse_loss_out <- function(out, self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("out", "self", "target", "reduction"))
 expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", reduction = "int64_t")
@@ -10621,6 +11253,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mul
 torch_mul <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
@@ -10637,6 +11270,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mul_out
 torch_mul_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor")
@@ -10653,6 +11287,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_multi_margin_loss
 torch_multi_margin_loss <- function(self, target, p = 1L, margin = 1L, weight = list(), reduction = torch_reduction_mean()) {
   args <- mget(x = c("self", "target", "p", "margin", "weight", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", p = "Scalar", margin = "Scalar", 
@@ -10670,6 +11305,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_multi_margin_loss_backward
 torch_multi_margin_loss_backward <- function(grad_output, self, target, p, margin, weight = list(), reduction = torch_reduction_mean()) {
   args <- mget(x = c("grad_output", "self", "target", "p", "margin", "weight", "reduction"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", 
@@ -10687,6 +11323,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_multi_margin_loss_backward_out
 torch_multi_margin_loss_backward_out <- function(grad_input, grad_output, self, target, p, margin, weight = list(), reduction = torch_reduction_mean()) {
   args <- mget(x = c("grad_input", "grad_output", "self", "target", "p", "margin", "weight", "reduction"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -10706,6 +11343,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_multi_margin_loss_out
 torch_multi_margin_loss_out <- function(out, self, target, p = 1L, margin = 1L, weight = list(), reduction = torch_reduction_mean()) {
   args <- mget(x = c("out", "self", "target", "p", "margin", "weight", "reduction"))
 expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", p = "Scalar", 
@@ -10723,6 +11361,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_multilabel_margin_loss
 torch_multilabel_margin_loss <- function(self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("self", "target", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t")
@@ -10739,6 +11378,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_multilabel_margin_loss_backward
 torch_multilabel_margin_loss_backward <- function(grad_output, self, target, reduction, is_target) {
   args <- mget(x = c("grad_output", "self", "target", "reduction", "is_target"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", 
@@ -10756,6 +11396,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_multilabel_margin_loss_backward_out
 torch_multilabel_margin_loss_backward_out <- function(grad_input, grad_output, self, target, reduction, is_target) {
   args <- mget(x = c("grad_input", "grad_output", "self", "target", "reduction", "is_target"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -10774,6 +11415,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_multilabel_margin_loss_forward
 torch_multilabel_margin_loss_forward <- function(self, target, reduction) {
   args <- mget(x = c("self", "target", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t")
@@ -10790,6 +11432,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_multilabel_margin_loss_forward_out
 torch_multilabel_margin_loss_forward_out <- function(output, is_target, self, target, reduction) {
   args <- mget(x = c("output", "is_target", "self", "target", "reduction"))
 expected_types <- list(output = "Tensor", is_target = "Tensor", self = "Tensor", 
@@ -10807,6 +11450,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_multilabel_margin_loss_out
 torch_multilabel_margin_loss_out <- function(out, self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("out", "self", "target", "reduction"))
 expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", reduction = "int64_t")
@@ -10823,6 +11467,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_multinomial
 torch_multinomial <- function(self, num_samples, replacement = FALSE, generator = NULL) {
   args <- mget(x = c("self", "num_samples", "replacement", "generator"))
 expected_types <- list(self = "Tensor", num_samples = "int64_t", replacement = "bool", 
@@ -10840,6 +11485,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_multinomial_out
 torch_multinomial_out <- function(out, self, num_samples, replacement = FALSE, generator = NULL) {
   args <- mget(x = c("out", "self", "num_samples", "replacement", "generator"))
 expected_types <- list(out = "Tensor", self = "Tensor", num_samples = "int64_t", 
@@ -10857,6 +11503,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mv
 torch_mv <- function(self, vec) {
   args <- mget(x = c("self", "vec"))
 expected_types <- list(self = "Tensor", vec = "Tensor")
@@ -10873,6 +11520,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mv_out
 torch_mv_out <- function(out, self, vec) {
   args <- mget(x = c("out", "self", "vec"))
 expected_types <- list(out = "Tensor", self = "Tensor", vec = "Tensor")
@@ -10889,6 +11537,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_mvlgamma
 torch_mvlgamma <- function(self, p) {
   args <- mget(x = c("self", "p"))
 expected_types <- list(self = "Tensor", p = "int64_t")
@@ -10905,6 +11554,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_narrow
 torch_narrow <- function(self, dim, start, length) {
   args <- mget(x = c("self", "dim", "start", "length"))
 expected_types <- list(self = "Tensor", dim = "int64_t", start = c("int64_t", "Tensor"
@@ -10922,6 +11572,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_native_batch_norm
 torch_native_batch_norm <- function(input, weight, bias, running_mean, running_var, training, momentum, eps) {
   args <- mget(x = c("input", "weight", "bias", "running_mean", "running_var", "training", "momentum", "eps"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", running_mean = "Tensor", 
@@ -10941,6 +11592,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_native_batch_norm_backward
 torch_native_batch_norm_backward <- function(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask) {
   args <- mget(x = c("grad_out", "input", "weight", "running_mean", "running_var", "save_mean", "save_invstd", "train", "eps", "output_mask"))
 expected_types <- list(grad_out = "Tensor", input = "Tensor", weight = "Tensor", 
@@ -10960,6 +11612,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_native_batch_norm_out
 torch_native_batch_norm_out <- function(out, save_mean, save_invstd, input, weight, bias, running_mean, running_var, training, momentum, eps) {
   args <- mget(x = c("out", "save_mean", "save_invstd", "input", "weight", "bias", "running_mean", "running_var", "training", "momentum", "eps"))
 expected_types <- list(out = "Tensor", save_mean = "Tensor", save_invstd = "Tensor", 
@@ -10980,6 +11633,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_native_layer_norm
 torch_native_layer_norm <- function(input, weight, bias, M, False, eps) {
   args <- mget(x = c("input", "weight", "bias", "M", "False", "eps"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", M = "int64_t", 
@@ -10997,6 +11651,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_native_layer_norm_backward
 torch_native_layer_norm_backward <- function(grad_out, input, mean, rstd, weight, M, False, output_mask) {
   args <- mget(x = c("grad_out", "input", "mean", "rstd", "weight", "M", "False", "output_mask"))
 expected_types <- list(grad_out = "Tensor", input = "Tensor", mean = "Tensor", 
@@ -11016,6 +11671,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_native_norm
 torch_native_norm <- function(self, p = 2L) {
   args <- mget(x = c("self", "p"))
 expected_types <- list(self = "Tensor", p = "Scalar")
@@ -11032,6 +11688,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ne
 torch_ne <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -11048,6 +11705,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ne_out
 torch_ne_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor"
@@ -11065,6 +11723,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_neg
 torch_neg <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -11081,6 +11740,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_neg_
 torch_neg_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -11097,6 +11757,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_neg_out
 torch_neg_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -11113,6 +11774,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_nll_loss
 .torch_nll_loss <- function(self, target, weight = list(), reduction = torch_reduction_mean(), ignore_index = -100L) {
   args <- mget(x = c("self", "target", "weight", "reduction", "ignore_index"))
 expected_types <- list(self = "Tensor", target = "Tensor", weight = "Tensor", reduction = "int64_t", 
@@ -11130,6 +11792,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nll_loss_backward
 torch_nll_loss_backward <- function(grad_output, self, target, weight, reduction, ignore_index, total_weight) {
   args <- mget(x = c("grad_output", "self", "target", "weight", "reduction", "ignore_index", "total_weight"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", 
@@ -11149,6 +11812,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nll_loss_backward_out
 torch_nll_loss_backward_out <- function(grad_input, grad_output, self, target, weight, reduction, ignore_index, total_weight) {
   args <- mget(x = c("grad_input", "grad_output", "self", "target", "weight", "reduction", "ignore_index", "total_weight"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -11168,6 +11832,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nll_loss_forward
 torch_nll_loss_forward <- function(self, target, weight, reduction, ignore_index) {
   args <- mget(x = c("self", "target", "weight", "reduction", "ignore_index"))
 expected_types <- list(self = "Tensor", target = "Tensor", weight = "Tensor", reduction = "int64_t", 
@@ -11185,6 +11850,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nll_loss_forward_out
 torch_nll_loss_forward_out <- function(output, total_weight, self, target, weight, reduction, ignore_index) {
   args <- mget(x = c("output", "total_weight", "self", "target", "weight", "reduction", "ignore_index"))
 expected_types <- list(output = "Tensor", total_weight = "Tensor", self = "Tensor", 
@@ -11204,6 +11870,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nll_loss_out
 torch_nll_loss_out <- function(out, self, target, weight = list(), reduction = torch_reduction_mean(), ignore_index = -100L) {
   args <- mget(x = c("out", "self", "target", "weight", "reduction", "ignore_index"))
 expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", weight = "Tensor", 
@@ -11221,6 +11888,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_nll_loss2d
 .torch_nll_loss2d <- function(self, target, weight = list(), reduction = torch_reduction_mean(), ignore_index = -100L) {
   args <- mget(x = c("self", "target", "weight", "reduction", "ignore_index"))
 expected_types <- list(self = "Tensor", target = "Tensor", weight = "Tensor", reduction = "int64_t", 
@@ -11238,6 +11906,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nll_loss2d_backward
 torch_nll_loss2d_backward <- function(grad_output, self, target, weight, reduction, ignore_index, total_weight) {
   args <- mget(x = c("grad_output", "self", "target", "weight", "reduction", "ignore_index", "total_weight"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", 
@@ -11257,6 +11926,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nll_loss2d_backward_out
 torch_nll_loss2d_backward_out <- function(grad_input, grad_output, self, target, weight, reduction, ignore_index, total_weight) {
   args <- mget(x = c("grad_input", "grad_output", "self", "target", "weight", "reduction", "ignore_index", "total_weight"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -11276,6 +11946,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nll_loss2d_forward
 torch_nll_loss2d_forward <- function(self, target, weight, reduction, ignore_index) {
   args <- mget(x = c("self", "target", "weight", "reduction", "ignore_index"))
 expected_types <- list(self = "Tensor", target = "Tensor", weight = "Tensor", reduction = "int64_t", 
@@ -11293,6 +11964,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nll_loss2d_forward_out
 torch_nll_loss2d_forward_out <- function(output, total_weight, self, target, weight, reduction, ignore_index) {
   args <- mget(x = c("output", "total_weight", "self", "target", "weight", "reduction", "ignore_index"))
 expected_types <- list(output = "Tensor", total_weight = "Tensor", self = "Tensor", 
@@ -11312,6 +11984,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nll_loss2d_out
 torch_nll_loss2d_out <- function(out, self, target, weight = list(), reduction = torch_reduction_mean(), ignore_index = -100L) {
   args <- mget(x = c("out", "self", "target", "weight", "reduction", "ignore_index"))
 expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", weight = "Tensor", 
@@ -11329,6 +12002,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nonzero
 torch_nonzero <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -11345,6 +12019,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nonzero_numpy
 torch_nonzero_numpy <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -11361,6 +12036,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nonzero_out
 torch_nonzero_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -11377,6 +12053,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_norm
 torch_norm <- function(self, p = 2L, dim, keepdim = FALSE, dtype) {
   args <- mget(x = c("self", "p", "dim", "keepdim", "dtype"))
 expected_types <- list(self = "Tensor", p = "Scalar", dim = c("IntArrayRef", "DimnameList"
@@ -11394,6 +12071,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_norm_except_dim
 torch_norm_except_dim <- function(v, pow = 2L, dim = 1L) {
   args <- mget(x = c("v", "pow", "dim"))
 expected_types <- list(v = "Tensor", pow = "int64_t", dim = "int64_t")
@@ -11410,6 +12088,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_norm_out
 torch_norm_out <- function(out, self, p, dim, keepdim = FALSE, dtype) {
   args <- mget(x = c("out", "self", "p", "dim", "keepdim", "dtype"))
 expected_types <- list(out = "Tensor", self = "Tensor", p = "Scalar", dim = c("IntArrayRef", 
@@ -11427,6 +12106,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_normal
 torch_normal <- function(mean, std = 1L, size, generator = NULL, options = list()) {
   args <- mget(x = c("mean", "std", "size", "generator", "options"))
 expected_types <- list(mean = c("Tensor", "double"), std = c("double", "Tensor"
@@ -11444,6 +12124,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_normal_out
 torch_normal_out <- function(out, mean, std = 1L, size, generator = NULL) {
   args <- mget(x = c("out", "mean", "std", "size", "generator"))
 expected_types <- list(out = "Tensor", mean = c("Tensor", "double"), std = c("double", 
@@ -11461,6 +12142,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nuclear_norm
 torch_nuclear_norm <- function(self, dim, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "keepdim"))
 expected_types <- list(self = "Tensor", dim = "IntArrayRef", keepdim = "bool")
@@ -11477,6 +12159,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_nuclear_norm_out
 torch_nuclear_norm_out <- function(out, self, dim, keepdim = FALSE) {
   args <- mget(x = c("out", "self", "dim", "keepdim"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = "IntArrayRef", keepdim = "bool")
@@ -11493,6 +12176,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_one_hot
 torch_one_hot <- function(self, num_classes = -1L) {
   args <- mget(x = c("self", "num_classes"))
 expected_types <- list(self = "Tensor", num_classes = "int64_t")
@@ -11509,6 +12193,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_ones
 .torch_ones <- function(size, names, options = list()) {
   args <- mget(x = c("size", "names", "options"))
 expected_types <- list(size = "IntArrayRef", names = "DimnameList", options = "TensorOptions")
@@ -11525,6 +12210,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_ones_like
 .torch_ones_like <- function(self, options = list(), memory_format = NULL) {
   args <- mget(x = c("self", "options", "memory_format"))
 expected_types <- list(self = "Tensor", options = "TensorOptions", memory_format = "MemoryFormat")
@@ -11541,6 +12227,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ones_out
 torch_ones_out <- function(out, size) {
   args <- mget(x = c("out", "size"))
 expected_types <- list(out = "Tensor", size = "IntArrayRef")
@@ -11557,6 +12244,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_orgqr
 torch_orgqr <- function(self, input2) {
   args <- mget(x = c("self", "input2"))
 expected_types <- list(self = "Tensor", input2 = "Tensor")
@@ -11573,6 +12261,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_orgqr_out
 torch_orgqr_out <- function(out, self, input2) {
   args <- mget(x = c("out", "self", "input2"))
 expected_types <- list(out = "Tensor", self = "Tensor", input2 = "Tensor")
@@ -11589,6 +12278,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ormqr
 torch_ormqr <- function(self, input2, input3, left = TRUE, transpose = FALSE) {
   args <- mget(x = c("self", "input2", "input3", "left", "transpose"))
 expected_types <- list(self = "Tensor", input2 = "Tensor", input3 = "Tensor", left = "bool", 
@@ -11606,6 +12296,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_ormqr_out
 torch_ormqr_out <- function(out, self, input2, input3, left = TRUE, transpose = FALSE) {
   args <- mget(x = c("out", "self", "input2", "input3", "left", "transpose"))
 expected_types <- list(out = "Tensor", self = "Tensor", input2 = "Tensor", input3 = "Tensor", 
@@ -11623,6 +12314,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_pairwise_distance
 torch_pairwise_distance <- function(x1, x2, p = 2L, eps = 0.000001, keepdim = FALSE) {
   args <- mget(x = c("x1", "x2", "p", "eps", "keepdim"))
 expected_types <- list(x1 = "Tensor", x2 = "Tensor", p = "double", eps = "double", 
@@ -11640,6 +12332,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_pdist
 torch_pdist <- function(self, p = 2L) {
   args <- mget(x = c("self", "p"))
 expected_types <- list(self = "Tensor", p = "double")
@@ -11656,6 +12349,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_pinverse
 torch_pinverse <- function(self, rcond = 0.000000) {
   args <- mget(x = c("self", "rcond"))
 expected_types <- list(self = "Tensor", rcond = "double")
@@ -11672,6 +12366,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_pixel_shuffle
 torch_pixel_shuffle <- function(self, upscale_factor) {
   args <- mget(x = c("self", "upscale_factor"))
 expected_types <- list(self = "Tensor", upscale_factor = "int64_t")
@@ -11688,6 +12383,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_poisson
 torch_poisson <- function(self, generator = NULL) {
   args <- mget(x = c("self", "generator"))
 expected_types <- list(self = "Tensor", generator = "Generator *")
@@ -11704,6 +12400,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_poisson_nll_loss
 torch_poisson_nll_loss <- function(input, target, log_input, full, eps, reduction) {
   args <- mget(x = c("input", "target", "log_input", "full", "eps", "reduction"))
 expected_types <- list(input = "Tensor", target = "Tensor", log_input = "bool", 
@@ -11721,6 +12418,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_polygamma
 torch_polygamma <- function(n, self) {
   args <- mget(x = c("n", "self"))
 expected_types <- list(n = "int64_t", self = "Tensor")
@@ -11737,6 +12435,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_polygamma_out
 torch_polygamma_out <- function(out, n, self) {
   args <- mget(x = c("out", "n", "self"))
 expected_types <- list(out = "Tensor", n = "int64_t", self = "Tensor")
@@ -11753,6 +12452,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_pow
 torch_pow <- function(self, exponent) {
   args <- mget(x = c("self", "exponent"))
 expected_types <- list(self = c("Tensor", "Scalar"), exponent = c("Scalar", "Tensor"
@@ -11770,6 +12470,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_pow_out
 torch_pow_out <- function(out, self, exponent) {
   args <- mget(x = c("out", "self", "exponent"))
 expected_types <- list(out = "Tensor", self = c("Tensor", "Scalar"), exponent = c("Scalar", 
@@ -11787,6 +12488,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_prelu
 torch_prelu <- function(self, weight) {
   args <- mget(x = c("self", "weight"))
 expected_types <- list(self = "Tensor", weight = "Tensor")
@@ -11803,6 +12505,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_prelu_backward
 torch_prelu_backward <- function(grad_output, self, weight) {
   args <- mget(x = c("grad_output", "self", "weight"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor")
@@ -11819,6 +12522,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_prod
 torch_prod <- function(self, dim, keepdim = FALSE, dtype = NULL) {
   args <- mget(x = c("self", "dim", "keepdim", "dtype"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool", 
@@ -11836,6 +12540,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_prod_out
 torch_prod_out <- function(out, self, dim, keepdim = FALSE, dtype = NULL) {
   args <- mget(x = c("out", "self", "dim", "keepdim", "dtype"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname"
@@ -11853,6 +12558,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_promote_types
 torch_promote_types <- function(type1, type2) {
   args <- mget(x = c("type1", "type2"))
 expected_types <- list(type1 = "ScalarType", type2 = "ScalarType")
@@ -11869,6 +12575,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_q_per_channel_axis
 torch_q_per_channel_axis <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -11885,6 +12592,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_q_per_channel_scales
 torch_q_per_channel_scales <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -11901,6 +12609,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_q_per_channel_zero_points
 torch_q_per_channel_zero_points <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -11917,6 +12626,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_q_scale
 torch_q_scale <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -11933,6 +12643,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_q_zero_point
 torch_q_zero_point <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -11949,6 +12660,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_qr
 torch_qr <- function(self, some = TRUE) {
   args <- mget(x = c("self", "some"))
 expected_types <- list(self = "Tensor", some = "bool")
@@ -11965,6 +12677,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_qr_out
 torch_qr_out <- function(Q, R, self, some = TRUE) {
   args <- mget(x = c("Q", "R", "self", "some"))
 expected_types <- list(Q = "Tensor", R = "Tensor", self = "Tensor", some = "bool")
@@ -11981,6 +12694,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_quantize_per_channel
 torch_quantize_per_channel <- function(self, scales, zero_points, axis, dtype) {
   args <- mget(x = c("self", "scales", "zero_points", "axis", "dtype"))
 expected_types <- list(self = "Tensor", scales = "Tensor", zero_points = "Tensor", 
@@ -11998,6 +12712,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_quantize_per_tensor
 torch_quantize_per_tensor <- function(self, scale, zero_point, dtype) {
   args <- mget(x = c("self", "scale", "zero_point", "dtype"))
 expected_types <- list(self = "Tensor", scale = "double", zero_point = "int64_t", 
@@ -12015,6 +12730,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_quantized_batch_norm
 torch_quantized_batch_norm <- function(input, weight, bias, mean, var, eps, output_scale, output_zero_point) {
   args <- mget(x = c("input", "weight", "bias", "mean", "var", "eps", "output_scale", "output_zero_point"))
 expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", mean = "Tensor", 
@@ -12034,6 +12750,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_quantized_gru
 torch_quantized_gru <- function(data, input, batch_sizes, hx, params, has_biases, num_layers, dropout, train, batch_first, bidirectional) {
   args <- mget(x = c("data", "input", "batch_sizes", "hx", "params", "has_biases", "num_layers", "dropout", "train", "batch_first", "bidirectional"))
 expected_types <- list(data = "Tensor", input = "Tensor", batch_sizes = "Tensor", 
@@ -12055,6 +12772,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_quantized_gru_cell
 torch_quantized_gru_cell <- function(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh) {
   args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh", "packed_ih", "packed_hh", "col_offsets_ih", "col_offsets_hh", "scale_ih", "scale_hh", "zero_point_ih", "zero_point_hh"))
 expected_types <- list(input = "Tensor", hx = "Tensor", w_ih = "Tensor", w_hh = "Tensor", 
@@ -12076,6 +12794,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_quantized_lstm
 torch_quantized_lstm <- function(data, input, batch_sizes, hx, params, has_biases, num_layers, dropout, train, batch_first, bidirectional, dtype = NULL, use_dynamic = FALSE) {
   args <- mget(x = c("data", "input", "batch_sizes", "hx", "params", "has_biases", "num_layers", "dropout", "train", "batch_first", "bidirectional", "dtype", "use_dynamic"))
 expected_types <- list(data = "Tensor", input = "Tensor", batch_sizes = "Tensor", 
@@ -12098,6 +12817,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_quantized_lstm_cell
 torch_quantized_lstm_cell <- function(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh) {
   args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh", "packed_ih", "packed_hh", "col_offsets_ih", "col_offsets_hh", "scale_ih", "scale_hh", "zero_point_ih", "zero_point_hh"))
 expected_types <- list(input = "Tensor", hx = "TensorList", w_ih = "Tensor", w_hh = "Tensor", 
@@ -12119,6 +12839,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_quantized_max_pool2d
 torch_quantized_max_pool2d <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) {
   args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode"))
 expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", 
@@ -12136,6 +12857,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_quantized_rnn_relu_cell
 torch_quantized_rnn_relu_cell <- function(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh) {
   args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh", "packed_ih", "packed_hh", "col_offsets_ih", "col_offsets_hh", "scale_ih", "scale_hh", "zero_point_ih", "zero_point_hh"))
 expected_types <- list(input = "Tensor", hx = "Tensor", w_ih = "Tensor", w_hh = "Tensor", 
@@ -12157,6 +12879,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_quantized_rnn_tanh_cell
 torch_quantized_rnn_tanh_cell <- function(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh) {
   args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh", "packed_ih", "packed_hh", "col_offsets_ih", "col_offsets_hh", "scale_ih", "scale_hh", "zero_point_ih", "zero_point_hh"))
 expected_types <- list(input = "Tensor", hx = "Tensor", w_ih = "Tensor", w_hh = "Tensor", 
@@ -12178,6 +12901,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_rand
 .torch_rand <- function(size, generator, names, options = list()) {
   args <- mget(x = c("size", "generator", "names", "options"))
 expected_types <- list(size = "IntArrayRef", generator = "Generator *", names = "DimnameList", 
@@ -12195,6 +12919,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_rand_like
 .torch_rand_like <- function(self, options = list(), memory_format = NULL) {
   args <- mget(x = c("self", "options", "memory_format"))
 expected_types <- list(self = "Tensor", options = "TensorOptions", memory_format = "MemoryFormat")
@@ -12211,6 +12936,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rand_out
 torch_rand_out <- function(out, size, generator) {
   args <- mget(x = c("out", "size", "generator"))
 expected_types <- list(out = "Tensor", size = "IntArrayRef", generator = "Generator *")
@@ -12227,6 +12953,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_randint
 .torch_randint <- function(low, high, size, generator, options = list()) {
   args <- mget(x = c("low", "high", "size", "generator", "options"))
 expected_types <- list(low = "int64_t", high = "int64_t", size = "IntArrayRef", 
@@ -12244,6 +12971,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_randint_like
 .torch_randint_like <- function(self, low, high, options = list(), memory_format = NULL) {
   args <- mget(x = c("self", "low", "high", "options", "memory_format"))
 expected_types <- list(self = "Tensor", low = "int64_t", high = "int64_t", options = "TensorOptions", 
@@ -12261,6 +12989,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_randint_out
 torch_randint_out <- function(out, low, high, size, generator) {
   args <- mget(x = c("out", "low", "high", "size", "generator"))
 expected_types <- list(out = "Tensor", low = "int64_t", high = "int64_t", size = "IntArrayRef", 
@@ -12278,6 +13007,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_randn
 .torch_randn <- function(size, generator, names, options = list()) {
   args <- mget(x = c("size", "generator", "names", "options"))
 expected_types <- list(size = "IntArrayRef", generator = "Generator *", names = "DimnameList", 
@@ -12295,6 +13025,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_randn_like
 .torch_randn_like <- function(self, options = list(), memory_format = NULL) {
   args <- mget(x = c("self", "options", "memory_format"))
 expected_types <- list(self = "Tensor", options = "TensorOptions", memory_format = "MemoryFormat")
@@ -12311,6 +13042,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_randn_out
 torch_randn_out <- function(out, size, generator) {
   args <- mget(x = c("out", "size", "generator"))
 expected_types <- list(out = "Tensor", size = "IntArrayRef", generator = "Generator *")
@@ -12327,6 +13059,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_randperm
 .torch_randperm <- function(n, generator, options = list()) {
   args <- mget(x = c("n", "generator", "options"))
 expected_types <- list(n = "int64_t", generator = "Generator *", options = "TensorOptions")
@@ -12343,6 +13076,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_randperm_out
 torch_randperm_out <- function(out, n, generator) {
   args <- mget(x = c("out", "n", "generator"))
 expected_types <- list(out = "Tensor", n = "int64_t", generator = "Generator *")
@@ -12359,6 +13093,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_range
 .torch_range <- function(start, end, step = 1L, options = list()) {
   args <- mget(x = c("start", "end", "step", "options"))
 expected_types <- list(start = "Scalar", end = "Scalar", step = "Scalar", options = "TensorOptions")
@@ -12375,6 +13110,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_range_out
 torch_range_out <- function(out, start, end, step = 1L) {
   args <- mget(x = c("out", "start", "end", "step"))
 expected_types <- list(out = "Tensor", start = "Scalar", end = "Scalar", step = "Scalar")
@@ -12391,6 +13127,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_real
 torch_real <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -12407,6 +13144,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_reciprocal
 torch_reciprocal <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -12423,6 +13161,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_reciprocal_
 torch_reciprocal_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -12439,6 +13178,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_reciprocal_out
 torch_reciprocal_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -12455,6 +13195,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_reflection_pad1d
 torch_reflection_pad1d <- function(self, padding) {
   args <- mget(x = c("self", "padding"))
 expected_types <- list(self = "Tensor", padding = "IntArrayRef")
@@ -12471,6 +13212,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_reflection_pad1d_backward
 torch_reflection_pad1d_backward <- function(grad_output, self, padding) {
   args <- mget(x = c("grad_output", "self", "padding"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", padding = "IntArrayRef")
@@ -12487,6 +13229,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_reflection_pad1d_backward_out
 torch_reflection_pad1d_backward_out <- function(grad_input, grad_output, self, padding) {
   args <- mget(x = c("grad_input", "grad_output", "self", "padding"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -12504,6 +13247,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_reflection_pad1d_out
 torch_reflection_pad1d_out <- function(out, self, padding) {
   args <- mget(x = c("out", "self", "padding"))
 expected_types <- list(out = "Tensor", self = "Tensor", padding = "IntArrayRef")
@@ -12520,6 +13264,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_reflection_pad2d
 torch_reflection_pad2d <- function(self, padding) {
   args <- mget(x = c("self", "padding"))
 expected_types <- list(self = "Tensor", padding = "IntArrayRef")
@@ -12536,6 +13281,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_reflection_pad2d_backward
 torch_reflection_pad2d_backward <- function(grad_output, self, padding) {
   args <- mget(x = c("grad_output", "self", "padding"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", padding = "IntArrayRef")
@@ -12552,6 +13298,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_reflection_pad2d_backward_out
 torch_reflection_pad2d_backward_out <- function(grad_input, grad_output, self, padding) {
   args <- mget(x = c("grad_input", "grad_output", "self", "padding"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -12569,6 +13316,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_reflection_pad2d_out
 torch_reflection_pad2d_out <- function(out, self, padding) {
   args <- mget(x = c("out", "self", "padding"))
 expected_types <- list(out = "Tensor", self = "Tensor", padding = "IntArrayRef")
@@ -12585,6 +13333,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_relu
 torch_relu <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -12601,6 +13350,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_relu_
 torch_relu_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -12617,6 +13367,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_remainder
 torch_remainder <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor"))
@@ -12633,6 +13384,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_remainder_out
 torch_remainder_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor"
@@ -12650,6 +13402,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_renorm
 torch_renorm <- function(self, p, dim, maxnorm) {
   args <- mget(x = c("self", "p", "dim", "maxnorm"))
 expected_types <- list(self = "Tensor", p = "Scalar", dim = "int64_t", maxnorm = "Scalar")
@@ -12666,6 +13419,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_renorm_out
 torch_renorm_out <- function(out, self, p, dim, maxnorm) {
   args <- mget(x = c("out", "self", "p", "dim", "maxnorm"))
 expected_types <- list(out = "Tensor", self = "Tensor", p = "Scalar", dim = "int64_t", 
@@ -12683,6 +13437,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_repeat_interleave
 torch_repeat_interleave <- function(self, repeats, dim = NULL) {
   args <- mget(x = c("self", "repeats", "dim"))
 expected_types <- list(self = "Tensor", repeats = c("Tensor", "int64_t"), dim = "int64_t")
@@ -12699,6 +13454,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_replication_pad1d
 torch_replication_pad1d <- function(self, padding) {
   args <- mget(x = c("self", "padding"))
 expected_types <- list(self = "Tensor", padding = "IntArrayRef")
@@ -12715,6 +13471,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_replication_pad1d_backward
 torch_replication_pad1d_backward <- function(grad_output, self, padding) {
   args <- mget(x = c("grad_output", "self", "padding"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", padding = "IntArrayRef")
@@ -12731,6 +13488,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_replication_pad1d_backward_out
 torch_replication_pad1d_backward_out <- function(grad_input, grad_output, self, padding) {
   args <- mget(x = c("grad_input", "grad_output", "self", "padding"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -12748,6 +13506,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_replication_pad1d_out
 torch_replication_pad1d_out <- function(out, self, padding) {
   args <- mget(x = c("out", "self", "padding"))
 expected_types <- list(out = "Tensor", self = "Tensor", padding = "IntArrayRef")
@@ -12764,6 +13523,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_replication_pad2d
 torch_replication_pad2d <- function(self, padding) {
   args <- mget(x = c("self", "padding"))
 expected_types <- list(self = "Tensor", padding = "IntArrayRef")
@@ -12780,6 +13540,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_replication_pad2d_backward
 torch_replication_pad2d_backward <- function(grad_output, self, padding) {
   args <- mget(x = c("grad_output", "self", "padding"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", padding = "IntArrayRef")
@@ -12796,6 +13557,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_replication_pad2d_backward_out
 torch_replication_pad2d_backward_out <- function(grad_input, grad_output, self, padding) {
   args <- mget(x = c("grad_input", "grad_output", "self", "padding"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -12813,6 +13575,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_replication_pad2d_out
 torch_replication_pad2d_out <- function(out, self, padding) {
   args <- mget(x = c("out", "self", "padding"))
 expected_types <- list(out = "Tensor", self = "Tensor", padding = "IntArrayRef")
@@ -12829,6 +13592,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_replication_pad3d
 torch_replication_pad3d <- function(self, padding) {
   args <- mget(x = c("self", "padding"))
 expected_types <- list(self = "Tensor", padding = "IntArrayRef")
@@ -12845,6 +13609,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_replication_pad3d_backward
 torch_replication_pad3d_backward <- function(grad_output, self, padding) {
   args <- mget(x = c("grad_output", "self", "padding"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", padding = "IntArrayRef")
@@ -12861,6 +13626,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_replication_pad3d_backward_out
 torch_replication_pad3d_backward_out <- function(grad_input, grad_output, self, padding) {
   args <- mget(x = c("grad_input", "grad_output", "self", "padding"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -12878,6 +13644,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_replication_pad3d_out
 torch_replication_pad3d_out <- function(out, self, padding) {
   args <- mget(x = c("out", "self", "padding"))
 expected_types <- list(out = "Tensor", self = "Tensor", padding = "IntArrayRef")
@@ -12894,6 +13661,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_reshape
 torch_reshape <- function(self, shape) {
   args <- mget(x = c("self", "shape"))
 expected_types <- list(self = "Tensor", shape = "IntArrayRef")
@@ -12910,6 +13678,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_resize_as_
 torch_resize_as_ <- function(self, the_template, memory_format = NULL) {
   args <- mget(x = c("self", "the_template", "memory_format"))
 expected_types <- list(self = "Tensor", the_template = "Tensor", memory_format = "MemoryFormat")
@@ -12926,6 +13695,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_result_type
 torch_result_type <- function(scalar, scalar1, other, scalar2, tensor) {
   args <- mget(x = c("scalar", "scalar1", "other", "scalar2", "tensor"))
 expected_types <- list(scalar = "Scalar", scalar1 = "Scalar", other = c("Tensor", 
@@ -12943,6 +13713,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rfft
 torch_rfft <- function(self, signal_ndim, normalized = FALSE, onesided = TRUE) {
   args <- mget(x = c("self", "signal_ndim", "normalized", "onesided"))
 expected_types <- list(self = "Tensor", signal_ndim = "int64_t", normalized = "bool", 
@@ -12960,6 +13731,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rnn_relu
 torch_rnn_relu <- function(data, input, batch_sizes, hx, params, has_biases, num_layers, dropout, train, batch_first, bidirectional) {
   args <- mget(x = c("data", "input", "batch_sizes", "hx", "params", "has_biases", "num_layers", "dropout", "train", "batch_first", "bidirectional"))
 expected_types <- list(data = "Tensor", input = "Tensor", batch_sizes = "Tensor", 
@@ -12981,6 +13753,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rnn_relu_cell
 torch_rnn_relu_cell <- function(input, hx, w_ih, w_hh, b_ih = list(), b_hh = list()) {
   args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh"))
 expected_types <- list(input = "Tensor", hx = "Tensor", w_ih = "Tensor", w_hh = "Tensor", 
@@ -12998,6 +13771,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rnn_tanh
 torch_rnn_tanh <- function(data, input, batch_sizes, hx, params, has_biases, num_layers, dropout, train, batch_first, bidirectional) {
   args <- mget(x = c("data", "input", "batch_sizes", "hx", "params", "has_biases", "num_layers", "dropout", "train", "batch_first", "bidirectional"))
 expected_types <- list(data = "Tensor", input = "Tensor", batch_sizes = "Tensor", 
@@ -13019,6 +13793,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rnn_tanh_cell
 torch_rnn_tanh_cell <- function(input, hx, w_ih, w_hh, b_ih = list(), b_hh = list()) {
   args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh"))
 expected_types <- list(input = "Tensor", hx = "Tensor", w_ih = "Tensor", w_hh = "Tensor", 
@@ -13036,6 +13811,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_roll
 torch_roll <- function(self, shifts, dims = list()) {
   args <- mget(x = c("self", "shifts", "dims"))
 expected_types <- list(self = "Tensor", shifts = "IntArrayRef", dims = "IntArrayRef")
@@ -13052,6 +13828,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rot90
 torch_rot90 <- function(self, k = 1L, dims = c(0,1)) {
   args <- mget(x = c("self", "k", "dims"))
 expected_types <- list(self = "Tensor", k = "int64_t", dims = "IntArrayRef")
@@ -13068,6 +13845,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_round
 torch_round <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13084,6 +13862,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_round_
 torch_round_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13100,6 +13879,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_round_out
 torch_round_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -13116,6 +13896,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rrelu
 torch_rrelu <- function(self, lower = 0.125000, upper = 0.333333, training = FALSE, generator = NULL) {
   args <- mget(x = c("self", "lower", "upper", "training", "generator"))
 expected_types <- list(self = "Tensor", lower = "Scalar", upper = "Scalar", training = "bool", 
@@ -13133,6 +13914,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rrelu_
 torch_rrelu_ <- function(self, lower = 0.125000, upper = 0.333333, training = FALSE, generator = NULL) {
   args <- mget(x = c("self", "lower", "upper", "training", "generator"))
 expected_types <- list(self = "Tensor", lower = "Scalar", upper = "Scalar", training = "bool", 
@@ -13150,6 +13932,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rrelu_with_noise
 torch_rrelu_with_noise <- function(self, noise, lower = 0.125000, upper = 0.333333, training = FALSE, generator = NULL) {
   args <- mget(x = c("self", "noise", "lower", "upper", "training", "generator"))
 expected_types <- list(self = "Tensor", noise = "Tensor", lower = "Scalar", upper = "Scalar", 
@@ -13167,6 +13950,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rrelu_with_noise_
 torch_rrelu_with_noise_ <- function(self, noise, lower = 0.125000, upper = 0.333333, training = FALSE, generator = NULL) {
   args <- mget(x = c("self", "noise", "lower", "upper", "training", "generator"))
 expected_types <- list(self = "Tensor", noise = "Tensor", lower = "Scalar", upper = "Scalar", 
@@ -13184,6 +13968,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rrelu_with_noise_backward
 torch_rrelu_with_noise_backward <- function(grad_output, self, noise, lower, upper, training, self_is_result) {
   args <- mget(x = c("grad_output", "self", "noise", "lower", "upper", "training", "self_is_result"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", noise = "Tensor", 
@@ -13202,6 +13987,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rrelu_with_noise_out
 torch_rrelu_with_noise_out <- function(out, self, noise, lower = 0.125000, upper = 0.333333, training = FALSE, generator = NULL) {
   args <- mget(x = c("out", "self", "noise", "lower", "upper", "training", "generator"))
 expected_types <- list(out = "Tensor", self = "Tensor", noise = "Tensor", lower = "Scalar", 
@@ -13219,6 +14005,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rsqrt
 torch_rsqrt <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13235,6 +14022,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rsqrt_
 torch_rsqrt_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13251,6 +14039,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rsqrt_out
 torch_rsqrt_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -13267,6 +14056,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_rsub
 torch_rsub <- function(self, other, alpha = 1L) {
   args <- mget(x = c("self", "other", "alpha"))
 expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), alpha = "Scalar")
@@ -13283,6 +14073,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_scalar_tensor
 torch_scalar_tensor <- function(s, options = list()) {
   args <- mget(x = c("s", "options"))
 expected_types <- list(s = "Scalar", options = "TensorOptions")
@@ -13299,6 +14090,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_scatter
 torch_scatter <- function(self, dim, index, src, value) {
   args <- mget(x = c("self", "dim", "index", "src", "value"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor", 
@@ -13316,6 +14108,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_scatter_add
 torch_scatter_add <- function(self, dim, index, src) {
   args <- mget(x = c("self", "dim", "index", "src"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor", 
@@ -13333,6 +14126,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_select
 torch_select <- function(self, dim, index) {
   args <- mget(x = c("self", "dim", "index"))
 expected_types <- list(self = "Tensor", dim = c("Dimname", "int64_t"), index = "int64_t")
@@ -13349,6 +14143,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_selu
 torch_selu <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13365,6 +14160,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_selu_
 torch_selu_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13381,6 +14177,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sigmoid
 torch_sigmoid <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13397,6 +14194,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sigmoid_
 torch_sigmoid_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13413,6 +14211,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sigmoid_backward
 torch_sigmoid_backward <- function(grad_output, output) {
   args <- mget(x = c("grad_output", "output"))
 expected_types <- list(grad_output = "Tensor", output = "Tensor")
@@ -13429,6 +14228,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sigmoid_backward_out
 torch_sigmoid_backward_out <- function(grad_input, grad_output, output) {
   args <- mget(x = c("grad_input", "grad_output", "output"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output = "Tensor")
@@ -13445,6 +14245,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sigmoid_out
 torch_sigmoid_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -13461,6 +14262,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sign
 torch_sign <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13477,6 +14279,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sign_out
 torch_sign_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -13493,6 +14296,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sin
 torch_sin <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13509,6 +14313,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sin_
 torch_sin_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13525,6 +14330,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sin_out
 torch_sin_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -13541,6 +14347,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sinh
 torch_sinh <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13557,6 +14364,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sinh_
 torch_sinh_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13573,6 +14381,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sinh_out
 torch_sinh_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -13589,6 +14398,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_size
 torch_size <- function(self, dim) {
   args <- mget(x = c("self", "dim"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
@@ -13605,6 +14415,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slice
 torch_slice <- function(self, dim = 1L, start = 0L, end = 9223372036854775807, step = 1L) {
   args <- mget(x = c("self", "dim", "start", "end", "step"))
 expected_types <- list(self = "Tensor", dim = "int64_t", start = "int64_t", end = "int64_t", 
@@ -13622,6 +14433,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slogdet
 torch_slogdet <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -13638,6 +14450,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv_dilated2d
 torch_slow_conv_dilated2d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, dilation = 1L) {
   args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding", "dilation"))
 expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -13656,6 +14469,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv_dilated2d_backward
 torch_slow_conv_dilated2d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, dilation, output_mask) {
   args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "dilation", "output_mask"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", 
@@ -13675,6 +14489,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv_dilated3d
 torch_slow_conv_dilated3d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, dilation = 1L) {
   args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding", "dilation"))
 expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -13693,6 +14508,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv_dilated3d_backward
 torch_slow_conv_dilated3d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, dilation, output_mask) {
   args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "dilation", "output_mask"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", 
@@ -13712,6 +14528,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv_transpose2d
 torch_slow_conv_transpose2d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, dilation = 1L) {
   args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding", "output_padding", "dilation"))
 expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -13730,6 +14547,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv_transpose2d_backward
 torch_slow_conv_transpose2d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, columns, ones, output_mask) {
   args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "output_padding", "dilation", "columns", "ones", "output_mask"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", 
@@ -13751,6 +14569,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv_transpose2d_backward_out
 torch_slow_conv_transpose2d_backward_out <- function(grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, columns, ones) {
   args <- mget(x = c("grad_input", "grad_weight", "grad_bias", "grad_output", "self", "weight", "kernel_size", "stride", "padding", "output_padding", "dilation", "columns", "ones"))
 expected_types <- list(grad_input = "Tensor", grad_weight = "Tensor", grad_bias = "Tensor", 
@@ -13773,6 +14592,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv_transpose2d_out
 torch_slow_conv_transpose2d_out <- function(out, self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, dilation = 1L) {
   args <- mget(x = c("out", "self", "weight", "kernel_size", "bias", "stride", "padding", "output_padding", "dilation"))
 expected_types <- list(out = "Tensor", self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -13791,6 +14611,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv_transpose3d
 torch_slow_conv_transpose3d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, dilation = 1L) {
   args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding", "output_padding", "dilation"))
 expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -13809,6 +14630,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv_transpose3d_backward
 torch_slow_conv_transpose3d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, finput, fgrad_input, output_mask) {
   args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "output_padding", "dilation", "finput", "fgrad_input", "output_mask"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", 
@@ -13830,6 +14652,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv_transpose3d_backward_out
 torch_slow_conv_transpose3d_backward_out <- function(grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, finput, fgrad_input) {
   args <- mget(x = c("grad_input", "grad_weight", "grad_bias", "grad_output", "self", "weight", "kernel_size", "stride", "padding", "output_padding", "dilation", "finput", "fgrad_input"))
 expected_types <- list(grad_input = "Tensor", grad_weight = "Tensor", grad_bias = "Tensor", 
@@ -13852,6 +14675,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv_transpose3d_out
 torch_slow_conv_transpose3d_out <- function(out, self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, dilation = 1L) {
   args <- mget(x = c("out", "self", "weight", "kernel_size", "bias", "stride", "padding", "output_padding", "dilation"))
 expected_types <- list(out = "Tensor", self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -13870,6 +14694,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv3d
 torch_slow_conv3d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L) {
   args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding"))
 expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -13887,6 +14712,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv3d_backward
 torch_slow_conv3d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input, output_mask) {
   args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "finput", "fgrad_input", "output_mask"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", 
@@ -13906,6 +14732,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv3d_backward_out
 torch_slow_conv3d_backward_out <- function(grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input) {
   args <- mget(x = c("grad_input", "grad_weight", "grad_bias", "grad_output", "self", "weight", "kernel_size", "stride", "padding", "finput", "fgrad_input"))
 expected_types <- list(grad_input = "Tensor", grad_weight = "Tensor", grad_bias = "Tensor", 
@@ -13927,6 +14754,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv3d_forward
 torch_slow_conv3d_forward <- function(self, weight, kernel_size, bias, stride, padding) {
   args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding"))
 expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -13945,6 +14773,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv3d_forward_out
 torch_slow_conv3d_forward_out <- function(output, finput, fgrad_input, self, weight, kernel_size, bias, stride, padding) {
   args <- mget(x = c("output", "finput", "fgrad_input", "self", "weight", "kernel_size", "bias", "stride", "padding"))
 expected_types <- list(output = "Tensor", finput = "Tensor", fgrad_input = "Tensor", 
@@ -13964,6 +14793,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_slow_conv3d_out
 torch_slow_conv3d_out <- function(out, self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L) {
   args <- mget(x = c("out", "self", "weight", "kernel_size", "bias", "stride", "padding"))
 expected_types <- list(out = "Tensor", self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -13981,6 +14811,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_smm
 torch_smm <- function(self, mat2) {
   args <- mget(x = c("self", "mat2"))
 expected_types <- list(self = "Tensor", mat2 = "Tensor")
@@ -13997,6 +14828,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_smooth_l1_loss
 torch_smooth_l1_loss <- function(self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("self", "target", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t")
@@ -14013,6 +14845,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_smooth_l1_loss_backward
 torch_smooth_l1_loss_backward <- function(grad_output, self, target, reduction) {
   args <- mget(x = c("grad_output", "self", "target", "reduction"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", 
@@ -14030,6 +14863,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_smooth_l1_loss_backward_out
 torch_smooth_l1_loss_backward_out <- function(grad_input, grad_output, self, target, reduction) {
   args <- mget(x = c("grad_input", "grad_output", "self", "target", "reduction"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -14047,6 +14881,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_smooth_l1_loss_out
 torch_smooth_l1_loss_out <- function(out, self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("out", "self", "target", "reduction"))
 expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", reduction = "int64_t")
@@ -14063,6 +14898,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_soft_margin_loss
 torch_soft_margin_loss <- function(self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("self", "target", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t")
@@ -14079,6 +14915,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_soft_margin_loss_backward
 torch_soft_margin_loss_backward <- function(grad_output, self, target, reduction) {
   args <- mget(x = c("grad_output", "self", "target", "reduction"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", 
@@ -14096,6 +14933,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_soft_margin_loss_backward_out
 torch_soft_margin_loss_backward_out <- function(grad_input, grad_output, self, target, reduction) {
   args <- mget(x = c("grad_input", "grad_output", "self", "target", "reduction"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -14113,6 +14951,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_soft_margin_loss_out
 torch_soft_margin_loss_out <- function(out, self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("out", "self", "target", "reduction"))
 expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", reduction = "int64_t")
@@ -14129,6 +14968,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_softmax
 torch_softmax <- function(self, dim, dtype = NULL) {
   args <- mget(x = c("self", "dim", "dtype"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType")
@@ -14145,6 +14985,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_softplus
 torch_softplus <- function(self, beta = 1L, threshold = 20L) {
   args <- mget(x = c("self", "beta", "threshold"))
 expected_types <- list(self = "Tensor", beta = "Scalar", threshold = "Scalar")
@@ -14161,6 +15002,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_softplus_backward
 torch_softplus_backward <- function(grad_output, self, beta, threshold, output) {
   args <- mget(x = c("grad_output", "self", "beta", "threshold", "output"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", beta = "Scalar", 
@@ -14178,6 +15020,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_softplus_backward_out
 torch_softplus_backward_out <- function(grad_input, grad_output, self, beta, threshold, output) {
   args <- mget(x = c("grad_input", "grad_output", "self", "beta", "threshold", "output"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -14196,6 +15039,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_softplus_out
 torch_softplus_out <- function(out, self, beta = 1L, threshold = 20L) {
   args <- mget(x = c("out", "self", "beta", "threshold"))
 expected_types <- list(out = "Tensor", self = "Tensor", beta = "Scalar", threshold = "Scalar")
@@ -14212,6 +15056,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_softshrink
 torch_softshrink <- function(self, lambd = 0.500000) {
   args <- mget(x = c("self", "lambd"))
 expected_types <- list(self = "Tensor", lambd = "Scalar")
@@ -14228,6 +15073,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_softshrink_backward
 torch_softshrink_backward <- function(grad_output, self, lambd) {
   args <- mget(x = c("grad_output", "self", "lambd"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", lambd = "Scalar")
@@ -14244,6 +15090,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_softshrink_backward_out
 torch_softshrink_backward_out <- function(grad_input, grad_output, self, lambd) {
   args <- mget(x = c("grad_input", "grad_output", "self", "lambd"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", 
@@ -14261,6 +15108,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_softshrink_out
 torch_softshrink_out <- function(out, self, lambd = 0.500000) {
   args <- mget(x = c("out", "self", "lambd"))
 expected_types <- list(out = "Tensor", self = "Tensor", lambd = "Scalar")
@@ -14277,6 +15125,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_solve
 torch_solve <- function(self, A) {
   args <- mget(x = c("self", "A"))
 expected_types <- list(self = "Tensor", A = "Tensor")
@@ -14293,6 +15142,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_solve_out
 torch_solve_out <- function(solution, lu, self, A) {
   args <- mget(x = c("solution", "lu", "self", "A"))
 expected_types <- list(solution = "Tensor", lu = "Tensor", self = "Tensor", A = "Tensor")
@@ -14309,6 +15159,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sort
 torch_sort <- function(self, dim = -1L, descending = FALSE) {
   args <- mget(x = c("self", "dim", "descending"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), descending = "bool")
@@ -14325,6 +15176,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sort_out
 torch_sort_out <- function(values, indices, self, dim = -1L, descending = FALSE) {
   args <- mget(x = c("values", "indices", "self", "dim", "descending"))
 expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", 
@@ -14342,6 +15194,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sparse_coo_tensor
 torch_sparse_coo_tensor <- function(indices, values, size, options = list()) {
   args <- mget(x = c("indices", "values", "size", "options"))
 expected_types <- list(indices = "Tensor", values = "Tensor", size = "IntArrayRef", 
@@ -14359,6 +15212,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_split
 torch_split <- function(self, split_size, dim = 1L) {
   args <- mget(x = c("self", "split_size", "dim"))
 expected_types <- list(self = "Tensor", split_size = "int64_t", dim = "int64_t")
@@ -14375,6 +15229,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_split_with_sizes
 torch_split_with_sizes <- function(self, split_sizes, dim = 1L) {
   args <- mget(x = c("self", "split_sizes", "dim"))
 expected_types <- list(self = "Tensor", split_sizes = "IntArrayRef", dim = "int64_t")
@@ -14391,6 +15246,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sqrt
 torch_sqrt <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -14407,6 +15263,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sqrt_
 torch_sqrt_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -14423,6 +15280,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sqrt_out
 torch_sqrt_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -14439,6 +15297,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_square
 torch_square <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -14455,6 +15314,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_square_
 torch_square_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -14471,6 +15331,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_squeeze
 torch_squeeze <- function(self, dim) {
   args <- mget(x = c("self", "dim"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
@@ -14487,6 +15348,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sspaddmm
 torch_sspaddmm <- function(self, mat1, mat2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("self", "mat1", "mat2", "beta", "alpha"))
 expected_types <- list(self = "Tensor", mat1 = "Tensor", mat2 = "Tensor", beta = "Scalar", 
@@ -14504,6 +15366,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sspaddmm_out
 torch_sspaddmm_out <- function(out, self, mat1, mat2, beta = 1L, alpha = 1L) {
   args <- mget(x = c("out", "self", "mat1", "mat2", "beta", "alpha"))
 expected_types <- list(out = "Tensor", self = "Tensor", mat1 = "Tensor", mat2 = "Tensor", 
@@ -14521,6 +15384,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_stack
 torch_stack <- function(tensors, dim = 1L) {
   args <- mget(x = c("tensors", "dim"))
 expected_types <- list(tensors = "TensorList", dim = "int64_t")
@@ -14537,6 +15401,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_stack_out
 torch_stack_out <- function(out, tensors, dim = 1L) {
   args <- mget(x = c("out", "tensors", "dim"))
 expected_types <- list(out = "Tensor", tensors = "TensorList", dim = "int64_t")
@@ -14553,6 +15418,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_std
 torch_std <- function(self, dim, unbiased = TRUE, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "unbiased", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), 
@@ -14570,6 +15436,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_std_mean
 torch_std_mean <- function(self, dim, unbiased = TRUE, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "unbiased", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), 
@@ -14587,6 +15454,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_std_out
 torch_std_out <- function(out, self, dim, unbiased = TRUE, keepdim = FALSE) {
   args <- mget(x = c("out", "self", "dim", "unbiased", "keepdim"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = c("IntArrayRef", 
@@ -14604,6 +15472,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_stft
 torch_stft <- function(self, n_fft, hop_length = NULL, win_length = NULL, window = list(), normalized = FALSE, onesided = TRUE) {
   args <- mget(x = c("self", "n_fft", "hop_length", "win_length", "window", "normalized", "onesided"))
 expected_types <- list(self = "Tensor", n_fft = "int64_t", hop_length = "int64_t", 
@@ -14622,6 +15491,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_stride
 torch_stride <- function(self, dim) {
   args <- mget(x = c("self", "dim"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
@@ -14638,6 +15508,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sub
 torch_sub <- function(self, other, alpha = 1L) {
   args <- mget(x = c("self", "other", "alpha"))
 expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), alpha = "Scalar")
@@ -14654,6 +15525,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sub_out
 torch_sub_out <- function(out, self, other, alpha = 1L) {
   args <- mget(x = c("out", "self", "other", "alpha"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor", alpha = "Scalar")
@@ -14670,6 +15542,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sum
 torch_sum <- function(self, dim, keepdim = FALSE, dtype = NULL) {
   args <- mget(x = c("self", "dim", "keepdim", "dtype"))
 expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), 
@@ -14687,6 +15560,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_sum_out
 torch_sum_out <- function(out, self, dim, keepdim = FALSE, dtype = NULL) {
   args <- mget(x = c("out", "self", "dim", "keepdim", "dtype"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = c("IntArrayRef", 
@@ -14704,6 +15578,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_svd
 torch_svd <- function(self, some = TRUE, compute_uv = TRUE) {
   args <- mget(x = c("self", "some", "compute_uv"))
 expected_types <- list(self = "Tensor", some = "bool", compute_uv = "bool")
@@ -14720,6 +15595,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_svd_out
 torch_svd_out <- function(U, S, V, self, some = TRUE, compute_uv = TRUE) {
   args <- mget(x = c("U", "S", "V", "self", "some", "compute_uv"))
 expected_types <- list(U = "Tensor", S = "Tensor", V = "Tensor", self = "Tensor", 
@@ -14737,6 +15613,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_symeig
 torch_symeig <- function(self, eigenvectors = FALSE, upper = TRUE) {
   args <- mget(x = c("self", "eigenvectors", "upper"))
 expected_types <- list(self = "Tensor", eigenvectors = "bool", upper = "bool")
@@ -14753,6 +15630,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_symeig_out
 torch_symeig_out <- function(e, V, self, eigenvectors = FALSE, upper = TRUE) {
   args <- mget(x = c("e", "V", "self", "eigenvectors", "upper"))
 expected_types <- list(e = "Tensor", V = "Tensor", self = "Tensor", eigenvectors = "bool", 
@@ -14770,6 +15648,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_t
 torch_t <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -14786,6 +15665,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_take
 torch_take <- function(self, index) {
   args <- mget(x = c("self", "index"))
 expected_types <- list(self = "Tensor", index = "Tensor")
@@ -14802,6 +15682,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_take_out
 torch_take_out <- function(out, self, index) {
   args <- mget(x = c("out", "self", "index"))
 expected_types <- list(out = "Tensor", self = "Tensor", index = "Tensor")
@@ -14818,6 +15699,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_tan
 torch_tan <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -14834,6 +15716,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_tan_
 torch_tan_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -14850,6 +15733,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_tan_out
 torch_tan_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -14866,6 +15750,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_tanh
 torch_tanh <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -14882,6 +15767,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_tanh_
 torch_tanh_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -14898,6 +15784,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_tanh_backward
 torch_tanh_backward <- function(grad_output, output) {
   args <- mget(x = c("grad_output", "output"))
 expected_types <- list(grad_output = "Tensor", output = "Tensor")
@@ -14914,6 +15801,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_tanh_backward_out
 torch_tanh_backward_out <- function(grad_input, grad_output, output) {
   args <- mget(x = c("grad_input", "grad_output", "output"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output = "Tensor")
@@ -14930,6 +15818,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_tanh_out
 torch_tanh_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -14946,6 +15835,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_tensordot
 torch_tensordot <- function(self, other, dims_self, dims_other) {
   args <- mget(x = c("self", "other", "dims_self", "dims_other"))
 expected_types <- list(self = "Tensor", other = "Tensor", dims_self = "IntArrayRef", 
@@ -14963,6 +15853,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_thnn_conv_depthwise2d
 torch_thnn_conv_depthwise2d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, dilation = 1L) {
   args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding", "dilation"))
 expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -14981,6 +15872,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_thnn_conv_depthwise2d_backward
 torch_thnn_conv_depthwise2d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, dilation, output_mask) {
   args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "dilation", "output_mask"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", 
@@ -15000,6 +15892,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_thnn_conv_depthwise2d_backward_out
 torch_thnn_conv_depthwise2d_backward_out <- function(grad_input, grad_weight, grad_output, self, weight, kernel_size, stride, padding, dilation) {
   args <- mget(x = c("grad_input", "grad_weight", "grad_output", "self", "weight", "kernel_size", "stride", "padding", "dilation"))
 expected_types <- list(grad_input = "Tensor", grad_weight = "Tensor", grad_output = "Tensor", 
@@ -15019,6 +15912,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_thnn_conv_depthwise2d_forward
 torch_thnn_conv_depthwise2d_forward <- function(self, weight, kernel_size, bias, stride, padding, dilation) {
   args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding", "dilation"))
 expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -15038,6 +15932,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_thnn_conv_depthwise2d_forward_out
 torch_thnn_conv_depthwise2d_forward_out <- function(out, self, weight, kernel_size, bias, stride, padding, dilation) {
   args <- mget(x = c("out", "self", "weight", "kernel_size", "bias", "stride", "padding", "dilation"))
 expected_types <- list(out = "Tensor", self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -15057,6 +15952,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_thnn_conv_depthwise2d_out
 torch_thnn_conv_depthwise2d_out <- function(out, self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, dilation = 1L) {
   args <- mget(x = c("out", "self", "weight", "kernel_size", "bias", "stride", "padding", "dilation"))
 expected_types <- list(out = "Tensor", self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -15075,6 +15971,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_thnn_conv2d
 torch_thnn_conv2d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L) {
   args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding"))
 expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -15092,6 +15989,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_thnn_conv2d_backward
 torch_thnn_conv2d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input, output_mask) {
   args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "finput", "fgrad_input", "output_mask"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", 
@@ -15111,6 +16009,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_thnn_conv2d_backward_out
 torch_thnn_conv2d_backward_out <- function(grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input) {
   args <- mget(x = c("grad_input", "grad_weight", "grad_bias", "grad_output", "self", "weight", "kernel_size", "stride", "padding", "finput", "fgrad_input"))
 expected_types <- list(grad_input = "Tensor", grad_weight = "Tensor", grad_bias = "Tensor", 
@@ -15132,6 +16031,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_thnn_conv2d_forward
 torch_thnn_conv2d_forward <- function(self, weight, kernel_size, bias, stride, padding) {
   args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding"))
 expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -15150,6 +16050,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_thnn_conv2d_forward_out
 torch_thnn_conv2d_forward_out <- function(output, finput, fgrad_input, self, weight, kernel_size, bias, stride, padding) {
   args <- mget(x = c("output", "finput", "fgrad_input", "self", "weight", "kernel_size", "bias", "stride", "padding"))
 expected_types <- list(output = "Tensor", finput = "Tensor", fgrad_input = "Tensor", 
@@ -15169,6 +16070,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_thnn_conv2d_out
 torch_thnn_conv2d_out <- function(out, self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L) {
   args <- mget(x = c("out", "self", "weight", "kernel_size", "bias", "stride", "padding"))
 expected_types <- list(out = "Tensor", self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", 
@@ -15186,6 +16088,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_threshold
 torch_threshold <- function(self, threshold, value) {
   args <- mget(x = c("self", "threshold", "value"))
 expected_types <- list(self = "Tensor", threshold = "Scalar", value = "Scalar")
@@ -15202,6 +16105,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_threshold_
 torch_threshold_ <- function(self, threshold, value) {
   args <- mget(x = c("self", "threshold", "value"))
 expected_types <- list(self = "Tensor", threshold = "Scalar", value = "Scalar")
@@ -15218,6 +16122,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_threshold_backward
 torch_threshold_backward <- function(grad_output, self, threshold) {
   args <- mget(x = c("grad_output", "self", "threshold"))
 expected_types <- list(grad_output = "Tensor", self = "Tensor", threshold = "Scalar")
@@ -15234,6 +16139,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_threshold_out
 torch_threshold_out <- function(out, self, threshold, value) {
   args <- mget(x = c("out", "self", "threshold", "value"))
 expected_types <- list(out = "Tensor", self = "Tensor", threshold = "Scalar", value = "Scalar")
@@ -15250,6 +16156,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_to_dense_backward
 torch_to_dense_backward <- function(grad, input) {
   args <- mget(x = c("grad", "input"))
 expected_types <- list(grad = "Tensor", input = "Tensor")
@@ -15266,6 +16173,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_to_mkldnn_backward
 torch_to_mkldnn_backward <- function(grad, input) {
   args <- mget(x = c("grad", "input"))
 expected_types <- list(grad = "Tensor", input = "Tensor")
@@ -15282,6 +16190,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_topk
 torch_topk <- function(self, k, dim = -1L, largest = TRUE, sorted = TRUE) {
   args <- mget(x = c("self", "k", "dim", "largest", "sorted"))
 expected_types <- list(self = "Tensor", k = "int64_t", dim = "int64_t", largest = "bool", 
@@ -15299,6 +16208,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_topk_out
 torch_topk_out <- function(values, indices, self, k, dim = -1L, largest = TRUE, sorted = TRUE) {
   args <- mget(x = c("values", "indices", "self", "k", "dim", "largest", "sorted"))
 expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", 
@@ -15316,6 +16226,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_trace
 torch_trace <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -15332,6 +16243,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_transpose
 torch_transpose <- function(self, dim0, dim1) {
   args <- mget(x = c("self", "dim0", "dim1"))
 expected_types <- list(self = "Tensor", dim0 = c("int64_t", "Dimname"), dim1 = c("int64_t", 
@@ -15349,6 +16261,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_trapz
 torch_trapz <- function(y, dx = 1L, x, dim = -1L) {
   args <- mget(x = c("y", "dx", "x", "dim"))
 expected_types <- list(y = "Tensor", dx = "double", x = "Tensor", dim = "int64_t")
@@ -15365,6 +16278,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_triangular_solve
 torch_triangular_solve <- function(self, A, upper = TRUE, transpose = FALSE, unitriangular = FALSE) {
   args <- mget(x = c("self", "A", "upper", "transpose", "unitriangular"))
 expected_types <- list(self = "Tensor", A = "Tensor", upper = "bool", transpose = "bool", 
@@ -15382,6 +16296,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_triangular_solve_out
 torch_triangular_solve_out <- function(X, M, self, A, upper = TRUE, transpose = FALSE, unitriangular = FALSE) {
   args <- mget(x = c("X", "M", "self", "A", "upper", "transpose", "unitriangular"))
 expected_types <- list(X = "Tensor", M = "Tensor", self = "Tensor", A = "Tensor", 
@@ -15399,6 +16314,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_tril
 torch_tril <- function(self, diagonal = 0L) {
   args <- mget(x = c("self", "diagonal"))
 expected_types <- list(self = "Tensor", diagonal = "int64_t")
@@ -15415,6 +16331,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_tril_indices
 torch_tril_indices <- function(row, col, offset = 0L, options = torch_long()) {
   args <- mget(x = c("row", "col", "offset", "options"))
 expected_types <- list(row = "int64_t", col = "int64_t", offset = "int64_t", options = "TensorOptions")
@@ -15431,6 +16348,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_tril_out
 torch_tril_out <- function(out, self, diagonal = 0L) {
   args <- mget(x = c("out", "self", "diagonal"))
 expected_types <- list(out = "Tensor", self = "Tensor", diagonal = "int64_t")
@@ -15447,6 +16365,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_triplet_margin_loss
 torch_triplet_margin_loss <- function(anchor, positive, negative, margin = 1.000000, p = 2L, eps = 0.000001, swap = FALSE, reduction = torch_reduction_mean()) {
   args <- mget(x = c("anchor", "positive", "negative", "margin", "p", "eps", "swap", "reduction"))
 expected_types <- list(anchor = "Tensor", positive = "Tensor", negative = "Tensor", 
@@ -15465,6 +16384,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_triu
 torch_triu <- function(self, diagonal = 0L) {
   args <- mget(x = c("self", "diagonal"))
 expected_types <- list(self = "Tensor", diagonal = "int64_t")
@@ -15481,6 +16401,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_triu_indices
 torch_triu_indices <- function(row, col, offset = 0L, options = torch_long()) {
   args <- mget(x = c("row", "col", "offset", "options"))
 expected_types <- list(row = "int64_t", col = "int64_t", offset = "int64_t", options = "TensorOptions")
@@ -15497,6 +16418,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_triu_out
 torch_triu_out <- function(out, self, diagonal = 0L) {
   args <- mget(x = c("out", "self", "diagonal"))
 expected_types <- list(out = "Tensor", self = "Tensor", diagonal = "int64_t")
@@ -15513,6 +16435,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_true_divide
 torch_true_divide <- function(self, other) {
   args <- mget(x = c("self", "other"))
 expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"))
@@ -15529,6 +16452,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_true_divide_out
 torch_true_divide_out <- function(out, self, other) {
   args <- mget(x = c("out", "self", "other"))
 expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor")
@@ -15545,6 +16469,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_trunc
 torch_trunc <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -15561,6 +16486,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_trunc_
 torch_trunc_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -15577,6 +16503,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_trunc_out
 torch_trunc_out <- function(out, self) {
   args <- mget(x = c("out", "self"))
 expected_types <- list(out = "Tensor", self = "Tensor")
@@ -15593,6 +16520,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_unbind
 torch_unbind <- function(self, dim = 1L) {
   args <- mget(x = c("self", "dim"))
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
@@ -15609,6 +16537,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_unique_consecutive
 torch_unique_consecutive <- function(self, return_inverse = FALSE, return_counts = FALSE, dim = NULL) {
   args <- mget(x = c("self", "return_inverse", "return_counts", "dim"))
 expected_types <- list(self = "Tensor", return_inverse = "bool", return_counts = "bool", 
@@ -15626,6 +16555,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_unique_dim
 torch_unique_dim <- function(self, dim, sorted = TRUE, return_inverse = FALSE, return_counts = FALSE) {
   args <- mget(x = c("self", "dim", "sorted", "return_inverse", "return_counts"))
 expected_types <- list(self = "Tensor", dim = "int64_t", sorted = "bool", return_inverse = "bool", 
@@ -15643,6 +16573,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_unique_dim_consecutive
 torch_unique_dim_consecutive <- function(self, dim, return_inverse = FALSE, return_counts = FALSE) {
   args <- mget(x = c("self", "dim", "return_inverse", "return_counts"))
 expected_types <- list(self = "Tensor", dim = "int64_t", return_inverse = "bool", 
@@ -15660,6 +16591,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_unsqueeze
 torch_unsqueeze <- function(self, dim) {
   args <- mget(x = c("self", "dim"))
 expected_types <- list(self = "Tensor", dim = "int64_t")
@@ -15676,6 +16608,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_bicubic2d
 torch_upsample_bicubic2d <- function(self, output_size, align_corners, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("self", "output_size", "align_corners", "scales_h", "scales_w"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef", align_corners = "bool", 
@@ -15693,6 +16626,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_bicubic2d_backward
 torch_upsample_bicubic2d_backward <- function(grad_output, output_size, input_size, align_corners, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("grad_output", "output_size", "input_size", "align_corners", "scales_h", "scales_w"))
 expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", 
@@ -15711,6 +16645,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_bicubic2d_backward_out
 torch_upsample_bicubic2d_backward_out <- function(grad_input, grad_output, output_size, input_size, align_corners, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "align_corners", "scales_h", "scales_w"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", 
@@ -15730,6 +16665,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_bicubic2d_out
 torch_upsample_bicubic2d_out <- function(out, self, output_size, align_corners, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("out", "self", "output_size", "align_corners", "scales_h", "scales_w"))
 expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", 
@@ -15747,6 +16683,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_bilinear2d
 torch_upsample_bilinear2d <- function(self, output_size, align_corners, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("self", "output_size", "align_corners", "scales_h", "scales_w"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef", align_corners = "bool", 
@@ -15764,6 +16701,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_bilinear2d_backward
 torch_upsample_bilinear2d_backward <- function(grad_output, output_size, input_size, align_corners, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("grad_output", "output_size", "input_size", "align_corners", "scales_h", "scales_w"))
 expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", 
@@ -15782,6 +16720,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_bilinear2d_backward_out
 torch_upsample_bilinear2d_backward_out <- function(grad_input, grad_output, output_size, input_size, align_corners, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "align_corners", "scales_h", "scales_w"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", 
@@ -15801,6 +16740,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_bilinear2d_out
 torch_upsample_bilinear2d_out <- function(out, self, output_size, align_corners, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("out", "self", "output_size", "align_corners", "scales_h", "scales_w"))
 expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", 
@@ -15818,6 +16758,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_linear1d
 torch_upsample_linear1d <- function(self, output_size, align_corners, scales = NULL) {
   args <- mget(x = c("self", "output_size", "align_corners", "scales"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef", align_corners = "bool", 
@@ -15835,6 +16776,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_linear1d_backward
 torch_upsample_linear1d_backward <- function(grad_output, output_size, input_size, align_corners, scales = NULL) {
   args <- mget(x = c("grad_output", "output_size", "input_size", "align_corners", "scales"))
 expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", 
@@ -15853,6 +16795,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_linear1d_backward_out
 torch_upsample_linear1d_backward_out <- function(grad_input, grad_output, output_size, input_size, align_corners, scales = NULL) {
   args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "align_corners", "scales"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", 
@@ -15871,6 +16814,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_linear1d_out
 torch_upsample_linear1d_out <- function(out, self, output_size, align_corners, scales = NULL) {
   args <- mget(x = c("out", "self", "output_size", "align_corners", "scales"))
 expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", 
@@ -15888,6 +16832,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_nearest1d
 torch_upsample_nearest1d <- function(self, output_size, scales = NULL) {
   args <- mget(x = c("self", "output_size", "scales"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef", scales = "double")
@@ -15904,6 +16849,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_nearest1d_backward
 torch_upsample_nearest1d_backward <- function(grad_output, output_size, input_size, scales = NULL) {
   args <- mget(x = c("grad_output", "output_size", "input_size", "scales"))
 expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", 
@@ -15921,6 +16867,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_nearest1d_backward_out
 torch_upsample_nearest1d_backward_out <- function(grad_input, grad_output, output_size, input_size, scales = NULL) {
   args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "scales"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", 
@@ -15938,6 +16885,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_nearest1d_out
 torch_upsample_nearest1d_out <- function(out, self, output_size, scales = NULL) {
   args <- mget(x = c("out", "self", "output_size", "scales"))
 expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", 
@@ -15955,6 +16903,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_nearest2d
 torch_upsample_nearest2d <- function(self, output_size, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("self", "output_size", "scales_h", "scales_w"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef", scales_h = "double", 
@@ -15972,6 +16921,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_nearest2d_backward
 torch_upsample_nearest2d_backward <- function(grad_output, output_size, input_size, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("grad_output", "output_size", "input_size", "scales_h", "scales_w"))
 expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", 
@@ -15989,6 +16939,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_nearest2d_backward_out
 torch_upsample_nearest2d_backward_out <- function(grad_input, grad_output, output_size, input_size, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "scales_h", "scales_w"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", 
@@ -16006,6 +16957,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_nearest2d_out
 torch_upsample_nearest2d_out <- function(out, self, output_size, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("out", "self", "output_size", "scales_h", "scales_w"))
 expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", 
@@ -16023,6 +16975,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_nearest3d
 torch_upsample_nearest3d <- function(self, output_size, scales_d = NULL, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("self", "output_size", "scales_d", "scales_h", "scales_w"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef", scales_d = "double", 
@@ -16040,6 +16993,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_nearest3d_backward
 torch_upsample_nearest3d_backward <- function(grad_output, output_size, input_size, scales_d = NULL, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("grad_output", "output_size", "input_size", "scales_d", "scales_h", "scales_w"))
 expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", 
@@ -16057,6 +17011,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_nearest3d_backward_out
 torch_upsample_nearest3d_backward_out <- function(grad_input, grad_output, output_size, input_size, scales_d = NULL, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "scales_d", "scales_h", "scales_w"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", 
@@ -16075,6 +17030,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_nearest3d_out
 torch_upsample_nearest3d_out <- function(out, self, output_size, scales_d = NULL, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("out", "self", "output_size", "scales_d", "scales_h", "scales_w"))
 expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", 
@@ -16092,6 +17048,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_trilinear3d
 torch_upsample_trilinear3d <- function(self, output_size, align_corners, scales_d = NULL, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("self", "output_size", "align_corners", "scales_d", "scales_h", "scales_w"))
 expected_types <- list(self = "Tensor", output_size = "IntArrayRef", align_corners = "bool", 
@@ -16109,6 +17066,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_trilinear3d_backward
 torch_upsample_trilinear3d_backward <- function(grad_output, output_size, input_size, align_corners, scales_d = NULL, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("grad_output", "output_size", "input_size", "align_corners", "scales_d", "scales_h", "scales_w"))
 expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", 
@@ -16128,6 +17086,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_trilinear3d_backward_out
 torch_upsample_trilinear3d_backward_out <- function(grad_input, grad_output, output_size, input_size, align_corners, scales_d = NULL, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "align_corners", "scales_d", "scales_h", "scales_w"))
 expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", 
@@ -16147,6 +17106,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_upsample_trilinear3d_out
 torch_upsample_trilinear3d_out <- function(out, self, output_size, align_corners, scales_d = NULL, scales_h = NULL, scales_w = NULL) {
   args <- mget(x = c("out", "self", "output_size", "align_corners", "scales_d", "scales_h", "scales_w"))
 expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", 
@@ -16165,6 +17125,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_var
 torch_var <- function(self, dim, unbiased = TRUE, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "unbiased", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), 
@@ -16182,6 +17143,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_var_mean
 torch_var_mean <- function(self, dim, unbiased = TRUE, keepdim = FALSE) {
   args <- mget(x = c("self", "dim", "unbiased", "keepdim"))
 expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), 
@@ -16199,6 +17161,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_var_out
 torch_var_out <- function(out, self, dim, unbiased = TRUE, keepdim = FALSE) {
   args <- mget(x = c("out", "self", "dim", "unbiased", "keepdim"))
 expected_types <- list(out = "Tensor", self = "Tensor", dim = c("IntArrayRef", 
@@ -16216,6 +17179,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_where
 torch_where <- function(condition, self, other) {
   args <- mget(x = c("condition", "self", "other"))
 expected_types <- list(condition = "Tensor", self = "Tensor", other = "Tensor")
@@ -16232,6 +17196,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_zero_
 torch_zero_ <- function(self) {
   args <- mget(x = c("self"))
 expected_types <- list(self = "Tensor")
@@ -16248,6 +17213,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_zeros
 .torch_zeros <- function(size, names, options = list()) {
   args <- mget(x = c("size", "names", "options"))
 expected_types <- list(size = "IntArrayRef", names = "DimnameList", options = "TensorOptions")
@@ -16264,6 +17230,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname .torch_zeros_like
 .torch_zeros_like <- function(self, options = list(), memory_format = NULL) {
   args <- mget(x = c("self", "options", "memory_format"))
 expected_types <- list(self = "Tensor", options = "TensorOptions", memory_format = "MemoryFormat")
@@ -16280,6 +17247,7 @@ fun_type = 'namespace'
 }
 
 
+#' @rdname torch_zeros_out
 torch_zeros_out <- function(out, size) {
   args <- mget(x = c("out", "size"))
 expected_types <- list(out = "Tensor", size = "IntArrayRef")
-- 
GitLab


From e068fe13859f6b898c76532c9b01b3295ed48a79 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 2 Sep 2020 22:03:47 -0300
Subject: [PATCH 100/157] start reviewing the docs manually. now parameters
 must match usage.

---
 R/gen-namespace-docs.R | 1208 ++++++++++++++++++++--------------------
 1 file changed, 601 insertions(+), 607 deletions(-)

diff --git a/R/gen-namespace-docs.R b/R/gen-namespace-docs.R
index 41c63a453..65dbc50ef 100644
--- a/R/gen-namespace-docs.R
+++ b/R/gen-namespace-docs.R
@@ -1,6 +1,6 @@
 #' Abs
 #'
-#' @section abs(input, out=None) -> Tensor :
+#' @section abs(input) -> Tensor :
 #'
 #' Computes the element-wise absolute value of the given `input` tensor.
 #' 
@@ -9,8 +9,7 @@
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' @param self (Tensor) the input tensor.
 #'
 #' @name torch_abs
 #'
@@ -20,7 +19,7 @@ NULL
 
 #' Angle
 #'
-#' @section angle(input, out=None) -> Tensor :
+#' @section angle(input) -> Tensor :
 #'
 #' Computes the element-wise angle (in radians) of the given `input` tensor.
 #' 
@@ -29,8 +28,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' @param self (Tensor) the input tensor.
 #'
 #' @name torch_angle
 #'
@@ -40,7 +38,7 @@ NULL
 
 #' Real
 #'
-#' @section real(input, out=None) -> Tensor :
+#' @section real(input) -> Tensor :
 #'
 #' Returns the real part of the `input` tensor. If
 #' `input` is a real (non-complex) tensor, this function just
@@ -54,8 +52,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' @param self (Tensor) the input tensor.
 #'
 #' @name torch_real
 #'
@@ -65,7 +62,7 @@ NULL
 
 #' Imag
 #'
-#' @section imag(input, out=None) -> Tensor :
+#' @section imag(input) -> Tensor :
 #'
 #' Returns the imaginary part of the `input` tensor.
 #' 
@@ -77,8 +74,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' @param self (Tensor) the input tensor.
 #'
 #' @name torch_imag
 #'
@@ -88,7 +84,7 @@ NULL
 
 #' Conj
 #'
-#' @section conj(input, out=None) -> Tensor :
+#' @section conj(input) -> Tensor :
 #'
 #' Computes the element-wise conjugate of the given `input` tensor.
 #' 
@@ -97,8 +93,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' @param self (Tensor) the input tensor.
 #'
 #' @name torch_conj
 #'
@@ -108,7 +103,7 @@ NULL
 
 #' Acos
 #'
-#' @section acos(input, out=None) -> Tensor :
+#' @section acos(input) -> Tensor :
 #'
 #' Returns a new tensor with the arccosine  of the elements of `input`.
 #' 
@@ -117,8 +112,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' @param self (Tensor) the input tensor.
 #'
 #' @name torch_acos
 #'
@@ -128,20 +122,20 @@ NULL
 
 #' Avg_pool1d
 #'
-#' @section avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor :
+#' @section avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -> Tensor :
 #'
 #' Applies a 1D average pooling over an input signal composed of several
 #' input planes.
 #' 
-#' See `~torch.nn.AvgPool1d` for details and output shape.
+#' See [nn_avg_pool1d()] for details and output shape.
 #'
 #'
-#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
-#' @param kernel_size NA the size of the window. Can be a single number or a      tuple `(kW,)`
-#' @param stride NA the stride of the window. Can be a single number or a tuple      `(sW,)`. Default: `kernel_size`
-#' @param padding NA implicit zero paddings on both sides of the input. Can be a      single number or a tuple `(padW,)`. Default: 0
-#' @param ceil_mode NA when True, will use `ceil` instead of `floor` to compute the        output shape. Default: ``False``
-#' @param count_include_pad NA when True, will include the zero-padding in the        averaging calculation. Default: ``True``
+#' @param self input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
+#' @param kernel_size the size of the window. Can be a single number or a tuple `(kW,)`
+#' @param stride the stride of the window. Can be a single number or a tuple `(sW,)`. Default: `kernel_size`
+#' @param padding implicit zero paddings on both sides of the input. Can be a single number or a tuple `(padW,)`. Default: 0
+#' @param ceil_mode when `TRUE`, will use `ceil` instead of `floor` to compute the output shape. Default: `FALSE`
+#' @param count_include_pad when `TRUE`, will include the zero-padding in the averaging calculation. Default: `TRUE`
 #'
 #' @name torch_avg_pool1d
 #'
@@ -169,7 +163,7 @@ NULL
 
 #' Add
 #'
-#' @section add(input, other, out=None) :
+#' @section add(input, other, out=NULL) :
 #'
 #' Adds the scalar `other` to each element of the input `input`
 #' and returns a new resulting tensor.
@@ -180,7 +174,7 @@ NULL
 #' If `input` is of type FloatTensor or DoubleTensor, `other` must be
 #' a real number, otherwise it should be an integer.
 #'
-#' @section add(input, other, *, alpha=1, out=None) :
+#' @section add(input, other, *, alpha=1, out=NULL) :
 #'
 #' Each element of the tensor `other` is multiplied by the scalar
 #' `alpha` and added to each element of the tensor `input`.
@@ -196,7 +190,7 @@ NULL
 #' a real number, otherwise it should be an integer.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param value (Number) the number to be added to each element of `input`
 #' @param other (Tensor) the second input tensor
 #' @param alpha (Number) the scalar multiplier for `other`
@@ -209,7 +203,7 @@ NULL
 
 #' Addmv
 #'
-#' @section addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor :
+#' @section addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -> Tensor :
 #'
 #' Performs a matrix-vector product of the matrix `mat` and
 #' the vector `vec`.
@@ -230,7 +224,7 @@ NULL
 #' `alpha` must be real numbers, otherwise they should be integers
 #'
 #'
-#' @param input (Tensor) vector to be added
+#' @param self (Tensor) vector to be added
 #' @param mat (Tensor) matrix to be multiplied
 #' @param vec (Tensor) vector to be multiplied
 #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
@@ -245,7 +239,7 @@ NULL
 
 #' Addr
 #'
-#' @section addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor :
+#' @section addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -> Tensor :
 #'
 #' Performs the outer-product of vectors `vec1` and `vec2`
 #' and adds it to the matrix `input`.
@@ -267,7 +261,7 @@ NULL
 #' `alpha` must be real numbers, otherwise they should be integers
 #'
 #'
-#' @param input (Tensor) matrix to be added
+#' @param self (Tensor) matrix to be added
 #' @param vec1 (Tensor) the first vector of the outer product
 #' @param vec2 (Tensor) the second vector of the outer product
 #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
@@ -293,11 +287,11 @@ NULL
 #' `numpy.allclose <https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html>`_
 #'
 #'
-#' @param input (Tensor) first tensor to compare
+#' @param self (Tensor) first tensor to compare
 #' @param other (Tensor) second tensor to compare
 #' @param atol (float, optional) absolute tolerance. Default: 1e-08
 #' @param rtol (float, optional) relative tolerance. Default: 1e-05
-#' @param equal_nan (bool, optional) if ``True``, then two ``NaN`` s will be compared as equal. Default: ``False``
+#' @param equal_nan (bool, optional) if `TRUE`, then two `NaN` s will be compared as equal. Default: `FALSE`
 #'
 #' @name torch_allclose
 #'
@@ -307,7 +301,7 @@ NULL
 
 #' Arange
 #'
-#' @section arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Returns a 1-D tensor of size \eqn{\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil}
 #' with values from the interval ``[start, end)`` taken with common difference
@@ -326,10 +320,10 @@ NULL
 #' @param end (Number) the ending value for the set of points
 #' @param step (Number) the gap between each pair of adjacent points. Default: ``1``.
 #' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). If `dtype` is not given, infer the data type from the other input        arguments. If any of `start`, `end`, or `stop` are floating-point, the        `dtype` is inferred to be the default dtype, see        `~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to        be `torch.int64`.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). If `dtype` is not given, infer the data type from the other input        arguments. If any of `start`, `end`, or `stop` are floating-point, the        `dtype` is inferred to be the default dtype, see        `~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to        be `torch.int64`.
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_arange
 #'
@@ -354,9 +348,9 @@ NULL
 #' documentation for the exact semantics of this method.
 #'
 #'
-#' @param input (Tensor) the input tensor.
-#' @param dim (int) the dimension to reduce. If ``None``, the argmax of the flattened input is returned.
-#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if ``dim=None``.
+#' @param self (Tensor) the input tensor.
+#' @param dim (int) the dimension to reduce. If `NULL`, the argmax of the flattened input is returned.
+#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if ``dim=NULL``.
 #'
 #' @name torch_argmax
 #'
@@ -373,7 +367,7 @@ NULL
 #' This is the second value returned by `torch_min`. See its
 #' documentation for the exact semantics of this method.
 #'
-#' @section argmin(input, dim, keepdim=False, out=None) -> LongTensor :
+#' @section argmin(input, dim, keepdim=False, out=NULL) -> LongTensor :
 #'
 #' Returns the indices of the minimum values of a tensor across a dimension.
 #' 
@@ -381,9 +375,9 @@ NULL
 #' documentation for the exact semantics of this method.
 #'
 #'
-#' @param input (Tensor) the input tensor.
-#' @param dim (int) the dimension to reduce. If ``None``, the argmin of the flattened input is returned.
-#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if ``dim=None``.
+#' @param self (Tensor) the input tensor.
+#' @param dim (int) the dimension to reduce. If `NULL`, the argmin of the flattened input is returned.
+#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if ``dim=NULL``.
 #'
 #' @name torch_argmin
 #'
@@ -410,7 +404,7 @@ NULL
 #'     advisable to use.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param size (tuple or ints) the shape of the output tensor
 #' @param stride (tuple or ints) the stride of the output tensor
 #' @param storage_offset (int, optional) the offset in the underlying storage of the output tensor
@@ -423,7 +417,7 @@ NULL
 
 #' Asin
 #'
-#' @section asin(input, out=None) -> Tensor :
+#' @section asin(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the arcsine  of the elements of `input`.
 #' 
@@ -432,7 +426,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_asin
@@ -443,7 +437,7 @@ NULL
 
 #' Atan
 #'
-#' @section atan(input, out=None) -> Tensor :
+#' @section atan(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the arctangent  of the elements of `input`.
 #' 
@@ -452,7 +446,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_atan
@@ -463,7 +457,7 @@ NULL
 
 #' Baddbmm
 #'
-#' @section baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor :
+#' @section baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor :
 #'
 #' Performs a batch matrix-matrix product of matrices in `batch1`
 #' and `batch2`.
@@ -486,7 +480,7 @@ NULL
 #' `alpha` must be real numbers, otherwise they should be integers.
 #'
 #'
-#' @param input (Tensor) the tensor to be added
+#' @param self (Tensor) the tensor to be added
 #' @param batch1 (Tensor) the first batch of matrices to be multiplied
 #' @param batch2 (Tensor) the second batch of matrices to be multiplied
 #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
@@ -501,7 +495,7 @@ NULL
 
 #' Bartlett_window
 #'
-#' @section bartlett_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Bartlett window function.
 #' 
@@ -520,7 +514,7 @@ NULL
 #' ready to be used as a periodic window with functions like
 #' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in
 #' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-#' ``torch_bartlett_window(L, periodic=True)`` equal to
+#' ``torch_bartlett_window(L, periodic=TRUE)`` equal to
 #' ``torch_bartlett_window(L + 1, periodic=False)[:-1])``.
 #' 
 #' @note
@@ -528,11 +522,11 @@ NULL
 #'
 #'
 #' @param window_length (int) the size of returned window
-#' @param periodic (bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
+#' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
 #' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          ``torch_strided`` (dense layout) is supported.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_bartlett_window
 #'
@@ -542,7 +536,7 @@ NULL
 
 #' Bernoulli
 #'
-#' @section bernoulli(input, *, generator=None, out=None) -> Tensor :
+#' @section bernoulli(input, *, generator=NULL, out=NULL) -> Tensor :
 #'
 #' Draws binary random numbers (0 or 1) from a Bernoulli distribution.
 #' 
@@ -565,7 +559,7 @@ NULL
 #' point ``dtype``.
 #'
 #'
-#' @param input (Tensor) the input tensor of probability values for the Bernoulli distribution
+#' @param self (Tensor) the input tensor of probability values for the Bernoulli distribution
 #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -577,7 +571,7 @@ NULL
 
 #' Bincount
 #'
-#' @section bincount(input, weights=None, minlength=0) -> Tensor :
+#' @section bincount(input, weights=NULL, minlength=0) -> Tensor :
 #'
 #' Count the frequency of each value in an array of non-negative ints.
 #' 
@@ -592,7 +586,7 @@ NULL
 #' .. include:: cuda_deterministic.rst
 #'
 #'
-#' @param input (Tensor) 1-d int tensor
+#' @param self (Tensor) 1-d int tensor
 #' @param weights (Tensor) optional, weight for each value in the input tensor.        Should be of same size as input tensor.
 #' @param minlength (int) optional, minimum number of bins. Should be non-negative.
 #'
@@ -604,13 +598,13 @@ NULL
 
 #' Bitwise_not
 #'
-#' @section bitwise_not(input, out=None) -> Tensor :
+#' @section bitwise_not(input, out=NULL) -> Tensor :
 #'
 #' 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.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_bitwise_not
@@ -621,13 +615,13 @@ NULL
 
 #' Logical_not
 #'
-#' @section logical_not(input, out=None) -> Tensor :
+#' @section logical_not(input, out=NULL) -> Tensor :
 #'
 #' 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 ``False`` and non-zeros are treated as ``True``.
+#' dtype. If the input tensor is not a bool tensor, zeros are treated as `FALSE` and non-zeros are treated as `TRUE`.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_logical_not
@@ -638,13 +632,13 @@ NULL
 
 #' Logical_xor
 #'
-#' @section logical_xor(input, other, out=None) -> Tensor :
+#' @section logical_xor(input, other, out=NULL) -> Tensor :
 #'
-#' Computes the element-wise logical XOR of the given input tensors. Zeros are treated as ``False`` and nonzeros are
-#' treated as ``True``.
+#' Computes the element-wise logical XOR of the given input tensors. Zeros are treated as `FALSE` and nonzeros are
+#' treated as `TRUE`.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param other (Tensor) the tensor to compute XOR with
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -656,13 +650,13 @@ NULL
 
 #' Logical_and
 #'
-#' @section logical_and(input, other, out=None) -> Tensor :
+#' @section logical_and(input, other, out=NULL) -> Tensor :
 #'
-#' Computes the element-wise logical AND of the given input tensors. Zeros are treated as ``False`` and nonzeros are
-#' treated as ``True``.
+#' Computes the element-wise logical AND of the given input tensors. Zeros are treated as `FALSE` and nonzeros are
+#' treated as `TRUE`.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param other (Tensor) the tensor to compute AND with
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -674,13 +668,13 @@ NULL
 
 #' Logical_or
 #'
-#' @section logical_or(input, other, out=None) -> Tensor :
+#' @section logical_or(input, other, out=NULL) -> Tensor :
 #'
-#' Computes the element-wise logical OR of the given input tensors. Zeros are treated as ``False`` and nonzeros are
-#' treated as ``True``.
+#' Computes the element-wise logical OR of the given input tensors. Zeros are treated as `FALSE` and nonzeros are
+#' treated as `TRUE`.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param other (Tensor) the tensor to compute OR with
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -692,7 +686,7 @@ NULL
 
 #' Blackman_window
 #'
-#' @section blackman_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Blackman window function.
 #' 
@@ -707,7 +701,7 @@ NULL
 #' ready to be used as a periodic window with functions like
 #' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in
 #' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-#' ``torch_blackman_window(L, periodic=True)`` equal to
+#' ``torch_blackman_window(L, periodic=TRUE)`` equal to
 #' ``torch_blackman_window(L + 1, periodic=False)[:-1])``.
 #' 
 #' @note
@@ -715,11 +709,11 @@ NULL
 #'
 #'
 #' @param window_length (int) the size of returned window
-#' @param periodic (bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
+#' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
 #' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          ``torch_strided`` (dense layout) is supported.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_blackman_window
 #'
@@ -729,7 +723,7 @@ NULL
 
 #' Bmm
 #'
-#' @section bmm(input, mat2, out=None) -> Tensor :
+#' @section bmm(input, mat2, out=NULL) -> Tensor :
 #'
 #' Performs a batch matrix-matrix product of matrices stored in `input`
 #' and `mat2`.
@@ -748,7 +742,7 @@ NULL
 #'           For broadcasting matrix products, see [`torch_matmul`].
 #'
 #'
-#' @param input (Tensor) the first batch of matrices to be multiplied
+#' @param self (Tensor) the first batch of matrices to be multiplied
 #' @param mat2 (Tensor) the second batch of matrices to be multiplied
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -775,7 +769,7 @@ NULL
 
 #' Cat
 #'
-#' @section cat(tensors, dim=0, out=None) -> Tensor :
+#' @section cat(tensors, dim=0, out=NULL) -> Tensor :
 #'
 #' Concatenates the given sequence of `seq` tensors in the given dimension.
 #' All tensors must either have the same shape (except in the concatenating
@@ -799,7 +793,7 @@ NULL
 
 #' Ceil
 #'
-#' @section ceil(input, out=None) -> Tensor :
+#' @section ceil(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the ceil of the elements of `input`,
 #' the smallest integer greater than or equal to each element.
@@ -809,7 +803,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_ceil
@@ -848,7 +842,7 @@ NULL
 #' `dim` is not divisible by `chunks`.
 #'
 #'
-#' @param input (Tensor) the tensor to split
+#' @param self (Tensor) the tensor to split
 #' @param chunks (int) number of chunks to return
 #' @param dim (int) dimension along which to split the tensor
 #'
@@ -860,7 +854,7 @@ NULL
 
 #' Clamp
 #'
-#' @section clamp(input, min, max, out=None) -> Tensor :
+#' @section clamp(input, min, max, out=NULL) -> Tensor :
 #'
 #' Clamp all elements in `input` into the range `[` `min`, `max` `]` and return
 #' a resulting tensor:
@@ -876,14 +870,14 @@ NULL
 #' If `input` is of type `FloatTensor` or `DoubleTensor`, args `min`
 #' and `max` must be real numbers, otherwise they should be integers.
 #'
-#' @section clamp(input, *, min, out=None) -> Tensor :
+#' @section clamp(input, *, min, out=NULL) -> Tensor :
 #'
 #' Clamps all elements in `input` to be larger or equal `min`.
 #' 
 #' If `input` is of type `FloatTensor` or `DoubleTensor`, `value`
 #' should be a real number, otherwise it should be an integer.
 #'
-#' @section clamp(input, *, max, out=None) -> Tensor :
+#' @section clamp(input, *, max, out=NULL) -> Tensor :
 #'
 #' Clamps all elements in `input` to be smaller or equal `max`.
 #' 
@@ -891,7 +885,7 @@ NULL
 #' should be a real number, otherwise it should be an integer.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param min (Number) lower-bound of the range to be clamped to
 #' @param max (Number) upper-bound of the range to be clamped to
 #' @param out (Tensor, optional) the output tensor.
@@ -905,7 +899,7 @@ NULL
 
 #' Conv1d
 #'
-#' @section conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor :
+#' @section conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor :
 #'
 #' Applies a 1D convolution over an input signal composed of several input
 #' planes.
@@ -915,9 +909,9 @@ NULL
 #' .. include:: cudnn_deterministic.rst
 #'
 #'
-#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
+#' @param self NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
 #' @param weight NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)}
-#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: ``None``
+#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: `NULL`
 #' @param stride NA the stride of the convolving kernel. Can be a single number or      a one-element tuple `(sW,)`. Default: 1
 #' @param padding NA implicit paddings on both sides of the input. Can be a      single number or a one-element tuple `(padW,)`. Default: 0
 #' @param dilation NA the spacing between kernel elements. Can be a single number or      a one-element tuple `(dW,)`. Default: 1
@@ -931,7 +925,7 @@ NULL
 
 #' Conv2d
 #'
-#' @section conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor :
+#' @section conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor :
 #'
 #' Applies a 2D convolution over an input image composed of several input
 #' planes.
@@ -941,9 +935,9 @@ NULL
 #' .. include:: cudnn_deterministic.rst
 #'
 #'
-#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}
+#' @param self NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}
 #' @param weight NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)}
-#' @param bias NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: ``None``
+#' @param bias NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: `NULL`
 #' @param stride NA the stride of the convolving kernel. Can be a single number or a      tuple `(sH, sW)`. Default: 1
 #' @param padding NA implicit paddings on both sides of the input. Can be a      single number or a tuple `(padH, padW)`. Default: 0
 #' @param dilation NA the spacing between kernel elements. Can be a single number or      a tuple `(dH, dW)`. Default: 1
@@ -957,7 +951,7 @@ NULL
 
 #' Conv3d
 #'
-#' @section conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor :
+#' @section conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor :
 #'
 #' Applies a 3D convolution over an input image composed of several input
 #' planes.
@@ -967,9 +961,9 @@ NULL
 #' .. include:: cudnn_deterministic.rst
 #'
 #'
-#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}
+#' @param self NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}
 #' @param weight NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)}
-#' @param bias NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: None
+#' @param bias NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: NULL
 #' @param stride NA the stride of the convolving kernel. Can be a single number or a      tuple `(sT, sH, sW)`. Default: 1
 #' @param padding NA implicit paddings on both sides of the input. Can be a      single number or a tuple `(padT, padH, padW)`. Default: 0
 #' @param dilation NA the spacing between kernel elements. Can be a single number or      a tuple `(dT, dH, dW)`. Default: 1
@@ -989,7 +983,7 @@ NULL
 #' Input and output dimensions are (Time, Batch, Channels) - hence TBC.
 #'
 #'
-#' @param input NA input tensor of shape \eqn{(\mbox{sequence length} \times batch \times \mbox{in\_channels})}
+#' @param self NA input tensor of shape \eqn{(\mbox{sequence length} \times batch \times \mbox{in\_channels})}
 #' @param weight NA filter of shape (\eqn{\mbox{kernel width} \times \mbox{in\_channels} \times \mbox{out\_channels}})
 #' @param bias NA bias of shape (\eqn{\mbox{out\_channels}})
 #' @param pad NA number of timesteps to pad. Default: 0
@@ -1002,7 +996,7 @@ NULL
 
 #' Conv_transpose1d
 #'
-#' @section conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor :
+#' @section conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor :
 #'
 #' Applies a 1D transposed convolution operator over an input signal
 #' composed of several input planes, sometimes also called "deconvolution".
@@ -1012,9 +1006,9 @@ NULL
 #' .. include:: cudnn_deterministic.rst
 #'
 #'
-#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
+#' @param self NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
 #' @param weight NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)}
-#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None
+#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL
 #' @param stride NA the stride of the convolving kernel. Can be a single number or a      tuple ``(sW,)``. Default: 1
 #' @param padding NA ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      ``(padW,)``. Default: 0
 #' @param output_padding NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple ``(out_padW)``. Default: 0
@@ -1029,7 +1023,7 @@ NULL
 
 #' Conv_transpose2d
 #'
-#' @section conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor :
+#' @section conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor :
 #'
 #' Applies a 2D transposed convolution operator over an input image
 #' composed of several input planes, sometimes also called "deconvolution".
@@ -1039,9 +1033,9 @@ NULL
 #' .. include:: cudnn_deterministic.rst
 #'
 #'
-#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}
+#' @param self NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}
 #' @param weight NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)}
-#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None
+#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL
 #' @param stride NA the stride of the convolving kernel. Can be a single number or a      tuple ``(sH, sW)``. Default: 1
 #' @param padding NA ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      ``(padH, padW)``. Default: 0
 #' @param output_padding NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple ``(out_padH, out_padW)``.      Default: 0
@@ -1056,7 +1050,7 @@ NULL
 
 #' Conv_transpose3d
 #'
-#' @section conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor :
+#' @section conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor :
 #'
 #' Applies a 3D transposed convolution operator over an input image
 #' composed of several input planes, sometimes also called "deconvolution"
@@ -1066,9 +1060,9 @@ NULL
 #' .. include:: cudnn_deterministic.rst
 #'
 #'
-#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}
+#' @param self NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}
 #' @param weight NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)}
-#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None
+#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL
 #' @param stride NA the stride of the convolving kernel. Can be a single number or a      tuple ``(sT, sH, sW)``. Default: 1
 #' @param padding NA ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      ``(padT, padH, padW)``. Default: 0
 #' @param output_padding NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      ``(out_padT, out_padH, out_padW)``. Default: 0
@@ -1083,7 +1077,7 @@ NULL
 
 #' Cos
 #'
-#' @section cos(input, out=None) -> Tensor :
+#' @section cos(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the cosine  of the elements of `input`.
 #' 
@@ -1092,7 +1086,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_cos
@@ -1103,7 +1097,7 @@ NULL
 
 #' Cosh
 #'
-#' @section cosh(input, out=None) -> Tensor :
+#' @section cosh(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the hyperbolic cosine  of the elements of
 #' `input`.
@@ -1113,7 +1107,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_cosh
@@ -1124,7 +1118,7 @@ NULL
 
 #' Cummax
 #'
-#' @section cummax(input, dim, out=None) -> (Tensor, LongTensor) :
+#' @section cummax(input, dim, out=NULL) -> (Tensor, LongTensor) :
 #'
 #' Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative maximum of
 #' elements of `input` in the dimension `dim`. And ``indices`` is the index
@@ -1135,7 +1129,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to do the operation over
 #' @param out (tuple, optional) the result tuple of two output tensors (values, indices)
 #'
@@ -1147,7 +1141,7 @@ NULL
 
 #' Cummin
 #'
-#' @section cummin(input, dim, out=None) -> (Tensor, LongTensor) :
+#' @section cummin(input, dim, out=NULL) -> (Tensor, LongTensor) :
 #'
 #' Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative minimum of
 #' elements of `input` in the dimension `dim`. And ``indices`` is the index
@@ -1158,7 +1152,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to do the operation over
 #' @param out (tuple, optional) the result tuple of two output tensors (values, indices)
 #'
@@ -1170,7 +1164,7 @@ NULL
 
 #' Cumprod
 #'
-#' @section cumprod(input, dim, out=None, dtype=None) -> Tensor :
+#' @section cumprod(input, dim, out=NULL, dtype=NULL) -> Tensor :
 #'
 #' Returns the cumulative product of elements of `input` in the dimension
 #' `dim`.
@@ -1183,9 +1177,9 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to do the operation over
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: None.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_cumprod
@@ -1196,7 +1190,7 @@ NULL
 
 #' Cumsum
 #'
-#' @section cumsum(input, dim, out=None, dtype=None) -> Tensor :
+#' @section cumsum(input, dim, out=NULL, dtype=NULL) -> Tensor :
 #'
 #' Returns the cumulative sum of elements of `input` in the dimension
 #' `dim`.
@@ -1209,9 +1203,9 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to do the operation over
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: None.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_cumsum
@@ -1233,7 +1227,7 @@ NULL
 #'     `~torch.svd` for details.
 #'
 #'
-#' @param input (Tensor) the input tensor of size ``(*, n, n)`` where ``*`` is zero or more                batch dimensions.
+#' @param self (Tensor) the input tensor of size ``(*, n, n)`` where ``*`` is zero or more                batch dimensions.
 #'
 #' @name torch_det
 #'
@@ -1268,7 +1262,7 @@ NULL
 #' need to be explicitly specified.
 #'
 #'
-#' @param input (Tensor) the input tensor. Must be at least 1-dimensional.
+#' @param self (Tensor) the input tensor. Must be at least 1-dimensional.
 #' @param offset (int, optional) which diagonal to consider. Default: 0        (main diagonal).
 #' @param dim1 (int, optional) first dimension with respect to which to        take diagonal. Default: -2.
 #' @param dim2 (int, optional) second dimension with respect to which to        take diagonal. Default: -1.
@@ -1295,7 +1289,7 @@ NULL
 #' - If `offset` < 0, it is below the main diagonal.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param offset (int, optional) the diagonal to consider. Default: 0 (main        diagonal).
 #'
 #' @name torch_diagflat
@@ -1324,7 +1318,7 @@ NULL
 #' dimensions, so those need to be explicitly specified.
 #'
 #'
-#' @param input (Tensor) the input tensor. Must be at least 2-dimensional.
+#' @param self (Tensor) the input tensor. Must be at least 2-dimensional.
 #' @param offset (int, optional) which diagonal to consider. Default: 0        (main diagonal).
 #' @param dim1 (int, optional) first dimension with respect to which to        take diagonal. Default: 0.
 #' @param dim2 (int, optional) second dimension with respect to which to        take diagonal. Default: 1.
@@ -1337,7 +1331,7 @@ NULL
 
 #' Div
 #'
-#' @section div(input, other, out=None) -> Tensor :
+#' @section div(input, other, out=NULL) -> Tensor :
 #'
 #' Divides each element of the input ``input`` with the scalar ``other`` and
 #' returns a new resulting tensor.
@@ -1358,7 +1352,7 @@ NULL
 #' to the `torch_dtype` of the specified output tensor. Integral division
 #' by zero leads to undefined behavior.
 #'
-#' @section div(input, other, out=None) -> Tensor :
+#' @section div(input, other, out=NULL) -> Tensor :
 #'
 #' Each element of the tensor ``input`` is divided by each element of the tensor
 #' ``other``. The resulting tensor is returned.
@@ -1375,7 +1369,7 @@ NULL
 #' specified output tensor. Integral division by zero leads to undefined behavior.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param other (Number) the number to be divided to each element of ``input``
 #'
 #' @name torch_div
@@ -1420,7 +1414,7 @@ NULL
 
 #' Empty
 #'
-#' @section empty(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor :
+#' @section empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -> Tensor :
 #'
 #' Returns a tensor filled with uninitialized data. The shape of the tensor is
 #' defined by the variable argument `size`.
@@ -1428,11 +1422,11 @@ NULL
 #'
 #' @param size (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.
 #' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`).
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
-#' @param pin_memory (bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
+#' @param pin_memory (bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: `FALSE`.
 #' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_contiguous_format``.
 #'
 #' @name torch_empty
@@ -1443,18 +1437,18 @@ NULL
 
 #' Empty_like
 #'
-#' @section empty_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
+#' @section empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
 #'
 #' Returns an uninitialized tensor with the same size as `input`.
 #' ``torch_empty_like(input)`` is equivalent to
 #' ``torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
 #'
 #'
-#' @param input (Tensor) the size of `input` will determine size of the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if ``None``, defaults to the dtype of `input`.
-#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if ``None``, defaults to the layout of `input`.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, defaults to the device of `input`.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param self (Tensor) the size of `input` will determine size of the output tensor.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
+#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
 #'
 #' @name torch_empty_like
@@ -1465,7 +1459,7 @@ NULL
 
 #' Empty_strided
 #'
-#' @section empty_strided(size, stride, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -> Tensor :
+#' @section empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -> Tensor :
 #'
 #' Returns a tensor filled with uninitialized data. The shape and strides of the tensor is
 #' defined by the variable argument `size` and `stride` respectively.
@@ -1481,11 +1475,11 @@ NULL
 #'
 #' @param size (tuple of ints) the shape of the output tensor
 #' @param stride (tuple of ints) the strides of the output tensor
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`).
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
-#' @param pin_memory (bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
+#' @param pin_memory (bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: `FALSE`.
 #'
 #' @name torch_empty_strided
 #'
@@ -1495,7 +1489,7 @@ NULL
 
 #' Erf
 #'
-#' @section erf(input, out=None) -> Tensor :
+#' @section erf(input, out=NULL) -> Tensor :
 #'
 #' Computes the error function of each element. The error function is defined as follows:
 #' 
@@ -1504,7 +1498,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_erf
@@ -1515,7 +1509,7 @@ NULL
 
 #' Erfc
 #'
-#' @section erfc(input, out=None) -> Tensor :
+#' @section erfc(input, out=NULL) -> Tensor :
 #'
 #' Computes the complementary error function of each element of `input`.
 #' The complementary error function is defined as follows:
@@ -1525,7 +1519,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_erfc
@@ -1536,7 +1530,7 @@ NULL
 
 #' Exp
 #'
-#' @section exp(input, out=None) -> Tensor :
+#' @section exp(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the exponential of the elements
 #' of the input tensor `input`.
@@ -1546,7 +1540,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_exp
@@ -1557,7 +1551,7 @@ NULL
 
 #' Expm1
 #'
-#' @section expm1(input, out=None) -> Tensor :
+#' @section expm1(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the exponential of the elements minus 1
 #' of `input`.
@@ -1567,7 +1561,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_expm1
@@ -1578,7 +1572,7 @@ NULL
 
 #' Eye
 #'
-#' @section eye(n, m=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.
 #'
@@ -1586,10 +1580,10 @@ NULL
 #' @param n (int) the number of rows
 #' @param m (int, optional) the number of columns with default being `n`
 #' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`).
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_eye
 #'
@@ -1604,7 +1598,7 @@ NULL
 #' Flattens a contiguous range of dims in a tensor.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param start_dim (int) the first dim to flatten
 #' @param end_dim (int) the last dim to flatten
 #'
@@ -1616,7 +1610,7 @@ NULL
 
 #' Floor
 #'
-#' @section floor(input, out=None) -> Tensor :
+#' @section floor(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the floor of the elements of `input`,
 #' the largest integer less than or equal to each element.
@@ -1626,7 +1620,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_floor
@@ -1637,7 +1631,7 @@ NULL
 
 #' Floor_divide
 #'
-#' @section floor_divide(input, other, out=None) -> Tensor :
+#' @section floor_divide(input, other, out=NULL) -> Tensor :
 #'
 #' Return the division of the inputs rounded down to the nearest integer. See [`torch_div`]
 #' for type promotion and broadcasting rules.
@@ -1647,7 +1641,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the numerator tensor
+#' @param self (Tensor) the numerator tensor
 #' @param other (Tensor or Scalar) the denominator
 #'
 #' @name torch_floor_divide
@@ -1658,7 +1652,7 @@ NULL
 
 #' Frac
 #'
-#' @section frac(input, out=None) -> Tensor :
+#' @section frac(input, out=NULL) -> Tensor :
 #'
 #' Computes the fractional portion of each element in `input`.
 #' 
@@ -1677,7 +1671,7 @@ NULL
 
 #' Full
 #'
-#' @section full(size, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Returns a tensor of size `size` filled with `fill_value`.
 #' 
@@ -1692,10 +1686,10 @@ NULL
 #' @param size (int...) a list, tuple, or `torch_Size` of integers defining the        shape of the output tensor.
 #' @param fill_value NA the number to fill the output tensor with.
 #' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`).
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_full
 #'
@@ -1705,7 +1699,7 @@ NULL
 
 #' Full_like
 #'
-#' @section full_like(input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, :
+#' @section full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, :
 #'
 #' memory_format=torch.preserve_format) -> Tensor
 #' 
@@ -1714,12 +1708,12 @@ NULL
 #' ``torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)``.
 #'
 #'
-#' @param input (Tensor) the size of `input` will determine size of the output tensor.
+#' @param self (Tensor) the size of `input` will determine size of the output tensor.
 #' @param fill_value NA the number to fill the output tensor with.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if ``None``, defaults to the dtype of `input`.
-#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if ``None``, defaults to the layout of `input`.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, defaults to the device of `input`.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
+#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
 #'
 #' @name torch_full_like
@@ -1730,7 +1724,7 @@ NULL
 
 #' Hann_window
 #'
-#' @section hann_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Hann window function.
 #' 
@@ -1746,7 +1740,7 @@ NULL
 #' ready to be used as a periodic window with functions like
 #' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in
 #' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-#' ``torch_hann_window(L, periodic=True)`` equal to
+#' ``torch_hann_window(L, periodic=TRUE)`` equal to
 #' ``torch_hann_window(L + 1, periodic=False)[:-1])``.
 #' 
 #' @note
@@ -1754,11 +1748,11 @@ NULL
 #'
 #'
 #' @param window_length (int) the size of returned window
-#' @param periodic (bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
+#' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
 #' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          ``torch_strided`` (dense layout) is supported.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_hann_window
 #'
@@ -1768,7 +1762,7 @@ NULL
 
 #' Hamming_window
 #'
-#' @section hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Hamming window function.
 #' 
@@ -1783,7 +1777,7 @@ NULL
 #' ready to be used as a periodic window with functions like
 #' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in
 #' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-#' ``torch_hamming_window(L, periodic=True)`` equal to
+#' ``torch_hamming_window(L, periodic=TRUE)`` equal to
 #' ``torch_hamming_window(L + 1, periodic=False)[:-1])``.
 #' 
 #' @note
@@ -1794,13 +1788,13 @@ NULL
 #'
 #'
 #' @param window_length (int) the size of returned window
-#' @param periodic (bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.
+#' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.
 #' @param alpha (float, optional) The coefficient \eqn{\alpha} in the equation above
 #' @param beta (float, optional) The coefficient \eqn{\beta} in the equation above
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
 #' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          ``torch_strided`` (dense layout) is supported.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_hamming_window
 #'
@@ -1810,7 +1804,7 @@ NULL
 
 #' Ger
 #'
-#' @section ger(input, vec2, out=None) -> Tensor :
+#' @section ger(input, vec2, out=NULL) -> Tensor :
 #'
 #' Outer product of `input` and `vec2`.
 #' If `input` is a vector of size \eqn{n} and `vec2` is a vector of
@@ -1819,7 +1813,7 @@ NULL
 #' @note This function does not broadcast .
 #'
 #'
-#' @param input (Tensor) 1-D input vector
+#' @param self (Tensor) 1-D input vector
 #' @param vec2 (Tensor) 1-D input vector
 #' @param out (Tensor, optional) optional output matrix
 #'
@@ -1851,7 +1845,7 @@ NULL
 #' of size 2, representing the real and imaginary components of complex
 #' numbers, and should have at least ``signal_ndim + 1`` dimensions with optionally
 #' arbitrary number of leading batch dimensions. If `normalized` is set to
-#' ``True``, this normalizes the result by dividing it with
+#' `TRUE`, this normalizes the result by dividing it with
 #' \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is unitary.
 #' 
 #' Returns the real and the imaginary parts together as one tensor of the same
@@ -1870,9 +1864,9 @@ NULL
 #'     `torch_backends.mkl.is_available` to check if MKL is installed.
 #'
 #'
-#' @param input (Tensor) the input tensor of at least `signal_ndim` ``+ 1``        dimensions
+#' @param self (Tensor) the input tensor of at least `signal_ndim` ``+ 1``        dimensions
 #' @param signal_ndim (int) the number of dimensions in each signal.        `signal_ndim` can only be 1, 2 or 3
-#' @param normalized (bool, optional) controls whether to return normalized results.        Default: ``False``
+#' @param normalized (bool, optional) controls whether to return normalized results.        Default: `FALSE`
 #'
 #' @name torch_fft
 #'
@@ -1899,7 +1893,7 @@ NULL
 #' signal, and \eqn{N_i} is the size of signal dimension \eqn{i}.
 #' 
 #' The argument specifications are almost identical with [`torch_fft`].
-#' However, if `normalized` is set to ``True``, this instead returns the
+#' However, if `normalized` is set to `TRUE`, this instead returns the
 #' results multiplied by \eqn{\sqrt{\prod_{i=1}^d N_i}}, to become a unitary
 #' operator. Therefore, to invert a [`torch_fft`], the `normalized`
 #' argument should be set identically for [`torch_fft`].
@@ -1920,9 +1914,9 @@ NULL
 #'     `torch_backends.mkl.is_available` to check if MKL is installed.
 #'
 #'
-#' @param input (Tensor) the input tensor of at least `signal_ndim` ``+ 1``        dimensions
+#' @param self (Tensor) the input tensor of at least `signal_ndim` ``+ 1``        dimensions
 #' @param signal_ndim (int) the number of dimensions in each signal.        `signal_ndim` can only be 1, 2 or 3
-#' @param normalized (bool, optional) controls whether to return normalized results.        Default: ``False``
+#' @param normalized (bool, optional) controls whether to return normalized results.        Default: `FALSE`
 #'
 #' @name torch_ifft
 #'
@@ -1932,7 +1926,7 @@ NULL
 
 #' Rfft
 #'
-#' @section rfft(input, signal_ndim, normalized=False, onesided=True) -> Tensor :
+#' @section rfft(input, signal_ndim, normalized=False, onesided=TRUE) -> Tensor :
 #'
 #' Real-to-complex Discrete Fourier Transform
 #' 
@@ -1943,7 +1937,7 @@ NULL
 #' This method supports 1D, 2D and 3D real-to-complex transforms, indicated
 #' by `signal_ndim`. `input` must be a tensor with at least
 #' ``signal_ndim`` dimensions with optionally arbitrary number of leading batch
-#' dimensions. If `normalized` is set to ``True``, this normalizes the result
+#' dimensions. If `normalized` is set to `TRUE`, this normalizes the result
 #' by dividing it with \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is
 #' unitary, where \eqn{N_i} is the size of signal dimension \eqn{i}.
 #' 
@@ -1955,7 +1949,7 @@ NULL
 #' where the index arithmetic is computed modulus the size of the corresponding
 #' dimension, \eqn{\ ^*} is the conjugate operator, and
 #' \eqn{d} = `signal_ndim`. `onesided` flag controls whether to avoid
-#' redundancy in the output results. If set to ``True`` (default), the output will
+#' redundancy in the output results. If set to `TRUE` (default), the output will
 #' not be full complex result of shape \eqn{(*, 2)}, where \eqn{*} is the shape
 #' of `input`, but instead the last dimension will be halfed as of size
 #' \eqn{\lfloor \frac{N_d}{2} \rfloor + 1}.
@@ -1973,10 +1967,10 @@ NULL
 #'     `torch_backends.mkl.is_available` to check if MKL is installed.
 #'
 #'
-#' @param input (Tensor) the input tensor of at least `signal_ndim` dimensions
+#' @param self (Tensor) the input tensor of at least `signal_ndim` dimensions
 #' @param signal_ndim (int) the number of dimensions in each signal.        `signal_ndim` can only be 1, 2 or 3
-#' @param normalized (bool, optional) controls whether to return normalized results.        Default: ``False``
-#' @param onesided (bool, optional) controls whether to return half of results to        avoid redundancy. Default: ``True``
+#' @param normalized (bool, optional) controls whether to return normalized results.        Default: `FALSE`
+#' @param onesided (bool, optional) controls whether to return half of results to        avoid redundancy. Default: `TRUE`
 #'
 #' @name torch_rfft
 #'
@@ -1986,7 +1980,7 @@ NULL
 
 #' Irfft
 #'
-#' @section irfft(input, signal_ndim, normalized=False, onesided=True, signal_sizes=None) -> Tensor :
+#' @section irfft(input, signal_ndim, normalized=False, onesided=TRUE, signal_sizes=NULL) -> Tensor :
 #'
 #' Complex-to-real Inverse Discrete Fourier Transform
 #' 
@@ -1995,7 +1989,7 @@ NULL
 #' formats of the input and output.
 #' 
 #' The argument specifications are almost identical with [`torch_ifft`].
-#' Similar to [`torch_ifft`], if `normalized` is set to ``True``,
+#' Similar to [`torch_ifft`], if `normalized` is set to `TRUE`,
 #' this normalizes the result by multiplying it with
 #' \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is unitary, where
 #' \eqn{N_i} is the size of signal dimension \eqn{i}.
@@ -2004,8 +1998,8 @@ NULL
 #'     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
+#'     ``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.
@@ -2022,7 +2016,7 @@ NULL
 #' @section Warning:
 #'     Generally speaking, input to this function should contain values
 #'     following conjugate symmetry. Note that even if `onesided` is
-#'     ``True``, often symmetry on some part is still needed. When this
+#'     `TRUE`, often symmetry on some part is still needed. When this
 #'     requirement is not satisfied, the behavior of [`torch_irfft`] is
 #'     undefined. Since `torch_autograd.gradcheck` estimates numerical
 #'     Jacobian with point perturbations, [`torch_irfft`] will almost
@@ -2039,11 +2033,11 @@ NULL
 #'     `torch_backends.mkl.is_available` to check if MKL is installed.
 #'
 #'
-#' @param input (Tensor) the input tensor of at least `signal_ndim` ``+ 1``        dimensions
+#' @param self (Tensor) the input tensor of at least `signal_ndim` ``+ 1``        dimensions
 #' @param signal_ndim (int) the number of dimensions in each signal.        `signal_ndim` can only be 1, 2 or 3
-#' @param normalized (bool, optional) controls whether to return normalized results.        Default: ``False``
-#' @param onesided (bool, optional) controls whether `input` was halfed to avoid        redundancy, e.g., by [torch_rfft()]. Default: ``True``
-#' @param signal_sizes (list or `torch.Size`, optional) the size of the original        signal (without batch dimension). Default: ``None``
+#' @param normalized (bool, optional) controls whether to return normalized results.        Default: `FALSE`
+#' @param onesided (bool, optional) controls whether `input` was halfed to avoid        redundancy, e.g., by [torch_rfft()]. Default: `TRUE`
+#' @param signal_sizes (list or `torch.Size`, optional) the size of the original        signal (without batch dimension). Default: `NULL`
 #'
 #' @name torch_irfft
 #'
@@ -2053,7 +2047,7 @@ NULL
 
 #' Inverse
 #'
-#' @section inverse(input, out=None) -> Tensor :
+#' @section inverse(input, out=NULL) -> Tensor :
 #'
 #' Takes the inverse of the square matrix `input`. `input` can be batches
 #' of 2D square tensors, in which case this function would return a tensor composed of
@@ -2065,7 +2059,7 @@ NULL
 #'     transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`
 #'
 #'
-#' @param input (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more                    batch dimensions
+#' @param self (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more                    batch dimensions
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_inverse
@@ -2081,7 +2075,7 @@ NULL
 #' Returns a new tensor with boolean elements representing if each element is `NaN` or not.
 #'
 #'
-#' @param input (Tensor) A tensor to check
+#' @param self (Tensor) A tensor to check
 #'
 #' @name torch_isnan
 #'
@@ -2093,11 +2087,11 @@ NULL
 #'
 #' @section is_floating_point(input) -> (bool) :
 #'
-#' Returns True if the data type of `input` is a floating point data type i.e.,
+#' Returns TRUE if the data type of `input` is a floating point data type i.e.,
 #' one of ``torch_float64``, ``torch.float32`` and ``torch.float16``.
 #'
 #'
-#' @param input (Tensor) the PyTorch tensor to test
+#' @param self (Tensor) the PyTorch tensor to test
 #'
 #' @name torch_is_floating_point
 #'
@@ -2109,11 +2103,11 @@ NULL
 #'
 #' @section is_complex(input) -> (bool) :
 #'
-#' Returns True if the data type of `input` is a complex data type i.e.,
+#' Returns TRUE if the data type of `input` is a complex data type i.e.,
 #' one of ``torch_complex64``, and ``torch.complex128``.
 #'
 #'
-#' @param input (Tensor) the PyTorch tensor to test
+#' @param self (Tensor) the PyTorch tensor to test
 #'
 #' @name torch_is_complex
 #'
@@ -2123,7 +2117,7 @@ NULL
 
 #' Kthvalue
 #'
-#' @section kthvalue(input, k, dim=None, keepdim=False, out=None) -> (Tensor, LongTensor) :
+#' @section kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
 #'
 #' Returns a namedtuple ``(values, indices)`` where ``values`` is the `k` th
 #' smallest element of each row of the `input` tensor in the given dimension
@@ -2131,14 +2125,14 @@ NULL
 #' 
 #' If `dim` is not given, the last dimension of the `input` is chosen.
 #' 
-#' If `keepdim` is ``True``, both the `values` and `indices` tensors
+#' If `keepdim` is `TRUE`, both the `values` and `indices` tensors
 #' are the same size as `input`, except in the dimension `dim` where
 #' they are of size 1. Otherwise, `dim` is squeezed
 #' (see [`torch_squeeze`]), resulting in both the `values` and
 #' `indices` tensors having 1 fewer dimension than the `input` tensor.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param k (int) k for the k-th smallest element
 #' @param dim (int, optional) the dimension to find the kth value along
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
@@ -2152,7 +2146,7 @@ NULL
 
 #' Linspace
 #'
-#' @section linspace(start, end, steps=100, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Returns a one-dimensional tensor of `steps`
 #' equally spaced points between `start` and `end`.
@@ -2164,10 +2158,10 @@ NULL
 #' @param end (float) the ending value for the set of points
 #' @param steps (int) number of points to sample between `start`        and `end`. Default: ``100``.
 #' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`).
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_linspace
 #'
@@ -2177,7 +2171,7 @@ NULL
 
 #' Log
 #'
-#' @section log(input, out=None) -> Tensor :
+#' @section log(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the natural logarithm of the elements
 #' of `input`.
@@ -2187,7 +2181,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_log
@@ -2198,7 +2192,7 @@ NULL
 
 #' Log10
 #'
-#' @section log10(input, out=None) -> Tensor :
+#' @section log10(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the logarithm to the base 10 of the elements
 #' of `input`.
@@ -2208,7 +2202,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_log10
@@ -2219,7 +2213,7 @@ NULL
 
 #' Log1p
 #'
-#' @section log1p(input, out=None) -> Tensor :
+#' @section log1p(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the natural logarithm of (1 + `input`).
 #' 
@@ -2230,7 +2224,7 @@ NULL
 #'           values of `input`
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_log1p
@@ -2241,7 +2235,7 @@ NULL
 
 #' Log2
 #'
-#' @section log2(input, out=None) -> Tensor :
+#' @section log2(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the logarithm to the base 2 of the elements
 #' of `input`.
@@ -2251,7 +2245,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_log2
@@ -2267,7 +2261,7 @@ NULL
 #' Calculates log determinant of a square matrix or batches of square matrices.
 #' 
 #' @note
-#'     Result is ``-inf`` if `input` has zero log determinant, and is ``nan`` if
+#'     Result is ``-inf`` if `input` has zero log determinant, and is `NaN` if
 #'     `input` has negative determinant.
 #' 
 #' @note
@@ -2277,7 +2271,7 @@ NULL
 #'     `~torch.svd` for details.
 #'
 #'
-#' @param input (Tensor) the input tensor of size ``(*, n, n)`` where ``*`` is zero or more                batch dimensions.
+#' @param self (Tensor) the input tensor of size ``(*, n, n)`` where ``*`` is zero or more                batch dimensions.
 #'
 #' @name torch_logdet
 #'
@@ -2287,7 +2281,7 @@ NULL
 
 #' Logspace
 #'
-#' @section logspace(start, end, steps=100, base=10.0, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Returns a one-dimensional tensor of `steps` points
 #' logarithmically spaced with base `base` between
@@ -2301,10 +2295,10 @@ NULL
 #' @param steps (int) number of points to sample between `start`        and `end`. Default: ``100``.
 #' @param base (float) base of the logarithm function. Default: ``10.0``.
 #' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`).
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_logspace
 #'
@@ -2314,7 +2308,7 @@ NULL
 
 #' Logsumexp
 #'
-#' @section logsumexp(input, dim, keepdim=False, out=None) :
+#' @section logsumexp(input, dim, keepdim=False, out=NULL) :
 #'
 #' Returns the log of summed exponentials of each row of the `input`
 #' tensor in the given dimension `dim`. The computation is numerically
@@ -2326,13 +2320,13 @@ NULL
 #'         \mbox{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij})
 #' }
 #' 
-#' If `keepdim` is ``True``, the output tensor is of the same size
+#' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
 #' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
 #' @param out (Tensor, optional) the output tensor.
@@ -2345,7 +2339,7 @@ NULL
 
 #' Matmul
 #'
-#' @section matmul(input, other, out=None) -> Tensor :
+#' @section matmul(input, other, out=NULL) -> Tensor :
 #'
 #' Matrix product of two tensors.
 #' 
@@ -2373,7 +2367,7 @@ NULL
 #'     The 1-dimensional dot product version of this function does not support an `out` parameter.
 #'
 #'
-#' @param input (Tensor) the first tensor to be multiplied
+#' @param self (Tensor) the first tensor to be multiplied
 #' @param other (Tensor) the second tensor to be multiplied
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -2385,23 +2379,23 @@ NULL
 
 #' Matrix_rank
 #'
-#' @section matrix_rank(input, tol=None, symmetric=False) -> Tensor :
+#' @section matrix_rank(input, tol=NULL, symmetric=False) -> Tensor :
 #'
 #' Returns the numerical rank of a 2-D tensor. The method to compute the
-#' matrix rank is done using SVD by default. If `symmetric` is ``True``,
+#' matrix rank is done using SVD by default. If `symmetric` is `TRUE`,
 #' then `input` is assumed to be symmetric, and the computation of the
 #' rank is done by obtaining the eigenvalues.
 #' 
 #' `tol` is the threshold below which the singular values (or the eigenvalues
-#' when `symmetric` is ``True``) are considered to be 0. If `tol` is not
+#' when `symmetric` is `TRUE`) are considered to be 0. If `tol` is not
 #' specified, `tol` is set to ``S.max() * max(S.size()) * eps`` where `S` is the
-#' singular values (or the eigenvalues when `symmetric` is ``True``), and ``eps``
+#' singular values (or the eigenvalues when `symmetric` is `TRUE`), and ``eps``
 #' is the epsilon value for the datatype of `input`.
 #'
 #'
-#' @param input (Tensor) the input 2-D tensor
-#' @param tol (float, optional) the tolerance value. Default: ``None``
-#' @param symmetric (bool, optional) indicates whether `input` is symmetric.                               Default: ``False``
+#' @param self (Tensor) the input 2-D tensor
+#' @param tol (float, optional) the tolerance value. Default: `NULL`
+#' @param symmetric (bool, optional) indicates whether `input` is symmetric.                               Default: `FALSE`
 #'
 #' @name torch_matrix_rank
 #'
@@ -2422,7 +2416,7 @@ NULL
 #' is returned.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param n (int) the power to raise the matrix to
 #'
 #' @name torch_matrix_power
@@ -2437,7 +2431,7 @@ NULL
 #'
 #' Returns the maximum value of all elements in the ``input`` tensor.
 #'
-#' @section max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) :
+#' @section max(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
 #'
 #' Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum
 #' value of each row of the `input` tensor in the given dimension
@@ -2450,12 +2444,12 @@ NULL
 #'     The exact implementation details are device-specific.
 #'     Do not expect the same result when run on CPU and GPU in general.
 #' 
-#' If ``keepdim`` is ``True``, the output tensors are of the same size
+#' If ``keepdim`` is `TRUE`, the output tensors are of the same size
 #' as ``input`` except in the dimension ``dim`` where they are of size 1.
 #' Otherwise, ``dim`` is squeezed (see [`torch_squeeze`]), resulting
 #' in the output tensors having 1 fewer dimension than ``input``.
 #'
-#' @section max(input, other, out=None) -> Tensor :
+#' @section max(input, other, out=NULL) -> Tensor :
 #'
 #' Each element of the tensor ``input`` is compared with the corresponding
 #' element of the tensor ``other`` and an element-wise maximum is taken.
@@ -2470,9 +2464,9 @@ NULL
 #'           follows the broadcasting rules .
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to reduce.
-#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Default: ``False``.
+#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Default: `FALSE`.
 #' @param out (tuple, optional) the result tuple of two output tensors (max, max_indices)
 #' @param other (Tensor) the second input tensor
 #'
@@ -2488,20 +2482,20 @@ NULL
 #'
 #' Returns the mean value of all elements in the `input` tensor.
 #'
-#' @section mean(input, dim, keepdim=False, out=None) -> Tensor :
+#' @section mean(input, dim, keepdim=False, out=NULL) -> Tensor :
 #'
 #' Returns the mean value of each row of the `input` tensor in the given
 #' dimension `dim`. If `dim` is a list of dimensions,
 #' reduce over all of them.
 #' 
 #' 
-#' If `keepdim` is ``True``, the output tensor is of the same size
+#' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
 #' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
 #' @param out (Tensor, optional) the output tensor.
@@ -2518,7 +2512,7 @@ NULL
 #'
 #' Returns the median value of all elements in the `input` tensor.
 #'
-#' @section median(input, dim=-1, keepdim=False, out=None) -> (Tensor, LongTensor) :
+#' @section median(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
 #'
 #' Returns a namedtuple ``(values, indices)`` where ``values`` is the median
 #' value of each row of the `input` tensor in the given dimension
@@ -2526,13 +2520,13 @@ NULL
 #' 
 #' By default, `dim` is the last dimension of the `input` tensor.
 #' 
-#' If `keepdim` is ``True``, the output tensors are of the same size
+#' If `keepdim` is `TRUE`, the output tensors are of the same size
 #' as `input` except in the dimension `dim` where they are of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in
 #' the outputs tensor having 1 fewer dimension than `input`.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
 #' @param out (tuple, optional) the result tuple of two output tensors (max, max_indices)
@@ -2549,7 +2543,7 @@ NULL
 #'
 #' Returns the minimum value of all elements in the `input` tensor.
 #'
-#' @section min(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) :
+#' @section min(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
 #'
 #' Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum
 #' value of each row of the `input` tensor in the given dimension
@@ -2562,12 +2556,12 @@ NULL
 #'     The exact implementation details are device-specific.
 #'     Do not expect the same result when run on CPU and GPU in general.
 #' 
-#' If `keepdim` is ``True``, the output tensors are of the same size as
+#' If `keepdim` is `TRUE`, the output tensors are of the same size as
 #' `input` except in the dimension `dim` where they are of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in
 #' the output tensors having 1 fewer dimension than `input`.
 #'
-#' @section min(input, other, out=None) -> Tensor :
+#' @section min(input, other, out=NULL) -> Tensor :
 #'
 #' Each element of the tensor `input` is compared with the corresponding
 #' element of the tensor `other` and an element-wise minimum is taken.
@@ -2583,7 +2577,7 @@ NULL
 #'           follows the broadcasting rules .
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
 #' @param out (tuple, optional) the tuple of two output tensors (min, min_indices)
@@ -2597,7 +2591,7 @@ NULL
 
 #' Mm
 #'
-#' @section mm(input, mat2, out=None) -> Tensor :
+#' @section mm(input, mat2, out=NULL) -> Tensor :
 #'
 #' Performs a matrix multiplication of the matrices `input` and `mat2`.
 #' 
@@ -2608,7 +2602,7 @@ NULL
 #'           For broadcasting matrix products, see [`torch_matmul`].
 #'
 #'
-#' @param input (Tensor) the first matrix to be multiplied
+#' @param self (Tensor) the first matrix to be multiplied
 #' @param mat2 (Tensor) the second matrix to be multiplied
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -2620,7 +2614,7 @@ NULL
 
 #' Mode
 #'
-#' @section mode(input, dim=-1, keepdim=False, out=None) -> (Tensor, LongTensor) :
+#' @section mode(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
 #'
 #' Returns a namedtuple ``(values, indices)`` where ``values`` is the mode
 #' value of each row of the `input` tensor in the given dimension
@@ -2629,7 +2623,7 @@ NULL
 #' 
 #' By default, `dim` is the last dimension of the `input` tensor.
 #' 
-#' If `keepdim` is ``True``, the output tensors are of the same size as
+#' If `keepdim` is `TRUE`, the output tensors are of the same size as
 #' `input` except in the dimension `dim` where they are of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting
 #' in the output tensors having 1 fewer dimension than `input`.
@@ -2637,7 +2631,7 @@ NULL
 #' @note This function is not defined for ``torch_cuda.Tensor`` yet.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
 #' @param out (tuple, optional) the result tuple of two output tensors (values, indices)
@@ -2650,7 +2644,7 @@ NULL
 
 #' Mul
 #'
-#' @section mul(input, other, out=None) :
+#' @section mul(input, other, out=NULL) :
 #'
 #' Multiplies each element of the input `input` with the scalar
 #' `other` and returns a new resulting tensor.
@@ -2661,7 +2655,7 @@ NULL
 #' If `input` is of type `FloatTensor` or `DoubleTensor`, `other`
 #' should be a real number, otherwise it should be an integer
 #'
-#' @section mul(input, other, out=None) :
+#' @section mul(input, other, out=NULL) :
 #'
 #' Each element of the tensor `input` is multiplied by the corresponding
 #' element of the Tensor `other`. The resulting tensor is returned.
@@ -2677,7 +2671,7 @@ NULL
 #' @param {input} NA 
 #' @param value (Number) the number to be multiplied to each element of `input`
 #' @param {out} NA 
-#' @param input (Tensor) the first multiplicand tensor
+#' @param self (Tensor) the first multiplicand tensor
 #' @param other (Tensor) the second multiplicand tensor
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -2689,7 +2683,7 @@ NULL
 
 #' Mv
 #'
-#' @section mv(input, vec, out=None) -> Tensor :
+#' @section mv(input, vec, out=NULL) -> Tensor :
 #'
 #' Performs a matrix-vector product of the matrix `input` and the vector
 #' `vec`.
@@ -2700,7 +2694,7 @@ NULL
 #' @note This function does not broadcast .
 #'
 #'
-#' @param input (Tensor) matrix to be multiplied
+#' @param self (Tensor) matrix to be multiplied
 #' @param vec (Tensor) vector to be multiplied
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -2726,7 +2720,7 @@ NULL
 #' All elements must be greater than \eqn{\frac{p - 1}{2}}, otherwise an error would be thrown.
 #'
 #'
-#' @param input (Tensor) the tensor to compute the multivariate log-gamma function
+#' @param self (Tensor) the tensor to compute the multivariate log-gamma function
 #' @param p (int) the number of dimensions
 #'
 #' @name torch_mvlgamma
@@ -2744,7 +2738,7 @@ NULL
 #' returned tensor and `input` tensor share the same underlying storage.
 #'
 #'
-#' @param input (Tensor) the tensor to narrow
+#' @param self (Tensor) the tensor to narrow
 #' @param dim (int) the dimension along which to narrow
 #' @param start (int) the starting dimension
 #' @param length (int) the distance to the ending dimension
@@ -2757,7 +2751,7 @@ NULL
 
 #' Ones
 #'
-#' @section ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Returns a tensor filled with the scalar value `1`, with the shape defined
 #' by the variable argument `size`.
@@ -2765,10 +2759,10 @@ NULL
 #'
 #' @param size (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.
 #' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`).
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_ones
 #'
@@ -2778,7 +2772,7 @@ NULL
 
 #' Ones_like
 #'
-#' @section ones_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
+#' @section ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
 #'
 #' Returns a tensor filled with the scalar value `1`, with the same size as
 #' `input`. ``torch_ones_like(input)`` is equivalent to
@@ -2790,11 +2784,11 @@ NULL
 #'     ``torch_ones(input.size(), out=output)``.
 #'
 #'
-#' @param input (Tensor) the size of `input` will determine size of the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if ``None``, defaults to the dtype of `input`.
-#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if ``None``, defaults to the layout of `input`.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, defaults to the device of `input`.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param self (Tensor) the size of `input` will determine size of the output tensor.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
+#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
 #'
 #' @name torch_ones_like
@@ -2827,7 +2821,7 @@ NULL
 #'
 #' 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
-#' `torch_norm(input[:, None] - input, dim=2, p=p)`. This function will be faster
+#' `torch_norm(input[:, NULL] - input, dim=2, p=p)`. This function will be faster
 #' if the rows are contiguous.
 #' 
 #' If input has shape \eqn{N \times M} then the output will have shape
@@ -2840,7 +2834,7 @@ NULL
 #' `scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max())`.
 #'
 #'
-#' @param input NA input tensor of shape \eqn{N \times M}.
+#' @param self NA input tensor of shape \eqn{N \times M}.
 #' @param p NA p value for the p-norm distance to calculate between each vector pair        \eqn{\in [0, \infty]}.
 #'
 #' @name torch_pdist
@@ -2881,7 +2875,7 @@ NULL
 #' See `~torch.nn.PixelShuffle` for details.
 #'
 #'
-#' @param input (Tensor) the input tensor
+#' @param self (Tensor) the input tensor
 #' @param upscale_factor (int) factor to increase spatial resolution by
 #'
 #' @name torch_pixel_shuffle
@@ -2908,7 +2902,7 @@ NULL
 #'     See `~torch.svd` for more details.
 #'
 #'
-#' @param input (Tensor) The input tensor of size \eqn{(*, m, n)} where \eqn{*} is zero or more batch dimensions
+#' @param self (Tensor) The input tensor of size \eqn{(*, m, n)} where \eqn{*} is zero or more batch dimensions
 #' @param rcond (float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15
 #'
 #' @name torch_pinverse
@@ -2919,7 +2913,7 @@ NULL
 
 #' Rand
 #'
-#' @section rand(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Returns a tensor filled with random numbers from a uniform distribution
 #' on the interval \eqn{[0, 1)}
@@ -2929,10 +2923,10 @@ NULL
 #'
 #' @param size (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.
 #' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`).
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_rand
 #'
@@ -2942,7 +2936,7 @@ NULL
 
 #' Rand_like
 #'
-#' @section rand_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
+#' @section rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
 #'
 #' Returns a tensor with the same size as `input` that is filled with
 #' random numbers from a uniform distribution on the interval \eqn{[0, 1)}.
@@ -2950,11 +2944,11 @@ NULL
 #' ``torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
 #'
 #'
-#' @param input (Tensor) the size of `input` will determine size of the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if ``None``, defaults to the dtype of `input`.
-#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if ``None``, defaults to the layout of `input`.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, defaults to the device of `input`.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param self (Tensor) the size of `input` will determine size of the output tensor.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
+#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
 #'
 #' @name torch_rand_like
@@ -2965,9 +2959,9 @@ NULL
 
 #' Randint
 #'
-#' @section randint(low=0, high, size, *, generator=None, out=None, \ :
+#' @section randint(low=0, high, size, *, generator=NULL, out=NULL, \ :
 #'
-#' dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
+#' dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor
 #' 
 #' Returns a tensor filled with random integers generated uniformly
 #' between `low` (inclusive) and `high` (exclusive).
@@ -2984,10 +2978,10 @@ NULL
 #' @param size (tuple) a tuple defining the shape of the output tensor.
 #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling
 #' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`).
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_randint
 #'
@@ -2997,7 +2991,7 @@ NULL
 
 #' Randint_like
 #'
-#' @section randint_like(input, low=0, high, dtype=None, layout=torch.strided, device=None, requires_grad=False, :
+#' @section randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, :
 #'
 #' memory_format=torch.preserve_format) -> Tensor
 #' 
@@ -3010,13 +3004,13 @@ NULL
 #'     a tensor with dtype ``torch_int64``.
 #'
 #'
-#' @param input (Tensor) the size of `input` will determine size of the output tensor.
+#' @param self (Tensor) the size of `input` will determine size of the output tensor.
 #' @param low (int, optional) Lowest integer to be drawn from the distribution. Default: 0.
 #' @param high (int) One above the highest integer to be drawn from the distribution.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if ``None``, defaults to the dtype of `input`.
-#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if ``None``, defaults to the layout of `input`.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, defaults to the device of `input`.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
+#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
 #'
 #' @name torch_randint_like
@@ -3027,7 +3021,7 @@ NULL
 
 #' Randn
 #'
-#' @section randn(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Returns a tensor filled with random numbers from a normal distribution
 #' with mean `0` and variance `1` (also called the standard normal
@@ -3041,10 +3035,10 @@ NULL
 #'
 #' @param size (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.
 #' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`).
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_randn
 #'
@@ -3054,7 +3048,7 @@ NULL
 
 #' Randn_like
 #'
-#' @section randn_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
+#' @section randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
 #'
 #' Returns a tensor with the same size as `input` that is filled with
 #' random numbers from a normal distribution with mean 0 and variance 1.
@@ -3062,11 +3056,11 @@ NULL
 #' ``torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
 #'
 #'
-#' @param input (Tensor) the size of `input` will determine size of the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if ``None``, defaults to the dtype of `input`.
-#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if ``None``, defaults to the layout of `input`.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, defaults to the device of `input`.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param self (Tensor) the size of `input` will determine size of the output tensor.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
+#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
 #'
 #' @name torch_randn_like
@@ -3077,7 +3071,7 @@ NULL
 
 #' Randperm
 #'
-#' @section randperm(n, out=None, dtype=torch.int64, layout=torch.strided, device=None, requires_grad=False) -> LongTensor :
+#' @section randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -> LongTensor :
 #'
 #' Returns a random permutation of integers from ``0`` to ``n - 1``.
 #'
@@ -3086,8 +3080,8 @@ NULL
 #' @param out (Tensor, optional) the output tensor.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: ``torch_int64``.
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_randperm
 #'
@@ -3097,7 +3091,7 @@ NULL
 
 #' Range
 #'
-#' @section range(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Returns a 1-D tensor of size \eqn{\left\lfloor \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rfloor + 1}
 #' with values from `start` to `end` with step `step`. Step is
@@ -3114,10 +3108,10 @@ NULL
 #' @param end (float) the ending value for the set of points
 #' @param step (float) the gap between each pair of adjacent points. Default: ``1``.
 #' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). If `dtype` is not given, infer the data type from the other input        arguments. If any of `start`, `end`, or `stop` are floating-point, the        `dtype` is inferred to be the default dtype, see        `~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to        be `torch.int64`.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). If `dtype` is not given, infer the data type from the other input        arguments. If any of `start`, `end`, or `stop` are floating-point, the        `dtype` is inferred to be the default dtype, see        `~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to        be `torch.int64`.
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_range
 #'
@@ -3127,7 +3121,7 @@ NULL
 
 #' Reciprocal
 #'
-#' @section reciprocal(input, out=None) -> Tensor :
+#' @section reciprocal(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the reciprocal of the elements of `input`
 #' 
@@ -3136,7 +3130,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_reciprocal
@@ -3147,7 +3141,7 @@ NULL
 
 #' Neg
 #'
-#' @section neg(input, out=None) -> Tensor :
+#' @section neg(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the negative of the elements of `input`.
 #' 
@@ -3156,7 +3150,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_neg
@@ -3167,7 +3161,7 @@ NULL
 
 #' Repeat_interleave
 #'
-#' @section repeat_interleave(input, repeats, dim=None) -> Tensor :
+#' @section repeat_interleave(input, repeats, dim=NULL) -> Tensor :
 #'
 #' Repeat elements of a tensor.
 #' 
@@ -3182,7 +3176,7 @@ NULL
 #' `1` appears `n2` times, `2` appears `n3` times, etc.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param repeats (Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.
 #' @param dim (int, optional) The dimension along which to repeat values.        By default, use the flattened input array, and return a flat output        array.
 #'
@@ -3208,7 +3202,7 @@ NULL
 #' dimensions and the number of elements in `input`.
 #'
 #'
-#' @param input (Tensor) the tensor to be reshaped
+#' @param self (Tensor) the tensor to be reshaped
 #' @param shape (tuple of ints) the new shape
 #'
 #' @name torch_reshape
@@ -3219,13 +3213,13 @@ NULL
 
 #' Round
 #'
-#' @section round(input, out=None) -> Tensor :
+#' @section round(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with each of the elements of `input` rounded
 #' to the closest integer.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_round
@@ -3266,7 +3260,7 @@ NULL
 
 #' Rsqrt
 #'
-#' @section rsqrt(input, out=None) -> Tensor :
+#' @section rsqrt(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the reciprocal of the square-root of each of
 #' the elements of `input`.
@@ -3276,7 +3270,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_rsqrt
@@ -3317,7 +3311,7 @@ NULL
 
 #' Sigmoid
 #'
-#' @section sigmoid(input, out=None) -> Tensor :
+#' @section sigmoid(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the sigmoid of the elements of `input`.
 #' 
@@ -3326,7 +3320,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_sigmoid
@@ -3337,7 +3331,7 @@ NULL
 
 #' Sin
 #'
-#' @section sin(input, out=None) -> Tensor :
+#' @section sin(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the sine of the elements of `input`.
 #' 
@@ -3346,7 +3340,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_sin
@@ -3357,7 +3351,7 @@ NULL
 
 #' Sinh
 #'
-#' @section sinh(input, out=None) -> Tensor :
+#' @section sinh(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the hyperbolic sine of the elements of
 #' `input`.
@@ -3367,7 +3361,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_sinh
@@ -3392,7 +3386,7 @@ NULL
 #'     See `~torch.svd` for details.
 #'
 #'
-#' @param input (Tensor) the input tensor of size ``(*, n, n)`` where ``*`` is zero or more                batch dimensions.
+#' @param self (Tensor) the input tensor of size ``(*, n, n)`` where ``*`` is zero or more                batch dimensions.
 #'
 #' @name torch_slogdet
 #'
@@ -3428,7 +3422,7 @@ NULL
 
 #' Squeeze
 #'
-#' @section squeeze(input, dim=None, out=None) -> Tensor :
+#' @section squeeze(input, dim=NULL, out=NULL) -> Tensor :
 #'
 #' Returns a tensor with all the dimensions of `input` of size `1` removed.
 #' 
@@ -3445,7 +3439,7 @@ NULL
 #'           so changing the contents of one will change the contents of the other.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int, optional) if given, the input will be squeezed only in           this dimension
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -3457,7 +3451,7 @@ NULL
 
 #' Stack
 #'
-#' @section stack(tensors, dim=0, out=None) -> Tensor :
+#' @section stack(tensors, dim=0, out=NULL) -> Tensor :
 #'
 #' Concatenates sequence of tensors along a new dimension.
 #' 
@@ -3490,38 +3484,38 @@ NULL
 #' }
 #'     where \eqn{m} is the index of the sliding window, and \eqn{\omega} is
 #'     the frequency that \eqn{0 \leq \omega < \mbox{n\_fft}}. When
-#'     `onesided` is the default value ``True``,
+#'     `onesided` is the default value `TRUE`,
 #' 
 #'     * `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
+#'     * If `hop_length` is `NULL` (default), it is treated as equal to
 #'       ``floor(n_fft / 4)``.
 #' 
-#'     * If `win_length` is ``None`` (default), it is treated as equal to
+#'     * 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 ``None`` (default), it is
+#'       `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} < \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
+#'     * 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
+#'       `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}
+#'     * 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
+#'     * 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
@@ -3536,15 +3530,15 @@ NULL
 #'       previous signature may cause error or return incorrect result.
 #'
 #'
-#' @param input (Tensor) the input tensor
+#' @param self (Tensor) the input tensor
 #' @param n_fft (int) size of Fourier transform
-#' @param hop_length (int, optional) the distance between neighboring sliding window        frames. Default: ``None`` (treated as equal to ``floor(n_fft / 4)``)
-#' @param win_length (int, optional) the size of window frame and STFT filter.        Default: ``None``  (treated as equal to `n_fft`)
-#' @param window (Tensor, optional) the optional window function.        Default: ``None`` (treated as window of all \eqn{1} s)
-#' @param center (bool, optional) whether to pad `input` on both sides so        that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}.        Default: ``True``
-#' @param pad_mode (string, optional) controls the padding method used when        `center` is ``True``. Default: ``"reflect"``
-#' @param normalized (bool, optional) controls whether to return the normalized STFT results         Default: ``False``
-#' @param onesided (bool, optional) controls whether to return half of results to        avoid redundancy Default: ``True``
+#' @param hop_length (int, optional) the distance between neighboring sliding window        frames. Default: `NULL` (treated as equal to ``floor(n_fft / 4)``)
+#' @param win_length (int, optional) the size of window frame and STFT filter.        Default: `NULL`  (treated as equal to `n_fft`)
+#' @param window (Tensor, optional) the optional window function.        Default: `NULL` (treated as window of all \eqn{1} s)
+#' @param center (bool, optional) whether to pad `input` on both sides so        that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}.        Default: `TRUE`
+#' @param pad_mode (string, optional) controls the padding method used when        `center` is `TRUE`. Default: ``"reflect"``
+#' @param normalized (bool, optional) controls whether to return the normalized STFT results         Default: `FALSE`
+#' @param onesided (bool, optional) controls whether to return half of results to        avoid redundancy Default: `TRUE`
 #'
 #' @name torch_stft
 #'
@@ -3554,25 +3548,25 @@ NULL
 
 #' Sum
 #'
-#' @section sum(input, dtype=None) -> Tensor :
+#' @section sum(input, dtype=NULL) -> Tensor :
 #'
 #' Returns the sum of all elements in the `input` tensor.
 #'
-#' @section sum(input, dim, keepdim=False, dtype=None) -> Tensor :
+#' @section sum(input, dim, keepdim=False, dtype=NULL) -> Tensor :
 #'
 #' Returns the sum of each row of the `input` tensor in the given
 #' dimension `dim`. If `dim` is a list of dimensions,
 #' reduce over all of them.
 #' 
 #' 
-#' If `keepdim` is ``True``, the output tensor is of the same size
+#' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
 #' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
 #'
 #'
-#' @param input (Tensor) the input tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: None.
+#' @param self (Tensor) the input tensor.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.
 #' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
 #'
@@ -3584,7 +3578,7 @@ NULL
 
 #' Sqrt
 #'
-#' @section sqrt(input, out=None) -> Tensor :
+#' @section sqrt(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the square-root of the elements of `input`.
 #' 
@@ -3593,7 +3587,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_sqrt
@@ -3604,12 +3598,12 @@ NULL
 
 #' Square
 #'
-#' @section square(input, out=None) -> Tensor :
+#' @section square(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the square of the elements of `input`.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_square
@@ -3620,31 +3614,31 @@ NULL
 
 #' Std
 #'
-#' @section std(input, unbiased=True) -> Tensor :
+#' @section std(input, unbiased=TRUE) -> Tensor :
 #'
 #' Returns the standard-deviation of all elements in the `input` tensor.
 #' 
-#' If `unbiased` is ``False``, then the standard-deviation will be calculated
+#' If `unbiased` is `FALSE`, then the standard-deviation will be calculated
 #' via the biased estimator. Otherwise, Bessel's correction will be used.
 #'
-#' @section std(input, dim, unbiased=True, keepdim=False, out=None) -> Tensor :
+#' @section std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -> Tensor :
 #'
 #' Returns the standard-deviation of each row of the `input` tensor in the
 #' dimension `dim`. If `dim` is a list of dimensions,
 #' reduce over all of them.
 #' 
 #' 
-#' If `keepdim` is ``True``, the output tensor is of the same size
+#' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
 #' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
 #' 
 #' 
-#' If `unbiased` is ``False``, then the standard-deviation will be calculated
+#' If `unbiased` is `FALSE`, then the standard-deviation will be calculated
 #' via the biased estimator. Otherwise, Bessel's correction will be used.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param unbiased (bool) whether to use the unbiased estimation or not
 #' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
@@ -3658,31 +3652,31 @@ NULL
 
 #' Std_mean
 #'
-#' @section std_mean(input, unbiased=True) -> (Tensor, Tensor) :
+#' @section std_mean(input, unbiased=TRUE) -> (Tensor, Tensor) :
 #'
 #' Returns the standard-deviation and mean of all elements in the `input` tensor.
 #' 
-#' If `unbiased` is ``False``, then the standard-deviation will be calculated
+#' If `unbiased` is `FALSE`, then the standard-deviation will be calculated
 #' via the biased estimator. Otherwise, Bessel's correction will be used.
 #'
-#' @section std_mean(input, dim, unbiased=True, keepdim=False) -> (Tensor, Tensor) :
+#' @section std_mean(input, dim, unbiased=TRUE, keepdim=False) -> (Tensor, Tensor) :
 #'
 #' Returns the standard-deviation and mean of each row of the `input` tensor in the
 #' dimension `dim`. If `dim` is a list of dimensions,
 #' reduce over all of them.
 #' 
 #' 
-#' If `keepdim` is ``True``, the output tensor is of the same size
+#' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
 #' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
 #' 
 #' 
-#' If `unbiased` is ``False``, then the standard-deviation will be calculated
+#' If `unbiased` is `FALSE`, then the standard-deviation will be calculated
 #' via the biased estimator. Otherwise, Bessel's correction will be used.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param unbiased (bool) whether to use the unbiased estimation or not
 #' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
@@ -3695,23 +3689,23 @@ NULL
 
 #' Prod
 #'
-#' @section prod(input, dtype=None) -> Tensor :
+#' @section prod(input, dtype=NULL) -> Tensor :
 #'
 #' Returns the product of all elements in the `input` tensor.
 #'
-#' @section prod(input, dim, keepdim=False, dtype=None) -> Tensor :
+#' @section prod(input, dim, keepdim=False, dtype=NULL) -> Tensor :
 #'
 #' Returns the product of each row of the `input` tensor in the given
 #' dimension `dim`.
 #' 
-#' If `keepdim` is ``True``, the output tensor is of the same size
+#' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in
 #' the output tensor having 1 fewer dimension than `input`.
 #'
 #'
-#' @param input (Tensor) the input tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: None.
+#' @param self (Tensor) the input tensor.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.
 #' @param dim (int) the dimension to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
 #'
@@ -3732,7 +3726,7 @@ NULL
 #' is equivalent to ``transpose(input, 0, 1)``.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #'
 #' @name torch_t
 #'
@@ -3742,7 +3736,7 @@ NULL
 
 #' Tan
 #'
-#' @section tan(input, out=None) -> Tensor :
+#' @section tan(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the tangent of the elements of `input`.
 #' 
@@ -3751,7 +3745,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_tan
@@ -3762,7 +3756,7 @@ NULL
 
 #' Tanh
 #'
-#' @section tanh(input, out=None) -> Tensor :
+#' @section tanh(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the hyperbolic tangent of the elements
 #' of `input`.
@@ -3772,7 +3766,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_tanh
@@ -3827,7 +3821,7 @@ NULL
 #' of the other.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim0 (int) the first dimension to be transposed
 #' @param dim1 (int) the second dimension to be transposed
 #'
@@ -3844,7 +3838,7 @@ NULL
 #' Reverse the order of a n-D tensor along given axis in dims.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dims (a list or tuple) axis to flip on
 #'
 #' @name torch_flip
@@ -3855,7 +3849,7 @@ NULL
 
 #' Roll
 #'
-#' @section roll(input, shifts, dims=None) -> Tensor :
+#' @section roll(input, shifts, dims=NULL) -> Tensor :
 #'
 #' 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
@@ -3863,7 +3857,7 @@ NULL
 #' to the original shape.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param shifts (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
 #' @param dims (int or tuple of ints) Axis along which to roll
 #'
@@ -3881,7 +3875,7 @@ NULL
 #' Rotation direction is from the first towards the second axis if k > 0, and from the second towards the first for k < 0.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param k (int) number of times to rotate
 #' @param dims (a list or tuple) axis to rotate
 #'
@@ -3913,7 +3907,7 @@ NULL
 NULL
 
 
-#' True_divide
+#' TRUE_divide
 #'
 #' @section true_divide(dividend, divisor) -> Tensor :
 #'
@@ -3938,13 +3932,13 @@ NULL
 
 #' Trunc
 #'
-#' @section trunc(input, out=None) -> Tensor :
+#' @section trunc(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the truncated integer values of
 #' the elements of `input`.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_trunc
@@ -3964,10 +3958,10 @@ NULL
 #'         in C++.
 #'
 #'
-#' @param input (Tensor) the input tensor
+#' @param self (Tensor) the input tensor
 #' @param return_inverse (bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.
 #' @param return_counts (bool) Whether to also return the counts for each unique        element.
-#' @param dim (int) the dimension to apply unique. If ``None``, the unique of the        flattened input is returned. default: ``None``
+#' @param dim (int) the dimension to apply unique. If `NULL`, the unique of the        flattened input is returned. default: `NULL`
 #'
 #' @name torch_unique_consecutive
 #'
@@ -3989,7 +3983,7 @@ NULL
 #' applied at `dim` = ``dim + input.dim() + 1``.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int) the index at which to insert the singleton dimension
 #'
 #' @name torch_unsqueeze
@@ -4000,30 +3994,30 @@ NULL
 
 #' Var
 #'
-#' @section var(input, unbiased=True) -> Tensor :
+#' @section var(input, unbiased=TRUE) -> Tensor :
 #'
 #' Returns the variance of all elements in the `input` tensor.
 #' 
-#' If `unbiased` is ``False``, then the variance will be calculated via the
+#' If `unbiased` is `FALSE`, then the variance will be calculated via the
 #' biased estimator. Otherwise, Bessel's correction will be used.
 #'
-#' @section var(input, dim, keepdim=False, unbiased=True, out=None) -> Tensor :
+#' @section var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -> Tensor :
 #'
 #' Returns the variance of each row of the `input` tensor in the given
 #' dimension `dim`.
 #' 
 #' 
-#' If `keepdim` is ``True``, the output tensor is of the same size
+#' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
 #' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
 #' 
 #' 
-#' If `unbiased` is ``False``, then the variance will be calculated via the
+#' If `unbiased` is `FALSE`, then the variance will be calculated via the
 #' biased estimator. Otherwise, Bessel's correction will be used.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param unbiased (bool) whether to use the unbiased estimation or not
 #' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
@@ -4037,30 +4031,30 @@ NULL
 
 #' Var_mean
 #'
-#' @section var_mean(input, unbiased=True) -> (Tensor, Tensor) :
+#' @section var_mean(input, unbiased=TRUE) -> (Tensor, Tensor) :
 #'
 #' Returns the variance and mean of all elements in the `input` tensor.
 #' 
-#' If `unbiased` is ``False``, then the variance will be calculated via the
+#' If `unbiased` is `FALSE`, then the variance will be calculated via the
 #' biased estimator. Otherwise, Bessel's correction will be used.
 #'
-#' @section var_mean(input, dim, keepdim=False, unbiased=True) -> (Tensor, Tensor) :
+#' @section var_mean(input, dim, keepdim=False, unbiased=TRUE) -> (Tensor, Tensor) :
 #'
 #' Returns the variance and mean of each row of the `input` tensor in the given
 #' dimension `dim`.
 #' 
 #' 
-#' If `keepdim` is ``True``, the output tensor is of the same size
+#' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
 #' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
 #' 
 #' 
-#' If `unbiased` is ``False``, then the variance will be calculated via the
+#' If `unbiased` is `FALSE`, then the variance will be calculated via the
 #' biased estimator. Otherwise, Bessel's correction will be used.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param unbiased (bool) whether to use the unbiased estimation or not
 #' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
@@ -4092,15 +4086,15 @@ NULL
 #' @section where(condition) -> tuple of LongTensor :
 #'
 #' ``torch_where(condition)`` is identical to
-#' ``torch_nonzero(condition, as_tuple=True)``.
+#' ``torch_nonzero(condition, as_tuple=TRUE)``.
 #' 
 #' @note
 #'     See also [`torch_nonzero`].
 #'
 #'
-#' @param condition (BoolTensor) When True (nonzero), yield x, otherwise yield y
-#' @param x (Tensor) values selected at indices where `condition` is ``True``
-#' @param y (Tensor) values selected at indices where `condition` is ``False``
+#' @param condition (BoolTensor) When TRUE (nonzero), yield x, otherwise yield y
+#' @param x (Tensor) values selected at indices where `condition` is `TRUE`
+#' @param y (Tensor) values selected at indices where `condition` is `FALSE`
 #'
 #' @name torch_where
 #'
@@ -4110,7 +4104,7 @@ NULL
 
 #' Zeros
 #'
-#' @section zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor :
+#' @section zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Returns a tensor filled with the scalar value `0`, with the shape defined
 #' by the variable argument `size`.
@@ -4118,10 +4112,10 @@ NULL
 #'
 #' @param size (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.
 #' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`).
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_zeros
 #'
@@ -4131,7 +4125,7 @@ NULL
 
 #' Zeros_like
 #'
-#' @section zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
+#' @section zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
 #'
 #' Returns a tensor filled with the scalar value `0`, with the same size as
 #' `input`. ``torch_zeros_like(input)`` is equivalent to
@@ -4143,11 +4137,11 @@ NULL
 #'     ``torch_zeros(input.size(), out=output)``.
 #'
 #'
-#' @param input (Tensor) the size of `input` will determine size of the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if ``None``, defaults to the dtype of `input`.
-#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if ``None``, defaults to the layout of `input`.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, defaults to the device of `input`.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param self (Tensor) the size of `input` will determine size of the output tensor.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
+#' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
 #'
 #' @name torch_zeros_like
@@ -4158,7 +4152,7 @@ NULL
 
 #' Poisson
 #'
-#' @section poisson(input *, generator=None) -> Tensor :
+#' @section poisson(input *, generator=NULL) -> Tensor :
 #'
 #' Returns a tensor of the same size as `input` with each element
 #' sampled from a Poisson distribution with rate parameter given by the corresponding
@@ -4169,7 +4163,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor containing the rates of the Poisson distribution
+#' @param self (Tensor) the input tensor containing the rates of the Poisson distribution
 #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling
 #'
 #' @name torch_poisson
@@ -4185,12 +4179,12 @@ NULL
 #' Returns the matrix norm or vector norm of a given tensor.
 #'
 #'
-#' @param input (Tensor) the input tensor
-#' @param p (int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: ``'fro'``        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)**ord)**(1./ord)        =====  ============================  ==========================
-#' @param dim (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.
-#' @param keepdim (bool, optional) whether the output tensors have `dim`        retained or not. Ignored if `dim` = ``None`` and        `out` = ``None``. Default: ``False``
-#' @param out (Tensor, optional) the output tensor. Ignored if        `dim` = ``None`` and `out` = ``None``.
-#' @param dtype (`torch.dtype`, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: None.
+#' @param self (Tensor) the input tensor
+#' @param p (int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: ``'fro'``        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)**ord)**(1./ord)        =====  ============================  ==========================
+#' @param dim (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.
+#' @param keepdim (bool, optional) whether the output tensors have `dim`        retained or not. Ignored if `dim` = `NULL` and        `out` = `NULL`. Default: `FALSE`
+#' @param out (Tensor, optional) the output tensor. Ignored if        `dim` = `NULL` and `out` = `NULL`.
+#' @param dtype (`torch.dtype`, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: NULL.
 #'
 #' @name torch_norm
 #'
@@ -4200,7 +4194,7 @@ NULL
 
 #' Pow
 #'
-#' @section pow(input, exponent, out=None) -> Tensor :
+#' @section pow(input, exponent, out=NULL) -> Tensor :
 #'
 #' Takes the power of each element in `input` with `exponent` and
 #' returns a tensor with the result.
@@ -4221,7 +4215,7 @@ NULL
 #' When `exponent` is a tensor, the shapes of `input`
 #' and `exponent` must be broadcastable .
 #'
-#' @section pow(self, exponent, out=None) -> Tensor :
+#' @section pow(self, exponent, out=NULL) -> Tensor :
 #'
 #' `self` is a scalar ``float`` value, and `exponent` is a tensor.
 #' The returned tensor `out` is of the same shape as `exponent`
@@ -4233,7 +4227,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param exponent (float or tensor) the exponent value
 #' @param out (Tensor, optional) the output tensor.
 #' @param self (float) the scalar base value for the power operation
@@ -4246,7 +4240,7 @@ NULL
 
 #' Addmm
 #'
-#' @section addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor :
+#' @section addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -> Tensor :
 #'
 #' Performs a matrix multiplication of the matrices `mat1` and `mat2`.
 #' The matrix `input` is added to the final result.
@@ -4266,7 +4260,7 @@ NULL
 #' `alpha` must be real numbers, otherwise they should be integers.
 #'
 #'
-#' @param input (Tensor) matrix to be added
+#' @param self (Tensor) matrix to be added
 #' @param mat1 (Tensor) the first matrix to be multiplied
 #' @param mat2 (Tensor) the second matrix to be multiplied
 #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
@@ -4281,7 +4275,7 @@ NULL
 
 #' Sparse_coo_tensor
 #'
-#' @section sparse_coo_tensor(indices, values, size=None, dtype=None, device=None, requires_grad=False) -> Tensor :
+#' @section sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given `indices`
 #' with the given `values`. A sparse tensor can be `uncoalesced`, in that case, there are duplicate
@@ -4292,9 +4286,9 @@ NULL
 #' @param indices (array_like) Initial data for the tensor. Can be a list, tuple,        NumPy ``ndarray``, scalar, and other types. Will be cast to a `torch_LongTensor`        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.
 #' @param values (array_like) Initial values for the tensor. Can be a list, tuple,        NumPy ``ndarray``, scalar, and other types.
 #' @param size (list, tuple, or `torch.Size`, 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.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if None, infers data type from `values`.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: ``False``.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if NULL, infers data type from `values`.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if NULL, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
 #' @name torch_sparse_coo_tensor
 #'
@@ -4311,7 +4305,7 @@ NULL
 #' Returns a tuple of all slices along a given dimension, already without it.
 #'
 #'
-#' @param input (Tensor) the tensor to unbind
+#' @param self (Tensor) the tensor to unbind
 #' @param dim (int) dimension to remove
 #'
 #' @name torch_unbind
@@ -4327,7 +4321,7 @@ NULL
 #' Converts a float tensor to quantized tensor with given scale and zero point.
 #'
 #'
-#' @param input (Tensor) float tensor to quantize
+#' @param self (Tensor) float tensor to quantize
 #' @param scale (float) scale to apply in quantization formula
 #' @param zero_point (int) offset in integer value that maps to float zero
 #' @param dtype (`torch.dtype`) the desired data type of returned tensor.        Has to be one of the quantized dtypes: ``torch_quint8``, ``torch.qint8``, ``torch.qint32``
@@ -4345,7 +4339,7 @@ NULL
 #' Converts a float tensor to per-channel quantized tensor with given scales and zero points.
 #'
 #'
-#' @param input (Tensor) float tensor to quantize
+#' @param self (Tensor) float tensor to quantize
 #' @param scales (Tensor) float 1D tensor of scales to use, size should match ``input.size(axis)``
 #' @param zero_points (int) integer 1D tensor of offset to use, size should match ``input.size(axis)``
 #' @param axis (int) dimension on which apply per-channel quantization
@@ -4397,10 +4391,10 @@ NULL
 #'
 #' Compute combinations of length \eqn{r} of the given tensor. The behavior is similar to
 #' python's `itertools.combinations` when `with_replacement` is set to `False`, and
-#' `itertools.combinations_with_replacement` when `with_replacement` is set to `True`.
+#' `itertools.combinations_with_replacement` when `with_replacement` is set to `TRUE`.
 #'
 #'
-#' @param input (Tensor) 1D vector.
+#' @param self (Tensor) 1D vector.
 #' @param r (int, optional) number of elements to combine
 #' @param with_replacement (boolean, optional) whether to allow duplication in combination
 #'
@@ -4466,13 +4460,13 @@ NULL
 
 #' Bitwise_and
 #'
-#' @section bitwise_and(input, other, out=None) -> Tensor :
+#' @section bitwise_and(input, other, out=NULL) -> Tensor :
 #'
 #' Computes the bitwise AND of `input` and `other`. The input tensor must be of
 #' integral or Boolean types. For bool tensors, it computes the logical AND.
 #'
 #'
-#' @param input NA the first input tensor
+#' @param self NA the first input tensor
 #' @param other NA the second input tensor
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -4484,13 +4478,13 @@ NULL
 
 #' Bitwise_or
 #'
-#' @section bitwise_or(input, other, out=None) -> Tensor :
+#' @section bitwise_or(input, other, out=NULL) -> Tensor :
 #'
 #' Computes the bitwise OR of `input` and `other`. The input tensor must be of
 #' integral or Boolean types. For bool tensors, it computes the logical OR.
 #'
 #'
-#' @param input NA the first input tensor
+#' @param self NA the first input tensor
 #' @param other NA the second input tensor
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -4502,13 +4496,13 @@ NULL
 
 #' Bitwise_xor
 #'
-#' @section bitwise_xor(input, other, out=None) -> Tensor :
+#' @section bitwise_xor(input, other, out=NULL) -> Tensor :
 #'
 #' Computes the bitwise XOR of `input` and `other`. The input tensor must be of
 #' integral or Boolean types. For bool tensors, it computes the logical XOR.
 #'
 #'
-#' @param input NA the first input tensor
+#' @param self NA the first input tensor
 #' @param other NA the second input tensor
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -4520,7 +4514,7 @@ NULL
 
 #' Addbmm
 #'
-#' @section addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor :
+#' @section addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor :
 #'
 #' Performs a batch matrix-matrix product of matrices stored
 #' in `batch1` and `batch2`,
@@ -4546,7 +4540,7 @@ NULL
 #' @param batch1 (Tensor) the first batch of matrices to be multiplied
 #' @param batch2 (Tensor) the second batch of matrices to be multiplied
 #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
-#' @param input (Tensor) matrix to be added
+#' @param self (Tensor) matrix to be added
 #' @param alpha (Number, optional) multiplier for `batch1 @ batch2` (\eqn{\alpha})
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -4558,7 +4552,7 @@ NULL
 
 #' Diag
 #'
-#' @section diag(input, diagonal=0, out=None) -> Tensor :
+#' @section diag(input, diagonal=0, out=NULL) -> Tensor :
 #'
 #' - If `input` is a vector (1-D tensor), then returns a 2-D square tensor
 #'   with the elements of `input` as the diagonal.
@@ -4572,7 +4566,7 @@ NULL
 #' - If `diagonal` < 0, it is below the main diagonal.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param diagonal (int, optional) the diagonal to consider
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -4584,7 +4578,7 @@ NULL
 
 #' Cross
 #'
-#' @section cross(input, other, dim=-1, out=None) -> Tensor :
+#' @section cross(input, other, dim=-1, out=NULL) -> Tensor :
 #'
 #' Returns the cross product of vectors in dimension `dim` of `input`
 #' and `other`.
@@ -4596,7 +4590,7 @@ NULL
 #' size 3.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param other (Tensor) the second input tensor
 #' @param dim (int, optional) the dimension to take the cross-product in.
 #' @param out (Tensor, optional) the output tensor.
@@ -4609,7 +4603,7 @@ NULL
 
 #' Triu
 #'
-#' @section triu(input, diagonal=0, out=None) -> Tensor :
+#' @section triu(input, diagonal=0, out=NULL) -> Tensor :
 #'
 #' Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
 #' `input`, the other elements of the result tensor `out` are set to 0.
@@ -4626,7 +4620,7 @@ NULL
 #' \eqn{d_{1}, d_{2}} are the dimensions of the matrix.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param diagonal (int, optional) the diagonal to consider
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -4638,7 +4632,7 @@ NULL
 
 #' Tril
 #'
-#' @section tril(input, diagonal=0, out=None) -> Tensor :
+#' @section tril(input, diagonal=0, out=NULL) -> Tensor :
 #'
 #' Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices
 #' `input`, the other elements of the result tensor `out` are set to 0.
@@ -4655,7 +4649,7 @@ NULL
 #' \eqn{d_{1}, d_{2}} are the dimensions of the matrix.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param diagonal (int, optional) the diagonal to consider
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -4693,8 +4687,8 @@ NULL
 #' @param row (``int``) number of rows in the 2-D matrix.
 #' @param col (``int``) number of columns in the 2-D matrix.
 #' @param offset (``int``) diagonal offset from the main diagonal.        Default: if not provided, 0.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, ``torch_long``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, ``torch_long``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param layout (`torch.layout`, optional) currently only support ``torch_strided``.
 #'
 #' @name torch_tril_indices
@@ -4731,8 +4725,8 @@ NULL
 #' @param row (``int``) number of rows in the 2-D matrix.
 #' @param col (``int``) number of columns in the 2-D matrix.
 #' @param offset (``int``) diagonal offset from the main diagonal.        Default: if not provided, 0.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if ``None``, ``torch_long``.
-#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if ``None``, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, ``torch_long``.
+#' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param layout (`torch.layout`, optional) currently only support ``torch_strided``.
 #'
 #' @name torch_triu_indices
@@ -4758,7 +4752,7 @@ NULL
 
 #' Ne
 #'
-#' @section ne(input, other, out=None) -> Tensor :
+#' @section ne(input, other, out=NULL) -> Tensor :
 #'
 #' Computes \eqn{input \neq other} element-wise.
 #' 
@@ -4766,7 +4760,7 @@ NULL
 #' broadcastable  with the first argument.
 #'
 #'
-#' @param input (Tensor) the tensor to compare
+#' @param self (Tensor) the tensor to compare
 #' @param other (Tensor or float) the tensor or value to compare
 #' @param out (Tensor, optional) the output tensor that must be a `BoolTensor`
 #'
@@ -4778,7 +4772,7 @@ NULL
 
 #' Eq
 #'
-#' @section eq(input, other, out=None) -> Tensor :
+#' @section eq(input, other, out=NULL) -> Tensor :
 #'
 #' Computes element-wise equality
 #' 
@@ -4786,7 +4780,7 @@ NULL
 #' broadcastable  with the first argument.
 #'
 #'
-#' @param input (Tensor) the tensor to compare
+#' @param self (Tensor) the tensor to compare
 #' @param other (Tensor or float) the tensor or value to compare
 #' @param out (Tensor, optional) the output tensor. Must be a `ByteTensor`
 #'
@@ -4798,7 +4792,7 @@ NULL
 
 #' Ge
 #'
-#' @section ge(input, other, out=None) -> Tensor :
+#' @section ge(input, other, out=NULL) -> Tensor :
 #'
 #' Computes \eqn{\mbox{input} \geq \mbox{other}} element-wise.
 #' 
@@ -4806,7 +4800,7 @@ NULL
 #' broadcastable  with the first argument.
 #'
 #'
-#' @param input (Tensor) the tensor to compare
+#' @param self (Tensor) the tensor to compare
 #' @param other (Tensor or float) the tensor or value to compare
 #' @param out (Tensor, optional) the output tensor that must be a `BoolTensor`
 #'
@@ -4818,7 +4812,7 @@ NULL
 
 #' Le
 #'
-#' @section le(input, other, out=None) -> Tensor :
+#' @section le(input, other, out=NULL) -> Tensor :
 #'
 #' Computes \eqn{\mbox{input} \leq \mbox{other}} element-wise.
 #' 
@@ -4826,7 +4820,7 @@ NULL
 #' broadcastable  with the first argument.
 #'
 #'
-#' @param input (Tensor) the tensor to compare
+#' @param self (Tensor) the tensor to compare
 #' @param other (Tensor or float) the tensor or value to compare
 #' @param out (Tensor, optional) the output tensor that must be a `BoolTensor`
 #'
@@ -4838,7 +4832,7 @@ NULL
 
 #' Gt
 #'
-#' @section gt(input, other, out=None) -> Tensor :
+#' @section gt(input, other, out=NULL) -> Tensor :
 #'
 #' Computes \eqn{\mbox{input} > \mbox{other}} element-wise.
 #' 
@@ -4846,7 +4840,7 @@ NULL
 #' broadcastable  with the first argument.
 #'
 #'
-#' @param input (Tensor) the tensor to compare
+#' @param self (Tensor) the tensor to compare
 #' @param other (Tensor or float) the tensor or value to compare
 #' @param out (Tensor, optional) the output tensor that must be a `BoolTensor`
 #'
@@ -4858,7 +4852,7 @@ NULL
 
 #' Lt
 #'
-#' @section lt(input, other, out=None) -> Tensor :
+#' @section lt(input, other, out=NULL) -> Tensor :
 #'
 #' Computes \eqn{\mbox{input} < \mbox{other}} element-wise.
 #' 
@@ -4866,7 +4860,7 @@ NULL
 #' broadcastable  with the first argument.
 #'
 #'
-#' @param input (Tensor) the tensor to compare
+#' @param self (Tensor) the tensor to compare
 #' @param other (Tensor or float) the tensor or value to compare
 #' @param out (Tensor, optional) the output tensor that must be a `BoolTensor`
 #'
@@ -4885,7 +4879,7 @@ NULL
 #' takes the same shape as the indices.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param indices (LongTensor) the indices into tensor
 #'
 #' @name torch_take
@@ -4896,7 +4890,7 @@ NULL
 
 #' Index_select
 #'
-#' @section index_select(input, dim, index, out=None) -> Tensor :
+#' @section index_select(input, dim, index, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor which indexes the `input` tensor along dimension
 #' `dim` using the entries in `index` which is a `LongTensor`.
@@ -4911,7 +4905,7 @@ NULL
 #'           storage if necessary.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension in which we index
 #' @param index (LongTensor) the 1-D tensor containing the indices to index
 #' @param out (Tensor, optional) the output tensor.
@@ -4924,7 +4918,7 @@ NULL
 
 #' Masked_select
 #'
-#' @section masked_select(input, mask, out=None) -> Tensor :
+#' @section masked_select(input, mask, out=NULL) -> Tensor :
 #'
 #' Returns a new 1-D tensor which indexes the `input` tensor according to
 #' the boolean mask `mask` which is a `BoolTensor`.
@@ -4936,7 +4930,7 @@ NULL
 #'           as the original tensor
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param mask (BoolTensor) the tensor containing the binary mask to index with
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -4952,16 +4946,16 @@ NULL
 #'     [`torch_nonzero(..., as_tuple=False) <torch.nonzero>`] (default) returns a
 #'     2-D tensor where each row is the index for a nonzero value.
 #' 
-#'     [`torch_nonzero(..., as_tuple=True) <torch.nonzero>`] returns a tuple of 1-D
-#'     index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]``
+#'     [`torch_nonzero(..., as_tuple=TRUE) <torch.nonzero>`] 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.
 #'     
-#' @section nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors :
+#' @section nonzero(input, *, out=NULL, as_tuple=False) -> LongTensor or tuple of LongTensors :
 #' 
-#' **When** `as_tuple` **is ``False`` (default)**:
+#' **When** `as_tuple` **is `FALSE` (default)**:
 #' 
 #' Returns a tensor containing the indices of all non-zero elements of
 #' `input`.  Each row in the result contains the indices of a non-zero
@@ -4972,7 +4966,7 @@ NULL
 #' `out` is of size \eqn{(z \times n)}, where \eqn{z} is the total number of
 #' non-zero elements in the `input` tensor.
 #' 
-#' **When** `as_tuple` **is ``True``**:
+#' **When** `as_tuple` **is `TRUE`**:
 #' 
 #' Returns a tuple of 1-D tensors, one for each dimension in `input`,
 #' each containing the indices (in that dimension) of all non-zero elements of
@@ -4986,7 +4980,7 @@ NULL
 #' value, it is treated as a one-dimensional tensor with one element.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (LongTensor, optional) the output tensor containing indices
 #'
 #' @name torch_nonzero
@@ -4997,7 +4991,7 @@ NULL
 
 #' Gather
 #'
-#' @section gather(input, dim, index, out=None, sparse_grad=False) -> Tensor :
+#' @section gather(input, dim, index, out=NULL, sparse_grad=False) -> Tensor :
 #'
 #' Gathers values along an axis specified by `dim`.
 #' 
@@ -5014,11 +5008,11 @@ NULL
 #' and `out` will have the same size as `index`.
 #'
 #'
-#' @param input (Tensor) the source tensor
+#' @param self (Tensor) the source tensor
 #' @param dim (int) the axis along which to index
 #' @param index (LongTensor) the indices of elements to gather
 #' @param out (Tensor, optional) the destination tensor
-#' @param sparse_grad (bool,optional) If ``True``, gradient w.r.t. `input` will be a sparse tensor.
+#' @param sparse_grad (bool,optional) If `TRUE`, gradient w.r.t. `input` will be a sparse tensor.
 #'
 #' @name torch_gather
 #'
@@ -5028,7 +5022,7 @@ NULL
 
 #' Addcmul
 #'
-#' @section addcmul(input, tensor1, tensor2, *, value=1, out=None) -> Tensor :
+#' @section addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor :
 #'
 #' Performs the element-wise multiplication of `tensor1`
 #' by `tensor2`, multiply the result by the scalar `value`
@@ -5044,7 +5038,7 @@ NULL
 #' a real number, otherwise an integer.
 #'
 #'
-#' @param input (Tensor) the tensor to be added
+#' @param self (Tensor) the tensor to be added
 #' @param tensor1 (Tensor) the tensor to be multiplied
 #' @param tensor2 (Tensor) the tensor to be multiplied
 #' @param value (Number, optional) multiplier for \eqn{tensor1 .* tensor2}
@@ -5058,7 +5052,7 @@ NULL
 
 #' Addcdiv
 #'
-#' @section addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor :
+#' @section addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor :
 #'
 #' Performs the element-wise division of `tensor1` by `tensor2`,
 #' multiply the result by the scalar `value` and add it to `input`.
@@ -5086,7 +5080,7 @@ NULL
 #' a real number, otherwise an integer.
 #'
 #'
-#' @param input (Tensor) the tensor to be added
+#' @param self (Tensor) the tensor to be added
 #' @param tensor1 (Tensor) the numerator tensor
 #' @param tensor2 (Tensor) the denominator tensor
 #' @param value (Number, optional) multiplier for \eqn{\mbox{tensor1} / \mbox{tensor2}}
@@ -5100,7 +5094,7 @@ NULL
 
 #' Lstsq
 #'
-#' @section lstsq(input, A, out=None) -> Tensor :
+#' @section lstsq(input, A, out=NULL) -> Tensor :
 #'
 #' Computes the solution to the least squares and least norm problems for a full
 #' rank matrix \eqn{A} of size \eqn{(m \times n)} and a matrix \eqn{B} of
@@ -5129,7 +5123,7 @@ NULL
 #'     The case when \eqn{m < n} is not supported on the GPU.
 #'
 #'
-#' @param input (Tensor) the matrix \eqn{B}
+#' @param self (Tensor) the matrix \eqn{B}
 #' @param A (Tensor) the \eqn{m} by \eqn{n} matrix \eqn{A}
 #' @param out (tuple, optional) the optional destination tensor
 #'
@@ -5141,7 +5135,7 @@ NULL
 
 #' Triangular_solve
 #'
-#' @section triangular_solve(input, A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor) :
+#' @section triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -> (Tensor, Tensor) :
 #'
 #' Solves a system of equations with a triangular coefficient matrix \eqn{A}
 #' and multiple right-hand sides \eqn{b}.
@@ -5154,11 +5148,11 @@ NULL
 #' batched outputs `X`
 #'
 #'
-#' @param input (Tensor) multiple right-hand sides of size \eqn{(*, m, k)} where                \eqn{*} is zero of more batch dimensions (\eqn{b})
+#' @param self (Tensor) multiple right-hand sides of size \eqn{(*, m, k)} where                \eqn{*} is zero of more batch dimensions (\eqn{b})
 #' @param A (Tensor) the input triangular coefficient matrix of size \eqn{(*, m, m)}                where \eqn{*} is zero or more batch dimensions
-#' @param upper (bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: ``True``.
-#' @param transpose (bool, optional) whether \eqn{A} should be transposed before        being sent into the solver. Default: ``False``.
-#' @param unitriangular (bool, optional) whether \eqn{A} is unit triangular.        If True, the diagonal elements of \eqn{A} are assumed to be        1 and not referenced from \eqn{A}. Default: ``False``.
+#' @param upper (bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: `TRUE`.
+#' @param transpose (bool, optional) whether \eqn{A} should be transposed before        being sent into the solver. Default: `FALSE`.
+#' @param unitriangular (bool, optional) whether \eqn{A} is unit triangular.        If TRUE, the diagonal elements of \eqn{A} are assumed to be        1 and not referenced from \eqn{A}. Default: `FALSE`.
 #'
 #' @name torch_triangular_solve
 #'
@@ -5168,7 +5162,7 @@ NULL
 
 #' Symeig
 #'
-#' @section symeig(input, eigenvectors=False, upper=True, out=None) -> (Tensor, Tensor) :
+#' @section symeig(input, eigenvectors=False, upper=TRUE, out=NULL) -> (Tensor, Tensor) :
 #'
 #' This function returns eigenvalues and eigenvectors
 #' of a real symmetric matrix `input` or a batch of real symmetric matrices,
@@ -5180,13 +5174,13 @@ NULL
 #' The boolean argument `eigenvectors` defines computation of
 #' both eigenvectors and eigenvalues or eigenvalues only.
 #' 
-#' If it is ``False``, only eigenvalues are computed. If it is ``True``,
+#' If it is `FALSE`, only eigenvalues are computed. If it is `TRUE`,
 #' both eigenvalues and eigenvectors are computed.
 #' 
 #' Since the input matrix `input` is supposed to be symmetric,
 #' only the upper triangular portion is used by default.
 #' 
-#' If `upper` is ``False``, then lower triangular portion is used.
+#' If `upper` is `FALSE`, then lower triangular portion is used.
 #' 
 #' @note The eigenvalues are returned in ascending order. If `input` is a batch of matrices,
 #'           then the eigenvalues of each matrix in the batch is returned in ascending order.
@@ -5196,10 +5190,10 @@ NULL
 #' 
 #' @note Extra care needs to be taken when backward through outputs. Such
 #'           operation is really only stable when all eigenvalues are distinct.
-#'           Otherwise, ``NaN`` can appear as the gradients are not properly defined.
+#'           Otherwise, `NaN` can appear as the gradients are not properly defined.
 #'
 #'
-#' @param input (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more                    batch dimensions consisting of symmetric matrices.
+#' @param self (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more                    batch dimensions consisting of symmetric matrices.
 #' @param eigenvectors (boolean, optional) controls whether eigenvectors have to be computed
 #' @param upper (boolean, optional) controls whether to consider upper-triangular or lower-triangular region
 #' @param out (tuple, optional) the output tuple of (Tensor, Tensor)
@@ -5212,7 +5206,7 @@ NULL
 
 #' Eig
 #'
-#' @section eig(input, eigenvectors=False, out=None) -> (Tensor, Tensor) :
+#' @section eig(input, eigenvectors=False, out=NULL) -> (Tensor, Tensor) :
 #'
 #' Computes the eigenvalues and eigenvectors of a real square matrix.
 #' 
@@ -5221,8 +5215,8 @@ NULL
 #'     for [`torch_symeig`]
 #'
 #'
-#' @param input (Tensor) the square matrix of shape \eqn{(n \times n)} for which the eigenvalues and eigenvectors        will be computed
-#' @param eigenvectors (bool) ``True`` to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed
+#' @param self (Tensor) the square matrix of shape \eqn{(n \times n)} for which the eigenvalues and eigenvectors        will be computed
+#' @param eigenvectors (bool) `TRUE` to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed
 #' @param out (tuple, optional) the output tensors
 #'
 #' @name torch_eig
@@ -5233,17 +5227,17 @@ NULL
 
 #' Svd
 #'
-#' @section svd(input, some=True, compute_uv=True, out=None) -> (Tensor, Tensor, Tensor) :
+#' @section svd(input, some=TRUE, compute_uv=TRUE, out=NULL) -> (Tensor, Tensor, Tensor) :
 #'
 #' This function returns a namedtuple ``(U, S, V)`` which is the singular value
 #' decomposition of a input real matrix or batches of real matrices `input` such that
 #' \eqn{input = U \times diag(S) \times V^T}.
 #' 
-#' If `some` is ``True`` (default), the method returns the reduced singular value decomposition
+#' If `some` is `TRUE` (default), the method returns the reduced singular value decomposition
 #' i.e., if the last two dimensions of `input` are ``m`` and ``n``, then the returned
 #' `U` and `V` matrices will contain only \eqn{min(n, m)} orthonormal columns.
 #' 
-#' If `compute_uv` is ``False``, the returned `U` and `V` matrices will be zero matrices
+#' If `compute_uv` is `FALSE`, the returned `U` and `V` matrices will be zero matrices
 #' of shape \eqn{(m \times m)} and \eqn{(n \times n)} respectively. `some` will be ignored here.
 #' 
 #' @note The singular values are returned in descending order. If `input` is a batch of matrices,
@@ -5258,20 +5252,20 @@ NULL
 #' 
 #' @note Extra care needs to be taken when backward through `U` and `V`
 #'           outputs. Such operation is really only stable when `input` is
-#'           full rank with all distinct singular values. Otherwise, ``NaN`` can
+#'           full rank with all distinct singular values. Otherwise, `NaN` can
 #'           appear as the gradients are not properly defined. Also, notice that
 #'           double backward will usually do an additional backward through `U` and
 #'           `V` even if the original backward is only on `S`.
 #' 
-#' @note When `some` = ``False``, the gradients on `U[..., :, min(m, n):]`
+#' @note When `some` = `FALSE`, the gradients on `U[..., :, min(m, n):]`
 #'           and `V[..., :, min(m, n):]` will be ignored in backward as those vectors
 #'           can be arbitrary bases of the subspaces.
 #' 
-#' @note When `compute_uv` = ``False``, backward cannot be performed since `U` and `V`
+#' @note When `compute_uv` = `FALSE`, backward cannot be performed since `U` and `V`
 #'           from the forward pass is required for the backward operation.
 #'
 #'
-#' @param input (Tensor) the input tensor of size \eqn{(*, m, n)} where `*` is zero or more                    batch dimensions consisting of \eqn{m \times n} matrices.
+#' @param self (Tensor) the input tensor of size \eqn{(*, m, n)} where `*` is zero or more                    batch dimensions consisting of \eqn{m \times n} matrices.
 #' @param some (bool, optional) controls the shape of returned `U` and `V`
 #' @param compute_uv (bool, optional) option whether to compute `U` and `V` or not
 #' @param out (tuple, optional) the output tuple of tensors
@@ -5284,32 +5278,32 @@ NULL
 
 #' Cholesky
 #'
-#' @section cholesky(input, upper=False, out=None) -> Tensor :
+#' @section cholesky(input, upper=False, out=NULL) -> Tensor :
 #'
 #' Computes the Cholesky decomposition of a symmetric positive-definite
 #' matrix \eqn{A} or for batches of symmetric positive-definite matrices.
 #' 
-#' If `upper` is ``True``, the returned matrix ``U`` is upper-triangular, and
+#' If `upper` is `TRUE`, the returned matrix ``U`` is upper-triangular, and
 #' the decomposition has the form:
 #' 
 #' \deqn{
 #'   A = U^TU
 #' }
-#' If `upper` is ``False``, the returned matrix ``L`` is lower-triangular, and
+#' If `upper` is `FALSE`, the returned matrix ``L`` is lower-triangular, and
 #' the decomposition has the form:
 #' 
 #' \deqn{
 #'     A = LL^T
 #' }
-#' If `upper` is ``True``, and \eqn{A} is a batch of symmetric positive-definite
+#' If `upper` is `TRUE`, and \eqn{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 `upper` is ``False``, the returned
+#' of each of the individual matrices. Similarly, when `upper` is `FALSE`, the returned
 #' tensor will be composed of lower-triangular Cholesky factors of each of the individual
 #' matrices.
 #'
 #'
-#' @param input (Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where `*` is zero or more                batch dimensions consisting of symmetric positive-definite matrices.
-#' @param upper (bool, optional) flag that indicates whether to return a                            upper or lower triangular matrix. Default: ``False``
+#' @param self (Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where `*` is zero or more                batch dimensions consisting of symmetric positive-definite matrices.
+#' @param upper (bool, optional) flag that indicates whether to return a                            upper or lower triangular matrix. Default: `FALSE`
 #' @param out (Tensor, optional) the output matrix
 #'
 #' @name torch_cholesky
@@ -5320,18 +5314,18 @@ NULL
 
 #' Cholesky_solve
 #'
-#' @section cholesky_solve(input, input2, upper=False, out=None) -> Tensor :
+#' @section cholesky_solve(input, input2, upper=False, out=NULL) -> Tensor :
 #'
 #' Solves a linear system of equations with a positive semidefinite
 #' matrix to be inverted given its Cholesky factor matrix \eqn{u}.
 #' 
-#' If `upper` is ``False``, \eqn{u} is and lower triangular and `c` is
+#' If `upper` is `FALSE`, \eqn{u} is and lower triangular and `c` is
 #' returned such that:
 #' 
 #' \deqn{
 #'     c = (u u^T)^{{-1}} b
 #' }
-#' If `upper` is ``True`` or not provided, \eqn{u} is upper triangular
+#' If `upper` is `TRUE` or not provided, \eqn{u} is upper triangular
 #' and `c` is returned such that:
 #' 
 #' \deqn{
@@ -5342,9 +5336,9 @@ NULL
 #' batched outputs `c`
 #'
 #'
-#' @param input (Tensor) input matrix \eqn{b} of size \eqn{(*, m, k)},                where \eqn{*} is zero or more batch dimensions
-#' @param input2 (Tensor) input matrix \eqn{u} of size \eqn{(*, m, m)},                where \eqn{*} is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor
-#' @param upper (bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: ``False``.
+#' @param self (Tensor) input matrix \eqn{b} of size \eqn{(*, m, k)},                where \eqn{*} is zero or more batch dimensions
+#' @param self2 (Tensor) input matrix \eqn{u} of size \eqn{(*, m, m)},                where \eqn{*} is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor
+#' @param upper (bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: `FALSE`.
 #' @param out (Tensor, optional) the output tensor for `c`
 #'
 #' @name torch_cholesky_solve
@@ -5355,7 +5349,7 @@ NULL
 
 #' Solve
 #'
-#' @section torch.solve(input, A, out=None) -> (Tensor, Tensor) :
+#' @section torch.solve(input, A, out=NULL) -> (Tensor, Tensor) :
 #'
 #' This function returns the solution to the system of linear
 #' equations represented by \eqn{AX = B} and the LU factorization of
@@ -5375,7 +5369,7 @@ NULL
 #'     `A.contiguous().transpose(-1, -2).stride()` respectively.
 #'
 #'
-#' @param input (Tensor) input matrix \eqn{B} of size \eqn{(*, m, k)} , where \eqn{*}                is zero or more batch dimensions.
+#' @param self (Tensor) input matrix \eqn{B} of size \eqn{(*, m, k)} , where \eqn{*}                is zero or more batch dimensions.
 #' @param A (Tensor) input square matrix of size \eqn{(*, m, m)}, where                \eqn{*} is zero or more batch dimensions.
 #' @param out ((Tensor, Tensor) optional output tuple.
 #'
@@ -5387,19 +5381,19 @@ NULL
 
 #' Cholesky_inverse
 #'
-#' @section cholesky_inverse(input, upper=False, out=None) -> Tensor :
+#' @section cholesky_inverse(input, upper=False, out=NULL) -> Tensor :
 #'
 #' Computes the inverse of a symmetric positive-definite matrix \eqn{A} using its
 #' Cholesky factor \eqn{u}: returns matrix ``inv``. The inverse is computed using
 #' LAPACK routines ``dpotri`` and ``spotri`` (and the corresponding MAGMA routines).
 #' 
-#' If `upper` is ``False``, \eqn{u} is lower triangular
+#' If `upper` is `FALSE`, \eqn{u} is lower triangular
 #' such that the returned tensor is
 #' 
 #' \deqn{
 #'     inv = (uu^{{T}})^{{-1}}
 #' }
-#' If `upper` is ``True`` or not provided, \eqn{u} is upper
+#' If `upper` is `TRUE` or not provided, \eqn{u} is upper
 #' triangular such that the returned tensor is
 #' 
 #' \deqn{
@@ -5407,7 +5401,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input 2-D tensor \eqn{u}, a upper or lower triangular           Cholesky factor
+#' @param self (Tensor) the input 2-D tensor \eqn{u}, a upper or lower triangular           Cholesky factor
 #' @param upper (bool, optional) whether to return a lower (default) or upper triangular matrix
 #' @param out (Tensor, optional) the output tensor for `inv`
 #'
@@ -5419,15 +5413,15 @@ NULL
 
 #' Qr
 #'
-#' @section qr(input, some=True, out=None) -> (Tensor, Tensor) :
+#' @section qr(input, some=TRUE, out=NULL) -> (Tensor, Tensor) :
 #'
 #' Computes the QR decomposition of a matrix or a batch of matrices `input`,
 #' and returns a namedtuple (Q, R) of tensors such that \eqn{\mbox{input} = Q R}
 #' with \eqn{Q} being an orthogonal matrix or batch of orthogonal matrices and
 #' \eqn{R} being an upper triangular matrix or batch of upper triangular matrices.
 #' 
-#' If `some` is ``True``, then this function returns the thin (reduced) QR factorization.
-#' Otherwise, if `some` is ``False``, this function returns the complete QR factorization.
+#' If `some` is `TRUE`, then this function returns the thin (reduced) QR factorization.
+#' Otherwise, if `some` is `FALSE`, this function returns the complete QR factorization.
 #' 
 #' @note precision may be lost if the magnitudes of the elements of `input`
 #'           are large
@@ -5437,9 +5431,9 @@ NULL
 #'           LAPACK implementation.
 #'
 #'
-#' @param input (Tensor) the input tensor of size \eqn{(*, m, n)} where `*` is zero or more                batch dimensions consisting of matrices of dimension \eqn{m \times n}.
-#' @param some (bool, optional) Set to ``True`` for reduced QR decomposition and ``False`` for                complete QR decomposition.
-#' @param out (tuple, optional) tuple of `Q` and `R` tensors                satisfying `input = torch.matmul(Q, R)`.                The dimensions of `Q` and `R` are \eqn{(*, m, k)} and \eqn{(*, k, n)}                respectively, where \eqn{k = \min(m, n)} if `some:` is ``True`` and                \eqn{k = m} otherwise.
+#' @param self (Tensor) the input tensor of size \eqn{(*, m, n)} where `*` is zero or more                batch dimensions consisting of matrices of dimension \eqn{m \times n}.
+#' @param some (bool, optional) Set to `TRUE` for reduced QR decomposition and `FALSE` for                complete QR decomposition.
+#' @param out (tuple, optional) tuple of `Q` and `R` tensors                satisfying `input = torch.matmul(Q, R)`.                The dimensions of `Q` and `R` are \eqn{(*, m, k)} and \eqn{(*, k, n)}                respectively, where \eqn{k = \min(m, n)} if `some:` is `TRUE` and                \eqn{k = m} otherwise.
 #'
 #' @name torch_qr
 #'
@@ -5449,7 +5443,7 @@ NULL
 
 #' Geqrf
 #'
-#' @section geqrf(input, out=None) -> (Tensor, Tensor) :
+#' @section geqrf(input, out=NULL) -> (Tensor, Tensor) :
 #'
 #' This is a low-level function for calling LAPACK directly. This function
 #' returns a namedtuple (a, tau) as defined in `LAPACK documentation for geqrf`_ .
@@ -5465,7 +5459,7 @@ NULL
 #' See `LAPACK documentation for geqrf`_ for further details.
 #'
 #'
-#' @param input (Tensor) the input matrix
+#' @param self (Tensor) the input matrix
 #' @param out (tuple, optional) the output tuple of (Tensor, Tensor)
 #'
 #' @name torch_geqrf
@@ -5485,8 +5479,8 @@ NULL
 #' See `LAPACK documentation for orgqr`_ for further details.
 #'
 #'
-#' @param input (Tensor) the `a` from [`torch_geqrf`].
-#' @param input2 (Tensor) the `tau` from [`torch_geqrf`].
+#' @param self (Tensor) the `a` from [`torch_geqrf`].
+#' @param self2 (Tensor) the `tau` from [`torch_geqrf`].
 #'
 #' @name torch_orgqr
 #'
@@ -5496,7 +5490,7 @@ NULL
 
 #' Ormqr
 #'
-#' @section ormqr(input, input2, input3, left=True, transpose=False) -> Tensor :
+#' @section ormqr(input, input2, input3, left=TRUE, transpose=False) -> Tensor :
 #'
 #' Multiplies `mat` (given by `input3`) by the orthogonal `Q` matrix of the QR factorization
 #' formed by [`torch_geqrf`] that is represented by `(a, tau)` (given by (`input`, `input2`)).
@@ -5505,9 +5499,9 @@ NULL
 #' See `LAPACK documentation for ormqr`_ for further details.
 #'
 #'
-#' @param input (Tensor) the `a` from [`torch_geqrf`].
-#' @param input2 (Tensor) the `tau` from [`torch_geqrf`].
-#' @param input3 (Tensor) the matrix to be multiplied.
+#' @param self (Tensor) the `a` from [`torch_geqrf`].
+#' @param self2 (Tensor) the `tau` from [`torch_geqrf`].
+#' @param self3 (Tensor) the matrix to be multiplied.
 #'
 #' @name torch_ormqr
 #'
@@ -5517,7 +5511,7 @@ NULL
 
 #' Lu_solve
 #'
-#' @section lu_solve(input, LU_data, LU_pivots, out=None) -> Tensor :
+#' @section lu_solve(input, LU_data, LU_pivots, out=NULL) -> Tensor :
 #'
 #' Returns the LU solve of the linear system \eqn{Ax = b} using the partially pivoted
 #' LU factorization of A from `torch_lu`.
@@ -5536,7 +5530,7 @@ NULL
 
 #' Multinomial
 #'
-#' @section multinomial(input, num_samples, replacement=False, *, generator=None, out=None) -> LongTensor :
+#' @section multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -> LongTensor :
 #'
 #' Returns a tensor where each row contains `num_samples` indices sampled
 #' from the multinomial probability distribution located in the corresponding row
@@ -5555,7 +5549,7 @@ NULL
 #' If `input` is a matrix with `m` rows, `out` is an matrix of shape
 #' \eqn{(m \times \mbox{num\_samples})}.
 #' 
-#' If replacement is ``True``, samples are drawn with replacement.
+#' If replacement is `TRUE`, samples are drawn with replacement.
 #' 
 #' 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.
@@ -5566,7 +5560,7 @@ NULL
 #'     elements in each row of `input` if it is a matrix).
 #'
 #'
-#' @param input (Tensor) the input tensor containing probabilities
+#' @param self (Tensor) the input tensor containing probabilities
 #' @param num_samples (int) number of samples to draw
 #' @param replacement (bool, optional) whether to draw with replacement or not
 #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling
@@ -5580,7 +5574,7 @@ NULL
 
 #' Lgamma
 #'
-#' @section lgamma(input, out=None) -> Tensor :
+#' @section lgamma(input, out=NULL) -> Tensor :
 #'
 #' Computes the logarithm of the gamma function on `input`.
 #' 
@@ -5589,7 +5583,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_lgamma
@@ -5600,7 +5594,7 @@ NULL
 
 #' Digamma
 #'
-#' @section digamma(input, out=None) -> Tensor :
+#' @section digamma(input, out=NULL) -> Tensor :
 #'
 #' Computes the logarithmic derivative of the gamma function on `input`.
 #' 
@@ -5609,7 +5603,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the tensor to compute the digamma function on
+#' @param self (Tensor) the tensor to compute the digamma function on
 #'
 #' @name torch_digamma
 #'
@@ -5619,7 +5613,7 @@ NULL
 
 #' Polygamma
 #'
-#' @section polygamma(n, input, out=None) -> Tensor :
+#' @section polygamma(n, input, out=NULL) -> Tensor :
 #'
 #' Computes the \eqn{n^{th}} derivative of the digamma function on `input`.
 #' \eqn{n \geq 0} is called the order of the polygamma function.
@@ -5632,7 +5626,7 @@ NULL
 #'
 #'
 #' @param n (int) the order of the polygamma function
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_polygamma
@@ -5643,7 +5637,7 @@ NULL
 
 #' Erfinv
 #'
-#' @section erfinv(input, out=None) -> Tensor :
+#' @section erfinv(input, out=NULL) -> Tensor :
 #'
 #' Computes the inverse error function of each element of `input`.
 #' The inverse error function is defined in the range \eqn{(-1, 1)} as:
@@ -5653,7 +5647,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_erfinv
@@ -5664,7 +5658,7 @@ NULL
 
 #' Sign
 #'
-#' @section sign(input, out=None) -> Tensor :
+#' @section sign(input, out=NULL) -> Tensor :
 #'
 #' Returns a new tensor with the signs of the elements of `input`.
 #' 
@@ -5673,7 +5667,7 @@ NULL
 #' }
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param out (Tensor, optional) the output tensor.
 #'
 #' @name torch_sign
@@ -5692,7 +5686,7 @@ NULL
 #' broadcastable .
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param other (Tensor) the Right-hand-side input tensor
 #' @param p (float, optional) the norm to be computed
 #'
@@ -5704,7 +5698,7 @@ NULL
 
 #' Atan2
 #'
-#' @section atan2(input, other, out=None) -> Tensor :
+#' @section atan2(input, other, out=NULL) -> Tensor :
 #'
 #' Element-wise arctangent of \eqn{\mbox{input}_{i} / \mbox{other}_{i}}
 #' with consideration of the quadrant. Returns a new tensor with the signed angles
@@ -5717,7 +5711,7 @@ NULL
 #' broadcastable .
 #'
 #'
-#' @param input (Tensor) the first input tensor
+#' @param self (Tensor) the first input tensor
 #' @param other (Tensor) the second input tensor
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -5729,7 +5723,7 @@ NULL
 
 #' Lerp
 #'
-#' @section lerp(input, end, weight, out=None) :
+#' @section lerp(input, end, weight, out=NULL) :
 #'
 #' Does a linear interpolation of two tensors `start` (given by `input`) and `end` based
 #' on a scalar or tensor `weight` and returns the resulting `out` tensor.
@@ -5742,7 +5736,7 @@ NULL
 #' the shapes of `weight`, `start`, and `end` must be broadcastable .
 #'
 #'
-#' @param input (Tensor) the tensor with the starting points
+#' @param self (Tensor) the tensor with the starting points
 #' @param end (Tensor) the tensor with the ending points
 #' @param weight (float or tensor) the weight for the interpolation formula
 #' @param out (Tensor, optional) the output tensor.
@@ -5755,7 +5749,7 @@ NULL
 
 #' Histc
 #'
-#' @section histc(input, bins=100, min=0, max=0, out=None) -> Tensor :
+#' @section histc(input, bins=100, min=0, max=0, out=NULL) -> Tensor :
 #'
 #' Computes the histogram of a tensor.
 #' 
@@ -5764,7 +5758,7 @@ NULL
 #' maximum values of the data are used.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param bins (int) number of histogram bins
 #' @param min (int) lower end of the range (inclusive)
 #' @param max (int) upper end of the range (inclusive)
@@ -5778,7 +5772,7 @@ NULL
 
 #' Fmod
 #'
-#' @section fmod(input, other, out=None) -> Tensor :
+#' @section fmod(input, other, out=NULL) -> Tensor :
 #'
 #' Computes the element-wise remainder of division.
 #' 
@@ -5789,7 +5783,7 @@ NULL
 #' `other` must be broadcastable .
 #'
 #'
-#' @param input (Tensor) the dividend
+#' @param self (Tensor) the dividend
 #' @param other (Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -5801,7 +5795,7 @@ NULL
 
 #' Remainder
 #'
-#' @section remainder(input, other, out=None) -> Tensor :
+#' @section remainder(input, other, out=NULL) -> Tensor :
 #'
 #' Computes the element-wise remainder of division.
 #' 
@@ -5812,7 +5806,7 @@ NULL
 #' `other` must be broadcastable .
 #'
 #'
-#' @param input (Tensor) the dividend
+#' @param self (Tensor) the dividend
 #' @param other (Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend
 #' @param out (Tensor, optional) the output tensor.
 #'
@@ -5824,14 +5818,14 @@ NULL
 
 #' Sort
 #'
-#' @section sort(input, dim=-1, descending=False, out=None) -> (Tensor, LongTensor) :
+#' @section sort(input, dim=-1, descending=False, out=NULL) -> (Tensor, LongTensor) :
 #'
 #' Sorts the elements of the `input` tensor along a given dimension
 #' in ascending order by value.
 #' 
 #' If `dim` is not given, the last dimension of the `input` is chosen.
 #' 
-#' If `descending` is ``True`` then the elements are sorted in descending
+#' If `descending` is `TRUE` then the elements are sorted in descending
 #' order by value.
 #' 
 #' A namedtuple of (values, indices) is returned, where the `values` are the
@@ -5839,7 +5833,7 @@ NULL
 #' `input` tensor.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int, optional) the dimension to sort along
 #' @param descending (bool, optional) controls the sorting order (ascending or descending)
 #' @param out (tuple, optional) the output tuple of (`Tensor`, `LongTensor`) that can        be optionally given to be used as output buffers
@@ -5861,7 +5855,7 @@ NULL
 #' for the exact semantics of this method.
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param dim (int, optional) the dimension to sort along
 #' @param descending (bool, optional) controls the sorting order (ascending or descending)
 #'
@@ -5873,23 +5867,23 @@ NULL
 
 #' Topk
 #'
-#' @section topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor) :
+#' @section topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE, out=NULL) -> (Tensor, LongTensor) :
 #'
 #' Returns the `k` largest elements of the given `input` tensor along
 #' a given dimension.
 #' 
 #' If `dim` is not given, the last dimension of the `input` is chosen.
 #' 
-#' If `largest` is ``False`` then the `k` smallest elements are returned.
+#' If `largest` is `FALSE` then the `k` smallest elements are returned.
 #' 
 #' A namedtuple of `(values, indices)` is returned, where the `indices` are the indices
 #' of the elements in the original `input` tensor.
 #' 
-#' The boolean option `sorted` if ``True``, will make sure that the returned
+#' The boolean option `sorted` if `TRUE`, will make sure that the returned
 #' `k` elements are themselves sorted
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param k (int) the k in "top-k"
 #' @param dim (int, optional) the dimension to sort along
 #' @param largest (bool, optional) controls whether to return largest or           smallest elements
@@ -5904,7 +5898,7 @@ NULL
 
 #' Renorm
 #'
-#' @section renorm(input, p, dim, maxnorm, out=None) -> Tensor :
+#' @section renorm(input, p, dim, maxnorm, out=NULL) -> Tensor :
 #'
 #' Returns a tensor where each sub-tensor of `input` along dimension
 #' `dim` is normalized such that the `p`-norm of the sub-tensor is lower
@@ -5913,7 +5907,7 @@ NULL
 #' @note If the norm of a row is lower than `maxnorm`, the row is unchanged
 #'
 #'
-#' @param input (Tensor) the input tensor.
+#' @param self (Tensor) the input tensor.
 #' @param p (float) the power for the norm computation
 #' @param dim (int) the dimension to slice over to get the sub-tensors
 #' @param maxnorm (float) the maximum norm to keep each sub-tensor under
@@ -5929,7 +5923,7 @@ NULL
 #'
 #' @section equal(input, other) -> bool :
 #'
-#' ``True`` if two tensors have the same size and elements, ``False`` otherwise.
+#' `TRUE` if two tensors have the same size and elements, `FALSE` otherwise.
 #'
 #'
 #'
@@ -5942,7 +5936,7 @@ NULL
 
 #' Normal
 #'
-#' @section normal(mean, std, *, generator=None, out=None) -> Tensor :
+#' @section normal(mean, std, *, generator=NULL, out=NULL) -> Tensor :
 #'
 #' Returns a tensor of random numbers drawn from separate normal distributions
 #' whose mean and standard deviation are given.
@@ -5959,17 +5953,17 @@ NULL
 #' @note When the shapes do not match, the shape of `mean`
 #'           is used as the shape for the returned output tensor
 #'
-#' @section normal(mean=0.0, std, out=None) -> Tensor :
+#' @section normal(mean=0.0, std, out=NULL) -> Tensor :
 #'
 #' Similar to the function above, but the means are shared among all drawn
 #' elements.
 #'
-#' @section normal(mean, std=1.0, out=None) -> Tensor :
+#' @section normal(mean, std=1.0, out=NULL) -> Tensor :
 #'
 #' Similar to the function above, but the standard-deviations are shared among
 #' all drawn elements.
 #'
-#' @section normal(mean, std, size, *, out=None) -> Tensor :
+#' @section normal(mean, std, size, *, out=NULL) -> Tensor :
 #'
 #' Similar to the function above, but the means and standard deviations are shared
 #' among all drawn elements. The resulting tensor has size given by `size`.
-- 
GitLab


From 67b248a7e8b601475cf371030cc40c7c3e041808 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 2 Sep 2020 22:04:09 -0300
Subject: [PATCH 101/157] allows @subsection

---
 tools/document.R | 38 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/tools/document.R b/tools/document.R
index 9acd96451..91fe76e0c 100644
--- a/tools/document.R
+++ b/tools/document.R
@@ -1,5 +1,9 @@
 library(roxygen2)
 
+
+# Patch examples ----------------------------------------------------------
+# we path examples so we add the `if` conditional.
+
 .S3method("roxy_tag_parse", "roxy_tag_examples", function(x) {
   roxygen2::tag_examples(x)
 })
@@ -16,5 +20,39 @@ library(roxygen2)
   )
 })
 
+# Subsection --------------------------------------------------------------
+
+.S3method("roxy_tag_parse", "roxy_tag_subsection", function(x) {
+  roxygen2::tag_markdown(x)
+})
+
+.S3method("roxy_tag_rd", "roxy_tag_subsection", function(x, base_path, env) {
+  pieces <- stringr::str_split(x$val, ":", n = 2)[[1]]
+  title <- stringr::str_split(pieces[1], "\n")[[1]]
+  
+  if (length(title) > 1) {
+    roxygen2:::roxy_tag_warning(x, paste0(
+      "Section title spans multiple lines.\n",
+      "Did you forget a colon (:) at the end of the title?"
+    ))
+    return()
+  }
+  
+  roxygen2:::rd_section_section(pieces[1], pieces[2])
+})
+
+.S3method("format", "rd_section_subsection", function(x, ...) {
+  paste0(
+    "\\subsection{", x$value$title, "}{\n", x$value$content, "\n}\n",
+    collapse = "\n"
+  )
+})
+
+# Params ------------------------------------------------------------------
+# avoids using the \arguments{} so we can have two sections with parameters
+# for different signatures.
+
+
+
 
 devtools::document()
\ No newline at end of file
-- 
GitLab


From 771504b8ab4d150a4650fc5e62a9e949bebc397f Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 2 Sep 2020 22:04:23 -0300
Subject: [PATCH 102/157] redocument

---
 man/torch_abs.Rd                  | 11 ++++++-----
 man/torch_acos.Rd                 |  7 +++++--
 man/torch_adaptive_avg_pool1d.Rd  |  5 ++++-
 man/torch_add.Rd                  | 11 +++++++----
 man/torch_addbmm.Rd               |  9 ++++++---
 man/torch_addcdiv.Rd              |  7 +++++--
 man/torch_addcmul.Rd              |  7 +++++--
 man/torch_addmm.Rd                |  7 +++++--
 man/torch_addmv.Rd                |  7 +++++--
 man/torch_addr.Rd                 |  7 +++++--
 man/torch_allclose.Rd             | 11 +++++++----
 man/torch_angle.Rd                |  7 +++++--
 man/torch_arange.Rd               | 17 ++++++++++++++---
 man/torch_argmax.Rd               |  7 +++++--
 man/torch_argmin.Rd               |  7 +++++--
 man/torch_argsort.Rd              |  7 +++++--
 man/torch_as_strided.Rd           |  7 +++++--
 man/torch_asin.Rd                 |  7 +++++--
 man/torch_atan.Rd                 |  7 +++++--
 man/torch_atan2.Rd                |  7 +++++--
 man/torch_avg_pool1d.Rd           | 14 ++++++++++++--
 man/torch_baddbmm.Rd              |  7 +++++--
 man/torch_bartlett_window.Rd      |  5 ++++-
 man/torch_bernoulli.Rd            |  7 +++++--
 man/torch_bincount.Rd             |  7 +++++--
 man/torch_bitwise_and.Rd          |  7 +++++--
 man/torch_bitwise_not.Rd          |  7 +++++--
 man/torch_bitwise_or.Rd           |  7 +++++--
 man/torch_bitwise_xor.Rd          |  7 +++++--
 man/torch_blackman_window.Rd      |  5 ++++-
 man/torch_bmm.Rd                  |  7 +++++--
 man/torch_broadcast_tensors.Rd    |  5 ++++-
 man/torch_can_cast.Rd             |  5 ++++-
 man/torch_cartesian_prod.Rd       |  5 ++++-
 man/torch_cat.Rd                  |  5 ++++-
 man/torch_cdist.Rd                |  5 ++++-
 man/torch_ceil.Rd                 |  7 +++++--
 man/torch_celu_.Rd                |  5 ++++-
 man/torch_chain_matmul.Rd         |  5 ++++-
 man/torch_cholesky.Rd             |  7 +++++--
 man/torch_cholesky_inverse.Rd     |  7 +++++--
 man/torch_cholesky_solve.Rd       | 11 +++++++----
 man/torch_chunk.Rd                |  7 +++++--
 man/torch_clamp.Rd                |  7 +++++--
 man/torch_combinations.Rd         |  7 +++++--
 man/torch_conj.Rd                 |  7 +++++--
 man/torch_conv1d.Rd               | 17 ++++++++++++++---
 man/torch_conv2d.Rd               | 17 ++++++++++++++---
 man/torch_conv3d.Rd               | 17 ++++++++++++++---
 man/torch_conv_tbc.Rd             |  7 +++++--
 man/torch_conv_transpose1d.Rd     | 18 +++++++++++++++---
 man/torch_conv_transpose2d.Rd     | 18 +++++++++++++++---
 man/torch_conv_transpose3d.Rd     | 18 +++++++++++++++---
 man/torch_cos.Rd                  |  7 +++++--
 man/torch_cosh.Rd                 |  7 +++++--
 man/torch_cosine_similarity.Rd    |  5 ++++-
 man/torch_cross.Rd                |  7 +++++--
 man/torch_cummax.Rd               |  7 +++++--
 man/torch_cummin.Rd               |  7 +++++--
 man/torch_cumprod.Rd              |  7 +++++--
 man/torch_cumsum.Rd               |  7 +++++--
 man/torch_det.Rd                  |  7 +++++--
 man/torch_diag.Rd                 |  7 +++++--
 man/torch_diag_embed.Rd           |  7 +++++--
 man/torch_diagflat.Rd             |  7 +++++--
 man/torch_diagonal.Rd             | 11 +++++++----
 man/torch_digamma.Rd              |  7 +++++--
 man/torch_dist.Rd                 |  7 +++++--
 man/torch_div.Rd                  |  7 +++++--
 man/torch_dot.Rd                  |  5 ++++-
 man/torch_eig.Rd                  |  7 +++++--
 man/torch_einsum.Rd               |  5 ++++-
 man/torch_empty.Rd                | 20 +++++++++++++++-----
 man/torch_empty_like.Rd           | 16 +++++++++++++---
 man/torch_empty_strided.Rd        | 13 ++++++++++++-
 man/torch_eq.Rd                   |  7 +++++--
 man/torch_equal.Rd                |  5 ++++-
 man/torch_erf.Rd                  |  7 +++++--
 man/torch_erfc.Rd                 |  7 +++++--
 man/torch_erfinv.Rd               |  7 +++++--
 man/torch_exp.Rd                  |  7 +++++--
 man/torch_expm1.Rd                |  7 +++++--
 man/torch_eye.Rd                  | 16 +++++++++++++---
 man/torch_fft.Rd                  |  7 +++++--
 man/torch_flatten.Rd              |  7 +++++--
 man/torch_flip.Rd                 |  7 +++++--
 man/torch_floor.Rd                |  7 +++++--
 man/torch_floor_divide.Rd         |  7 +++++--
 man/torch_fmod.Rd                 |  7 +++++--
 man/torch_frac.Rd                 |  5 ++++-
 man/torch_full.Rd                 | 17 ++++++++++++++---
 man/torch_full_like.Rd            | 17 ++++++++++++++---
 man/torch_gather.Rd               | 11 +++++++----
 man/torch_ge.Rd                   |  7 +++++--
 man/torch_geqrf.Rd                |  7 +++++--
 man/torch_ger.Rd                  |  7 +++++--
 man/torch_gt.Rd                   |  7 +++++--
 man/torch_hamming_window.Rd       |  5 ++++-
 man/torch_hann_window.Rd          |  5 ++++-
 man/torch_histc.Rd                |  7 +++++--
 man/torch_ifft.Rd                 |  7 +++++--
 man/torch_imag.Rd                 |  7 +++++--
 man/torch_index_select.Rd         |  7 +++++--
 man/torch_inverse.Rd              |  7 +++++--
 man/torch_irfft.Rd                | 13 +++++++++++--
 man/torch_is_complex.Rd           |  7 +++++--
 man/torch_is_floating_point.Rd    |  7 +++++--
 man/torch_isfinite.Rd             |  5 ++++-
 man/torch_isinf.Rd                |  5 ++++-
 man/torch_isnan.Rd                |  7 +++++--
 man/torch_kthvalue.Rd             |  7 +++++--
 man/torch_le.Rd                   |  7 +++++--
 man/torch_lerp.Rd                 |  7 +++++--
 man/torch_lgamma.Rd               |  7 +++++--
 man/torch_linspace.Rd             | 29 ++++++++++++++++++++++++++---
 man/torch_log.Rd                  |  7 +++++--
 man/torch_log10.Rd                |  7 +++++--
 man/torch_log1p.Rd                |  7 +++++--
 man/torch_log2.Rd                 |  7 +++++--
 man/torch_logdet.Rd               |  7 +++++--
 man/torch_logical_and.Rd          |  7 +++++--
 man/torch_logical_not.Rd          |  2 +-
 man/torch_logical_or.Rd           |  7 +++++--
 man/torch_logical_xor.Rd          |  7 +++++--
 man/torch_logsumexp.Rd            |  7 +++++--
 man/torch_lstsq.Rd                |  7 +++++--
 man/torch_lt.Rd                   |  7 +++++--
 man/torch_lu_solve.Rd             |  9 ++++++---
 man/torch_masked_select.Rd        |  7 +++++--
 man/torch_matmul.Rd               |  7 +++++--
 man/torch_matrix_power.Rd         |  7 +++++--
 man/torch_matrix_rank.Rd          |  7 +++++--
 man/torch_max.Rd                  |  2 +-
 man/torch_mean.Rd                 |  7 +++++--
 man/torch_median.Rd               |  7 +++++--
 man/torch_meshgrid.Rd             |  5 ++++-
 man/torch_min.Rd                  |  2 +-
 man/torch_mm.Rd                   |  7 +++++--
 man/torch_mode.Rd                 |  7 +++++--
 man/torch_mul.Rd                  | 13 ++++++++-----
 man/torch_multinomial.Rd          |  7 +++++--
 man/torch_mv.Rd                   |  7 +++++--
 man/torch_mvlgamma.Rd             |  7 +++++--
 man/torch_narrow.Rd               |  7 +++++--
 man/torch_ne.Rd                   |  7 +++++--
 man/torch_neg.Rd                  |  7 +++++--
 man/torch_nonzero.Rd              |  7 +++++--
 man/torch_norm.Rd                 | 11 +++++++----
 man/torch_normal.Rd               |  9 ++++++---
 man/torch_ones.Rd                 | 20 +++++++++++++++-----
 man/torch_ones_like.Rd            | 16 +++++++++++++---
 man/torch_orgqr.Rd                |  9 ++++++---
 man/torch_ormqr.Rd                | 11 +++++++----
 man/torch_pdist.Rd                |  7 +++++--
 man/torch_pinverse.Rd             |  7 +++++--
 man/torch_pixel_shuffle.Rd        |  7 +++++--
 man/torch_poisson.Rd              |  7 +++++--
 man/torch_polygamma.Rd            |  7 +++++--
 man/torch_pow.Rd                  |  9 +++++----
 man/torch_prod.Rd                 | 11 +++++++----
 man/torch_promote_types.Rd        |  5 ++++-
 man/torch_qr.Rd                   |  7 +++++--
 man/torch_quantize_per_channel.Rd |  7 +++++--
 man/torch_quantize_per_tensor.Rd  |  7 +++++--
 man/torch_rand.Rd                 | 20 +++++++++++++++-----
 man/torch_rand_like.Rd            | 26 +++++++++++++++++++++++---
 man/torch_randint.Rd              | 19 ++++++++++++++++---
 man/torch_randint_like.Rd         | 17 ++++++++++++++---
 man/torch_randn.Rd                | 20 +++++++++++++++-----
 man/torch_randn_like.Rd           |  2 +-
 man/torch_randperm.Rd             | 15 ++++++++++++---
 man/torch_range.Rd                | 17 ++++++++++++++---
 man/torch_real.Rd                 |  7 +++++--
 man/torch_reciprocal.Rd           |  7 +++++--
 man/torch_relu_.Rd                |  5 ++++-
 man/torch_remainder.Rd            |  7 +++++--
 man/torch_renorm.Rd               |  7 +++++--
 man/torch_repeat_interleave.Rd    |  7 +++++--
 man/torch_reshape.Rd              |  7 +++++--
 man/torch_result_type.Rd          |  5 ++++-
 man/torch_rfft.Rd                 |  7 +++++--
 man/torch_roll.Rd                 |  7 +++++--
 man/torch_rot90.Rd                |  7 +++++--
 man/torch_round.Rd                |  7 +++++--
 man/torch_rrelu_.Rd               | 11 ++++++++++-
 man/torch_rsqrt.Rd                |  7 +++++--
 man/torch_selu_.Rd                |  5 ++++-
 man/torch_sigmoid.Rd              |  7 +++++--
 man/torch_sign.Rd                 |  7 +++++--
 man/torch_sin.Rd                  |  7 +++++--
 man/torch_sinh.Rd                 |  7 +++++--
 man/torch_slogdet.Rd              |  7 +++++--
 man/torch_solve.Rd                |  7 +++++--
 man/torch_sort.Rd                 |  7 +++++--
 man/torch_sparse_coo_tensor.Rd    |  5 ++++-
 man/torch_split.Rd                |  9 ++++++---
 man/torch_sqrt.Rd                 |  7 +++++--
 man/torch_square.Rd               |  7 +++++--
 man/torch_squeeze.Rd              |  7 +++++--
 man/torch_stack.Rd                |  5 ++++-
 man/torch_std.Rd                  | 11 +++++++----
 man/torch_std_mean.Rd             | 11 +++++++----
 man/torch_stft.Rd                 | 23 +++++++++++++++++------
 man/torch_sum.Rd                  | 11 +++++++----
 man/torch_svd.Rd                  |  7 +++++--
 man/torch_symeig.Rd               |  7 +++++--
 man/torch_t.Rd                    |  7 +++++--
 man/torch_take.Rd                 |  7 +++++--
 man/torch_tan.Rd                  |  7 +++++--
 man/torch_tanh.Rd                 |  7 +++++--
 man/torch_tensordot.Rd            |  5 ++++-
 man/torch_threshold_.Rd           |  5 ++++-
 man/torch_topk.Rd                 |  7 +++++--
 man/torch_trace.Rd                |  5 ++++-
 man/torch_transpose.Rd            |  7 +++++--
 man/torch_trapz.Rd                |  9 ++++++---
 man/torch_triangular_solve.Rd     | 13 +++++++++++--
 man/torch_tril.Rd                 |  7 +++++--
 man/torch_tril_indices.Rd         |  5 ++++-
 man/torch_triu.Rd                 |  7 +++++--
 man/torch_triu_indices.Rd         |  5 ++++-
 man/torch_true_divide.Rd          |  5 ++++-
 man/torch_trunc.Rd                |  7 +++++--
 man/torch_unbind.Rd               |  7 +++++--
 man/torch_unique_consecutive.Rd   | 12 ++++++++++--
 man/torch_unsqueeze.Rd            |  7 +++++--
 man/torch_var.Rd                  | 11 +++++++----
 man/torch_var_mean.Rd             | 11 +++++++----
 man/torch_where.Rd                |  5 ++++-
 man/torch_zeros.Rd                | 20 +++++++++++++++-----
 man/torch_zeros_like.Rd           | 16 +++++++++++++---
 231 files changed, 1431 insertions(+), 501 deletions(-)

diff --git a/man/torch_abs.Rd b/man/torch_abs.Rd
index 576f39c6a..4bb5b383f 100644
--- a/man/torch_abs.Rd
+++ b/man/torch_abs.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_abs}
 \alias{torch_abs}
 \title{Abs}
+\usage{
+torch_abs(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Abs
 }
-\section{abs(input, out=None) -> Tensor }{
+\section{abs(input) -> Tensor }{
 
 
 Computes the element-wise absolute value of the given \code{input} tensor.
diff --git a/man/torch_acos.Rd b/man/torch_acos.Rd
index a35172d9d..8b869192f 100644
--- a/man/torch_acos.Rd
+++ b/man/torch_acos.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_acos}
 \alias{torch_acos}
 \title{Acos}
+\usage{
+torch_acos(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_adaptive_avg_pool1d.Rd b/man/torch_adaptive_avg_pool1d.Rd
index 024801cd4..9d8a7bc08 100644
--- a/man/torch_adaptive_avg_pool1d.Rd
+++ b/man/torch_adaptive_avg_pool1d.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_adaptive_avg_pool1d}
 \alias{torch_adaptive_avg_pool1d}
 \title{Adaptive_avg_pool1d}
+\usage{
+torch_adaptive_avg_pool1d(self, output_size)
+}
 \arguments{
 \item{output_size}{NA the target output size (single integer)}
 }
diff --git a/man/torch_add.Rd b/man/torch_add.Rd
index e28ed4ebe..9e901f64d 100644
--- a/man/torch_add.Rd
+++ b/man/torch_add.Rd
@@ -1,17 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_add}
 \alias{torch_add}
 \title{Add}
+\usage{
+torch_add(self, other, alpha = 1L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{value}{(Number) the number to be added to each element of \code{input}}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the second input tensor}
 
 \item{alpha}{(Number) the scalar multiplier for \code{other}}
+
+\item{value}{(Number) the number to be added to each element of \code{input}}
 }
 \description{
 Add
diff --git a/man/torch_addbmm.Rd b/man/torch_addbmm.Rd
index 8c6ec8fdf..b33e3f147 100644
--- a/man/torch_addbmm.Rd
+++ b/man/torch_addbmm.Rd
@@ -1,18 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_addbmm}
 \alias{torch_addbmm}
 \title{Addbmm}
+\usage{
+torch_addbmm(self, batch1, batch2, beta = 1L, alpha = 1L)
+}
 \arguments{
+\item{self}{(Tensor) matrix to be added}
+
 \item{batch1}{(Tensor) the first batch of matrices to be multiplied}
 
 \item{batch2}{(Tensor) the second batch of matrices to be multiplied}
 
 \item{beta}{(Number, optional) multiplier for \code{input} (\eqn{\beta})}
 
-\item{input}{(Tensor) matrix to be added}
-
 \item{alpha}{(Number, optional) multiplier for \code{batch1 @ batch2} (\eqn{\alpha})}
 
 \item{out}{(Tensor, optional) the output tensor.}
diff --git a/man/torch_addcdiv.Rd b/man/torch_addcdiv.Rd
index b8cc67385..79e31f7a5 100644
--- a/man/torch_addcdiv.Rd
+++ b/man/torch_addcdiv.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_addcdiv}
 \alias{torch_addcdiv}
 \title{Addcdiv}
+\usage{
+torch_addcdiv(self, tensor1, tensor2, value = 1L)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to be added}
+\item{self}{(Tensor) the tensor to be added}
 
 \item{tensor1}{(Tensor) the numerator tensor}
 
diff --git a/man/torch_addcmul.Rd b/man/torch_addcmul.Rd
index 55571d3d1..7661458a9 100644
--- a/man/torch_addcmul.Rd
+++ b/man/torch_addcmul.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_addcmul}
 \alias{torch_addcmul}
 \title{Addcmul}
+\usage{
+torch_addcmul(self, tensor1, tensor2, value = 1L)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to be added}
+\item{self}{(Tensor) the tensor to be added}
 
 \item{tensor1}{(Tensor) the tensor to be multiplied}
 
diff --git a/man/torch_addmm.Rd b/man/torch_addmm.Rd
index ea9b40ff1..f2de8781e 100644
--- a/man/torch_addmm.Rd
+++ b/man/torch_addmm.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_addmm}
 \alias{torch_addmm}
 \title{Addmm}
+\usage{
+torch_addmm(self, mat1, mat2, beta = 1L, alpha = 1L)
+}
 \arguments{
-\item{input}{(Tensor) matrix to be added}
+\item{self}{(Tensor) matrix to be added}
 
 \item{mat1}{(Tensor) the first matrix to be multiplied}
 
diff --git a/man/torch_addmv.Rd b/man/torch_addmv.Rd
index 5b32d1806..08e2bb652 100644
--- a/man/torch_addmv.Rd
+++ b/man/torch_addmv.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_addmv}
 \alias{torch_addmv}
 \title{Addmv}
+\usage{
+torch_addmv(self, mat, vec, beta = 1L, alpha = 1L)
+}
 \arguments{
-\item{input}{(Tensor) vector to be added}
+\item{self}{(Tensor) vector to be added}
 
 \item{mat}{(Tensor) matrix to be multiplied}
 
diff --git a/man/torch_addr.Rd b/man/torch_addr.Rd
index ffb890bbd..5bac2973f 100644
--- a/man/torch_addr.Rd
+++ b/man/torch_addr.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_addr}
 \alias{torch_addr}
 \title{Addr}
+\usage{
+torch_addr(self, vec1, vec2, beta = 1L, alpha = 1L)
+}
 \arguments{
-\item{input}{(Tensor) matrix to be added}
+\item{self}{(Tensor) matrix to be added}
 
 \item{vec1}{(Tensor) the first vector of the outer product}
 
diff --git a/man/torch_allclose.Rd b/man/torch_allclose.Rd
index 66c9ba584..8729cfe59 100644
--- a/man/torch_allclose.Rd
+++ b/man/torch_allclose.Rd
@@ -1,18 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_allclose}
 \alias{torch_allclose}
 \title{Allclose}
+\usage{
+torch_allclose(self, other, rtol = 1e-05, atol = 0, equal_nan = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) first tensor to compare}
+\item{self}{(Tensor) first tensor to compare}
 
 \item{other}{(Tensor) second tensor to compare}
 
-\item{atol}{(float, optional) absolute tolerance. Default: 1e-08}
-
 \item{rtol}{(float, optional) relative tolerance. Default: 1e-05}
 
+\item{atol}{(float, optional) absolute tolerance. Default: 1e-08}
+
 \item{equal_nan}{(bool, optional) if \code{True}, then two \code{NaN} s will be compared as equal. Default: \code{False}}
 }
 \description{
diff --git a/man/torch_angle.Rd b/man/torch_angle.Rd
index f64198baf..67d6fee6f 100644
--- a/man/torch_angle.Rd
+++ b/man/torch_angle.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_angle}
 \alias{torch_angle}
 \title{Angle}
+\usage{
+torch_angle(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_arange.Rd b/man/torch_arange.Rd
index 12d1200c6..b2bfd6440 100644
--- a/man/torch_arange.Rd
+++ b/man/torch_arange.Rd
@@ -1,9 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_arange}
 \alias{torch_arange}
 \title{Arange}
+\usage{
+torch_arange(
+  start,
+  end,
+  step = 1,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{start}{(Number) the starting value for the set of points. Default: \code{0}.}
 
@@ -11,8 +22,6 @@
 
 \item{step}{(Number) the gap between each pair of adjacent points. Default: \code{1}.}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). If \code{dtype} is not given, infer the data type from the other input        arguments. If any of \code{start}, \code{end}, or \code{stop} are floating-point, the        \code{dtype} is inferred to be the default dtype, see        \code{~torch.get_default_dtype}. Otherwise, the \code{dtype} is inferred to        be \code{torch.int64}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -20,6 +29,8 @@
 \item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+
+\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Arange
diff --git a/man/torch_argmax.Rd b/man/torch_argmax.Rd
index b40d5ceb5..0b0aeb022 100644
--- a/man/torch_argmax.Rd
+++ b/man/torch_argmax.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_argmax}
 \alias{torch_argmax}
 \title{Argmax}
+\usage{
+torch_argmax(self, dim = NULL, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to reduce. If \code{None}, the argmax of the flattened input is returned.}
 
diff --git a/man/torch_argmin.Rd b/man/torch_argmin.Rd
index d028f7db4..c0f256ad4 100644
--- a/man/torch_argmin.Rd
+++ b/man/torch_argmin.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_argmin}
 \alias{torch_argmin}
 \title{Argmin}
+\usage{
+torch_argmin(self, dim = NULL, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to reduce. If \code{None}, the argmin of the flattened input is returned.}
 
diff --git a/man/torch_argsort.Rd b/man/torch_argsort.Rd
index 706e61687..a8a3463a1 100644
--- a/man/torch_argsort.Rd
+++ b/man/torch_argsort.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_argsort}
 \alias{torch_argsort}
 \title{Argsort}
+\usage{
+torch_argsort(self, dim = -1L, descending = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int, optional) the dimension to sort along}
 
diff --git a/man/torch_as_strided.Rd b/man/torch_as_strided.Rd
index a07d9ae48..703a5010f 100644
--- a/man/torch_as_strided.Rd
+++ b/man/torch_as_strided.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_as_strided}
 \alias{torch_as_strided}
 \title{As_strided}
+\usage{
+torch_as_strided(self, size, stride, storage_offset = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{size}{(tuple or ints) the shape of the output tensor}
 
diff --git a/man/torch_asin.Rd b/man/torch_asin.Rd
index 844eb97c9..369cd29e4 100644
--- a/man/torch_asin.Rd
+++ b/man/torch_asin.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_asin}
 \alias{torch_asin}
 \title{Asin}
+\usage{
+torch_asin(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_atan.Rd b/man/torch_atan.Rd
index fd065feca..f0d8e376d 100644
--- a/man/torch_atan.Rd
+++ b/man/torch_atan.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_atan}
 \alias{torch_atan}
 \title{Atan}
+\usage{
+torch_atan(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_atan2.Rd b/man/torch_atan2.Rd
index 8cb88e954..439f6dc3c 100644
--- a/man/torch_atan2.Rd
+++ b/man/torch_atan2.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_atan2}
 \alias{torch_atan2}
 \title{Atan2}
+\usage{
+torch_atan2(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the first input tensor}
+\item{self}{(Tensor) the first input tensor}
 
 \item{other}{(Tensor) the second input tensor}
 
diff --git a/man/torch_avg_pool1d.Rd b/man/torch_avg_pool1d.Rd
index bc242b13a..a9f5825d6 100644
--- a/man/torch_avg_pool1d.Rd
+++ b/man/torch_avg_pool1d.Rd
@@ -1,11 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_avg_pool1d}
 \alias{torch_avg_pool1d}
 \title{Avg_pool1d}
+\usage{
+torch_avg_pool1d(
+  self,
+  kernel_size,
+  stride = list(),
+  padding = 0L,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
+\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
 
 \item{kernel_size}{NA the size of the window. Can be a single number or a      tuple \verb{(kW,)}}
 
diff --git a/man/torch_baddbmm.Rd b/man/torch_baddbmm.Rd
index 170192019..446bfb2d6 100644
--- a/man/torch_baddbmm.Rd
+++ b/man/torch_baddbmm.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_baddbmm}
 \alias{torch_baddbmm}
 \title{Baddbmm}
+\usage{
+torch_baddbmm(self, batch1, batch2, beta = 1L, alpha = 1L)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to be added}
+\item{self}{(Tensor) the tensor to be added}
 
 \item{batch1}{(Tensor) the first batch of matrices to be multiplied}
 
diff --git a/man/torch_bartlett_window.Rd b/man/torch_bartlett_window.Rd
index 7e22d7eb8..8f5c8c5ea 100644
--- a/man/torch_bartlett_window.Rd
+++ b/man/torch_bartlett_window.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bartlett_window}
 \alias{torch_bartlett_window}
 \title{Bartlett_window}
+\usage{
+torch_bartlett_window(window_length, periodic, options = list())
+}
 \arguments{
 \item{window_length}{(int) the size of returned window}
 
diff --git a/man/torch_bernoulli.Rd b/man/torch_bernoulli.Rd
index 236cef0ec..3b2d11a1b 100644
--- a/man/torch_bernoulli.Rd
+++ b/man/torch_bernoulli.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bernoulli}
 \alias{torch_bernoulli}
 \title{Bernoulli}
+\usage{
+torch_bernoulli(self, p, generator = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of probability values for the Bernoulli distribution}
+\item{self}{(Tensor) the input tensor of probability values for the Bernoulli distribution}
 
 \item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
 
diff --git a/man/torch_bincount.Rd b/man/torch_bincount.Rd
index d0c7d7002..a08e06828 100644
--- a/man/torch_bincount.Rd
+++ b/man/torch_bincount.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bincount}
 \alias{torch_bincount}
 \title{Bincount}
+\usage{
+torch_bincount(self, weights = list(), minlength = 0L)
+}
 \arguments{
-\item{input}{(Tensor) 1-d int tensor}
+\item{self}{(Tensor) 1-d int tensor}
 
 \item{weights}{(Tensor) optional, weight for each value in the input tensor.        Should be of same size as input tensor.}
 
diff --git a/man/torch_bitwise_and.Rd b/man/torch_bitwise_and.Rd
index 6ad1f5854..e64507777 100644
--- a/man/torch_bitwise_and.Rd
+++ b/man/torch_bitwise_and.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bitwise_and}
 \alias{torch_bitwise_and}
 \title{Bitwise_and}
+\usage{
+torch_bitwise_and(self, other)
+}
 \arguments{
-\item{input}{NA the first input tensor}
+\item{self}{NA the first input tensor}
 
 \item{other}{NA the second input tensor}
 
diff --git a/man/torch_bitwise_not.Rd b/man/torch_bitwise_not.Rd
index 63e275584..a5460d5b9 100644
--- a/man/torch_bitwise_not.Rd
+++ b/man/torch_bitwise_not.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bitwise_not}
 \alias{torch_bitwise_not}
 \title{Bitwise_not}
+\usage{
+torch_bitwise_not(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_bitwise_or.Rd b/man/torch_bitwise_or.Rd
index 08886e511..9dfe9e6ed 100644
--- a/man/torch_bitwise_or.Rd
+++ b/man/torch_bitwise_or.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bitwise_or}
 \alias{torch_bitwise_or}
 \title{Bitwise_or}
+\usage{
+torch_bitwise_or(self, other)
+}
 \arguments{
-\item{input}{NA the first input tensor}
+\item{self}{NA the first input tensor}
 
 \item{other}{NA the second input tensor}
 
diff --git a/man/torch_bitwise_xor.Rd b/man/torch_bitwise_xor.Rd
index 3cb50dc4c..0ce1859d7 100644
--- a/man/torch_bitwise_xor.Rd
+++ b/man/torch_bitwise_xor.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bitwise_xor}
 \alias{torch_bitwise_xor}
 \title{Bitwise_xor}
+\usage{
+torch_bitwise_xor(self, other)
+}
 \arguments{
-\item{input}{NA the first input tensor}
+\item{self}{NA the first input tensor}
 
 \item{other}{NA the second input tensor}
 
diff --git a/man/torch_blackman_window.Rd b/man/torch_blackman_window.Rd
index e06037d77..2b8775610 100644
--- a/man/torch_blackman_window.Rd
+++ b/man/torch_blackman_window.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_blackman_window}
 \alias{torch_blackman_window}
 \title{Blackman_window}
+\usage{
+torch_blackman_window(window_length, periodic, options = list())
+}
 \arguments{
 \item{window_length}{(int) the size of returned window}
 
diff --git a/man/torch_bmm.Rd b/man/torch_bmm.Rd
index 2818210ad..27e57e0e2 100644
--- a/man/torch_bmm.Rd
+++ b/man/torch_bmm.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bmm}
 \alias{torch_bmm}
 \title{Bmm}
+\usage{
+torch_bmm(self, mat2)
+}
 \arguments{
-\item{input}{(Tensor) the first batch of matrices to be multiplied}
+\item{self}{(Tensor) the first batch of matrices to be multiplied}
 
 \item{mat2}{(Tensor) the second batch of matrices to be multiplied}
 
diff --git a/man/torch_broadcast_tensors.Rd b/man/torch_broadcast_tensors.Rd
index 1b2184e7a..5ba547b18 100644
--- a/man/torch_broadcast_tensors.Rd
+++ b/man/torch_broadcast_tensors.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_broadcast_tensors}
 \alias{torch_broadcast_tensors}
 \title{Broadcast_tensors}
+\usage{
+torch_broadcast_tensors(tensors)
+}
 \arguments{
 \item{*tensors}{NA any number of tensors of the same type}
 }
diff --git a/man/torch_can_cast.Rd b/man/torch_can_cast.Rd
index b6918f152..deb6e08a6 100644
--- a/man/torch_can_cast.Rd
+++ b/man/torch_can_cast.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_can_cast}
 \alias{torch_can_cast}
 \title{Can_cast}
+\usage{
+torch_can_cast(from, to)
+}
 \arguments{
 \item{from}{(dtype) The original \code{torch_dtype}.}
 
diff --git a/man/torch_cartesian_prod.Rd b/man/torch_cartesian_prod.Rd
index 0a8f7278a..1f52ca165 100644
--- a/man/torch_cartesian_prod.Rd
+++ b/man/torch_cartesian_prod.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cartesian_prod}
 \alias{torch_cartesian_prod}
 \title{Cartesian_prod}
+\usage{
+torch_cartesian_prod(tensors)
+}
 \arguments{
 \item{*tensors}{NA any number of 1 dimensional tensors.}
 }
diff --git a/man/torch_cat.Rd b/man/torch_cat.Rd
index aa135d3cf..0890fcb13 100644
--- a/man/torch_cat.Rd
+++ b/man/torch_cat.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cat}
 \alias{torch_cat}
 \title{Cat}
+\usage{
+torch_cat(tensors, dim = 1L)
+}
 \arguments{
 \item{tensors}{(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.}
 
diff --git a/man/torch_cdist.Rd b/man/torch_cdist.Rd
index a27c82a14..c825fe6e2 100644
--- a/man/torch_cdist.Rd
+++ b/man/torch_cdist.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cdist}
 \alias{torch_cdist}
 \title{Cdist}
+\usage{
+torch_cdist(x1, x2, p = 2L, compute_mode = NULL)
+}
 \arguments{
 \item{x1}{(Tensor) input tensor of shape \eqn{B \times P \times M}.}
 
diff --git a/man/torch_ceil.Rd b/man/torch_ceil.Rd
index 76b664b69..e93ca2704 100644
--- a/man/torch_ceil.Rd
+++ b/man/torch_ceil.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_ceil}
 \alias{torch_ceil}
 \title{Ceil}
+\usage{
+torch_ceil(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_celu_.Rd b/man/torch_celu_.Rd
index 2fc6f929d..ed1372944 100644
--- a/man/torch_celu_.Rd
+++ b/man/torch_celu_.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_celu_}
 \alias{torch_celu_}
 \title{Celu_}
+\usage{
+torch_celu_(self, alpha = 1)
+}
 \description{
 Celu_
 }
diff --git a/man/torch_chain_matmul.Rd b/man/torch_chain_matmul.Rd
index 408cd87fb..55e57f074 100644
--- a/man/torch_chain_matmul.Rd
+++ b/man/torch_chain_matmul.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_chain_matmul}
 \alias{torch_chain_matmul}
 \title{Chain_matmul}
+\usage{
+torch_chain_matmul(matrices)
+}
 \arguments{
 \item{matrices}{(Tensors...) a sequence of 2 or more 2-D tensors whose product is to be determined.}
 }
diff --git a/man/torch_cholesky.Rd b/man/torch_cholesky.Rd
index 6e7246df4..1e5f1d15c 100644
--- a/man/torch_cholesky.Rd
+++ b/man/torch_cholesky.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cholesky}
 \alias{torch_cholesky}
 \title{Cholesky}
+\usage{
+torch_cholesky(self, upper = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where \code{*} is zero or more                batch dimensions consisting of symmetric positive-definite matrices.}
+\item{self}{(Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where \code{*} is zero or more                batch dimensions consisting of symmetric positive-definite matrices.}
 
 \item{upper}{(bool, optional) flag that indicates whether to return a                            upper or lower triangular matrix. Default: \code{False}}
 
diff --git a/man/torch_cholesky_inverse.Rd b/man/torch_cholesky_inverse.Rd
index 4c62050bc..f2ae5fb48 100644
--- a/man/torch_cholesky_inverse.Rd
+++ b/man/torch_cholesky_inverse.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cholesky_inverse}
 \alias{torch_cholesky_inverse}
 \title{Cholesky_inverse}
+\usage{
+torch_cholesky_inverse(self, upper = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input 2-D tensor \eqn{u}, a upper or lower triangular           Cholesky factor}
+\item{self}{(Tensor) the input 2-D tensor \eqn{u}, a upper or lower triangular           Cholesky factor}
 
 \item{upper}{(bool, optional) whether to return a lower (default) or upper triangular matrix}
 
diff --git a/man/torch_cholesky_solve.Rd b/man/torch_cholesky_solve.Rd
index 71c4ee6e9..91424f0fb 100644
--- a/man/torch_cholesky_solve.Rd
+++ b/man/torch_cholesky_solve.Rd
@@ -1,16 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cholesky_solve}
 \alias{torch_cholesky_solve}
 \title{Cholesky_solve}
+\usage{
+torch_cholesky_solve(self, input2, upper = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) input matrix \eqn{b} of size \eqn{(*, m, k)},                where \eqn{*} is zero or more batch dimensions}
-
-\item{input2}{(Tensor) input matrix \eqn{u} of size \eqn{(*, m, m)},                where \eqn{*} is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor}
+\item{self}{(Tensor) input matrix \eqn{b} of size \eqn{(*, m, k)},                where \eqn{*} is zero or more batch dimensions}
 
 \item{upper}{(bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: \code{False}.}
 
+\item{self2}{(Tensor) input matrix \eqn{u} of size \eqn{(*, m, m)},                where \eqn{*} is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor}
+
 \item{out}{(Tensor, optional) the output tensor for \code{c}}
 }
 \description{
diff --git a/man/torch_chunk.Rd b/man/torch_chunk.Rd
index 7776f4cd7..7950c02ae 100644
--- a/man/torch_chunk.Rd
+++ b/man/torch_chunk.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_chunk}
 \alias{torch_chunk}
 \title{Chunk}
+\usage{
+torch_chunk(self, chunks, dim = 1L)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to split}
+\item{self}{(Tensor) the tensor to split}
 
 \item{chunks}{(int) number of chunks to return}
 
diff --git a/man/torch_clamp.Rd b/man/torch_clamp.Rd
index 52796d108..34bcc1fe7 100644
--- a/man/torch_clamp.Rd
+++ b/man/torch_clamp.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_clamp}
 \alias{torch_clamp}
 \title{Clamp}
+\usage{
+torch_clamp(self, min = NULL, max = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{min}{(Number) lower-bound of the range to be clamped to}
 
diff --git a/man/torch_combinations.Rd b/man/torch_combinations.Rd
index da508224f..0eec951c7 100644
--- a/man/torch_combinations.Rd
+++ b/man/torch_combinations.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_combinations}
 \alias{torch_combinations}
 \title{Combinations}
+\usage{
+torch_combinations(self, r = 2L, with_replacement = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) 1D vector.}
+\item{self}{(Tensor) 1D vector.}
 
 \item{r}{(int, optional) number of elements to combine}
 
diff --git a/man/torch_conj.Rd b/man/torch_conj.Rd
index f385d54bb..c0643b3fb 100644
--- a/man/torch_conj.Rd
+++ b/man/torch_conj.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conj}
 \alias{torch_conj}
 \title{Conj}
+\usage{
+torch_conj(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_conv1d.Rd b/man/torch_conv1d.Rd
index cdfa36eb3..d43320c45 100644
--- a/man/torch_conv1d.Rd
+++ b/man/torch_conv1d.Rd
@@ -1,12 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv1d}
 \alias{torch_conv1d}
 \title{Conv1d}
+\usage{
+torch_conv1d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  dilation = 1L,
+  groups = 1L
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
-
 \item{weight}{NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)}}
 
 \item{bias}{NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: \code{None}}
@@ -18,6 +27,8 @@
 \item{dilation}{NA the spacing between kernel elements. Can be a single number or      a one-element tuple \verb{(dW,)}. Default: 1}
 
 \item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1}
+
+\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
 }
 \description{
 Conv1d
diff --git a/man/torch_conv2d.Rd b/man/torch_conv2d.Rd
index 185226a64..e90d24bf6 100644
--- a/man/torch_conv2d.Rd
+++ b/man/torch_conv2d.Rd
@@ -1,12 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv2d}
 \alias{torch_conv2d}
 \title{Conv2d}
+\usage{
+torch_conv2d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  dilation = 1L,
+  groups = 1L
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}}
-
 \item{weight}{NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)}}
 
 \item{bias}{NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: \code{None}}
@@ -18,6 +27,8 @@
 \item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dH, dW)}. Default: 1}
 
 \item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
+
+\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}}
 }
 \description{
 Conv2d
diff --git a/man/torch_conv3d.Rd b/man/torch_conv3d.Rd
index 5b544a164..a64d709c2 100644
--- a/man/torch_conv3d.Rd
+++ b/man/torch_conv3d.Rd
@@ -1,12 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv3d}
 \alias{torch_conv3d}
 \title{Conv3d}
+\usage{
+torch_conv3d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  dilation = 1L,
+  groups = 1L
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}}
-
 \item{weight}{NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)}}
 
 \item{bias}{NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: None}
@@ -18,6 +27,8 @@
 \item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dT, dH, dW)}. Default: 1}
 
 \item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1}
+
+\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}}
 }
 \description{
 Conv3d
diff --git a/man/torch_conv_tbc.Rd b/man/torch_conv_tbc.Rd
index 2508aa621..b4eda21f2 100644
--- a/man/torch_conv_tbc.Rd
+++ b/man/torch_conv_tbc.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv_tbc}
 \alias{torch_conv_tbc}
 \title{Conv_tbc}
+\usage{
+torch_conv_tbc(self, weight, bias, pad = 0L)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{sequence length} \times batch \times \mbox{in\_channels})}}
+\item{self}{NA input tensor of shape \eqn{(\mbox{sequence length} \times batch \times \mbox{in\_channels})}}
 
 \item{weight}{NA filter of shape (\eqn{\mbox{kernel width} \times \mbox{in\_channels} \times \mbox{out\_channels}})}
 
diff --git a/man/torch_conv_transpose1d.Rd b/man/torch_conv_transpose1d.Rd
index 9b7ff3cf2..edff98833 100644
--- a/man/torch_conv_transpose1d.Rd
+++ b/man/torch_conv_transpose1d.Rd
@@ -1,12 +1,22 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv_transpose1d}
 \alias{torch_conv_transpose1d}
 \title{Conv_transpose1d}
+\usage{
+torch_conv_transpose1d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  output_padding = 0L,
+  groups = 1L,
+  dilation = 1L
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
-
 \item{weight}{NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)}}
 
 \item{bias}{NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None}
@@ -20,6 +30,8 @@
 \item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
 
 \item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dW,)}. Default: 1}
+
+\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
 }
 \description{
 Conv_transpose1d
diff --git a/man/torch_conv_transpose2d.Rd b/man/torch_conv_transpose2d.Rd
index 8a91d86ce..26f5baf2c 100644
--- a/man/torch_conv_transpose2d.Rd
+++ b/man/torch_conv_transpose2d.Rd
@@ -1,12 +1,22 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv_transpose2d}
 \alias{torch_conv_transpose2d}
 \title{Conv_transpose2d}
+\usage{
+torch_conv_transpose2d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  output_padding = 0L,
+  groups = 1L,
+  dilation = 1L
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}}
-
 \item{weight}{NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)}}
 
 \item{bias}{NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None}
@@ -20,6 +30,8 @@
 \item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
 
 \item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dH, dW)}. Default: 1}
+
+\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}}
 }
 \description{
 Conv_transpose2d
diff --git a/man/torch_conv_transpose3d.Rd b/man/torch_conv_transpose3d.Rd
index c5c2ad239..c9374d9a3 100644
--- a/man/torch_conv_transpose3d.Rd
+++ b/man/torch_conv_transpose3d.Rd
@@ -1,12 +1,22 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv_transpose3d}
 \alias{torch_conv_transpose3d}
 \title{Conv_transpose3d}
+\usage{
+torch_conv_transpose3d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  output_padding = 0L,
+  groups = 1L,
+  dilation = 1L
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}}
-
 \item{weight}{NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)}}
 
 \item{bias}{NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None}
@@ -20,6 +30,8 @@
 \item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
 
 \item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dT, dH, dW)}. Default: 1}
+
+\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}}
 }
 \description{
 Conv_transpose3d
diff --git a/man/torch_cos.Rd b/man/torch_cos.Rd
index 2381bcff7..5e3709d64 100644
--- a/man/torch_cos.Rd
+++ b/man/torch_cos.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cos}
 \alias{torch_cos}
 \title{Cos}
+\usage{
+torch_cos(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_cosh.Rd b/man/torch_cosh.Rd
index 285862ba3..10f19bf1f 100644
--- a/man/torch_cosh.Rd
+++ b/man/torch_cosh.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cosh}
 \alias{torch_cosh}
 \title{Cosh}
+\usage{
+torch_cosh(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_cosine_similarity.Rd b/man/torch_cosine_similarity.Rd
index b88054d82..60b7b421e 100644
--- a/man/torch_cosine_similarity.Rd
+++ b/man/torch_cosine_similarity.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cosine_similarity}
 \alias{torch_cosine_similarity}
 \title{Cosine_similarity}
+\usage{
+torch_cosine_similarity(x1, x2, dim = 2L, eps = 0)
+}
 \arguments{
 \item{x1}{(Tensor) First input.}
 
diff --git a/man/torch_cross.Rd b/man/torch_cross.Rd
index b860e4bf9..1039bd242 100644
--- a/man/torch_cross.Rd
+++ b/man/torch_cross.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cross}
 \alias{torch_cross}
 \title{Cross}
+\usage{
+torch_cross(self, other, dim = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the second input tensor}
 
diff --git a/man/torch_cummax.Rd b/man/torch_cummax.Rd
index a8a8e3f7f..28afe1459 100644
--- a/man/torch_cummax.Rd
+++ b/man/torch_cummax.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cummax}
 \alias{torch_cummax}
 \title{Cummax}
+\usage{
+torch_cummax(self, dim)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to do the operation over}
 
diff --git a/man/torch_cummin.Rd b/man/torch_cummin.Rd
index 81679be46..c1f1b6c2c 100644
--- a/man/torch_cummin.Rd
+++ b/man/torch_cummin.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cummin}
 \alias{torch_cummin}
 \title{Cummin}
+\usage{
+torch_cummin(self, dim)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to do the operation over}
 
diff --git a/man/torch_cumprod.Rd b/man/torch_cumprod.Rd
index 7b414808b..6ec0ad78b 100644
--- a/man/torch_cumprod.Rd
+++ b/man/torch_cumprod.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cumprod}
 \alias{torch_cumprod}
 \title{Cumprod}
+\usage{
+torch_cumprod(self, dim, dtype = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to do the operation over}
 
diff --git a/man/torch_cumsum.Rd b/man/torch_cumsum.Rd
index 8ea1d01a1..9169cacd8 100644
--- a/man/torch_cumsum.Rd
+++ b/man/torch_cumsum.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cumsum}
 \alias{torch_cumsum}
 \title{Cumsum}
+\usage{
+torch_cumsum(self, dim, dtype = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to do the operation over}
 
diff --git a/man/torch_det.Rd b/man/torch_det.Rd
index 4030c7fd9..e248a5846 100644
--- a/man/torch_det.Rd
+++ b/man/torch_det.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_det}
 \alias{torch_det}
 \title{Det}
+\usage{
+torch_det(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \verb{(*, n, n)} where \code{*} is zero or more                batch dimensions.}
+\item{self}{(Tensor) the input tensor of size \verb{(*, n, n)} where \code{*} is zero or more                batch dimensions.}
 }
 \description{
 Det
diff --git a/man/torch_diag.Rd b/man/torch_diag.Rd
index 0f5640c94..dc3efb1f4 100644
--- a/man/torch_diag.Rd
+++ b/man/torch_diag.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_diag}
 \alias{torch_diag}
 \title{Diag}
+\usage{
+torch_diag(self, diagonal = 0L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{diagonal}{(int, optional) the diagonal to consider}
 
diff --git a/man/torch_diag_embed.Rd b/man/torch_diag_embed.Rd
index 00fbde12e..1a2eb842d 100644
--- a/man/torch_diag_embed.Rd
+++ b/man/torch_diag_embed.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_diag_embed}
 \alias{torch_diag_embed}
 \title{Diag_embed}
+\usage{
+torch_diag_embed(self, offset = 0L, dim1 = -2L, dim2 = -1L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor. Must be at least 1-dimensional.}
+\item{self}{(Tensor) the input tensor. Must be at least 1-dimensional.}
 
 \item{offset}{(int, optional) which diagonal to consider. Default: 0        (main diagonal).}
 
diff --git a/man/torch_diagflat.Rd b/man/torch_diagflat.Rd
index c4d1b7079..b38a3bf70 100644
--- a/man/torch_diagflat.Rd
+++ b/man/torch_diagflat.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_diagflat}
 \alias{torch_diagflat}
 \title{Diagflat}
+\usage{
+torch_diagflat(self, offset = 0L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{offset}{(int, optional) the diagonal to consider. Default: 0 (main        diagonal).}
 }
diff --git a/man/torch_diagonal.Rd b/man/torch_diagonal.Rd
index 480ca6dda..42b3d6377 100644
--- a/man/torch_diagonal.Rd
+++ b/man/torch_diagonal.Rd
@@ -1,17 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_diagonal}
 \alias{torch_diagonal}
 \title{Diagonal}
+\usage{
+torch_diagonal(self, outdim, dim1 = 1L, dim2 = 2L, offset = 0L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor. Must be at least 2-dimensional.}
-
-\item{offset}{(int, optional) which diagonal to consider. Default: 0        (main diagonal).}
+\item{self}{(Tensor) the input tensor. Must be at least 2-dimensional.}
 
 \item{dim1}{(int, optional) first dimension with respect to which to        take diagonal. Default: 0.}
 
 \item{dim2}{(int, optional) second dimension with respect to which to        take diagonal. Default: 1.}
+
+\item{offset}{(int, optional) which diagonal to consider. Default: 0        (main diagonal).}
 }
 \description{
 Diagonal
diff --git a/man/torch_digamma.Rd b/man/torch_digamma.Rd
index 315c1916c..9751ab73e 100644
--- a/man/torch_digamma.Rd
+++ b/man/torch_digamma.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_digamma}
 \alias{torch_digamma}
 \title{Digamma}
+\usage{
+torch_digamma(self)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compute the digamma function on}
+\item{self}{(Tensor) the tensor to compute the digamma function on}
 }
 \description{
 Digamma
diff --git a/man/torch_dist.Rd b/man/torch_dist.Rd
index 457463436..cb2bd173d 100644
--- a/man/torch_dist.Rd
+++ b/man/torch_dist.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_dist}
 \alias{torch_dist}
 \title{Dist}
+\usage{
+torch_dist(self, other, p = 2L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the Right-hand-side input tensor}
 
diff --git a/man/torch_div.Rd b/man/torch_div.Rd
index c0d3e8f03..7e4e61a7e 100644
--- a/man/torch_div.Rd
+++ b/man/torch_div.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_div}
 \alias{torch_div}
 \title{Div}
+\usage{
+torch_div(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Number) the number to be divided to each element of \code{input}}
 }
diff --git a/man/torch_dot.Rd b/man/torch_dot.Rd
index 26435dd78..f0c71ccb0 100644
--- a/man/torch_dot.Rd
+++ b/man/torch_dot.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_dot}
 \alias{torch_dot}
 \title{Dot}
+\usage{
+torch_dot(self, tensor)
+}
 \description{
 Dot
 }
diff --git a/man/torch_eig.Rd b/man/torch_eig.Rd
index abd282e4a..02ec7402c 100644
--- a/man/torch_eig.Rd
+++ b/man/torch_eig.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_eig}
 \alias{torch_eig}
 \title{Eig}
+\usage{
+torch_eig(self, eigenvectors = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the square matrix of shape \eqn{(n \times n)} for which the eigenvalues and eigenvectors        will be computed}
+\item{self}{(Tensor) the square matrix of shape \eqn{(n \times n)} for which the eigenvalues and eigenvectors        will be computed}
 
 \item{eigenvectors}{(bool) \code{True} to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed}
 
diff --git a/man/torch_einsum.Rd b/man/torch_einsum.Rd
index c01dcc553..ecf334d09 100644
--- a/man/torch_einsum.Rd
+++ b/man/torch_einsum.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_einsum}
 \alias{torch_einsum}
 \title{Einsum}
+\usage{
+torch_einsum(equation, tensors)
+}
 \arguments{
 \item{equation}{(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 \verb{->} and gives the indices for the output.           If the \verb{->} 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{...} represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.}
 
diff --git a/man/torch_empty.Rd b/man/torch_empty.Rd
index 2f59b2426..cbe9877d8 100644
--- a/man/torch_empty.Rd
+++ b/man/torch_empty.Rd
@@ -1,14 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_empty}
 \alias{torch_empty}
 \title{Empty}
+\usage{
+torch_empty(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
-\item{size}{(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.}
-
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -17,6 +23,10 @@
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
 
+\item{size}{(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.}
+
+\item{out}{(Tensor, optional) the output tensor.}
+
 \item{pin_memory}{(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: \code{False}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_contiguous_format}.}
diff --git a/man/torch_empty_like.Rd b/man/torch_empty_like.Rd
index d6d6aefce..325c6436c 100644
--- a/man/torch_empty_like.Rd
+++ b/man/torch_empty_like.Rd
@@ -1,12 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_empty_like}
 \alias{torch_empty_like}
 \title{Empty_like}
+\usage{
+torch_empty_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
-\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
@@ -16,6 +24,8 @@
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
+
+\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 }
 \description{
 Empty_like
diff --git a/man/torch_empty_strided.Rd b/man/torch_empty_strided.Rd
index 67240ac86..f4ab25603 100644
--- a/man/torch_empty_strided.Rd
+++ b/man/torch_empty_strided.Rd
@@ -1,9 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_empty_strided}
 \alias{torch_empty_strided}
 \title{Empty_strided}
+\usage{
+torch_empty_strided(
+  size,
+  stride,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  pin_memory = FALSE
+)
+}
 \arguments{
 \item{size}{(tuple of ints) the shape of the output tensor}
 
diff --git a/man/torch_eq.Rd b/man/torch_eq.Rd
index e13863200..9b5047cdd 100644
--- a/man/torch_eq.Rd
+++ b/man/torch_eq.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_eq}
 \alias{torch_eq}
 \title{Eq}
+\usage{
+torch_eq(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compare}
+\item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
 
diff --git a/man/torch_equal.Rd b/man/torch_equal.Rd
index 6830abb8b..a3283f218 100644
--- a/man/torch_equal.Rd
+++ b/man/torch_equal.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_equal}
 \alias{torch_equal}
 \title{Equal}
+\usage{
+torch_equal(self, other)
+}
 \description{
 Equal
 }
diff --git a/man/torch_erf.Rd b/man/torch_erf.Rd
index d18ec53a5..6ecef8f56 100644
--- a/man/torch_erf.Rd
+++ b/man/torch_erf.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_erf}
 \alias{torch_erf}
 \title{Erf}
+\usage{
+torch_erf(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_erfc.Rd b/man/torch_erfc.Rd
index f58465cac..51f1932af 100644
--- a/man/torch_erfc.Rd
+++ b/man/torch_erfc.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_erfc}
 \alias{torch_erfc}
 \title{Erfc}
+\usage{
+torch_erfc(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_erfinv.Rd b/man/torch_erfinv.Rd
index 1e36f2799..db8483573 100644
--- a/man/torch_erfinv.Rd
+++ b/man/torch_erfinv.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_erfinv}
 \alias{torch_erfinv}
 \title{Erfinv}
+\usage{
+torch_erfinv(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_exp.Rd b/man/torch_exp.Rd
index 5942c916c..9162359a4 100644
--- a/man/torch_exp.Rd
+++ b/man/torch_exp.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_exp}
 \alias{torch_exp}
 \title{Exp}
+\usage{
+torch_exp(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_expm1.Rd b/man/torch_expm1.Rd
index cb06a0ac0..e75ab1c0a 100644
--- a/man/torch_expm1.Rd
+++ b/man/torch_expm1.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_expm1}
 \alias{torch_expm1}
 \title{Expm1}
+\usage{
+torch_expm1(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_eye.Rd b/man/torch_eye.Rd
index 0533d3e40..d6a744bf4 100644
--- a/man/torch_eye.Rd
+++ b/man/torch_eye.Rd
@@ -1,16 +1,24 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_eye}
 \alias{torch_eye}
 \title{Eye}
+\usage{
+torch_eye(
+  n,
+  m = n,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{n}{(int) the number of rows}
 
 \item{m}{(int, optional) the number of columns with default being \code{n}}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -18,6 +26,8 @@
 \item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+
+\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Eye
diff --git a/man/torch_fft.Rd b/man/torch_fft.Rd
index a94c0dbb5..2a70bbf78 100644
--- a/man/torch_fft.Rd
+++ b/man/torch_fft.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_fft}
 \alias{torch_fft}
 \title{Fft}
+\usage{
+torch_fft(self, signal_ndim, normalized = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of at least \code{signal_ndim} \code{+ 1}        dimensions}
+\item{self}{(Tensor) the input tensor of at least \code{signal_ndim} \code{+ 1}        dimensions}
 
 \item{signal_ndim}{(int) the number of dimensions in each signal.        \code{signal_ndim} can only be 1, 2 or 3}
 
diff --git a/man/torch_flatten.Rd b/man/torch_flatten.Rd
index fc2b8fee4..429e0fe83 100644
--- a/man/torch_flatten.Rd
+++ b/man/torch_flatten.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_flatten}
 \alias{torch_flatten}
 \title{Flatten}
+\usage{
+torch_flatten(self, dims, start_dim = 1L, end_dim = -1L, out_dim)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{start_dim}{(int) the first dim to flatten}
 
diff --git a/man/torch_flip.Rd b/man/torch_flip.Rd
index d9d104c7b..c478daf48 100644
--- a/man/torch_flip.Rd
+++ b/man/torch_flip.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_flip}
 \alias{torch_flip}
 \title{Flip}
+\usage{
+torch_flip(self, dims)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dims}{(a list or tuple) axis to flip on}
 }
diff --git a/man/torch_floor.Rd b/man/torch_floor.Rd
index fc743a901..18366318e 100644
--- a/man/torch_floor.Rd
+++ b/man/torch_floor.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_floor}
 \alias{torch_floor}
 \title{Floor}
+\usage{
+torch_floor(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_floor_divide.Rd b/man/torch_floor_divide.Rd
index 9d4cae433..86ec7abcd 100644
--- a/man/torch_floor_divide.Rd
+++ b/man/torch_floor_divide.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_floor_divide}
 \alias{torch_floor_divide}
 \title{Floor_divide}
+\usage{
+torch_floor_divide(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the numerator tensor}
+\item{self}{(Tensor) the numerator tensor}
 
 \item{other}{(Tensor or Scalar) the denominator}
 }
diff --git a/man/torch_fmod.Rd b/man/torch_fmod.Rd
index 337e60708..21335a1b3 100644
--- a/man/torch_fmod.Rd
+++ b/man/torch_fmod.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_fmod}
 \alias{torch_fmod}
 \title{Fmod}
+\usage{
+torch_fmod(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the dividend}
+\item{self}{(Tensor) the dividend}
 
 \item{other}{(Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend}
 
diff --git a/man/torch_frac.Rd b/man/torch_frac.Rd
index b7a468f39..5327ea1d0 100644
--- a/man/torch_frac.Rd
+++ b/man/torch_frac.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_frac}
 \alias{torch_frac}
 \title{Frac}
+\usage{
+torch_frac(self)
+}
 \description{
 Frac
 }
diff --git a/man/torch_full.Rd b/man/torch_full.Rd
index 6e9ff662c..d3b5ffeaf 100644
--- a/man/torch_full.Rd
+++ b/man/torch_full.Rd
@@ -1,16 +1,25 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_full}
 \alias{torch_full}
 \title{Full}
+\usage{
+torch_full(
+  size,
+  fill_value,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{size}{(int...) a list, tuple, or \code{torch_Size} of integers defining the        shape of the output tensor.}
 
 \item{fill_value}{NA the number to fill the output tensor with.}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -18,6 +27,8 @@
 \item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+
+\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Full
diff --git a/man/torch_full_like.Rd b/man/torch_full_like.Rd
index 625370ec4..5471e913b 100644
--- a/man/torch_full_like.Rd
+++ b/man/torch_full_like.Rd
@@ -1,12 +1,21 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_full_like}
 \alias{torch_full_like}
 \title{Full_like}
+\usage{
+torch_full_like(
+  input,
+  fill_value,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
-\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
-
 \item{fill_value}{NA the number to fill the output tensor with.}
 
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
@@ -18,6 +27,8 @@
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
+
+\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 }
 \description{
 Full_like
diff --git a/man/torch_gather.Rd b/man/torch_gather.Rd
index 2e6c7ed5b..7f0354cc4 100644
--- a/man/torch_gather.Rd
+++ b/man/torch_gather.Rd
@@ -1,19 +1,22 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_gather}
 \alias{torch_gather}
 \title{Gather}
+\usage{
+torch_gather(self, dim, index, sparse_grad = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the source tensor}
+\item{self}{(Tensor) the source tensor}
 
 \item{dim}{(int) the axis along which to index}
 
 \item{index}{(LongTensor) the indices of elements to gather}
 
-\item{out}{(Tensor, optional) the destination tensor}
-
 \item{sparse_grad}{(bool,optional) If \code{True}, gradient w.r.t. \code{input} will be a sparse tensor.}
+
+\item{out}{(Tensor, optional) the destination tensor}
 }
 \description{
 Gather
diff --git a/man/torch_ge.Rd b/man/torch_ge.Rd
index f4c332397..669d0101d 100644
--- a/man/torch_ge.Rd
+++ b/man/torch_ge.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_ge}
 \alias{torch_ge}
 \title{Ge}
+\usage{
+torch_ge(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compare}
+\item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
 
diff --git a/man/torch_geqrf.Rd b/man/torch_geqrf.Rd
index 997e72843..bc70bcb9e 100644
--- a/man/torch_geqrf.Rd
+++ b/man/torch_geqrf.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_geqrf}
 \alias{torch_geqrf}
 \title{Geqrf}
+\usage{
+torch_geqrf(self)
+}
 \arguments{
-\item{input}{(Tensor) the input matrix}
+\item{self}{(Tensor) the input matrix}
 
 \item{out}{(tuple, optional) the output tuple of (Tensor, Tensor)}
 }
diff --git a/man/torch_ger.Rd b/man/torch_ger.Rd
index 8640dbe20..9f476fe58 100644
--- a/man/torch_ger.Rd
+++ b/man/torch_ger.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_ger}
 \alias{torch_ger}
 \title{Ger}
+\usage{
+torch_ger(self, vec2)
+}
 \arguments{
-\item{input}{(Tensor) 1-D input vector}
+\item{self}{(Tensor) 1-D input vector}
 
 \item{vec2}{(Tensor) 1-D input vector}
 
diff --git a/man/torch_gt.Rd b/man/torch_gt.Rd
index 9cc4dad89..ba8e988ea 100644
--- a/man/torch_gt.Rd
+++ b/man/torch_gt.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_gt}
 \alias{torch_gt}
 \title{Gt}
+\usage{
+torch_gt(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compare}
+\item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
 
diff --git a/man/torch_hamming_window.Rd b/man/torch_hamming_window.Rd
index 0225361a9..b2c7bc3e8 100644
--- a/man/torch_hamming_window.Rd
+++ b/man/torch_hamming_window.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_hamming_window}
 \alias{torch_hamming_window}
 \title{Hamming_window}
+\usage{
+torch_hamming_window(window_length, periodic, alpha, beta, options = list())
+}
 \arguments{
 \item{window_length}{(int) the size of returned window}
 
diff --git a/man/torch_hann_window.Rd b/man/torch_hann_window.Rd
index a92e60667..29560f3ae 100644
--- a/man/torch_hann_window.Rd
+++ b/man/torch_hann_window.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_hann_window}
 \alias{torch_hann_window}
 \title{Hann_window}
+\usage{
+torch_hann_window(window_length, periodic, options = list())
+}
 \arguments{
 \item{window_length}{(int) the size of returned window}
 
diff --git a/man/torch_histc.Rd b/man/torch_histc.Rd
index 9e1ab1830..8892ea0e1 100644
--- a/man/torch_histc.Rd
+++ b/man/torch_histc.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_histc}
 \alias{torch_histc}
 \title{Histc}
+\usage{
+torch_histc(self, bins = 100L, min = 0L, max = 0L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{bins}{(int) number of histogram bins}
 
diff --git a/man/torch_ifft.Rd b/man/torch_ifft.Rd
index e157efab1..20b549d80 100644
--- a/man/torch_ifft.Rd
+++ b/man/torch_ifft.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_ifft}
 \alias{torch_ifft}
 \title{Ifft}
+\usage{
+torch_ifft(self, signal_ndim, normalized = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of at least \code{signal_ndim} \code{+ 1}        dimensions}
+\item{self}{(Tensor) the input tensor of at least \code{signal_ndim} \code{+ 1}        dimensions}
 
 \item{signal_ndim}{(int) the number of dimensions in each signal.        \code{signal_ndim} can only be 1, 2 or 3}
 
diff --git a/man/torch_imag.Rd b/man/torch_imag.Rd
index 39a396f71..f38ef5168 100644
--- a/man/torch_imag.Rd
+++ b/man/torch_imag.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_imag}
 \alias{torch_imag}
 \title{Imag}
+\usage{
+torch_imag(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_index_select.Rd b/man/torch_index_select.Rd
index 942c3bd15..bfe819f3c 100644
--- a/man/torch_index_select.Rd
+++ b/man/torch_index_select.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_index_select}
 \alias{torch_index_select}
 \title{Index_select}
+\usage{
+torch_index_select(self, dim, index)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension in which we index}
 
diff --git a/man/torch_inverse.Rd b/man/torch_inverse.Rd
index 19fac6cb3..80e27b6a7 100644
--- a/man/torch_inverse.Rd
+++ b/man/torch_inverse.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_inverse}
 \alias{torch_inverse}
 \title{Inverse}
+\usage{
+torch_inverse(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \eqn{(*, n, n)} where \code{*} is zero or more                    batch dimensions}
+\item{self}{(Tensor) the input tensor of size \eqn{(*, n, n)} where \code{*} is zero or more                    batch dimensions}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_irfft.Rd b/man/torch_irfft.Rd
index 83473fb0b..a4e0db908 100644
--- a/man/torch_irfft.Rd
+++ b/man/torch_irfft.Rd
@@ -1,11 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_irfft}
 \alias{torch_irfft}
 \title{Irfft}
+\usage{
+torch_irfft(
+  self,
+  signal_ndim,
+  normalized = FALSE,
+  onesided = TRUE,
+  signal_sizes = list()
+)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of at least \code{signal_ndim} \code{+ 1}        dimensions}
+\item{self}{(Tensor) the input tensor of at least \code{signal_ndim} \code{+ 1}        dimensions}
 
 \item{signal_ndim}{(int) the number of dimensions in each signal.        \code{signal_ndim} can only be 1, 2 or 3}
 
diff --git a/man/torch_is_complex.Rd b/man/torch_is_complex.Rd
index 3a972733e..2e01757c9 100644
--- a/man/torch_is_complex.Rd
+++ b/man/torch_is_complex.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_is_complex}
 \alias{torch_is_complex}
 \title{Is_complex}
+\usage{
+torch_is_complex(self)
+}
 \arguments{
-\item{input}{(Tensor) the PyTorch tensor to test}
+\item{self}{(Tensor) the PyTorch tensor to test}
 }
 \description{
 Is_complex
diff --git a/man/torch_is_floating_point.Rd b/man/torch_is_floating_point.Rd
index 7e537ca02..8ecac3426 100644
--- a/man/torch_is_floating_point.Rd
+++ b/man/torch_is_floating_point.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_is_floating_point}
 \alias{torch_is_floating_point}
 \title{Is_floating_point}
+\usage{
+torch_is_floating_point(self)
+}
 \arguments{
-\item{input}{(Tensor) the PyTorch tensor to test}
+\item{self}{(Tensor) the PyTorch tensor to test}
 }
 \description{
 Is_floating_point
diff --git a/man/torch_isfinite.Rd b/man/torch_isfinite.Rd
index cf7f41e87..76591ebd8 100644
--- a/man/torch_isfinite.Rd
+++ b/man/torch_isfinite.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_isfinite}
 \alias{torch_isfinite}
 \title{Isfinite}
+\usage{
+torch_isfinite(self)
+}
 \arguments{
 \item{tensor}{(Tensor) A tensor to check}
 }
diff --git a/man/torch_isinf.Rd b/man/torch_isinf.Rd
index 19c01319d..857e8335d 100644
--- a/man/torch_isinf.Rd
+++ b/man/torch_isinf.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_isinf}
 \alias{torch_isinf}
 \title{Isinf}
+\usage{
+torch_isinf(self)
+}
 \arguments{
 \item{tensor}{(Tensor) A tensor to check}
 }
diff --git a/man/torch_isnan.Rd b/man/torch_isnan.Rd
index 764fde453..1c321274f 100644
--- a/man/torch_isnan.Rd
+++ b/man/torch_isnan.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_isnan}
 \alias{torch_isnan}
 \title{Isnan}
+\usage{
+torch_isnan(self)
+}
 \arguments{
-\item{input}{(Tensor) A tensor to check}
+\item{self}{(Tensor) A tensor to check}
 }
 \description{
 Isnan
diff --git a/man/torch_kthvalue.Rd b/man/torch_kthvalue.Rd
index 3616916cb..518b4e231 100644
--- a/man/torch_kthvalue.Rd
+++ b/man/torch_kthvalue.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_kthvalue}
 \alias{torch_kthvalue}
 \title{Kthvalue}
+\usage{
+torch_kthvalue(self, k, dim = -1L, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{k}{(int) k for the k-th smallest element}
 
diff --git a/man/torch_le.Rd b/man/torch_le.Rd
index 85e497f05..843f3b9c2 100644
--- a/man/torch_le.Rd
+++ b/man/torch_le.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_le}
 \alias{torch_le}
 \title{Le}
+\usage{
+torch_le(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compare}
+\item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
 
diff --git a/man/torch_lerp.Rd b/man/torch_lerp.Rd
index 66be2f225..dcae4c000 100644
--- a/man/torch_lerp.Rd
+++ b/man/torch_lerp.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_lerp}
 \alias{torch_lerp}
 \title{Lerp}
+\usage{
+torch_lerp(self, end, weight)
+}
 \arguments{
-\item{input}{(Tensor) the tensor with the starting points}
+\item{self}{(Tensor) the tensor with the starting points}
 
 \item{end}{(Tensor) the tensor with the ending points}
 
diff --git a/man/torch_lgamma.Rd b/man/torch_lgamma.Rd
index 6ae297ded..07089b88e 100644
--- a/man/torch_lgamma.Rd
+++ b/man/torch_lgamma.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_lgamma}
 \alias{torch_lgamma}
 \title{Lgamma}
+\usage{
+torch_lgamma(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_linspace.Rd b/man/torch_linspace.Rd
index 8c57a248f..6c140a1b3 100644
--- a/man/torch_linspace.Rd
+++ b/man/torch_linspace.Rd
@@ -1,9 +1,32 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_linspace}
 \alias{torch_linspace}
+\alias{torch_logspace}
 \title{Linspace}
+\usage{
+torch_linspace(
+  start,
+  end,
+  steps = 100,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+
+torch_logspace(
+  start,
+  end,
+  steps = 100,
+  base = 10,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{start}{(float) the starting value for the set of points}
 
@@ -11,8 +34,6 @@
 
 \item{steps}{(int) number of points to sample between \code{start}        and \code{end}. Default: \code{100}.}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -20,6 +41,8 @@
 \item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+
+\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Linspace
diff --git a/man/torch_log.Rd b/man/torch_log.Rd
index 95e5c80ec..b1c66ac7c 100644
--- a/man/torch_log.Rd
+++ b/man/torch_log.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_log}
 \alias{torch_log}
 \title{Log}
+\usage{
+torch_log(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_log10.Rd b/man/torch_log10.Rd
index c087b1e93..62c435289 100644
--- a/man/torch_log10.Rd
+++ b/man/torch_log10.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_log10}
 \alias{torch_log10}
 \title{Log10}
+\usage{
+torch_log10(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_log1p.Rd b/man/torch_log1p.Rd
index 075bf23d4..8cb005d50 100644
--- a/man/torch_log1p.Rd
+++ b/man/torch_log1p.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_log1p}
 \alias{torch_log1p}
 \title{Log1p}
+\usage{
+torch_log1p(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_log2.Rd b/man/torch_log2.Rd
index a7175d328..338c91a63 100644
--- a/man/torch_log2.Rd
+++ b/man/torch_log2.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_log2}
 \alias{torch_log2}
 \title{Log2}
+\usage{
+torch_log2(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_logdet.Rd b/man/torch_logdet.Rd
index cb922c129..cde18c09e 100644
--- a/man/torch_logdet.Rd
+++ b/man/torch_logdet.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_logdet}
 \alias{torch_logdet}
 \title{Logdet}
+\usage{
+torch_logdet(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \verb{(*, n, n)} where \code{*} is zero or more                batch dimensions.}
+\item{self}{(Tensor) the input tensor of size \verb{(*, n, n)} where \code{*} is zero or more                batch dimensions.}
 }
 \description{
 Logdet
diff --git a/man/torch_logical_and.Rd b/man/torch_logical_and.Rd
index 7179072a6..0cc258784 100644
--- a/man/torch_logical_and.Rd
+++ b/man/torch_logical_and.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_logical_and}
 \alias{torch_logical_and}
 \title{Logical_and}
+\usage{
+torch_logical_and(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the tensor to compute AND with}
 
diff --git a/man/torch_logical_not.Rd b/man/torch_logical_not.Rd
index b9345cfdb..984df1dc6 100644
--- a/man/torch_logical_not.Rd
+++ b/man/torch_logical_not.Rd
@@ -5,7 +5,7 @@
 \alias{torch_logical_not}
 \title{Logical_not}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_logical_or.Rd b/man/torch_logical_or.Rd
index b4b66d9cb..2aced74bb 100644
--- a/man/torch_logical_or.Rd
+++ b/man/torch_logical_or.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_logical_or}
 \alias{torch_logical_or}
 \title{Logical_or}
+\usage{
+torch_logical_or(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the tensor to compute OR with}
 
diff --git a/man/torch_logical_xor.Rd b/man/torch_logical_xor.Rd
index 4c32ced8f..0064323af 100644
--- a/man/torch_logical_xor.Rd
+++ b/man/torch_logical_xor.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_logical_xor}
 \alias{torch_logical_xor}
 \title{Logical_xor}
+\usage{
+torch_logical_xor(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the tensor to compute XOR with}
 
diff --git a/man/torch_logsumexp.Rd b/man/torch_logsumexp.Rd
index 398f92ccd..7496a09b8 100644
--- a/man/torch_logsumexp.Rd
+++ b/man/torch_logsumexp.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_logsumexp}
 \alias{torch_logsumexp}
 \title{Logsumexp}
+\usage{
+torch_logsumexp(self, dim, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
diff --git a/man/torch_lstsq.Rd b/man/torch_lstsq.Rd
index 4778caffa..85d07e48d 100644
--- a/man/torch_lstsq.Rd
+++ b/man/torch_lstsq.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_lstsq}
 \alias{torch_lstsq}
 \title{Lstsq}
+\usage{
+torch_lstsq(self, A)
+}
 \arguments{
-\item{input}{(Tensor) the matrix \eqn{B}}
+\item{self}{(Tensor) the matrix \eqn{B}}
 
 \item{A}{(Tensor) the \eqn{m} by \eqn{n} matrix \eqn{A}}
 
diff --git a/man/torch_lt.Rd b/man/torch_lt.Rd
index 567fd30df..f327143f5 100644
--- a/man/torch_lt.Rd
+++ b/man/torch_lt.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_lt}
 \alias{torch_lt}
 \title{Lt}
+\usage{
+torch_lt(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compare}
+\item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
 
diff --git a/man/torch_lu_solve.Rd b/man/torch_lu_solve.Rd
index 7fbbfe45c..7f21b910e 100644
--- a/man/torch_lu_solve.Rd
+++ b/man/torch_lu_solve.Rd
@@ -1,16 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_lu_solve}
 \alias{torch_lu_solve}
 \title{Lu_solve}
+\usage{
+torch_lu_solve(self, LU_data, LU_pivots)
+}
 \arguments{
-\item{b}{(Tensor) the RHS tensor of size \eqn{(*, m, k)}, where \eqn{*}                is zero or more batch dimensions.}
-
 \item{LU_data}{(Tensor) the pivoted LU factorization of A from \code{torch_lu} of size \eqn{(*, m, m)},                       where \eqn{*} is zero or more batch dimensions.}
 
 \item{LU_pivots}{(IntTensor) the pivots of the LU factorization from \code{torch_lu} of size \eqn{(*, m)},                           where \eqn{*} is zero or more batch dimensions.                           The batch dimensions of \code{LU_pivots} must be equal to the batch dimensions of                           \code{LU_data}.}
 
+\item{b}{(Tensor) the RHS tensor of size \eqn{(*, m, k)}, where \eqn{*}                is zero or more batch dimensions.}
+
 \item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
diff --git a/man/torch_masked_select.Rd b/man/torch_masked_select.Rd
index 065c0b7be..89c3ee5b0 100644
--- a/man/torch_masked_select.Rd
+++ b/man/torch_masked_select.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_masked_select}
 \alias{torch_masked_select}
 \title{Masked_select}
+\usage{
+torch_masked_select(self, mask)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{mask}{(BoolTensor) the tensor containing the binary mask to index with}
 
diff --git a/man/torch_matmul.Rd b/man/torch_matmul.Rd
index f11474fe7..5de50dfc9 100644
--- a/man/torch_matmul.Rd
+++ b/man/torch_matmul.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_matmul}
 \alias{torch_matmul}
 \title{Matmul}
+\usage{
+torch_matmul(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the first tensor to be multiplied}
+\item{self}{(Tensor) the first tensor to be multiplied}
 
 \item{other}{(Tensor) the second tensor to be multiplied}
 
diff --git a/man/torch_matrix_power.Rd b/man/torch_matrix_power.Rd
index 5202822e1..2c4a899c6 100644
--- a/man/torch_matrix_power.Rd
+++ b/man/torch_matrix_power.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_matrix_power}
 \alias{torch_matrix_power}
 \title{Matrix_power}
+\usage{
+torch_matrix_power(self, n)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{n}{(int) the power to raise the matrix to}
 }
diff --git a/man/torch_matrix_rank.Rd b/man/torch_matrix_rank.Rd
index 811d3efc3..8758a836f 100644
--- a/man/torch_matrix_rank.Rd
+++ b/man/torch_matrix_rank.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_matrix_rank}
 \alias{torch_matrix_rank}
 \title{Matrix_rank}
+\usage{
+torch_matrix_rank(self, tol, symmetric = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input 2-D tensor}
+\item{self}{(Tensor) the input 2-D tensor}
 
 \item{tol}{(float, optional) the tolerance value. Default: \code{None}}
 
diff --git a/man/torch_max.Rd b/man/torch_max.Rd
index 2b4e7c154..5f8dfd1ca 100644
--- a/man/torch_max.Rd
+++ b/man/torch_max.Rd
@@ -5,7 +5,7 @@
 \alias{torch_max}
 \title{Max}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to reduce.}
 
diff --git a/man/torch_mean.Rd b/man/torch_mean.Rd
index 5d597f841..f1ffbd38f 100644
--- a/man/torch_mean.Rd
+++ b/man/torch_mean.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_mean}
 \alias{torch_mean}
 \title{Mean}
+\usage{
+torch_mean(self, dim, keepdim = FALSE, dtype = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
diff --git a/man/torch_median.Rd b/man/torch_median.Rd
index 49a5ce580..05d069452 100644
--- a/man/torch_median.Rd
+++ b/man/torch_median.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_median}
 \alias{torch_median}
 \title{Median}
+\usage{
+torch_median(self, dim, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to reduce.}
 
diff --git a/man/torch_meshgrid.Rd b/man/torch_meshgrid.Rd
index fd681386c..64b51b098 100644
--- a/man/torch_meshgrid.Rd
+++ b/man/torch_meshgrid.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_meshgrid}
 \alias{torch_meshgrid}
 \title{Meshgrid}
+\usage{
+torch_meshgrid(tensors)
+}
 \arguments{
 \item{tensors}{(list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be}
 
diff --git a/man/torch_min.Rd b/man/torch_min.Rd
index 4dbc40bd3..398a9d1c3 100644
--- a/man/torch_min.Rd
+++ b/man/torch_min.Rd
@@ -5,7 +5,7 @@
 \alias{torch_min}
 \title{Min}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to reduce.}
 
diff --git a/man/torch_mm.Rd b/man/torch_mm.Rd
index d7a95a389..3789c6c81 100644
--- a/man/torch_mm.Rd
+++ b/man/torch_mm.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_mm}
 \alias{torch_mm}
 \title{Mm}
+\usage{
+torch_mm(self, mat2)
+}
 \arguments{
-\item{input}{(Tensor) the first matrix to be multiplied}
+\item{self}{(Tensor) the first matrix to be multiplied}
 
 \item{mat2}{(Tensor) the second matrix to be multiplied}
 
diff --git a/man/torch_mode.Rd b/man/torch_mode.Rd
index 77110e48a..57e597ad0 100644
--- a/man/torch_mode.Rd
+++ b/man/torch_mode.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_mode}
 \alias{torch_mode}
 \title{Mode}
+\usage{
+torch_mode(self, dim = -1L, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to reduce.}
 
diff --git a/man/torch_mul.Rd b/man/torch_mul.Rd
index dac0045bf..cfdd1df0c 100644
--- a/man/torch_mul.Rd
+++ b/man/torch_mul.Rd
@@ -1,20 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_mul}
 \alias{torch_mul}
 \title{Mul}
+\usage{
+torch_mul(self, other)
+}
 \arguments{
+\item{self}{(Tensor) the first multiplicand tensor}
+
+\item{other}{(Tensor) the second multiplicand tensor}
+
 \item{{input}}{NA}
 
 \item{value}{(Number) the number to be multiplied to each element of \code{input}}
 
 \item{{out}}{NA}
 
-\item{input}{(Tensor) the first multiplicand tensor}
-
-\item{other}{(Tensor) the second multiplicand tensor}
-
 \item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
diff --git a/man/torch_multinomial.Rd b/man/torch_multinomial.Rd
index 8726beaf2..1bbf48fe1 100644
--- a/man/torch_multinomial.Rd
+++ b/man/torch_multinomial.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_multinomial}
 \alias{torch_multinomial}
 \title{Multinomial}
+\usage{
+torch_multinomial(self, num_samples, replacement = FALSE, generator = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor containing probabilities}
+\item{self}{(Tensor) the input tensor containing probabilities}
 
 \item{num_samples}{(int) number of samples to draw}
 
diff --git a/man/torch_mv.Rd b/man/torch_mv.Rd
index e0e0a8611..083a5d706 100644
--- a/man/torch_mv.Rd
+++ b/man/torch_mv.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_mv}
 \alias{torch_mv}
 \title{Mv}
+\usage{
+torch_mv(self, vec)
+}
 \arguments{
-\item{input}{(Tensor) matrix to be multiplied}
+\item{self}{(Tensor) matrix to be multiplied}
 
 \item{vec}{(Tensor) vector to be multiplied}
 
diff --git a/man/torch_mvlgamma.Rd b/man/torch_mvlgamma.Rd
index 58bb475f4..913346d3b 100644
--- a/man/torch_mvlgamma.Rd
+++ b/man/torch_mvlgamma.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_mvlgamma}
 \alias{torch_mvlgamma}
 \title{Mvlgamma}
+\usage{
+torch_mvlgamma(self, p)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compute the multivariate log-gamma function}
+\item{self}{(Tensor) the tensor to compute the multivariate log-gamma function}
 
 \item{p}{(int) the number of dimensions}
 }
diff --git a/man/torch_narrow.Rd b/man/torch_narrow.Rd
index 1ca8bb3e7..5934d04c1 100644
--- a/man/torch_narrow.Rd
+++ b/man/torch_narrow.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_narrow}
 \alias{torch_narrow}
 \title{Narrow}
+\usage{
+torch_narrow(self, dim, start, length)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to narrow}
+\item{self}{(Tensor) the tensor to narrow}
 
 \item{dim}{(int) the dimension along which to narrow}
 
diff --git a/man/torch_ne.Rd b/man/torch_ne.Rd
index fc31508f9..4dd93c2e2 100644
--- a/man/torch_ne.Rd
+++ b/man/torch_ne.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_ne}
 \alias{torch_ne}
 \title{Ne}
+\usage{
+torch_ne(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compare}
+\item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
 
diff --git a/man/torch_neg.Rd b/man/torch_neg.Rd
index de4a5133f..43c8c3390 100644
--- a/man/torch_neg.Rd
+++ b/man/torch_neg.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_neg}
 \alias{torch_neg}
 \title{Neg}
+\usage{
+torch_neg(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_nonzero.Rd b/man/torch_nonzero.Rd
index 85eedee9d..e06697230 100644
--- a/man/torch_nonzero.Rd
+++ b/man/torch_nonzero.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_nonzero}
 \alias{torch_nonzero}
 \title{Nonzero}
+\usage{
+torch_nonzero(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(LongTensor, optional) the output tensor containing indices}
 }
diff --git a/man/torch_norm.Rd b/man/torch_norm.Rd
index 7600be494..6aa346560 100644
--- a/man/torch_norm.Rd
+++ b/man/torch_norm.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_norm}
 \alias{torch_norm}
 \title{Norm}
+\usage{
+torch_norm(self, p = 2L, dim, keepdim = FALSE, dtype)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor}
+\item{self}{(Tensor) the input tensor}
 
 \item{p}{(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: \code{'fro'}        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)}(1./ord)        =====  ============================  ==========================}
 
@@ -13,9 +16,9 @@
 
 \item{keepdim}{(bool, optional) whether the output tensors have \code{dim}        retained or not. Ignored if \code{dim} = \code{None} and        \code{out} = \code{None}. Default: \code{False}}
 
-\item{out}{(Tensor, optional) the output tensor. Ignored if        \code{dim} = \code{None} and \code{out} = \code{None}.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: None.}
+
+\item{out}{(Tensor, optional) the output tensor. Ignored if        \code{dim} = \code{None} and \code{out} = \code{None}.}
 }
 \description{
 Norm
diff --git a/man/torch_normal.Rd b/man/torch_normal.Rd
index 18601ca32..335dbe3ee 100644
--- a/man/torch_normal.Rd
+++ b/man/torch_normal.Rd
@@ -1,19 +1,22 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_normal}
 \alias{torch_normal}
 \title{Normal}
+\usage{
+torch_normal(mean, std = 1L, size, generator = NULL, options = list())
+}
 \arguments{
 \item{mean}{(Tensor) the tensor of per-element means}
 
 \item{std}{(Tensor) the tensor of per-element standard deviations}
 
+\item{size}{(int...) a sequence of integers defining the shape of the output tensor.}
+
 \item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
 
 \item{out}{(Tensor, optional) the output tensor.}
-
-\item{size}{(int...) a sequence of integers defining the shape of the output tensor.}
 }
 \description{
 Normal
diff --git a/man/torch_ones.Rd b/man/torch_ones.Rd
index d2bbc86e9..0c86b3ab3 100644
--- a/man/torch_ones.Rd
+++ b/man/torch_ones.Rd
@@ -1,14 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_ones}
 \alias{torch_ones}
 \title{Ones}
+\usage{
+torch_ones(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
-\item{size}{(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.}
-
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -16,6 +22,10 @@
 \item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+
+\item{size}{(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.}
+
+\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Ones
diff --git a/man/torch_ones_like.Rd b/man/torch_ones_like.Rd
index be0df6d18..a6859858a 100644
--- a/man/torch_ones_like.Rd
+++ b/man/torch_ones_like.Rd
@@ -1,12 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_ones_like}
 \alias{torch_ones_like}
 \title{Ones_like}
+\usage{
+torch_ones_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
-\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
@@ -16,6 +24,8 @@
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
+
+\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 }
 \description{
 Ones_like
diff --git a/man/torch_orgqr.Rd b/man/torch_orgqr.Rd
index df61b8795..ca36b9ad6 100644
--- a/man/torch_orgqr.Rd
+++ b/man/torch_orgqr.Rd
@@ -1,13 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_orgqr}
 \alias{torch_orgqr}
 \title{Orgqr}
+\usage{
+torch_orgqr(self, input2)
+}
 \arguments{
-\item{input}{(Tensor) the \code{a} from \code{\link{torch_geqrf}}.}
+\item{self}{(Tensor) the \code{a} from \code{\link{torch_geqrf}}.}
 
-\item{input2}{(Tensor) the \code{tau} from \code{\link{torch_geqrf}}.}
+\item{self2}{(Tensor) the \code{tau} from \code{\link{torch_geqrf}}.}
 }
 \description{
 Orgqr
diff --git a/man/torch_ormqr.Rd b/man/torch_ormqr.Rd
index f519b4506..db32fd2a5 100644
--- a/man/torch_ormqr.Rd
+++ b/man/torch_ormqr.Rd
@@ -1,15 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_ormqr}
 \alias{torch_ormqr}
 \title{Ormqr}
+\usage{
+torch_ormqr(self, input2, input3, left = TRUE, transpose = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the \code{a} from \code{\link{torch_geqrf}}.}
+\item{self}{(Tensor) the \code{a} from \code{\link{torch_geqrf}}.}
 
-\item{input2}{(Tensor) the \code{tau} from \code{\link{torch_geqrf}}.}
+\item{self2}{(Tensor) the \code{tau} from \code{\link{torch_geqrf}}.}
 
-\item{input3}{(Tensor) the matrix to be multiplied.}
+\item{self3}{(Tensor) the matrix to be multiplied.}
 }
 \description{
 Ormqr
diff --git a/man/torch_pdist.Rd b/man/torch_pdist.Rd
index 7b0dcca0e..399363e4a 100644
--- a/man/torch_pdist.Rd
+++ b/man/torch_pdist.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_pdist}
 \alias{torch_pdist}
 \title{Pdist}
+\usage{
+torch_pdist(self, p = 2L)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{N \times M}.}
+\item{self}{NA input tensor of shape \eqn{N \times M}.}
 
 \item{p}{NA p value for the p-norm distance to calculate between each vector pair        \eqn{\in [0, \infty]}.}
 }
diff --git a/man/torch_pinverse.Rd b/man/torch_pinverse.Rd
index 131b5149f..c3bed5c29 100644
--- a/man/torch_pinverse.Rd
+++ b/man/torch_pinverse.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_pinverse}
 \alias{torch_pinverse}
 \title{Pinverse}
+\usage{
+torch_pinverse(self, rcond = 0)
+}
 \arguments{
-\item{input}{(Tensor) The input tensor of size \eqn{(*, m, n)} where \eqn{*} is zero or more batch dimensions}
+\item{self}{(Tensor) The input tensor of size \eqn{(*, m, n)} where \eqn{*} is zero or more batch dimensions}
 
 \item{rcond}{(float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15}
 }
diff --git a/man/torch_pixel_shuffle.Rd b/man/torch_pixel_shuffle.Rd
index 1df105203..5af008b54 100644
--- a/man/torch_pixel_shuffle.Rd
+++ b/man/torch_pixel_shuffle.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_pixel_shuffle}
 \alias{torch_pixel_shuffle}
 \title{Pixel_shuffle}
+\usage{
+torch_pixel_shuffle(self, upscale_factor)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor}
+\item{self}{(Tensor) the input tensor}
 
 \item{upscale_factor}{(int) factor to increase spatial resolution by}
 }
diff --git a/man/torch_poisson.Rd b/man/torch_poisson.Rd
index 6bb3122c8..0f6fd808d 100644
--- a/man/torch_poisson.Rd
+++ b/man/torch_poisson.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_poisson}
 \alias{torch_poisson}
 \title{Poisson}
+\usage{
+torch_poisson(self, generator = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor containing the rates of the Poisson distribution}
+\item{self}{(Tensor) the input tensor containing the rates of the Poisson distribution}
 
 \item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
 }
diff --git a/man/torch_polygamma.Rd b/man/torch_polygamma.Rd
index 4ab7249f8..b213de299 100644
--- a/man/torch_polygamma.Rd
+++ b/man/torch_polygamma.Rd
@@ -1,13 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_polygamma}
 \alias{torch_polygamma}
 \title{Polygamma}
+\usage{
+torch_polygamma(n, self)
+}
 \arguments{
 \item{n}{(int) the order of the polygamma function}
 
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_pow.Rd b/man/torch_pow.Rd
index b11720826..ff7a9aac1 100644
--- a/man/torch_pow.Rd
+++ b/man/torch_pow.Rd
@@ -1,17 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_pow}
 \alias{torch_pow}
 \title{Pow}
+\usage{
+torch_pow(self, exponent)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(float) the scalar base value for the power operation}
 
 \item{exponent}{(float or tensor) the exponent value}
 
 \item{out}{(Tensor, optional) the output tensor.}
-
-\item{self}{(float) the scalar base value for the power operation}
 }
 \description{
 Pow
diff --git a/man/torch_prod.Rd b/man/torch_prod.Rd
index b2fc17d06..52e76ea84 100644
--- a/man/torch_prod.Rd
+++ b/man/torch_prod.Rd
@@ -1,17 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_prod}
 \alias{torch_prod}
 \title{Prod}
+\usage{
+torch_prod(self, dim, keepdim = FALSE, dtype = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: None.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to reduce.}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
+
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: None.}
 }
 \description{
 Prod
diff --git a/man/torch_promote_types.Rd b/man/torch_promote_types.Rd
index a6e05dcb3..11e5bed85 100644
--- a/man/torch_promote_types.Rd
+++ b/man/torch_promote_types.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_promote_types}
 \alias{torch_promote_types}
 \title{Promote_types}
+\usage{
+torch_promote_types(type1, type2)
+}
 \arguments{
 \item{type1}{(\code{torch.dtype})}
 
diff --git a/man/torch_qr.Rd b/man/torch_qr.Rd
index 6463d155a..b9b078f3a 100644
--- a/man/torch_qr.Rd
+++ b/man/torch_qr.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_qr}
 \alias{torch_qr}
 \title{Qr}
+\usage{
+torch_qr(self, some = TRUE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \eqn{(*, m, n)} where \code{*} is zero or more                batch dimensions consisting of matrices of dimension \eqn{m \times n}.}
+\item{self}{(Tensor) the input tensor of size \eqn{(*, m, n)} where \code{*} is zero or more                batch dimensions consisting of matrices of dimension \eqn{m \times n}.}
 
 \item{some}{(bool, optional) Set to \code{True} for reduced QR decomposition and \code{False} for                complete QR decomposition.}
 
diff --git a/man/torch_quantize_per_channel.Rd b/man/torch_quantize_per_channel.Rd
index 683a9576a..6011f1726 100644
--- a/man/torch_quantize_per_channel.Rd
+++ b/man/torch_quantize_per_channel.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_quantize_per_channel}
 \alias{torch_quantize_per_channel}
 \title{Quantize_per_channel}
+\usage{
+torch_quantize_per_channel(self, scales, zero_points, axis, dtype)
+}
 \arguments{
-\item{input}{(Tensor) float tensor to quantize}
+\item{self}{(Tensor) float tensor to quantize}
 
 \item{scales}{(Tensor) float 1D tensor of scales to use, size should match \code{input.size(axis)}}
 
diff --git a/man/torch_quantize_per_tensor.Rd b/man/torch_quantize_per_tensor.Rd
index 962a816aa..b327d5e42 100644
--- a/man/torch_quantize_per_tensor.Rd
+++ b/man/torch_quantize_per_tensor.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_quantize_per_tensor}
 \alias{torch_quantize_per_tensor}
 \title{Quantize_per_tensor}
+\usage{
+torch_quantize_per_tensor(self, scale, zero_point, dtype)
+}
 \arguments{
-\item{input}{(Tensor) float tensor to quantize}
+\item{self}{(Tensor) float tensor to quantize}
 
 \item{scale}{(float) scale to apply in quantization formula}
 
diff --git a/man/torch_rand.Rd b/man/torch_rand.Rd
index 49e2fbb0e..32c5fd09e 100644
--- a/man/torch_rand.Rd
+++ b/man/torch_rand.Rd
@@ -1,14 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_rand}
 \alias{torch_rand}
 \title{Rand}
+\usage{
+torch_rand(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
-\item{size}{(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.}
-
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -16,6 +22,10 @@
 \item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+
+\item{size}{(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.}
+
+\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Rand
diff --git a/man/torch_rand_like.Rd b/man/torch_rand_like.Rd
index e13f1a8e2..7666d2750 100644
--- a/man/torch_rand_like.Rd
+++ b/man/torch_rand_like.Rd
@@ -1,12 +1,30 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_rand_like}
 \alias{torch_rand_like}
+\alias{torch_randn_like}
 \title{Rand_like}
+\usage{
+torch_rand_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+
+torch_randn_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
-\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
@@ -16,6 +34,8 @@
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
+
+\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 }
 \description{
 Rand_like
diff --git a/man/torch_randint.Rd b/man/torch_randint.Rd
index f806ea80f..d7348ed1d 100644
--- a/man/torch_randint.Rd
+++ b/man/torch_randint.Rd
@@ -1,9 +1,22 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_randint}
 \alias{torch_randint}
 \title{Randint}
+\usage{
+torch_randint(
+  low,
+  high,
+  size,
+  generator = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
 \item{low}{(int, optional) Lowest integer to be drawn from the distribution. Default: 0.}
 
@@ -13,8 +26,6 @@
 
 \item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -22,6 +33,8 @@
 \item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+
+\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Randint
diff --git a/man/torch_randint_like.Rd b/man/torch_randint_like.Rd
index 971f489d8..756800662 100644
--- a/man/torch_randint_like.Rd
+++ b/man/torch_randint_like.Rd
@@ -1,12 +1,21 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_randint_like}
 \alias{torch_randint_like}
 \title{Randint_like}
+\usage{
+torch_randint_like(
+  input,
+  low,
+  high,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
-\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
-
 \item{low}{(int, optional) Lowest integer to be drawn from the distribution. Default: 0.}
 
 \item{high}{(int) One above the highest integer to be drawn from the distribution.}
@@ -19,6 +28,8 @@
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
 
+\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
+
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 }
 \description{
diff --git a/man/torch_randn.Rd b/man/torch_randn.Rd
index 2e2e77387..24f4d08e4 100644
--- a/man/torch_randn.Rd
+++ b/man/torch_randn.Rd
@@ -1,14 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_randn}
 \alias{torch_randn}
 \title{Randn}
+\usage{
+torch_randn(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
-\item{size}{(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.}
-
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -16,6 +22,10 @@
 \item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+
+\item{size}{(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.}
+
+\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Randn
diff --git a/man/torch_randn_like.Rd b/man/torch_randn_like.Rd
index 7592a38e5..2fcde1a5f 100644
--- a/man/torch_randn_like.Rd
+++ b/man/torch_randn_like.Rd
@@ -5,7 +5,7 @@
 \alias{torch_randn_like}
 \title{Randn_like}
 \arguments{
-\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
+\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
 
diff --git a/man/torch_randperm.Rd b/man/torch_randperm.Rd
index b1e9fea4a..3319c7963 100644
--- a/man/torch_randperm.Rd
+++ b/man/torch_randperm.Rd
@@ -1,14 +1,21 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_randperm}
 \alias{torch_randperm}
 \title{Randperm}
+\usage{
+torch_randperm(
+  n,
+  dtype = torch_int64(),
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{n}{(int) the upper bound (exclusive)}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: \code{torch_int64}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -16,6 +23,8 @@
 \item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+
+\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Randperm
diff --git a/man/torch_range.Rd b/man/torch_range.Rd
index 306c49123..c2c42eb8e 100644
--- a/man/torch_range.Rd
+++ b/man/torch_range.Rd
@@ -1,9 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_range}
 \alias{torch_range}
 \title{Range}
+\usage{
+torch_range(
+  start,
+  end,
+  step = 1,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{start}{(float) the starting value for the set of points. Default: \code{0}.}
 
@@ -11,8 +22,6 @@
 
 \item{step}{(float) the gap between each pair of adjacent points. Default: \code{1}.}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). If \code{dtype} is not given, infer the data type from the other input        arguments. If any of \code{start}, \code{end}, or \code{stop} are floating-point, the        \code{dtype} is inferred to be the default dtype, see        \code{~torch.get_default_dtype}. Otherwise, the \code{dtype} is inferred to        be \code{torch.int64}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -20,6 +29,8 @@
 \item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+
+\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Range
diff --git a/man/torch_real.Rd b/man/torch_real.Rd
index a9b86c77e..dcb3dd4f4 100644
--- a/man/torch_real.Rd
+++ b/man/torch_real.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_real}
 \alias{torch_real}
 \title{Real}
+\usage{
+torch_real(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_reciprocal.Rd b/man/torch_reciprocal.Rd
index 6009938b7..b82af29ab 100644
--- a/man/torch_reciprocal.Rd
+++ b/man/torch_reciprocal.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_reciprocal}
 \alias{torch_reciprocal}
 \title{Reciprocal}
+\usage{
+torch_reciprocal(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_relu_.Rd b/man/torch_relu_.Rd
index a8e54ff0f..c75b09a0f 100644
--- a/man/torch_relu_.Rd
+++ b/man/torch_relu_.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_relu_}
 \alias{torch_relu_}
 \title{Relu_}
+\usage{
+torch_relu_(self)
+}
 \description{
 Relu_
 }
diff --git a/man/torch_remainder.Rd b/man/torch_remainder.Rd
index 10fd367f8..162de6c3c 100644
--- a/man/torch_remainder.Rd
+++ b/man/torch_remainder.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_remainder}
 \alias{torch_remainder}
 \title{Remainder}
+\usage{
+torch_remainder(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the dividend}
+\item{self}{(Tensor) the dividend}
 
 \item{other}{(Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend}
 
diff --git a/man/torch_renorm.Rd b/man/torch_renorm.Rd
index 84755448c..b0f678186 100644
--- a/man/torch_renorm.Rd
+++ b/man/torch_renorm.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_renorm}
 \alias{torch_renorm}
 \title{Renorm}
+\usage{
+torch_renorm(self, p, dim, maxnorm)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{p}{(float) the power for the norm computation}
 
diff --git a/man/torch_repeat_interleave.Rd b/man/torch_repeat_interleave.Rd
index aa40042bd..a9d4d9551 100644
--- a/man/torch_repeat_interleave.Rd
+++ b/man/torch_repeat_interleave.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_repeat_interleave}
 \alias{torch_repeat_interleave}
 \title{Repeat_interleave}
+\usage{
+torch_repeat_interleave(self, repeats, dim = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{repeats}{(Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.}
 
diff --git a/man/torch_reshape.Rd b/man/torch_reshape.Rd
index dd06c69eb..a86e9efd8 100644
--- a/man/torch_reshape.Rd
+++ b/man/torch_reshape.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_reshape}
 \alias{torch_reshape}
 \title{Reshape}
+\usage{
+torch_reshape(self, shape)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to be reshaped}
+\item{self}{(Tensor) the tensor to be reshaped}
 
 \item{shape}{(tuple of ints) the new shape}
 }
diff --git a/man/torch_result_type.Rd b/man/torch_result_type.Rd
index 6519045eb..d60023a6d 100644
--- a/man/torch_result_type.Rd
+++ b/man/torch_result_type.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_result_type}
 \alias{torch_result_type}
 \title{Result_type}
+\usage{
+torch_result_type(scalar, scalar1, other, scalar2, tensor)
+}
 \arguments{
 \item{tensor1}{(Tensor or Number) an input tensor or number}
 
diff --git a/man/torch_rfft.Rd b/man/torch_rfft.Rd
index b94c2515f..f2882f62c 100644
--- a/man/torch_rfft.Rd
+++ b/man/torch_rfft.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_rfft}
 \alias{torch_rfft}
 \title{Rfft}
+\usage{
+torch_rfft(self, signal_ndim, normalized = FALSE, onesided = TRUE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of at least \code{signal_ndim} dimensions}
+\item{self}{(Tensor) the input tensor of at least \code{signal_ndim} dimensions}
 
 \item{signal_ndim}{(int) the number of dimensions in each signal.        \code{signal_ndim} can only be 1, 2 or 3}
 
diff --git a/man/torch_roll.Rd b/man/torch_roll.Rd
index 5d237d9f7..992e6961f 100644
--- a/man/torch_roll.Rd
+++ b/man/torch_roll.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_roll}
 \alias{torch_roll}
 \title{Roll}
+\usage{
+torch_roll(self, shifts, dims = list())
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{shifts}{(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}
 
diff --git a/man/torch_rot90.Rd b/man/torch_rot90.Rd
index 980f1f98c..bb14b1b9d 100644
--- a/man/torch_rot90.Rd
+++ b/man/torch_rot90.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_rot90}
 \alias{torch_rot90}
 \title{Rot90}
+\usage{
+torch_rot90(self, k = 1L, dims = c(0, 1))
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{k}{(int) number of times to rotate}
 
diff --git a/man/torch_round.Rd b/man/torch_round.Rd
index 26d7258df..abf72ad13 100644
--- a/man/torch_round.Rd
+++ b/man/torch_round.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_round}
 \alias{torch_round}
 \title{Round}
+\usage{
+torch_round(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_rrelu_.Rd b/man/torch_rrelu_.Rd
index be9c9c01e..d1a7988dd 100644
--- a/man/torch_rrelu_.Rd
+++ b/man/torch_rrelu_.Rd
@@ -1,9 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_rrelu_}
 \alias{torch_rrelu_}
 \title{Rrelu_}
+\usage{
+torch_rrelu_(
+  self,
+  lower = 0.125,
+  upper = 0.333333,
+  training = FALSE,
+  generator = NULL
+)
+}
 \description{
 Rrelu_
 }
diff --git a/man/torch_rsqrt.Rd b/man/torch_rsqrt.Rd
index bc8a54ba6..e909f01ab 100644
--- a/man/torch_rsqrt.Rd
+++ b/man/torch_rsqrt.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_rsqrt}
 \alias{torch_rsqrt}
 \title{Rsqrt}
+\usage{
+torch_rsqrt(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_selu_.Rd b/man/torch_selu_.Rd
index e74d3d439..4eaf380e7 100644
--- a/man/torch_selu_.Rd
+++ b/man/torch_selu_.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_selu_}
 \alias{torch_selu_}
 \title{Selu_}
+\usage{
+torch_selu_(self)
+}
 \description{
 Selu_
 }
diff --git a/man/torch_sigmoid.Rd b/man/torch_sigmoid.Rd
index bd3459c60..e447405f2 100644
--- a/man/torch_sigmoid.Rd
+++ b/man/torch_sigmoid.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sigmoid}
 \alias{torch_sigmoid}
 \title{Sigmoid}
+\usage{
+torch_sigmoid(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_sign.Rd b/man/torch_sign.Rd
index 81b8eb32c..bdfd12c70 100644
--- a/man/torch_sign.Rd
+++ b/man/torch_sign.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sign}
 \alias{torch_sign}
 \title{Sign}
+\usage{
+torch_sign(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_sin.Rd b/man/torch_sin.Rd
index 3d61e9032..a799f1d40 100644
--- a/man/torch_sin.Rd
+++ b/man/torch_sin.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sin}
 \alias{torch_sin}
 \title{Sin}
+\usage{
+torch_sin(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_sinh.Rd b/man/torch_sinh.Rd
index b164ae5d5..fbc37f304 100644
--- a/man/torch_sinh.Rd
+++ b/man/torch_sinh.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sinh}
 \alias{torch_sinh}
 \title{Sinh}
+\usage{
+torch_sinh(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_slogdet.Rd b/man/torch_slogdet.Rd
index ec5cb000f..6271ffab3 100644
--- a/man/torch_slogdet.Rd
+++ b/man/torch_slogdet.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_slogdet}
 \alias{torch_slogdet}
 \title{Slogdet}
+\usage{
+torch_slogdet(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \verb{(*, n, n)} where \code{*} is zero or more                batch dimensions.}
+\item{self}{(Tensor) the input tensor of size \verb{(*, n, n)} where \code{*} is zero or more                batch dimensions.}
 }
 \description{
 Slogdet
diff --git a/man/torch_solve.Rd b/man/torch_solve.Rd
index d5332079f..156f71ef4 100644
--- a/man/torch_solve.Rd
+++ b/man/torch_solve.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_solve}
 \alias{torch_solve}
 \title{Solve}
+\usage{
+torch_solve(self, A)
+}
 \arguments{
-\item{input}{(Tensor) input matrix \eqn{B} of size \eqn{(*, m, k)} , where \eqn{*}                is zero or more batch dimensions.}
+\item{self}{(Tensor) input matrix \eqn{B} of size \eqn{(*, m, k)} , where \eqn{*}                is zero or more batch dimensions.}
 
 \item{A}{(Tensor) input square matrix of size \eqn{(*, m, m)}, where                \eqn{*} is zero or more batch dimensions.}
 
diff --git a/man/torch_sort.Rd b/man/torch_sort.Rd
index d74974a10..9048f7520 100644
--- a/man/torch_sort.Rd
+++ b/man/torch_sort.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sort}
 \alias{torch_sort}
 \title{Sort}
+\usage{
+torch_sort(self, dim = -1L, descending = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int, optional) the dimension to sort along}
 
diff --git a/man/torch_sparse_coo_tensor.Rd b/man/torch_sparse_coo_tensor.Rd
index d0814f66b..62696b9b9 100644
--- a/man/torch_sparse_coo_tensor.Rd
+++ b/man/torch_sparse_coo_tensor.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sparse_coo_tensor}
 \alias{torch_sparse_coo_tensor}
 \title{Sparse_coo_tensor}
+\usage{
+torch_sparse_coo_tensor(indices, values, size, options = list())
+}
 \arguments{
 \item{indices}{(array_like) Initial data for the tensor. Can be a list, tuple,        NumPy \code{ndarray}, scalar, and other types. Will be cast to a \code{torch_LongTensor}        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.}
 
diff --git a/man/torch_split.Rd b/man/torch_split.Rd
index b6c7e2943..def245048 100644
--- a/man/torch_split.Rd
+++ b/man/torch_split.Rd
@@ -1,15 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_split}
 \alias{torch_split}
 \title{Split}
+\usage{
+torch_split(self, split_size, dim = 1L)
+}
 \arguments{
+\item{dim}{(int) dimension along which to split the tensor.}
+
 \item{tensor}{(Tensor) tensor to split.}
 
 \item{split_size_or_sections}{(int) size of a single chunk or        list of sizes for each chunk}
-
-\item{dim}{(int) dimension along which to split the tensor.}
 }
 \description{
 Split
diff --git a/man/torch_sqrt.Rd b/man/torch_sqrt.Rd
index 3757f1820..d5337afcd 100644
--- a/man/torch_sqrt.Rd
+++ b/man/torch_sqrt.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sqrt}
 \alias{torch_sqrt}
 \title{Sqrt}
+\usage{
+torch_sqrt(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_square.Rd b/man/torch_square.Rd
index 428cfb2d2..24def3ba9 100644
--- a/man/torch_square.Rd
+++ b/man/torch_square.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_square}
 \alias{torch_square}
 \title{Square}
+\usage{
+torch_square(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_squeeze.Rd b/man/torch_squeeze.Rd
index a35aa7e53..022a62e49 100644
--- a/man/torch_squeeze.Rd
+++ b/man/torch_squeeze.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_squeeze}
 \alias{torch_squeeze}
 \title{Squeeze}
+\usage{
+torch_squeeze(self, dim)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int, optional) if given, the input will be squeezed only in           this dimension}
 
diff --git a/man/torch_stack.Rd b/man/torch_stack.Rd
index 929ad9950..2718b34aa 100644
--- a/man/torch_stack.Rd
+++ b/man/torch_stack.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_stack}
 \alias{torch_stack}
 \title{Stack}
+\usage{
+torch_stack(tensors, dim = 1L)
+}
 \arguments{
 \item{tensors}{(sequence of Tensors) sequence of tensors to concatenate}
 
diff --git a/man/torch_std.Rd b/man/torch_std.Rd
index 8b01e57c1..403084549 100644
--- a/man/torch_std.Rd
+++ b/man/torch_std.Rd
@@ -1,16 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_std}
 \alias{torch_std}
 \title{Std}
+\usage{
+torch_std(self, dim, unbiased = TRUE, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
+\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
 
 \item{out}{(Tensor, optional) the output tensor.}
diff --git a/man/torch_std_mean.Rd b/man/torch_std_mean.Rd
index c763aff18..234b421e8 100644
--- a/man/torch_std_mean.Rd
+++ b/man/torch_std_mean.Rd
@@ -1,16 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_std_mean}
 \alias{torch_std_mean}
 \title{Std_mean}
+\usage{
+torch_std_mean(self, dim, unbiased = TRUE, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
+\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
 }
 \description{
diff --git a/man/torch_stft.Rd b/man/torch_stft.Rd
index 3757eb866..4734c2ae7 100644
--- a/man/torch_stft.Rd
+++ b/man/torch_stft.Rd
@@ -1,11 +1,22 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_stft}
 \alias{torch_stft}
 \title{Stft}
+\usage{
+torch_stft(
+  self,
+  n_fft,
+  hop_length = NULL,
+  win_length = NULL,
+  window = list(),
+  normalized = FALSE,
+  onesided = TRUE
+)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor}
+\item{self}{(Tensor) the input tensor}
 
 \item{n_fft}{(int) size of Fourier transform}
 
@@ -15,13 +26,13 @@
 
 \item{window}{(Tensor, optional) the optional window function.        Default: \code{None} (treated as window of all \eqn{1} s)}
 
-\item{center}{(bool, optional) whether to pad \code{input} on both sides so        that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}.        Default: \code{True}}
-
-\item{pad_mode}{(string, optional) controls the padding method used when        \code{center} is \code{True}. Default: \code{"reflect"}}
-
 \item{normalized}{(bool, optional) controls whether to return the normalized STFT results         Default: \code{False}}
 
 \item{onesided}{(bool, optional) controls whether to return half of results to        avoid redundancy Default: \code{True}}
+
+\item{center}{(bool, optional) whether to pad \code{input} on both sides so        that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}.        Default: \code{True}}
+
+\item{pad_mode}{(string, optional) controls the padding method used when        \code{center} is \code{True}. Default: \code{"reflect"}}
 }
 \description{
 Stft
diff --git a/man/torch_sum.Rd b/man/torch_sum.Rd
index 1a1c56e81..fa803b9a0 100644
--- a/man/torch_sum.Rd
+++ b/man/torch_sum.Rd
@@ -1,17 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sum}
 \alias{torch_sum}
 \title{Sum}
+\usage{
+torch_sum(self, dim, keepdim = FALSE, dtype = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: None.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
+
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: None.}
 }
 \description{
 Sum
diff --git a/man/torch_svd.Rd b/man/torch_svd.Rd
index a47d7621b..61b2a0057 100644
--- a/man/torch_svd.Rd
+++ b/man/torch_svd.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_svd}
 \alias{torch_svd}
 \title{Svd}
+\usage{
+torch_svd(self, some = TRUE, compute_uv = TRUE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \eqn{(*, m, n)} where \code{*} is zero or more                    batch dimensions consisting of \eqn{m \times n} matrices.}
+\item{self}{(Tensor) the input tensor of size \eqn{(*, m, n)} where \code{*} is zero or more                    batch dimensions consisting of \eqn{m \times n} matrices.}
 
 \item{some}{(bool, optional) controls the shape of returned \code{U} and \code{V}}
 
diff --git a/man/torch_symeig.Rd b/man/torch_symeig.Rd
index 85871a34f..121cdc7d0 100644
--- a/man/torch_symeig.Rd
+++ b/man/torch_symeig.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_symeig}
 \alias{torch_symeig}
 \title{Symeig}
+\usage{
+torch_symeig(self, eigenvectors = FALSE, upper = TRUE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \eqn{(*, n, n)} where \code{*} is zero or more                    batch dimensions consisting of symmetric matrices.}
+\item{self}{(Tensor) the input tensor of size \eqn{(*, n, n)} where \code{*} is zero or more                    batch dimensions consisting of symmetric matrices.}
 
 \item{eigenvectors}{(boolean, optional) controls whether eigenvectors have to be computed}
 
diff --git a/man/torch_t.Rd b/man/torch_t.Rd
index 5f74e590f..ccaa48f7c 100644
--- a/man/torch_t.Rd
+++ b/man/torch_t.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_t}
 \alias{torch_t}
 \title{T}
+\usage{
+torch_t(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 T
diff --git a/man/torch_take.Rd b/man/torch_take.Rd
index 0de56ae01..54af7784b 100644
--- a/man/torch_take.Rd
+++ b/man/torch_take.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_take}
 \alias{torch_take}
 \title{Take}
+\usage{
+torch_take(self, index)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{indices}{(LongTensor) the indices into tensor}
 }
diff --git a/man/torch_tan.Rd b/man/torch_tan.Rd
index 6b75f0017..84b3b2a82 100644
--- a/man/torch_tan.Rd
+++ b/man/torch_tan.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_tan}
 \alias{torch_tan}
 \title{Tan}
+\usage{
+torch_tan(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_tanh.Rd b/man/torch_tanh.Rd
index 36e79c323..e35dc93f3 100644
--- a/man/torch_tanh.Rd
+++ b/man/torch_tanh.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_tanh}
 \alias{torch_tanh}
 \title{Tanh}
+\usage{
+torch_tanh(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_tensordot.Rd b/man/torch_tensordot.Rd
index fc8bae339..5c04daacb 100644
--- a/man/torch_tensordot.Rd
+++ b/man/torch_tensordot.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_tensordot}
 \alias{torch_tensordot}
 \title{Tensordot}
+\usage{
+torch_tensordot(self, other, dims_self, dims_other)
+}
 \arguments{
 \item{a}{(Tensor) Left tensor to contract}
 
diff --git a/man/torch_threshold_.Rd b/man/torch_threshold_.Rd
index f93e84d64..62f35e637 100644
--- a/man/torch_threshold_.Rd
+++ b/man/torch_threshold_.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_threshold_}
 \alias{torch_threshold_}
 \title{Threshold_}
+\usage{
+torch_threshold_(self, threshold, value)
+}
 \description{
 Threshold_
 }
diff --git a/man/torch_topk.Rd b/man/torch_topk.Rd
index a37b2d99b..de6c7d32b 100644
--- a/man/torch_topk.Rd
+++ b/man/torch_topk.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_topk}
 \alias{torch_topk}
 \title{Topk}
+\usage{
+torch_topk(self, k, dim = -1L, largest = TRUE, sorted = TRUE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{k}{(int) the k in "top-k"}
 
diff --git a/man/torch_trace.Rd b/man/torch_trace.Rd
index 5961dd02a..93fbe4fa1 100644
--- a/man/torch_trace.Rd
+++ b/man/torch_trace.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_trace}
 \alias{torch_trace}
 \title{Trace}
+\usage{
+torch_trace(self)
+}
 \description{
 Trace
 }
diff --git a/man/torch_transpose.Rd b/man/torch_transpose.Rd
index 937c04347..425ea49e5 100644
--- a/man/torch_transpose.Rd
+++ b/man/torch_transpose.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_transpose}
 \alias{torch_transpose}
 \title{Transpose}
+\usage{
+torch_transpose(self, dim0, dim1)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim0}{(int) the first dimension to be transposed}
 
diff --git a/man/torch_trapz.Rd b/man/torch_trapz.Rd
index 81042d8d4..8c3916657 100644
--- a/man/torch_trapz.Rd
+++ b/man/torch_trapz.Rd
@@ -1,17 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_trapz}
 \alias{torch_trapz}
 \title{Trapz}
+\usage{
+torch_trapz(y, dx = 1L, x, dim = -1L)
+}
 \arguments{
 \item{y}{(Tensor) The values of the function to integrate}
 
+\item{dx}{(float) The distance between points at which \code{y} is sampled.}
+
 \item{x}{(Tensor) The points at which the function \code{y} is sampled.        If \code{x} is not in ascending order, intervals on which it is decreasing        contribute negatively to the estimated integral (i.e., the convention        \eqn{\int_a^b f = -\int_b^a f} is followed).}
 
 \item{dim}{(int) The dimension along which to integrate.        By default, use the last dimension.}
-
-\item{dx}{(float) The distance between points at which \code{y} is sampled.}
 }
 \description{
 Trapz
diff --git a/man/torch_triangular_solve.Rd b/man/torch_triangular_solve.Rd
index c01554484..26e39faf6 100644
--- a/man/torch_triangular_solve.Rd
+++ b/man/torch_triangular_solve.Rd
@@ -1,11 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_triangular_solve}
 \alias{torch_triangular_solve}
 \title{Triangular_solve}
+\usage{
+torch_triangular_solve(
+  self,
+  A,
+  upper = TRUE,
+  transpose = FALSE,
+  unitriangular = FALSE
+)
+}
 \arguments{
-\item{input}{(Tensor) multiple right-hand sides of size \eqn{(*, m, k)} where                \eqn{*} is zero of more batch dimensions (\eqn{b})}
+\item{self}{(Tensor) multiple right-hand sides of size \eqn{(*, m, k)} where                \eqn{*} is zero of more batch dimensions (\eqn{b})}
 
 \item{A}{(Tensor) the input triangular coefficient matrix of size \eqn{(*, m, m)}                where \eqn{*} is zero or more batch dimensions}
 
diff --git a/man/torch_tril.Rd b/man/torch_tril.Rd
index e09c3c4a4..de8dd189f 100644
--- a/man/torch_tril.Rd
+++ b/man/torch_tril.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_tril}
 \alias{torch_tril}
 \title{Tril}
+\usage{
+torch_tril(self, diagonal = 0L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{diagonal}{(int, optional) the diagonal to consider}
 
diff --git a/man/torch_tril_indices.Rd b/man/torch_tril_indices.Rd
index 36bac6c9d..639ae3ed8 100644
--- a/man/torch_tril_indices.Rd
+++ b/man/torch_tril_indices.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_tril_indices}
 \alias{torch_tril_indices}
 \title{Tril_indices}
+\usage{
+torch_tril_indices(row, col, offset = 0L, options = torch_long())
+}
 \arguments{
 \item{row}{(\code{int}) number of rows in the 2-D matrix.}
 
diff --git a/man/torch_triu.Rd b/man/torch_triu.Rd
index 57ff765f6..a2ac32e33 100644
--- a/man/torch_triu.Rd
+++ b/man/torch_triu.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_triu}
 \alias{torch_triu}
 \title{Triu}
+\usage{
+torch_triu(self, diagonal = 0L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{diagonal}{(int, optional) the diagonal to consider}
 
diff --git a/man/torch_triu_indices.Rd b/man/torch_triu_indices.Rd
index 7a1f3cf06..810428d12 100644
--- a/man/torch_triu_indices.Rd
+++ b/man/torch_triu_indices.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_triu_indices}
 \alias{torch_triu_indices}
 \title{Triu_indices}
+\usage{
+torch_triu_indices(row, col, offset = 0L, options = torch_long())
+}
 \arguments{
 \item{row}{(\code{int}) number of rows in the 2-D matrix.}
 
diff --git a/man/torch_true_divide.Rd b/man/torch_true_divide.Rd
index 493543389..6a845856c 100644
--- a/man/torch_true_divide.Rd
+++ b/man/torch_true_divide.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_true_divide}
 \alias{torch_true_divide}
 \title{True_divide}
+\usage{
+torch_true_divide(self, other)
+}
 \arguments{
 \item{dividend}{(Tensor) the dividend}
 
diff --git a/man/torch_trunc.Rd b/man/torch_trunc.Rd
index a0599bf98..92ae7ab65 100644
--- a/man/torch_trunc.Rd
+++ b/man/torch_trunc.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_trunc}
 \alias{torch_trunc}
 \title{Trunc}
+\usage{
+torch_trunc(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{out}{(Tensor, optional) the output tensor.}
 }
diff --git a/man/torch_unbind.Rd b/man/torch_unbind.Rd
index 2f37ed2c1..5a47d648a 100644
--- a/man/torch_unbind.Rd
+++ b/man/torch_unbind.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_unbind}
 \alias{torch_unbind}
 \title{Unbind}
+\usage{
+torch_unbind(self, dim = 1L)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to unbind}
+\item{self}{(Tensor) the tensor to unbind}
 
 \item{dim}{(int) dimension to remove}
 }
diff --git a/man/torch_unique_consecutive.Rd b/man/torch_unique_consecutive.Rd
index a362da738..c106f866d 100644
--- a/man/torch_unique_consecutive.Rd
+++ b/man/torch_unique_consecutive.Rd
@@ -1,11 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_unique_consecutive}
 \alias{torch_unique_consecutive}
 \title{Unique_consecutive}
+\usage{
+torch_unique_consecutive(
+  self,
+  return_inverse = FALSE,
+  return_counts = FALSE,
+  dim = NULL
+)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor}
+\item{self}{(Tensor) the input tensor}
 
 \item{return_inverse}{(bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.}
 
diff --git a/man/torch_unsqueeze.Rd b/man/torch_unsqueeze.Rd
index 3d9b23a12..12154c973 100644
--- a/man/torch_unsqueeze.Rd
+++ b/man/torch_unsqueeze.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_unsqueeze}
 \alias{torch_unsqueeze}
 \title{Unsqueeze}
+\usage{
+torch_unsqueeze(self, dim)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the index at which to insert the singleton dimension}
 }
diff --git a/man/torch_var.Rd b/man/torch_var.Rd
index 91d5092d9..4e7495952 100644
--- a/man/torch_var.Rd
+++ b/man/torch_var.Rd
@@ -1,16 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_var}
 \alias{torch_var}
 \title{Var}
+\usage{
+torch_var(self, dim, unbiased = TRUE, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
+\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
 
 \item{out}{(Tensor, optional) the output tensor.}
diff --git a/man/torch_var_mean.Rd b/man/torch_var_mean.Rd
index 95b42720c..e8157e315 100644
--- a/man/torch_var_mean.Rd
+++ b/man/torch_var_mean.Rd
@@ -1,16 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_var_mean}
 \alias{torch_var_mean}
 \title{Var_mean}
+\usage{
+torch_var_mean(self, dim, unbiased = TRUE, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
+\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
 }
 \description{
diff --git a/man/torch_where.Rd b/man/torch_where.Rd
index 2e119d46e..9b95e2ff0 100644
--- a/man/torch_where.Rd
+++ b/man/torch_where.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_where}
 \alias{torch_where}
 \title{Where}
+\usage{
+torch_where(condition, self, other)
+}
 \arguments{
 \item{condition}{(BoolTensor) When True (nonzero), yield x, otherwise yield y}
 
diff --git a/man/torch_zeros.Rd b/man/torch_zeros.Rd
index e060eac58..c8d9208eb 100644
--- a/man/torch_zeros.Rd
+++ b/man/torch_zeros.Rd
@@ -1,14 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_zeros}
 \alias{torch_zeros}
 \title{Zeros}
+\usage{
+torch_zeros(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
-\item{size}{(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.}
-
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -16,6 +22,10 @@
 \item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+
+\item{size}{(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.}
+
+\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Zeros
diff --git a/man/torch_zeros_like.Rd b/man/torch_zeros_like.Rd
index 3c82caf7e..65533b538 100644
--- a/man/torch_zeros_like.Rd
+++ b/man/torch_zeros_like.Rd
@@ -1,12 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_zeros_like}
 \alias{torch_zeros_like}
 \title{Zeros_like}
+\usage{
+torch_zeros_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
-\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
@@ -16,6 +24,8 @@
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
+
+\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 }
 \description{
 Zeros_like
-- 
GitLab


From f25f40edf8d3c952ab352c5c8ae698f7d651d577 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 3 Sep 2020 17:10:53 -0300
Subject: [PATCH 103/157] keep organizing docs

---
 NAMESPACE                        |   2 +
 R/creation-ops.R                 |   2 +-
 R/gen-namespace-docs.R           | 818 +++++++++++++++----------------
 man/torch_acos.Rd                |   4 +-
 man/torch_adaptive_avg_pool1d.Rd |   6 +-
 man/torch_add.Rd                 |   8 +-
 man/torch_addbmm.Rd              |   4 +-
 man/torch_addcdiv.Rd             |   4 +-
 man/torch_addcmul.Rd             |   4 +-
 man/torch_addmm.Rd               |   4 +-
 man/torch_addmv.Rd               |   4 +-
 man/torch_addr.Rd                |   4 +-
 man/torch_allclose.Rd            |   2 +-
 man/torch_angle.Rd               |   4 +-
 man/torch_arange.Rd              |  10 +-
 man/torch_argmax.Rd              |   4 +-
 man/torch_argmin.Rd              |   6 +-
 man/torch_asin.Rd                |   4 +-
 man/torch_atan.Rd                |   4 +-
 man/torch_atan2.Rd               |   4 +-
 man/torch_avg_pool1d.Rd          |  16 +-
 man/torch_baddbmm.Rd             |   4 +-
 man/torch_bartlett_window.Rd     |  12 +-
 man/torch_bernoulli.Rd           |  10 +-
 man/torch_bincount.Rd            |   2 +-
 man/torch_bitwise_and.Rd         |   4 +-
 man/torch_bitwise_not.Rd         |   4 +-
 man/torch_bitwise_or.Rd          |   4 +-
 man/torch_bitwise_xor.Rd         |   4 +-
 man/torch_blackman_window.Rd     |  12 +-
 man/torch_bmm.Rd                 |   4 +-
 man/torch_broadcast_tensors.Rd   |   4 +-
 man/torch_cartesian_prod.Rd      |  11 +-
 man/torch_cat.Rd                 |   4 +-
 man/torch_ceil.Rd                |   4 +-
 man/torch_celu.Rd                |  22 +
 man/torch_celu_.Rd               |   7 +-
 man/torch_cholesky.Rd            |  18 +-
 man/torch_cholesky_inverse.Rd    |   8 +-
 man/torch_cholesky_solve.Rd      |  12 +-
 man/torch_clamp.Rd               |  10 +-
 man/torch_combinations.Rd        |   2 +-
 man/torch_conj.Rd                |   4 +-
 man/torch_conv1d.Rd              |  20 +-
 man/torch_conv2d.Rd              |  20 +-
 man/torch_conv3d.Rd              |  20 +-
 man/torch_conv_transpose1d.Rd    |  22 +-
 man/torch_conv_transpose2d.Rd    |  22 +-
 man/torch_conv_transpose3d.Rd    |  22 +-
 man/torch_cos.Rd                 |   4 +-
 man/torch_cosh.Rd                |   4 +-
 man/torch_cross.Rd               |   4 +-
 man/torch_cummax.Rd              |   4 +-
 man/torch_cummin.Rd              |   4 +-
 man/torch_cumprod.Rd             |   6 +-
 man/torch_cumsum.Rd              |   6 +-
 man/torch_diag.Rd                |   4 +-
 man/torch_diagonal.Rd            |   2 +
 man/torch_digamma.Rd             |   2 +-
 man/torch_div.Rd                 |   2 +-
 man/torch_dot.Rd                 |   5 +
 man/torch_eig.Rd                 |   6 +-
 man/torch_einsum.Rd              |   2 +-
 man/torch_empty.Rd               |  18 +-
 man/torch_empty_like.Rd          |  14 +-
 man/torch_empty_strided.Rd       |  10 +-
 man/torch_eq.Rd                  |   7 +-
 man/torch_equal.Rd               |   7 +-
 man/torch_erf.Rd                 |   4 +-
 man/torch_erfc.Rd                |   4 +-
 man/torch_erfinv.Rd              |   4 +-
 man/torch_exp.Rd                 |   4 +-
 man/torch_expm1.Rd               |   4 +-
 man/torch_eye.Rd                 |  10 +-
 man/torch_fft.Rd                 |   4 +-
 man/torch_flatten.Rd             |   5 +
 man/torch_floor.Rd               |   4 +-
 man/torch_floor_divide.Rd        |   2 +-
 man/torch_fmod.Rd                |   4 +-
 man/torch_frac.Rd                |   5 +-
 man/torch_full.Rd                |  12 +-
 man/torch_full_like.Rd           |  16 +-
 man/torch_gather.Rd              |   6 +-
 man/torch_ge.Rd                  |   4 +-
 man/torch_geqrf.Rd               |   4 +-
 man/torch_ger.Rd                 |   4 +-
 man/torch_gt.Rd                  |   4 +-
 man/torch_hamming_window.Rd      |  12 +-
 man/torch_hann_window.Rd         |  12 +-
 man/torch_histc.Rd               |   4 +-
 man/torch_ifft.Rd                |   4 +-
 man/torch_imag.Rd                |   4 +-
 man/torch_index_select.Rd        |   4 +-
 man/torch_inverse.Rd             |   4 +-
 man/torch_irfft.Rd               |  16 +-
 man/torch_is_complex.Rd          |   2 +-
 man/torch_is_floating_point.Rd   |   2 +-
 man/torch_isfinite.Rd            |   2 +-
 man/torch_isinf.Rd               |   2 +-
 man/torch_kthvalue.Rd            |   6 +-
 man/torch_le.Rd                  |   4 +-
 man/torch_lerp.Rd                |   4 +-
 man/torch_lgamma.Rd              |   4 +-
 man/torch_linspace.Rd            |  22 +-
 man/torch_log.Rd                 |   4 +-
 man/torch_log10.Rd               |   4 +-
 man/torch_log1p.Rd               |   4 +-
 man/torch_log2.Rd                |   4 +-
 man/torch_logdet.Rd              |   2 +-
 man/torch_logical_and.Rd         |   8 +-
 man/torch_logical_not.Rd         |   6 +-
 man/torch_logical_or.Rd          |   8 +-
 man/torch_logical_xor.Rd         |   8 +-
 man/torch_logspace.Rd            |  24 +-
 man/torch_logsumexp.Rd           |   6 +-
 man/torch_lstsq.Rd               |   4 +-
 man/torch_lt.Rd                  |   4 +-
 man/torch_lu_solve.Rd            |   8 +-
 man/torch_masked_select.Rd       |   4 +-
 man/torch_matmul.Rd              |   4 +-
 man/torch_matrix_rank.Rd         |  12 +-
 man/torch_max.Rd                 |   8 +-
 man/torch_mean.Rd                |   6 +-
 man/torch_median.Rd              |   6 +-
 man/torch_meshgrid.Rd            |   5 +-
 man/torch_min.Rd                 |   6 +-
 man/torch_mm.Rd                  |   4 +-
 man/torch_mode.Rd                |   6 +-
 man/torch_mul.Rd                 |  10 +-
 man/torch_multinomial.Rd         |   6 +-
 man/torch_mv.Rd                  |   4 +-
 man/torch_ne.Rd                  |   4 +-
 man/torch_neg.Rd                 |   4 +-
 man/torch_nonzero.Rd             |  14 +-
 man/torch_norm.Rd                |  11 +-
 man/torch_normal.Rd              |  10 +-
 man/torch_ones.Rd                |  14 +-
 man/torch_ones_like.Rd           |  14 +-
 man/torch_orgqr.Rd               |   2 +-
 man/torch_ormqr.Rd               |   6 +-
 man/torch_pdist.Rd               |   2 +-
 man/torch_poisson.Rd             |   2 +-
 man/torch_polygamma.Rd           |   4 +-
 man/torch_pow.Rd                 |   6 +-
 man/torch_prod.Rd                |   8 +-
 man/torch_qr.Rd                  |  10 +-
 man/torch_rand.Rd                |  14 +-
 man/torch_rand_like.Rd           |  14 +-
 man/torch_randint.Rd             |  12 +-
 man/torch_randint_like.Rd        |  16 +-
 man/torch_randn.Rd               |  14 +-
 man/torch_randn_like.Rd          |  10 +-
 man/torch_randperm.Rd            |   8 +-
 man/torch_range.Rd               |  10 +-
 man/torch_real.Rd                |   4 +-
 man/torch_reciprocal.Rd          |   4 +-
 man/torch_relu.Rd                |  18 +
 man/torch_relu_.Rd               |   5 +-
 man/torch_remainder.Rd           |   4 +-
 man/torch_renorm.Rd              |   4 +-
 man/torch_repeat_interleave.Rd   |   4 +-
 man/torch_rfft.Rd                |  10 +-
 man/torch_roll.Rd                |   2 +-
 man/torch_round.Rd               |   4 +-
 man/torch_rrelu_.Rd              |  11 +
 man/torch_rsqrt.Rd               |   4 +-
 man/torch_sigmoid.Rd             |   4 +-
 man/torch_sign.Rd                |   4 +-
 man/torch_sin.Rd                 |   4 +-
 man/torch_sinh.Rd                |   4 +-
 man/torch_slogdet.Rd             |   2 +-
 man/torch_solve.Rd               |   2 +-
 man/torch_sort.Rd                |   4 +-
 man/torch_sparse_coo_tensor.Rd   |   8 +-
 man/torch_split.Rd               |   2 +-
 man/torch_sqrt.Rd                |   4 +-
 man/torch_square.Rd              |   4 +-
 man/torch_squeeze.Rd             |   4 +-
 man/torch_stack.Rd               |   4 +-
 man/torch_std.Rd                 |  12 +-
 man/torch_std_mean.Rd            |  10 +-
 man/torch_stft.Rd                |  34 +-
 man/torch_sum.Rd                 |   8 +-
 man/torch_svd.Rd                 |  10 +-
 man/torch_symeig.Rd              |   6 +-
 man/torch_tan.Rd                 |   4 +-
 man/torch_tanh.Rd                |   4 +-
 man/torch_topk.Rd                |   6 +-
 man/torch_triangular_solve.Rd    |   8 +-
 man/torch_tril.Rd                |   4 +-
 man/torch_tril_indices.Rd        |   6 +-
 man/torch_triu.Rd                |   4 +-
 man/torch_triu_indices.Rd        |   6 +-
 man/torch_true_divide.Rd         |   4 +-
 man/torch_trunc.Rd               |   4 +-
 man/torch_unique_consecutive.Rd  |   2 +-
 man/torch_var.Rd                 |  12 +-
 man/torch_var_mean.Rd            |  10 +-
 man/torch_where.Rd               |   8 +-
 man/torch_zeros.Rd               |  10 +-
 man/torch_zeros_like.Rd          |  10 +-
 201 files changed, 1026 insertions(+), 1197 deletions(-)
 create mode 100644 man/torch_celu.Rd
 create mode 100644 man/torch_relu.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 4c39b9219..00ed918e5 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -335,6 +335,7 @@ export(torch_cartesian_prod)
 export(torch_cat)
 export(torch_cdist)
 export(torch_ceil)
+export(torch_celu)
 export(torch_celu_)
 export(torch_chain_matmul)
 export(torch_channels_last_format)
@@ -507,6 +508,7 @@ export(torch_reciprocal)
 export(torch_reduction_mean)
 export(torch_reduction_none)
 export(torch_reduction_sum)
+export(torch_relu)
 export(torch_relu_)
 export(torch_remainder)
 export(torch_renorm)
diff --git a/R/creation-ops.R b/R/creation-ops.R
index 39faeceb4..2b3a01455 100644
--- a/R/creation-ops.R
+++ b/R/creation-ops.R
@@ -280,7 +280,7 @@ torch_linspace <- function(start, end, steps=100, dtype = NULL, layout = torch_s
   do.call(.torch_linspace, args)
 }
 
-#' @rdname torch_linspace
+#' @rdname torch_logspace
 torch_logspace <- function(start, end, steps=100, base=10, dtype = NULL, layout = torch_strided(), 
                            device=NULL, requires_grad = FALSE) {
   args <- list(
diff --git a/R/gen-namespace-docs.R b/R/gen-namespace-docs.R
index 65dbc50ef..b21e14423 100644
--- a/R/gen-namespace-docs.R
+++ b/R/gen-namespace-docs.R
@@ -150,10 +150,10 @@ NULL
 #' Applies a 1D adaptive average pooling over an input signal composed of
 #' several input planes.
 #' 
-#' See `~torch.nn.AdaptiveAvgPool1d` for details and output shape.
+#' See [nn_adaptive_avg_pool1d()] for details and output shape.
 #'
-#'
-#' @param output_size NA the target output size (single integer)
+#' @param self the input tensor
+#' @param output_size the target output size (single integer)
 #'
 #' @name torch_adaptive_avg_pool1d
 #'
@@ -191,8 +191,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param value (Number) the number to be added to each element of `input`
-#' @param other (Tensor) the second input tensor
+#' @param other (Tensor/Number) the second input tensor/number.
 #' @param alpha (Number) the scalar multiplier for `other`
 #'
 #' @name torch_add
@@ -229,7 +228,7 @@ NULL
 #' @param vec (Tensor) vector to be multiplied
 #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
 #' @param alpha (Number, optional) multiplier for \eqn{mat @ vec} (\eqn{\alpha})
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_addmv
 #'
@@ -266,7 +265,7 @@ NULL
 #' @param vec2 (Tensor) the second vector of the outer product
 #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
 #' @param alpha (Number, optional) multiplier for \eqn{\mbox{vec1} \otimes \mbox{vec2}} (\eqn{\alpha})
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_addr
 #'
@@ -304,7 +303,7 @@ NULL
 #' @section arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor :
 #'
 #' Returns a 1-D tensor of size \eqn{\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil}
-#' with values from the interval ``[start, end)`` taken with common difference
+#' with values from the interval `[start, end)` taken with common difference
 #' `step` beginning from `start`.
 #' 
 #' Note that non-integer `step` is subject to floating point rounding errors when
@@ -316,12 +315,12 @@ NULL
 #' }
 #'
 #'
-#' @param start (Number) the starting value for the set of points. Default: ``0``.
+#' @param start (Number) the starting value for the set of points. Default: `0`.
 #' @param end (Number) the ending value for the set of points
-#' @param step (Number) the gap between each pair of adjacent points. Default: ``1``.
-#' @param out (Tensor, optional) the output tensor.
+#' @param step (Number) the gap between each pair of adjacent points. Default: `1`.
+#' 
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). If `dtype` is not given, infer the data type from the other input        arguments. If any of `start`, `end`, or `stop` are floating-point, the        `dtype` is inferred to be the default dtype, see        `~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to        be `torch.int64`.
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
@@ -350,7 +349,7 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to reduce. If `NULL`, the argmax of the flattened input is returned.
-#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if ``dim=NULL``.
+#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if `dim=NULL`.
 #'
 #' @name torch_argmax
 #'
@@ -377,7 +376,7 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to reduce. If `NULL`, the argmin of the flattened input is returned.
-#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if ``dim=NULL``.
+#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if `dim=NULL`.
 #'
 #' @name torch_argmin
 #'
@@ -427,7 +426,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_asin
 #'
@@ -447,7 +446,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_atan
 #'
@@ -485,7 +484,7 @@ NULL
 #' @param batch2 (Tensor) the second batch of matrices to be multiplied
 #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
 #' @param alpha (Number, optional) multiplier for \eqn{\mbox{batch1} \mathbin{@} \mbox{batch2}} (\eqn{\alpha})
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_baddbmm
 #'
@@ -514,8 +513,8 @@ NULL
 #' ready to be used as a periodic window with functions like
 #' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in
 #' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-#' ``torch_bartlett_window(L, periodic=TRUE)`` equal to
-#' ``torch_bartlett_window(L + 1, periodic=False)[:-1])``.
+#' `torch_bartlett_window(L, periodic=TRUE)` equal to
+#' `torch_bartlett_window(L + 1, periodic=False)[:-1])`.
 #' 
 #' @note
 #'     If `window_length` \eqn{=1}, the returned window contains a single value 1.
@@ -524,7 +523,7 @@ NULL
 #' @param window_length (int) the size of returned window
 #' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
-#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          ``torch_strided`` (dense layout) is supported.
+#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          `torch_strided` (dense layout) is supported.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
@@ -555,13 +554,16 @@ NULL
 #' The returned `out` tensor only has values 0 or 1 and is of the same
 #' shape as `input`.
 #' 
-#' `out` can have integral ``dtype``, but `input` must have floating
-#' point ``dtype``.
+#' `out` can have integral `dtype`, but `input` must have floating
+#' point `dtype`.
 #'
 #'
-#' @param self (Tensor) the input tensor of probability values for the Bernoulli distribution
+#' @param self (Tensor) the input tensor of probability values for the Bernoulli 
+#'   distribution
+#' @param p (Number) a probability value. If `p` is passed than it's used instead of 
+#'   the values in `self` tensor.
 #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_bernoulli
 #'
@@ -579,9 +581,9 @@ NULL
 #' `input` unless `input` is empty, in which case the result is a
 #' tensor of size 0. If `minlength` is specified, the number of bins is at least
 #' `minlength` and if `input` is empty, then the result is tensor of size
-#' `minlength` filled with zeros. If ``n`` is the value at position ``i``,
-#' ``out[n] += weights[i]`` if `weights` is specified else
-#' ``out[n] += 1``.
+#' `minlength` filled with zeros. If `n` is the value at position `i`,
+#' `out[n] += weights[i]` if `weights` is specified else
+#' `out[n] += 1`.
 #' 
 #' .. include:: cuda_deterministic.rst
 #'
@@ -605,7 +607,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_bitwise_not
 #'
@@ -622,7 +624,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_logical_not
 #'
@@ -640,7 +642,7 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param other (Tensor) the tensor to compute XOR with
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_logical_xor
 #'
@@ -658,7 +660,7 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param other (Tensor) the tensor to compute AND with
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_logical_and
 #'
@@ -676,7 +678,7 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param other (Tensor) the tensor to compute OR with
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_logical_or
 #'
@@ -701,8 +703,8 @@ NULL
 #' ready to be used as a periodic window with functions like
 #' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in
 #' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-#' ``torch_blackman_window(L, periodic=TRUE)`` equal to
-#' ``torch_blackman_window(L + 1, periodic=False)[:-1])``.
+#' `torch_blackman_window(L, periodic=TRUE)` equal to
+#' `torch_blackman_window(L + 1, periodic=False)[:-1])`.
 #' 
 #' @note
 #'     If `window_length` \eqn{=1}, the returned window contains a single value 1.
@@ -711,7 +713,7 @@ NULL
 #' @param window_length (int) the size of returned window
 #' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
-#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          ``torch_strided`` (dense layout) is supported.
+#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          `torch_strided` (dense layout) is supported.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
@@ -744,7 +746,7 @@ NULL
 #'
 #' @param self (Tensor) the first batch of matrices to be multiplied
 #' @param mat2 (Tensor) the second batch of matrices to be multiplied
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_bmm
 #'
@@ -754,12 +756,11 @@ NULL
 
 #' Broadcast_tensors
 #'
-#' @section broadcast_tensors(*tensors) -> List of Tensors :
+#' @section broadcast_tensors(tensors) -> List of Tensors :
 #'
 #' Broadcasts the given tensors according to broadcasting-semantics.
 #'
-#'
-#' @param *tensors NA any number of tensors of the same type
+#' @param tensors a list containing any number of tensors of the same type
 #'
 #' @name torch_broadcast_tensors
 #'
@@ -783,7 +784,7 @@ NULL
 #'
 #' @param tensors (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.
 #' @param dim (int, optional) the dimension over which the tensors are concatenated
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_cat
 #'
@@ -804,7 +805,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_ceil
 #'
@@ -888,8 +889,7 @@ NULL
 #' @param self (Tensor) the input tensor.
 #' @param min (Number) lower-bound of the range to be clamped to
 #' @param max (Number) upper-bound of the range to be clamped to
-#' @param out (Tensor, optional) the output tensor.
-#' @param value (Number) minimal value of each element in the output
+#' 
 #'
 #' @name torch_clamp
 #'
@@ -904,18 +904,16 @@ NULL
 #' Applies a 1D convolution over an input signal composed of several input
 #' planes.
 #' 
-#' See `~torch.nn.Conv1d` for details and output shape.
-#' 
-#' .. include:: cudnn_deterministic.rst
+#' See [nn_conv1d()] for details and output shape.
 #'
 #'
-#' @param self NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
-#' @param weight NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)}
-#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: `NULL`
-#' @param stride NA the stride of the convolving kernel. Can be a single number or      a one-element tuple `(sW,)`. Default: 1
-#' @param padding NA implicit paddings on both sides of the input. Can be a      single number or a one-element tuple `(padW,)`. Default: 0
-#' @param dilation NA the spacing between kernel elements. Can be a single number or      a one-element tuple `(dW,)`. Default: 1
-#' @param groups NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1
+#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
+#' @param weight filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)}
+#' @param bias optional bias of shape \eqn{(\mbox{out\_channels})}. Default: `NULL`
+#' @param stride the stride of the convolving kernel. Can be a single number or      a one-element tuple `(sW,)`. Default: 1
+#' @param padding implicit paddings on both sides of the input. Can be a      single number or a one-element tuple `(padW,)`. Default: 0
+#' @param dilation the spacing between kernel elements. Can be a single number or      a one-element tuple `(dW,)`. Default: 1
+#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1
 #'
 #' @name torch_conv1d
 #'
@@ -930,18 +928,16 @@ NULL
 #' Applies a 2D convolution over an input image composed of several input
 #' planes.
 #' 
-#' See `~torch.nn.Conv2d` for details and output shape.
-#' 
-#' .. include:: cudnn_deterministic.rst
+#' See [nn_conv2d()] for details and output shape.
 #'
 #'
-#' @param self NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}
-#' @param weight NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)}
-#' @param bias NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: `NULL`
-#' @param stride NA the stride of the convolving kernel. Can be a single number or a      tuple `(sH, sW)`. Default: 1
-#' @param padding NA implicit paddings on both sides of the input. Can be a      single number or a tuple `(padH, padW)`. Default: 0
-#' @param dilation NA the spacing between kernel elements. Can be a single number or      a tuple `(dH, dW)`. Default: 1
-#' @param groups NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1
+#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}
+#' @param weight filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)}
+#' @param bias optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: `NULL`
+#' @param stride the stride of the convolving kernel. Can be a single number or a      tuple `(sH, sW)`. Default: 1
+#' @param padding implicit paddings on both sides of the input. Can be a      single number or a tuple `(padH, padW)`. Default: 0
+#' @param dilation the spacing between kernel elements. Can be a single number or      a tuple `(dH, dW)`. Default: 1
+#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1
 #'
 #' @name torch_conv2d
 #'
@@ -956,18 +952,16 @@ NULL
 #' Applies a 3D convolution over an input image composed of several input
 #' planes.
 #' 
-#' See `~torch.nn.Conv3d` for details and output shape.
-#' 
-#' .. include:: cudnn_deterministic.rst
+#' See [nn_conv3d()] for details and output shape.
 #'
 #'
-#' @param self NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}
-#' @param weight NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)}
-#' @param bias NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: NULL
-#' @param stride NA the stride of the convolving kernel. Can be a single number or a      tuple `(sT, sH, sW)`. Default: 1
-#' @param padding NA implicit paddings on both sides of the input. Can be a      single number or a tuple `(padT, padH, padW)`. Default: 0
-#' @param dilation NA the spacing between kernel elements. Can be a single number or      a tuple `(dT, dH, dW)`. Default: 1
-#' @param groups NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1
+#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}
+#' @param weight filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)}
+#' @param bias optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: NULL
+#' @param stride the stride of the convolving kernel. Can be a single number or a      tuple `(sT, sH, sW)`. Default: 1
+#' @param padding implicit paddings on both sides of the input. Can be a      single number or a tuple `(padT, padH, padW)`. Default: 0
+#' @param dilation the spacing between kernel elements. Can be a single number or      a tuple `(dT, dH, dW)`. Default: 1
+#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1
 #'
 #' @name torch_conv3d
 #'
@@ -1001,19 +995,16 @@ NULL
 #' Applies a 1D transposed convolution operator over an input signal
 #' composed of several input planes, sometimes also called "deconvolution".
 #' 
-#' See `~torch.nn.ConvTranspose1d` for details and output shape.
-#' 
-#' .. include:: cudnn_deterministic.rst
-#'
+#' See [nn_conv_transpose1d()]  for details and output shape.
 #'
-#' @param self NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
-#' @param weight NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)}
-#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL
-#' @param stride NA the stride of the convolving kernel. Can be a single number or a      tuple ``(sW,)``. Default: 1
-#' @param padding NA ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      ``(padW,)``. Default: 0
-#' @param output_padding NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple ``(out_padW)``. Default: 0
-#' @param groups NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1
-#' @param dilation NA the spacing between kernel elements. Can be a single number or      a tuple ``(dW,)``. Default: 1
+#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}
+#' @param weight filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)}
+#' @param bias optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL
+#' @param stride the stride of the convolving kernel. Can be a single number or a      tuple `(sW,)`. Default: 1
+#' @param padding `dilation * (kernel_size - 1) - padding` zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      `(padW,)`. Default: 0
+#' @param output_padding additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple `(out_padW)`. Default: 0
+#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1
+#' @param dilation the spacing between kernel elements. Can be a single number or      a tuple `(dW,)`. Default: 1
 #'
 #' @name torch_conv_transpose1d
 #'
@@ -1028,19 +1019,17 @@ NULL
 #' Applies a 2D transposed convolution operator over an input image
 #' composed of several input planes, sometimes also called "deconvolution".
 #' 
-#' See `~torch.nn.ConvTranspose2d` for details and output shape.
-#' 
-#' .. include:: cudnn_deterministic.rst
+#' See [nn_conv_transpose2d()] for details and output shape.
 #'
 #'
-#' @param self NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}
-#' @param weight NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)}
-#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL
-#' @param stride NA the stride of the convolving kernel. Can be a single number or a      tuple ``(sH, sW)``. Default: 1
-#' @param padding NA ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      ``(padH, padW)``. Default: 0
-#' @param output_padding NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple ``(out_padH, out_padW)``.      Default: 0
-#' @param groups NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1
-#' @param dilation NA the spacing between kernel elements. Can be a single number or      a tuple ``(dH, dW)``. Default: 1
+#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}
+#' @param weight filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)}
+#' @param bias optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL
+#' @param stride the stride of the convolving kernel. Can be a single number or a      tuple `(sH, sW)`. Default: 1
+#' @param padding `dilation * (kernel_size - 1) - padding` zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      `(padH, padW)`. Default: 0
+#' @param output_padding additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple `(out_padH, out_padW)`.      Default: 0
+#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1
+#' @param dilation the spacing between kernel elements. Can be a single number or      a tuple `(dH, dW)`. Default: 1
 #'
 #' @name torch_conv_transpose2d
 #'
@@ -1055,19 +1044,16 @@ NULL
 #' Applies a 3D transposed convolution operator over an input image
 #' composed of several input planes, sometimes also called "deconvolution"
 #' 
-#' See `~torch.nn.ConvTranspose3d` for details and output shape.
-#' 
-#' .. include:: cudnn_deterministic.rst
+#' See [nn_conv_transpose3d()] for details and output shape.
 #'
-#'
-#' @param self NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}
-#' @param weight NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)}
-#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL
-#' @param stride NA the stride of the convolving kernel. Can be a single number or a      tuple ``(sT, sH, sW)``. Default: 1
-#' @param padding NA ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      ``(padT, padH, padW)``. Default: 0
-#' @param output_padding NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      ``(out_padT, out_padH, out_padW)``. Default: 0
-#' @param groups NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1
-#' @param dilation NA the spacing between kernel elements. Can be a single number or      a tuple `(dT, dH, dW)`. Default: 1
+#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}
+#' @param weight filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)}
+#' @param bias optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL
+#' @param stride the stride of the convolving kernel. Can be a single number or a      tuple `(sT, sH, sW)`. Default: 1
+#' @param padding `dilation * (kernel_size - 1) - padding` zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      `(padT, padH, padW)`. Default: 0
+#' @param output_padding additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      `(out_padT, out_padH, out_padW)`. Default: 0
+#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1
+#' @param dilation the spacing between kernel elements. Can be a single number or      a tuple `(dT, dH, dW)`. Default: 1
 #'
 #' @name torch_conv_transpose3d
 #'
@@ -1087,7 +1073,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_cos
 #'
@@ -1108,7 +1094,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_cosh
 #'
@@ -1118,10 +1104,10 @@ NULL
 
 #' Cummax
 #'
-#' @section cummax(input, dim, out=NULL) -> (Tensor, LongTensor) :
+#' @section cummax(input, dim) -> (Tensor, LongTensor) :
 #'
-#' Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative maximum of
-#' elements of `input` in the dimension `dim`. And ``indices`` is the index
+#' Returns a namedtuple `(values, indices)` where `values` is the cumulative maximum of
+#' elements of `input` in the dimension `dim`. And `indices` is the index
 #' location of each maximum value found in the dimension `dim`.
 #' 
 #' \deqn{
@@ -1131,7 +1117,6 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to do the operation over
-#' @param out (tuple, optional) the result tuple of two output tensors (values, indices)
 #'
 #' @name torch_cummax
 #'
@@ -1141,10 +1126,10 @@ NULL
 
 #' Cummin
 #'
-#' @section cummin(input, dim, out=NULL) -> (Tensor, LongTensor) :
+#' @section cummin(input, dim) -> (Tensor, LongTensor) :
 #'
-#' Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative minimum of
-#' elements of `input` in the dimension `dim`. And ``indices`` is the index
+#' Returns a namedtuple `(values, indices)` where `values` is the cumulative minimum of
+#' elements of `input` in the dimension `dim`. And `indices` is the index
 #' location of each maximum value found in the dimension `dim`.
 #' 
 #' \deqn{
@@ -1154,7 +1139,6 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to do the operation over
-#' @param out (tuple, optional) the result tuple of two output tensors (values, indices)
 #'
 #' @name torch_cummin
 #'
@@ -1180,7 +1164,7 @@ NULL
 #' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to do the operation over
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_cumprod
 #'
@@ -1206,7 +1190,7 @@ NULL
 #' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to do the operation over
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to `dtype` before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_cumsum
 #'
@@ -1227,7 +1211,7 @@ NULL
 #'     `~torch.svd` for details.
 #'
 #'
-#' @param self (Tensor) the input tensor of size ``(*, n, n)`` where ``*`` is zero or more                batch dimensions.
+#' @param self (Tensor) the input tensor of size `(*, n, n)` where `*` is zero or more                batch dimensions.
 #'
 #' @name torch_det
 #'
@@ -1266,7 +1250,7 @@ NULL
 #' @param offset (int, optional) which diagonal to consider. Default: 0        (main diagonal).
 #' @param dim1 (int, optional) first dimension with respect to which to        take diagonal. Default: -2.
 #' @param dim2 (int, optional) second dimension with respect to which to        take diagonal. Default: -1.
-#'
+#' 
 #' @name torch_diag_embed
 #'
 #' @export
@@ -1322,6 +1306,7 @@ NULL
 #' @param offset (int, optional) which diagonal to consider. Default: 0        (main diagonal).
 #' @param dim1 (int, optional) first dimension with respect to which to        take diagonal. Default: 0.
 #' @param dim2 (int, optional) second dimension with respect to which to        take diagonal. Default: 1.
+#' @param outdim dimension name if `self` is a named tensor.
 #'
 #' @name torch_diagonal
 #'
@@ -1333,7 +1318,7 @@ NULL
 #'
 #' @section div(input, other, out=NULL) -> Tensor :
 #'
-#' Divides each element of the input ``input`` with the scalar ``other`` and
+#' Divides each element of the input `input` with the scalar `other` and
 #' returns a new resulting tensor.
 #' 
 #' @section Warning:
@@ -1345,32 +1330,32 @@ NULL
 #' \deqn{
 #'     \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}}
 #' }
-#' If the `torch_dtype` of ``input`` and ``other`` differ, the
+#' If the `torch_dtype` of `input` and `other` differ, the
 #' `torch_dtype` of the result tensor is determined following rules
 #' described in the type promotion documentation . If
-#' ``out`` is specified, the result must be castable 
+#' `out` is specified, the result must be castable 
 #' to the `torch_dtype` of the specified output tensor. Integral division
 #' by zero leads to undefined behavior.
 #'
 #' @section div(input, other, out=NULL) -> Tensor :
 #'
-#' Each element of the tensor ``input`` is divided by each element of the tensor
-#' ``other``. The resulting tensor is returned.
+#' Each element of the tensor `input` is divided by each element of the tensor
+#' `other`. The resulting tensor is returned.
 #' 
 #' \deqn{
 #'     \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}_i}
 #' }
-#' The shapes of ``input`` and ``other`` must be broadcastable
-#' . If the `torch_dtype` of ``input`` and
-#' ``other`` differ, the `torch_dtype` of the result tensor is determined
+#' The shapes of `input` and `other` must be broadcastable
+#' . If the `torch_dtype` of `input` and
+#' `other` differ, the `torch_dtype` of the result tensor is determined
 #' following rules described in the type promotion documentation
-#' . If ``out`` is specified, the result must be
+#' . If `out` is specified, the result must be
 #' castable  to the `torch_dtype` of the
 #' specified output tensor. Integral division by zero leads to undefined behavior.
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param other (Number) the number to be divided to each element of ``input``
+#' @param other (Number) the number to be divided to each element of `input`
 #'
 #' @name torch_div
 #'
@@ -1386,8 +1371,8 @@ NULL
 #' 
 #' @note This function does not broadcast .
 #'
-#'
-#'
+#' @param self the input tensor
+#' @param tensor the other input tensor
 #'
 #' @name torch_dot
 #'
@@ -1402,9 +1387,8 @@ NULL
 #' This function provides a way of computing multilinear expressions (i.e. sums of products) using the
 #' Einstein summation convention.
 #'
-#'
 #' @param equation (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 `->` and gives the indices for the output.           If the `->` 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 `...` represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.
-#' @param operands (Tensor) The operands to compute the Einstein sum of.
+#' @param tensors (Tensor) The operands to compute the Einstein sum of.
 #'
 #' @name torch_einsum
 #'
@@ -1420,15 +1404,13 @@ NULL
 #' defined by the variable argument `size`.
 #'
 #'
-#' @param size (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.
-#' @param out (Tensor, optional) the output tensor.
+#' @param ... a sequence of integers defining the shape of the output tensor.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
-#' @param pin_memory (bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: `FALSE`.
-#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_contiguous_format``.
-#'
+#' @param names optional character vector naming each dimension.
+#' 
 #' @name torch_empty
 #'
 #' @export
@@ -1440,16 +1422,16 @@ NULL
 #' @section empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
 #'
 #' Returns an uninitialized tensor with the same size as `input`.
-#' ``torch_empty_like(input)`` is equivalent to
-#' ``torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
+#' `torch_empty_like(input)` is equivalent to
+#' `torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`.
 #'
 #'
-#' @param self (Tensor) the size of `input` will determine size of the output tensor.
+#' @param input (Tensor) the size of `input` will determine size of the output tensor.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
 #' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
-#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
+#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: `torch_preserve_format`.
 #'
 #' @name torch_empty_like
 #'
@@ -1463,8 +1445,8 @@ NULL
 #'
 #' Returns a tensor filled with uninitialized data. The shape and strides of the tensor is
 #' defined by the variable argument `size` and `stride` respectively.
-#' ``torch_empty_strided(size, stride)`` is equivalent to
-#' ``torch_empty(size).as_strided(size, stride)``.
+#' `torch_empty_strided(size, stride)` is equivalent to
+#' `torch_empty(size).as_strided(size, stride)`.
 #' 
 #' @section Warning:
 #'     More than one element of the created tensor may refer to a single memory
@@ -1476,7 +1458,7 @@ NULL
 #' @param size (tuple of ints) the shape of the output tensor
 #' @param stride (tuple of ints) the strides of the output tensor
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #' @param pin_memory (bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: `FALSE`.
@@ -1499,7 +1481,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_erf
 #'
@@ -1520,7 +1502,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_erfc
 #'
@@ -1541,7 +1523,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_exp
 #'
@@ -1562,7 +1544,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_expm1
 #'
@@ -1579,9 +1561,9 @@ NULL
 #'
 #' @param n (int) the number of rows
 #' @param m (int, optional) the number of columns with default being `n`
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
@@ -1601,6 +1583,9 @@ NULL
 #' @param self (Tensor) the input tensor.
 #' @param start_dim (int) the first dim to flatten
 #' @param end_dim (int) the last dim to flatten
+#' @param dims if tensor is named you can pass the name of the dimensions to 
+#'   flatten
+#' @param out_dim the name of the resulting dimension if a named tensor.
 #'
 #' @name torch_flatten
 #'
@@ -1621,7 +1606,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_floor
 #'
@@ -1659,6 +1644,8 @@ NULL
 #' \deqn{
 #'     \mbox{out}_{i} = \mbox{input}_{i} - \left\lfloor |\mbox{input}_{i}| \right\rfloor * \mbox{sgn}(\mbox{input}_{i})
 #' }
+#' 
+#' @param self the input tensor.
 #'
 #'
 #'
@@ -1685,11 +1672,12 @@ NULL
 #'
 #' @param size (int...) a list, tuple, or `torch_Size` of integers defining the        shape of the output tensor.
 #' @param fill_value NA the number to fill the output tensor with.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
+#' @param names optional names of the dimensions
 #'
 #' @name torch_full
 #'
@@ -1704,17 +1692,17 @@ NULL
 #' memory_format=torch.preserve_format) -> Tensor
 #' 
 #' Returns a tensor with the same size as `input` filled with `fill_value`.
-#' ``torch_full_like(input, fill_value)`` is equivalent to
-#' ``torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)``.
+#' `torch_full_like(input, fill_value)` is equivalent to
+#' `torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)`.
 #'
 #'
-#' @param self (Tensor) the size of `input` will determine size of the output tensor.
-#' @param fill_value NA the number to fill the output tensor with.
+#' @param input (Tensor) the size of `input` will determine size of the output tensor.
+#' @param fill_value the number to fill the output tensor with.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
 #' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
-#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
+#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: `torch_preserve_format`.
 #'
 #' @name torch_full_like
 #'
@@ -1740,8 +1728,8 @@ NULL
 #' ready to be used as a periodic window with functions like
 #' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in
 #' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-#' ``torch_hann_window(L, periodic=TRUE)`` equal to
-#' ``torch_hann_window(L + 1, periodic=False)[:-1])``.
+#' `torch_hann_window(L, periodic=TRUE)` equal to
+#' `torch_hann_window(L + 1, periodic=False)[:-1])`.
 #' 
 #' @note
 #'     If `window_length` \eqn{=1}, the returned window contains a single value 1.
@@ -1750,7 +1738,7 @@ NULL
 #' @param window_length (int) the size of returned window
 #' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
-#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          ``torch_strided`` (dense layout) is supported.
+#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          `torch_strided` (dense layout) is supported.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
@@ -1777,8 +1765,8 @@ NULL
 #' ready to be used as a periodic window with functions like
 #' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in
 #' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-#' ``torch_hamming_window(L, periodic=TRUE)`` equal to
-#' ``torch_hamming_window(L + 1, periodic=False)[:-1])``.
+#' `torch_hamming_window(L, periodic=TRUE)` equal to
+#' `torch_hamming_window(L + 1, periodic=False)[:-1])`.
 #' 
 #' @note
 #'     If `window_length` \eqn{=1}, the returned window contains a single value 1.
@@ -1792,7 +1780,7 @@ NULL
 #' @param alpha (float, optional) The coefficient \eqn{\alpha} in the equation above
 #' @param beta (float, optional) The coefficient \eqn{\beta} in the equation above
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
-#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          ``torch_strided`` (dense layout) is supported.
+#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only          `torch_strided` (dense layout) is supported.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
@@ -1815,7 +1803,6 @@ NULL
 #'
 #' @param self (Tensor) 1-D input vector
 #' @param vec2 (Tensor) 1-D input vector
-#' @param out (Tensor, optional) optional output matrix
 #'
 #' @name torch_ger
 #'
@@ -1843,7 +1830,7 @@ NULL
 #' This method supports 1D, 2D and 3D complex-to-complex transforms, indicated
 #' by `signal_ndim`. `input` must be a tensor with last dimension
 #' of size 2, representing the real and imaginary components of complex
-#' numbers, and should have at least ``signal_ndim + 1`` dimensions with optionally
+#' numbers, and should have at least `signal_ndim + 1` dimensions with optionally
 #' arbitrary number of leading batch dimensions. If `normalized` is set to
 #' `TRUE`, this normalizes the result by dividing it with
 #' \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is unitary.
@@ -1864,7 +1851,7 @@ NULL
 #'     `torch_backends.mkl.is_available` to check if MKL is installed.
 #'
 #'
-#' @param self (Tensor) the input tensor of at least `signal_ndim` ``+ 1``        dimensions
+#' @param self (Tensor) the input tensor of at least `signal_ndim` `+ 1`        dimensions
 #' @param signal_ndim (int) the number of dimensions in each signal.        `signal_ndim` can only be 1, 2 or 3
 #' @param normalized (bool, optional) controls whether to return normalized results.        Default: `FALSE`
 #'
@@ -1914,7 +1901,7 @@ NULL
 #'     `torch_backends.mkl.is_available` to check if MKL is installed.
 #'
 #'
-#' @param self (Tensor) the input tensor of at least `signal_ndim` ``+ 1``        dimensions
+#' @param self (Tensor) the input tensor of at least `signal_ndim` `+ 1`        dimensions
 #' @param signal_ndim (int) the number of dimensions in each signal.        `signal_ndim` can only be 1, 2 or 3
 #' @param normalized (bool, optional) controls whether to return normalized results.        Default: `FALSE`
 #'
@@ -1936,7 +1923,7 @@ NULL
 #' 
 #' This method supports 1D, 2D and 3D real-to-complex transforms, indicated
 #' by `signal_ndim`. `input` must be a tensor with at least
-#' ``signal_ndim`` dimensions with optionally arbitrary number of leading batch
+#' `signal_ndim` dimensions with optionally arbitrary number of leading batch
 #' dimensions. If `normalized` is set to `TRUE`, this normalizes the result
 #' by dividing it with \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is
 #' unitary, where \eqn{N_i} is the size of signal dimension \eqn{i}.
@@ -1998,7 +1985,7 @@ NULL
 #'     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`
+#'     `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
@@ -2033,7 +2020,7 @@ NULL
 #'     `torch_backends.mkl.is_available` to check if MKL is installed.
 #'
 #'
-#' @param self (Tensor) the input tensor of at least `signal_ndim` ``+ 1``        dimensions
+#' @param self (Tensor) the input tensor of at least `signal_ndim` `+ 1`        dimensions
 #' @param signal_ndim (int) the number of dimensions in each signal.        `signal_ndim` can only be 1, 2 or 3
 #' @param normalized (bool, optional) controls whether to return normalized results.        Default: `FALSE`
 #' @param onesided (bool, optional) controls whether `input` was halfed to avoid        redundancy, e.g., by [torch_rfft()]. Default: `TRUE`
@@ -2060,7 +2047,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more                    batch dimensions
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_inverse
 #'
@@ -2088,7 +2075,7 @@ NULL
 #' @section is_floating_point(input) -> (bool) :
 #'
 #' Returns TRUE if the data type of `input` is a floating point data type i.e.,
-#' one of ``torch_float64``, ``torch.float32`` and ``torch.float16``.
+#' one of `torch_float64`, `torch.float32` and `torch.float16`.
 #'
 #'
 #' @param self (Tensor) the PyTorch tensor to test
@@ -2104,7 +2091,7 @@ NULL
 #' @section is_complex(input) -> (bool) :
 #'
 #' Returns TRUE if the data type of `input` is a complex data type i.e.,
-#' one of ``torch_complex64``, and ``torch.complex128``.
+#' one of `torch_complex64`, and `torch.complex128`.
 #'
 #'
 #' @param self (Tensor) the PyTorch tensor to test
@@ -2119,9 +2106,9 @@ NULL
 #'
 #' @section kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
 #'
-#' Returns a namedtuple ``(values, indices)`` where ``values`` is the `k` th
+#' Returns a namedtuple `(values, indices)` where `values` is the `k` th
 #' smallest element of each row of the `input` tensor in the given dimension
-#' `dim`. And ``indices`` is the index location of each element found.
+#' `dim`. And `indices` is the index location of each element found.
 #' 
 #' If `dim` is not given, the last dimension of the `input` is chosen.
 #' 
@@ -2136,7 +2123,6 @@ NULL
 #' @param k (int) k for the k-th smallest element
 #' @param dim (int, optional) the dimension to find the kth value along
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
-#' @param out (tuple, optional) the output tuple of (Tensor, LongTensor)                           can be optionally given to be used as output buffers
 #'
 #' @name torch_kthvalue
 #'
@@ -2156,10 +2142,10 @@ NULL
 #'
 #' @param start (float) the starting value for the set of points
 #' @param end (float) the ending value for the set of points
-#' @param steps (int) number of points to sample between `start`        and `end`. Default: ``100``.
-#' @param out (Tensor, optional) the output tensor.
+#' @param steps (int) number of points to sample between `start`        and `end`. Default: `100`.
+#' 
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
@@ -2182,7 +2168,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_log
 #'
@@ -2203,7 +2189,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_log10
 #'
@@ -2225,7 +2211,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_log1p
 #'
@@ -2246,7 +2232,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_log2
 #'
@@ -2261,7 +2247,7 @@ NULL
 #' Calculates log determinant of a square matrix or batches of square matrices.
 #' 
 #' @note
-#'     Result is ``-inf`` if `input` has zero log determinant, and is `NaN` if
+#'     Result is `-inf` if `input` has zero log determinant, and is `NaN` if
 #'     `input` has negative determinant.
 #' 
 #' @note
@@ -2271,7 +2257,7 @@ NULL
 #'     `~torch.svd` for details.
 #'
 #'
-#' @param self (Tensor) the input tensor of size ``(*, n, n)`` where ``*`` is zero or more                batch dimensions.
+#' @param self (Tensor) the input tensor of size `(*, n, n)` where `*` is zero or more                batch dimensions.
 #'
 #' @name torch_logdet
 #'
@@ -2292,11 +2278,11 @@ NULL
 #'
 #' @param start (float) the starting value for the set of points
 #' @param end (float) the ending value for the set of points
-#' @param steps (int) number of points to sample between `start`        and `end`. Default: ``100``.
-#' @param base (float) base of the logarithm function. Default: ``10.0``.
-#' @param out (Tensor, optional) the output tensor.
+#' @param steps (int) number of points to sample between `start`        and `end`. Default: `100`.
+#' @param base (float) base of the logarithm function. Default: `10.0`.
+#' 
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
@@ -2323,13 +2309,13 @@ NULL
 #' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
-#' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
+#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
 #'
 #'
 #' @param self (Tensor) the input tensor.
 #' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_logsumexp
 #'
@@ -2369,7 +2355,7 @@ NULL
 #'
 #' @param self (Tensor) the first tensor to be multiplied
 #' @param other (Tensor) the second tensor to be multiplied
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_matmul
 #'
@@ -2388,8 +2374,8 @@ NULL
 #' 
 #' `tol` is the threshold below which the singular values (or the eigenvalues
 #' when `symmetric` is `TRUE`) are considered to be 0. If `tol` is not
-#' specified, `tol` is set to ``S.max() * max(S.size()) * eps`` where `S` is the
-#' singular values (or the eigenvalues when `symmetric` is `TRUE`), and ``eps``
+#' specified, `tol` is set to `S.max() * max(S.size()) * eps` where `S` is the
+#' singular values (or the eigenvalues when `symmetric` is `TRUE`), and `eps`
 #' is the epsilon value for the datatype of `input`.
 #'
 #'
@@ -2429,32 +2415,32 @@ NULL
 #'
 #' @section max(input) -> Tensor :
 #'
-#' Returns the maximum value of all elements in the ``input`` tensor.
+#' Returns the maximum value of all elements in the `input` tensor.
 #'
 #' @section max(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
 #'
-#' Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum
+#' Returns a namedtuple `(values, indices)` where `values` is the maximum
 #' value of each row of the `input` tensor in the given dimension
-#' `dim`. And ``indices`` is the index location of each maximum value found
+#' `dim`. And `indices` is the index location of each maximum value found
 #' (argmax).
 #' 
 #' @section Warning:
-#'     ``indices`` does not necessarily contain the first occurrence of each
+#'     `indices` 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.
 #' 
-#' If ``keepdim`` is `TRUE`, the output tensors are of the same size
-#' as ``input`` except in the dimension ``dim`` where they are of size 1.
-#' Otherwise, ``dim`` is squeezed (see [`torch_squeeze`]), resulting
-#' in the output tensors having 1 fewer dimension than ``input``.
+#' If `keepdim` is `TRUE`, the output tensors are of the same size
+#' as `input` except in the dimension `dim` where they are of size 1.
+#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting
+#' in the output tensors having 1 fewer dimension than `input`.
 #'
 #' @section max(input, other, out=NULL) -> Tensor :
 #'
-#' Each element of the tensor ``input`` is compared with the corresponding
-#' element of the tensor ``other`` and an element-wise maximum is taken.
+#' Each element of the tensor `input` is compared with the corresponding
+#' element of the tensor `other` and an element-wise maximum is taken.
 #' 
-#' The shapes of ``input`` and ``other`` don't need to match,
+#' The shapes of `input` and `other` don't need to match,
 #' but they must be broadcastable .
 #' 
 #' \deqn{
@@ -2492,13 +2478,13 @@ NULL
 #' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
-#' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
+#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
 #'
 #'
 #' @param self (Tensor) the input tensor.
 #' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
-#' @param out (Tensor, optional) the output tensor.
+#' @param dtype the resulting data type.
 #'
 #' @name torch_mean
 #'
@@ -2514,9 +2500,9 @@ NULL
 #'
 #' @section median(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
 #'
-#' Returns a namedtuple ``(values, indices)`` where ``values`` is the median
+#' Returns a namedtuple `(values, indices)` where `values` is the median
 #' value of each row of the `input` tensor in the given dimension
-#' `dim`. And ``indices`` is the index location of each median value found.
+#' `dim`. And `indices` is the index location of each median value found.
 #' 
 #' By default, `dim` is the last dimension of the `input` tensor.
 #' 
@@ -2529,7 +2515,6 @@ NULL
 #' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
-#' @param out (tuple, optional) the result tuple of two output tensors (max, max_indices)
 #'
 #' @name torch_median
 #'
@@ -2545,13 +2530,13 @@ NULL
 #'
 #' @section min(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
 #'
-#' Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum
+#' Returns a namedtuple `(values, indices)` where `values` is the minimum
 #' value of each row of the `input` tensor in the given dimension
-#' `dim`. And ``indices`` is the index location of each minimum value found
+#' `dim`. And `indices` is the index location of each minimum value found
 #' (argmin).
 #' 
 #' @section Warning:
-#'     ``indices`` does not necessarily contain the first occurrence of each
+#'     `indices` 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.
@@ -2604,7 +2589,7 @@ NULL
 #'
 #' @param self (Tensor) the first matrix to be multiplied
 #' @param mat2 (Tensor) the second matrix to be multiplied
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_mm
 #'
@@ -2616,10 +2601,10 @@ NULL
 #'
 #' @section mode(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor) :
 #'
-#' Returns a namedtuple ``(values, indices)`` where ``values`` is the mode
+#' Returns a namedtuple `(values, indices)` where `values` is the mode
 #' value of each row of the `input` tensor in the given dimension
 #' `dim`, i.e. a value which appears most often
-#' in that row, and ``indices`` is the index location of each mode value found.
+#' in that row, and `indices` is the index location of each mode value found.
 #' 
 #' By default, `dim` is the last dimension of the `input` tensor.
 #' 
@@ -2628,13 +2613,12 @@ NULL
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting
 #' in the output tensors having 1 fewer dimension than `input`.
 #' 
-#' @note This function is not defined for ``torch_cuda.Tensor`` yet.
+#' @note This function is not defined for `torch_cuda.Tensor` yet.
 #'
 #'
 #' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
-#' @param out (tuple, optional) the result tuple of two output tensors (values, indices)
 #'
 #' @name torch_mode
 #'
@@ -2667,13 +2651,9 @@ NULL
 #'     \mbox{out}_i = \mbox{input}_i \times \mbox{other}_i
 #' }
 #'
-#'
-#' @param {input} NA 
-#' @param value (Number) the number to be multiplied to each element of `input`
-#' @param {out} NA 
 #' @param self (Tensor) the first multiplicand tensor
 #' @param other (Tensor) the second multiplicand tensor
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_mul
 #'
@@ -2696,7 +2676,7 @@ NULL
 #'
 #' @param self (Tensor) matrix to be multiplied
 #' @param vec (Tensor) vector to be multiplied
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_mv
 #'
@@ -2757,12 +2737,13 @@ NULL
 #' by the variable argument `size`.
 #'
 #'
-#' @param size (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.
-#' @param out (Tensor, optional) the output tensor.
+#' @param ... (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.
+#' 
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
+#' @param names optional names for the dimensions
 #'
 #' @name torch_ones
 #'
@@ -2775,21 +2756,21 @@ NULL
 #' @section ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
 #'
 #' Returns a tensor filled with the scalar value `1`, with the same size as
-#' `input`. ``torch_ones_like(input)`` is equivalent to
-#' ``torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
+#' `input`. `torch_ones_like(input)` is equivalent to
+#' `torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`.
 #' 
 #' @section Warning:
 #'     As of 0.4, this function does not support an `out` keyword. As an alternative,
-#'     the old ``torch_ones_like(input, out=output)`` is equivalent to
-#'     ``torch_ones(input.size(), out=output)``.
+#'     the old `torch_ones_like(input, out=output)` is equivalent to
+#'     `torch_ones(input.size(), out=output)`.
 #'
 #'
-#' @param self (Tensor) the size of `input` will determine size of the output tensor.
+#' @param input (Tensor) the size of `input` will determine size of the output tensor.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
 #' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
-#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
+#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: `torch_preserve_format`.
 #'
 #' @name torch_ones_like
 #'
@@ -2921,12 +2902,13 @@ NULL
 #' The shape of the tensor is defined by the variable argument `size`.
 #'
 #'
-#' @param size (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.
-#' @param out (Tensor, optional) the output tensor.
+#' @param ... (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.
+#' 
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
+#' @param names optional dimension names
 #'
 #' @name torch_rand
 #'
@@ -2940,16 +2922,16 @@ NULL
 #'
 #' Returns a tensor with the same size as `input` that is filled with
 #' random numbers from a uniform distribution on the interval \eqn{[0, 1)}.
-#' ``torch_rand_like(input)`` is equivalent to
-#' ``torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
+#' `torch_rand_like(input)` is equivalent to
+#' `torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`.
 #'
 #'
-#' @param self (Tensor) the size of `input` will determine size of the output tensor.
+#' @param input (Tensor) the size of `input` will determine size of the output tensor.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
 #' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
-#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
+#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: `torch_preserve_format`.
 #'
 #' @name torch_rand_like
 #'
@@ -2969,20 +2951,21 @@ NULL
 #' The shape of the tensor is defined by the variable argument `size`.
 #' 
 #' .. note:
-#'     With the global dtype default (``torch_float32``), this function returns
-#'     a tensor with dtype ``torch_int64``.
+#'     With the global dtype default (`torch_float32`), this function returns
+#'     a tensor with dtype `torch_int64`.
 #'
 #'
 #' @param low (int, optional) Lowest integer to be drawn from the distribution. Default: 0.
 #' @param high (int) One above the highest integer to be drawn from the distribution.
 #' @param size (tuple) a tuple defining the shape of the output tensor.
 #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
-#'
+#' @param memory_format memory format for the resulting tensor.
+#' 
 #' @name torch_randint
 #'
 #' @export
@@ -3000,18 +2983,17 @@ NULL
 #' `high` (exclusive).
 #' 
 #' .. note:
-#'     With the global dtype default (``torch_float32``), this function returns
-#'     a tensor with dtype ``torch_int64``.
+#'     With the global dtype default (`torch_float32`), this function returns
+#'     a tensor with dtype `torch_int64`.
 #'
 #'
-#' @param self (Tensor) the size of `input` will determine size of the output tensor.
+#' @param input (Tensor) the size of `input` will determine size of the output tensor.
 #' @param low (int, optional) Lowest integer to be drawn from the distribution. Default: 0.
 #' @param high (int) One above the highest integer to be drawn from the distribution.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
 #' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
-#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
 #'
 #' @name torch_randint_like
 #'
@@ -3033,12 +3015,13 @@ NULL
 #' The shape of the tensor is defined by the variable argument `size`.
 #'
 #'
-#' @param size (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.
-#' @param out (Tensor, optional) the output tensor.
+#' @param ... (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.
+#' 
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
+#' @param names optional names for the dimensions
 #'
 #' @name torch_randn
 #'
@@ -3052,8 +3035,8 @@ NULL
 #'
 #' Returns a tensor with the same size as `input` that is filled with
 #' random numbers from a normal distribution with mean 0 and variance 1.
-#' ``torch_randn_like(input)`` is equivalent to
-#' ``torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
+#' `torch_randn_like(input)` is equivalent to
+#' `torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`.
 #'
 #'
 #' @param self (Tensor) the size of `input` will determine size of the output tensor.
@@ -3061,7 +3044,7 @@ NULL
 #' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
-#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
+#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: `torch_preserve_format`.
 #'
 #' @name torch_randn_like
 #'
@@ -3073,13 +3056,13 @@ NULL
 #'
 #' @section randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -> LongTensor :
 #'
-#' Returns a random permutation of integers from ``0`` to ``n - 1``.
+#' Returns a random permutation of integers from `0` to `n - 1`.
 #'
 #'
 #' @param n (int) the upper bound (exclusive)
-#' @param out (Tensor, optional) the output tensor.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: ``torch_int64``.
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' 
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: `torch_int64`.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
@@ -3104,12 +3087,12 @@ NULL
 #'     This function is deprecated in favor of [`torch_arange`].
 #'
 #'
-#' @param start (float) the starting value for the set of points. Default: ``0``.
+#' @param start (float) the starting value for the set of points. Default: `0`.
 #' @param end (float) the ending value for the set of points
-#' @param step (float) the gap between each pair of adjacent points. Default: ``1``.
-#' @param out (Tensor, optional) the output tensor.
+#' @param step (float) the gap between each pair of adjacent points. Default: `1`.
+#' 
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). If `dtype` is not given, infer the data type from the other input        arguments. If any of `start`, `end`, or `stop` are floating-point, the        `dtype` is inferred to be the default dtype, see        `~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to        be `torch.int64`.
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
@@ -3131,7 +3114,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_reciprocal
 #'
@@ -3151,7 +3134,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_neg
 #'
@@ -3167,7 +3150,7 @@ NULL
 #' 
 #' @section Warning:
 #' 
-#'     This is different from `torch_Tensor.repeat` but similar to ``numpy.repeat``.
+#'     This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`.
 #'
 #' @section repeat_interleave(repeats) -> Tensor :
 #'
@@ -3220,7 +3203,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_round
 #'
@@ -3233,6 +3216,10 @@ NULL
 #' @section rrelu_(input, lower=1./8, upper=1./3, training=False) -> Tensor :
 #'
 #' In-place version of `torch_rrelu`.
+#' 
+#' @param self the input tensor
+#' @param generator random number generator
+#' @inheritParams nnf_rrelu
 #'
 #'
 #'
@@ -3242,15 +3229,24 @@ NULL
 #' @export
 NULL
 
-
-#' Relu_
+#' Relu
 #'
-#' @section relu_(input) -> Tensor :
+#' @section relu(input) -> Tensor :
+#' 
+#' @param self the input tensor
 #'
-#' In-place version of `torch_relu`.
+#' @name torch_relu
 #'
+#' @export
+NULL
+
+#' Relu_
 #'
+#' @section relu_(input) -> Tensor :
 #'
+#' In-place version of [torch_relu()].
+#' 
+#' @param self the input tensor
 #'
 #' @name torch_relu_
 #'
@@ -3271,7 +3267,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_rsqrt
 #'
@@ -3293,16 +3289,30 @@ NULL
 #' @export
 NULL
 
+#' Celu
+#'
+#' @section celu(input, alpha=1.) -> Tensor :
+#' 
+#' See [nnf_celu()] for more info.
+#' 
+#' @param self the input tensor
+#' @param alpha the alpha value for the CELU formulation. Default: 1.0
+#' 
+#' @name torch_celu
+#'
+#' @export
+NULL
+
 
 #' Celu_
 #'
 #' @section celu_(input, alpha=1.) -> Tensor :
 #'
-#' In-place version of `torch_celu`.
-#'
-#'
-#'
-#'
+#' In-place version of [torch_celu()].
+#' 
+#' @param self the input tensor
+#' @param alpha the alpha value for the CELU formulation. Default: 1.0
+#' 
 #' @name torch_celu_
 #'
 #' @export
@@ -3321,7 +3331,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_sigmoid
 #'
@@ -3341,7 +3351,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_sin
 #'
@@ -3362,7 +3372,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_sinh
 #'
@@ -3377,7 +3387,7 @@ NULL
 #' Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices.
 #' 
 #' @note
-#'     If ``input`` has zero determinant, this returns ``(0, -inf)``.
+#'     If `input` has zero determinant, this returns `(0, -inf)`.
 #' 
 #' @note
 #'     Backward through `slogdet` internally uses SVD results when `input`
@@ -3386,7 +3396,7 @@ NULL
 #'     See `~torch.svd` for details.
 #'
 #'
-#' @param self (Tensor) the input tensor of size ``(*, n, n)`` where ``*`` is zero or more                batch dimensions.
+#' @param self (Tensor) the input tensor of size `(*, n, n)` where `*` is zero or more                batch dimensions.
 #'
 #' @name torch_slogdet
 #'
@@ -3406,7 +3416,7 @@ NULL
 #'     `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
+#'     into `len(split_size_or_sections)` chunks with sizes in `dim` according
 #'     to `split_size_or_sections`.
 #'
 #'
@@ -3432,7 +3442,7 @@ NULL
 #' 
 #' When `dim` is given, a squeeze operation is done only in the given
 #' dimension. If `input` is of shape: \eqn{(A \times 1 \times B)},
-#' ``squeeze(input, 0)`` leaves the tensor unchanged, but ``squeeze(input, 1)``
+#' `squeeze(input, 0)` leaves the tensor unchanged, but `squeeze(input, 1)`
 #' will squeeze the tensor to the shape \eqn{(A \times B)}.
 #' 
 #' @note The returned tensor shares the storage with the input tensor,
@@ -3441,7 +3451,7 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param dim (int, optional) if given, the input will be squeezed only in           this dimension
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_squeeze
 #'
@@ -3460,7 +3470,7 @@ NULL
 #'
 #' @param tensors (sequence of Tensors) sequence of tensors to concatenate
 #' @param dim (int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_stack
 #'
@@ -3490,7 +3500,7 @@ NULL
 #'       sequences.
 #' 
 #'     * If `hop_length` is `NULL` (default), it is treated as equal to
-#'       ``floor(n_fft / 4)``.
+#'       `floor(n_fft / 4)`.
 #' 
 #'     * If `win_length` is `NULL` (default), it is treated as equal to
 #'       `n_fft`.
@@ -3508,7 +3518,7 @@ NULL
 #' 
 #'     * `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"``.
+#'       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]}
@@ -3532,11 +3542,11 @@ NULL
 #'
 #' @param self (Tensor) the input tensor
 #' @param n_fft (int) size of Fourier transform
-#' @param hop_length (int, optional) the distance between neighboring sliding window        frames. Default: `NULL` (treated as equal to ``floor(n_fft / 4)``)
+#' @param hop_length (int, optional) the distance between neighboring sliding window        frames. Default: `NULL` (treated as equal to `floor(n_fft / 4)`)
 #' @param win_length (int, optional) the size of window frame and STFT filter.        Default: `NULL`  (treated as equal to `n_fft`)
 #' @param window (Tensor, optional) the optional window function.        Default: `NULL` (treated as window of all \eqn{1} s)
 #' @param center (bool, optional) whether to pad `input` on both sides so        that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}.        Default: `TRUE`
-#' @param pad_mode (string, optional) controls the padding method used when        `center` is `TRUE`. Default: ``"reflect"``
+#' @param pad_mode (string, optional) controls the padding method used when        `center` is `TRUE`. Default: `"reflect"`
 #' @param normalized (bool, optional) controls whether to return the normalized STFT results         Default: `FALSE`
 #' @param onesided (bool, optional) controls whether to return half of results to        avoid redundancy Default: `TRUE`
 #'
@@ -3562,7 +3572,7 @@ NULL
 #' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
-#' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
+#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
 #'
 #'
 #' @param self (Tensor) the input tensor.
@@ -3588,7 +3598,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_sqrt
 #'
@@ -3604,7 +3614,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_square
 #'
@@ -3631,7 +3641,7 @@ NULL
 #' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
-#' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
+#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
 #' 
 #' 
 #' If `unbiased` is `FALSE`, then the standard-deviation will be calculated
@@ -3642,7 +3652,7 @@ NULL
 #' @param unbiased (bool) whether to use the unbiased estimation or not
 #' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_std
 #'
@@ -3669,7 +3679,7 @@ NULL
 #' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
-#' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
+#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
 #' 
 #' 
 #' If `unbiased` is `FALSE`, then the standard-deviation will be calculated
@@ -3723,7 +3733,7 @@ NULL
 #' and 1.
 #' 
 #' 0-D and 1-D tensors are returned as is. When input is a 2-D tensor this
-#' is equivalent to ``transpose(input, 0, 1)``.
+#' is equivalent to `transpose(input, 0, 1)`.
 #'
 #'
 #' @param self (Tensor) the input tensor.
@@ -3746,7 +3756,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_tan
 #'
@@ -3767,7 +3777,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_tanh
 #'
@@ -3939,7 +3949,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_trunc
 #'
@@ -3978,9 +3988,9 @@ NULL
 #' 
 #' The returned tensor shares the same underlying data with this tensor.
 #' 
-#' A `dim` value within the range ``[-input.dim() - 1, input.dim() + 1)``
+#' A `dim` value within the range `[-input.dim() - 1, input.dim() + 1)`
 #' can be used. Negative `dim` will correspond to `unsqueeze`
-#' applied at `dim` = ``dim + input.dim() + 1``.
+#' applied at `dim` = `dim + input.dim() + 1`.
 #'
 #'
 #' @param self (Tensor) the input tensor.
@@ -4010,7 +4020,7 @@ NULL
 #' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
-#' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
+#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
 #' 
 #' 
 #' If `unbiased` is `FALSE`, then the variance will be calculated via the
@@ -4021,7 +4031,7 @@ NULL
 #' @param unbiased (bool) whether to use the unbiased estimation or not
 #' @param dim (int or tuple of ints) the dimension or dimensions to reduce.
 #' @param keepdim (bool) whether the output tensor has `dim` retained or not.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_var
 #'
@@ -4047,7 +4057,7 @@ NULL
 #' If `keepdim` is `TRUE`, the output tensor is of the same size
 #' as `input` except in the dimension(s) `dim` where it is of size 1.
 #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the
-#' output tensor having 1 (or ``len(dim)``) fewer dimension(s).
+#' output tensor having 1 (or `len(dim)`) fewer dimension(s).
 #' 
 #' 
 #' If `unbiased` is `FALSE`, then the variance will be calculated via the
@@ -4085,8 +4095,8 @@ NULL
 #'
 #' @section where(condition) -> tuple of LongTensor :
 #'
-#' ``torch_where(condition)`` is identical to
-#' ``torch_nonzero(condition, as_tuple=TRUE)``.
+#' `torch_where(condition)` is identical to
+#' `torch_nonzero(condition, as_tuple=TRUE)`.
 #' 
 #' @note
 #'     See also [`torch_nonzero`].
@@ -4111,9 +4121,9 @@ NULL
 #'
 #'
 #' @param size (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.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
-#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: ``torch_strided``.
+#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
 #'
@@ -4128,13 +4138,13 @@ NULL
 #' @section zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor :
 #'
 #' Returns a tensor filled with the scalar value `0`, with the same size as
-#' `input`. ``torch_zeros_like(input)`` is equivalent to
-#' ``torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
+#' `input`. `torch_zeros_like(input)` is equivalent to
+#' `torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`.
 #' 
 #' @section Warning:
 #'     As of 0.4, this function does not support an `out` keyword. As an alternative,
-#'     the old ``torch_zeros_like(input, out=output)`` is equivalent to
-#'     ``torch_zeros(input.size(), out=output)``.
+#'     the old `torch_zeros_like(input, out=output)` is equivalent to
+#'     `torch_zeros(input.size(), out=output)`.
 #'
 #'
 #' @param self (Tensor) the size of `input` will determine size of the output tensor.
@@ -4142,7 +4152,7 @@ NULL
 #' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
-#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: ``torch_preserve_format``.
+#' @param memory_format (`torch.memory_format`, optional) the desired memory format of        returned Tensor. Default: `torch_preserve_format`.
 #'
 #' @name torch_zeros_like
 #'
@@ -4180,10 +4190,10 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor
-#' @param p (int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: ``'fro'``        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)**ord)**(1./ord)        =====  ============================  ==========================
+#' @param p (int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: `'fro'`        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)**ord)**(1./ord)        =====  ============================  ==========================
 #' @param dim (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.
 #' @param keepdim (bool, optional) whether the output tensors have `dim`        retained or not. Ignored if `dim` = `NULL` and        `out` = `NULL`. Default: `FALSE`
-#' @param out (Tensor, optional) the output tensor. Ignored if        `dim` = `NULL` and `out` = `NULL`.
+#'  Ignored if        `dim` = `NULL` and `out` = `NULL`.
 #' @param dtype (`torch.dtype`, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: NULL.
 #'
 #' @name torch_norm
@@ -4199,7 +4209,7 @@ NULL
 #' Takes the power of each element in `input` with `exponent` and
 #' returns a tensor with the result.
 #' 
-#' `exponent` can be either a single ``float`` number or a `Tensor`
+#' `exponent` can be either a single `float` number or a `Tensor`
 #' with the same number of elements as `input`.
 #' 
 #' When `exponent` is a scalar value, the operation applied is:
@@ -4217,7 +4227,7 @@ NULL
 #'
 #' @section pow(self, exponent, out=NULL) -> Tensor :
 #'
-#' `self` is a scalar ``float`` value, and `exponent` is a tensor.
+#' `self` is a scalar `float` value, and `exponent` is a tensor.
 #' The returned tensor `out` is of the same shape as `exponent`
 #' 
 #' The operation applied is:
@@ -4229,7 +4239,7 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param exponent (float or tensor) the exponent value
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #' @param self (float) the scalar base value for the power operation
 #'
 #' @name torch_pow
@@ -4265,7 +4275,7 @@ NULL
 #' @param mat2 (Tensor) the second matrix to be multiplied
 #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
 #' @param alpha (Number, optional) multiplier for \eqn{mat1 @ mat2} (\eqn{\alpha})
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_addmm
 #'
@@ -4283,8 +4293,8 @@ NULL
 #' `torch_sparse`_.
 #'
 #'
-#' @param indices (array_like) Initial data for the tensor. Can be a list, tuple,        NumPy ``ndarray``, scalar, and other types. Will be cast to a `torch_LongTensor`        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.
-#' @param values (array_like) Initial values for the tensor. Can be a list, tuple,        NumPy ``ndarray``, scalar, and other types.
+#' @param indices (array_like) Initial data for the tensor. Can be a list, tuple,        NumPy `ndarray`, scalar, and other types. Will be cast to a `torch_LongTensor`        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.
+#' @param values (array_like) Initial values for the tensor. Can be a list, tuple,        NumPy `ndarray`, scalar, and other types.
 #' @param size (list, tuple, or `torch.Size`, 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.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if NULL, infers data type from `values`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if NULL, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
@@ -4324,7 +4334,7 @@ NULL
 #' @param self (Tensor) float tensor to quantize
 #' @param scale (float) scale to apply in quantization formula
 #' @param zero_point (int) offset in integer value that maps to float zero
-#' @param dtype (`torch.dtype`) the desired data type of returned tensor.        Has to be one of the quantized dtypes: ``torch_quint8``, ``torch.qint8``, ``torch.qint32``
+#' @param dtype (`torch.dtype`) the desired data type of returned tensor.        Has to be one of the quantized dtypes: `torch_quint8`, `torch.qint8`, `torch.qint32`
 #'
 #' @name torch_quantize_per_tensor
 #'
@@ -4340,10 +4350,10 @@ NULL
 #'
 #'
 #' @param self (Tensor) float tensor to quantize
-#' @param scales (Tensor) float 1D tensor of scales to use, size should match ``input.size(axis)``
-#' @param zero_points (int) integer 1D tensor of offset to use, size should match ``input.size(axis)``
+#' @param scales (Tensor) float 1D tensor of scales to use, size should match `input.size(axis)`
+#' @param zero_points (int) integer 1D tensor of offset to use, size should match `input.size(axis)`
 #' @param axis (int) dimension on which apply per-channel quantization
-#' @param dtype (`torch.dtype`) the desired data type of returned tensor.        Has to be one of the quantized dtypes: ``torch_quint8``, ``torch.qint8``, ``torch.qint32``
+#' @param dtype (`torch.dtype`) the desired data type of returned tensor.        Has to be one of the quantized dtypes: `torch_quint8`, `torch.qint8`, `torch.qint32`
 #'
 #' @name torch_quantize_per_channel
 #'
@@ -4361,7 +4371,7 @@ NULL
 #'
 #'
 #' @param tensors (list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be
-#' @param treated (1,) 
+#'  treated (1,).
 #'
 #' @name torch_meshgrid
 #'
@@ -4371,13 +4381,9 @@ NULL
 
 #' Cartesian_prod
 #'
-#' @section TEST :
-#'
-#' Do cartesian product of the given sequence of tensors. The behavior is similar to
-#'     python's `itertools.product`.
+#' Do cartesian product of the given sequence of tensors.
 #'
-#'
-#' @param *tensors NA any number of 1 dimensional tensors.
+#' @param tensors a list containing any number of 1 dimensional tensors.
 #'
 #' @name torch_cartesian_prod
 #'
@@ -4468,7 +4474,7 @@ NULL
 #'
 #' @param self NA the first input tensor
 #' @param other NA the second input tensor
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_bitwise_and
 #'
@@ -4486,7 +4492,7 @@ NULL
 #'
 #' @param self NA the first input tensor
 #' @param other NA the second input tensor
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_bitwise_or
 #'
@@ -4504,7 +4510,7 @@ NULL
 #'
 #' @param self NA the first input tensor
 #' @param other NA the second input tensor
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_bitwise_xor
 #'
@@ -4542,7 +4548,7 @@ NULL
 #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta})
 #' @param self (Tensor) matrix to be added
 #' @param alpha (Number, optional) multiplier for `batch1 @ batch2` (\eqn{\alpha})
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_addbmm
 #'
@@ -4568,7 +4574,7 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param diagonal (int, optional) the diagonal to consider
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_diag
 #'
@@ -4593,7 +4599,7 @@ NULL
 #' @param self (Tensor) the input tensor.
 #' @param other (Tensor) the second input tensor
 #' @param dim (int, optional) the dimension to take the cross-product in.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_cross
 #'
@@ -4622,7 +4628,7 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param diagonal (int, optional) the diagonal to consider
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_triu
 #'
@@ -4651,7 +4657,7 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param diagonal (int, optional) the diagonal to consider
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_tril
 #'
@@ -4680,16 +4686,16 @@ NULL
 #' where \eqn{d_{1}, d_{2}} are the dimensions of the matrix.
 #' 
 #' @note
-#'     When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to
+#'     When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
 #'     prevent overflow during calculation.
 #'
 #'
-#' @param row (``int``) number of rows in the 2-D matrix.
-#' @param col (``int``) number of columns in the 2-D matrix.
-#' @param offset (``int``) diagonal offset from the main diagonal.        Default: if not provided, 0.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, ``torch_long``.
+#' @param row (`int`) number of rows in the 2-D matrix.
+#' @param col (`int`) number of columns in the 2-D matrix.
+#' @param offset (`int`) diagonal offset from the main diagonal.        Default: if not provided, 0.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, `torch_long`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param layout (`torch.layout`, optional) currently only support ``torch_strided``.
+#' @param layout (`torch.layout`, optional) currently only support `torch_strided`.
 #'
 #' @name torch_tril_indices
 #'
@@ -4718,16 +4724,16 @@ NULL
 #' where \eqn{d_{1}, d_{2}} are the dimensions of the matrix.
 #' 
 #' @note
-#'     When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to
+#'     When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
 #'     prevent overflow during calculation.
 #'
 #'
-#' @param row (``int``) number of rows in the 2-D matrix.
-#' @param col (``int``) number of columns in the 2-D matrix.
-#' @param offset (``int``) diagonal offset from the main diagonal.        Default: if not provided, 0.
-#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, ``torch_long``.
+#' @param row (`int`) number of rows in the 2-D matrix.
+#' @param col (`int`) number of columns in the 2-D matrix.
+#' @param offset (`int`) diagonal offset from the main diagonal.        Default: if not provided, 0.
+#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, `torch_long`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
-#' @param layout (`torch.layout`, optional) currently only support ``torch_strided``.
+#' @param layout (`torch.layout`, optional) currently only support `torch_strided`.
 #'
 #' @name torch_triu_indices
 #'
@@ -4762,7 +4768,6 @@ NULL
 #'
 #' @param self (Tensor) the tensor to compare
 #' @param other (Tensor or float) the tensor or value to compare
-#' @param out (Tensor, optional) the output tensor that must be a `BoolTensor`
 #'
 #' @name torch_ne
 #'
@@ -4782,7 +4787,7 @@ NULL
 #'
 #' @param self (Tensor) the tensor to compare
 #' @param other (Tensor or float) the tensor or value to compare
-#' @param out (Tensor, optional) the output tensor. Must be a `ByteTensor`
+#'  Must be a `ByteTensor`
 #'
 #' @name torch_eq
 #'
@@ -4802,7 +4807,6 @@ NULL
 #'
 #' @param self (Tensor) the tensor to compare
 #' @param other (Tensor or float) the tensor or value to compare
-#' @param out (Tensor, optional) the output tensor that must be a `BoolTensor`
 #'
 #' @name torch_ge
 #'
@@ -4822,7 +4826,6 @@ NULL
 #'
 #' @param self (Tensor) the tensor to compare
 #' @param other (Tensor or float) the tensor or value to compare
-#' @param out (Tensor, optional) the output tensor that must be a `BoolTensor`
 #'
 #' @name torch_le
 #'
@@ -4842,7 +4845,6 @@ NULL
 #'
 #' @param self (Tensor) the tensor to compare
 #' @param other (Tensor or float) the tensor or value to compare
-#' @param out (Tensor, optional) the output tensor that must be a `BoolTensor`
 #'
 #' @name torch_gt
 #'
@@ -4862,7 +4864,6 @@ NULL
 #'
 #' @param self (Tensor) the tensor to compare
 #' @param other (Tensor or float) the tensor or value to compare
-#' @param out (Tensor, optional) the output tensor that must be a `BoolTensor`
 #'
 #' @name torch_lt
 #'
@@ -4908,7 +4909,7 @@ NULL
 #' @param self (Tensor) the input tensor.
 #' @param dim (int) the dimension in which we index
 #' @param index (LongTensor) the 1-D tensor containing the indices to index
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_index_select
 #'
@@ -4932,7 +4933,7 @@ NULL
 #'
 #' @param self (Tensor) the input tensor.
 #' @param mask (BoolTensor) the tensor containing the binary mask to index with
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_masked_select
 #'
@@ -4947,8 +4948,8 @@ NULL
 #'     2-D tensor where each row is the index for a nonzero value.
 #' 
 #'     [`torch_nonzero(..., as_tuple=TRUE) <torch.nonzero>`] 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
+#'     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.
@@ -4981,7 +4982,6 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (LongTensor, optional) the output tensor containing indices
 #'
 #' @name torch_nonzero
 #'
@@ -4991,7 +4991,7 @@ NULL
 
 #' Gather
 #'
-#' @section gather(input, dim, index, out=NULL, sparse_grad=False) -> Tensor :
+#' @section gather(input, dim, index, sparse_grad=FALSE) -> Tensor :
 #'
 #' Gathers values along an axis specified by `dim`.
 #' 
@@ -5003,7 +5003,7 @@ NULL
 #' 
 #' If `input` is an n-dimensional tensor with size
 #' \eqn{(x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})}
-#' and ``dim = i``, then `index` must be an \eqn{n}-dimensional tensor with
+#' and `dim = i`, then `index` must be an \eqn{n}-dimensional tensor with
 #' size \eqn{(x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})} where \eqn{y \geq 1}
 #' and `out` will have the same size as `index`.
 #'
@@ -5011,7 +5011,6 @@ NULL
 #' @param self (Tensor) the source tensor
 #' @param dim (int) the axis along which to index
 #' @param index (LongTensor) the indices of elements to gather
-#' @param out (Tensor, optional) the destination tensor
 #' @param sparse_grad (bool,optional) If `TRUE`, gradient w.r.t. `input` will be a sparse tensor.
 #'
 #' @name torch_gather
@@ -5042,7 +5041,7 @@ NULL
 #' @param tensor1 (Tensor) the tensor to be multiplied
 #' @param tensor2 (Tensor) the tensor to be multiplied
 #' @param value (Number, optional) multiplier for \eqn{tensor1 .* tensor2}
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_addcmul
 #'
@@ -5084,7 +5083,7 @@ NULL
 #' @param tensor1 (Tensor) the numerator tensor
 #' @param tensor2 (Tensor) the denominator tensor
 #' @param value (Number, optional) multiplier for \eqn{\mbox{tensor1} / \mbox{tensor2}}
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_addcdiv
 #'
@@ -5125,7 +5124,6 @@ NULL
 #'
 #' @param self (Tensor) the matrix \eqn{B}
 #' @param A (Tensor) the \eqn{m} by \eqn{n} matrix \eqn{A}
-#' @param out (tuple, optional) the optional destination tensor
 #'
 #' @name torch_lstsq
 #'
@@ -5217,7 +5215,6 @@ NULL
 #'
 #' @param self (Tensor) the square matrix of shape \eqn{(n \times n)} for which the eigenvalues and eigenvectors        will be computed
 #' @param eigenvectors (bool) `TRUE` to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed
-#' @param out (tuple, optional) the output tensors
 #'
 #' @name torch_eig
 #'
@@ -5229,12 +5226,12 @@ NULL
 #'
 #' @section svd(input, some=TRUE, compute_uv=TRUE, out=NULL) -> (Tensor, Tensor, Tensor) :
 #'
-#' This function returns a namedtuple ``(U, S, V)`` which is the singular value
+#' This function returns a namedtuple `(U, S, V)` which is the singular value
 #' decomposition of a input real matrix or batches of real matrices `input` such that
 #' \eqn{input = U \times diag(S) \times V^T}.
 #' 
 #' If `some` is `TRUE` (default), the method returns the reduced singular value decomposition
-#' i.e., if the last two dimensions of `input` are ``m`` and ``n``, then the returned
+#' i.e., if the last two dimensions of `input` are `m` and `n`, then the returned
 #' `U` and `V` matrices will contain only \eqn{min(n, m)} orthonormal columns.
 #' 
 #' If `compute_uv` is `FALSE`, the returned `U` and `V` matrices will be zero matrices
@@ -5283,13 +5280,13 @@ NULL
 #' Computes the Cholesky decomposition of a symmetric positive-definite
 #' matrix \eqn{A} or for batches of symmetric positive-definite matrices.
 #' 
-#' If `upper` is `TRUE`, the returned matrix ``U`` is upper-triangular, and
+#' If `upper` is `TRUE`, the returned matrix `U` is upper-triangular, and
 #' the decomposition has the form:
 #' 
 #' \deqn{
 #'   A = U^TU
 #' }
-#' If `upper` is `FALSE`, the returned matrix ``L`` is lower-triangular, and
+#' If `upper` is `FALSE`, the returned matrix `L` is lower-triangular, and
 #' the decomposition has the form:
 #' 
 #' \deqn{
@@ -5302,9 +5299,10 @@ NULL
 #' matrices.
 #'
 #'
-#' @param self (Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where `*` is zero or more                batch dimensions consisting of symmetric positive-definite matrices.
-#' @param upper (bool, optional) flag that indicates whether to return a                            upper or lower triangular matrix. Default: `FALSE`
-#' @param out (Tensor, optional) the output matrix
+#' @param self (Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where `*` is zero or more                
+#'   batch dimensions consisting of symmetric positive-definite matrices.
+#' @param upper (bool, optional) flag that indicates whether to return a                            
+#'   upper or lower triangular matrix. Default: `FALSE`
 #'
 #' @name torch_cholesky
 #'
@@ -5337,9 +5335,8 @@ NULL
 #'
 #'
 #' @param self (Tensor) input matrix \eqn{b} of size \eqn{(*, m, k)},                where \eqn{*} is zero or more batch dimensions
-#' @param self2 (Tensor) input matrix \eqn{u} of size \eqn{(*, m, m)},                where \eqn{*} is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor
+#' @param input2 (Tensor) input matrix \eqn{u} of size \eqn{(*, m, m)},                where \eqn{*} is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor
 #' @param upper (bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: `FALSE`.
-#' @param out (Tensor, optional) the output tensor for `c`
 #'
 #' @name torch_cholesky_solve
 #'
@@ -5384,8 +5381,8 @@ NULL
 #' @section cholesky_inverse(input, upper=False, out=NULL) -> Tensor :
 #'
 #' Computes the inverse of a symmetric positive-definite matrix \eqn{A} using its
-#' Cholesky factor \eqn{u}: returns matrix ``inv``. The inverse is computed using
-#' LAPACK routines ``dpotri`` and ``spotri`` (and the corresponding MAGMA routines).
+#' Cholesky factor \eqn{u}: returns matrix `inv`. The inverse is computed using
+#' LAPACK routines `dpotri` and `spotri` (and the corresponding MAGMA routines).
 #' 
 #' If `upper` is `FALSE`, \eqn{u} is lower triangular
 #' such that the returned tensor is
@@ -5403,7 +5400,6 @@ NULL
 #'
 #' @param self (Tensor) the input 2-D tensor \eqn{u}, a upper or lower triangular           Cholesky factor
 #' @param upper (bool, optional) whether to return a lower (default) or upper triangular matrix
-#' @param out (Tensor, optional) the output tensor for `inv`
 #'
 #' @name torch_cholesky_inverse
 #'
@@ -5433,7 +5429,6 @@ NULL
 #'
 #' @param self (Tensor) the input tensor of size \eqn{(*, m, n)} where `*` is zero or more                batch dimensions consisting of matrices of dimension \eqn{m \times n}.
 #' @param some (bool, optional) Set to `TRUE` for reduced QR decomposition and `FALSE` for                complete QR decomposition.
-#' @param out (tuple, optional) tuple of `Q` and `R` tensors                satisfying `input = torch.matmul(Q, R)`.                The dimensions of `Q` and `R` are \eqn{(*, m, k)} and \eqn{(*, k, n)}                respectively, where \eqn{k = \min(m, n)} if `some:` is `TRUE` and                \eqn{k = m} otherwise.
 #'
 #' @name torch_qr
 #'
@@ -5460,7 +5455,6 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input matrix
-#' @param out (tuple, optional) the output tuple of (Tensor, Tensor)
 #'
 #' @name torch_geqrf
 #'
@@ -5480,7 +5474,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the `a` from [`torch_geqrf`].
-#' @param self2 (Tensor) the `tau` from [`torch_geqrf`].
+#' @param input2 (Tensor) the `tau` from [`torch_geqrf`].
 #'
 #' @name torch_orgqr
 #'
@@ -5500,8 +5494,8 @@ NULL
 #'
 #'
 #' @param self (Tensor) the `a` from [`torch_geqrf`].
-#' @param self2 (Tensor) the `tau` from [`torch_geqrf`].
-#' @param self3 (Tensor) the matrix to be multiplied.
+#' @param input2 (Tensor) the `tau` from [`torch_geqrf`].
+#' @param input3 (Tensor) the matrix to be multiplied.
 #'
 #' @name torch_ormqr
 #'
@@ -5517,10 +5511,10 @@ NULL
 #' LU factorization of A from `torch_lu`.
 #'
 #'
-#' @param b (Tensor) the RHS tensor of size \eqn{(*, m, k)}, where \eqn{*}                is zero or more batch dimensions.
+#' @param self (Tensor) the RHS tensor of size \eqn{(*, m, k)}, where \eqn{*}                is zero or more batch dimensions.
 #' @param LU_data (Tensor) the pivoted LU factorization of A from `torch_lu` of size \eqn{(*, m, m)},                       where \eqn{*} is zero or more batch dimensions.
 #' @param LU_pivots (IntTensor) the pivots of the LU factorization from `torch_lu` of size \eqn{(*, m)},                           where \eqn{*} is zero or more batch dimensions.                           The batch dimensions of `LU_pivots` must be equal to the batch dimensions of                           `LU_data`.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_lu_solve
 #'
@@ -5564,7 +5558,7 @@ NULL
 #' @param num_samples (int) number of samples to draw
 #' @param replacement (bool, optional) whether to draw with replacement or not
 #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_multinomial
 #'
@@ -5584,7 +5578,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_lgamma
 #'
@@ -5627,7 +5621,7 @@ NULL
 #'
 #' @param n (int) the order of the polygamma function
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_polygamma
 #'
@@ -5648,7 +5642,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_erfinv
 #'
@@ -5668,7 +5662,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_sign
 #'
@@ -5707,13 +5701,13 @@ NULL
 #' parameter, is the x-coordinate, while \eqn{\mbox{input}_{i}}, the first
 #' parameter, is the y-coordinate.)
 #' 
-#' The shapes of ``input`` and ``other`` must be
+#' The shapes of `input` and `other` must be
 #' broadcastable .
 #'
 #'
 #' @param self (Tensor) the first input tensor
 #' @param other (Tensor) the second input tensor
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_atan2
 #'
@@ -5739,7 +5733,7 @@ NULL
 #' @param self (Tensor) the tensor with the starting points
 #' @param end (Tensor) the tensor with the ending points
 #' @param weight (float or tensor) the weight for the interpolation formula
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_lerp
 #'
@@ -5762,7 +5756,7 @@ NULL
 #' @param bins (int) number of histogram bins
 #' @param min (int) lower end of the range (inclusive)
 #' @param max (int) upper end of the range (inclusive)
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_histc
 #'
@@ -5785,7 +5779,7 @@ NULL
 #'
 #' @param self (Tensor) the dividend
 #' @param other (Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_fmod
 #'
@@ -5808,7 +5802,7 @@ NULL
 #'
 #' @param self (Tensor) the dividend
 #' @param other (Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_remainder
 #'
@@ -5911,7 +5905,7 @@ NULL
 #' @param p (float) the power for the norm computation
 #' @param dim (int) the dimension to slice over to get the sub-tensors
 #' @param maxnorm (float) the maximum norm to keep each sub-tensor under
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #'
 #' @name torch_renorm
 #'
@@ -5925,6 +5919,8 @@ NULL
 #'
 #' `TRUE` if two tensors have the same size and elements, `FALSE` otherwise.
 #'
+#' @param self the input tensor
+#' @param other the other input tensor
 #'
 #'
 #'
@@ -5972,7 +5968,7 @@ NULL
 #' @param mean (Tensor) the tensor of per-element means
 #' @param std (Tensor) the tensor of per-element standard deviations
 #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling
-#' @param out (Tensor, optional) the output tensor.
+#' 
 #' @param size (int...) a sequence of integers defining the shape of the output tensor.
 #'
 #' @name torch_normal
@@ -5988,7 +5984,7 @@ NULL
 #' Returns a new tensor with boolean elements representing if each element is `Finite` or not.
 #'
 #'
-#' @param tensor (Tensor) A tensor to check
+#' @param self (Tensor) A tensor to check
 #'
 #' @name torch_isfinite
 #'
@@ -6003,7 +5999,7 @@ NULL
 #' Returns a new tensor with boolean elements representing if each element is `+/-INF` or not.
 #'
 #'
-#' @param tensor (Tensor) A tensor to check
+#' @param self (Tensor) A tensor to check
 #'
 #' @name torch_isinf
 #'
diff --git a/man/torch_acos.Rd b/man/torch_acos.Rd
index 8b869192f..e2f5873f4 100644
--- a/man/torch_acos.Rd
+++ b/man/torch_acos.Rd
@@ -9,13 +9,11 @@ torch_acos(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Acos
 }
-\section{acos(input, out=None) -> Tensor }{
+\section{acos(input) -> Tensor }{
 
 
 Returns a new tensor with the arccosine  of the elements of \code{input}.
diff --git a/man/torch_adaptive_avg_pool1d.Rd b/man/torch_adaptive_avg_pool1d.Rd
index 9d8a7bc08..a602da3ef 100644
--- a/man/torch_adaptive_avg_pool1d.Rd
+++ b/man/torch_adaptive_avg_pool1d.Rd
@@ -8,7 +8,9 @@
 torch_adaptive_avg_pool1d(self, output_size)
 }
 \arguments{
-\item{output_size}{NA the target output size (single integer)}
+\item{self}{the input tensor}
+
+\item{output_size}{the target output size (single integer)}
 }
 \description{
 Adaptive_avg_pool1d
@@ -19,6 +21,6 @@ Adaptive_avg_pool1d
 Applies a 1D adaptive average pooling over an input signal composed of
 several input planes.
 
-See \code{~torch.nn.AdaptiveAvgPool1d} for details and output shape.
+See \code{\link[=nn_adaptive_avg_pool1d]{nn_adaptive_avg_pool1d()}} for details and output shape.
 }
 
diff --git a/man/torch_add.Rd b/man/torch_add.Rd
index 9e901f64d..577bda478 100644
--- a/man/torch_add.Rd
+++ b/man/torch_add.Rd
@@ -10,16 +10,14 @@ torch_add(self, other, alpha = 1L)
 \arguments{
 \item{self}{(Tensor) the input tensor.}
 
-\item{other}{(Tensor) the second input tensor}
+\item{other}{(Tensor/Number) the second input tensor/number.}
 
 \item{alpha}{(Number) the scalar multiplier for \code{other}}
-
-\item{value}{(Number) the number to be added to each element of \code{input}}
 }
 \description{
 Add
 }
-\section{add(input, other, out=None) }{
+\section{add(input, other, out=NULL) }{
 
 
 Adds the scalar \code{other} to each element of the input \code{input}
@@ -32,7 +30,7 @@ If \code{input} is of type FloatTensor or DoubleTensor, \code{other} must be
 a real number, otherwise it should be an integer.
 }
 
-\section{add(input, other, *, alpha=1, out=None) }{
+\section{add(input, other, *, alpha=1, out=NULL) }{
 
 
 Each element of the tensor \code{other} is multiplied by the scalar
diff --git a/man/torch_addbmm.Rd b/man/torch_addbmm.Rd
index b33e3f147..a344a17c7 100644
--- a/man/torch_addbmm.Rd
+++ b/man/torch_addbmm.Rd
@@ -17,13 +17,11 @@ torch_addbmm(self, batch1, batch2, beta = 1L, alpha = 1L)
 \item{beta}{(Number, optional) multiplier for \code{input} (\eqn{\beta})}
 
 \item{alpha}{(Number, optional) multiplier for \code{batch1 @ batch2} (\eqn{\alpha})}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Addbmm
 }
-\section{addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor }{
+\section{addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor }{
 
 
 Performs a batch matrix-matrix product of matrices stored
diff --git a/man/torch_addcdiv.Rd b/man/torch_addcdiv.Rd
index 79e31f7a5..cf93b9124 100644
--- a/man/torch_addcdiv.Rd
+++ b/man/torch_addcdiv.Rd
@@ -15,13 +15,11 @@ torch_addcdiv(self, tensor1, tensor2, value = 1L)
 \item{tensor2}{(Tensor) the denominator tensor}
 
 \item{value}{(Number, optional) multiplier for \eqn{\mbox{tensor1} / \mbox{tensor2}}}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Addcdiv
 }
-\section{addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor }{
+\section{addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor }{
 
 
 Performs the element-wise division of \code{tensor1} by \code{tensor2},
diff --git a/man/torch_addcmul.Rd b/man/torch_addcmul.Rd
index 7661458a9..b9a1170ae 100644
--- a/man/torch_addcmul.Rd
+++ b/man/torch_addcmul.Rd
@@ -15,13 +15,11 @@ torch_addcmul(self, tensor1, tensor2, value = 1L)
 \item{tensor2}{(Tensor) the tensor to be multiplied}
 
 \item{value}{(Number, optional) multiplier for \eqn{tensor1 .* tensor2}}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Addcmul
 }
-\section{addcmul(input, tensor1, tensor2, *, value=1, out=None) -> Tensor }{
+\section{addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor }{
 
 
 Performs the element-wise multiplication of \code{tensor1}
diff --git a/man/torch_addmm.Rd b/man/torch_addmm.Rd
index f2de8781e..d025e5e1b 100644
--- a/man/torch_addmm.Rd
+++ b/man/torch_addmm.Rd
@@ -17,13 +17,11 @@ torch_addmm(self, mat1, mat2, beta = 1L, alpha = 1L)
 \item{beta}{(Number, optional) multiplier for \code{input} (\eqn{\beta})}
 
 \item{alpha}{(Number, optional) multiplier for \eqn{mat1 @ mat2} (\eqn{\alpha})}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Addmm
 }
-\section{addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor }{
+\section{addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -> Tensor }{
 
 
 Performs a matrix multiplication of the matrices \code{mat1} and \code{mat2}.
diff --git a/man/torch_addmv.Rd b/man/torch_addmv.Rd
index 08e2bb652..cb3940101 100644
--- a/man/torch_addmv.Rd
+++ b/man/torch_addmv.Rd
@@ -17,13 +17,11 @@ torch_addmv(self, mat, vec, beta = 1L, alpha = 1L)
 \item{beta}{(Number, optional) multiplier for \code{input} (\eqn{\beta})}
 
 \item{alpha}{(Number, optional) multiplier for \eqn{mat @ vec} (\eqn{\alpha})}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Addmv
 }
-\section{addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor }{
+\section{addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -> Tensor }{
 
 
 Performs a matrix-vector product of the matrix \code{mat} and
diff --git a/man/torch_addr.Rd b/man/torch_addr.Rd
index 5bac2973f..91329566a 100644
--- a/man/torch_addr.Rd
+++ b/man/torch_addr.Rd
@@ -17,13 +17,11 @@ torch_addr(self, vec1, vec2, beta = 1L, alpha = 1L)
 \item{beta}{(Number, optional) multiplier for \code{input} (\eqn{\beta})}
 
 \item{alpha}{(Number, optional) multiplier for \eqn{\mbox{vec1} \otimes \mbox{vec2}} (\eqn{\alpha})}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Addr
 }
-\section{addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor }{
+\section{addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -> Tensor }{
 
 
 Performs the outer-product of vectors \code{vec1} and \code{vec2}
diff --git a/man/torch_allclose.Rd b/man/torch_allclose.Rd
index 8729cfe59..108f0f4ff 100644
--- a/man/torch_allclose.Rd
+++ b/man/torch_allclose.Rd
@@ -16,7 +16,7 @@ torch_allclose(self, other, rtol = 1e-05, atol = 0, equal_nan = FALSE)
 
 \item{atol}{(float, optional) absolute tolerance. Default: 1e-08}
 
-\item{equal_nan}{(bool, optional) if \code{True}, then two \code{NaN} s will be compared as equal. Default: \code{False}}
+\item{equal_nan}{(bool, optional) if \code{TRUE}, then two \code{NaN} s will be compared as equal. Default: \code{FALSE}}
 }
 \description{
 Allclose
diff --git a/man/torch_angle.Rd b/man/torch_angle.Rd
index 67d6fee6f..ecaa37e05 100644
--- a/man/torch_angle.Rd
+++ b/man/torch_angle.Rd
@@ -9,13 +9,11 @@ torch_angle(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Angle
 }
-\section{angle(input, out=None) -> Tensor }{
+\section{angle(input) -> Tensor }{
 
 
 Computes the element-wise angle (in radians) of the given \code{input} tensor.
diff --git a/man/torch_arange.Rd b/man/torch_arange.Rd
index b2bfd6440..47d0782ea 100644
--- a/man/torch_arange.Rd
+++ b/man/torch_arange.Rd
@@ -22,20 +22,18 @@ torch_arange(
 
 \item{step}{(Number) the gap between each pair of adjacent points. Default: \code{1}.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). If \code{dtype} is not given, infer the data type from the other input        arguments. If any of \code{start}, \code{end}, or \code{stop} are floating-point, the        \code{dtype} is inferred to be the default dtype, see        \code{~torch.get_default_dtype}. Otherwise, the \code{dtype} is inferred to        be \code{torch.int64}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}). If \code{dtype} is not given, infer the data type from the other input        arguments. If any of \code{start}, \code{end}, or \code{stop} are floating-point, the        \code{dtype} is inferred to be the default dtype, see        \code{~torch.get_default_dtype}. Otherwise, the \code{dtype} is inferred to        be \code{torch.int64}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Arange
 }
-\section{arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a 1-D tensor of size \eqn{\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil}
diff --git a/man/torch_argmax.Rd b/man/torch_argmax.Rd
index 0b0aeb022..ba3df7660 100644
--- a/man/torch_argmax.Rd
+++ b/man/torch_argmax.Rd
@@ -10,9 +10,9 @@ torch_argmax(self, dim = NULL, keepdim = FALSE)
 \arguments{
 \item{self}{(Tensor) the input tensor.}
 
-\item{dim}{(int) the dimension to reduce. If \code{None}, the argmax of the flattened input is returned.}
+\item{dim}{(int) the dimension to reduce. If \code{NULL}, the argmax of the flattened input is returned.}
 
-\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not. Ignored if \code{dim=None}.}
+\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not. Ignored if \code{dim=NULL}.}
 }
 \description{
 Argmax
diff --git a/man/torch_argmin.Rd b/man/torch_argmin.Rd
index c0f256ad4..34f621181 100644
--- a/man/torch_argmin.Rd
+++ b/man/torch_argmin.Rd
@@ -10,9 +10,9 @@ torch_argmin(self, dim = NULL, keepdim = FALSE)
 \arguments{
 \item{self}{(Tensor) the input tensor.}
 
-\item{dim}{(int) the dimension to reduce. If \code{None}, the argmin of the flattened input is returned.}
+\item{dim}{(int) the dimension to reduce. If \code{NULL}, the argmin of the flattened input is returned.}
 
-\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not. Ignored if \code{dim=None}.}
+\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not. Ignored if \code{dim=NULL}.}
 }
 \description{
 Argmin
@@ -26,7 +26,7 @@ This is the second value returned by \code{torch_min}. See its
 documentation for the exact semantics of this method.
 }
 
-\section{argmin(input, dim, keepdim=False, out=None) -> LongTensor }{
+\section{argmin(input, dim, keepdim=False, out=NULL) -> LongTensor }{
 
 
 Returns the indices of the minimum values of a tensor across a dimension.
diff --git a/man/torch_asin.Rd b/man/torch_asin.Rd
index 369cd29e4..5e087de01 100644
--- a/man/torch_asin.Rd
+++ b/man/torch_asin.Rd
@@ -9,13 +9,11 @@ torch_asin(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Asin
 }
-\section{asin(input, out=None) -> Tensor }{
+\section{asin(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the arcsine  of the elements of \code{input}.
diff --git a/man/torch_atan.Rd b/man/torch_atan.Rd
index f0d8e376d..016b42ed4 100644
--- a/man/torch_atan.Rd
+++ b/man/torch_atan.Rd
@@ -9,13 +9,11 @@ torch_atan(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Atan
 }
-\section{atan(input, out=None) -> Tensor }{
+\section{atan(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the arctangent  of the elements of \code{input}.
diff --git a/man/torch_atan2.Rd b/man/torch_atan2.Rd
index 439f6dc3c..5dcfb72ae 100644
--- a/man/torch_atan2.Rd
+++ b/man/torch_atan2.Rd
@@ -11,13 +11,11 @@ torch_atan2(self, other)
 \item{self}{(Tensor) the first input tensor}
 
 \item{other}{(Tensor) the second input tensor}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Atan2
 }
-\section{atan2(input, other, out=None) -> Tensor }{
+\section{atan2(input, other, out=NULL) -> Tensor }{
 
 
 Element-wise arctangent of \eqn{\mbox{input}_{i} / \mbox{other}_{i}}
diff --git a/man/torch_avg_pool1d.Rd b/man/torch_avg_pool1d.Rd
index a9f5825d6..ba19f2e51 100644
--- a/man/torch_avg_pool1d.Rd
+++ b/man/torch_avg_pool1d.Rd
@@ -15,27 +15,27 @@ torch_avg_pool1d(
 )
 }
 \arguments{
-\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
+\item{self}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
 
-\item{kernel_size}{NA the size of the window. Can be a single number or a      tuple \verb{(kW,)}}
+\item{kernel_size}{the size of the window. Can be a single number or a tuple \verb{(kW,)}}
 
-\item{stride}{NA the stride of the window. Can be a single number or a tuple      \verb{(sW,)}. Default: \code{kernel_size}}
+\item{stride}{the stride of the window. Can be a single number or a tuple \verb{(sW,)}. Default: \code{kernel_size}}
 
-\item{padding}{NA implicit zero paddings on both sides of the input. Can be a      single number or a tuple \verb{(padW,)}. Default: 0}
+\item{padding}{implicit zero paddings on both sides of the input. Can be a single number or a tuple \verb{(padW,)}. Default: 0}
 
-\item{ceil_mode}{NA when True, will use \code{ceil} instead of \code{floor} to compute the        output shape. Default: \code{False}}
+\item{ceil_mode}{when \code{TRUE}, will use \code{ceil} instead of \code{floor} to compute the output shape. Default: \code{FALSE}}
 
-\item{count_include_pad}{NA when True, will include the zero-padding in the        averaging calculation. Default: \code{True}}
+\item{count_include_pad}{when \code{TRUE}, will include the zero-padding in the averaging calculation. Default: \code{TRUE}}
 }
 \description{
 Avg_pool1d
 }
-\section{avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor }{
+\section{avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -> Tensor }{
 
 
 Applies a 1D average pooling over an input signal composed of several
 input planes.
 
-See \code{~torch.nn.AvgPool1d} for details and output shape.
+See \code{\link[=nn_avg_pool1d]{nn_avg_pool1d()}} for details and output shape.
 }
 
diff --git a/man/torch_baddbmm.Rd b/man/torch_baddbmm.Rd
index 446bfb2d6..7d16fda5f 100644
--- a/man/torch_baddbmm.Rd
+++ b/man/torch_baddbmm.Rd
@@ -17,13 +17,11 @@ torch_baddbmm(self, batch1, batch2, beta = 1L, alpha = 1L)
 \item{beta}{(Number, optional) multiplier for \code{input} (\eqn{\beta})}
 
 \item{alpha}{(Number, optional) multiplier for \eqn{\mbox{batch1} \mathbin{@} \mbox{batch2}} (\eqn{\alpha})}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Baddbmm
 }
-\section{baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor }{
+\section{baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor }{
 
 
 Performs a batch matrix-matrix product of matrices in \code{batch1}
diff --git a/man/torch_bartlett_window.Rd b/man/torch_bartlett_window.Rd
index 8f5c8c5ea..050bb90b2 100644
--- a/man/torch_bartlett_window.Rd
+++ b/man/torch_bartlett_window.Rd
@@ -10,15 +10,15 @@ torch_bartlett_window(window_length, periodic, options = list())
 \arguments{
 \item{window_length}{(int) the size of returned window}
 
-\item{periodic}{(bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.}
+\item{periodic}{(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned window tensor. Only          \code{torch_strided} (dense layout) is supported.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Bartlett_window
@@ -27,7 +27,7 @@ Bartlett_window
 \preformatted{If `window_length` \eqn{=1}, the returned window contains a single value 1.
 }
 }
-\section{bartlett_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Bartlett window function.
@@ -47,7 +47,7 @@ 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}. Therefore, if \code{periodic} is true, the \eqn{N} in
 above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-\code{torch_bartlett_window(L, periodic=True)} equal to
+\code{torch_bartlett_window(L, periodic=TRUE)} equal to
 \verb{torch_bartlett_window(L + 1, periodic=False)[:-1])}.
 }
 
diff --git a/man/torch_bernoulli.Rd b/man/torch_bernoulli.Rd
index 3b2d11a1b..6ad61490d 100644
--- a/man/torch_bernoulli.Rd
+++ b/man/torch_bernoulli.Rd
@@ -8,16 +8,18 @@
 torch_bernoulli(self, p, generator = NULL)
 }
 \arguments{
-\item{self}{(Tensor) the input tensor of probability values for the Bernoulli distribution}
+\item{self}{(Tensor) the input tensor of probability values for the Bernoulli
+distribution}
 
-\item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
+\item{p}{(Number) a probability value. If \code{p} is passed than it's used instead of
+the values in \code{self} tensor.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
 }
 \description{
 Bernoulli
 }
-\section{bernoulli(input, *, generator=None, out=None) -> Tensor }{
+\section{bernoulli(input, *, generator=NULL, out=NULL) -> Tensor }{
 
 
 Draws binary random numbers (0 or 1) from a Bernoulli distribution.
diff --git a/man/torch_bincount.Rd b/man/torch_bincount.Rd
index a08e06828..3a1bcdafc 100644
--- a/man/torch_bincount.Rd
+++ b/man/torch_bincount.Rd
@@ -17,7 +17,7 @@ torch_bincount(self, weights = list(), minlength = 0L)
 \description{
 Bincount
 }
-\section{bincount(input, weights=None, minlength=0) -> Tensor }{
+\section{bincount(input, weights=NULL, minlength=0) -> Tensor }{
 
 
 Count the frequency of each value in an array of non-negative ints.
diff --git a/man/torch_bitwise_and.Rd b/man/torch_bitwise_and.Rd
index e64507777..a68be3e31 100644
--- a/man/torch_bitwise_and.Rd
+++ b/man/torch_bitwise_and.Rd
@@ -11,13 +11,11 @@ torch_bitwise_and(self, other)
 \item{self}{NA the first input tensor}
 
 \item{other}{NA the second input tensor}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Bitwise_and
 }
-\section{bitwise_and(input, other, out=None) -> Tensor }{
+\section{bitwise_and(input, other, out=NULL) -> Tensor }{
 
 
 Computes the bitwise AND of \code{input} and \code{other}. The input tensor must be of
diff --git a/man/torch_bitwise_not.Rd b/man/torch_bitwise_not.Rd
index a5460d5b9..62d2fbb0a 100644
--- a/man/torch_bitwise_not.Rd
+++ b/man/torch_bitwise_not.Rd
@@ -9,13 +9,11 @@ torch_bitwise_not(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Bitwise_not
 }
-\section{bitwise_not(input, out=None) -> Tensor }{
+\section{bitwise_not(input, out=NULL) -> Tensor }{
 
 
 Computes the bitwise NOT of the given input tensor. The input tensor must be of
diff --git a/man/torch_bitwise_or.Rd b/man/torch_bitwise_or.Rd
index 9dfe9e6ed..0b8a49121 100644
--- a/man/torch_bitwise_or.Rd
+++ b/man/torch_bitwise_or.Rd
@@ -11,13 +11,11 @@ torch_bitwise_or(self, other)
 \item{self}{NA the first input tensor}
 
 \item{other}{NA the second input tensor}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Bitwise_or
 }
-\section{bitwise_or(input, other, out=None) -> Tensor }{
+\section{bitwise_or(input, other, out=NULL) -> Tensor }{
 
 
 Computes the bitwise OR of \code{input} and \code{other}. The input tensor must be of
diff --git a/man/torch_bitwise_xor.Rd b/man/torch_bitwise_xor.Rd
index 0ce1859d7..dc675020a 100644
--- a/man/torch_bitwise_xor.Rd
+++ b/man/torch_bitwise_xor.Rd
@@ -11,13 +11,11 @@ torch_bitwise_xor(self, other)
 \item{self}{NA the first input tensor}
 
 \item{other}{NA the second input tensor}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Bitwise_xor
 }
-\section{bitwise_xor(input, other, out=None) -> Tensor }{
+\section{bitwise_xor(input, other, out=NULL) -> Tensor }{
 
 
 Computes the bitwise XOR of \code{input} and \code{other}. The input tensor must be of
diff --git a/man/torch_blackman_window.Rd b/man/torch_blackman_window.Rd
index 2b8775610..6b495b040 100644
--- a/man/torch_blackman_window.Rd
+++ b/man/torch_blackman_window.Rd
@@ -10,15 +10,15 @@ torch_blackman_window(window_length, periodic, options = list())
 \arguments{
 \item{window_length}{(int) the size of returned window}
 
-\item{periodic}{(bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.}
+\item{periodic}{(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned window tensor. Only          \code{torch_strided} (dense layout) is supported.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Blackman_window
@@ -27,7 +27,7 @@ Blackman_window
 \preformatted{If `window_length` \eqn{=1}, the returned window contains a single value 1.
 }
 }
-\section{blackman_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Blackman window function.
@@ -43,7 +43,7 @@ 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}. Therefore, if \code{periodic} is true, the \eqn{N} in
 above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-\code{torch_blackman_window(L, periodic=True)} equal to
+\code{torch_blackman_window(L, periodic=TRUE)} equal to
 \verb{torch_blackman_window(L + 1, periodic=False)[:-1])}.
 }
 
diff --git a/man/torch_bmm.Rd b/man/torch_bmm.Rd
index 27e57e0e2..4fd68eb31 100644
--- a/man/torch_bmm.Rd
+++ b/man/torch_bmm.Rd
@@ -11,8 +11,6 @@ torch_bmm(self, mat2)
 \item{self}{(Tensor) the first batch of matrices to be multiplied}
 
 \item{mat2}{(Tensor) the second batch of matrices to be multiplied}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Bmm
@@ -21,7 +19,7 @@ Bmm
 This function does not broadcast .
 For broadcasting matrix products, see \code{\link{torch_matmul}}.
 }
-\section{bmm(input, mat2, out=None) -> Tensor }{
+\section{bmm(input, mat2, out=NULL) -> Tensor }{
 
 
 Performs a batch matrix-matrix product of matrices stored in \code{input}
diff --git a/man/torch_broadcast_tensors.Rd b/man/torch_broadcast_tensors.Rd
index 5ba547b18..21ef4485c 100644
--- a/man/torch_broadcast_tensors.Rd
+++ b/man/torch_broadcast_tensors.Rd
@@ -8,12 +8,12 @@
 torch_broadcast_tensors(tensors)
 }
 \arguments{
-\item{*tensors}{NA any number of tensors of the same type}
+\item{tensors}{a list containing any number of tensors of the same type}
 }
 \description{
 Broadcast_tensors
 }
-\section{broadcast_tensors(*tensors) -> List of Tensors }{
+\section{broadcast_tensors(tensors) -> List of Tensors }{
 
 
 Broadcasts the given tensors according to broadcasting-semantics.
diff --git a/man/torch_cartesian_prod.Rd b/man/torch_cartesian_prod.Rd
index 1f52ca165..a621dbdb5 100644
--- a/man/torch_cartesian_prod.Rd
+++ b/man/torch_cartesian_prod.Rd
@@ -8,18 +8,11 @@
 torch_cartesian_prod(tensors)
 }
 \arguments{
-\item{*tensors}{NA any number of 1 dimensional tensors.}
+\item{tensors}{a list containing any number of 1 dimensional tensors.}
 }
 \description{
-Cartesian_prod
+Do cartesian product of the given sequence of tensors.
 }
-\section{TEST }{
-
-
-Do cartesian product of the given sequence of tensors. The behavior is similar to
-python's \code{itertools.product}.
-}
-
 \examples{
 if (torch_is_installed()) {
 
diff --git a/man/torch_cat.Rd b/man/torch_cat.Rd
index 0890fcb13..1b20e65b0 100644
--- a/man/torch_cat.Rd
+++ b/man/torch_cat.Rd
@@ -11,13 +11,11 @@ torch_cat(tensors, dim = 1L)
 \item{tensors}{(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.}
 
 \item{dim}{(int, optional) the dimension over which the tensors are concatenated}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Cat
 }
-\section{cat(tensors, dim=0, out=None) -> Tensor }{
+\section{cat(tensors, dim=0, out=NULL) -> Tensor }{
 
 
 Concatenates the given sequence of \code{seq} tensors in the given dimension.
diff --git a/man/torch_ceil.Rd b/man/torch_ceil.Rd
index e93ca2704..e87a78163 100644
--- a/man/torch_ceil.Rd
+++ b/man/torch_ceil.Rd
@@ -9,13 +9,11 @@ torch_ceil(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Ceil
 }
-\section{ceil(input, out=None) -> Tensor }{
+\section{ceil(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the ceil of the elements of \code{input},
diff --git a/man/torch_celu.Rd b/man/torch_celu.Rd
new file mode 100644
index 000000000..7898283fb
--- /dev/null
+++ b/man/torch_celu.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gen-namespace-docs.R, R/gen-namespace.R
+\name{torch_celu}
+\alias{torch_celu}
+\title{Celu}
+\usage{
+torch_celu(self, alpha = 1)
+}
+\arguments{
+\item{self}{the input tensor}
+
+\item{alpha}{the alpha value for the CELU formulation. Default: 1.0}
+}
+\description{
+Celu
+}
+\section{celu(input, alpha=1.) -> Tensor }{
+
+
+See \code{\link[=nnf_celu]{nnf_celu()}} for more info.
+}
+
diff --git a/man/torch_celu_.Rd b/man/torch_celu_.Rd
index ed1372944..a77937165 100644
--- a/man/torch_celu_.Rd
+++ b/man/torch_celu_.Rd
@@ -7,12 +7,17 @@
 \usage{
 torch_celu_(self, alpha = 1)
 }
+\arguments{
+\item{self}{the input tensor}
+
+\item{alpha}{the alpha value for the CELU formulation. Default: 1.0}
+}
 \description{
 Celu_
 }
 \section{celu_(input, alpha=1.) -> Tensor }{
 
 
-In-place version of \code{torch_celu}.
+In-place version of \code{\link[=torch_celu]{torch_celu()}}.
 }
 
diff --git a/man/torch_cholesky.Rd b/man/torch_cholesky.Rd
index 1e5f1d15c..a9381a8f5 100644
--- a/man/torch_cholesky.Rd
+++ b/man/torch_cholesky.Rd
@@ -8,36 +8,36 @@
 torch_cholesky(self, upper = FALSE)
 }
 \arguments{
-\item{self}{(Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where \code{*} is zero or more                batch dimensions consisting of symmetric positive-definite matrices.}
+\item{self}{(Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where \code{*} is zero or more
+batch dimensions consisting of symmetric positive-definite matrices.}
 
-\item{upper}{(bool, optional) flag that indicates whether to return a                            upper or lower triangular matrix. Default: \code{False}}
-
-\item{out}{(Tensor, optional) the output matrix}
+\item{upper}{(bool, optional) flag that indicates whether to return a
+upper or lower triangular matrix. Default: \code{FALSE}}
 }
 \description{
 Cholesky
 }
-\section{cholesky(input, upper=False, out=None) -> Tensor }{
+\section{cholesky(input, upper=False, out=NULL) -> Tensor }{
 
 
 Computes the Cholesky decomposition of a symmetric positive-definite
 matrix \eqn{A} or for batches of symmetric positive-definite matrices.
 
-If \code{upper} is \code{True}, the returned matrix \code{U} is upper-triangular, and
+If \code{upper} is \code{TRUE}, the returned matrix \code{U} is upper-triangular, and
 the decomposition has the form:
 
 \deqn{
   A = U^TU
 }
-If \code{upper} is \code{False}, the returned matrix \code{L} is lower-triangular, and
+If \code{upper} is \code{FALSE}, the returned matrix \code{L} is lower-triangular, and
 the decomposition has the form:
 
 \deqn{
     A = LL^T
 }
-If \code{upper} is \code{True}, and \eqn{A} is a batch of symmetric positive-definite
+If \code{upper} is \code{TRUE}, and \eqn{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} is \code{False}, the returned
+of each of the individual matrices. Similarly, when \code{upper} is \code{FALSE}, the returned
 tensor will be composed of lower-triangular Cholesky factors of each of the individual
 matrices.
 }
diff --git a/man/torch_cholesky_inverse.Rd b/man/torch_cholesky_inverse.Rd
index f2ae5fb48..5d34bccc2 100644
--- a/man/torch_cholesky_inverse.Rd
+++ b/man/torch_cholesky_inverse.Rd
@@ -11,26 +11,24 @@ torch_cholesky_inverse(self, upper = FALSE)
 \item{self}{(Tensor) the input 2-D tensor \eqn{u}, a upper or lower triangular           Cholesky factor}
 
 \item{upper}{(bool, optional) whether to return a lower (default) or upper triangular matrix}
-
-\item{out}{(Tensor, optional) the output tensor for \code{inv}}
 }
 \description{
 Cholesky_inverse
 }
-\section{cholesky_inverse(input, upper=False, out=None) -> Tensor }{
+\section{cholesky_inverse(input, upper=False, out=NULL) -> Tensor }{
 
 
 Computes the inverse of a symmetric positive-definite matrix \eqn{A} using its
 Cholesky factor \eqn{u}: returns matrix \code{inv}. The inverse is computed using
 LAPACK routines \code{dpotri} and \code{spotri} (and the corresponding MAGMA routines).
 
-If \code{upper} is \code{False}, \eqn{u} is lower triangular
+If \code{upper} is \code{FALSE}, \eqn{u} is lower triangular
 such that the returned tensor is
 
 \deqn{
     inv = (uu^{{T}})^{{-1}}
 }
-If \code{upper} is \code{True} or not provided, \eqn{u} is upper
+If \code{upper} is \code{TRUE} or not provided, \eqn{u} is upper
 triangular such that the returned tensor is
 
 \deqn{
diff --git a/man/torch_cholesky_solve.Rd b/man/torch_cholesky_solve.Rd
index 91424f0fb..7d2ba2282 100644
--- a/man/torch_cholesky_solve.Rd
+++ b/man/torch_cholesky_solve.Rd
@@ -10,28 +10,26 @@ torch_cholesky_solve(self, input2, upper = FALSE)
 \arguments{
 \item{self}{(Tensor) input matrix \eqn{b} of size \eqn{(*, m, k)},                where \eqn{*} is zero or more batch dimensions}
 
-\item{upper}{(bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: \code{False}.}
+\item{input2}{(Tensor) input matrix \eqn{u} of size \eqn{(*, m, m)},                where \eqn{*} is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor}
 
-\item{self2}{(Tensor) input matrix \eqn{u} of size \eqn{(*, m, m)},                where \eqn{*} is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor}
-
-\item{out}{(Tensor, optional) the output tensor for \code{c}}
+\item{upper}{(bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: \code{FALSE}.}
 }
 \description{
 Cholesky_solve
 }
-\section{cholesky_solve(input, input2, upper=False, out=None) -> Tensor }{
+\section{cholesky_solve(input, input2, upper=False, out=NULL) -> Tensor }{
 
 
 Solves a linear system of equations with a positive semidefinite
 matrix to be inverted given its Cholesky factor matrix \eqn{u}.
 
-If \code{upper} is \code{False}, \eqn{u} is and lower triangular and \code{c} is
+If \code{upper} is \code{FALSE}, \eqn{u} is and lower triangular and \code{c} is
 returned such that:
 
 \deqn{
     c = (u u^T)^{{-1}} b
 }
-If \code{upper} is \code{True} or not provided, \eqn{u} is upper triangular
+If \code{upper} is \code{TRUE} or not provided, \eqn{u} is upper triangular
 and \code{c} is returned such that:
 
 \deqn{
diff --git a/man/torch_clamp.Rd b/man/torch_clamp.Rd
index 34bcc1fe7..12284d21b 100644
--- a/man/torch_clamp.Rd
+++ b/man/torch_clamp.Rd
@@ -13,15 +13,11 @@ torch_clamp(self, min = NULL, max = NULL)
 \item{min}{(Number) lower-bound of the range to be clamped to}
 
 \item{max}{(Number) upper-bound of the range to be clamped to}
-
-\item{out}{(Tensor, optional) the output tensor.}
-
-\item{value}{(Number) minimal value of each element in the output}
 }
 \description{
 Clamp
 }
-\section{clamp(input, min, max, out=None) -> Tensor }{
+\section{clamp(input, min, max, out=NULL) -> Tensor }{
 
 
 Clamp all elements in \code{input} into the range \code{[} \code{min}, \code{max} \verb{]} and return
@@ -39,7 +35,7 @@ If \code{input} is of type \code{FloatTensor} or \code{DoubleTensor}, args \code
 and \code{max} must be real numbers, otherwise they should be integers.
 }
 
-\section{clamp(input, *, min, out=None) -> Tensor }{
+\section{clamp(input, *, min, out=NULL) -> Tensor }{
 
 
 Clamps all elements in \code{input} to be larger or equal \code{min}.
@@ -48,7 +44,7 @@ If \code{input} is of type \code{FloatTensor} or \code{DoubleTensor}, \code{valu
 should be a real number, otherwise it should be an integer.
 }
 
-\section{clamp(input, *, max, out=None) -> Tensor }{
+\section{clamp(input, *, max, out=NULL) -> Tensor }{
 
 
 Clamps all elements in \code{input} to be smaller or equal \code{max}.
diff --git a/man/torch_combinations.Rd b/man/torch_combinations.Rd
index 0eec951c7..90315cd17 100644
--- a/man/torch_combinations.Rd
+++ b/man/torch_combinations.Rd
@@ -22,7 +22,7 @@ Combinations
 
 Compute combinations of length \eqn{r} of the given tensor. The behavior is similar to
 python's \code{itertools.combinations} when \code{with_replacement} is set to \code{False}, and
-\code{itertools.combinations_with_replacement} when \code{with_replacement} is set to \code{True}.
+\code{itertools.combinations_with_replacement} when \code{with_replacement} is set to \code{TRUE}.
 }
 
 \examples{
diff --git a/man/torch_conj.Rd b/man/torch_conj.Rd
index c0643b3fb..96825cfd7 100644
--- a/man/torch_conj.Rd
+++ b/man/torch_conj.Rd
@@ -9,13 +9,11 @@ torch_conj(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Conj
 }
-\section{conj(input, out=None) -> Tensor }{
+\section{conj(input) -> Tensor }{
 
 
 Computes the element-wise conjugate of the given \code{input} tensor.
diff --git a/man/torch_conv1d.Rd b/man/torch_conv1d.Rd
index d43320c45..f4465a33e 100644
--- a/man/torch_conv1d.Rd
+++ b/man/torch_conv1d.Rd
@@ -16,32 +16,30 @@ torch_conv1d(
 )
 }
 \arguments{
-\item{weight}{NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)}}
+\item{input}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
 
-\item{bias}{NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: \code{None}}
+\item{weight}{filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)}}
 
-\item{stride}{NA the stride of the convolving kernel. Can be a single number or      a one-element tuple \verb{(sW,)}. Default: 1}
+\item{bias}{optional bias of shape \eqn{(\mbox{out\_channels})}. Default: \code{NULL}}
 
-\item{padding}{NA implicit paddings on both sides of the input. Can be a      single number or a one-element tuple \verb{(padW,)}. Default: 0}
+\item{stride}{the stride of the convolving kernel. Can be a single number or      a one-element tuple \verb{(sW,)}. Default: 1}
 
-\item{dilation}{NA the spacing between kernel elements. Can be a single number or      a one-element tuple \verb{(dW,)}. Default: 1}
+\item{padding}{implicit paddings on both sides of the input. Can be a      single number or a one-element tuple \verb{(padW,)}. Default: 0}
 
-\item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1}
+\item{dilation}{the spacing between kernel elements. Can be a single number or      a one-element tuple \verb{(dW,)}. Default: 1}
 
-\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
+\item{groups}{split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1}
 }
 \description{
 Conv1d
 }
-\section{conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor }{
+\section{conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor }{
 
 
 Applies a 1D convolution over an input signal composed of several input
 planes.
 
-See \code{~torch.nn.Conv1d} for details and output shape.
-
-.. include:: cudnn_deterministic.rst
+See \code{\link[=nn_conv1d]{nn_conv1d()}} for details and output shape.
 }
 
 \examples{
diff --git a/man/torch_conv2d.Rd b/man/torch_conv2d.Rd
index e90d24bf6..21c7ebd1f 100644
--- a/man/torch_conv2d.Rd
+++ b/man/torch_conv2d.Rd
@@ -16,32 +16,30 @@ torch_conv2d(
 )
 }
 \arguments{
-\item{weight}{NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)}}
+\item{input}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}}
 
-\item{bias}{NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: \code{None}}
+\item{weight}{filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)}}
 
-\item{stride}{NA the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sH, sW)}. Default: 1}
+\item{bias}{optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: \code{NULL}}
 
-\item{padding}{NA implicit paddings on both sides of the input. Can be a      single number or a tuple \verb{(padH, padW)}. Default: 0}
+\item{stride}{the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sH, sW)}. Default: 1}
 
-\item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dH, dW)}. Default: 1}
+\item{padding}{implicit paddings on both sides of the input. Can be a      single number or a tuple \verb{(padH, padW)}. Default: 0}
 
-\item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
+\item{dilation}{the spacing between kernel elements. Can be a single number or      a tuple \verb{(dH, dW)}. Default: 1}
 
-\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}}
+\item{groups}{split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
 }
 \description{
 Conv2d
 }
-\section{conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor }{
+\section{conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor }{
 
 
 Applies a 2D convolution over an input image composed of several input
 planes.
 
-See \code{~torch.nn.Conv2d} for details and output shape.
-
-.. include:: cudnn_deterministic.rst
+See \code{\link[=nn_conv2d]{nn_conv2d()}} for details and output shape.
 }
 
 \examples{
diff --git a/man/torch_conv3d.Rd b/man/torch_conv3d.Rd
index a64d709c2..fcc3f8c4e 100644
--- a/man/torch_conv3d.Rd
+++ b/man/torch_conv3d.Rd
@@ -16,32 +16,30 @@ torch_conv3d(
 )
 }
 \arguments{
-\item{weight}{NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)}}
+\item{input}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}}
 
-\item{bias}{NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: None}
+\item{weight}{filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)}}
 
-\item{stride}{NA the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sT, sH, sW)}. Default: 1}
+\item{bias}{optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: NULL}
 
-\item{padding}{NA implicit paddings on both sides of the input. Can be a      single number or a tuple \verb{(padT, padH, padW)}. Default: 0}
+\item{stride}{the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sT, sH, sW)}. Default: 1}
 
-\item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dT, dH, dW)}. Default: 1}
+\item{padding}{implicit paddings on both sides of the input. Can be a      single number or a tuple \verb{(padT, padH, padW)}. Default: 0}
 
-\item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1}
+\item{dilation}{the spacing between kernel elements. Can be a single number or      a tuple \verb{(dT, dH, dW)}. Default: 1}
 
-\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}}
+\item{groups}{split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1}
 }
 \description{
 Conv3d
 }
-\section{conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor }{
+\section{conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor }{
 
 
 Applies a 3D convolution over an input image composed of several input
 planes.
 
-See \code{~torch.nn.Conv3d} for details and output shape.
-
-.. include:: cudnn_deterministic.rst
+See \code{\link[=nn_conv3d]{nn_conv3d()}} for details and output shape.
 }
 
 \examples{
diff --git a/man/torch_conv_transpose1d.Rd b/man/torch_conv_transpose1d.Rd
index edff98833..ffdd5f23a 100644
--- a/man/torch_conv_transpose1d.Rd
+++ b/man/torch_conv_transpose1d.Rd
@@ -17,34 +17,32 @@ torch_conv_transpose1d(
 )
 }
 \arguments{
-\item{weight}{NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)}}
+\item{input}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
 
-\item{bias}{NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None}
+\item{weight}{filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)}}
 
-\item{stride}{NA the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sW,)}. Default: 1}
+\item{bias}{optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL}
 
-\item{padding}{NA \code{dilation * (kernel_size - 1) - padding} zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      \verb{(padW,)}. Default: 0}
+\item{stride}{the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sW,)}. Default: 1}
 
-\item{output_padding}{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)}. Default: 0}
+\item{padding}{\code{dilation * (kernel_size - 1) - padding} zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      \verb{(padW,)}. Default: 0}
 
-\item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
+\item{output_padding}{additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple \code{(out_padW)}. Default: 0}
 
-\item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dW,)}. Default: 1}
+\item{groups}{split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
 
-\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
+\item{dilation}{the spacing between kernel elements. Can be a single number or      a tuple \verb{(dW,)}. Default: 1}
 }
 \description{
 Conv_transpose1d
 }
-\section{conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor }{
+\section{conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor }{
 
 
 Applies a 1D transposed convolution operator over an input signal
 composed of several input planes, sometimes also called "deconvolution".
 
-See \code{~torch.nn.ConvTranspose1d} for details and output shape.
-
-.. include:: cudnn_deterministic.rst
+See \code{\link[=nn_conv_transpose1d]{nn_conv_transpose1d()}}  for details and output shape.
 }
 
 \examples{
diff --git a/man/torch_conv_transpose2d.Rd b/man/torch_conv_transpose2d.Rd
index 26f5baf2c..beb9e6a98 100644
--- a/man/torch_conv_transpose2d.Rd
+++ b/man/torch_conv_transpose2d.Rd
@@ -17,34 +17,32 @@ torch_conv_transpose2d(
 )
 }
 \arguments{
-\item{weight}{NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)}}
+\item{input}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}}
 
-\item{bias}{NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None}
+\item{weight}{filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)}}
 
-\item{stride}{NA the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sH, sW)}. Default: 1}
+\item{bias}{optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL}
 
-\item{padding}{NA \code{dilation * (kernel_size - 1) - padding} zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      \verb{(padH, padW)}. Default: 0}
+\item{stride}{the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sH, sW)}. Default: 1}
 
-\item{output_padding}{NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple \verb{(out_padH, out_padW)}.      Default: 0}
+\item{padding}{\code{dilation * (kernel_size - 1) - padding} zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      \verb{(padH, padW)}. Default: 0}
 
-\item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
+\item{output_padding}{additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple \verb{(out_padH, out_padW)}.      Default: 0}
 
-\item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dH, dW)}. Default: 1}
+\item{groups}{split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
 
-\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}}
+\item{dilation}{the spacing between kernel elements. Can be a single number or      a tuple \verb{(dH, dW)}. Default: 1}
 }
 \description{
 Conv_transpose2d
 }
-\section{conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor }{
+\section{conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor }{
 
 
 Applies a 2D transposed convolution operator over an input image
 composed of several input planes, sometimes also called "deconvolution".
 
-See \code{~torch.nn.ConvTranspose2d} for details and output shape.
-
-.. include:: cudnn_deterministic.rst
+See \code{\link[=nn_conv_transpose2d]{nn_conv_transpose2d()}} for details and output shape.
 }
 
 \examples{
diff --git a/man/torch_conv_transpose3d.Rd b/man/torch_conv_transpose3d.Rd
index c9374d9a3..cde9c094c 100644
--- a/man/torch_conv_transpose3d.Rd
+++ b/man/torch_conv_transpose3d.Rd
@@ -17,34 +17,32 @@ torch_conv_transpose3d(
 )
 }
 \arguments{
-\item{weight}{NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)}}
+\item{input}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}}
 
-\item{bias}{NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None}
+\item{weight}{filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)}}
 
-\item{stride}{NA the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sT, sH, sW)}. Default: 1}
+\item{bias}{optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL}
 
-\item{padding}{NA \code{dilation * (kernel_size - 1) - padding} zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      \verb{(padT, padH, padW)}. Default: 0}
+\item{stride}{the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sT, sH, sW)}. Default: 1}
 
-\item{output_padding}{NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      \verb{(out_padT, out_padH, out_padW)}. Default: 0}
+\item{padding}{\code{dilation * (kernel_size - 1) - padding} zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      \verb{(padT, padH, padW)}. Default: 0}
 
-\item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
+\item{output_padding}{additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      \verb{(out_padT, out_padH, out_padW)}. Default: 0}
 
-\item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dT, dH, dW)}. Default: 1}
+\item{groups}{split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
 
-\item{self}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}}
+\item{dilation}{the spacing between kernel elements. Can be a single number or      a tuple \verb{(dT, dH, dW)}. Default: 1}
 }
 \description{
 Conv_transpose3d
 }
-\section{conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor }{
+\section{conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor }{
 
 
 Applies a 3D transposed convolution operator over an input image
 composed of several input planes, sometimes also called "deconvolution"
 
-See \code{~torch.nn.ConvTranspose3d} for details and output shape.
-
-.. include:: cudnn_deterministic.rst
+See \code{\link[=nn_conv_transpose3d]{nn_conv_transpose3d()}} for details and output shape.
 }
 
 \examples{
diff --git a/man/torch_cos.Rd b/man/torch_cos.Rd
index 5e3709d64..feca69803 100644
--- a/man/torch_cos.Rd
+++ b/man/torch_cos.Rd
@@ -9,13 +9,11 @@ torch_cos(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Cos
 }
-\section{cos(input, out=None) -> Tensor }{
+\section{cos(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the cosine  of the elements of \code{input}.
diff --git a/man/torch_cosh.Rd b/man/torch_cosh.Rd
index 10f19bf1f..cfec6e0bc 100644
--- a/man/torch_cosh.Rd
+++ b/man/torch_cosh.Rd
@@ -9,13 +9,11 @@ torch_cosh(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Cosh
 }
-\section{cosh(input, out=None) -> Tensor }{
+\section{cosh(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the hyperbolic cosine  of the elements of
diff --git a/man/torch_cross.Rd b/man/torch_cross.Rd
index 1039bd242..f29ce6267 100644
--- a/man/torch_cross.Rd
+++ b/man/torch_cross.Rd
@@ -13,13 +13,11 @@ torch_cross(self, other, dim = NULL)
 \item{other}{(Tensor) the second input tensor}
 
 \item{dim}{(int, optional) the dimension to take the cross-product in.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Cross
 }
-\section{cross(input, other, dim=-1, out=None) -> Tensor }{
+\section{cross(input, other, dim=-1, out=NULL) -> Tensor }{
 
 
 Returns the cross product of vectors in dimension \code{dim} of \code{input}
diff --git a/man/torch_cummax.Rd b/man/torch_cummax.Rd
index 28afe1459..07da9b8c1 100644
--- a/man/torch_cummax.Rd
+++ b/man/torch_cummax.Rd
@@ -11,13 +11,11 @@ torch_cummax(self, dim)
 \item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to do the operation over}
-
-\item{out}{(tuple, optional) the result tuple of two output tensors (values, indices)}
 }
 \description{
 Cummax
 }
-\section{cummax(input, dim, out=None) -> (Tensor, LongTensor) }{
+\section{cummax(input, dim) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the cumulative maximum of
diff --git a/man/torch_cummin.Rd b/man/torch_cummin.Rd
index c1f1b6c2c..8950e566a 100644
--- a/man/torch_cummin.Rd
+++ b/man/torch_cummin.Rd
@@ -11,13 +11,11 @@ torch_cummin(self, dim)
 \item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to do the operation over}
-
-\item{out}{(tuple, optional) the result tuple of two output tensors (values, indices)}
 }
 \description{
 Cummin
 }
-\section{cummin(input, dim, out=None) -> (Tensor, LongTensor) }{
+\section{cummin(input, dim) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the cumulative minimum of
diff --git a/man/torch_cumprod.Rd b/man/torch_cumprod.Rd
index 6ec0ad78b..0b30bcc72 100644
--- a/man/torch_cumprod.Rd
+++ b/man/torch_cumprod.Rd
@@ -12,14 +12,12 @@ torch_cumprod(self, dim, dtype = NULL)
 
 \item{dim}{(int) the dimension to do the operation over}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: None.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.}
 }
 \description{
 Cumprod
 }
-\section{cumprod(input, dim, out=None, dtype=None) -> Tensor }{
+\section{cumprod(input, dim, out=NULL, dtype=NULL) -> Tensor }{
 
 
 Returns the cumulative product of elements of \code{input} in the dimension
diff --git a/man/torch_cumsum.Rd b/man/torch_cumsum.Rd
index 9169cacd8..4caecb807 100644
--- a/man/torch_cumsum.Rd
+++ b/man/torch_cumsum.Rd
@@ -12,14 +12,12 @@ torch_cumsum(self, dim, dtype = NULL)
 
 \item{dim}{(int) the dimension to do the operation over}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: None.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.}
 }
 \description{
 Cumsum
 }
-\section{cumsum(input, dim, out=None, dtype=None) -> Tensor }{
+\section{cumsum(input, dim, out=NULL, dtype=NULL) -> Tensor }{
 
 
 Returns the cumulative sum of elements of \code{input} in the dimension
diff --git a/man/torch_diag.Rd b/man/torch_diag.Rd
index dc3efb1f4..536398651 100644
--- a/man/torch_diag.Rd
+++ b/man/torch_diag.Rd
@@ -11,13 +11,11 @@ torch_diag(self, diagonal = 0L)
 \item{self}{(Tensor) the input tensor.}
 
 \item{diagonal}{(int, optional) the diagonal to consider}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Diag
 }
-\section{diag(input, diagonal=0, out=None) -> Tensor }{
+\section{diag(input, diagonal=0, out=NULL) -> Tensor }{
 
 \itemize{
 \item If \code{input} is a vector (1-D tensor), then returns a 2-D square tensor
diff --git a/man/torch_diagonal.Rd b/man/torch_diagonal.Rd
index 42b3d6377..16f35d401 100644
--- a/man/torch_diagonal.Rd
+++ b/man/torch_diagonal.Rd
@@ -10,6 +10,8 @@ torch_diagonal(self, outdim, dim1 = 1L, dim2 = 2L, offset = 0L)
 \arguments{
 \item{self}{(Tensor) the input tensor. Must be at least 2-dimensional.}
 
+\item{outdim}{dimension name if \code{self} is a named tensor.}
+
 \item{dim1}{(int, optional) first dimension with respect to which to        take diagonal. Default: 0.}
 
 \item{dim2}{(int, optional) second dimension with respect to which to        take diagonal. Default: 1.}
diff --git a/man/torch_digamma.Rd b/man/torch_digamma.Rd
index 9751ab73e..5fd0f3738 100644
--- a/man/torch_digamma.Rd
+++ b/man/torch_digamma.Rd
@@ -13,7 +13,7 @@ torch_digamma(self)
 \description{
 Digamma
 }
-\section{digamma(input, out=None) -> Tensor }{
+\section{digamma(input, out=NULL) -> Tensor }{
 
 
 Computes the logarithmic derivative of the gamma function on \code{input}.
diff --git a/man/torch_div.Rd b/man/torch_div.Rd
index 7e4e61a7e..e02219aad 100644
--- a/man/torch_div.Rd
+++ b/man/torch_div.Rd
@@ -15,7 +15,7 @@ torch_div(self, other)
 \description{
 Div
 }
-\section{div(input, other, out=None) -> Tensor }{
+\section{div(input, other, out=NULL) -> Tensor }{
 
 
 Divides each element of the input \code{input} with the scalar \code{other} and
diff --git a/man/torch_dot.Rd b/man/torch_dot.Rd
index f0c71ccb0..edcf24ea6 100644
--- a/man/torch_dot.Rd
+++ b/man/torch_dot.Rd
@@ -7,6 +7,11 @@
 \usage{
 torch_dot(self, tensor)
 }
+\arguments{
+\item{self}{the input tensor}
+
+\item{tensor}{the other input tensor}
+}
 \description{
 Dot
 }
diff --git a/man/torch_eig.Rd b/man/torch_eig.Rd
index 02ec7402c..ed48c72c3 100644
--- a/man/torch_eig.Rd
+++ b/man/torch_eig.Rd
@@ -10,9 +10,7 @@ torch_eig(self, eigenvectors = FALSE)
 \arguments{
 \item{self}{(Tensor) the square matrix of shape \eqn{(n \times n)} for which the eigenvalues and eigenvectors        will be computed}
 
-\item{eigenvectors}{(bool) \code{True} to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed}
-
-\item{out}{(tuple, optional) the output tensors}
+\item{eigenvectors}{(bool) \code{TRUE} to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed}
 }
 \description{
 Eig
@@ -22,7 +20,7 @@ Eig
 for [`torch_symeig`]
 }
 }
-\section{eig(input, eigenvectors=False, out=None) -> (Tensor, Tensor) }{
+\section{eig(input, eigenvectors=False, out=NULL) -> (Tensor, Tensor) }{
 
 
 Computes the eigenvalues and eigenvectors of a real square matrix.
diff --git a/man/torch_einsum.Rd b/man/torch_einsum.Rd
index ecf334d09..4658a5d61 100644
--- a/man/torch_einsum.Rd
+++ b/man/torch_einsum.Rd
@@ -10,7 +10,7 @@ torch_einsum(equation, tensors)
 \arguments{
 \item{equation}{(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 \verb{->} and gives the indices for the output.           If the \verb{->} 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{...} represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.}
 
-\item{operands}{(Tensor) The operands to compute the Einstein sum of.}
+\item{tensors}{(Tensor) The operands to compute the Einstein sum of.}
 }
 \description{
 Einsum
diff --git a/man/torch_empty.Rd b/man/torch_empty.Rd
index cbe9877d8..f465eafaa 100644
--- a/man/torch_empty.Rd
+++ b/man/torch_empty.Rd
@@ -15,26 +15,22 @@ torch_empty(
 )
 }
 \arguments{
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{...}{a sequence of integers defining the shape of the output tensor.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
-
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{names}{optional character vector naming each dimension.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
-\item{size}{(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.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{pin_memory}{(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: \code{False}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_contiguous_format}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Empty
 }
-\section{empty(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor }{
+\section{empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -> Tensor }{
 
 
 Returns a tensor filled with uninitialized data. The shape of the tensor is
diff --git a/man/torch_empty_like.Rd b/man/torch_empty_like.Rd
index 325c6436c..dcc40bac0 100644
--- a/man/torch_empty_like.Rd
+++ b/man/torch_empty_like.Rd
@@ -15,22 +15,22 @@ torch_empty_like(
 )
 }
 \arguments{
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
+\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
-\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
+\item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 }
 \description{
 Empty_like
 }
-\section{empty_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
+\section{empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
 
 
 Returns an uninitialized tensor with the same size as \code{input}.
diff --git a/man/torch_empty_strided.Rd b/man/torch_empty_strided.Rd
index f4ab25603..8e2ee213d 100644
--- a/man/torch_empty_strided.Rd
+++ b/man/torch_empty_strided.Rd
@@ -20,20 +20,20 @@ torch_empty_strided(
 
 \item{stride}{(tuple of ints) the strides of the output tensor}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
-\item{pin_memory}{(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: \code{False}.}
+\item{pin_memory}{(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: \code{FALSE}.}
 }
 \description{
 Empty_strided
 }
-\section{empty_strided(size, stride, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -> Tensor }{
+\section{empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -> Tensor }{
 
 
 Returns a tensor filled with uninitialized data. The shape and strides of the tensor is
diff --git a/man/torch_eq.Rd b/man/torch_eq.Rd
index 9b5047cdd..16aa5fbb8 100644
--- a/man/torch_eq.Rd
+++ b/man/torch_eq.Rd
@@ -10,14 +10,13 @@ torch_eq(self, other)
 \arguments{
 \item{self}{(Tensor) the tensor to compare}
 
-\item{other}{(Tensor or float) the tensor or value to compare}
-
-\item{out}{(Tensor, optional) the output tensor. Must be a \code{ByteTensor}}
+\item{other}{(Tensor or float) the tensor or value to compare
+Must be a \code{ByteTensor}}
 }
 \description{
 Eq
 }
-\section{eq(input, other, out=None) -> Tensor }{
+\section{eq(input, other, out=NULL) -> Tensor }{
 
 
 Computes element-wise equality
diff --git a/man/torch_equal.Rd b/man/torch_equal.Rd
index a3283f218..c24a68773 100644
--- a/man/torch_equal.Rd
+++ b/man/torch_equal.Rd
@@ -7,13 +7,18 @@
 \usage{
 torch_equal(self, other)
 }
+\arguments{
+\item{self}{the input tensor}
+
+\item{other}{the other input tensor}
+}
 \description{
 Equal
 }
 \section{equal(input, other) -> bool }{
 
 
-\code{True} if two tensors have the same size and elements, \code{False} otherwise.
+\code{TRUE} if two tensors have the same size and elements, \code{FALSE} otherwise.
 }
 
 \examples{
diff --git a/man/torch_erf.Rd b/man/torch_erf.Rd
index 6ecef8f56..093bff668 100644
--- a/man/torch_erf.Rd
+++ b/man/torch_erf.Rd
@@ -9,13 +9,11 @@ torch_erf(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Erf
 }
-\section{erf(input, out=None) -> Tensor }{
+\section{erf(input, out=NULL) -> Tensor }{
 
 
 Computes the error function of each element. The error function is defined as follows:
diff --git a/man/torch_erfc.Rd b/man/torch_erfc.Rd
index 51f1932af..eaf8fe8f3 100644
--- a/man/torch_erfc.Rd
+++ b/man/torch_erfc.Rd
@@ -9,13 +9,11 @@ torch_erfc(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Erfc
 }
-\section{erfc(input, out=None) -> Tensor }{
+\section{erfc(input, out=NULL) -> Tensor }{
 
 
 Computes the complementary error function of each element of \code{input}.
diff --git a/man/torch_erfinv.Rd b/man/torch_erfinv.Rd
index db8483573..b2a1703cc 100644
--- a/man/torch_erfinv.Rd
+++ b/man/torch_erfinv.Rd
@@ -9,13 +9,11 @@ torch_erfinv(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Erfinv
 }
-\section{erfinv(input, out=None) -> Tensor }{
+\section{erfinv(input, out=NULL) -> Tensor }{
 
 
 Computes the inverse error function of each element of \code{input}.
diff --git a/man/torch_exp.Rd b/man/torch_exp.Rd
index 9162359a4..e605f9fce 100644
--- a/man/torch_exp.Rd
+++ b/man/torch_exp.Rd
@@ -9,13 +9,11 @@ torch_exp(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Exp
 }
-\section{exp(input, out=None) -> Tensor }{
+\section{exp(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the exponential of the elements
diff --git a/man/torch_expm1.Rd b/man/torch_expm1.Rd
index e75ab1c0a..15b7a3dc4 100644
--- a/man/torch_expm1.Rd
+++ b/man/torch_expm1.Rd
@@ -9,13 +9,11 @@ torch_expm1(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Expm1
 }
-\section{expm1(input, out=None) -> Tensor }{
+\section{expm1(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the exponential of the elements minus 1
diff --git a/man/torch_eye.Rd b/man/torch_eye.Rd
index d6a744bf4..1933cad23 100644
--- a/man/torch_eye.Rd
+++ b/man/torch_eye.Rd
@@ -19,20 +19,18 @@ torch_eye(
 
 \item{m}{(int, optional) the number of columns with default being \code{n}}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Eye
 }
-\section{eye(n, m=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.
diff --git a/man/torch_fft.Rd b/man/torch_fft.Rd
index 2a70bbf78..9c07a4172 100644
--- a/man/torch_fft.Rd
+++ b/man/torch_fft.Rd
@@ -12,7 +12,7 @@ torch_fft(self, signal_ndim, normalized = FALSE)
 
 \item{signal_ndim}{(int) the number of dimensions in each signal.        \code{signal_ndim} can only be 1, 2 or 3}
 
-\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{False}}
+\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{FALSE}}
 }
 \description{
 Fft
@@ -45,7 +45,7 @@ by \code{signal_ndim}. \code{input} 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} dimensions with optionally
 arbitrary number of leading batch dimensions. If \code{normalized} is set to
-\code{True}, this normalizes the result by dividing it with
+\code{TRUE}, this normalizes the result by dividing it with
 \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is unitary.
 
 Returns the real and the imaginary parts together as one tensor of the same
diff --git a/man/torch_flatten.Rd b/man/torch_flatten.Rd
index 429e0fe83..7e75d7330 100644
--- a/man/torch_flatten.Rd
+++ b/man/torch_flatten.Rd
@@ -10,9 +10,14 @@ torch_flatten(self, dims, start_dim = 1L, end_dim = -1L, out_dim)
 \arguments{
 \item{self}{(Tensor) the input tensor.}
 
+\item{dims}{if tensor is named you can pass the name of the dimensions to
+flatten}
+
 \item{start_dim}{(int) the first dim to flatten}
 
 \item{end_dim}{(int) the last dim to flatten}
+
+\item{out_dim}{the name of the resulting dimension if a named tensor.}
 }
 \description{
 Flatten
diff --git a/man/torch_floor.Rd b/man/torch_floor.Rd
index 18366318e..ed7351411 100644
--- a/man/torch_floor.Rd
+++ b/man/torch_floor.Rd
@@ -9,13 +9,11 @@ torch_floor(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Floor
 }
-\section{floor(input, out=None) -> Tensor }{
+\section{floor(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the floor of the elements of \code{input},
diff --git a/man/torch_floor_divide.Rd b/man/torch_floor_divide.Rd
index 86ec7abcd..85f1c2f88 100644
--- a/man/torch_floor_divide.Rd
+++ b/man/torch_floor_divide.Rd
@@ -15,7 +15,7 @@ torch_floor_divide(self, other)
 \description{
 Floor_divide
 }
-\section{floor_divide(input, other, out=None) -> Tensor }{
+\section{floor_divide(input, other, out=NULL) -> Tensor }{
 
 
 Return the division of the inputs rounded down to the nearest integer. See \code{\link{torch_div}}
diff --git a/man/torch_fmod.Rd b/man/torch_fmod.Rd
index 21335a1b3..c6b3a6a8f 100644
--- a/man/torch_fmod.Rd
+++ b/man/torch_fmod.Rd
@@ -11,13 +11,11 @@ torch_fmod(self, other)
 \item{self}{(Tensor) the dividend}
 
 \item{other}{(Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Fmod
 }
-\section{fmod(input, other, out=None) -> Tensor }{
+\section{fmod(input, other, out=NULL) -> Tensor }{
 
 
 Computes the element-wise remainder of division.
diff --git a/man/torch_frac.Rd b/man/torch_frac.Rd
index 5327ea1d0..761f3281f 100644
--- a/man/torch_frac.Rd
+++ b/man/torch_frac.Rd
@@ -7,10 +7,13 @@
 \usage{
 torch_frac(self)
 }
+\arguments{
+\item{self}{the input tensor.}
+}
 \description{
 Frac
 }
-\section{frac(input, out=None) -> Tensor }{
+\section{frac(input, out=NULL) -> Tensor }{
 
 
 Computes the fractional portion of each element in \code{input}.
diff --git a/man/torch_full.Rd b/man/torch_full.Rd
index d3b5ffeaf..c54b7cbdf 100644
--- a/man/torch_full.Rd
+++ b/man/torch_full.Rd
@@ -20,20 +20,20 @@ torch_full(
 
 \item{fill_value}{NA the number to fill the output tensor with.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{names}{optional names of the dimensions}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Full
 }
-\section{full(size, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a tensor of size \code{size} filled with \code{fill_value}.
diff --git a/man/torch_full_like.Rd b/man/torch_full_like.Rd
index 5471e913b..a9a7a2a48 100644
--- a/man/torch_full_like.Rd
+++ b/man/torch_full_like.Rd
@@ -16,24 +16,24 @@ torch_full_like(
 )
 }
 \arguments{
-\item{fill_value}{NA the number to fill the output tensor with.}
+\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
+\item{fill_value}{the number to fill the output tensor with.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
-\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
+\item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 }
 \description{
 Full_like
 }
-\section{full_like(input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, }{
+\section{full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, }{
 
 
 memory_format=torch.preserve_format) -> Tensor
diff --git a/man/torch_gather.Rd b/man/torch_gather.Rd
index 7f0354cc4..efb6bf96e 100644
--- a/man/torch_gather.Rd
+++ b/man/torch_gather.Rd
@@ -14,14 +14,12 @@ torch_gather(self, dim, index, sparse_grad = FALSE)
 
 \item{index}{(LongTensor) the indices of elements to gather}
 
-\item{sparse_grad}{(bool,optional) If \code{True}, gradient w.r.t. \code{input} will be a sparse tensor.}
-
-\item{out}{(Tensor, optional) the destination tensor}
+\item{sparse_grad}{(bool,optional) If \code{TRUE}, gradient w.r.t. \code{input} will be a sparse tensor.}
 }
 \description{
 Gather
 }
-\section{gather(input, dim, index, out=None, sparse_grad=False) -> Tensor }{
+\section{gather(input, dim, index, sparse_grad=FALSE) -> Tensor }{
 
 
 Gathers values along an axis specified by \code{dim}.
diff --git a/man/torch_ge.Rd b/man/torch_ge.Rd
index 669d0101d..77599e332 100644
--- a/man/torch_ge.Rd
+++ b/man/torch_ge.Rd
@@ -11,13 +11,11 @@ torch_ge(self, other)
 \item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
-
-\item{out}{(Tensor, optional) the output tensor that must be a \code{BoolTensor}}
 }
 \description{
 Ge
 }
-\section{ge(input, other, out=None) -> Tensor }{
+\section{ge(input, other, out=NULL) -> Tensor }{
 
 
 Computes \eqn{\mbox{input} \geq \mbox{other}} element-wise.
diff --git a/man/torch_geqrf.Rd b/man/torch_geqrf.Rd
index bc70bcb9e..326af5e97 100644
--- a/man/torch_geqrf.Rd
+++ b/man/torch_geqrf.Rd
@@ -9,13 +9,11 @@ torch_geqrf(self)
 }
 \arguments{
 \item{self}{(Tensor) the input matrix}
-
-\item{out}{(tuple, optional) the output tuple of (Tensor, Tensor)}
 }
 \description{
 Geqrf
 }
-\section{geqrf(input, out=None) -> (Tensor, Tensor) }{
+\section{geqrf(input, out=NULL) -> (Tensor, Tensor) }{
 
 
 This is a low-level function for calling LAPACK directly. This function
diff --git a/man/torch_ger.Rd b/man/torch_ger.Rd
index 9f476fe58..8517af354 100644
--- a/man/torch_ger.Rd
+++ b/man/torch_ger.Rd
@@ -11,8 +11,6 @@ torch_ger(self, vec2)
 \item{self}{(Tensor) 1-D input vector}
 
 \item{vec2}{(Tensor) 1-D input vector}
-
-\item{out}{(Tensor, optional) optional output matrix}
 }
 \description{
 Ger
@@ -20,7 +18,7 @@ Ger
 \note{
 This function does not broadcast .
 }
-\section{ger(input, vec2, out=None) -> Tensor }{
+\section{ger(input, vec2, out=NULL) -> Tensor }{
 
 
 Outer product of \code{input} and \code{vec2}.
diff --git a/man/torch_gt.Rd b/man/torch_gt.Rd
index ba8e988ea..df54138d2 100644
--- a/man/torch_gt.Rd
+++ b/man/torch_gt.Rd
@@ -11,13 +11,11 @@ torch_gt(self, other)
 \item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
-
-\item{out}{(Tensor, optional) the output tensor that must be a \code{BoolTensor}}
 }
 \description{
 Gt
 }
-\section{gt(input, other, out=None) -> Tensor }{
+\section{gt(input, other, out=NULL) -> Tensor }{
 
 
 Computes \eqn{\mbox{input} > \mbox{other}} element-wise.
diff --git a/man/torch_hamming_window.Rd b/man/torch_hamming_window.Rd
index b2c7bc3e8..e33d445bf 100644
--- a/man/torch_hamming_window.Rd
+++ b/man/torch_hamming_window.Rd
@@ -10,19 +10,19 @@ torch_hamming_window(window_length, periodic, alpha, beta, options = list())
 \arguments{
 \item{window_length}{(int) the size of returned window}
 
-\item{periodic}{(bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.}
+\item{periodic}{(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.}
 
 \item{alpha}{(float, optional) The coefficient \eqn{\alpha} in the equation above}
 
 \item{beta}{(float, optional) The coefficient \eqn{\beta} in the equation above}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned window tensor. Only          \code{torch_strided} (dense layout) is supported.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Hamming_window
@@ -34,7 +34,7 @@ Hamming_window
 \preformatted{This is a generalized version of `torch_hann_window`.
 }
 }
-\section{hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Hamming window function.
@@ -50,7 +50,7 @@ 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}. Therefore, if \code{periodic} is true, the \eqn{N} in
 above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-\code{torch_hamming_window(L, periodic=True)} equal to
+\code{torch_hamming_window(L, periodic=TRUE)} equal to
 \verb{torch_hamming_window(L + 1, periodic=False)[:-1])}.
 }
 
diff --git a/man/torch_hann_window.Rd b/man/torch_hann_window.Rd
index 29560f3ae..533862970 100644
--- a/man/torch_hann_window.Rd
+++ b/man/torch_hann_window.Rd
@@ -10,15 +10,15 @@ torch_hann_window(window_length, periodic, options = list())
 \arguments{
 \item{window_length}{(int) the size of returned window}
 
-\item{periodic}{(bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.}
+\item{periodic}{(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned window tensor. Only          \code{torch_strided} (dense layout) is supported.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Hann_window
@@ -27,7 +27,7 @@ Hann_window
 \preformatted{If `window_length` \eqn{=1}, the returned window contains a single value 1.
 }
 }
-\section{hann_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Hann window function.
@@ -44,7 +44,7 @@ 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}. Therefore, if \code{periodic} is true, the \eqn{N} in
 above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-\code{torch_hann_window(L, periodic=True)} equal to
+\code{torch_hann_window(L, periodic=TRUE)} equal to
 \verb{torch_hann_window(L + 1, periodic=False)[:-1])}.
 }
 
diff --git a/man/torch_histc.Rd b/man/torch_histc.Rd
index 8892ea0e1..3bc8e3552 100644
--- a/man/torch_histc.Rd
+++ b/man/torch_histc.Rd
@@ -15,13 +15,11 @@ torch_histc(self, bins = 100L, min = 0L, max = 0L)
 \item{min}{(int) lower end of the range (inclusive)}
 
 \item{max}{(int) upper end of the range (inclusive)}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Histc
 }
-\section{histc(input, bins=100, min=0, max=0, out=None) -> Tensor }{
+\section{histc(input, bins=100, min=0, max=0, out=NULL) -> Tensor }{
 
 
 Computes the histogram of a tensor.
diff --git a/man/torch_ifft.Rd b/man/torch_ifft.Rd
index 20b549d80..c6fc27071 100644
--- a/man/torch_ifft.Rd
+++ b/man/torch_ifft.Rd
@@ -12,7 +12,7 @@ torch_ifft(self, signal_ndim, normalized = FALSE)
 
 \item{signal_ndim}{(int) the number of dimensions in each signal.        \code{signal_ndim} can only be 1, 2 or 3}
 
-\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{False}}
+\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{FALSE}}
 }
 \description{
 Ifft
@@ -42,7 +42,7 @@ where \eqn{d} = \code{signal_ndim} is number of dimensions for the
 signal, and \eqn{N_i} is the size of signal dimension \eqn{i}.
 
 The argument specifications are almost identical with \code{\link{torch_fft}}.
-However, if \code{normalized} is set to \code{True}, this instead returns the
+However, if \code{normalized} is set to \code{TRUE}, this instead returns the
 results multiplied by \eqn{\sqrt{\prod_{i=1}^d N_i}}, to become a unitary
 operator. Therefore, to invert a \code{\link{torch_fft}}, the \code{normalized}
 argument should be set identically for \code{\link{torch_fft}}.
diff --git a/man/torch_imag.Rd b/man/torch_imag.Rd
index f38ef5168..b54d3840c 100644
--- a/man/torch_imag.Rd
+++ b/man/torch_imag.Rd
@@ -9,13 +9,11 @@ torch_imag(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Imag
 }
-\section{imag(input, out=None) -> Tensor }{
+\section{imag(input) -> Tensor }{
 
 
 Returns the imaginary part of the \code{input} tensor.
diff --git a/man/torch_index_select.Rd b/man/torch_index_select.Rd
index bfe819f3c..8a7a586ab 100644
--- a/man/torch_index_select.Rd
+++ b/man/torch_index_select.Rd
@@ -13,8 +13,6 @@ torch_index_select(self, dim, index)
 \item{dim}{(int) the dimension in which we index}
 
 \item{index}{(LongTensor) the 1-D tensor containing the indices to index}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Index_select
@@ -25,7 +23,7 @@ tensor.  If \code{out} has a different shape than expected, we
 silently change it to the correct shape, reallocating the underlying
 storage if necessary.
 }
-\section{index_select(input, dim, index, out=None) -> Tensor }{
+\section{index_select(input, dim, index, out=NULL) -> Tensor }{
 
 
 Returns a new tensor which indexes the \code{input} tensor along dimension
diff --git a/man/torch_inverse.Rd b/man/torch_inverse.Rd
index 80e27b6a7..468e39984 100644
--- a/man/torch_inverse.Rd
+++ b/man/torch_inverse.Rd
@@ -9,8 +9,6 @@ torch_inverse(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor of size \eqn{(*, n, n)} where \code{*} is zero or more                    batch dimensions}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Inverse
@@ -20,7 +18,7 @@ Inverse
 transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`
 }
 }
-\section{inverse(input, out=None) -> Tensor }{
+\section{inverse(input, out=NULL) -> Tensor }{
 
 
 Takes the inverse of the square matrix \code{input}. \code{input} can be batches
diff --git a/man/torch_irfft.Rd b/man/torch_irfft.Rd
index a4e0db908..367b6eac9 100644
--- a/man/torch_irfft.Rd
+++ b/man/torch_irfft.Rd
@@ -18,11 +18,11 @@ torch_irfft(
 
 \item{signal_ndim}{(int) the number of dimensions in each signal.        \code{signal_ndim} can only be 1, 2 or 3}
 
-\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{False}}
+\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{FALSE}}
 
-\item{onesided}{(bool, optional) controls whether \code{input} was halfed to avoid        redundancy, e.g., by \code{\link[=torch_rfft]{torch_rfft()}}. Default: \code{True}}
+\item{onesided}{(bool, optional) controls whether \code{input} was halfed to avoid        redundancy, e.g., by \code{\link[=torch_rfft]{torch_rfft()}}. Default: \code{TRUE}}
 
-\item{signal_sizes}{(list or \code{torch.Size}, optional) the size of the original        signal (without batch dimension). Default: \code{None}}
+\item{signal_sizes}{(list or \code{torch.Size}, optional) the size of the original        signal (without batch dimension). Default: \code{NULL}}
 }
 \description{
 Irfft
@@ -31,8 +31,8 @@ Irfft
 \preformatted{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
+`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.
@@ -53,7 +53,7 @@ configuration. See cufft-plan-cache for more details on how to
 monitor and control the cache.
 }
 }
-\section{irfft(input, signal_ndim, normalized=False, onesided=True, signal_sizes=None) -> Tensor }{
+\section{irfft(input, signal_ndim, normalized=False, onesided=TRUE, signal_sizes=NULL) -> Tensor }{
 
 
 Complex-to-real Inverse Discrete Fourier Transform
@@ -63,7 +63,7 @@ It is mathematically equivalent with \code{\link{torch_ifft}} with differences o
 formats of the input and output.
 
 The argument specifications are almost identical with \code{\link{torch_ifft}}.
-Similar to \code{\link{torch_ifft}}, if \code{normalized} is set to \code{True},
+Similar to \code{\link{torch_ifft}}, if \code{normalized} is set to \code{TRUE},
 this normalizes the result by multiplying it with
 \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is unitary, where
 \eqn{N_i} is the size of signal dimension \eqn{i}.
@@ -73,7 +73,7 @@ this normalizes the result by multiplying it with
 
 Generally speaking, input to this function should contain values
 following conjugate symmetry. Note that even if \code{onesided} is
-\code{True}, often symmetry on some part is still needed. When this
+\code{TRUE}, often symmetry on some part is still needed. When this
 requirement is not satisfied, the behavior of \code{\link{torch_irfft}} is
 undefined. Since \code{torch_autograd.gradcheck} estimates numerical
 Jacobian with point perturbations, \code{\link{torch_irfft}} will almost
diff --git a/man/torch_is_complex.Rd b/man/torch_is_complex.Rd
index 2e01757c9..cfff9724a 100644
--- a/man/torch_is_complex.Rd
+++ b/man/torch_is_complex.Rd
@@ -16,7 +16,7 @@ Is_complex
 \section{is_complex(input) -> (bool) }{
 
 
-Returns True if the data type of \code{input} is a complex data type i.e.,
+Returns TRUE if the data type of \code{input} is a complex data type i.e.,
 one of \code{torch_complex64}, and \code{torch.complex128}.
 }
 
diff --git a/man/torch_is_floating_point.Rd b/man/torch_is_floating_point.Rd
index 8ecac3426..c8e609bc1 100644
--- a/man/torch_is_floating_point.Rd
+++ b/man/torch_is_floating_point.Rd
@@ -16,7 +16,7 @@ Is_floating_point
 \section{is_floating_point(input) -> (bool) }{
 
 
-Returns True if the data type of \code{input} is a floating point data type i.e.,
+Returns TRUE if the data type of \code{input} is a floating point data type i.e.,
 one of \code{torch_float64}, \code{torch.float32} and \code{torch.float16}.
 }
 
diff --git a/man/torch_isfinite.Rd b/man/torch_isfinite.Rd
index 76591ebd8..1e620295c 100644
--- a/man/torch_isfinite.Rd
+++ b/man/torch_isfinite.Rd
@@ -8,7 +8,7 @@
 torch_isfinite(self)
 }
 \arguments{
-\item{tensor}{(Tensor) A tensor to check}
+\item{self}{(Tensor) A tensor to check}
 }
 \description{
 Isfinite
diff --git a/man/torch_isinf.Rd b/man/torch_isinf.Rd
index 857e8335d..3f43dd8c9 100644
--- a/man/torch_isinf.Rd
+++ b/man/torch_isinf.Rd
@@ -8,7 +8,7 @@
 torch_isinf(self)
 }
 \arguments{
-\item{tensor}{(Tensor) A tensor to check}
+\item{self}{(Tensor) A tensor to check}
 }
 \description{
 Isinf
diff --git a/man/torch_kthvalue.Rd b/man/torch_kthvalue.Rd
index 518b4e231..8e687ccae 100644
--- a/man/torch_kthvalue.Rd
+++ b/man/torch_kthvalue.Rd
@@ -15,13 +15,11 @@ torch_kthvalue(self, k, dim = -1L, keepdim = FALSE)
 \item{dim}{(int, optional) the dimension to find the kth value along}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
-
-\item{out}{(tuple, optional) the output tuple of (Tensor, LongTensor)                           can be optionally given to be used as output buffers}
 }
 \description{
 Kthvalue
 }
-\section{kthvalue(input, k, dim=None, keepdim=False, out=None) -> (Tensor, LongTensor) }{
+\section{kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the \code{k} th
@@ -30,7 +28,7 @@ smallest element of each row of the \code{input} tensor in the given dimension
 
 If \code{dim} is not given, the last dimension of the \code{input} is chosen.
 
-If \code{keepdim} is \code{True}, both the \code{values} and \code{indices} tensors
+If \code{keepdim} is \code{TRUE}, both the \code{values} and \code{indices} tensors
 are the same size as \code{input}, except in the dimension \code{dim} where
 they are of size 1. Otherwise, \code{dim} is squeezed
 (see \code{\link{torch_squeeze}}), resulting in both the \code{values} and
diff --git a/man/torch_le.Rd b/man/torch_le.Rd
index 843f3b9c2..b5d3e1ea5 100644
--- a/man/torch_le.Rd
+++ b/man/torch_le.Rd
@@ -11,13 +11,11 @@ torch_le(self, other)
 \item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
-
-\item{out}{(Tensor, optional) the output tensor that must be a \code{BoolTensor}}
 }
 \description{
 Le
 }
-\section{le(input, other, out=None) -> Tensor }{
+\section{le(input, other, out=NULL) -> Tensor }{
 
 
 Computes \eqn{\mbox{input} \leq \mbox{other}} element-wise.
diff --git a/man/torch_lerp.Rd b/man/torch_lerp.Rd
index dcae4c000..2ed8cf208 100644
--- a/man/torch_lerp.Rd
+++ b/man/torch_lerp.Rd
@@ -13,13 +13,11 @@ torch_lerp(self, end, weight)
 \item{end}{(Tensor) the tensor with the ending points}
 
 \item{weight}{(float or tensor) the weight for the interpolation formula}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Lerp
 }
-\section{lerp(input, end, weight, out=None) }{
+\section{lerp(input, end, weight, out=NULL) }{
 
 
 Does a linear interpolation of two tensors \code{start} (given by \code{input}) and \code{end} based
diff --git a/man/torch_lgamma.Rd b/man/torch_lgamma.Rd
index 07089b88e..a13098dbc 100644
--- a/man/torch_lgamma.Rd
+++ b/man/torch_lgamma.Rd
@@ -9,13 +9,11 @@ torch_lgamma(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Lgamma
 }
-\section{lgamma(input, out=None) -> Tensor }{
+\section{lgamma(input, out=NULL) -> Tensor }{
 
 
 Computes the logarithm of the gamma function on \code{input}.
diff --git a/man/torch_linspace.Rd b/man/torch_linspace.Rd
index 6c140a1b3..b624775ed 100644
--- a/man/torch_linspace.Rd
+++ b/man/torch_linspace.Rd
@@ -3,7 +3,6 @@
 %   R/gen-namespace-examples.R
 \name{torch_linspace}
 \alias{torch_linspace}
-\alias{torch_logspace}
 \title{Linspace}
 \usage{
 torch_linspace(
@@ -15,17 +14,6 @@ torch_linspace(
   device = NULL,
   requires_grad = FALSE
 )
-
-torch_logspace(
-  start,
-  end,
-  steps = 100,
-  base = 10,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE
-)
 }
 \arguments{
 \item{start}{(float) the starting value for the set of points}
@@ -34,20 +22,18 @@ torch_logspace(
 
 \item{steps}{(int) number of points to sample between \code{start}        and \code{end}. Default: \code{100}.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
-
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Linspace
 }
-\section{linspace(start, end, steps=100, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a one-dimensional tensor of \code{steps}
diff --git a/man/torch_log.Rd b/man/torch_log.Rd
index b1c66ac7c..f91a1b1cb 100644
--- a/man/torch_log.Rd
+++ b/man/torch_log.Rd
@@ -9,13 +9,11 @@ torch_log(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Log
 }
-\section{log(input, out=None) -> Tensor }{
+\section{log(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the natural logarithm of the elements
diff --git a/man/torch_log10.Rd b/man/torch_log10.Rd
index 62c435289..7919429b1 100644
--- a/man/torch_log10.Rd
+++ b/man/torch_log10.Rd
@@ -9,13 +9,11 @@ torch_log10(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Log10
 }
-\section{log10(input, out=None) -> Tensor }{
+\section{log10(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the logarithm to the base 10 of the elements
diff --git a/man/torch_log1p.Rd b/man/torch_log1p.Rd
index 8cb005d50..835b2d183 100644
--- a/man/torch_log1p.Rd
+++ b/man/torch_log1p.Rd
@@ -9,8 +9,6 @@ torch_log1p(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Log1p
@@ -19,7 +17,7 @@ Log1p
 This function is more accurate than \code{\link{torch_log}} for small
 values of \code{input}
 }
-\section{log1p(input, out=None) -> Tensor }{
+\section{log1p(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the natural logarithm of (1 + \code{input}).
diff --git a/man/torch_log2.Rd b/man/torch_log2.Rd
index 338c91a63..e58088d6b 100644
--- a/man/torch_log2.Rd
+++ b/man/torch_log2.Rd
@@ -9,13 +9,11 @@ torch_log2(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Log2
 }
-\section{log2(input, out=None) -> Tensor }{
+\section{log2(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the logarithm to the base 2 of the elements
diff --git a/man/torch_logdet.Rd b/man/torch_logdet.Rd
index cde18c09e..84a269e23 100644
--- a/man/torch_logdet.Rd
+++ b/man/torch_logdet.Rd
@@ -14,7 +14,7 @@ torch_logdet(self)
 Logdet
 }
 \note{
-\preformatted{Result is ``-inf`` if `input` has zero log determinant, and is ``nan`` if
+\preformatted{Result is `-inf` if `input` has zero log determinant, and is `NaN` if
 `input` has negative determinant.
 }
 
diff --git a/man/torch_logical_and.Rd b/man/torch_logical_and.Rd
index 0cc258784..bc3e31e12 100644
--- a/man/torch_logical_and.Rd
+++ b/man/torch_logical_and.Rd
@@ -11,17 +11,15 @@ torch_logical_and(self, other)
 \item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the tensor to compute AND with}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Logical_and
 }
-\section{logical_and(input, other, out=None) -> Tensor }{
+\section{logical_and(input, other, out=NULL) -> Tensor }{
 
 
-Computes the element-wise logical AND of the given input tensors. Zeros are treated as \code{False} and nonzeros are
-treated as \code{True}.
+Computes the element-wise logical AND of the given input tensors. Zeros are treated as \code{FALSE} and nonzeros are
+treated as \code{TRUE}.
 }
 
 \examples{
diff --git a/man/torch_logical_not.Rd b/man/torch_logical_not.Rd
index 984df1dc6..b0ff17929 100644
--- a/man/torch_logical_not.Rd
+++ b/man/torch_logical_not.Rd
@@ -6,17 +6,15 @@
 \title{Logical_not}
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Logical_not
 }
-\section{logical_not(input, out=None) -> Tensor }{
+\section{logical_not(input, out=NULL) -> Tensor }{
 
 
 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} and non-zeros are treated as \code{True}.
+dtype. If the input tensor is not a bool tensor, zeros are treated as \code{FALSE} and non-zeros are treated as \code{TRUE}.
 }
 
 \examples{
diff --git a/man/torch_logical_or.Rd b/man/torch_logical_or.Rd
index 2aced74bb..6b35e1f6e 100644
--- a/man/torch_logical_or.Rd
+++ b/man/torch_logical_or.Rd
@@ -11,17 +11,15 @@ torch_logical_or(self, other)
 \item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the tensor to compute OR with}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Logical_or
 }
-\section{logical_or(input, other, out=None) -> Tensor }{
+\section{logical_or(input, other, out=NULL) -> Tensor }{
 
 
-Computes the element-wise logical OR of the given input tensors. Zeros are treated as \code{False} and nonzeros are
-treated as \code{True}.
+Computes the element-wise logical OR of the given input tensors. Zeros are treated as \code{FALSE} and nonzeros are
+treated as \code{TRUE}.
 }
 
 \examples{
diff --git a/man/torch_logical_xor.Rd b/man/torch_logical_xor.Rd
index 0064323af..452921212 100644
--- a/man/torch_logical_xor.Rd
+++ b/man/torch_logical_xor.Rd
@@ -11,17 +11,15 @@ torch_logical_xor(self, other)
 \item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the tensor to compute XOR with}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Logical_xor
 }
-\section{logical_xor(input, other, out=None) -> Tensor }{
+\section{logical_xor(input, other, out=NULL) -> Tensor }{
 
 
-Computes the element-wise logical XOR of the given input tensors. Zeros are treated as \code{False} and nonzeros are
-treated as \code{True}.
+Computes the element-wise logical XOR of the given input tensors. Zeros are treated as \code{FALSE} and nonzeros are
+treated as \code{TRUE}.
 }
 
 \examples{
diff --git a/man/torch_logspace.Rd b/man/torch_logspace.Rd
index 25d6ac11c..a71e7a16f 100644
--- a/man/torch_logspace.Rd
+++ b/man/torch_logspace.Rd
@@ -1,9 +1,21 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_logspace}
 \alias{torch_logspace}
 \title{Logspace}
+\usage{
+torch_logspace(
+  start,
+  end,
+  steps = 100,
+  base = 10,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{start}{(float) the starting value for the set of points}
 
@@ -13,20 +25,18 @@
 
 \item{base}{(float) base of the logarithm function. Default: \code{10.0}.}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Logspace
 }
-\section{logspace(start, end, steps=100, base=10.0, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a one-dimensional tensor of \code{steps} points
diff --git a/man/torch_logsumexp.Rd b/man/torch_logsumexp.Rd
index 7496a09b8..9bd2548f6 100644
--- a/man/torch_logsumexp.Rd
+++ b/man/torch_logsumexp.Rd
@@ -13,13 +13,11 @@ torch_logsumexp(self, dim, keepdim = FALSE)
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Logsumexp
 }
-\section{logsumexp(input, dim, keepdim=False, out=None) }{
+\section{logsumexp(input, dim, keepdim=False, out=NULL) }{
 
 
 Returns the log of summed exponentials of each row of the \code{input}
@@ -32,7 +30,7 @@ For summation index \eqn{j} given by \code{dim} and other indices \eqn{i}, the r
         \mbox{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij})
 }
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
diff --git a/man/torch_lstsq.Rd b/man/torch_lstsq.Rd
index 85d07e48d..9fcae8a32 100644
--- a/man/torch_lstsq.Rd
+++ b/man/torch_lstsq.Rd
@@ -11,8 +11,6 @@ torch_lstsq(self, A)
 \item{self}{(Tensor) the matrix \eqn{B}}
 
 \item{A}{(Tensor) the \eqn{m} by \eqn{n} matrix \eqn{A}}
-
-\item{out}{(tuple, optional) the optional destination tensor}
 }
 \description{
 Lstsq
@@ -21,7 +19,7 @@ Lstsq
 \preformatted{The case when \eqn{m < n} is not supported on the GPU.
 }
 }
-\section{lstsq(input, A, out=None) -> Tensor }{
+\section{lstsq(input, A, out=NULL) -> Tensor }{
 
 
 Computes the solution to the least squares and least norm problems for a full
diff --git a/man/torch_lt.Rd b/man/torch_lt.Rd
index f327143f5..dad4d06f3 100644
--- a/man/torch_lt.Rd
+++ b/man/torch_lt.Rd
@@ -11,13 +11,11 @@ torch_lt(self, other)
 \item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
-
-\item{out}{(Tensor, optional) the output tensor that must be a \code{BoolTensor}}
 }
 \description{
 Lt
 }
-\section{lt(input, other, out=None) -> Tensor }{
+\section{lt(input, other, out=NULL) -> Tensor }{
 
 
 Computes \eqn{\mbox{input} < \mbox{other}} element-wise.
diff --git a/man/torch_lu_solve.Rd b/man/torch_lu_solve.Rd
index 7f21b910e..f5675ce12 100644
--- a/man/torch_lu_solve.Rd
+++ b/man/torch_lu_solve.Rd
@@ -8,18 +8,16 @@
 torch_lu_solve(self, LU_data, LU_pivots)
 }
 \arguments{
+\item{self}{(Tensor) the RHS tensor of size \eqn{(*, m, k)}, where \eqn{*}                is zero or more batch dimensions.}
+
 \item{LU_data}{(Tensor) the pivoted LU factorization of A from \code{torch_lu} of size \eqn{(*, m, m)},                       where \eqn{*} is zero or more batch dimensions.}
 
 \item{LU_pivots}{(IntTensor) the pivots of the LU factorization from \code{torch_lu} of size \eqn{(*, m)},                           where \eqn{*} is zero or more batch dimensions.                           The batch dimensions of \code{LU_pivots} must be equal to the batch dimensions of                           \code{LU_data}.}
-
-\item{b}{(Tensor) the RHS tensor of size \eqn{(*, m, k)}, where \eqn{*}                is zero or more batch dimensions.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Lu_solve
 }
-\section{lu_solve(input, LU_data, LU_pivots, out=None) -> Tensor }{
+\section{lu_solve(input, LU_data, LU_pivots, out=NULL) -> Tensor }{
 
 
 Returns the LU solve of the linear system \eqn{Ax = b} using the partially pivoted
diff --git a/man/torch_masked_select.Rd b/man/torch_masked_select.Rd
index 89c3ee5b0..fbad7f295 100644
--- a/man/torch_masked_select.Rd
+++ b/man/torch_masked_select.Rd
@@ -11,8 +11,6 @@ torch_masked_select(self, mask)
 \item{self}{(Tensor) the input tensor.}
 
 \item{mask}{(BoolTensor) the tensor containing the binary mask to index with}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Masked_select
@@ -21,7 +19,7 @@ Masked_select
 The returned tensor does \strong{not} use the same storage
 as the original tensor
 }
-\section{masked_select(input, mask, out=None) -> Tensor }{
+\section{masked_select(input, mask, out=NULL) -> Tensor }{
 
 
 Returns a new 1-D tensor which indexes the \code{input} tensor according to
diff --git a/man/torch_matmul.Rd b/man/torch_matmul.Rd
index 5de50dfc9..aa6e08f81 100644
--- a/man/torch_matmul.Rd
+++ b/man/torch_matmul.Rd
@@ -11,8 +11,6 @@ torch_matmul(self, other)
 \item{self}{(Tensor) the first tensor to be multiplied}
 
 \item{other}{(Tensor) the second tensor to be multiplied}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Matmul
@@ -21,7 +19,7 @@ Matmul
 \preformatted{The 1-dimensional dot product version of this function does not support an `out` parameter.
 }
 }
-\section{matmul(input, other, out=None) -> Tensor }{
+\section{matmul(input, other, out=NULL) -> Tensor }{
 
 
 Matrix product of two tensors.
diff --git a/man/torch_matrix_rank.Rd b/man/torch_matrix_rank.Rd
index 8758a836f..fdad76339 100644
--- a/man/torch_matrix_rank.Rd
+++ b/man/torch_matrix_rank.Rd
@@ -10,25 +10,25 @@ torch_matrix_rank(self, tol, symmetric = FALSE)
 \arguments{
 \item{self}{(Tensor) the input 2-D tensor}
 
-\item{tol}{(float, optional) the tolerance value. Default: \code{None}}
+\item{tol}{(float, optional) the tolerance value. Default: \code{NULL}}
 
-\item{symmetric}{(bool, optional) indicates whether \code{input} is symmetric.                               Default: \code{False}}
+\item{symmetric}{(bool, optional) indicates whether \code{input} is symmetric.                               Default: \code{FALSE}}
 }
 \description{
 Matrix_rank
 }
-\section{matrix_rank(input, tol=None, symmetric=False) -> Tensor }{
+\section{matrix_rank(input, tol=NULL, symmetric=False) -> Tensor }{
 
 
 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} is \code{True},
+matrix rank is done using SVD by default. If \code{symmetric} is \code{TRUE},
 then \code{input} is assumed to be symmetric, and the computation of the
 rank is done by obtaining the eigenvalues.
 
 \code{tol} is the threshold below which the singular values (or the eigenvalues
-when \code{symmetric} is \code{True}) are considered to be 0. If \code{tol} is not
+when \code{symmetric} is \code{TRUE}) are considered to be 0. If \code{tol} is not
 specified, \code{tol} is set to \code{S.max() * max(S.size()) * eps} where \code{S} is the
-singular values (or the eigenvalues when \code{symmetric} is \code{True}), and \code{eps}
+singular values (or the eigenvalues when \code{symmetric} is \code{TRUE}), and \code{eps}
 is the epsilon value for the datatype of \code{input}.
 }
 
diff --git a/man/torch_max.Rd b/man/torch_max.Rd
index 5f8dfd1ca..52214e44b 100644
--- a/man/torch_max.Rd
+++ b/man/torch_max.Rd
@@ -9,7 +9,7 @@
 
 \item{dim}{(int) the dimension to reduce.}
 
-\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not. Default: \code{False}.}
+\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not. Default: \code{FALSE}.}
 
 \item{out}{(tuple, optional) the result tuple of two output tensors (max, max_indices)}
 
@@ -28,7 +28,7 @@ follows the broadcasting rules .
 Returns the maximum value of all elements in the \code{input} tensor.
 }
 
-\section{max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) }{
+\section{max(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the maximum
@@ -44,13 +44,13 @@ 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.
 
-If \code{keepdim} is \code{True}, the output tensors are of the same size
+If \code{keepdim} is \code{TRUE}, the output tensors are of the same size
 as \code{input} except in the dimension \code{dim} where they are of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting
 in the output tensors having 1 fewer dimension than \code{input}.
 }
 
-\section{max(input, other, out=None) -> Tensor }{
+\section{max(input, other, out=NULL) -> Tensor }{
 
 
 Each element of the tensor \code{input} is compared with the corresponding
diff --git a/man/torch_mean.Rd b/man/torch_mean.Rd
index f1ffbd38f..c67738ada 100644
--- a/man/torch_mean.Rd
+++ b/man/torch_mean.Rd
@@ -14,7 +14,7 @@ torch_mean(self, dim, keepdim = FALSE, dtype = NULL)
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{dtype}{the resulting data type.}
 }
 \description{
 Mean
@@ -25,14 +25,14 @@ Mean
 Returns the mean value of all elements in the \code{input} tensor.
 }
 
-\section{mean(input, dim, keepdim=False, out=None) -> Tensor }{
+\section{mean(input, dim, keepdim=False, out=NULL) -> Tensor }{
 
 
 Returns the mean value of each row of the \code{input} tensor in the given
 dimension \code{dim}. If \code{dim} is a list of dimensions,
 reduce over all of them.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
diff --git a/man/torch_median.Rd b/man/torch_median.Rd
index 05d069452..c01b50b12 100644
--- a/man/torch_median.Rd
+++ b/man/torch_median.Rd
@@ -13,8 +13,6 @@ torch_median(self, dim, keepdim = FALSE)
 \item{dim}{(int) the dimension to reduce.}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
-
-\item{out}{(tuple, optional) the result tuple of two output tensors (max, max_indices)}
 }
 \description{
 Median
@@ -25,7 +23,7 @@ Median
 Returns the median value of all elements in the \code{input} tensor.
 }
 
-\section{median(input, dim=-1, keepdim=False, out=None) -> (Tensor, LongTensor) }{
+\section{median(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the median
@@ -34,7 +32,7 @@ value of each row of the \code{input} tensor in the given dimension
 
 By default, \code{dim} is the last dimension of the \code{input} tensor.
 
-If \code{keepdim} is \code{True}, the output tensors are of the same size
+If \code{keepdim} is \code{TRUE}, the output tensors are of the same size
 as \code{input} except in the dimension \code{dim} where they are of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in
 the outputs tensor having 1 fewer dimension than \code{input}.
diff --git a/man/torch_meshgrid.Rd b/man/torch_meshgrid.Rd
index 64b51b098..d65be9294 100644
--- a/man/torch_meshgrid.Rd
+++ b/man/torch_meshgrid.Rd
@@ -8,9 +8,8 @@
 torch_meshgrid(tensors)
 }
 \arguments{
-\item{tensors}{(list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be}
-
-\item{treated}{(1,)}
+\item{tensors}{(list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be
+treated (1,).}
 }
 \description{
 Meshgrid
diff --git a/man/torch_min.Rd b/man/torch_min.Rd
index 398a9d1c3..cae165f1d 100644
--- a/man/torch_min.Rd
+++ b/man/torch_min.Rd
@@ -28,7 +28,7 @@ follows the broadcasting rules .
 Returns the minimum value of all elements in the \code{input} tensor.
 }
 
-\section{min(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) }{
+\section{min(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the minimum
@@ -44,13 +44,13 @@ 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.
 
-If \code{keepdim} is \code{True}, the output tensors are of the same size as
+If \code{keepdim} is \code{TRUE}, the output tensors are of the same size as
 \code{input} except in the dimension \code{dim} where they are of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in
 the output tensors having 1 fewer dimension than \code{input}.
 }
 
-\section{min(input, other, out=None) -> Tensor }{
+\section{min(input, other, out=NULL) -> Tensor }{
 
 
 Each element of the tensor \code{input} is compared with the corresponding
diff --git a/man/torch_mm.Rd b/man/torch_mm.Rd
index 3789c6c81..15f215198 100644
--- a/man/torch_mm.Rd
+++ b/man/torch_mm.Rd
@@ -11,8 +11,6 @@ torch_mm(self, mat2)
 \item{self}{(Tensor) the first matrix to be multiplied}
 
 \item{mat2}{(Tensor) the second matrix to be multiplied}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Mm
@@ -21,7 +19,7 @@ Mm
 This function does not broadcast .
 For broadcasting matrix products, see \code{\link{torch_matmul}}.
 }
-\section{mm(input, mat2, out=None) -> Tensor }{
+\section{mm(input, mat2, out=NULL) -> Tensor }{
 
 
 Performs a matrix multiplication of the matrices \code{input} and \code{mat2}.
diff --git a/man/torch_mode.Rd b/man/torch_mode.Rd
index 57e597ad0..546a6f656 100644
--- a/man/torch_mode.Rd
+++ b/man/torch_mode.Rd
@@ -13,8 +13,6 @@ torch_mode(self, dim = -1L, keepdim = FALSE)
 \item{dim}{(int) the dimension to reduce.}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
-
-\item{out}{(tuple, optional) the result tuple of two output tensors (values, indices)}
 }
 \description{
 Mode
@@ -22,7 +20,7 @@ Mode
 \note{
 This function is not defined for \code{torch_cuda.Tensor} yet.
 }
-\section{mode(input, dim=-1, keepdim=False, out=None) -> (Tensor, LongTensor) }{
+\section{mode(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the mode
@@ -32,7 +30,7 @@ in that row, and \code{indices} is the index location of each mode value found.
 
 By default, \code{dim} is the last dimension of the \code{input} tensor.
 
-If \code{keepdim} is \code{True}, the output tensors are of the same size as
+If \code{keepdim} is \code{TRUE}, the output tensors are of the same size as
 \code{input} except in the dimension \code{dim} where they are of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting
 in the output tensors having 1 fewer dimension than \code{input}.
diff --git a/man/torch_mul.Rd b/man/torch_mul.Rd
index cfdd1df0c..cde66445a 100644
--- a/man/torch_mul.Rd
+++ b/man/torch_mul.Rd
@@ -11,19 +11,11 @@ torch_mul(self, other)
 \item{self}{(Tensor) the first multiplicand tensor}
 
 \item{other}{(Tensor) the second multiplicand tensor}
-
-\item{{input}}{NA}
-
-\item{value}{(Number) the number to be multiplied to each element of \code{input}}
-
-\item{{out}}{NA}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Mul
 }
-\section{mul(input, other, out=None) }{
+\section{mul(input, other, out=NULL) }{
 
 
 Multiplies each element of the input \code{input} with the scalar
diff --git a/man/torch_multinomial.Rd b/man/torch_multinomial.Rd
index 1bbf48fe1..ae6702208 100644
--- a/man/torch_multinomial.Rd
+++ b/man/torch_multinomial.Rd
@@ -15,8 +15,6 @@ torch_multinomial(self, num_samples, replacement = FALSE, generator = NULL)
 \item{replacement}{(bool, optional) whether to draw with replacement or not}
 
 \item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Multinomial
@@ -35,7 +33,7 @@ If \code{input} is a vector, \code{out} is a vector of size \code{num_samples}.
 If \code{input} is a matrix with \code{m} rows, \code{out} is an matrix of shape
 \eqn{(m \times \mbox{num\_samples})}.
 
-If replacement is \code{True}, samples are drawn with replacement.
+If replacement is \code{TRUE}, samples are drawn with replacement.
 
 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.
@@ -45,7 +43,7 @@ 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).
 }
 }
-\section{multinomial(input, num_samples, replacement=False, *, generator=None, out=None) -> LongTensor }{
+\section{multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -> LongTensor }{
 
 
 Returns a tensor where each row contains \code{num_samples} indices sampled
diff --git a/man/torch_mv.Rd b/man/torch_mv.Rd
index 083a5d706..d02c3f20c 100644
--- a/man/torch_mv.Rd
+++ b/man/torch_mv.Rd
@@ -11,8 +11,6 @@ torch_mv(self, vec)
 \item{self}{(Tensor) matrix to be multiplied}
 
 \item{vec}{(Tensor) vector to be multiplied}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Mv
@@ -20,7 +18,7 @@ Mv
 \note{
 This function does not broadcast .
 }
-\section{mv(input, vec, out=None) -> Tensor }{
+\section{mv(input, vec, out=NULL) -> Tensor }{
 
 
 Performs a matrix-vector product of the matrix \code{input} and the vector
diff --git a/man/torch_ne.Rd b/man/torch_ne.Rd
index 4dd93c2e2..cc8c81d23 100644
--- a/man/torch_ne.Rd
+++ b/man/torch_ne.Rd
@@ -11,13 +11,11 @@ torch_ne(self, other)
 \item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
-
-\item{out}{(Tensor, optional) the output tensor that must be a \code{BoolTensor}}
 }
 \description{
 Ne
 }
-\section{ne(input, other, out=None) -> Tensor }{
+\section{ne(input, other, out=NULL) -> Tensor }{
 
 
 Computes \eqn{input \neq other} element-wise.
diff --git a/man/torch_neg.Rd b/man/torch_neg.Rd
index 43c8c3390..13a05bc3e 100644
--- a/man/torch_neg.Rd
+++ b/man/torch_neg.Rd
@@ -9,13 +9,11 @@ torch_neg(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Neg
 }
-\section{neg(input, out=None) -> Tensor }{
+\section{neg(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the negative of the elements of \code{input}.
diff --git a/man/torch_nonzero.Rd b/man/torch_nonzero.Rd
index e06697230..dcd929d69 100644
--- a/man/torch_nonzero.Rd
+++ b/man/torch_nonzero.Rd
@@ -9,8 +9,6 @@ torch_nonzero(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(LongTensor, optional) the output tensor containing indices}
 }
 \description{
 Nonzero
@@ -19,18 +17,18 @@ Nonzero
 \preformatted{[`torch_nonzero(..., as_tuple=False) <torch.nonzero>`] (default) returns a
 2-D tensor where each row is the index for a nonzero value.
 
-[`torch_nonzero(..., as_tuple=True) <torch.nonzero>`] 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
+[`torch_nonzero(..., as_tuple=TRUE) <torch.nonzero>`] 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.
 }
 }
-\section{nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors }{
+\section{nonzero(input, *, out=NULL, as_tuple=False) -> LongTensor or tuple of LongTensors }{
 
 
-\strong{When} \code{as_tuple} \strong{is \code{False} (default)}:
+\strong{When} \code{as_tuple} \strong{is \code{FALSE} (default)}:
 
 Returns a tensor containing the indices of all non-zero elements of
 \code{input}.  Each row in the result contains the indices of a non-zero
@@ -41,7 +39,7 @@ If \code{input} has \eqn{n} dimensions, then the resulting indices tensor
 \code{out} is of size \eqn{(z \times n)}, where \eqn{z} is the total number of
 non-zero elements in the \code{input} tensor.
 
-\strong{When} \code{as_tuple} \strong{is \code{True}}:
+\strong{When} \code{as_tuple} \strong{is \code{TRUE}}:
 
 Returns a tuple of 1-D tensors, one for each dimension in \code{input},
 each containing the indices (in that dimension) of all non-zero elements of
diff --git a/man/torch_norm.Rd b/man/torch_norm.Rd
index 6aa346560..cb8bc741d 100644
--- a/man/torch_norm.Rd
+++ b/man/torch_norm.Rd
@@ -10,15 +10,14 @@ torch_norm(self, p = 2L, dim, keepdim = FALSE, dtype)
 \arguments{
 \item{self}{(Tensor) the input tensor}
 
-\item{p}{(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: \code{'fro'}        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)}(1./ord)        =====  ============================  ==========================}
+\item{p}{(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: \code{'fro'}        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)}(1./ord)        =====  ============================  ==========================}
 
-\item{dim}{(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.}
+\item{dim}{(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.}
 
-\item{keepdim}{(bool, optional) whether the output tensors have \code{dim}        retained or not. Ignored if \code{dim} = \code{None} and        \code{out} = \code{None}. Default: \code{False}}
+\item{keepdim}{(bool, optional) whether the output tensors have \code{dim}        retained or not. Ignored if \code{dim} = \code{NULL} and        \code{out} = \code{NULL}. Default: \code{FALSE}
+Ignored if        \code{dim} = \code{NULL} and \code{out} = \code{NULL}.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: None.}
-
-\item{out}{(Tensor, optional) the output tensor. Ignored if        \code{dim} = \code{None} and \code{out} = \code{None}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: NULL.}
 }
 \description{
 Norm
diff --git a/man/torch_normal.Rd b/man/torch_normal.Rd
index 335dbe3ee..49d58cd0d 100644
--- a/man/torch_normal.Rd
+++ b/man/torch_normal.Rd
@@ -15,8 +15,6 @@ torch_normal(mean, std = 1L, size, generator = NULL, options = list())
 \item{size}{(int...) a sequence of integers defining the shape of the output tensor.}
 
 \item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Normal
@@ -25,7 +23,7 @@ Normal
 When the shapes do not match, the shape of \code{mean}
 is used as the shape for the returned output tensor
 }
-\section{normal(mean, std, *, generator=None, out=None) -> Tensor }{
+\section{normal(mean, std, *, generator=NULL, out=NULL) -> Tensor }{
 
 
 Returns a tensor of random numbers drawn from separate normal distributions
@@ -41,21 +39,21 @@ The shapes of \code{mean} and \code{std} don't need to match, but the
 total number of elements in each tensor need to be the same.
 }
 
-\section{normal(mean=0.0, std, out=None) -> Tensor }{
+\section{normal(mean=0.0, std, out=NULL) -> Tensor }{
 
 
 Similar to the function above, but the means are shared among all drawn
 elements.
 }
 
-\section{normal(mean, std=1.0, out=None) -> Tensor }{
+\section{normal(mean, std=1.0, out=NULL) -> Tensor }{
 
 
 Similar to the function above, but the standard-deviations are shared among
 all drawn elements.
 }
 
-\section{normal(mean, std, size, *, out=None) -> Tensor }{
+\section{normal(mean, std, size, *, out=NULL) -> Tensor }{
 
 
 Similar to the function above, but the means and standard deviations are shared
diff --git a/man/torch_ones.Rd b/man/torch_ones.Rd
index 0c86b3ab3..eaf9bbae8 100644
--- a/man/torch_ones.Rd
+++ b/man/torch_ones.Rd
@@ -15,22 +15,22 @@ torch_ones(
 )
 }
 \arguments{
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{...}{(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.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
+\item{names}{optional names for the dimensions}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{size}{(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.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Ones
 }
-\section{ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a tensor filled with the scalar value \code{1}, with the shape defined
diff --git a/man/torch_ones_like.Rd b/man/torch_ones_like.Rd
index a6859858a..5ebdd0a61 100644
--- a/man/torch_ones_like.Rd
+++ b/man/torch_ones_like.Rd
@@ -15,22 +15,22 @@ torch_ones_like(
 )
 }
 \arguments{
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
+\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
-\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
+\item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 }
 \description{
 Ones_like
 }
-\section{ones_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
+\section{ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
 
 
 Returns a tensor filled with the scalar value \code{1}, with the same size as
diff --git a/man/torch_orgqr.Rd b/man/torch_orgqr.Rd
index ca36b9ad6..bf22176b6 100644
--- a/man/torch_orgqr.Rd
+++ b/man/torch_orgqr.Rd
@@ -10,7 +10,7 @@ torch_orgqr(self, input2)
 \arguments{
 \item{self}{(Tensor) the \code{a} from \code{\link{torch_geqrf}}.}
 
-\item{self2}{(Tensor) the \code{tau} from \code{\link{torch_geqrf}}.}
+\item{input2}{(Tensor) the \code{tau} from \code{\link{torch_geqrf}}.}
 }
 \description{
 Orgqr
diff --git a/man/torch_ormqr.Rd b/man/torch_ormqr.Rd
index db32fd2a5..f669de96d 100644
--- a/man/torch_ormqr.Rd
+++ b/man/torch_ormqr.Rd
@@ -10,14 +10,14 @@ torch_ormqr(self, input2, input3, left = TRUE, transpose = FALSE)
 \arguments{
 \item{self}{(Tensor) the \code{a} from \code{\link{torch_geqrf}}.}
 
-\item{self2}{(Tensor) the \code{tau} from \code{\link{torch_geqrf}}.}
+\item{input2}{(Tensor) the \code{tau} from \code{\link{torch_geqrf}}.}
 
-\item{self3}{(Tensor) the matrix to be multiplied.}
+\item{input3}{(Tensor) the matrix to be multiplied.}
 }
 \description{
 Ormqr
 }
-\section{ormqr(input, input2, input3, left=True, transpose=False) -> Tensor }{
+\section{ormqr(input, input2, input3, left=TRUE, transpose=False) -> Tensor }{
 
 
 Multiplies \code{mat} (given by \code{input3}) by the orthogonal \code{Q} matrix of the QR factorization
diff --git a/man/torch_pdist.Rd b/man/torch_pdist.Rd
index 399363e4a..e6f5c075b 100644
--- a/man/torch_pdist.Rd
+++ b/man/torch_pdist.Rd
@@ -20,7 +20,7 @@ Pdist
 
 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
-\verb{torch_norm(input[:, None] - input, dim=2, p=p)}. This function will be faster
+\verb{torch_norm(input[:, NULL] - input, dim=2, p=p)}. This function will be faster
 if the rows are contiguous.
 
 If input has shape \eqn{N \times M} then the output will have shape
diff --git a/man/torch_poisson.Rd b/man/torch_poisson.Rd
index 0f6fd808d..c5a33794e 100644
--- a/man/torch_poisson.Rd
+++ b/man/torch_poisson.Rd
@@ -15,7 +15,7 @@ torch_poisson(self, generator = NULL)
 \description{
 Poisson
 }
-\section{poisson(input *, generator=None) -> Tensor }{
+\section{poisson(input *, generator=NULL) -> Tensor }{
 
 
 Returns a tensor of the same size as \code{input} with each element
diff --git a/man/torch_polygamma.Rd b/man/torch_polygamma.Rd
index b213de299..68fe882d2 100644
--- a/man/torch_polygamma.Rd
+++ b/man/torch_polygamma.Rd
@@ -11,8 +11,6 @@ torch_polygamma(n, self)
 \item{n}{(int) the order of the polygamma function}
 
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Polygamma
@@ -21,7 +19,7 @@ Polygamma
 \preformatted{This function is not implemented for \eqn{n \geq 2}.
 }
 }
-\section{polygamma(n, input, out=None) -> Tensor }{
+\section{polygamma(n, input, out=NULL) -> Tensor }{
 
 
 Computes the \eqn{n^{th}} derivative of the digamma function on \code{input}.
diff --git a/man/torch_pow.Rd b/man/torch_pow.Rd
index ff7a9aac1..01212160c 100644
--- a/man/torch_pow.Rd
+++ b/man/torch_pow.Rd
@@ -11,13 +11,11 @@ torch_pow(self, exponent)
 \item{self}{(float) the scalar base value for the power operation}
 
 \item{exponent}{(float or tensor) the exponent value}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Pow
 }
-\section{pow(input, exponent, out=None) -> Tensor }{
+\section{pow(input, exponent, out=NULL) -> Tensor }{
 
 
 Takes the power of each element in \code{input} with \code{exponent} and
@@ -40,7 +38,7 @@ When \code{exponent} is a tensor, the shapes of \code{input}
 and \code{exponent} must be broadcastable .
 }
 
-\section{pow(self, exponent, out=None) -> Tensor }{
+\section{pow(self, exponent, out=NULL) -> Tensor }{
 
 
 \code{self} is a scalar \code{float} value, and \code{exponent} is a tensor.
diff --git a/man/torch_prod.Rd b/man/torch_prod.Rd
index 52e76ea84..1fd3c582a 100644
--- a/man/torch_prod.Rd
+++ b/man/torch_prod.Rd
@@ -14,24 +14,24 @@ torch_prod(self, dim, keepdim = FALSE, dtype = NULL)
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: None.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.}
 }
 \description{
 Prod
 }
-\section{prod(input, dtype=None) -> Tensor }{
+\section{prod(input, dtype=NULL) -> Tensor }{
 
 
 Returns the product of all elements in the \code{input} tensor.
 }
 
-\section{prod(input, dim, keepdim=False, dtype=None) -> Tensor }{
+\section{prod(input, dim, keepdim=False, dtype=NULL) -> Tensor }{
 
 
 Returns the product of each row of the \code{input} tensor in the given
 dimension \code{dim}.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in
 the output tensor having 1 fewer dimension than \code{input}.
diff --git a/man/torch_qr.Rd b/man/torch_qr.Rd
index b9b078f3a..7c8b1b0d2 100644
--- a/man/torch_qr.Rd
+++ b/man/torch_qr.Rd
@@ -10,9 +10,7 @@ torch_qr(self, some = TRUE)
 \arguments{
 \item{self}{(Tensor) the input tensor of size \eqn{(*, m, n)} where \code{*} is zero or more                batch dimensions consisting of matrices of dimension \eqn{m \times n}.}
 
-\item{some}{(bool, optional) Set to \code{True} for reduced QR decomposition and \code{False} for                complete QR decomposition.}
-
-\item{out}{(tuple, optional) tuple of \code{Q} and \code{R} tensors                satisfying \code{input = torch.matmul(Q, R)}.                The dimensions of \code{Q} and \code{R} are \eqn{(*, m, k)} and \eqn{(*, k, n)}                respectively, where \eqn{k = \min(m, n)} if \verb{some:} is \code{True} and                \eqn{k = m} otherwise.}
+\item{some}{(bool, optional) Set to \code{TRUE} for reduced QR decomposition and \code{FALSE} for                complete QR decomposition.}
 }
 \description{
 Qr
@@ -25,7 +23,7 @@ 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.
 }
-\section{qr(input, some=True, out=None) -> (Tensor, Tensor) }{
+\section{qr(input, some=TRUE, out=NULL) -> (Tensor, Tensor) }{
 
 
 Computes the QR decomposition of a matrix or a batch of matrices \code{input},
@@ -33,8 +31,8 @@ and returns a namedtuple (Q, R) of tensors such that \eqn{\mbox{input} = Q R}
 with \eqn{Q} being an orthogonal matrix or batch of orthogonal matrices and
 \eqn{R} being an upper triangular matrix or batch of upper triangular matrices.
 
-If \code{some} is \code{True}, then this function returns the thin (reduced) QR factorization.
-Otherwise, if \code{some} is \code{False}, this function returns the complete QR factorization.
+If \code{some} is \code{TRUE}, then this function returns the thin (reduced) QR factorization.
+Otherwise, if \code{some} is \code{FALSE}, this function returns the complete QR factorization.
 }
 
 \examples{
diff --git a/man/torch_rand.Rd b/man/torch_rand.Rd
index 32c5fd09e..a2f9f4e53 100644
--- a/man/torch_rand.Rd
+++ b/man/torch_rand.Rd
@@ -15,22 +15,22 @@ torch_rand(
 )
 }
 \arguments{
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{...}{(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.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
+\item{names}{optional dimension names}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{size}{(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.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Rand
 }
-\section{rand(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a tensor filled with random numbers from a uniform distribution
diff --git a/man/torch_rand_like.Rd b/man/torch_rand_like.Rd
index 7666d2750..ad7bab9fe 100644
--- a/man/torch_rand_like.Rd
+++ b/man/torch_rand_like.Rd
@@ -25,22 +25,22 @@ torch_randn_like(
 )
 }
 \arguments{
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
+\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
-\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
+\item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 }
 \description{
 Rand_like
 }
-\section{rand_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
+\section{rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
 
 
 Returns a tensor with the same size as \code{input} that is filled with
diff --git a/man/torch_randint.Rd b/man/torch_randint.Rd
index d7348ed1d..6e75e16d3 100644
--- a/man/torch_randint.Rd
+++ b/man/torch_randint.Rd
@@ -26,23 +26,23 @@ torch_randint(
 
 \item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{memory_format}{memory format for the resulting tensor.}
 }
 \description{
 Randint
 }
-\section{randint(low=0, high, size, *, generator=None, out=None, \ }{
+\section{randint(low=0, high, size, *, generator=NULL, out=NULL, \ }{
 
 
-dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
+dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor
 
 Returns a tensor filled with random integers generated uniformly
 between \code{low} (inclusive) and \code{high} (exclusive).
diff --git a/man/torch_randint_like.Rd b/man/torch_randint_like.Rd
index 756800662..7d1c05a57 100644
--- a/man/torch_randint_like.Rd
+++ b/man/torch_randint_like.Rd
@@ -16,26 +16,24 @@ torch_randint_like(
 )
 }
 \arguments{
+\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
+
 \item{low}{(int, optional) Lowest integer to be drawn from the distribution. Default: 0.}
 
 \item{high}{(int) One above the highest integer to be drawn from the distribution.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
-
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
-
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Randint_like
 }
-\section{randint_like(input, low=0, high, dtype=None, layout=torch.strided, device=None, requires_grad=False, }{
+\section{randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, }{
 
 
 memory_format=torch.preserve_format) -> Tensor
diff --git a/man/torch_randn.Rd b/man/torch_randn.Rd
index 24f4d08e4..49ce9feac 100644
--- a/man/torch_randn.Rd
+++ b/man/torch_randn.Rd
@@ -15,22 +15,22 @@ torch_randn(
 )
 }
 \arguments{
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{...}{(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.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
+\item{names}{optional names for the dimensions}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{size}{(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.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Randn
 }
-\section{randn(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a tensor filled with random numbers from a normal distribution
diff --git a/man/torch_randn_like.Rd b/man/torch_randn_like.Rd
index 2fcde1a5f..ecaad2285 100644
--- a/man/torch_randn_like.Rd
+++ b/man/torch_randn_like.Rd
@@ -7,20 +7,20 @@
 \arguments{
 \item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 }
 \description{
 Randn_like
 }
-\section{randn_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
+\section{randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
 
 
 Returns a tensor with the same size as \code{input} that is filled with
diff --git a/man/torch_randperm.Rd b/man/torch_randperm.Rd
index 3319c7963..705596370 100644
--- a/man/torch_randperm.Rd
+++ b/man/torch_randperm.Rd
@@ -20,16 +20,14 @@ torch_randperm(
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Randperm
 }
-\section{randperm(n, out=None, dtype=torch.int64, layout=torch.strided, device=None, requires_grad=False) -> LongTensor }{
+\section{randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -> LongTensor }{
 
 
 Returns a random permutation of integers from \code{0} to \code{n - 1}.
diff --git a/man/torch_range.Rd b/man/torch_range.Rd
index c2c42eb8e..078c19113 100644
--- a/man/torch_range.Rd
+++ b/man/torch_range.Rd
@@ -22,20 +22,18 @@ torch_range(
 
 \item{step}{(float) the gap between each pair of adjacent points. Default: \code{1}.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). If \code{dtype} is not given, infer the data type from the other input        arguments. If any of \code{start}, \code{end}, or \code{stop} are floating-point, the        \code{dtype} is inferred to be the default dtype, see        \code{~torch.get_default_dtype}. Otherwise, the \code{dtype} is inferred to        be \code{torch.int64}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}). If \code{dtype} is not given, infer the data type from the other input        arguments. If any of \code{start}, \code{end}, or \code{stop} are floating-point, the        \code{dtype} is inferred to be the default dtype, see        \code{~torch.get_default_dtype}. Otherwise, the \code{dtype} is inferred to        be \code{torch.int64}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Range
 }
-\section{range(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a 1-D tensor of size \eqn{\left\lfloor \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rfloor + 1}
diff --git a/man/torch_real.Rd b/man/torch_real.Rd
index dcb3dd4f4..15cdbc172 100644
--- a/man/torch_real.Rd
+++ b/man/torch_real.Rd
@@ -9,13 +9,11 @@ torch_real(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Real
 }
-\section{real(input, out=None) -> Tensor }{
+\section{real(input) -> Tensor }{
 
 
 Returns the real part of the \code{input} tensor. If
diff --git a/man/torch_reciprocal.Rd b/man/torch_reciprocal.Rd
index b82af29ab..f064f2b0f 100644
--- a/man/torch_reciprocal.Rd
+++ b/man/torch_reciprocal.Rd
@@ -9,13 +9,11 @@ torch_reciprocal(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Reciprocal
 }
-\section{reciprocal(input, out=None) -> Tensor }{
+\section{reciprocal(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the reciprocal of the elements of \code{input}
diff --git a/man/torch_relu.Rd b/man/torch_relu.Rd
new file mode 100644
index 000000000..3f02fda62
--- /dev/null
+++ b/man/torch_relu.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gen-namespace-docs.R, R/gen-namespace.R
+\name{torch_relu}
+\alias{torch_relu}
+\title{Relu}
+\usage{
+torch_relu(self)
+}
+\arguments{
+\item{self}{the input tensor}
+}
+\description{
+Relu
+}
+\section{relu(input) -> Tensor }{
+
+}
+
diff --git a/man/torch_relu_.Rd b/man/torch_relu_.Rd
index c75b09a0f..6687e4d1c 100644
--- a/man/torch_relu_.Rd
+++ b/man/torch_relu_.Rd
@@ -7,12 +7,15 @@
 \usage{
 torch_relu_(self)
 }
+\arguments{
+\item{self}{the input tensor}
+}
 \description{
 Relu_
 }
 \section{relu_(input) -> Tensor }{
 
 
-In-place version of \code{torch_relu}.
+In-place version of \code{\link[=torch_relu]{torch_relu()}}.
 }
 
diff --git a/man/torch_remainder.Rd b/man/torch_remainder.Rd
index 162de6c3c..326e05928 100644
--- a/man/torch_remainder.Rd
+++ b/man/torch_remainder.Rd
@@ -11,13 +11,11 @@ torch_remainder(self, other)
 \item{self}{(Tensor) the dividend}
 
 \item{other}{(Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Remainder
 }
-\section{remainder(input, other, out=None) -> Tensor }{
+\section{remainder(input, other, out=NULL) -> Tensor }{
 
 
 Computes the element-wise remainder of division.
diff --git a/man/torch_renorm.Rd b/man/torch_renorm.Rd
index b0f678186..72e7b816f 100644
--- a/man/torch_renorm.Rd
+++ b/man/torch_renorm.Rd
@@ -15,8 +15,6 @@ torch_renorm(self, p, dim, maxnorm)
 \item{dim}{(int) the dimension to slice over to get the sub-tensors}
 
 \item{maxnorm}{(float) the maximum norm to keep each sub-tensor under}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Renorm
@@ -24,7 +22,7 @@ Renorm
 \note{
 If the norm of a row is lower than \code{maxnorm}, the row is unchanged
 }
-\section{renorm(input, p, dim, maxnorm, out=None) -> Tensor }{
+\section{renorm(input, p, dim, maxnorm, out=NULL) -> Tensor }{
 
 
 Returns a tensor where each sub-tensor of \code{input} along dimension
diff --git a/man/torch_repeat_interleave.Rd b/man/torch_repeat_interleave.Rd
index a9d4d9551..d7a37c7c3 100644
--- a/man/torch_repeat_interleave.Rd
+++ b/man/torch_repeat_interleave.Rd
@@ -17,14 +17,14 @@ torch_repeat_interleave(self, repeats, dim = NULL)
 \description{
 Repeat_interleave
 }
-\section{repeat_interleave(input, repeats, dim=None) -> Tensor }{
+\section{repeat_interleave(input, repeats, dim=NULL) -> Tensor }{
 
 
 Repeat elements of a tensor.
 }
 
 \section{Warning}{
-\preformatted{This is different from `torch_Tensor.repeat` but similar to ``numpy.repeat``.
+\preformatted{This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`.
 }
 }
 
diff --git a/man/torch_rfft.Rd b/man/torch_rfft.Rd
index f2882f62c..a74c14e87 100644
--- a/man/torch_rfft.Rd
+++ b/man/torch_rfft.Rd
@@ -12,9 +12,9 @@ torch_rfft(self, signal_ndim, normalized = FALSE, onesided = TRUE)
 
 \item{signal_ndim}{(int) the number of dimensions in each signal.        \code{signal_ndim} can only be 1, 2 or 3}
 
-\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{False}}
+\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{FALSE}}
 
-\item{onesided}{(bool, optional) controls whether to return half of results to        avoid redundancy. Default: \code{True}}
+\item{onesided}{(bool, optional) controls whether to return half of results to        avoid redundancy. Default: \code{TRUE}}
 }
 \description{
 Rfft
@@ -26,7 +26,7 @@ configuration. See cufft-plan-cache for more details on how to
 monitor and control the cache.
 }
 }
-\section{rfft(input, signal_ndim, normalized=False, onesided=True) -> Tensor }{
+\section{rfft(input, signal_ndim, normalized=False, onesided=TRUE) -> Tensor }{
 
 
 Real-to-complex Discrete Fourier Transform
@@ -38,7 +38,7 @@ formats of the input and output.
 This method supports 1D, 2D and 3D real-to-complex transforms, indicated
 by \code{signal_ndim}. \code{input} must be a tensor with at least
 \code{signal_ndim} dimensions with optionally arbitrary number of leading batch
-dimensions. If \code{normalized} is set to \code{True}, this normalizes the result
+dimensions. If \code{normalized} is set to \code{TRUE}, this normalizes the result
 by dividing it with \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is
 unitary, where \eqn{N_i} is the size of signal dimension \eqn{i}.
 
@@ -50,7 +50,7 @@ The real-to-complex Fourier transform results follow conjugate symmetry:
 where the index arithmetic is computed modulus the size of the corresponding
 dimension, \eqn{\ ^*} is the conjugate operator, and
 \eqn{d} = \code{signal_ndim}. \code{onesided} flag controls whether to avoid
-redundancy in the output results. If set to \code{True} (default), the output will
+redundancy in the output results. If set to \code{TRUE} (default), the output will
 not be full complex result of shape \eqn{(*, 2)}, where \eqn{*} is the shape
 of \code{input}, but instead the last dimension will be halfed as of size
 \eqn{\lfloor \frac{N_d}{2} \rfloor + 1}.
diff --git a/man/torch_roll.Rd b/man/torch_roll.Rd
index 992e6961f..6ba28ae42 100644
--- a/man/torch_roll.Rd
+++ b/man/torch_roll.Rd
@@ -17,7 +17,7 @@ torch_roll(self, shifts, dims = list())
 \description{
 Roll
 }
-\section{roll(input, shifts, dims=None) -> Tensor }{
+\section{roll(input, shifts, dims=NULL) -> Tensor }{
 
 
 Roll the tensor along the given dimension(s). Elements that are shifted beyond the
diff --git a/man/torch_round.Rd b/man/torch_round.Rd
index abf72ad13..05551e9b9 100644
--- a/man/torch_round.Rd
+++ b/man/torch_round.Rd
@@ -9,13 +9,11 @@ torch_round(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Round
 }
-\section{round(input, out=None) -> Tensor }{
+\section{round(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with each of the elements of \code{input} rounded
diff --git a/man/torch_rrelu_.Rd b/man/torch_rrelu_.Rd
index d1a7988dd..50c434878 100644
--- a/man/torch_rrelu_.Rd
+++ b/man/torch_rrelu_.Rd
@@ -13,6 +13,17 @@ torch_rrelu_(
   generator = NULL
 )
 }
+\arguments{
+\item{self}{the input tensor}
+
+\item{lower}{lower bound of the uniform distribution. Default: 1/8}
+
+\item{upper}{upper bound of the uniform distribution. Default: 1/3}
+
+\item{training}{bool wether it's a training pass. DEfault: FALSE}
+
+\item{generator}{random number generator}
+}
 \description{
 Rrelu_
 }
diff --git a/man/torch_rsqrt.Rd b/man/torch_rsqrt.Rd
index e909f01ab..3109c1fc5 100644
--- a/man/torch_rsqrt.Rd
+++ b/man/torch_rsqrt.Rd
@@ -9,13 +9,11 @@ torch_rsqrt(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Rsqrt
 }
-\section{rsqrt(input, out=None) -> Tensor }{
+\section{rsqrt(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the reciprocal of the square-root of each of
diff --git a/man/torch_sigmoid.Rd b/man/torch_sigmoid.Rd
index e447405f2..f45b8442a 100644
--- a/man/torch_sigmoid.Rd
+++ b/man/torch_sigmoid.Rd
@@ -9,13 +9,11 @@ torch_sigmoid(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Sigmoid
 }
-\section{sigmoid(input, out=None) -> Tensor }{
+\section{sigmoid(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the sigmoid of the elements of \code{input}.
diff --git a/man/torch_sign.Rd b/man/torch_sign.Rd
index bdfd12c70..992776e79 100644
--- a/man/torch_sign.Rd
+++ b/man/torch_sign.Rd
@@ -9,13 +9,11 @@ torch_sign(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Sign
 }
-\section{sign(input, out=None) -> Tensor }{
+\section{sign(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the signs of the elements of \code{input}.
diff --git a/man/torch_sin.Rd b/man/torch_sin.Rd
index a799f1d40..dbad49db5 100644
--- a/man/torch_sin.Rd
+++ b/man/torch_sin.Rd
@@ -9,13 +9,11 @@ torch_sin(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Sin
 }
-\section{sin(input, out=None) -> Tensor }{
+\section{sin(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the sine of the elements of \code{input}.
diff --git a/man/torch_sinh.Rd b/man/torch_sinh.Rd
index fbc37f304..b09e7cd7d 100644
--- a/man/torch_sinh.Rd
+++ b/man/torch_sinh.Rd
@@ -9,13 +9,11 @@ torch_sinh(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Sinh
 }
-\section{sinh(input, out=None) -> Tensor }{
+\section{sinh(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the hyperbolic sine of the elements of
diff --git a/man/torch_slogdet.Rd b/man/torch_slogdet.Rd
index 6271ffab3..49767ea68 100644
--- a/man/torch_slogdet.Rd
+++ b/man/torch_slogdet.Rd
@@ -14,7 +14,7 @@ torch_slogdet(self)
 Slogdet
 }
 \note{
-\preformatted{If ``input`` has zero determinant, this returns ``(0, -inf)``.
+\preformatted{If `input` has zero determinant, this returns `(0, -inf)`.
 }
 
 \preformatted{Backward through `slogdet` internally uses SVD results when `input`
diff --git a/man/torch_solve.Rd b/man/torch_solve.Rd
index 156f71ef4..319da9395 100644
--- a/man/torch_solve.Rd
+++ b/man/torch_solve.Rd
@@ -24,7 +24,7 @@ Solve
 `A.contiguous().transpose(-1, -2).stride()` respectively.
 }
 }
-\section{torch.solve(input, A, out=None) -> (Tensor, Tensor) }{
+\section{torch.solve(input, A, out=NULL) -> (Tensor, Tensor) }{
 
 
 This function returns the solution to the system of linear
diff --git a/man/torch_sort.Rd b/man/torch_sort.Rd
index 9048f7520..3587e1334 100644
--- a/man/torch_sort.Rd
+++ b/man/torch_sort.Rd
@@ -19,7 +19,7 @@ torch_sort(self, dim = -1L, descending = FALSE)
 \description{
 Sort
 }
-\section{sort(input, dim=-1, descending=False, out=None) -> (Tensor, LongTensor) }{
+\section{sort(input, dim=-1, descending=False, out=NULL) -> (Tensor, LongTensor) }{
 
 
 Sorts the elements of the \code{input} tensor along a given dimension
@@ -27,7 +27,7 @@ in ascending order by value.
 
 If \code{dim} is not given, the last dimension of the \code{input} is chosen.
 
-If \code{descending} is \code{True} then the elements are sorted in descending
+If \code{descending} is \code{TRUE} then the elements are sorted in descending
 order by value.
 
 A namedtuple of (values, indices) is returned, where the \code{values} are the
diff --git a/man/torch_sparse_coo_tensor.Rd b/man/torch_sparse_coo_tensor.Rd
index 62696b9b9..baffc1fc9 100644
--- a/man/torch_sparse_coo_tensor.Rd
+++ b/man/torch_sparse_coo_tensor.Rd
@@ -14,16 +14,16 @@ torch_sparse_coo_tensor(indices, values, size, options = list())
 
 \item{size}{(list, tuple, or \code{torch.Size}, 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.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if None, infers data type from \code{values}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if NULL, infers data type from \code{values}.}
 
-\item{device}{(\code{torch.device}, 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{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, 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{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Sparse_coo_tensor
 }
-\section{sparse_coo_tensor(indices, values, size=None, dtype=None, device=None, requires_grad=False) -> Tensor }{
+\section{sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given \code{indices}
diff --git a/man/torch_split.Rd b/man/torch_split.Rd
index def245048..ea5b5989a 100644
--- a/man/torch_split.Rd
+++ b/man/torch_split.Rd
@@ -26,7 +26,7 @@ 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
+into `len(split_size_or_sections)` chunks with sizes in `dim` according
 to `split_size_or_sections`.
 }
 }
diff --git a/man/torch_sqrt.Rd b/man/torch_sqrt.Rd
index d5337afcd..618a548ec 100644
--- a/man/torch_sqrt.Rd
+++ b/man/torch_sqrt.Rd
@@ -9,13 +9,11 @@ torch_sqrt(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Sqrt
 }
-\section{sqrt(input, out=None) -> Tensor }{
+\section{sqrt(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the square-root of the elements of \code{input}.
diff --git a/man/torch_square.Rd b/man/torch_square.Rd
index 24def3ba9..b674e341b 100644
--- a/man/torch_square.Rd
+++ b/man/torch_square.Rd
@@ -9,13 +9,11 @@ torch_square(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Square
 }
-\section{square(input, out=None) -> Tensor }{
+\section{square(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the square of the elements of \code{input}.
diff --git a/man/torch_squeeze.Rd b/man/torch_squeeze.Rd
index 022a62e49..e2f57a834 100644
--- a/man/torch_squeeze.Rd
+++ b/man/torch_squeeze.Rd
@@ -11,8 +11,6 @@ torch_squeeze(self, dim)
 \item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int, optional) if given, the input will be squeezed only in           this dimension}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Squeeze
@@ -21,7 +19,7 @@ Squeeze
 The returned tensor shares the storage with the input tensor,
 so changing the contents of one will change the contents of the other.
 }
-\section{squeeze(input, dim=None, out=None) -> Tensor }{
+\section{squeeze(input, dim=NULL, out=NULL) -> Tensor }{
 
 
 Returns a tensor with all the dimensions of \code{input} of size \code{1} removed.
diff --git a/man/torch_stack.Rd b/man/torch_stack.Rd
index 2718b34aa..7910c8535 100644
--- a/man/torch_stack.Rd
+++ b/man/torch_stack.Rd
@@ -11,13 +11,11 @@ torch_stack(tensors, dim = 1L)
 \item{tensors}{(sequence of Tensors) sequence of tensors to concatenate}
 
 \item{dim}{(int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Stack
 }
-\section{stack(tensors, dim=0, out=None) -> Tensor }{
+\section{stack(tensors, dim=0, out=NULL) -> Tensor }{
 
 
 Concatenates sequence of tensors along a new dimension.
diff --git a/man/torch_std.Rd b/man/torch_std.Rd
index 403084549..1d8d67d7a 100644
--- a/man/torch_std.Rd
+++ b/man/torch_std.Rd
@@ -15,34 +15,32 @@ torch_std(self, dim, unbiased = TRUE, keepdim = FALSE)
 \item{unbiased}{(bool) whether to use the unbiased estimation or not}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Std
 }
-\section{std(input, unbiased=True) -> Tensor }{
+\section{std(input, unbiased=TRUE) -> Tensor }{
 
 
 Returns the standard-deviation of all elements in the \code{input} tensor.
 
-If \code{unbiased} is \code{False}, then the standard-deviation will be calculated
+If \code{unbiased} is \code{FALSE}, then the standard-deviation will be calculated
 via the biased estimator. Otherwise, Bessel's correction will be used.
 }
 
-\section{std(input, dim, unbiased=True, keepdim=False, out=None) -> Tensor }{
+\section{std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -> Tensor }{
 
 
 Returns the standard-deviation of each row of the \code{input} tensor in the
 dimension \code{dim}. If \code{dim} is a list of dimensions,
 reduce over all of them.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 
-If \code{unbiased} is \code{False}, then the standard-deviation will be calculated
+If \code{unbiased} is \code{FALSE}, then the standard-deviation will be calculated
 via the biased estimator. Otherwise, Bessel's correction will be used.
 }
 
diff --git a/man/torch_std_mean.Rd b/man/torch_std_mean.Rd
index 234b421e8..65b7470b6 100644
--- a/man/torch_std_mean.Rd
+++ b/man/torch_std_mean.Rd
@@ -19,28 +19,28 @@ torch_std_mean(self, dim, unbiased = TRUE, keepdim = FALSE)
 \description{
 Std_mean
 }
-\section{std_mean(input, unbiased=True) -> (Tensor, Tensor) }{
+\section{std_mean(input, unbiased=TRUE) -> (Tensor, Tensor) }{
 
 
 Returns the standard-deviation and mean of all elements in the \code{input} tensor.
 
-If \code{unbiased} is \code{False}, then the standard-deviation will be calculated
+If \code{unbiased} is \code{FALSE}, then the standard-deviation will be calculated
 via the biased estimator. Otherwise, Bessel's correction will be used.
 }
 
-\section{std_mean(input, dim, unbiased=True, keepdim=False) -> (Tensor, Tensor) }{
+\section{std_mean(input, dim, unbiased=TRUE, keepdim=False) -> (Tensor, Tensor) }{
 
 
 Returns the standard-deviation and mean of each row of the \code{input} tensor in the
 dimension \code{dim}. If \code{dim} is a list of dimensions,
 reduce over all of them.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 
-If \code{unbiased} is \code{False}, then the standard-deviation will be calculated
+If \code{unbiased} is \code{FALSE}, then the standard-deviation will be calculated
 via the biased estimator. Otherwise, Bessel's correction will be used.
 }
 
diff --git a/man/torch_stft.Rd b/man/torch_stft.Rd
index 4734c2ae7..bb37275c9 100644
--- a/man/torch_stft.Rd
+++ b/man/torch_stft.Rd
@@ -20,19 +20,19 @@ torch_stft(
 
 \item{n_fft}{(int) size of Fourier transform}
 
-\item{hop_length}{(int, optional) the distance between neighboring sliding window        frames. Default: \code{None} (treated as equal to \code{floor(n_fft / 4)})}
+\item{hop_length}{(int, optional) the distance between neighboring sliding window        frames. Default: \code{NULL} (treated as equal to \code{floor(n_fft / 4)})}
 
-\item{win_length}{(int, optional) the size of window frame and STFT filter.        Default: \code{None}  (treated as equal to \code{n_fft})}
+\item{win_length}{(int, optional) the size of window frame and STFT filter.        Default: \code{NULL}  (treated as equal to \code{n_fft})}
 
-\item{window}{(Tensor, optional) the optional window function.        Default: \code{None} (treated as window of all \eqn{1} s)}
+\item{window}{(Tensor, optional) the optional window function.        Default: \code{NULL} (treated as window of all \eqn{1} s)}
 
-\item{normalized}{(bool, optional) controls whether to return the normalized STFT results         Default: \code{False}}
+\item{normalized}{(bool, optional) controls whether to return the normalized STFT results         Default: \code{FALSE}}
 
-\item{onesided}{(bool, optional) controls whether to return half of results to        avoid redundancy Default: \code{True}}
+\item{onesided}{(bool, optional) controls whether to return half of results to        avoid redundancy Default: \code{TRUE}}
 
-\item{center}{(bool, optional) whether to pad \code{input} on both sides so        that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}.        Default: \code{True}}
+\item{center}{(bool, optional) whether to pad \code{input} on both sides so        that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}.        Default: \code{TRUE}}
 
-\item{pad_mode}{(string, optional) controls the padding method used when        \code{center} is \code{True}. Default: \code{"reflect"}}
+\item{pad_mode}{(string, optional) controls the padding method used when        \code{center} is \code{TRUE}. Default: \code{"reflect"}}
 }
 \description{
 Stft
@@ -51,36 +51,36 @@ expression:
 }
 where \eqn{m} is the index of the sliding window, and \eqn{\omega} is
 the frequency that \eqn{0 \leq \omega < \mbox{n\_fft}}. When
-\code{onesided} is the default value \code{True},\preformatted{* `input` must be either a 1-D time sequence or a 2-D batch of time
+\code{onesided} is the default value \code{TRUE},\preformatted{* `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 `hop_length` is `NULL` (default), it is treated as equal to
+  `floor(n_fft / 4)`.
 
-* If `win_length` is ``None`` (default), it is treated as equal to
+* 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 ``None`` (default), it is
+  `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} < \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
+* 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"``.
+  `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}
+* 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
+* 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
diff --git a/man/torch_sum.Rd b/man/torch_sum.Rd
index fa803b9a0..5f9b06c89 100644
--- a/man/torch_sum.Rd
+++ b/man/torch_sum.Rd
@@ -14,25 +14,25 @@ torch_sum(self, dim, keepdim = FALSE, dtype = NULL)
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: None.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.}
 }
 \description{
 Sum
 }
-\section{sum(input, dtype=None) -> Tensor }{
+\section{sum(input, dtype=NULL) -> Tensor }{
 
 
 Returns the sum of all elements in the \code{input} tensor.
 }
 
-\section{sum(input, dim, keepdim=False, dtype=None) -> Tensor }{
+\section{sum(input, dim, keepdim=False, dtype=NULL) -> Tensor }{
 
 
 Returns the sum of each row of the \code{input} tensor in the given
 dimension \code{dim}. If \code{dim} is a list of dimensions,
 reduce over all of them.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
diff --git a/man/torch_svd.Rd b/man/torch_svd.Rd
index 61b2a0057..4525ae423 100644
--- a/man/torch_svd.Rd
+++ b/man/torch_svd.Rd
@@ -37,25 +37,25 @@ appear as the gradients are not properly defined. Also, notice that
 double backward will usually do an additional backward through \code{U} and
 \code{V} even if the original backward is only on \code{S}.
 
-When \code{some} = \code{False}, the gradients on \verb{U[..., :, min(m, n):]}
+When \code{some} = \code{FALSE}, the gradients on \verb{U[..., :, min(m, n):]}
 and \verb{V[..., :, min(m, n):]} will be ignored in backward as those vectors
 can be arbitrary bases of the subspaces.
 
-When \code{compute_uv} = \code{False}, backward cannot be performed since \code{U} and \code{V}
+When \code{compute_uv} = \code{FALSE}, backward cannot be performed since \code{U} and \code{V}
 from the forward pass is required for the backward operation.
 }
-\section{svd(input, some=True, compute_uv=True, out=None) -> (Tensor, Tensor, Tensor) }{
+\section{svd(input, some=TRUE, compute_uv=TRUE, out=NULL) -> (Tensor, Tensor, Tensor) }{
 
 
 This function returns a namedtuple \verb{(U, S, V)} which is the singular value
 decomposition of a input real matrix or batches of real matrices \code{input} such that
 \eqn{input = U \times diag(S) \times V^T}.
 
-If \code{some} is \code{True} (default), the method returns the reduced singular value decomposition
+If \code{some} is \code{TRUE} (default), the method returns the reduced singular value decomposition
 i.e., if the last two dimensions of \code{input} are \code{m} and \code{n}, then the returned
 \code{U} and \code{V} matrices will contain only \eqn{min(n, m)} orthonormal columns.
 
-If \code{compute_uv} is \code{False}, the returned \code{U} and \code{V} matrices will be zero matrices
+If \code{compute_uv} is \code{FALSE}, the returned \code{U} and \code{V} matrices will be zero matrices
 of shape \eqn{(m \times m)} and \eqn{(n \times n)} respectively. \code{some} will be ignored here.
 }
 
diff --git a/man/torch_symeig.Rd b/man/torch_symeig.Rd
index 121cdc7d0..5121431f6 100644
--- a/man/torch_symeig.Rd
+++ b/man/torch_symeig.Rd
@@ -30,7 +30,7 @@ Extra care needs to be taken when backward through outputs. Such
 operation is really only stable when all eigenvalues are distinct.
 Otherwise, \code{NaN} can appear as the gradients are not properly defined.
 }
-\section{symeig(input, eigenvectors=False, upper=True, out=None) -> (Tensor, Tensor) }{
+\section{symeig(input, eigenvectors=False, upper=TRUE, out=NULL) -> (Tensor, Tensor) }{
 
 
 This function returns eigenvalues and eigenvectors
@@ -43,13 +43,13 @@ such that \eqn{\mbox{input} = V \mbox{diag}(e) V^T}.
 The boolean argument \code{eigenvectors} defines computation of
 both eigenvectors and eigenvalues or eigenvalues only.
 
-If it is \code{False}, only eigenvalues are computed. If it is \code{True},
+If it is \code{FALSE}, only eigenvalues are computed. If it is \code{TRUE},
 both eigenvalues and eigenvectors are computed.
 
 Since the input matrix \code{input} is supposed to be symmetric,
 only the upper triangular portion is used by default.
 
-If \code{upper} is \code{False}, then lower triangular portion is used.
+If \code{upper} is \code{FALSE}, then lower triangular portion is used.
 }
 
 \examples{
diff --git a/man/torch_tan.Rd b/man/torch_tan.Rd
index 84b3b2a82..63e59d3e3 100644
--- a/man/torch_tan.Rd
+++ b/man/torch_tan.Rd
@@ -9,13 +9,11 @@ torch_tan(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Tan
 }
-\section{tan(input, out=None) -> Tensor }{
+\section{tan(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the tangent of the elements of \code{input}.
diff --git a/man/torch_tanh.Rd b/man/torch_tanh.Rd
index e35dc93f3..10aebdbc6 100644
--- a/man/torch_tanh.Rd
+++ b/man/torch_tanh.Rd
@@ -9,13 +9,11 @@ torch_tanh(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Tanh
 }
-\section{tanh(input, out=None) -> Tensor }{
+\section{tanh(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the hyperbolic tangent of the elements
diff --git a/man/torch_topk.Rd b/man/torch_topk.Rd
index de6c7d32b..886bfa847 100644
--- a/man/torch_topk.Rd
+++ b/man/torch_topk.Rd
@@ -23,7 +23,7 @@ torch_topk(self, k, dim = -1L, largest = TRUE, sorted = TRUE)
 \description{
 Topk
 }
-\section{topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor) }{
+\section{topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE, out=NULL) -> (Tensor, LongTensor) }{
 
 
 Returns the \code{k} largest elements of the given \code{input} tensor along
@@ -31,12 +31,12 @@ a given dimension.
 
 If \code{dim} is not given, the last dimension of the \code{input} is chosen.
 
-If \code{largest} is \code{False} then the \code{k} smallest elements are returned.
+If \code{largest} is \code{FALSE} then the \code{k} smallest elements are returned.
 
 A namedtuple of \verb{(values, indices)} is returned, where the \code{indices} are the indices
 of the elements in the original \code{input} tensor.
 
-The boolean option \code{sorted} if \code{True}, will make sure that the returned
+The boolean option \code{sorted} if \code{TRUE}, will make sure that the returned
 \code{k} elements are themselves sorted
 }
 
diff --git a/man/torch_triangular_solve.Rd b/man/torch_triangular_solve.Rd
index 26e39faf6..2bef20a90 100644
--- a/man/torch_triangular_solve.Rd
+++ b/man/torch_triangular_solve.Rd
@@ -18,16 +18,16 @@ torch_triangular_solve(
 
 \item{A}{(Tensor) the input triangular coefficient matrix of size \eqn{(*, m, m)}                where \eqn{*} is zero or more batch dimensions}
 
-\item{upper}{(bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: \code{True}.}
+\item{upper}{(bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: \code{TRUE}.}
 
-\item{transpose}{(bool, optional) whether \eqn{A} should be transposed before        being sent into the solver. Default: \code{False}.}
+\item{transpose}{(bool, optional) whether \eqn{A} should be transposed before        being sent into the solver. Default: \code{FALSE}.}
 
-\item{unitriangular}{(bool, optional) whether \eqn{A} is unit triangular.        If True, the diagonal elements of \eqn{A} are assumed to be        1 and not referenced from \eqn{A}. Default: \code{False}.}
+\item{unitriangular}{(bool, optional) whether \eqn{A} is unit triangular.        If TRUE, the diagonal elements of \eqn{A} are assumed to be        1 and not referenced from \eqn{A}. Default: \code{FALSE}.}
 }
 \description{
 Triangular_solve
 }
-\section{triangular_solve(input, A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor) }{
+\section{triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -> (Tensor, Tensor) }{
 
 
 Solves a system of equations with a triangular coefficient matrix \eqn{A}
diff --git a/man/torch_tril.Rd b/man/torch_tril.Rd
index de8dd189f..700e50b01 100644
--- a/man/torch_tril.Rd
+++ b/man/torch_tril.Rd
@@ -11,13 +11,11 @@ torch_tril(self, diagonal = 0L)
 \item{self}{(Tensor) the input tensor.}
 
 \item{diagonal}{(int, optional) the diagonal to consider}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Tril
 }
-\section{tril(input, diagonal=0, out=None) -> Tensor }{
+\section{tril(input, diagonal=0, out=NULL) -> Tensor }{
 
 
 Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices
diff --git a/man/torch_tril_indices.Rd b/man/torch_tril_indices.Rd
index 639ae3ed8..11e20c1d0 100644
--- a/man/torch_tril_indices.Rd
+++ b/man/torch_tril_indices.Rd
@@ -14,9 +14,9 @@ torch_tril_indices(row, col, offset = 0L, options = torch_long())
 
 \item{offset}{(\code{int}) diagonal offset from the main diagonal.        Default: if not provided, 0.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, \code{torch_long}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, \code{torch_long}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{layout}{(\code{torch.layout}, optional) currently only support \code{torch_strided}.}
 }
@@ -24,7 +24,7 @@ torch_tril_indices(row, col, offset = 0L, options = torch_long())
 Tril_indices
 }
 \note{
-\preformatted{When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to
+\preformatted{When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
 prevent overflow during calculation.
 }
 }
diff --git a/man/torch_triu.Rd b/man/torch_triu.Rd
index a2ac32e33..c0e5f26bd 100644
--- a/man/torch_triu.Rd
+++ b/man/torch_triu.Rd
@@ -11,13 +11,11 @@ torch_triu(self, diagonal = 0L)
 \item{self}{(Tensor) the input tensor.}
 
 \item{diagonal}{(int, optional) the diagonal to consider}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Triu
 }
-\section{triu(input, diagonal=0, out=None) -> Tensor }{
+\section{triu(input, diagonal=0, out=NULL) -> Tensor }{
 
 
 Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
diff --git a/man/torch_triu_indices.Rd b/man/torch_triu_indices.Rd
index 810428d12..80b87c177 100644
--- a/man/torch_triu_indices.Rd
+++ b/man/torch_triu_indices.Rd
@@ -14,9 +14,9 @@ torch_triu_indices(row, col, offset = 0L, options = torch_long())
 
 \item{offset}{(\code{int}) diagonal offset from the main diagonal.        Default: if not provided, 0.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, \code{torch_long}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, \code{torch_long}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{layout}{(\code{torch.layout}, optional) currently only support \code{torch_strided}.}
 }
@@ -24,7 +24,7 @@ torch_triu_indices(row, col, offset = 0L, options = torch_long())
 Triu_indices
 }
 \note{
-\preformatted{When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to
+\preformatted{When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
 prevent overflow during calculation.
 }
 }
diff --git a/man/torch_true_divide.Rd b/man/torch_true_divide.Rd
index 6a845856c..d6bc956aa 100644
--- a/man/torch_true_divide.Rd
+++ b/man/torch_true_divide.Rd
@@ -3,7 +3,7 @@
 %   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_true_divide}
 \alias{torch_true_divide}
-\title{True_divide}
+\title{TRUE_divide}
 \usage{
 torch_true_divide(self, other)
 }
@@ -13,7 +13,7 @@ torch_true_divide(self, other)
 \item{divisor}{(Tensor or Scalar) the divisor}
 }
 \description{
-True_divide
+TRUE_divide
 }
 \section{true_divide(dividend, divisor) -> Tensor }{
 
diff --git a/man/torch_trunc.Rd b/man/torch_trunc.Rd
index 92ae7ab65..95665a160 100644
--- a/man/torch_trunc.Rd
+++ b/man/torch_trunc.Rd
@@ -9,13 +9,11 @@ torch_trunc(self)
 }
 \arguments{
 \item{self}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Trunc
 }
-\section{trunc(input, out=None) -> Tensor }{
+\section{trunc(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the truncated integer values of
diff --git a/man/torch_unique_consecutive.Rd b/man/torch_unique_consecutive.Rd
index c106f866d..b34752edd 100644
--- a/man/torch_unique_consecutive.Rd
+++ b/man/torch_unique_consecutive.Rd
@@ -19,7 +19,7 @@ torch_unique_consecutive(
 
 \item{return_counts}{(bool) Whether to also return the counts for each unique        element.}
 
-\item{dim}{(int) the dimension to apply unique. If \code{None}, the unique of the        flattened input is returned. default: \code{None}}
+\item{dim}{(int) the dimension to apply unique. If \code{NULL}, the unique of the        flattened input is returned. default: \code{NULL}}
 }
 \description{
 Unique_consecutive
diff --git a/man/torch_var.Rd b/man/torch_var.Rd
index 4e7495952..ad5462719 100644
--- a/man/torch_var.Rd
+++ b/man/torch_var.Rd
@@ -15,33 +15,31 @@ torch_var(self, dim, unbiased = TRUE, keepdim = FALSE)
 \item{unbiased}{(bool) whether to use the unbiased estimation or not}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Var
 }
-\section{var(input, unbiased=True) -> Tensor }{
+\section{var(input, unbiased=TRUE) -> Tensor }{
 
 
 Returns the variance of all elements in the \code{input} tensor.
 
-If \code{unbiased} is \code{False}, then the variance will be calculated via the
+If \code{unbiased} is \code{FALSE}, then the variance will be calculated via the
 biased estimator. Otherwise, Bessel's correction will be used.
 }
 
-\section{var(input, dim, keepdim=False, unbiased=True, out=None) -> Tensor }{
+\section{var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -> Tensor }{
 
 
 Returns the variance of each row of the \code{input} tensor in the given
 dimension \code{dim}.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 
-If \code{unbiased} is \code{False}, then the variance will be calculated via the
+If \code{unbiased} is \code{FALSE}, then the variance will be calculated via the
 biased estimator. Otherwise, Bessel's correction will be used.
 }
 
diff --git a/man/torch_var_mean.Rd b/man/torch_var_mean.Rd
index e8157e315..4cda19876 100644
--- a/man/torch_var_mean.Rd
+++ b/man/torch_var_mean.Rd
@@ -19,27 +19,27 @@ torch_var_mean(self, dim, unbiased = TRUE, keepdim = FALSE)
 \description{
 Var_mean
 }
-\section{var_mean(input, unbiased=True) -> (Tensor, Tensor) }{
+\section{var_mean(input, unbiased=TRUE) -> (Tensor, Tensor) }{
 
 
 Returns the variance and mean of all elements in the \code{input} tensor.
 
-If \code{unbiased} is \code{False}, then the variance will be calculated via the
+If \code{unbiased} is \code{FALSE}, then the variance will be calculated via the
 biased estimator. Otherwise, Bessel's correction will be used.
 }
 
-\section{var_mean(input, dim, keepdim=False, unbiased=True) -> (Tensor, Tensor) }{
+\section{var_mean(input, dim, keepdim=False, unbiased=TRUE) -> (Tensor, Tensor) }{
 
 
 Returns the variance and mean of each row of the \code{input} tensor in the given
 dimension \code{dim}.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 
-If \code{unbiased} is \code{False}, then the variance will be calculated via the
+If \code{unbiased} is \code{FALSE}, then the variance will be calculated via the
 biased estimator. Otherwise, Bessel's correction will be used.
 }
 
diff --git a/man/torch_where.Rd b/man/torch_where.Rd
index 9b95e2ff0..0270be799 100644
--- a/man/torch_where.Rd
+++ b/man/torch_where.Rd
@@ -8,11 +8,11 @@
 torch_where(condition, self, other)
 }
 \arguments{
-\item{condition}{(BoolTensor) When True (nonzero), yield x, otherwise yield y}
+\item{condition}{(BoolTensor) When TRUE (nonzero), yield x, otherwise yield y}
 
-\item{x}{(Tensor) values selected at indices where \code{condition} is \code{True}}
+\item{x}{(Tensor) values selected at indices where \code{condition} is \code{TRUE}}
 
-\item{y}{(Tensor) values selected at indices where \code{condition} is \code{False}}
+\item{y}{(Tensor) values selected at indices where \code{condition} is \code{FALSE}}
 }
 \description{
 Where
@@ -44,7 +44,7 @@ The operation is defined as:
 
 
 \code{torch_where(condition)} is identical to
-\code{torch_nonzero(condition, as_tuple=True)}.
+\code{torch_nonzero(condition, as_tuple=TRUE)}.
 }
 
 \examples{
diff --git a/man/torch_zeros.Rd b/man/torch_zeros.Rd
index c8d9208eb..02ceca196 100644
--- a/man/torch_zeros.Rd
+++ b/man/torch_zeros.Rd
@@ -15,22 +15,20 @@ torch_zeros(
 )
 }
 \arguments{
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
 \item{size}{(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.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Zeros
 }
-\section{zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a tensor filled with the scalar value \code{0}, with the shape defined
diff --git a/man/torch_zeros_like.Rd b/man/torch_zeros_like.Rd
index 65533b538..556d55890 100644
--- a/man/torch_zeros_like.Rd
+++ b/man/torch_zeros_like.Rd
@@ -15,13 +15,13 @@ torch_zeros_like(
 )
 }
 \arguments{
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 
@@ -30,7 +30,7 @@ torch_zeros_like(
 \description{
 Zeros_like
 }
-\section{zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
+\section{zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
 
 
 Returns a tensor filled with the scalar value \code{0}, with the same size as
-- 
GitLab


From d6c3447f284012214f11523839fabcb79cd8dc96 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 9 Sep 2020 16:45:52 -0300
Subject: [PATCH 104/157] some more docs fixes

---
 NAMESPACE                |  1 +
 R/gen-namespace-docs.R   | 90 +++++++++++++++++++++-------------------
 man/torch_div.Rd         |  4 +-
 man/torch_ormqr.Rd       |  8 +++-
 man/torch_selu.Rd        | 18 ++++++++
 man/torch_selu_.Rd       |  5 ++-
 man/torch_solve.Rd       |  8 ++--
 man/torch_sort.Rd        |  4 +-
 man/torch_split.Rd       |  6 +--
 man/torch_stft.Rd        | 10 +++--
 man/torch_svd.Rd         |  4 +-
 man/torch_symeig.Rd      |  4 +-
 man/torch_take.Rd        |  2 +-
 man/torch_tensordot.Rd   | 10 +----
 man/torch_threshold_.Rd  |  7 ++++
 man/torch_topk.Rd        |  4 +-
 man/torch_trace.Rd       |  3 ++
 man/torch_true_divide.Rd |  4 +-
 man/torch_where.Rd       |  7 ++--
 man/torch_zeros.Rd       |  6 ++-
 man/torch_zeros_like.Rd  |  4 +-
 21 files changed, 119 insertions(+), 90 deletions(-)
 create mode 100644 man/torch_selu.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 00ed918e5..5eacbca71 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -522,6 +522,7 @@ export(torch_round)
 export(torch_rrelu_)
 export(torch_rsqrt)
 export(torch_save)
+export(torch_selu)
 export(torch_selu_)
 export(torch_set_default_dtype)
 export(torch_short)
diff --git a/R/gen-namespace-docs.R b/R/gen-namespace-docs.R
index b21e14423..1c9400301 100644
--- a/R/gen-namespace-docs.R
+++ b/R/gen-namespace-docs.R
@@ -1323,8 +1323,8 @@ NULL
 #' 
 #' @section Warning:
 #'     Integer division using div is deprecated, and in a future release div will
-#'     perform true division like [`torch_true_divide`].
-#'     Use [`torch_floor_divide`] (// in Python) to perform integer division,
+#'     perform true division like [torch_true_divide()].
+#'     Use [torch_floor_divide()] to perform integer division,
 #'     instead.
 #' 
 #' \deqn{
@@ -3274,14 +3274,25 @@ NULL
 #' @export
 NULL
 
+#' Selu
+#'
+#' @section selu(input) -> Tensor :
+#'
+#' @param self the input tensor
+#'
+#' @name torch_selu
+#'
+#' @export
+NULL
+
 
 #' Selu_
 #'
 #' @section selu_(input) -> Tensor :
 #'
-#' In-place version of `toch_selu`.
-#'
-#'
+#' In-place version of [toch_selu()].
+#' 
+#' @param self the input tensor
 #'
 #'
 #' @name torch_selu_
@@ -3420,8 +3431,8 @@ NULL
 #'     to `split_size_or_sections`.
 #'
 #'
-#' @param tensor (Tensor) tensor to split.
-#' @param split_size_or_sections (int) size of a single chunk or        list of sizes for each chunk
+#' @param self (Tensor) tensor to split.
+#' @param split_size (int) size of a single chunk or        list of sizes for each chunk
 #' @param dim (int) dimension along which to split the tensor.
 #'
 #' @name torch_split
@@ -3535,9 +3546,9 @@ NULL
 #'     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.
+#' @section Warning:
+#' This function changed signature at version 0.4.1. Calling with the
+#' previous signature may cause error or return incorrect result.
 #'
 #'
 #' @param self (Tensor) the input tensor
@@ -3787,11 +3798,8 @@ NULL
 
 #' Tensordot
 #'
-#' @section TEST :
-#'
 #' Returns a contraction of a and b over multiple dimensions.
-#' 
-#'     `tensordot` implements a generalized matrix product.
+#' `tensordot` implements a generalized matrix product.
 #'
 #'
 #' @param a (Tensor) Left tensor to contract
@@ -3810,8 +3818,9 @@ NULL
 #'
 #' In-place version of `torch_threshold`.
 #'
-#'
-#'
+#' @param self input tensor
+#' @param threshold The value to threshold at
+#' @param value The value to replace with
 #'
 #' @name torch_threshold_
 #'
@@ -3931,8 +3940,8 @@ NULL
 #' }
 #'
 #'
-#' @param dividend (Tensor) the dividend
-#' @param divisor (Tensor or Scalar) the divisor
+#' @param self (Tensor) the dividend
+#' @param other (Tensor or Scalar) the divisor
 #'
 #' @name torch_true_divide
 #'
@@ -4099,12 +4108,12 @@ NULL
 #' `torch_nonzero(condition, as_tuple=TRUE)`.
 #' 
 #' @note
-#'     See also [`torch_nonzero`].
+#' See also [torch_nonzero()].
 #'
 #'
 #' @param condition (BoolTensor) When TRUE (nonzero), yield x, otherwise yield y
-#' @param x (Tensor) values selected at indices where `condition` is `TRUE`
-#' @param y (Tensor) values selected at indices where `condition` is `FALSE`
+#' @param self (Tensor) values selected at indices where `condition` is `TRUE`
+#' @param other (Tensor) values selected at indices where `condition` is `FALSE`
 #'
 #' @name torch_where
 #'
@@ -4120,13 +4129,14 @@ NULL
 #' by the variable argument `size`.
 #'
 #'
-#' @param size (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.
+#' @param ... 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.
 #' 
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor.        Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`).
 #' @param layout (`torch.layout`, optional) the desired layout of returned Tensor.        Default: `torch_strided`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, uses the current device for the default tensor type        (see `torch_set_default_tensor_type`). `device` will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
 #' @param requires_grad (bool, optional) If autograd should record operations on the        returned tensor. Default: `FALSE`.
-#'
+#' @param names optional dimension names
+#' 
 #' @name torch_zeros
 #'
 #' @export
@@ -4147,7 +4157,7 @@ NULL
 #'     `torch_zeros(input.size(), out=output)`.
 #'
 #'
-#' @param self (Tensor) the size of `input` will determine size of the output tensor.
+#' @param input (Tensor) the size of `input` will determine size of the output tensor.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
 #' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
@@ -4418,9 +4428,9 @@ NULL
 #' operation on the provided input tensors. See type promotion documentation 
 #' for more information on the type promotion logic.
 #'
-#'
 #' @param tensor1 (Tensor or Number) an input tensor or number
 #' @param tensor2 (Tensor or Number) an input tensor or number
+#' 
 #'
 #' @name torch_result_type
 #'
@@ -4747,8 +4757,7 @@ NULL
 #'
 #' Returns the sum of the elements of the diagonal of the input 2-D matrix.
 #'
-#'
-#'
+#' @param self the input tensor
 #'
 #' @name torch_trace
 #'
@@ -4881,7 +4890,7 @@ NULL
 #'
 #'
 #' @param self (Tensor) the input tensor.
-#' @param indices (LongTensor) the indices into tensor
+#' @param index (LongTensor) the indices into tensor
 #'
 #' @name torch_take
 #'
@@ -5160,7 +5169,7 @@ NULL
 
 #' Symeig
 #'
-#' @section symeig(input, eigenvectors=False, upper=TRUE, out=NULL) -> (Tensor, Tensor) :
+#' @section symeig(input, eigenvectors=False, upper=TRUE) -> (Tensor, Tensor) :
 #'
 #' This function returns eigenvalues and eigenvectors
 #' of a real symmetric matrix `input` or a batch of real symmetric matrices,
@@ -5194,7 +5203,6 @@ NULL
 #' @param self (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more                    batch dimensions consisting of symmetric matrices.
 #' @param eigenvectors (boolean, optional) controls whether eigenvectors have to be computed
 #' @param upper (boolean, optional) controls whether to consider upper-triangular or lower-triangular region
-#' @param out (tuple, optional) the output tuple of (Tensor, Tensor)
 #'
 #' @name torch_symeig
 #'
@@ -5224,7 +5232,7 @@ NULL
 
 #' Svd
 #'
-#' @section svd(input, some=TRUE, compute_uv=TRUE, out=NULL) -> (Tensor, Tensor, Tensor) :
+#' @section svd(input, some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor) :
 #'
 #' This function returns a namedtuple `(U, S, V)` which is the singular value
 #' decomposition of a input real matrix or batches of real matrices `input` such that
@@ -5265,7 +5273,6 @@ NULL
 #' @param self (Tensor) the input tensor of size \eqn{(*, m, n)} where `*` is zero or more                    batch dimensions consisting of \eqn{m \times n} matrices.
 #' @param some (bool, optional) controls the shape of returned `U` and `V`
 #' @param compute_uv (bool, optional) option whether to compute `U` and `V` or not
-#' @param out (tuple, optional) the output tuple of tensors
 #'
 #' @name torch_svd
 #'
@@ -5346,7 +5353,7 @@ NULL
 
 #' Solve
 #'
-#' @section torch.solve(input, A, out=NULL) -> (Tensor, Tensor) :
+#' @section solve(input, A) -> (Tensor, Tensor) :
 #'
 #' This function returns the solution to the system of linear
 #' equations represented by \eqn{AX = B} and the LU factorization of
@@ -5362,13 +5369,12 @@ NULL
 #' 
 #'     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.
+#'     `B$contiguous()$transpose(-1, -2)$stride()` and
+#'     `A$contiguous()$transpose(-1, -2)$stride()` respectively.
 #'
 #'
 #' @param self (Tensor) input matrix \eqn{B} of size \eqn{(*, m, k)} , where \eqn{*}                is zero or more batch dimensions.
 #' @param A (Tensor) input square matrix of size \eqn{(*, m, m)}, where                \eqn{*} is zero or more batch dimensions.
-#' @param out ((Tensor, Tensor) optional output tuple.
 #'
 #' @name torch_solve
 #'
@@ -5487,15 +5493,17 @@ NULL
 #' @section ormqr(input, input2, input3, left=TRUE, transpose=False) -> Tensor :
 #'
 #' Multiplies `mat` (given by `input3`) by the orthogonal `Q` matrix of the QR factorization
-#' formed by [`torch_geqrf`] that is represented by `(a, tau)` (given by (`input`, `input2`)).
+#' formed by [torch_geqrf()] that is represented by `(a, tau)` (given by (`input`, `input2`)).
 #' 
 #' This directly calls the underlying LAPACK function `?ormqr`.
-#' See `LAPACK documentation for ormqr`_ for further details.
+#' See [LAPACK documentation for ormqr](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) for further details.
 #'
 #'
 #' @param self (Tensor) the `a` from [`torch_geqrf`].
 #' @param input2 (Tensor) the `tau` from [`torch_geqrf`].
 #' @param input3 (Tensor) the matrix to be multiplied.
+#' @param left see LAPACK documentation
+#' @param transpose see LAPACK documentation
 #'
 #' @name torch_ormqr
 #'
@@ -5812,7 +5820,7 @@ NULL
 
 #' Sort
 #'
-#' @section sort(input, dim=-1, descending=False, out=NULL) -> (Tensor, LongTensor) :
+#' @section sort(input, dim=-1, descending=FALSE) -> (Tensor, LongTensor) :
 #'
 #' Sorts the elements of the `input` tensor along a given dimension
 #' in ascending order by value.
@@ -5830,7 +5838,6 @@ NULL
 #' @param self (Tensor) the input tensor.
 #' @param dim (int, optional) the dimension to sort along
 #' @param descending (bool, optional) controls the sorting order (ascending or descending)
-#' @param out (tuple, optional) the output tuple of (`Tensor`, `LongTensor`) that can        be optionally given to be used as output buffers
 #'
 #' @name torch_sort
 #'
@@ -5861,7 +5868,7 @@ NULL
 
 #' Topk
 #'
-#' @section topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE, out=NULL) -> (Tensor, LongTensor) :
+#' @section topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor) :
 #'
 #' Returns the `k` largest elements of the given `input` tensor along
 #' a given dimension.
@@ -5882,7 +5889,6 @@ NULL
 #' @param dim (int, optional) the dimension to sort along
 #' @param largest (bool, optional) controls whether to return largest or           smallest elements
 #' @param sorted (bool, optional) controls whether to return the elements           in sorted order
-#' @param out (tuple, optional) the output tuple of (Tensor, LongTensor) that can be        optionally given to be used as output buffers
 #'
 #' @name torch_topk
 #'
diff --git a/man/torch_div.Rd b/man/torch_div.Rd
index e02219aad..b88ebc4c7 100644
--- a/man/torch_div.Rd
+++ b/man/torch_div.Rd
@@ -41,8 +41,8 @@ specified output tensor. Integral division by zero leads to undefined behavior.
 \section{Warning}{
 
 Integer division using div is deprecated, and in a future release div will
-perform true division like \code{\link{torch_true_divide}}.
-Use \code{\link{torch_floor_divide}} (// in Python) to perform integer division,
+perform true division like \code{\link[=torch_true_divide]{torch_true_divide()}}.
+Use \code{\link[=torch_floor_divide]{torch_floor_divide()}} to perform integer division,
 instead.
 
 \deqn{
diff --git a/man/torch_ormqr.Rd b/man/torch_ormqr.Rd
index f669de96d..d62c33426 100644
--- a/man/torch_ormqr.Rd
+++ b/man/torch_ormqr.Rd
@@ -13,6 +13,10 @@ torch_ormqr(self, input2, input3, left = TRUE, transpose = FALSE)
 \item{input2}{(Tensor) the \code{tau} from \code{\link{torch_geqrf}}.}
 
 \item{input3}{(Tensor) the matrix to be multiplied.}
+
+\item{left}{see LAPACK documentation}
+
+\item{transpose}{see LAPACK documentation}
 }
 \description{
 Ormqr
@@ -21,9 +25,9 @@ Ormqr
 
 
 Multiplies \code{mat} (given by \code{input3}) by the orthogonal \code{Q} matrix of the QR factorization
-formed by \code{\link{torch_geqrf}} that is represented by \verb{(a, tau)} (given by (\code{input}, \code{input2})).
+formed by \code{\link[=torch_geqrf]{torch_geqrf()}} that is represented by \verb{(a, tau)} (given by (\code{input}, \code{input2})).
 
 This directly calls the underlying LAPACK function \code{?ormqr}.
-See \verb{LAPACK documentation for ormqr}_ for further details.
+See \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} for further details.
 }
 
diff --git a/man/torch_selu.Rd b/man/torch_selu.Rd
new file mode 100644
index 000000000..7bd9f5023
--- /dev/null
+++ b/man/torch_selu.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gen-namespace-docs.R, R/gen-namespace.R
+\name{torch_selu}
+\alias{torch_selu}
+\title{Selu}
+\usage{
+torch_selu(self)
+}
+\arguments{
+\item{self}{the input tensor}
+}
+\description{
+Selu
+}
+\section{selu(input) -> Tensor }{
+
+}
+
diff --git a/man/torch_selu_.Rd b/man/torch_selu_.Rd
index 4eaf380e7..7b2f9280a 100644
--- a/man/torch_selu_.Rd
+++ b/man/torch_selu_.Rd
@@ -7,12 +7,15 @@
 \usage{
 torch_selu_(self)
 }
+\arguments{
+\item{self}{the input tensor}
+}
 \description{
 Selu_
 }
 \section{selu_(input) -> Tensor }{
 
 
-In-place version of \code{toch_selu}.
+In-place version of \code{\link[=toch_selu]{toch_selu()}}.
 }
 
diff --git a/man/torch_solve.Rd b/man/torch_solve.Rd
index 319da9395..0d0a66bd4 100644
--- a/man/torch_solve.Rd
+++ b/man/torch_solve.Rd
@@ -11,8 +11,6 @@ torch_solve(self, A)
 \item{self}{(Tensor) input matrix \eqn{B} of size \eqn{(*, m, k)} , where \eqn{*}                is zero or more batch dimensions.}
 
 \item{A}{(Tensor) input square matrix of size \eqn{(*, m, m)}, where                \eqn{*} is zero or more batch dimensions.}
-
-\item{out}{((Tensor, Tensor) optional output tuple.}
 }
 \description{
 Solve
@@ -20,11 +18,11 @@ Solve
 \note{
 \preformatted{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.
+`B$contiguous()$transpose(-1, -2)$stride()` and
+`A$contiguous()$transpose(-1, -2)$stride()` respectively.
 }
 }
-\section{torch.solve(input, A, out=NULL) -> (Tensor, Tensor) }{
+\section{solve(input, A) -> (Tensor, Tensor) }{
 
 
 This function returns the solution to the system of linear
diff --git a/man/torch_sort.Rd b/man/torch_sort.Rd
index 3587e1334..eeb3e9e19 100644
--- a/man/torch_sort.Rd
+++ b/man/torch_sort.Rd
@@ -13,13 +13,11 @@ torch_sort(self, dim = -1L, descending = FALSE)
 \item{dim}{(int, optional) the dimension to sort along}
 
 \item{descending}{(bool, optional) controls the sorting order (ascending or descending)}
-
-\item{out}{(tuple, optional) the output tuple of (\code{Tensor}, \code{LongTensor}) that can        be optionally given to be used as output buffers}
 }
 \description{
 Sort
 }
-\section{sort(input, dim=-1, descending=False, out=NULL) -> (Tensor, LongTensor) }{
+\section{sort(input, dim=-1, descending=FALSE) -> (Tensor, LongTensor) }{
 
 
 Sorts the elements of the \code{input} tensor along a given dimension
diff --git a/man/torch_split.Rd b/man/torch_split.Rd
index ea5b5989a..ca222f03b 100644
--- a/man/torch_split.Rd
+++ b/man/torch_split.Rd
@@ -8,11 +8,11 @@
 torch_split(self, split_size, dim = 1L)
 }
 \arguments{
-\item{dim}{(int) dimension along which to split the tensor.}
+\item{self}{(Tensor) tensor to split.}
 
-\item{tensor}{(Tensor) tensor to split.}
+\item{split_size}{(int) size of a single chunk or        list of sizes for each chunk}
 
-\item{split_size_or_sections}{(int) size of a single chunk or        list of sizes for each chunk}
+\item{dim}{(int) dimension along which to split the tensor.}
 }
 \description{
 Split
diff --git a/man/torch_stft.Rd b/man/torch_stft.Rd
index bb37275c9..48f5f90e0 100644
--- a/man/torch_stft.Rd
+++ b/man/torch_stft.Rd
@@ -89,10 +89,12 @@ 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.
 }
 }
 
+\section{Warning}{
+
+This function changed signature at version 0.4.1. Calling with the
+previous signature may cause error or return incorrect result.
+}
+
diff --git a/man/torch_svd.Rd b/man/torch_svd.Rd
index 4525ae423..924ae245e 100644
--- a/man/torch_svd.Rd
+++ b/man/torch_svd.Rd
@@ -13,8 +13,6 @@ torch_svd(self, some = TRUE, compute_uv = TRUE)
 \item{some}{(bool, optional) controls the shape of returned \code{U} and \code{V}}
 
 \item{compute_uv}{(bool, optional) option whether to compute \code{U} and \code{V} or not}
-
-\item{out}{(tuple, optional) the output tuple of tensors}
 }
 \description{
 Svd
@@ -44,7 +42,7 @@ can be arbitrary bases of the subspaces.
 When \code{compute_uv} = \code{FALSE}, backward cannot be performed since \code{U} and \code{V}
 from the forward pass is required for the backward operation.
 }
-\section{svd(input, some=TRUE, compute_uv=TRUE, out=NULL) -> (Tensor, Tensor, Tensor) }{
+\section{svd(input, some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor) }{
 
 
 This function returns a namedtuple \verb{(U, S, V)} which is the singular value
diff --git a/man/torch_symeig.Rd b/man/torch_symeig.Rd
index 5121431f6..6be0c5944 100644
--- a/man/torch_symeig.Rd
+++ b/man/torch_symeig.Rd
@@ -13,8 +13,6 @@ torch_symeig(self, eigenvectors = FALSE, upper = TRUE)
 \item{eigenvectors}{(boolean, optional) controls whether eigenvectors have to be computed}
 
 \item{upper}{(boolean, optional) controls whether to consider upper-triangular or lower-triangular region}
-
-\item{out}{(tuple, optional) the output tuple of (Tensor, Tensor)}
 }
 \description{
 Symeig
@@ -30,7 +28,7 @@ Extra care needs to be taken when backward through outputs. Such
 operation is really only stable when all eigenvalues are distinct.
 Otherwise, \code{NaN} can appear as the gradients are not properly defined.
 }
-\section{symeig(input, eigenvectors=False, upper=TRUE, out=NULL) -> (Tensor, Tensor) }{
+\section{symeig(input, eigenvectors=False, upper=TRUE) -> (Tensor, Tensor) }{
 
 
 This function returns eigenvalues and eigenvectors
diff --git a/man/torch_take.Rd b/man/torch_take.Rd
index 54af7784b..b7442a56a 100644
--- a/man/torch_take.Rd
+++ b/man/torch_take.Rd
@@ -10,7 +10,7 @@ torch_take(self, index)
 \arguments{
 \item{self}{(Tensor) the input tensor.}
 
-\item{indices}{(LongTensor) the indices into tensor}
+\item{index}{(LongTensor) the indices into tensor}
 }
 \description{
 Take
diff --git a/man/torch_tensordot.Rd b/man/torch_tensordot.Rd
index 5c04daacb..eced5e1c5 100644
--- a/man/torch_tensordot.Rd
+++ b/man/torch_tensordot.Rd
@@ -15,15 +15,9 @@ torch_tensordot(self, other, dims_self, dims_other)
 \item{dims}{(int or tuple of two lists of integers) number of dimensions to     contract or explicit lists of dimensions for \code{a} and     \code{b} respectively}
 }
 \description{
-Tensordot
+Returns a contraction of a and b over multiple dimensions.
+\code{tensordot} implements a generalized matrix product.
 }
-\section{TEST }{
-
-
-Returns a contraction of a and b over multiple dimensions.\preformatted{`tensordot` implements a generalized matrix product.
-}
-}
-
 \examples{
 if (torch_is_installed()) {
 
diff --git a/man/torch_threshold_.Rd b/man/torch_threshold_.Rd
index 62f35e637..f09c56199 100644
--- a/man/torch_threshold_.Rd
+++ b/man/torch_threshold_.Rd
@@ -7,6 +7,13 @@
 \usage{
 torch_threshold_(self, threshold, value)
 }
+\arguments{
+\item{self}{input tensor}
+
+\item{threshold}{The value to threshold at}
+
+\item{value}{The value to replace with}
+}
 \description{
 Threshold_
 }
diff --git a/man/torch_topk.Rd b/man/torch_topk.Rd
index 886bfa847..cd138938c 100644
--- a/man/torch_topk.Rd
+++ b/man/torch_topk.Rd
@@ -17,13 +17,11 @@ torch_topk(self, k, dim = -1L, largest = TRUE, sorted = TRUE)
 \item{largest}{(bool, optional) controls whether to return largest or           smallest elements}
 
 \item{sorted}{(bool, optional) controls whether to return the elements           in sorted order}
-
-\item{out}{(tuple, optional) the output tuple of (Tensor, LongTensor) that can be        optionally given to be used as output buffers}
 }
 \description{
 Topk
 }
-\section{topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE, out=NULL) -> (Tensor, LongTensor) }{
+\section{topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor) }{
 
 
 Returns the \code{k} largest elements of the given \code{input} tensor along
diff --git a/man/torch_trace.Rd b/man/torch_trace.Rd
index 93fbe4fa1..23e2b0261 100644
--- a/man/torch_trace.Rd
+++ b/man/torch_trace.Rd
@@ -7,6 +7,9 @@
 \usage{
 torch_trace(self)
 }
+\arguments{
+\item{self}{the input tensor}
+}
 \description{
 Trace
 }
diff --git a/man/torch_true_divide.Rd b/man/torch_true_divide.Rd
index d6bc956aa..667c9a69d 100644
--- a/man/torch_true_divide.Rd
+++ b/man/torch_true_divide.Rd
@@ -8,9 +8,9 @@
 torch_true_divide(self, other)
 }
 \arguments{
-\item{dividend}{(Tensor) the dividend}
+\item{self}{(Tensor) the dividend}
 
-\item{divisor}{(Tensor or Scalar) the divisor}
+\item{other}{(Tensor or Scalar) the divisor}
 }
 \description{
 TRUE_divide
diff --git a/man/torch_where.Rd b/man/torch_where.Rd
index 0270be799..e3937156a 100644
--- a/man/torch_where.Rd
+++ b/man/torch_where.Rd
@@ -10,9 +10,9 @@ torch_where(condition, self, other)
 \arguments{
 \item{condition}{(BoolTensor) When TRUE (nonzero), yield x, otherwise yield y}
 
-\item{x}{(Tensor) values selected at indices where \code{condition} is \code{TRUE}}
+\item{self}{(Tensor) values selected at indices where \code{condition} is \code{TRUE}}
 
-\item{y}{(Tensor) values selected at indices where \code{condition} is \code{FALSE}}
+\item{other}{(Tensor) values selected at indices where \code{condition} is \code{FALSE}}
 }
 \description{
 Where
@@ -21,8 +21,7 @@ Where
 \preformatted{The tensors `condition`, `x`, `y` must be broadcastable .
 }
 
-\preformatted{See also [`torch_nonzero`].
-}
+See also \code{\link[=torch_nonzero]{torch_nonzero()}}.
 }
 \section{where(condition, x, y) -> Tensor }{
 
diff --git a/man/torch_zeros.Rd b/man/torch_zeros.Rd
index 02ceca196..af44a5138 100644
--- a/man/torch_zeros.Rd
+++ b/man/torch_zeros.Rd
@@ -15,6 +15,10 @@ torch_zeros(
 )
 }
 \arguments{
+\item{...}{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.}
+
+\item{names}{optional dimension names}
+
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
@@ -22,8 +26,6 @@ torch_zeros(
 \item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
-
-\item{size}{(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.}
 }
 \description{
 Zeros
diff --git a/man/torch_zeros_like.Rd b/man/torch_zeros_like.Rd
index 556d55890..bdbd430bc 100644
--- a/man/torch_zeros_like.Rd
+++ b/man/torch_zeros_like.Rd
@@ -15,6 +15,8 @@ torch_zeros_like(
 )
 }
 \arguments{
+\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
+
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
@@ -24,8 +26,6 @@ torch_zeros_like(
 \item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
-
-\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 }
 \description{
 Zeros_like
-- 
GitLab


From 8f90b7be86990e28500382d9d7719812909ff263 Mon Sep 17 00:00:00 2001
From: Javier Luraschi <javierluraschi@hotmail.com>
Date: Thu, 10 Sep 2020 09:36:08 -0700
Subject: [PATCH 105/157] consider jupyter and collab notebooks interactive

---
 R/package.R | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/R/package.R b/R/package.R
index 14694b0b9..0a2bd180e 100644
--- a/R/package.R
+++ b/R/package.R
@@ -11,8 +11,10 @@ globalVariables(c("..", "self", "private", "N"))
 
 .onLoad <- function(libname, pkgname){
   install_success <- TRUE
+  autoinstall <- interactive() || "JPY_PARENT_PID" %in% names(Sys.getenv())
+  
   if (!install_exists() && Sys.getenv("TORCH_INSTALL", unset = 2) != 0 && 
-      (interactive() || Sys.getenv("TORCH_INSTALL", unset = 2) == "1")) {
+      (autoinstall || Sys.getenv("TORCH_INSTALL", unset = 2) == "1")) {
     install_success <- tryCatch({
       install_torch()
       TRUE
-- 
GitLab


From 014c9e247b6018e89e685af7aa4672cebd09c7b5 Mon Sep 17 00:00:00 2001
From: Javier Luraschi <javierluraschi@hotmail.com>
Date: Thu, 10 Sep 2020 10:08:59 -0700
Subject: [PATCH 106/157] also check jupyer kernel option

---
 R/package.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/R/package.R b/R/package.R
index 0a2bd180e..bd2e52110 100644
--- a/R/package.R
+++ b/R/package.R
@@ -11,7 +11,9 @@ globalVariables(c("..", "self", "private", "N"))
 
 .onLoad <- function(libname, pkgname){
   install_success <- TRUE
-  autoinstall <- interactive() || "JPY_PARENT_PID" %in% names(Sys.getenv())
+  autoinstall <- interactive() ||
+                          "JPY_PARENT_PID" %in% names(Sys.getenv()) ||
+                          identical(getOption("jupyter.in_kernel"), TRUE)
   
   if (!install_exists() && Sys.getenv("TORCH_INSTALL", unset = 2) != 0 && 
       (autoinstall || Sys.getenv("TORCH_INSTALL", unset = 2) == "1")) {
@@ -42,4 +44,3 @@ globalVariables(c("..", "self", "private", "N"))
 }
 
 
-
-- 
GitLab


From e329f922a620f69b8e09ba0a5ff4bb70bd2279c4 Mon Sep 17 00:00:00 2001
From: Javier Luraschi <javierluraschi@hotmail.com>
Date: Thu, 10 Sep 2020 10:10:10 -0700
Subject: [PATCH 107/157] fix indentation for autoinstall

---
 R/package.R | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/R/package.R b/R/package.R
index bd2e52110..68dc2401e 100644
--- a/R/package.R
+++ b/R/package.R
@@ -12,8 +12,8 @@ globalVariables(c("..", "self", "private", "N"))
 .onLoad <- function(libname, pkgname){
   install_success <- TRUE
   autoinstall <- interactive() ||
-                          "JPY_PARENT_PID" %in% names(Sys.getenv()) ||
-                          identical(getOption("jupyter.in_kernel"), TRUE)
+                 "JPY_PARENT_PID" %in% names(Sys.getenv()) ||
+                 identical(getOption("jupyter.in_kernel"), TRUE)
   
   if (!install_exists() && Sys.getenv("TORCH_INSTALL", unset = 2) != 0 && 
       (autoinstall || Sys.getenv("TORCH_INSTALL", unset = 2) == "1")) {
@@ -43,4 +43,3 @@ globalVariables(c("..", "self", "private", "N"))
   
 }
 
-
-- 
GitLab


From fc4ab76b59c2e0043ebcd473da8650c0e9ca2371 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 10 Sep 2020 17:44:01 -0300
Subject: [PATCH 108/157] more documentation fixes

---
 R/creation-ops.R               |   2 +-
 R/gen-namespace-docs.R         |  11 ++-
 R/gen-namespace-examples.R     |   2 +-
 R/gen-namespace.R              |  44 +++++-----
 R/wrapers.R                    | 141 +++++++++++++++++++++++++++++++++
 man/torch_bartlett_window.Rd   |  11 ++-
 man/torch_blackman_window.Rd   |  11 ++-
 man/torch_hamming_window.Rd    |  13 ++-
 man/torch_hann_window.Rd       |  11 ++-
 man/torch_normal.Rd            |   4 +-
 man/torch_rand_like.Rd         |  10 ---
 man/torch_randn_like.Rd        |  14 +++-
 man/torch_relu.Rd              |   2 +
 man/torch_result_type.Rd       |   6 +-
 man/torch_selu.Rd              |   2 +
 man/torch_selu_.Rd             |   2 +-
 man/torch_sparse_coo_tensor.Rd |  11 ++-
 man/torch_stft.Rd              |  18 +++--
 man/torch_tensordot.Rd         |   4 +-
 man/torch_tril_indices.Rd      |  11 ++-
 man/torch_triu_indices.Rd      |  11 ++-
 tests/testthat/test-wrapers.R  |  31 ++++++++
 tools/torchgen/R/r.R           |   5 +-
 23 files changed, 307 insertions(+), 70 deletions(-)
 create mode 100644 tests/testthat/test-wrapers.R

diff --git a/R/creation-ops.R b/R/creation-ops.R
index 2b3a01455..3df0ec7e2 100644
--- a/R/creation-ops.R
+++ b/R/creation-ops.R
@@ -140,7 +140,7 @@ torch_randn <- function(..., names = NULL, dtype = NULL, layout = torch_strided(
   do.call(.torch_randn, args)
 }
 
-#' @rdname torch_rand_like
+#' @rdname torch_randn_like
 torch_randn_like <- function(input, dtype = NULL, layout = torch_strided(), 
                             device=NULL, requires_grad = FALSE, 
                             memory_format = torch_preserve_format()) {
diff --git a/R/gen-namespace-docs.R b/R/gen-namespace-docs.R
index 1c9400301..d4c3dfc10 100644
--- a/R/gen-namespace-docs.R
+++ b/R/gen-namespace-docs.R
@@ -3039,7 +3039,7 @@ NULL
 #' `torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`.
 #'
 #'
-#' @param self (Tensor) the size of `input` will determine size of the output tensor.
+#' @param input (Tensor) the size of `input` will determine size of the output tensor.
 #' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor.        Default: if `NULL`, defaults to the dtype of `input`.
 #' @param layout (`torch.layout`, optional) the desired layout of returned tensor.        Default: if `NULL`, defaults to the layout of `input`.
 #' @param device (`torch.device`, optional) the desired device of returned tensor.        Default: if `NULL`, defaults to the device of `input`.
@@ -3233,6 +3233,8 @@ NULL
 #'
 #' @section relu(input) -> Tensor :
 #' 
+#' Computes the relu tranformation.
+#' 
 #' @param self the input tensor
 #'
 #' @name torch_relu
@@ -3277,12 +3279,15 @@ NULL
 #' Selu
 #'
 #' @section selu(input) -> Tensor :
+#' 
+#' Computes the selu transformation.
 #'
 #' @param self the input tensor
 #'
 #' @name torch_selu
 #'
 #' @export
+#' 
 NULL
 
 
@@ -3290,7 +3295,7 @@ NULL
 #'
 #' @section selu_(input) -> Tensor :
 #'
-#' In-place version of [toch_selu()].
+#' In-place version of [torch_selu()].
 #' 
 #' @param self the input tensor
 #'
@@ -3551,7 +3556,7 @@ NULL
 #' previous signature may cause error or return incorrect result.
 #'
 #'
-#' @param self (Tensor) the input tensor
+#' @param input (Tensor) the input tensor
 #' @param n_fft (int) size of Fourier transform
 #' @param hop_length (int, optional) the distance between neighboring sliding window        frames. Default: `NULL` (treated as equal to `floor(n_fft / 4)`)
 #' @param win_length (int, optional) the size of window frame and STFT filter.        Default: `NULL`  (treated as equal to `n_fft`)
diff --git a/R/gen-namespace-examples.R b/R/gen-namespace-examples.R
index b4325298a..c7890be34 100644
--- a/R/gen-namespace-examples.R
+++ b/R/gen-namespace-examples.R
@@ -2094,7 +2094,7 @@ NULL
 #'
 #' @examples
 #'
-#' torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_int()), 1.0)
+#' torch_result_type(tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), tensor2 = 1)
 NULL
 # -> result_type <-
 
diff --git a/R/gen-namespace.R b/R/gen-namespace.R
index f9224a52b..e89478c94 100644
--- a/R/gen-namespace.R
+++ b/R/gen-namespace.R
@@ -3804,8 +3804,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_bartlett_window
-torch_bartlett_window <- function(window_length, periodic, options = list()) {
+#' @rdname .torch_bartlett_window
+.torch_bartlett_window <- function(window_length, periodic, options = list()) {
   args <- mget(x = c("window_length", "periodic", "options"))
 expected_types <- list(window_length = "int64_t", periodic = "bool", options = "TensorOptions")
 nd_args <- c("window_length", "periodic")
@@ -4307,8 +4307,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_blackman_window
-torch_blackman_window <- function(window_length, periodic, options = list()) {
+#' @rdname .torch_blackman_window
+.torch_blackman_window <- function(window_length, periodic, options = list()) {
   args <- mget(x = c("window_length", "periodic", "options"))
 expected_types <- list(window_length = "int64_t", periodic = "bool", options = "TensorOptions")
 nd_args <- c("window_length", "periodic")
@@ -7992,8 +7992,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_hamming_window
-torch_hamming_window <- function(window_length, periodic, alpha, beta, options = list()) {
+#' @rdname .torch_hamming_window
+.torch_hamming_window <- function(window_length, periodic, alpha, beta, options = list()) {
   args <- mget(x = c("window_length", "periodic", "alpha", "beta", "options"))
 expected_types <- list(window_length = "int64_t", periodic = "bool", alpha = "double", 
     beta = "double", options = "TensorOptions")
@@ -8010,8 +8010,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_hann_window
-torch_hann_window <- function(window_length, periodic, options = list()) {
+#' @rdname .torch_hann_window
+.torch_hann_window <- function(window_length, periodic, options = list()) {
   args <- mget(x = c("window_length", "periodic", "options"))
 expected_types <- list(window_length = "int64_t", periodic = "bool", options = "TensorOptions")
 nd_args <- c("window_length", "periodic")
@@ -12106,8 +12106,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_normal
-torch_normal <- function(mean, std = 1L, size, generator = NULL, options = list()) {
+#' @rdname .torch_normal
+.torch_normal <- function(mean, std = 1L, size, generator = NULL, options = list()) {
   args <- mget(x = c("mean", "std", "size", "generator", "options"))
 expected_types <- list(mean = c("Tensor", "double"), std = c("double", "Tensor"
 ), size = "IntArrayRef", generator = "Generator *", options = "TensorOptions")
@@ -13695,8 +13695,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_result_type
-torch_result_type <- function(scalar, scalar1, other, scalar2, tensor) {
+#' @rdname .torch_result_type
+.torch_result_type <- function(scalar, scalar1, other, scalar2, tensor) {
   args <- mget(x = c("scalar", "scalar1", "other", "scalar2", "tensor"))
 expected_types <- list(scalar = "Scalar", scalar1 = "Scalar", other = c("Tensor", 
 "Scalar"), scalar2 = "Scalar", tensor = "Tensor")
@@ -15194,8 +15194,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_sparse_coo_tensor
-torch_sparse_coo_tensor <- function(indices, values, size, options = list()) {
+#' @rdname .torch_sparse_coo_tensor
+.torch_sparse_coo_tensor <- function(indices, values, size, options = list()) {
   args <- mget(x = c("indices", "values", "size", "options"))
 expected_types <- list(indices = "Tensor", values = "Tensor", size = "IntArrayRef", 
     options = "TensorOptions")
@@ -15472,8 +15472,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_stft
-torch_stft <- function(self, n_fft, hop_length = NULL, win_length = NULL, window = list(), normalized = FALSE, onesided = TRUE) {
+#' @rdname .torch_stft
+.torch_stft <- function(self, n_fft, hop_length = NULL, win_length = NULL, window = list(), normalized = FALSE, onesided = TRUE) {
   args <- mget(x = c("self", "n_fft", "hop_length", "win_length", "window", "normalized", "onesided"))
 expected_types <- list(self = "Tensor", n_fft = "int64_t", hop_length = "int64_t", 
     win_length = "int64_t", window = "Tensor", normalized = "bool", 
@@ -15835,8 +15835,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_tensordot
-torch_tensordot <- function(self, other, dims_self, dims_other) {
+#' @rdname .torch_tensordot
+.torch_tensordot <- function(self, other, dims_self, dims_other) {
   args <- mget(x = c("self", "other", "dims_self", "dims_other"))
 expected_types <- list(self = "Tensor", other = "Tensor", dims_self = "IntArrayRef", 
     dims_other = "IntArrayRef")
@@ -16331,8 +16331,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_tril_indices
-torch_tril_indices <- function(row, col, offset = 0L, options = torch_long()) {
+#' @rdname .torch_tril_indices
+.torch_tril_indices <- function(row, col, offset = 0L, options = torch_long()) {
   args <- mget(x = c("row", "col", "offset", "options"))
 expected_types <- list(row = "int64_t", col = "int64_t", offset = "int64_t", options = "TensorOptions")
 nd_args <- c("row", "col")
@@ -16401,8 +16401,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_triu_indices
-torch_triu_indices <- function(row, col, offset = 0L, options = torch_long()) {
+#' @rdname .torch_triu_indices
+.torch_triu_indices <- function(row, col, offset = 0L, options = torch_long()) {
   args <- mget(x = c("row", "col", "offset", "options"))
 expected_types <- list(row = "int64_t", col = "int64_t", offset = "int64_t", options = "TensorOptions")
 nd_args <- c("row", "col")
diff --git a/R/wrapers.R b/R/wrapers.R
index 3693f6ec6..538ae4183 100644
--- a/R/wrapers.R
+++ b/R/wrapers.R
@@ -47,3 +47,144 @@ torch_lu <- function(A, pivot=TRUE, get_infos=FALSE, out=NULL) {
 torch_logical_not <- function(self) {
   .torch_logical_not(self)
 }
+
+#' @rdname torch_bartlett_window
+torch_bartlett_window <- function(window_length, periodic=TRUE, dtype=NULL, 
+                                  layout=torch_strided(), device=NULL, 
+                                  requires_grad=FALSE) {
+  opt <- torch_tensor_options(dtype = dtype, layout = layout, device = device,
+                              requires_grad = requires_grad)
+  .torch_bartlett_window(window_length = window_length, periodic = periodic,
+                         options = opt)
+}
+
+#' @rdname torch_blackman_window
+torch_blackman_window <- function(window_length, periodic=TRUE, dtype=NULL, 
+                                  layout=torch_strided(), device=NULL, 
+                                  requires_grad=FALSE) {
+  opt <- torch_tensor_options(dtype = dtype, layout = layout, device = device,
+                              requires_grad = requires_grad)
+  .torch_blackman_window(window_length = window_length, periodic = periodic,
+                         options = opt)
+}
+
+#' @rdname torch_hamming_window
+torch_hamming_window <- function(window_length, periodic=TRUE, alpha=0.54, 
+                                 beta=0.46, dtype=NULL, layout=torch_strided(), 
+                                 device=NULL, requires_grad=FALSE) {
+  opt <- torch_tensor_options(dtype = dtype, layout = layout, device = device,
+                              requires_grad = requires_grad)
+  .torch_hamming_window(window_length = window_length, periodic = periodic, 
+                        alpha = alpha, beta = beta, options = opt)
+}
+
+#' @rdname torch_hann_window
+torch_hann_window <- function(window_length, periodic=TRUE, dtype=NULL, 
+                              layout=torch_strided(), device=NULL, 
+                              requires_grad=FALSE) {
+  opt <- torch_tensor_options(dtype = dtype, layout = layout, device = device,
+                              requires_grad = requires_grad)
+  .torch_hann_window(window_length = window_length, periodic = periodic, 
+                     options = opt)
+}
+
+#' @rdname torch_normal
+torch_normal <- function(mean, std = 1L, size, generator = NULL) {
+  .torch_normal(mean, std, size, generator)
+}
+
+#' @rdname torch_result_type
+torch_result_type <- function(tensor1, tensor2) {
+  
+  if (is_torch_tensor(tensor1) && is_torch_tensor(tensor2)) {
+    o <- cpp_torch_namespace_result_type_tensor_Tensor_other_Tensor(
+      tensor1$ptr, 
+      tensor2$ptr
+    )
+  } else if (is_torch_tensor(tensor1) && !is_torch_tensor(tensor2)) {
+    o <- cpp_torch_namespace_result_type_tensor_Tensor_other_Scalar(
+      tensor1$ptr, 
+      torch_scalar(tensor2)$ptr
+    )
+  } else if (!is_torch_tensor(tensor1) && is_torch_tensor(tensor2)) {
+    o <- cpp_torch_namespace_result_type_scalar_Scalar_tensor_Tensor(
+      torch_scalar(tensor1)$ptr, 
+      tensor2$ptr
+    )
+  } else if (!is_torch_tensor(tensor1) && !is_torch_tensor(tensor2)) {
+    o <- cpp_torch_namespace_result_type_scalar1_Scalar_scalar2_Scalar(
+      torch_scalar(tensor1)$ptr, 
+      torch_scalar(tensor2)$ptr
+    )
+  }
+  
+  torch_dtype$new(ptr = o)
+}
+
+#' @rdname torch_sparse_coo_tensor
+torch_sparse_coo_tensor <- function(indices, values, size=NULL, dtype=NULL, 
+                                    device=NULL, requires_grad=FALSE) {
+  opt <- torch_tensor_options(dtype = dtype, device = device, 
+                              requires_grad = requires_grad)
+  .torch_sparse_coo_tensor(indices, values, size, options = opt)
+}
+
+#' @rdname torch_stft
+torch_stft <- function(input, n_fft, hop_length=NULL, win_length=NULL, 
+                       window=NULL, center=TRUE, pad_mode='reflect', 
+                       normalized=FALSE, onesided=TRUE) {
+  if (center) {
+    signal_dim <- input$dim()
+    extended_shape <- c(
+      rep(2, 3 - signal_dim),
+      input$size()
+    )
+    pad <- as.integer(n_fft %/% 2)
+    input <- nnf_pad(input$view(extended_shape), c(pad, pad), pad_mode)
+    input <- input$view(utils::tail(input$shape(), signal_dim))
+  }
+  
+  .torch_stft(self = input, n_fft = n_fft, hop_length = hop_length, 
+              win_length = win_length, window = window, 
+              normalized = normalized, onesided = onesided)
+}
+
+#' @rdname torch_tensordot
+torch_tensordot <- function(a, b, dims = 2) {
+  
+  if (is.list(dims)) {
+    dims_a <- dims[[1]]
+    dims_b <- dims[[2]]
+  } else if (is_torch_tensor(dims) && dims$numel() > 1) {
+    dims_a <- as_array(dims[1])
+    dims_b <- as_array(dims[2])
+  } else {
+    
+    if (is_torch_tensor(dims))
+      dims <- dims$item()
+    
+    if (dims < 1)
+      runtime_error("tensordot expects dims >= 1, but got {dims}")
+    
+    dims_a <- seq(from = -dims, to = 0)
+    dims_b <- seq(from = 1, to = dims)
+  }
+  
+  .torch_tensordot(a, b, dims_a, dims_b)
+}
+
+#' @rdname torch_tril_indices
+torch_tril_indices <- function(row, col, offset=0, dtype=torch_long(), 
+                               device='cpu', layout=torch_strided()) {
+  opt <- torch_tensor_options(dtype = dtype, device = device, layout = layout)
+  .torch_tril_indices(row, col, offset, options = opt)
+}
+
+#' @rdname torch_triu_indices
+torch_triu_indices <- function(row, col, offset=0, dtype=torch_long(), 
+                               device='cpu', layout=torch_strided()) {
+  opt <- torch_tensor_options(dtype = dtype, device = device, layout = layout)
+  .torch_triu_indices(row, col, offset, options = opt)
+}
+
+
diff --git a/man/torch_bartlett_window.Rd b/man/torch_bartlett_window.Rd
index 050bb90b2..fceba8e27 100644
--- a/man/torch_bartlett_window.Rd
+++ b/man/torch_bartlett_window.Rd
@@ -1,11 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R, R/gen-namespace.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_bartlett_window}
 \alias{torch_bartlett_window}
 \title{Bartlett_window}
 \usage{
-torch_bartlett_window(window_length, periodic, options = list())
+torch_bartlett_window(
+  window_length,
+  periodic = TRUE,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
 }
 \arguments{
 \item{window_length}{(int) the size of returned window}
diff --git a/man/torch_blackman_window.Rd b/man/torch_blackman_window.Rd
index 6b495b040..ccaf3851e 100644
--- a/man/torch_blackman_window.Rd
+++ b/man/torch_blackman_window.Rd
@@ -1,11 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R, R/gen-namespace.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_blackman_window}
 \alias{torch_blackman_window}
 \title{Blackman_window}
 \usage{
-torch_blackman_window(window_length, periodic, options = list())
+torch_blackman_window(
+  window_length,
+  periodic = TRUE,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
 }
 \arguments{
 \item{window_length}{(int) the size of returned window}
diff --git a/man/torch_hamming_window.Rd b/man/torch_hamming_window.Rd
index e33d445bf..1d02d9154 100644
--- a/man/torch_hamming_window.Rd
+++ b/man/torch_hamming_window.Rd
@@ -1,11 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R, R/gen-namespace.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_hamming_window}
 \alias{torch_hamming_window}
 \title{Hamming_window}
 \usage{
-torch_hamming_window(window_length, periodic, alpha, beta, options = list())
+torch_hamming_window(
+  window_length,
+  periodic = TRUE,
+  alpha = 0.54,
+  beta = 0.46,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
 }
 \arguments{
 \item{window_length}{(int) the size of returned window}
diff --git a/man/torch_hann_window.Rd b/man/torch_hann_window.Rd
index 533862970..4a21ee5a8 100644
--- a/man/torch_hann_window.Rd
+++ b/man/torch_hann_window.Rd
@@ -1,11 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R, R/gen-namespace.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_hann_window}
 \alias{torch_hann_window}
 \title{Hann_window}
 \usage{
-torch_hann_window(window_length, periodic, options = list())
+torch_hann_window(
+  window_length,
+  periodic = TRUE,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
 }
 \arguments{
 \item{window_length}{(int) the size of returned window}
diff --git a/man/torch_normal.Rd b/man/torch_normal.Rd
index 49d58cd0d..080f7bb87 100644
--- a/man/torch_normal.Rd
+++ b/man/torch_normal.Rd
@@ -1,11 +1,11 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R, R/gen-namespace.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_normal}
 \alias{torch_normal}
 \title{Normal}
 \usage{
-torch_normal(mean, std = 1L, size, generator = NULL, options = list())
+torch_normal(mean, std = 1L, size, generator = NULL)
 }
 \arguments{
 \item{mean}{(Tensor) the tensor of per-element means}
diff --git a/man/torch_rand_like.Rd b/man/torch_rand_like.Rd
index ad7bab9fe..c4532e0fb 100644
--- a/man/torch_rand_like.Rd
+++ b/man/torch_rand_like.Rd
@@ -3,7 +3,6 @@
 %   R/gen-namespace-examples.R
 \name{torch_rand_like}
 \alias{torch_rand_like}
-\alias{torch_randn_like}
 \title{Rand_like}
 \usage{
 torch_rand_like(
@@ -14,15 +13,6 @@ torch_rand_like(
   requires_grad = FALSE,
   memory_format = torch_preserve_format()
 )
-
-torch_randn_like(
-  input,
-  dtype = NULL,
-  layout = torch_strided(),
-  device = NULL,
-  requires_grad = FALSE,
-  memory_format = torch_preserve_format()
-)
 }
 \arguments{
 \item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
diff --git a/man/torch_randn_like.Rd b/man/torch_randn_like.Rd
index ecaad2285..c374cb085 100644
--- a/man/torch_randn_like.Rd
+++ b/man/torch_randn_like.Rd
@@ -1,11 +1,21 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_randn_like}
 \alias{torch_randn_like}
 \title{Randn_like}
+\usage{
+torch_randn_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
-\item{self}{(Tensor) the size of \code{input} will determine size of the output tensor.}
+\item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
diff --git a/man/torch_relu.Rd b/man/torch_relu.Rd
index 3f02fda62..b9a415aec 100644
--- a/man/torch_relu.Rd
+++ b/man/torch_relu.Rd
@@ -14,5 +14,7 @@ Relu
 }
 \section{relu(input) -> Tensor }{
 
+
+Computes the relu tranformation.
 }
 
diff --git a/man/torch_result_type.Rd b/man/torch_result_type.Rd
index d60023a6d..ad3b913e8 100644
--- a/man/torch_result_type.Rd
+++ b/man/torch_result_type.Rd
@@ -1,11 +1,11 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R, R/gen-namespace.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_result_type}
 \alias{torch_result_type}
 \title{Result_type}
 \usage{
-torch_result_type(scalar, scalar1, other, scalar2, tensor)
+torch_result_type(tensor1, tensor2)
 }
 \arguments{
 \item{tensor1}{(Tensor or Number) an input tensor or number}
@@ -26,6 +26,6 @@ for more information on the type promotion logic.
 \examples{
 if (torch_is_installed()) {
 
-torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_int()), 1.0)
+torch_result_type(tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), tensor2 = 1)
 }
 }
diff --git a/man/torch_selu.Rd b/man/torch_selu.Rd
index 7bd9f5023..efcf2e2c8 100644
--- a/man/torch_selu.Rd
+++ b/man/torch_selu.Rd
@@ -14,5 +14,7 @@ Selu
 }
 \section{selu(input) -> Tensor }{
 
+
+Computes the selu transformation.
 }
 
diff --git a/man/torch_selu_.Rd b/man/torch_selu_.Rd
index 7b2f9280a..81005438b 100644
--- a/man/torch_selu_.Rd
+++ b/man/torch_selu_.Rd
@@ -16,6 +16,6 @@ Selu_
 \section{selu_(input) -> Tensor }{
 
 
-In-place version of \code{\link[=toch_selu]{toch_selu()}}.
+In-place version of \code{\link[=torch_selu]{torch_selu()}}.
 }
 
diff --git a/man/torch_sparse_coo_tensor.Rd b/man/torch_sparse_coo_tensor.Rd
index baffc1fc9..5cddf5eaf 100644
--- a/man/torch_sparse_coo_tensor.Rd
+++ b/man/torch_sparse_coo_tensor.Rd
@@ -1,11 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R, R/gen-namespace.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_sparse_coo_tensor}
 \alias{torch_sparse_coo_tensor}
 \title{Sparse_coo_tensor}
 \usage{
-torch_sparse_coo_tensor(indices, values, size, options = list())
+torch_sparse_coo_tensor(
+  indices,
+  values,
+  size = NULL,
+  dtype = NULL,
+  device = NULL,
+  requires_grad = FALSE
+)
 }
 \arguments{
 \item{indices}{(array_like) Initial data for the tensor. Can be a list, tuple,        NumPy \code{ndarray}, scalar, and other types. Will be cast to a \code{torch_LongTensor}        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.}
diff --git a/man/torch_stft.Rd b/man/torch_stft.Rd
index 48f5f90e0..69d10a812 100644
--- a/man/torch_stft.Rd
+++ b/man/torch_stft.Rd
@@ -1,22 +1,24 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R, R/gen-namespace.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_stft}
 \alias{torch_stft}
 \title{Stft}
 \usage{
 torch_stft(
-  self,
+  input,
   n_fft,
   hop_length = NULL,
   win_length = NULL,
-  window = list(),
+  window = NULL,
+  center = TRUE,
+  pad_mode = "reflect",
   normalized = FALSE,
   onesided = TRUE
 )
 }
 \arguments{
-\item{self}{(Tensor) the input tensor}
+\item{input}{(Tensor) the input tensor}
 
 \item{n_fft}{(int) size of Fourier transform}
 
@@ -26,13 +28,13 @@ torch_stft(
 
 \item{window}{(Tensor, optional) the optional window function.        Default: \code{NULL} (treated as window of all \eqn{1} s)}
 
-\item{normalized}{(bool, optional) controls whether to return the normalized STFT results         Default: \code{FALSE}}
-
-\item{onesided}{(bool, optional) controls whether to return half of results to        avoid redundancy Default: \code{TRUE}}
-
 \item{center}{(bool, optional) whether to pad \code{input} on both sides so        that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}.        Default: \code{TRUE}}
 
 \item{pad_mode}{(string, optional) controls the padding method used when        \code{center} is \code{TRUE}. Default: \code{"reflect"}}
+
+\item{normalized}{(bool, optional) controls whether to return the normalized STFT results         Default: \code{FALSE}}
+
+\item{onesided}{(bool, optional) controls whether to return half of results to        avoid redundancy Default: \code{TRUE}}
 }
 \description{
 Stft
diff --git a/man/torch_tensordot.Rd b/man/torch_tensordot.Rd
index eced5e1c5..f618ff896 100644
--- a/man/torch_tensordot.Rd
+++ b/man/torch_tensordot.Rd
@@ -1,11 +1,11 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R, R/gen-namespace.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_tensordot}
 \alias{torch_tensordot}
 \title{Tensordot}
 \usage{
-torch_tensordot(self, other, dims_self, dims_other)
+torch_tensordot(a, b, dims = 2)
 }
 \arguments{
 \item{a}{(Tensor) Left tensor to contract}
diff --git a/man/torch_tril_indices.Rd b/man/torch_tril_indices.Rd
index 11e20c1d0..7fe840029 100644
--- a/man/torch_tril_indices.Rd
+++ b/man/torch_tril_indices.Rd
@@ -1,11 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R, R/gen-namespace.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_tril_indices}
 \alias{torch_tril_indices}
 \title{Tril_indices}
 \usage{
-torch_tril_indices(row, col, offset = 0L, options = torch_long())
+torch_tril_indices(
+  row,
+  col,
+  offset = 0,
+  dtype = torch_long(),
+  device = "cpu",
+  layout = torch_strided()
+)
 }
 \arguments{
 \item{row}{(\code{int}) number of rows in the 2-D matrix.}
diff --git a/man/torch_triu_indices.Rd b/man/torch_triu_indices.Rd
index 80b87c177..64e251588 100644
--- a/man/torch_triu_indices.Rd
+++ b/man/torch_triu_indices.Rd
@@ -1,11 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R, R/gen-namespace.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_triu_indices}
 \alias{torch_triu_indices}
 \title{Triu_indices}
 \usage{
-torch_triu_indices(row, col, offset = 0L, options = torch_long())
+torch_triu_indices(
+  row,
+  col,
+  offset = 0,
+  dtype = torch_long(),
+  device = "cpu",
+  layout = torch_strided()
+)
 }
 \arguments{
 \item{row}{(\code{int}) number of rows in the 2-D matrix.}
diff --git a/tests/testthat/test-wrapers.R b/tests/testthat/test-wrapers.R
new file mode 100644
index 000000000..5a82f66be
--- /dev/null
+++ b/tests/testthat/test-wrapers.R
@@ -0,0 +1,31 @@
+test_that("result_type", {
+  
+  x <- torch_result_type(
+    tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), 
+    tensor2 = 1
+  )
+  
+  expect_true(x == torch_float())
+  
+  x <- torch_result_type(
+    tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), 
+    tensor2 = torch_tensor(1:2)
+  )
+  
+  expect_true(x == torch_long())
+  
+  x <- torch_result_type(
+    tensor1 = 1, 
+    tensor2 = torch_tensor(1:2)
+  )
+  
+  expect_true(x == torch_float())
+  
+  x <- torch_result_type(
+    tensor1 = 1, 
+    tensor2 = 2L
+  )
+  
+  expect_true(x == torch_float())
+  
+})
diff --git a/tools/torchgen/R/r.R b/tools/torchgen/R/r.R
index 4e9de0fa8..8d1c698de 100644
--- a/tools/torchgen/R/r.R
+++ b/tools/torchgen/R/r.R
@@ -100,7 +100,10 @@ creation_ops <- c("ones", "ones_like", "rand", "rand_like", "randint",
 internal_funs <- c("logical_not", "max_pool1d_with_indices", "max_pool2d_with_indices",
                    "max_pool2d_with_indices_out", "max_pool3d_with_indices",
                    "max_pool3d_with_indices_out", "max", "min", "max_out", "min_out",
-                   "nll_loss", "nll_loss2d")
+                   "nll_loss", "nll_loss2d", "bartlett_window", "blackman_window",
+                   "hamming_window", "hann_window", "normal",
+                   "result_type", "sparse_coo_tensor", "stft",
+                   "tensordot", "tril_indices", "triu_indices")
 internal_funs <- c(internal_funs, creation_ops)
 
 
-- 
GitLab


From 267481e2e7e356f7465c92388f880a381de4ffb8 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 10 Sep 2020 18:21:12 -0300
Subject: [PATCH 109/157] fix examples

---
 R/gen-namespace-examples.R | 2 +-
 R/wrapers.R                | 6 +++++-
 man/torch_tensordot.Rd     | 2 +-
 3 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/R/gen-namespace-examples.R b/R/gen-namespace-examples.R
index c7890be34..c026063fc 100644
--- a/R/gen-namespace-examples.R
+++ b/R/gen-namespace-examples.R
@@ -1743,7 +1743,7 @@ NULL
 #'
 #' a = torch_arange(start = 0, end = 60.)$reshape(c(3, 4, 5))
 #' b = torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2))
-#' torch_tensordot(a, b, dims_self=c(2, 1), dims_other = c(1, 2))
+#' torch_tensordot(a, b, dims = list(c(2, 1), c(1, 2)))
 #' \dontrun{
 #' a = torch_randn(3, 4, 5, device='cuda')
 #' b = torch_randn(4, 5, 6, device='cuda')
diff --git a/R/wrapers.R b/R/wrapers.R
index 538ae4183..f2210680b 100644
--- a/R/wrapers.R
+++ b/R/wrapers.R
@@ -126,7 +126,11 @@ torch_sparse_coo_tensor <- function(indices, values, size=NULL, dtype=NULL,
                                     device=NULL, requires_grad=FALSE) {
   opt <- torch_tensor_options(dtype = dtype, device = device, 
                               requires_grad = requires_grad)
-  .torch_sparse_coo_tensor(indices, values, size, options = opt)
+  
+  if (is.null(size))
+    .torch_sparse_coo_tensor(indices, values, options = opt)
+  else
+    .torch_sparse_coo_tensor(indices, values, size = size, options = opt)
 }
 
 #' @rdname torch_stft
diff --git a/man/torch_tensordot.Rd b/man/torch_tensordot.Rd
index f618ff896..362089c9a 100644
--- a/man/torch_tensordot.Rd
+++ b/man/torch_tensordot.Rd
@@ -23,7 +23,7 @@ if (torch_is_installed()) {
 
 a = torch_arange(start = 0, end = 60.)$reshape(c(3, 4, 5))
 b = torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2))
-torch_tensordot(a, b, dims_self=c(2, 1), dims_other = c(1, 2))
+torch_tensordot(a, b, dims = list(c(2, 1), c(1, 2)))
 \dontrun{
 a = torch_randn(3, 4, 5, device='cuda')
 b = torch_randn(4, 5, 6, device='cuda')
-- 
GitLab


From 015ce207a2a8ed4ba76f7331d7a76e2b2f7d3ff4 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 10 Sep 2020 19:43:46 -0300
Subject: [PATCH 110/157] fix tensordot error message test

---
 tests/testthat/test-dim-error-messages.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/testthat/test-dim-error-messages.R b/tests/testthat/test-dim-error-messages.R
index 4ee5327ed..5a4444288 100644
--- a/tests/testthat/test-dim-error-messages.R
+++ b/tests/testthat/test-dim-error-messages.R
@@ -166,7 +166,7 @@ test_that("tensordot error message", {
   b <- torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2))
   
   expect_error(
-    torch_tensordot(a, b, dims_self=c(2, 1), dims_other = c(1, 3)),
+    torch_tensordot(a, b, list(c(2, 1), c(1, 3))),
     regex = "contracted dimensions need to match, but first has size 3 in dim 1 and second has size 2 in dim 3",
     fixed = TRUE
   )
-- 
GitLab


From 5fd7ef9ab3596c268ca945bbd101c4bcda495c44 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 10 Sep 2020 19:44:48 -0300
Subject: [PATCH 111/157] fix tensordot test for the new api

---
 tests/testthat/test-gen-namespace.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/testthat/test-gen-namespace.R b/tests/testthat/test-gen-namespace.R
index 4f04748a8..d4c3710ee 100644
--- a/tests/testthat/test-gen-namespace.R
+++ b/tests/testthat/test-gen-namespace.R
@@ -165,7 +165,7 @@ test_that("tensordot", {
   
   a <- torch_arange(start = 0, end = 60.)$reshape(c(3, 4, 5))
   b <- torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2))
-  out <- torch_tensordot(a, b, dims_self=c(2, 1), dims_other = c(1, 2))
+  out <- torch_tensordot(a, b, list(c(2, 1), c(1, 2)))
   
   expect_tensor_shape(out, c(5,2))
   
-- 
GitLab


From a76c75ce511b17a4dcebbaf27ae043ff0e88b16e Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 15 Sep 2020 16:24:08 -0300
Subject: [PATCH 112/157] start adding documentation for the tensor class.

---
 vignettes/tensor/index.Rmd | 3640 ++++++++++++++++++++++++++++++++++++
 1 file changed, 3640 insertions(+)
 create mode 100644 vignettes/tensor/index.Rmd

diff --git a/vignettes/tensor/index.Rmd b/vignettes/tensor/index.Rmd
new file mode 100644
index 000000000..804f9dbb3
--- /dev/null
+++ b/vignettes/tensor/index.Rmd
@@ -0,0 +1,3640 @@
+---
+title: "Tensor objects"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Tensor objects}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+```{r setup}
+library(torch)
+```
+
+Central to torch is the `torch_tensor` objects. `torch_tensor`'s are R objects
+very similar to R6 instances. Tensors have a large amount of methods that can
+be called using the `$` operator.
+
+Following is a list of all methods that can be called by tensor objects and their
+documentation. You can also look at [PyTorch's documentation](https://pytorch.org/docs/stable/tensors.html) for additional details. 
+
+
+## T
+
+Is this Tensor with its dimensions reversed.
+
+If `n` is the number of dimensions in `x`,
+`x$T` is equivalent to `x$permute(n-1, n-2, ..., 0)`.
+
+## abs
+
+abs() -> Tensor
+
+See [torch_abs()]
+
+## abs_
+
+abs_() -> Tensor
+
+In-place version of `$abs`
+
+## absolute
+
+absolute() -> Tensor
+
+Alias for [$abs()]
+
+## absolute_
+
+absolute_() -> Tensor
+
+In-place version of `$absolute`
+Alias for [$abs_()]
+
+## acos
+
+acos() -> Tensor
+
+See [torch_acos()]
+
+## acos_
+
+acos_() -> Tensor
+
+In-place version of `$acos`
+
+## acosh
+
+acosh() -> Tensor
+
+See [torch_acosh()]
+
+## acosh_
+
+acosh_() -> Tensor
+
+In-place version of `$acosh`
+
+## add
+
+add(other, *, alpha=1) -> Tensor
+
+Add a scalar or tensor to `self` tensor. If both `alpha`
+and `other` are specified, each element of `other` is scaled by
+`alpha` before being used.
+
+When `other` is a tensor, the shape of `other` must be
+broadcastable with the shape of the underlying
+tensor
+
+See [torch_add()]
+
+## add_
+
+add_(other, *, alpha=1) -> Tensor
+
+In-place version of `$add`
+
+## addbmm
+
+addbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor
+
+See [torch_addbmm()]
+
+## addbmm_
+
+addbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor
+
+In-place version of `$addbmm`
+
+## addcdiv
+
+addcdiv(tensor1, tensor2, *, value=1) -> Tensor
+
+See [torch_addcdiv()]
+
+## addcdiv_
+
+addcdiv_(tensor1, tensor2, *, value=1) -> Tensor
+
+In-place version of `$addcdiv`
+
+## addcmul
+
+addcmul(tensor1, tensor2, *, value=1) -> Tensor
+
+See [torch_addcmul()]
+
+## addcmul_
+
+addcmul_(tensor1, tensor2, *, value=1) -> Tensor
+
+In-place version of `$addcmul`
+
+## addmm
+
+addmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor
+
+See [torch_addmm()]
+
+## addmm_
+
+addmm_(mat1, mat2, *, beta=1, alpha=1) -> Tensor
+
+In-place version of `$addmm`
+
+## addmv
+
+addmv(mat, vec, *, beta=1, alpha=1) -> Tensor
+
+See [torch_addmv()]
+
+## addmv_
+
+addmv_(mat, vec, *, beta=1, alpha=1) -> Tensor
+
+In-place version of `$addmv`
+
+## addr
+
+addr(vec1, vec2, *, beta=1, alpha=1) -> Tensor
+
+See [torch_addr()]
+
+## addr_
+
+addr_(vec1, vec2, *, beta=1, alpha=1) -> Tensor
+
+In-place version of `$addr`
+
+## align_as
+
+align_as(other) -> Tensor
+
+Permutes the dimensions of the `self` tensor to match the dimension order
+in the `other` tensor, adding size-one dims for any new names.
+
+This operation is useful for explicit broadcasting by names (see examples).
+
+All of the dims of `self` must be named in order to use this method.
+The resulting tensor is a view on the original tensor.
+
+All dimension names of `self` must be present in `other$names`.
+`other` may contain named dimensions that are not in `self$names`;
+the output tensor has a size-one dimension for each of those new names.
+
+To align a tensor to a specific order, use `$align_to`.
+
+### Examples:
+
+```{r echo=FALSE}
+# Example 1: Applying a mask
+mask <- torch_randint(low = 0, high = 2, size = c(127, 128), dtype=torch_bool())$refine_names(c('W', 'H'))
+imgs <- torch_randn(32, 128, 127, 3, names=c('N', 'H', 'W', 'C'))
+imgs$masked_fill_(mask$align_as(imgs), 0)
+
+# Example 2: Applying a per-channel-scale
+scale_channels <- function(input, scale) {
+  scale <- scale$refine_names("C")
+  input * scale$align_as(input)
+}
+
+num_channels <- 3
+scale <- torch_randn(num_channels, names='C')
+imgs <- torch_rand(32, 128, 128, num_channels, names=c('N', 'H', 'W', 'C'))
+more_imgs = torch_rand(32, num_channels, 128, 128, names=c('N', 'C', 'H', 'W'))
+videos = torch_randn(3, num_channels, 128, 128, 128, names=c('N', 'C', 'H', 'W', 'D'))
+
+# scale_channels is agnostic to the dimension order of the input
+scale_channels(imgs, scale)
+scale_channels(more_imgs, scale)
+scale_channels(videos, scale)
+```
+
+### Warning:
+    
+The named tensor API is experimental and subject to change.
+
+
+## align_to
+
+Permutes the dimensions of the `self` tensor to match the order
+specified in `names`, adding size-one dims for any new names.
+
+All of the dims of `self` must be named in order to use this method.
+The resulting tensor is a view on the original tensor.
+
+All dimension names of `self` must be present in `names`.
+`names` may contain additional names that are not in `self$names`;
+the output tensor has a size-one dimension for each of those new names.
+
+### Arguments:
+
+* 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 `self`.
+
+### Examples:
+
+```{r echo = FALSE}
+tensor <- torch_randn(2, 2, 2, 2, 2, 2)
+named_tensor <- tensor$refine_names(names = c('A', 'B', 'C', 'D', 'E', 'F'))
+
+# Move the F and E dims to the front while keeping the rest in order
+named_tensor$align_to(c("A", "B", "F", "C", "E", "D"))
+```
+
+
+
+        .. warning::
+            The named tensor API is experimental and subject to change.
+
+        
+## all
+
+all() -> bool
+
+Returns TRUE if all elements in the tensor are TRUE, FALSE otherwise.
+
+#### Examples: 
+
+```{r}
+a <- torch_rand(1, 2)$to(dtype = torch_bool())
+a
+a$all()
+```
+
+all(dim, keepdim=FALSE, out=NULL) -> Tensor
+
+Returns TRUE if all elements in each row of the tensor in the given
+dimension `dim` are TRUE, FALSE otherwise.
+
+If `keepdim` is `TRUE`, the output tensor is of the same size as
+`input` except in the dimension `dim` where it is of size 1.
+Otherwise, `dim` is squeezed (see [torch_squeeze()]), resulting
+in the output tensor having 1 fewer dimension than `input`.
+
+#### Arguments: 
+
+* dim (int): the dimension to reduce
+* keepdim (bool): whether the output tensor has `dim` retained or not
+* out (Tensor, optional): the output tensor
+
+#### Examples: 
+
+```{r}
+a <- torch_rand(4, 2)to(dtype = torch_bool())
+a
+a$all(dim=2)
+a$all(dim=1)
+```
+    
+## allclose
+
+allclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor
+
+See [torch_allclose()]
+
+## angle
+
+angle() -> Tensor
+
+See [torch_angle()]
+
+## any
+
+any() -> bool
+
+Returns TRUE if any elements in the tensor are TRUE, FALSE otherwise.
+
+#### Examples: 
+
+```{r}
+a <- torch_rand(1, 2)$to(dtype = torch_bool())
+a
+a$any()
+```
+
+any(dim, keepdim=FALSE, out=NULL) -> Tensor
+
+Returns TRUE if any elements in each row of the tensor in the given
+dimension `dim` are TRUE, FALSE otherwise.
+
+If `keepdim` is `TRUE`, the output tensor is of the same size as
+`input` except in the dimension :attr:`dim` where it is of size 1.
+Otherwise, `dim` is squeezed (see [torch_squeeze()]), resulting
+in the output tensor having 1 fewer dimension than `input`.
+
+#### Arguments: 
+
+* dim (int): the dimension to reduce
+* keepdim (bool): whether the output tensor has `dim` retained or not
+* out (Tensor, optional): the output tensor
+
+#### Examples: 
+
+```{r}
+a <- torch_randn(4, 2) < 0
+a
+a$any(2)
+a$any(1)
+```
+
+## apply_
+
+apply_(callable) -> Tensor
+
+Applies the function `callable` to each element in the tensor, replacing
+each element with the value returned by `callable`.
+
+#### Note: 
+
+This function only works with CPU tensors and should not be used in code
+sections that require high performance.
+
+## argmax
+
+argmax(dim=NULL, keepdim=FALSE) -> LongTensor
+
+See [torch_argmax()]
+
+## argmin
+
+argmin(dim=NULL, keepdim=FALSE) -> LongTensor
+
+See [torch_argmin()]
+
+## argsort
+
+argsort(dim=-1, descending=FALSE) -> LongTensor
+
+See [torch_argsort()]
+
+## as_strided
+
+as_strided(size, stride, storage_offset=0) -> Tensor
+
+See [torch_as_strided()]
+
+## as_subclass
+
+as_subclass(cls) -> Tensor
+
+Makes a `cls` instance with the same data pointer as `self`. Changes
+in the output mirror changes in `self`, and the output stays attached
+to the autograd graph. `cls` must be a subclass of `Tensor`.
+
+## asin
+
+asin() -> Tensor
+
+See [torch_asin()]
+
+## asin_
+
+asin_() -> Tensor
+
+In-place version of `$asin`
+
+## asinh
+
+asinh() -> Tensor
+
+See [torch_asinh()]
+
+## asinh_
+
+asinh_() -> Tensor
+
+In-place version of `$asinh`
+
+## atan
+
+atan() -> Tensor
+
+See [torch_atan()]
+
+## atan2
+
+atan2(other) -> Tensor
+
+See [torch_atan2()]
+
+## atan2_
+
+atan2_(other) -> Tensor
+
+In-place version of `$atan2`
+
+## atan_
+
+atan_() -> Tensor
+
+In-place version of `$atan`
+
+## atanh
+
+atanh() -> Tensor
+
+See [torch_atanh()]
+
+## atanh_
+
+In-place version of `$atanh`
+
+## backward
+Computes the gradient of current tensor w.r.t. graph leaves.
+
+The graph is differentiated using the chain rule. If the tensor is
+non-scalar (i.e. its data has more than one element) and requires
+gradient, the function additionally requires specifying `gradient`.
+It should be a tensor of matching type and location, that contains
+the gradient of the differentiated function w.r.t. `self`.
+
+This function accumulates gradients in the leaves - you might need to zero
+`$grad` attributes or set them to `NULL` before calling it.
+See `Default gradient layouts<default-grad-layouts>`
+for details on the memory layout of accumulated gradients.
+
+#### Arguments:
+
+* 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 `create_graph` 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.
+* retain_graph (bool, optional): If `FALSE`, 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
+    `create_graph`.
+* create_graph (bool, optional): If `TRUE`, graph of the derivative will
+    be constructed, allowing to compute higher order derivative
+    products. Defaults to `FALSE`.
+        
+## baddbmm
+
+baddbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor
+
+See [torch_baddbmm()]
+
+## baddbmm_
+
+baddbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor
+
+In-place version of `$baddbmm`
+
+## bernoulli
+
+bernoulli(*, generator=NULL) -> Tensor
+
+Returns a result tensor where each `\texttt{result[i]}` is independently
+sampled from `\text{Bernoulli}(\texttt{self[i]})`. :attr:`self` must have
+floating point `dtype`, and the result will have the same `dtype`.
+
+See [torch_bernoulli()]
+
+## bernoulli_
+
+bernoulli_(p=0.5, *, generator=NULL) -> Tensor
+
+Fills each location of `self` with an independent sample from
+`\text{Bernoulli}(\texttt{p})`. :attr:`self` can have integral
+`dtype`.
+
+bernoulli_(p_tensor, *, generator=NULL) -> Tensor
+
+`p_tensor` should be a tensor containing probabilities to be used for
+drawing the binary random number.
+
+The `\text{i}^{th}` element of :attr:`self` tensor will be set to a
+value sampled from `\text{Bernoulli}(\texttt{p\_tensor[i]})`.
+
+`self` can have integral `dtype`, but :attr:`p_tensor` must have
+floating point `dtype`.
+
+See also `$bernoulli` and [torch_bernoulli()]
+
+## bfloat16
+
+bfloat16(memory_format=torch_preserve_format) -> Tensor
+`self$bfloat16()` is equivalent to `self$to(torch_bfloat16)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## bincount
+
+bincount(weights=NULL, minlength=0) -> Tensor
+
+See [torch_bincount()]
+
+## bitwise_and
+
+bitwise_and() -> Tensor
+
+See [torch_bitwise_and()]
+
+## bitwise_and_
+
+bitwise_and_() -> Tensor
+
+In-place version of `$bitwise_and`
+
+## bitwise_not
+
+bitwise_not() -> Tensor
+
+See [torch_bitwise_not()]
+
+## bitwise_not_
+
+bitwise_not_() -> Tensor
+
+In-place version of `$bitwise_not`
+
+## bitwise_or
+
+bitwise_or() -> Tensor
+
+See [torch_bitwise_or()]
+
+## bitwise_or_
+
+bitwise_or_() -> Tensor
+
+In-place version of `$bitwise_or`
+
+## bitwise_xor
+
+bitwise_xor() -> Tensor
+
+See [torch_bitwise_xor()]
+
+## bitwise_xor_
+
+bitwise_xor_() -> Tensor
+
+In-place version of `$bitwise_xor`
+
+## bmm
+
+bmm(batch2) -> Tensor
+
+See [torch_bmm()]
+
+## bool
+
+bool(memory_format=torch_preserve_format) -> Tensor
+
+`self$bool()` is equivalent to `self$to(torch_bool)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## byte
+
+byte(memory_format=torch_preserve_format) -> Tensor
+
+`self$byte()` is equivalent to `self$to(torch_uint8)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## cauchy_
+
+cauchy_(median=0, sigma=1, *, generator=NULL) -> Tensor
+
+Fills the tensor with numbers drawn from the Cauchy distribution:
+
+$$
+f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2}
+$$
+
+## ceil
+
+ceil() -> Tensor
+
+See [torch_ceil()]
+
+## ceil_
+
+ceil_() -> Tensor
+
+In-place version of `$ceil`
+
+## char
+
+char(memory_format=torch_preserve_format) -> Tensor
+
+`self$char()` is equivalent to `self$to(torch_int8)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## cholesky
+
+cholesky(upper=FALSE) -> Tensor
+
+See [torch_cholesky()]
+
+## cholesky_inverse
+
+cholesky_inverse(upper=FALSE) -> Tensor
+
+See [torch_cholesky_inverse()]
+
+## cholesky_solve
+
+cholesky_solve(input2, upper=FALSE) -> Tensor
+
+See [torch_cholesky_solve()]
+
+## chunk
+
+chunk(chunks, dim=0) -> List of Tensors
+
+See [torch_chunk()]
+
+## clamp
+
+clamp(min, max) -> Tensor
+
+See [torch_clamp()]
+
+## clamp_
+
+clamp_(min, max) -> Tensor
+
+In-place version of `$clamp`
+
+## clone
+
+clone(memory_format=torch_preserve_format) -> Tensor
+
+Returns a copy of the `self` tensor. The copy has the same size and data
+type as `self`.
+
+#### Note: 
+
+Unlike `copy_()`, this function is recorded in the computation graph. Gradients
+propagating to the cloned tensor will propagate to the original tensor.
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## conj
+
+conj() -> Tensor
+
+See [torch_conj()]
+
+## contiguous
+
+contiguous(memory_format=torch_contiguous_format) -> Tensor
+
+Returns a contiguous in memory tensor containing the same data as `self` tensor. If
+`self` tensor is already in the specified memory format, this function returns the
+`self` tensor.
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_contiguous_format`.
+
+## copy_
+
+copy_(src, non_blocking=FALSE) -> Tensor
+
+Copies the elements from `src` into :attr:`self` tensor and returns
+`self`.
+
+The `src` tensor must be :ref:`broadcastable <broadcasting-semantics>`
+with the `self` tensor. It may be of a different data type or reside on a
+different device.
+
+#### Arguments: 
+
+* src (Tensor): the source tensor to copy from
+* non_blocking (bool): if `TRUE` and this copy is between CPU and GPU,
+* the copy may occur asynchronously with respect to the host. For other
+* cases, this argument has no effect.
+
+## cos
+
+cos() -> Tensor
+
+See [torch_cos()]
+
+## cos_
+
+cos_() -> Tensor
+
+In-place version of `$cos`
+
+## cosh
+
+cosh() -> Tensor
+
+See [torch_cosh()]
+
+## cosh_
+
+cosh_() -> Tensor
+
+In-place version of `$cosh`
+
+## cpu
+
+cpu(memory_format=torch_preserve_format) -> Tensor
+
+Returns a copy of this object in CPU memory.
+
+If this object is already in CPU memory and on the correct device,
+then no copy is performed and the original object is returned.
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## cross
+
+cross(other, dim=-1) -> Tensor
+
+See [torch_cross()]
+
+## cuda
+
+cuda(device=NULL, non_blocking=FALSE, memory_format=torch_preserve_format) -> Tensor
+
+Returns a copy of this object in CUDA memory.
+
+If this object is already in CUDA memory and on the correct device,
+then no copy is performed and the original object is returned.
+
+#### Arguments: 
+
+* device (`torch_device`): The destination GPU device.
+  Defaults to the current CUDA device.
+* non_blocking (bool): If `TRUE` and the source is in pinned memory,
+  the copy will be asynchronous with respect to the host.
+  Otherwise, the argument has no effect. Default: `FALSE`.
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## cummax
+
+cummax(dim) -> (Tensor, Tensor)
+
+See [torch_cummax()]
+
+## cummin
+
+cummin(dim) -> (Tensor, Tensor)
+
+See [torch_cummin()]
+
+## cumprod
+
+cumprod(dim, dtype=NULL) -> Tensor
+
+See [torch_cumprod()]
+
+## cumsum
+
+cumsum(dim, dtype=NULL) -> Tensor
+
+See [torch_cumsum()]
+
+## data_ptr
+
+data_ptr() -> int
+
+Returns the address of the first element of `self` tensor.
+
+## deg2rad
+
+deg2rad() -> Tensor
+
+See [torch_deg2rad()]
+
+## deg2rad_
+
+deg2rad_() -> Tensor
+
+In-place version of `$deg2rad`
+
+## dense_dim
+
+dense_dim() -> int
+
+If `self` is a sparse COO tensor (i.e., with `torch_sparse_coo` layout),
+this returns the number of dense dimensions. Otherwise, this throws an error.
+
+See also `$sparse_dim`.
+
+## dequantize
+
+dequantize() -> Tensor
+
+Given a quantized Tensor, dequantize it and return the dequantized float Tensor.
+
+## det
+
+det() -> Tensor
+
+See [torch_det()]
+
+## detach
+
+Returns a new Tensor, detached from the current graph.
+
+The result will never require gradient.
+
+#### Note:
+
+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 `resize_` / `resize_as_` / `set_` / `transpose_`) 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 `zero_` / `copy_` / `add_`) to the
+returned tensor will not update the original tensor anymore, and will instead
+trigger an error.
+  
+## detach_
+
+Detaches the Tensor from the graph that created it, making it a leaf.
+Views cannot be detached in-place.
+    
+## device
+
+Is the `torch_device` where this Tensor is.
+
+## diag
+
+diag(diagonal=0) -> Tensor
+
+See [torch_diag()]
+
+## diag_embed
+
+diag_embed(offset=0, dim1=-2, dim2=-1) -> Tensor
+
+See [torch_diag_embed()]
+
+## diagflat
+
+diagflat(offset=0) -> Tensor
+
+See [torch_diagflat()]
+
+## diagonal
+
+diagonal(offset=0, dim1=0, dim2=1) -> Tensor
+
+See [torch_diagonal()]
+
+## digamma
+
+digamma() -> Tensor
+
+See [torch_digamma()]
+
+## digamma_
+
+digamma_() -> Tensor
+
+In-place version of `$digamma`
+
+## dim
+
+dim() -> int
+
+Returns the number of dimensions of `self` tensor.
+
+## dist
+
+dist(other, p=2) -> Tensor
+
+See [torch_dist()]
+
+## div
+
+div(value) -> Tensor
+
+See [torch_div()]
+
+## div_
+
+div_(value) -> Tensor
+
+In-place version of `$div`
+
+## dot
+
+dot(tensor2) -> Tensor
+
+See [torch_dot()]
+
+## double
+
+double(memory_format=torch_preserve_format) -> Tensor
+
+`self$double()` is equivalent to `self$to(torch_float64)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## eig
+
+eig(eigenvectors=FALSE) -> (Tensor, Tensor)
+
+See [torch_eig()]
+
+## element_size
+
+element_size() -> int
+
+Returns the size in bytes of an individual element.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+torch_tensor(c(1))$element_size()
+```
+
+## eq
+
+eq(other) -> Tensor
+
+See [torch_eq()]
+
+## eq_
+
+eq_(other) -> Tensor
+
+In-place version of `$eq`
+
+## equal
+
+equal(other) -> bool
+
+See [torch_equal()]
+
+## erf
+
+erf() -> Tensor
+
+See [torch_erf()]
+
+## erf_
+
+erf_() -> Tensor
+
+In-place version of `$erf`
+
+## erfc
+
+erfc() -> Tensor
+
+See [torch_erfc()]
+
+## erfc_
+
+erfc_() -> Tensor
+
+In-place version of `$erfc`
+
+## erfinv
+
+erfinv() -> Tensor
+
+See [torch_erfinv()]
+
+## erfinv_
+
+erfinv_() -> Tensor
+
+In-place version of `$erfinv`
+
+## exp
+
+exp() -> Tensor
+
+See [torch_exp()]
+
+## exp_
+
+exp_() -> Tensor
+
+In-place version of `$exp`
+
+## expand
+
+expand(*sizes) -> Tensor
+
+Returns a new view of the `self` tensor with singleton dimensions expanded
+to a larger size.
+
+Passing -1 as the size for a dimension means not changing the size of
+that dimension.
+
+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.
+
+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 `stride` to 0. Any dimension
+of size 1 can be expanded to an arbitrary value without allocating new
+memory.
+
+#### Arguments: 
+
+* sizes (torch_Size or int...): the desired expanded size
+
+#### Warning:
+
+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.
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(matrix(c(1,2,3), ncol = 1))
+x$size()
+x$expand(c(3, 4))
+x$expand(c(-1, 4))  # -1 means not changing the size of that dimension
+```
+
+## expand_as
+
+expand_as(other) -> Tensor
+
+Expand this tensor to the same size as `other`.
+`self$expand_as(other)` is equivalent to `self$expand(other.size())`.
+
+Please see `$expand` for more information about `expand`.
+
+#### Arguments: 
+
+* other (`$): The result tensor has the same size
+* as `other`.
+
+## expm1
+
+expm1() -> Tensor
+
+See [torch_expm1()]
+
+## expm1_
+
+expm1_() -> Tensor
+
+In-place version of `$expm1`
+
+## exponential_
+
+exponential_(lambd=1, *, generator=NULL) -> Tensor
+
+Fills `self` tensor with elements drawn from the exponential distribution:
+
+$$
+f(x) = \lambda e^{-\lambda x}
+$$
+
+## fft
+
+fft(signal_ndim, normalized=FALSE) -> Tensor
+
+See [torch_fft()]
+
+## fill_
+
+fill_(value) -> Tensor
+
+Fills `self` tensor with the specified value.
+
+## fill_diagonal_
+
+fill_diagonal_(fill_value, wrap=FALSE) -> Tensor
+
+Fill the main diagonal of a tensor that has at least 2-dimensions.
+When dims>2, all dimensions of input must be of equal length.
+This function modifies the input tensor in-place, and returns the input tensor.
+
+#### Arguments:
+    
+* fill_value (Scalar): the fill value
+* wrap (bool): the diagonal 'wrapped' after N columns for tall matrices.
+
+#### Examples: 
+
+```{r}
+a <- torch_zeros(3, 3)
+a$fill_diagonal_(5)
+b <- torch_zeros(7, 3)
+b$fill_diagonal_(5)
+c <- torch_zeros(7, 3)
+c$fill_diagonal_(5, wrap=TRUE)
+```
+    
+## flatten
+
+flatten(input, start_dim=0, end_dim=-1) -> Tensor
+
+see [torch_flatten()]
+
+## flip
+
+flip(dims) -> Tensor
+
+See [torch_flip()]
+
+## fliplr
+
+fliplr() -> Tensor
+
+See [torch_fliplr()]
+
+## flipud
+
+flipud() -> Tensor
+
+See [torch_flipud()]
+
+## float
+
+float(memory_format=torch_preserve_format) -> Tensor
+
+`self$float()` is equivalent to `self$to(torch_float32)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## floor
+
+floor() -> Tensor
+
+See [torch_floor()]
+
+## floor_
+
+floor_() -> Tensor
+
+In-place version of `$floor`
+
+## floor_divide
+
+floor_divide(value) -> Tensor
+
+See [torch_floor_divide()]
+
+## floor_divide_
+
+floor_divide_(value) -> Tensor
+
+In-place version of `$floor_divide`
+
+## fmod
+
+fmod(divisor) -> Tensor
+
+See [torch_fmod()]
+
+## fmod_
+
+fmod_(divisor) -> Tensor
+
+In-place version of `$fmod`
+
+## frac
+
+frac() -> Tensor
+
+See [torch_frac()]
+
+## frac_
+
+frac_() -> Tensor
+
+In-place version of `$frac`
+
+## gather
+
+gather(dim, index) -> Tensor
+
+See [torch_gather()]
+
+## ge
+
+ge(other) -> Tensor
+
+See [torch_ge()]
+
+## ge_
+
+ge_(other) -> Tensor
+
+In-place version of `$ge`
+
+## geometric_
+
+geometric_(p, *, generator=NULL) -> Tensor
+
+Fills `self` tensor with elements drawn from the geometric distribution:
+
+$$
+f(X=k) = p^{k - 1} (1 - p)
+$$
+
+## geqrf
+
+geqrf() -> (Tensor, Tensor)
+
+See [torch_geqrf()]
+
+## ger
+
+ger(vec2) -> Tensor
+
+See [torch_ger()]
+
+## get_device
+
+get_device() -> Device ordinal (Integer)
+
+For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides.
+For CPU tensors, an error is thrown.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+x <- torch_randn(3, 4, 5, device='cuda:0')
+x$get_device()
+x$cpu()$get_device()  # RuntimeError: get_device is not implemented for type torch_FloatTensor
+```
+## grad
+
+This attribute is `NULL` by default and becomes a Tensor the first time a call to
+`backward` computes gradients for `self`.
+The attribute will then contain the gradients computed and future calls to
+[backward()] will accumulate (add) gradients into it.
+        
+## gt
+
+gt(other) -> Tensor
+
+See [torch_gt()]
+
+## gt_
+
+gt_(other) -> Tensor
+
+In-place version of `$gt`
+
+## half
+
+half(memory_format=torch_preserve_format) -> Tensor
+
+`self$half()` is equivalent to `self$to(torch_float16)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## hardshrink
+
+hardshrink(lambd=0.5) -> Tensor
+
+See [torch_nn.functional.hardshrink()]
+
+## has_names
+
+Is `TRUE` if any of this tensor's dimensions are named. Otherwise, is `FALSE`.
+
+## histc
+
+histc(bins=100, min=0, max=0) -> Tensor
+
+See [torch_histc()]
+
+## ifft
+
+ifft(signal_ndim, normalized=FALSE) -> Tensor
+
+See [torch_ifft()]
+
+## imag
+
+Returns a new tensor containing imaginary values of the `self` tensor.
+The returned tensor and `self` share the same underlying storage.
+
+#### Warning:
+
+[imag()] is only supported for tensors with complex dtypes.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+x <- torch_randn(4, dtype=torch_cfloat())
+x
+x$imag
+```
+## index_add
+
+index_add(tensor1, dim, index, tensor2) -> Tensor
+
+Out-of-place version of `$index_add_`.
+`tensor1` corresponds to `self` in `$index_add_`.
+
+## index_add_
+
+index_add_(dim, index, tensor) -> Tensor
+
+Accumulate the elements of `tensor` into the :attr:`self` tensor by adding
+to the indices in the order given in `index`. For example, if `dim == 0`
+and `index[i] == j`, then the `i`\ th row of `tensor` is added to the
+`j`\ th row of `self`.
+
+The `dim`\ th dimension of :attr:`tensor` must have the same size as the
+length of `index` (which must be a vector), and all other dimensions must
+match `self`, or an error will be raised.
+
+#### Note:
+
+In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting `torch_backends.cudnn.deterministic =
+TRUE`.
+
+#### Arguments: 
+
+* dim (int): dimension along which to index
+* index (LongTensor): indices of `tensor` to select from
+* tensor (Tensor): the tensor containing values to add
+
+#### Examples: 
+
+```{r}
+x <- torch_ones(5, 3)
+t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
+index <- torch_tensor(c(1L, 4L, 3L))
+x$index_add_(1, index, t)
+```
+
+## index_copy
+
+index_copy(tensor1, dim, index, tensor2) -> Tensor
+
+Out-of-place version of `$index_copy_`.
+`tensor1` corresponds to `self` in `$index_copy_`.
+
+## index_copy_
+
+index_copy_(dim, index, tensor) -> Tensor
+
+Copies the elements of `tensor` into the :attr:`self` tensor by selecting
+the indices in the order given in `index`. For example, if `dim == 0`
+and `index[i] == j`, then the `i`\ th row of `tensor` is copied to the
+`j`\ th row of `self`.
+
+The `dim`\ th dimension of :attr:`tensor` must have the same size as the
+length of `index` (which must be a vector), and all other dimensions must
+match `self`, or an error will be raised.
+
+#### Arguments: 
+
+* dim (int): dimension along which to index
+* index (LongTensor): indices of `tensor` to select from
+* tensor (Tensor): the tensor containing values to copy
+
+#### Examples: 
+
+```{r}
+x <- torch_zeros(5, 3)
+t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
+index <- torch_tensor(c(1, 5, 3))
+x$index_copy_(1, index, t)
+```
+    
+
+## index_fill
+
+index_fill(tensor1, dim, index, value) -> Tensor
+
+Out-of-place version of `$index_fill_`.
+`tensor1` corresponds to `self` in `$index_fill_`.
+
+## index_fill_
+
+index_fill_(dim, index, val) -> Tensor
+
+Fills the elements of the `self` tensor with value :attr:`val` by
+selecting the indices in the order given in `index`.
+
+#### Arguments: 
+
+* dim (int): dimension along which to index
+* index (LongTensor): indices of `self` tensor to fill in
+* val (float): the value to fill with
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
+index <- torch_tensor(c(1, 3), dtype = torch_long())
+x$index_fill_(1, index, -1)
+```
+## index_put
+
+index_put(tensor1, indices, value, accumulate=FALSE) -> Tensor
+
+Out-place version of `$index_put_`.
+`tensor1` corresponds to `self` in `$index_put_`.
+
+## index_put_
+
+index_put_(indices, value, accumulate=FALSE) -> Tensor
+
+Puts values from the tensor `value` into the tensor :attr:`self` using
+the indices specified in `indices` (which is a tuple of Tensors). The
+expression `tensor.index_put_(indices, value)` is equivalent to
+`tensor[indices] = value`. Returns `self`.
+
+If `accumulate` is `TRUE`, the elements in :attr:`value` are added to
+`self`. If accumulate is `FALSE`, the behavior is undefined if indices
+contain duplicate elements.
+
+#### Arguments: 
+
+* indices (tuple of LongTensor): tensors used to index into `self`.
+* value (Tensor): tensor of same dtype as `self`.
+* accumulate (bool): whether to accumulate into self
+
+## index_select
+
+index_select(dim, index) -> Tensor
+
+See [torch_index_select()]
+
+## indices
+
+indices() -> Tensor
+
+If `self` is a sparse COO tensor (i.e., with `torch_sparse_coo` layout),
+this returns a view of the contained indices tensor. Otherwise, this throws an
+error.
+
+See also `Tensor.values`.
+
+#### Note: 
+
+
+This method can only be called on a coalesced sparse tensor. See
+`Tensor.coalesce` for details.
+
+## int
+
+int(memory_format=torch_preserve_format) -> Tensor
+
+`self$int()` is equivalent to `self$to(torch_int32)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## int_repr
+
+int_repr() -> Tensor
+
+Given a quantized Tensor,
+`self$int_repr()` returns a CPU Tensor with uint8_t as data type that stores the
+underlying uint8_t values of the given Tensor.
+
+## inverse
+
+inverse() -> Tensor
+
+See [torch_inverse()]
+
+## irfft
+
+irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -> Tensor
+
+See [torch_irfft()]
+
+## is_complex
+
+is_complex() -> bool
+
+Returns TRUE if the data type of `self` is a complex data type.
+
+## is_contiguous
+
+is_contiguous(memory_format=torch_contiguous_format) -> bool
+
+Returns TRUE if `self` tensor is contiguous in memory in the order specified
+by memory format.
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): Specifies memory allocation
+* order. Default: `torch_contiguous_format`.
+
+## is_cuda
+
+Is `TRUE` if the Tensor is stored on the GPU, `FALSE` otherwise.
+
+## is_floating_point
+
+is_floating_point() -> bool
+
+Returns TRUE if the data type of `self` is a floating point data type.
+
+## is_leaf
+
+All Tensors that have `requires_grad` which is `FALSE` will be leaf Tensors by convention.
+
+For Tensors that have `requires_grad` which is `TRUE`, they will be leaf Tensors if they were
+created by the user. This means that they are not the result of an operation and so
+`grad_fn` is NULL.
+
+Only leaf Tensors will have their `grad` populated during a call to [backward()].
+To get `grad` populated for non-leaf Tensors, you can use [retain_grad()].
+
+#### Examples: 
+
+```{r}
+a <- torch_rand(10, requires_grad=TRUE)
+a$is_leaf()
+
+# b <- torch_rand(10, requires_grad=TRUE)$cuda()
+# b$is_leaf()
+# FALSE
+# b was created by the operation that cast a cpu Tensor into a cuda Tensor
+
+c <- torch_rand(10, requires_grad=TRUE) + 2
+c$is_leaf()
+# c was created by the addition operation
+
+# d <- torch_rand(10)$cuda()
+# d$is_leaf()
+# TRUE
+# d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)
+
+# e <- torch_rand(10)$cuda()$requires_grad_()
+# e$is_leaf()
+# TRUE
+# e requires gradients and has no operations creating it
+
+# f <- torch_rand(10, requires_grad=TRUE, device="cuda")
+# f$is_leaf
+# TRUE
+# f requires grad, has no operation creating it
+```
+
+## is_meta
+
+Is `TRUE` if the Tensor is a meta tensor, `FALSE` otherwise.  Meta tensors
+are like normal tensors, but they carry no data.
+
+## is_pinned
+
+Returns true if this tensor resides in pinned memory.
+
+## is_quantized
+
+Is `TRUE` if the Tensor is quantized, `FALSE` otherwise.
+
+## is_set_to
+
+is_set_to(tensor) -> bool
+
+Returns TRUE if this object refers to the same `THTensor` object from the
+Torch C API as the given tensor.
+
+## is_shared
+
+Checks if tensor is in shared memory.
+
+This is always `TRUE` for CUDA tensors.
+        
+## is_signed
+
+is_signed() -> bool
+
+Returns TRUE if the data type of `self` is a signed data type.
+
+## isclose
+
+isclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor
+
+See [torch_isclose()]
+
+## isfinite
+
+isfinite() -> Tensor
+
+See [torch_isfinite()]
+
+## isinf
+
+isinf() -> Tensor
+
+See [torch_isinf()]
+
+## isnan
+
+isnan() -> Tensor
+
+See [torch_isnan()]
+
+## istft
+See [torch_istft()]
+## item
+
+item() -> number
+
+Returns the value of this tensor as a standard Python number. This only works
+for tensors with one element. For other cases, see `$tolist`.
+
+This operation is not differentiable.
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(1.0)
+x$item()
+```
+    
+## kthvalue
+
+kthvalue(k, dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
+
+See [torch_kthvalue()]
+
+## le
+
+le(other) -> Tensor
+
+See [torch_le()]
+
+## le_
+
+le_(other) -> Tensor
+
+In-place version of `$le`
+
+## lerp
+
+lerp(end, weight) -> Tensor
+
+See [torch_lerp()]
+
+## lerp_
+
+lerp_(end, weight) -> Tensor
+
+In-place version of `$lerp`
+
+## lgamma
+
+lgamma() -> Tensor
+
+See [torch_lgamma()]
+
+## lgamma_
+
+lgamma_() -> Tensor
+
+In-place version of `$lgamma`
+
+## log
+
+log() -> Tensor
+
+See [torch_log()]
+
+## log10
+
+log10() -> Tensor
+
+See [torch_log10()]
+
+## log10_
+
+log10_() -> Tensor
+
+In-place version of `$log10`
+
+## log1p
+
+log1p() -> Tensor
+
+See [torch_log1p()]
+
+## log1p_
+
+log1p_() -> Tensor
+
+In-place version of `$log1p`
+
+## log2
+
+log2() -> Tensor
+
+See [torch_log2()]
+
+## log2_
+
+log2_() -> Tensor
+
+In-place version of `$log2`
+
+## log_
+
+log_() -> Tensor
+
+In-place version of `$log`
+
+## log_normal_
+
+log_normal_(mean=1, std=2, *, generator=NULL)
+
+Fills `self` tensor with numbers samples from the log-normal distribution
+parameterized by the given mean `\mu` and standard deviation
+`\sigma`. Note that :attr:`mean` and :attr:`std` are the mean and
+standard deviation of the underlying normal distribution, and not of the
+returned distribution:
+
+$$
+f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
+$$
+
+## logaddexp
+
+logaddexp(other) -> Tensor
+
+See [torch_logaddexp()]
+
+## logaddexp2
+
+logaddexp2(other) -> Tensor
+
+See [torch_logaddexp2()]
+
+## logcumsumexp
+
+logcumsumexp(dim) -> Tensor
+
+See [torch_logcumsumexp()]
+
+## logdet
+
+logdet() -> Tensor
+
+See [torch_logdet()]
+
+## logical_and
+
+logical_and() -> Tensor
+
+See [torch_logical_and()]
+
+## logical_and_
+
+logical_and_() -> Tensor
+
+In-place version of `$logical_and`
+
+## logical_not
+
+logical_not() -> Tensor
+
+See [torch_logical_not()]
+
+## logical_not_
+
+logical_not_() -> Tensor
+
+In-place version of `$logical_not`
+
+## logical_or
+
+logical_or() -> Tensor
+
+See [torch_logical_or()]
+
+## logical_or_
+
+logical_or_() -> Tensor
+
+In-place version of `$logical_or`
+
+## logical_xor
+
+logical_xor() -> Tensor
+
+See [torch_logical_xor()]
+
+## logical_xor_
+
+logical_xor_() -> Tensor
+
+In-place version of `$logical_xor`
+
+## logsumexp
+
+logsumexp(dim, keepdim=FALSE) -> Tensor
+
+See [torch_logsumexp()]
+
+## long
+
+long(memory_format=torch_preserve_format) -> Tensor
+
+`self$long()` is equivalent to `self$to(torch_int64)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## lstsq
+
+lstsq(A) -> (Tensor, Tensor)
+
+See [torch_lstsq()]
+
+## lt
+
+lt(other) -> Tensor
+
+See [torch_lt()]
+
+## lt_
+
+lt_(other) -> Tensor
+
+In-place version of `$lt`
+
+## lu
+See [torch_lu()]
+## lu_solve
+
+lu_solve(LU_data, LU_pivots) -> Tensor
+
+See [torch_lu_solve()]
+
+## map_
+
+map_(tensor, callable)
+
+Applies `callable` for each element in `self` tensor and the given
+`tensor` and stores the results in `self` tensor. `self` tensor and
+the given `tensor` must be broadcastable.
+
+The `callable` should have the signature:
+
+`callable(a, b) -> number`
+
+## masked_fill
+
+masked_fill(mask, value) -> Tensor
+
+Out-of-place version of `$masked_fill_`
+
+## masked_fill_
+
+masked_fill_(mask, value)
+
+Fills elements of `self` tensor with :attr:`value` where :attr:`mask` is
+TRUE. The shape of `mask` must be
+`broadcastable <broadcasting-semantics>` with the shape of the underlying
+tensor.
+
+#### Arguments: 
+
+* mask (BoolTensor): the boolean mask
+* value (float): the value to fill in with
+
+## masked_scatter
+
+masked_scatter(mask, tensor) -> Tensor
+
+Out-of-place version of `$masked_scatter_`
+
+## masked_scatter_
+
+masked_scatter_(mask, source)
+
+Copies elements from `source` into :attr:`self` tensor at positions where
+the `mask` is TRUE.
+The shape of `mask` must be :ref:`broadcastable <broadcasting-semantics>`
+with the shape of the underlying tensor. The `source` should have at least
+as many elements as the number of ones in `mask`
+
+#### Arguments: 
+
+* mask (BoolTensor): the boolean mask
+* source (Tensor): the tensor to copy from
+
+#### Note: 
+
+The `mask` operates on the :attr:`self` tensor, not on the given
+`source` tensor.
+
+## masked_select
+
+masked_select(mask) -> Tensor
+
+See [torch_masked_select()]
+
+## matmul
+
+matmul(tensor2) -> Tensor
+
+See [torch_matmul()]
+
+## matrix_power
+
+matrix_power(n) -> Tensor
+
+See [torch_matrix_power()]
+
+## max
+
+max(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
+
+See [torch_max()]
+
+## mean
+
+mean(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
+
+See [torch_mean()]
+
+## median
+
+median(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
+
+See [torch_median()]
+
+## min
+
+min(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
+
+See [torch_min()]
+
+## mm
+
+mm(mat2) -> Tensor
+
+See [torch_mm()]
+
+## mode
+
+mode(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
+
+See [torch_mode()]
+
+## mul
+
+mul(value) -> Tensor
+
+See [torch_mul()]
+
+## mul_
+
+mul_(value)
+
+In-place version of `$mul`
+
+## multinomial
+
+multinomial(num_samples, replacement=FALSE, *, generator=NULL) -> Tensor
+
+See [torch_multinomial()]
+
+## mv
+
+mv(vec) -> Tensor
+
+See [torch_mv()]
+
+## mvlgamma
+
+mvlgamma(p) -> Tensor
+
+See [torch_mvlgamma()]
+
+## mvlgamma_
+
+mvlgamma_(p) -> Tensor
+
+In-place version of `$mvlgamma`
+
+## names
+
+Stores names for each of this tensor's dimensions.
+
+`names[idx]` corresponds to the name of tensor dimension `idx`.
+Names are either a string if the dimension is named or `NULL` if the
+dimension is unnamed.
+
+Dimension names may contain characters or underscore. Furthermore, a dimension
+name must be a valid Python variable name (i.e., does not start with underscore).
+
+Tensors may not have two named dimensions with the same name.
+
+#### Warning:
+
+The named tensor API is experimental and subject to change.
+
+
+## narrow
+
+narrow(dimension, start, length) -> Tensor
+
+See [torch_narrow()]
+
+#### Examples: 
+
+```{r, eval = FALSE}
+x <- torch_tensor(matrix(1:9, ncol = 3))
+x$narrow(1, 1, 3)
+x$narrow(1, 1, 2)
+```
+
+## narrow_copy
+
+narrow_copy(dimension, start, length) -> Tensor
+
+Same as `Tensor.narrow` except returning a copy rather
+than shared storage.  This is primarily for sparse tensors, which
+do not have a shared-storage narrow method.  Calling ``narrow_copy`
+with ``dimemsion > self$sparse_dim()`` will return a copy with the
+relevant dense dimension narrowed, and ``self$shape`` updated accordingly.
+
+## ndim
+
+Alias for `$dim()`
+
+## ndimension
+
+ndimension() -> int
+
+Alias for `$dim()`
+
+## ne
+
+ne(other) -> Tensor
+
+See [torch_ne()]
+
+## ne_
+
+ne_(other) -> Tensor
+
+In-place version of `$ne`
+
+## neg
+
+neg() -> Tensor
+
+See [torch_neg()]
+
+## neg_
+
+neg_() -> Tensor
+
+In-place version of `$neg`
+
+## nelement
+
+nelement() -> int
+
+Alias for `$numel`
+
+## new_empty
+
+new_empty(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
+
+Returns a Tensor of size `size` filled with uninitialized data.
+By default, the returned Tensor has the same `torch_dtype` and
+`torch_device` as this tensor.
+
+#### Arguments: 
+
+* dtype (`torch_dtype`, optional): the desired type of returned tensor.
+        Default: if NULL, same `torch_dtype` as this tensor.
+* device (`torch_device`, optional): the desired device of returned tensor.
+        Default: if NULL, same `torch_device` as this tensor.
+* requires_grad (bool, optional): If autograd should record operations on the
+* returned tensor. Default: `FALSE`.
+
+#### Examples: 
+
+```{r}
+tensor <- torch_ones(5)
+tensor$new_empty(c(2, 3))
+```
+
+## new_full
+
+new_full(size, fill_value, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
+
+Returns a Tensor of size `size` filled with :attr:`fill_value`.
+By default, the returned Tensor has the same `torch_dtype` and
+`torch_device` as this tensor.
+
+#### Arguments: 
+
+* fill_value (scalar): the number to fill the output tensor with.
+* dtype (`torch_dtype`, optional): the desired type of returned tensor.
+        Default: if NULL, same `torch_dtype` as this tensor.
+* device (`torch_device`, optional): the desired device of returned tensor.
+        Default: if NULL, same `torch_device` as this tensor.
+* requires_grad (bool, optional): If autograd should record operations on the
+* returned tensor. Default: `FALSE`.
+
+#### Examples: 
+
+```{r}
+tensor <- torch_ones(c(2), dtype=torch_float64())
+tensor$new_full(c(3, 4), 3.141592)
+```
+## new_ones
+
+new_ones(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
+
+Returns a Tensor of size `size` filled with `1`.
+By default, the returned Tensor has the same `torch_dtype` and
+`torch_device` as this tensor.
+
+#### Arguments: 
+
+* size (int...): a list, tuple, or `torch_Size` of integers defining the
+* shape of the output tensor.
+* dtype (`torch_dtype`, optional): the desired type of returned tensor.
+        Default: if NULL, same `torch_dtype` as this tensor.
+* device (`torch_device`, optional): the desired device of returned tensor.
+        Default: if NULL, same `torch_device` as this tensor.
+* requires_grad (bool, optional): If autograd should record operations on the
+* returned tensor. Default: `FALSE`.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+tensor <- torch_tensor(c(2), dtype=torch_int32())
+tensor$new_ones(c(2, 3))
+```
+
+## new_tensor
+
+new_tensor(data, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
+
+Returns a new Tensor with `data` as the tensor data.
+By default, the returned Tensor has the same `torch_dtype` and
+`torch_device` as this tensor.
+
+#### Warning: 
+
+`new_tensor` always copies `data(). If you have a Tensor
+`data` and want to avoid a copy, use [$requires_grad_()]
+or [$detach()].
+If you have a numpy array and want to avoid a copy, use
+[torch_from_numpy()].
+
+
+When data is a tensor `x`, [new_tensor()()] reads out 'the data' from whatever it is passed,
+and constructs a leaf variable. Therefore `tensor$new_tensor(x)` is equivalent to `x$clone()$detach()`
+and `tensor$new_tensor(x, requires_grad=TRUE)` is equivalent to `x$clone()$detach()$requires_grad_(TRUE)`.
+The equivalents using `clone()` and `detach()` are recommended.
+
+#### Arguments: 
+
+* data (array_like): The returned Tensor copies `data`.
+* dtype (`torch_dtype`, optional): the desired type of returned tensor.
+        Default: if NULL, same `torch_dtype` as this tensor.
+* device (`torch_device`, optional): the desired device of returned tensor.
+        Default: if NULL, same `torch_device` as this tensor.
+* requires_grad (bool, optional): If autograd should record operations on the
+* returned tensor. Default: `FALSE`.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+tensor <- torch_ones(c(2), dtype=torch_int8)
+data <- matrix(1:4, ncol = 2)
+tensor$new_tensor(data)
+```
+## new_zeros
+
+new_zeros(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
+
+Returns a Tensor of size `size` filled with `0`.
+By default, the returned Tensor has the same `torch_dtype` and
+`torch_device` as this tensor.
+
+#### Arguments: 
+
+* size (int...): a list, tuple, or `torch_Size` of integers defining the
+* shape of the output tensor.
+* dtype (`torch_dtype`, optional): the desired type of returned tensor.
+        Default: if NULL, same `torch_dtype` as this tensor.
+* device (`torch_device`, optional): the desired device of returned tensor.
+        Default: if NULL, same `torch_device` as this tensor.
+* requires_grad (bool, optional): If autograd should record operations on the
+* returned tensor. Default: `FALSE`.
+
+#### Examples: 
+
+```{r}
+tensor <- torch_tensor(c(1), dtype=torch_float64())
+tensor$new_zeros(c(2, 3))
+```
+
+## nonzero
+
+nonzero() -> LongTensor
+
+See [torch_nonzero()]
+
+## norm
+See [torch_norm()]
+## normal_
+
+normal_(mean=0, std=1, *, generator=NULL) -> Tensor
+
+Fills `self` tensor with elements samples from the normal distribution
+parameterized by `mean` and :attr:`std`.
+
+## numel
+
+numel() -> int
+
+See [torch_numel()]
+
+## numpy
+
+numpy() -> numpy.ndarray
+
+Returns `self` tensor as a NumPy :class:`ndarray`. This tensor and the
+returned `ndarray` share the same underlying storage. Changes to
+`self` tensor will be reflected in the :class:`ndarray` and vice versa.
+
+## orgqr
+
+orgqr(input2) -> Tensor
+
+See [torch_orgqr()]
+
+## ormqr
+
+ormqr(input2, input3, left=TRUE, transpose=FALSE) -> Tensor
+
+See [torch_ormqr()]
+
+## permute
+
+permute(*dims) -> Tensor
+
+Returns a view of the original tensor with its dimensions permuted.
+
+#### Arguments: 
+
+* dims (int...): The desired ordering of dimensions
+
+#### Examples:
+
+```{r}
+x <- torch_randn(2, 3, 5)
+x$size()
+x$permute(c(3, 1, 2))$size()
+```
+
+## pin_memory
+
+pin_memory() -> Tensor
+
+Copies the tensor to pinned memory, if it's not already pinned.
+
+## pinverse
+
+pinverse() -> Tensor
+
+See [torch_pinverse()]
+
+## polygamma
+
+polygamma(n) -> Tensor
+
+See [torch_polygamma()]
+
+## polygamma_
+
+polygamma_(n) -> Tensor
+
+In-place version of `$polygamma`
+
+## pow
+
+pow(exponent) -> Tensor
+
+See [torch_pow()]
+
+## pow_
+
+pow_(exponent) -> Tensor
+
+In-place version of `$pow`
+
+## prod
+
+prod(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor
+
+See [torch_prod()]
+
+## put_
+
+put_(indices, tensor, accumulate=FALSE) -> Tensor
+
+Copies the elements from `tensor` into the positions specified by
+indices. For the purpose of indexing, the `self` tensor is treated as if
+it were a 1-D tensor.
+
+If `accumulate` is `TRUE`, the elements in :attr:`tensor` are added to
+`self`. If accumulate is `FALSE`, the behavior is undefined if indices
+contain duplicate elements.
+
+#### Arguments: 
+
+* indices (LongTensor): the indices into self
+* tensor (Tensor): the tensor containing values to copy from
+* accumulate (bool): whether to accumulate into self
+
+#### Examples: 
+
+```{r}
+src <- torch_tensor(matrix(3:8, ncol = 3))
+src$put_(torch_tensor(1:2), torch_tensor(9:10))
+```
+   
+## q_per_channel_axis
+
+q_per_channel_axis() -> int
+
+Given a Tensor quantized by linear (affine) per-channel quantization,
+returns the index of dimension on which per-channel quantization is applied.
+
+## q_per_channel_scales
+
+q_per_channel_scales() -> Tensor
+
+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.
+
+## q_per_channel_zero_points
+
+q_per_channel_zero_points() -> Tensor
+
+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.
+
+## q_scale
+
+q_scale() -> float
+
+Given a Tensor quantized by linear(affine) quantization,
+returns the scale of the underlying quantizer().
+
+## q_zero_point
+
+q_zero_point() -> int
+
+Given a Tensor quantized by linear(affine) quantization,
+returns the zero_point of the underlying quantizer().
+
+## qr
+
+qr(some=TRUE) -> (Tensor, Tensor)
+
+See [torch_qr()]
+
+## qscheme
+
+qscheme() -> torch_qscheme
+
+Returns the quantization scheme of a given QTensor.
+
+## rad2deg
+
+rad2deg() -> Tensor
+
+See [torch_rad2deg()]
+
+## rad2deg_
+
+rad2deg_() -> Tensor
+
+In-place version of `$rad2deg`
+
+## random_
+
+random_(from=0, to=NULL, *, generator=NULL) -> Tensor
+
+Fills `self` tensor with numbers sampled from the discrete uniform
+distribution over `[from, to - 1]`. If not specified, the values are usually
+only bounded by `self` tensor's data type. However, for floating point
+types, if unspecified, range will be `[0, 2^mantissa]` to ensure that every
+value is representable. For example, `torch_tensor(1, dtype=torch_double).random_()`
+will be uniform in `[0, 2^53]`.
+
+## real
+
+Returns a new tensor containing real values of the `self` tensor.
+The returned tensor and `self` share the same underlying storage.
+
+#### Warning:
+
+[real()] is only supported for tensors with complex dtypes.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+x <- torch_randn(4, dtype=torch_cfloat())
+x
+x$real
+```
+
+## reciprocal
+
+reciprocal() -> Tensor
+
+See [torch_reciprocal()]
+
+## reciprocal_
+
+reciprocal_() -> Tensor
+
+In-place version of `$reciprocal`
+
+## record_stream
+
+record_stream(stream)
+
+Ensures that the tensor memory is not reused for another tensor until all
+current work queued on `stream` are complete.
+
+#### Note: 
+
+The caching allocator is aware of only the stream where a tensor was
+allocated. Due to the awareness, it already correctly manages the life
+cycle of tensors on only one stream. But if a tensor is used on a stream
+different from the stream of origin, the allocator might reuse the memory
+unexpectedly. Calling this method lets the allocator know which streams
+have used the tensor.
+
+
+## refine_names
+
+Refines the dimension names of `self` according to :attr:`names`.
+
+Refining is a special case of renaming that "lifts" unnamed dimensions.
+A `NULL` dim can be refined to have any name; a named dim can only be
+refined to have the same name.
+
+Because named tensors can coexist with unnamed tensors, refining names
+gives a nice way to write named-tensor-aware code that works with both
+named and unnamed tensors.
+
+`names` may contain up to one Ellipsis (`...`).
+The Ellipsis is expanded greedily; it is expanded in-place to fill
+`names` to the same length as `self$dim()` using names from the
+corresponding indices of `self$names`.
+
+#### Arguments:
+* names (iterable of str): The desired names of the output tensor. May
+  contain up to one Ellipsis.
+
+#### Examples:
+
+```{r, eval = FALSE}
+imgs <- torch_randn(32, 3, 128, 128)
+named_imgs <- imgs$refine_names(c('N', 'C', 'H', 'W'))
+named_imgs$names
+```
+        
+## register_hook
+Registers a backward hook.
+
+The hook will be called every time a gradient with respect to the
+Tensor is computed. The hook should have the following signature::
+
+hook(grad) -> Tensor or NULL
+
+The hook should not modify its argument, but it can optionally return
+a new gradient which will be used in place of `grad`.
+
+This function returns a handle with a method `handle$remove()`
+that removes the hook from the module.
+
+#### Example
+
+```{r}
+v <- torch_tensor(c(0., 0., 0.), requires_grad=TRUE)
+h <- v$register_hook(function(grad) grad * 2)  # double the gradient
+v$backward(torch_tensor(c(1., 2., 3.)))
+v$grad
+h$remove()
+```
+        
+## remainder
+
+remainder(divisor) -> Tensor
+
+See [torch_remainder()]
+
+## remainder_
+
+remainder_(divisor) -> Tensor
+
+In-place version of `$remainder`
+
+## rename
+
+Renames dimension names of `self`.
+
+There are two main usages:
+
+`self$rename(**rename_map)` returns a view on tensor that has dims
+renamed as specified in the mapping `rename_map`.
+
+`self$rename(*names)` returns a view on tensor, renaming all
+dimensions positionally using `names`.
+Use `self$rename(NULL)` to drop names on a tensor.
+
+One cannot specify both positional args `names` and keyword args
+`rename_map`.
+
+#### Examples:
+
+```{r}
+imgs <- torch_rand(2, 3, 5, 7, names=c('N', 'C', 'H', 'W'))
+renamed_imgs <- imgs$rename(c("Batch", "Channels", "Height", "Width"))
+```
+        
+## rename_
+
+In-place version of `$rename`.
+
+## renorm
+
+renorm(p, dim, maxnorm) -> Tensor
+
+See [torch_renorm()]
+
+## renorm_
+
+renorm_(p, dim, maxnorm) -> Tensor
+
+In-place version of `$renorm`
+
+## repeat
+
+repeat(*sizes) -> Tensor
+
+Repeats this tensor along the specified dimensions.
+
+Unlike `$expand`, this function copies the tensor's data.
+
+#### Arguments: 
+
+* sizes (torch_Size or int...): The number of times to repeat this tensor along each
+* dimension
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(c(1, 2, 3))
+x$`repeat`(c(4, 2))
+x$`repeat`(c(4, 2, 1))$size()
+```
+
+## repeat_interleave
+
+repeat_interleave(repeats, dim=NULL) -> Tensor
+
+See [torch_repeat_interleave()].
+
+## requires_grad
+
+Is `TRUE` if gradients need to be computed for this Tensor, `FALSE` otherwise.
+
+#### Note: 
+
+The fact that gradients need to be computed for a Tensor do not mean that the `grad`
+attribute will be populated, see `is_leaf` for more details.
+
+## requires_grad_
+
+requires_grad_(requires_grad=TRUE) -> Tensor
+
+Change if autograd should record operations on this tensor: sets this tensor's
+`requires_grad` attribute in-place. Returns this tensor.
+
+[requires_grad_()]'s main use case is to tell autograd to begin recording
+operations on a Tensor `tensor`. If `tensor` has `requires_grad=FALSE`
+(because it was obtained through a DataLoader, or required preprocessing or
+initialization), `tensor.requires_grad_()` makes it so that autograd will
+begin to record operations on `tensor`.
+
+#### Arguments: 
+
+* requires_grad (bool): If autograd should record operations on this tensor.
+        Default: `TRUE`.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+# Let's say we want to preprocess some saved weights and use
+# the result as new weights.
+saved_weights <- c(0.1, 0.2, 0.3, 0.25)
+loaded_weights <- torch_tensor(saved_weights)
+weights <- preprocess(loaded_weights)  # some function
+weights
+
+# Now, start to record operations done to weights
+weights$requires_grad_()
+out <- weights$pow(2)$sum()
+out$backward()
+weights$grad
+```
+    
+## reshape
+
+reshape(*shape) -> Tensor
+
+Returns a tensor with the same data and number of elements as `self`
+but with the specified shape. This method returns a view if `shape` is
+compatible with the current shape. See `$view` on when it is
+possible to return a view.
+
+See [torch_reshape()]
+
+#### Arguments: 
+
+* shape (tuple of ints or int...): the desired shape
+ 
+## reshape_as
+
+reshape_as(other) -> Tensor
+
+Returns this tensor as the same shape as `other`.
+`self$reshape_as(other)` is equivalent to `self$reshape(other.sizes())`.
+This method returns a view if `other.sizes()` is compatible with the current
+shape. See `$view` on when it is possible to return a view.
+
+Please see `reshape` for more information about `reshape`.
+
+#### Arguments: 
+
+* other (`$): The result tensor has the same shape
+* as `other`.
+
+## resize_
+
+resize_(*sizes, memory_format=torch_contiguous_format) -> Tensor
+
+Resizes `self` tensor to the specified size. If the number of elements is
+larger than the current storage size, then the underlying storage is resized
+to fit the new number of elements. If the number of elements is smaller, the
+underlying storage is not changed. Existing elements are preserved but any new
+memory is uninitialized.
+
+#### Warning:
+
+This is a low-level method. The storage is reinterpreted as C-contiguous,
+ignoring the current strides (unless the target size equals the current
+size, in which case the tensor is left unchanged). For most purposes, you
+will instead want to use `$view()`, which checks for
+contiguity, or `$reshape()`, which copies data if needed. To
+change the size in-place with custom strides, see `$set_()`.
+
+#### Arguments: 
+
+* sizes (torch_Size or int...): the desired size
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+  Tensor. Default: `torch_contiguous_format`. Note that memory format of
+  `self` is going to be unaffected if `self$size()` matches `sizes`.
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(matrix(1:6, ncol = 2))
+x$resize_(2, 2)
+```
+    
+## resize_as_
+
+resize_as_(tensor, memory_format=torch_contiguous_format) -> Tensor
+
+Resizes the `self` tensor to be the same size as the specified
+`tensor`. This is equivalent to `self$resize_(tensor.size())`.
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+    Tensor. Default: `torch_contiguous_format`. Note that memory format of
+    `self` is going to be unaffected if `self$size()` matches `tensor.size()`.
+
+
+## retain_grad
+
+Enables `$grad` attribute for non-leaf Tensors.
+
+## rfft
+
+rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -> Tensor
+
+See [torch_rfft()]
+
+## roll
+
+roll(shifts, dims) -> Tensor
+
+See [torch_roll()]
+
+## rot90
+
+rot90(k, dims) -> Tensor
+
+See [torch_rot90()]
+
+## round
+
+round() -> Tensor
+
+See [torch_round()]
+
+## round_
+
+round_() -> Tensor
+
+In-place version of `$round`
+
+## rsqrt
+
+rsqrt() -> Tensor
+
+See [torch_rsqrt()]
+
+## rsqrt_
+
+rsqrt_() -> Tensor
+
+In-place version of `$rsqrt`
+
+## scatter
+
+scatter(dim, index, src) -> Tensor
+
+Out-of-place version of `$scatter_`
+
+## scatter_
+
+scatter_(dim, index, src) -> Tensor
+
+Writes all values from the tensor `src` into :attr:`self` at the indices
+specified in the `index` tensor. For each value in :attr:`src`, its output
+index is specified by its index in `src` for `dimension != dim` and by
+the corresponding value in `index` for `dimension = dim`.
+
+For a 3-D tensor, `self` is updated as:
+
+```
+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
+```
+
+This is the reverse operation of the manner described in `$gather`.
+
+`self`, `index` and `src` (if it is a Tensor) should have same
+number of dimensions. It is also required that `index.size(d) <= src.size(d)`
+for all dimensions `d`, and that `index.size(d) <= self$size(d)` for all
+dimensions `d != dim`.
+
+Moreover, as for `$gather`, the values of `index` must be
+between `0` and `self$size(dim) - 1` inclusive, and all values in a row
+along the specified dimension `dim` must be unique.
+
+#### Arguments: 
+
+* dim (int): the axis along which to index
+* index (LongTensor): the indices of elements to scatter,
+* can be either empty or the same size of src.
+      When empty, the operation returns identity
+* src (Tensor): the source element(s) to scatter,
+* incase `value` is not specified
+* value (float): the source element(s) to scatter,
+* incase `src` is not specified
+
+#### Examples: 
+
+```{r}
+x <- torch_rand(2, 5)
+x
+torch_zeros(3, 5)$scatter_(
+        1, 
+        torch_tensor(rbind(c(2, 3, 3, 1, 1), c(3, 1, 1, 2, 3)), x)
+)
+
+z <- torch_zeros(2, 4)$scatter_(
+        2, 
+        torch_tensor(matrix(3:4, ncol = 1)), 1.23
+)
+```
+    
+
+## scatter_add
+
+scatter_add(dim, index, src) -> Tensor
+
+Out-of-place version of `$scatter_add_`
+
+## scatter_add_
+
+scatter_add_(dim, index, src) -> Tensor
+
+Adds all values from the tensor `other` into :attr:`self` at the indices
+specified in the `index` tensor in a similar fashion as
+`~$scatter_`. For each value in :attr:`src`, it is added to
+an index in `self` which is specified by its index in :attr:`src`
+for `dimension != dim` and by the corresponding value in `index` for
+`dimension = dim`.
+
+For a 3-D tensor, `self` is updated as::
+
+```
+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
+```
+
+`self`, :attr:`index` and :attr:`src` should have same number of
+dimensions. It is also required that `index.size(d) <= src.size(d)` for all
+dimensions `d`, and that `index.size(d) <= self$size(d)` for all dimensions
+`d != dim`.
+
+#### Note:
+    
+In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting `torch_backends.cudnn.deterministic = TRUE`.
+    
+#### Arguments: 
+
+* dim (int): the axis along which to index
+* index (LongTensor): the indices of elements to scatter and add,
+* can be either empty or the same size of src.
+      When empty, the operation returns identity.
+* src (Tensor): the source elements to scatter and add
+
+#### Examples: 
+
+```{r}
+x <- torch_rand(2, 5)
+x
+torch_ones(3, 5)$scatter_add_(1, torch_tensor(rbind(c(0, 1, 2, 0, 0), c(2, 0, 0, 1, 2))), x)
+```
+    
+## select
+
+select(dim, index) -> Tensor
+
+Slices the `self` tensor along the selected dimension at the given index.
+This function returns a view of the original tensor with the given dimension removed.
+
+#### Arguments: 
+
+* dim (int): the dimension to slice
+* index (int): the index to select with
+
+#### Note: 
+
+`select` is equivalent to slicing. For example,
+`tensor$select(0, index)` is equivalent to `tensor[index]` and
+`tensor$select(2, index)` is equivalent to `tensor[:,:,index]`.
+
+## set_
+
+set_(source=NULL, storage_offset=0, size=NULL, stride=NULL) -> Tensor
+
+Sets the underlying storage, size, and strides. If `source` is a tensor,
+`self` tensor will share the same storage and have the same size and
+strides as `source`. Changes to elements in one tensor will be reflected
+in the other.
+
+#### Arguments: 
+
+* source (Tensor or Storage): the tensor or storage to use
+* storage_offset (int, optional): the offset in the storage
+* size (torch_Size, optional): the desired size. Defaults to the size of the source.
+* stride (tuple, optional): the desired stride. Defaults to C-contiguous strides.
+
+## share_memory_
+
+Moves the underlying storage to shared memory.
+
+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.
+        
+## short
+
+short(memory_format=torch_preserve_format) -> Tensor
+
+`self$short()` is equivalent to `self$to(torch_int16)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## sigmoid
+
+sigmoid() -> Tensor
+
+See [torch_sigmoid()]
+
+## sigmoid_
+
+sigmoid_() -> Tensor
+
+In-place version of `$sigmoid`
+
+## sign
+
+sign() -> Tensor
+
+See [torch_sign()]
+
+## sign_
+
+sign_() -> Tensor
+
+In-place version of `$sign`
+
+## sin
+
+sin() -> Tensor
+
+See [torch_sin()]
+
+## sin_
+
+sin_() -> Tensor
+
+In-place version of `$sin`
+
+## sinh
+
+sinh() -> Tensor
+
+See [torch_sinh()]
+
+## sinh_
+
+sinh_() -> Tensor
+
+In-place version of `$sinh`
+
+## size
+
+size() -> torch_Size
+
+Returns the size of the `self` tensor. The returned value is a subclass of
+`tuple`.
+
+#### Examples: 
+
+```{r}
+torch_empty(3, 4, 5)$size()
+```
+    
+## slogdet
+
+slogdet() -> (Tensor, Tensor)
+
+See [torch_slogdet()]
+
+## solve
+
+solve(A) -> Tensor, Tensor
+
+See [torch_solve()]
+
+## sort
+
+sort(dim=-1, descending=FALSE) -> (Tensor, LongTensor)
+
+See [torch_sort()]
+
+## sparse_dim
+
+sparse_dim() -> int
+
+If `self` is a sparse COO tensor (i.e., with `torch_sparse_coo` layout),
+this returns the number of sparse dimensions. Otherwise, this throws an error.
+
+See also `Tensor.dense_dim`.
+
+## sparse_mask
+
+sparse_mask(input, mask) -> Tensor
+
+Returns a new SparseTensor with values from Tensor `input` filtered
+by indices of `mask` and values are ignored. :attr:`input` and :attr:`mask`
+must have the same shape.
+
+#### Arguments: 
+
+* input (Tensor): an input Tensor
+* mask (SparseTensor): a SparseTensor which we filter `input` based on its indices
+
+## split
+See [torch_split()]
+        
+## sqrt
+
+sqrt() -> Tensor
+
+See [torch_sqrt()]
+
+## sqrt_
+
+sqrt_() -> Tensor
+
+In-place version of `$sqrt`
+
+## square
+
+square() -> Tensor
+
+See [torch_square()]
+
+## square_
+
+square_() -> Tensor
+
+In-place version of `$square`
+
+## squeeze
+
+squeeze(dim=NULL) -> Tensor
+
+See [torch_squeeze()]
+
+## squeeze_
+
+squeeze_(dim=NULL) -> Tensor
+
+In-place version of `$squeeze`
+
+## std
+
+std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor
+
+See [torch_std()]
+
+## stft
+See [torch_stft()]
+
+        .. warning::
+          This function changed signature at version 0.4.1. Calling with
+          the previous signature may cause error or return incorrect result.
+        
+## storage
+
+storage() -> torch_Storage
+
+Returns the underlying storage.
+
+## storage_offset
+
+storage_offset() -> int
+
+Returns `self` tensor's offset in the underlying storage in terms of
+number of storage elements (not bytes).
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(c(1, 2, 3, 4, 5))
+x$storage_offset()
+x[3:N]$storage_offset()
+```
+
+## storage_type
+
+storage_type() -> type
+
+Returns the type of the underlying storage.
+
+## stride
+
+stride(dim) -> tuple or int
+
+Returns the stride of `self` tensor.
+
+Stride is the jump necessary to go from one element to the next one in the
+specified dimension `dim`. 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 `dim`.
+
+#### Arguments: 
+
+* dim (int, optional): the desired dimension in which stride is required
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(matrix(1:10, nrow = 2))
+x$stride()
+x$stride(0)
+x$stride(-1)
+```
+    
+## sub
+
+sub(other, *, alpha=1) -> Tensor
+
+Subtracts a scalar or tensor from `self` tensor. If both :attr:`alpha`
+and `other` are specified, each element of :attr:`other` is scaled by
+`alpha` before being used.
+
+When `other` is a tensor, the shape of :attr:`other` must be
+`broadcastable <broadcasting-semantics>` with the shape of the underlying
+tensor.
+
+
+## sub_
+
+sub_(other, *, alpha=1) -> Tensor
+
+In-place version of `$sub`
+
+## sum
+
+sum(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor
+
+See [torch_sum()]
+
+## sum_to_size
+
+sum_to_size(*size) -> Tensor
+
+Sum `this` tensor to `size`.
+`size` must be broadcastable to `this` tensor size.
+
+#### Arguments: 
+
+* size (int...): a sequence of integers defining the shape of the output tensor.
+
+## svd
+
+svd(some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor)
+
+See [torch_svd()]
+
+## symeig
+
+symeig(eigenvectors=FALSE, upper=TRUE) -> (Tensor, Tensor)
+
+See [torch_symeig()]
+
+## t
+
+t() -> Tensor
+
+See [torch_t()]
+
+## t_
+
+t_() -> Tensor
+
+In-place version of `$t`
+
+## take
+
+take(indices) -> Tensor
+
+See [torch_take()]
+
+## tan
+
+tan() -> Tensor
+
+See [torch_tan()]
+
+## tan_
+
+tan_() -> Tensor
+
+In-place version of `$tan`
+
+## tanh
+
+tanh() -> Tensor
+
+See [torch_tanh()]
+
+## tanh_
+
+tanh_() -> Tensor
+
+In-place version of `$tanh`
+
+## to
+
+to(*args, **kwargs) -> Tensor
+
+Performs Tensor dtype and/or device conversion. A `torch_dtype` and :class:`torch_device` are
+inferred from the arguments of `self$to(*args, **kwargs)`.
+
+#### Note: 
+
+If the `self` Tensor already
+has the correct `torch_dtype` and :class:`torch_device`, then `self` is returned.
+Otherwise, the returned tensor is a copy of `self` with the desired
+`torch_dtype` and :class:`torch_device`.
+
+Here are the ways to call `to`:
+
+to(dtype, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor
+
+Returns a Tensor with the specified `dtype`
+
+#### Arguments:
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+  returned Tensor. Default: `torch_preserve_format`.
+
+to(device=NULL, dtype=NULL, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor
+
+Returns a Tensor with the specified `device` and (optional)
+`dtype`. If :attr:`dtype` is `NULL` it is inferred to be `self$dtype`.
+When `non_blocking`, tries to convert asynchronously with respect to
+the host if possible, e.g., converting a CPU Tensor with pinned memory to a
+CUDA Tensor.
+
+When `copy` is set, a new Tensor is created even when the Tensor
+already matches the desired conversion.
+
+#### Arguments:
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+  returned Tensor. Default: `torch_preserve_format`.
+
+function:: to(other, non_blocking=FALSE, copy=FALSE) -> Tensor
+
+Returns a Tensor with same `torch_dtype` and :class:`torch_device` as
+the Tensor `other`. When :attr:`non_blocking`, tries to convert
+asynchronously with respect to the host if possible, e.g., converting a CPU
+Tensor with pinned memory to a CUDA Tensor.
+
+When `copy` is set, a new Tensor is created even when the Tensor
+already matches the desired conversion.
+
+#### Examples: 
+
+```{r}
+tensor <- torch_randn(2, 2)  # Initially dtype=float32, device=cpu
+tensor$to(dtype = torch_float64())
+
+other <- torch_randn(1, dtype=torch_float64())
+tensor$to(other = other, non_blocking=TRUE)
+```
+    
+## to_mkldnn
+
+to_mkldnn() -> Tensor
+Returns a copy of the tensor in `torch_mkldnn` layout.
+
+
+## to_sparse
+
+to_sparse(sparseDims) -> Tensor
+Returns a sparse copy of the tensor.  PyTorch supports sparse tensors in
+`coordinate format <sparse-docs>`.
+
+#### Arguments: 
+
+* sparseDims (int, optional): the number of sparse dimensions to include in the new sparse tensor
+
+## tolist
+
+tolist() -> list or number
+
+Returns the tensor as a (nested) list. For scalars, a standard
+Python number is returned, just like with `$item`.
+Tensors are automatically moved to the CPU first if necessary.
+
+This operation is not differentiable.
+
+## topk
+
+topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor)
+
+See [torch_topk()]
+
+## trace
+
+trace() -> Tensor
+
+See [torch_trace()]
+
+## transpose
+
+transpose(dim0, dim1) -> Tensor
+
+See [torch_transpose()]
+
+## transpose_
+
+transpose_(dim0, dim1) -> Tensor
+
+In-place version of `$transpose`
+
+## triangular_solve
+
+triangular_solve(A, upper=TRUE, transpose=FALSE, unitriangular=FALSE) -> (Tensor, Tensor)
+
+See [torch_triangular_solve()]
+
+## tril
+
+tril(k=0) -> Tensor
+
+See [torch_tril()]
+
+## tril_
+
+tril_(k=0) -> Tensor
+
+In-place version of `$tril`
+
+## triu
+
+triu(k=0) -> Tensor
+
+See [torch_triu()]
+
+## triu_
+
+triu_(k=0) -> Tensor
+
+In-place version of `$triu`
+
+## true_divide
+
+true_divide(value) -> Tensor
+
+See [torch_true_divide()]
+
+## true_divide_
+
+true_divide_(value) -> Tensor
+
+In-place version of `$true_divide_`
+
+## trunc
+
+trunc() -> Tensor
+
+See [torch_trunc()]
+
+## trunc_
+
+trunc_() -> Tensor
+
+In-place version of `$trunc`
+
+## type
+
+type(dtype=NULL, non_blocking=FALSE, **kwargs) -> str or Tensor
+Returns the type if `dtype` is not provided, else casts this object to
+the specified type.
+
+If this is already of the correct type, no copy is performed and the
+original object is returned.
+
+#### Arguments: 
+
+* dtype (type or string): The desired type
+* non_blocking (bool): If `TRUE`, and the source is in pinned memory
+* and destination is on the GPU or vice versa, the copy is performed
+* asynchronously with respect to the host. Otherwise, the argument
+* has no effect.
+    **kwargs: For compatibility, may contain the key `async` in place of
+* the `non_blocking` argument. The `async` arg is deprecated.
+
+## type_as
+
+type_as(tensor) -> Tensor
+
+Returns this tensor cast to the type of the given tensor.
+
+This is a no-op if the tensor is already of the correct type. This is
+equivalent to `self$type(tensor.type())`
+
+#### Arguments: 
+
+* tensor (Tensor): the tensor which has the desired type
+
+## unbind
+
+unbind(dim=0) -> seq
+
+See [torch_unbind()]
+
+## unflatten
+
+Unflattens the named dimension `dim`, viewing it in the shape
+specified by `namedshape`.
+
+#### Arguments:
+
+* namedshape: (iterable of `(name, size)` tuples).
+        
+## unfold
+
+unfold(dimension, size, step) -> Tensor
+
+Returns a view of the original tensor which contains all slices of size `size` from
+`self` tensor in the dimension :attr:`dimension`.
+
+Step between two slices is given by `step`.
+
+If `sizedim` is the size of dimension `dimension` for :attr:`self`, the size of
+dimension `dimension` in the returned tensor will be
+`(sizedim - size) / step + 1`.
+
+An additional dimension of size `size` is appended in the returned tensor.
+
+#### Arguments: 
+
+* dimension (int): dimension in which unfolding happens
+* size (int): the size of each slice that is unfolded
+* step (int): the step between each slice
+
+## uniform_
+
+uniform_(from=0, to=1) -> Tensor
+
+Fills `self` tensor with numbers sampled from the continuous uniform
+distribution:
+
+$$
+P(x) = \dfrac{1}{\text{to} - \text{from}}
+$$
+
+## unique
+
+Returns the unique elements of the input tensor.
+
+See [torch_unique()]
+        
+## unique_consecutive
+
+Eliminates all but the first element from every consecutive group of equivalent elements.
+
+See [torch_unique_consecutive()]
+        
+## unsqueeze
+
+unsqueeze(dim) -> Tensor
+
+See [torch_unsqueeze()]
+
+## unsqueeze_
+
+unsqueeze_(dim) -> Tensor
+
+In-place version of `$unsqueeze`
+
+## values
+
+values() -> Tensor
+
+If `self` is a sparse COO tensor (i.e., with `torch_sparse_coo` layout),
+this returns a view of the contained values tensor. Otherwise, this throws an
+error.
+
+
+#### Note: 
+
+This method can only be called on a coalesced sparse tensor. See
+`Tensor$coalesce` for details.
+
+## var
+
+var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor
+
+See [torch_var()]
+
+## view
+
+view(*shape) -> Tensor
+
+Returns a new tensor with the same data as the `self` tensor but of a
+different `shape`.
+
+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 `d, d+1, \dots, d+k` that satisfy the following
+contiguity-like condition that `\forall i = d, \dots, d+k-1`,
+
+$$
+\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]
+$$
+
+Otherwise, it will not be possible to view `self` tensor as :attr:`shape`
+without copying it (e.g., via `contiguous`). When it is unclear whether a
+`view` can be performed, it is advisable to use :meth:`reshape`, which
+returns a view if the shapes are compatible, and copies (equivalent to calling
+`contiguous`) otherwise.
+
+#### Arguments: 
+
+* shape (torch_Size or int...): the desired size
+
+## view_as
+
+view_as(other) -> Tensor
+
+View this tensor as the same size as `other`.
+`self$view_as(other)` is equivalent to `self$view(other.size())`.
+
+Please see `$view` for more information about `view`.
+
+#### Arguments: 
+
+* other (`$): The result tensor has the same size
+* as `other`.
+
+## where
+
+where(condition, y) -> Tensor
+
+`self$where(condition, y)` is equivalent to `torch_where(condition, self, y)`.
+See [torch_where()]
+
+## zero_
+
+zero_() -> Tensor
+
+Fills `self` tensor with zeros.
+
-- 
GitLab


From 8003d5ef4c2bc04b00d596478ccaf6c18bb60f36 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 16 Sep 2020 17:05:13 -0300
Subject: [PATCH 113/157] small fixes

---
 vignettes/tensor/index.Rmd | 83 ++++++++++++++++++--------------------
 1 file changed, 39 insertions(+), 44 deletions(-)

diff --git a/vignettes/tensor/index.Rmd b/vignettes/tensor/index.Rmd
index 804f9dbb3..c40b9747f 100644
--- a/vignettes/tensor/index.Rmd
+++ b/vignettes/tensor/index.Rmd
@@ -252,10 +252,9 @@ named_tensor <- tensor$refine_names(names = c('A', 'B', 'C', 'D', 'E', 'F'))
 named_tensor$align_to(c("A", "B", "F", "C", "E", "D"))
 ```
 
-
-
-        .. warning::
-            The named tensor API is experimental and subject to change.
+#### Warning:
+            
+The named tensor API is experimental and subject to change.
 
         
 ## all
@@ -329,7 +328,7 @@ Returns TRUE if any elements in each row of the tensor in the given
 dimension `dim` are TRUE, FALSE otherwise.
 
 If `keepdim` is `TRUE`, the output tensor is of the same size as
-`input` except in the dimension :attr:`dim` where it is of size 1.
+`input` except in the dimension `dim` where it is of size 1.
 Otherwise, `dim` is squeezed (see [torch_squeeze()]), resulting
 in the output tensor having 1 fewer dimension than `input`.
 
@@ -498,7 +497,7 @@ In-place version of `$baddbmm`
 bernoulli(*, generator=NULL) -> Tensor
 
 Returns a result tensor where each `\texttt{result[i]}` is independently
-sampled from `\text{Bernoulli}(\texttt{self[i]})`. :attr:`self` must have
+sampled from `\text{Bernoulli}(\texttt{self[i]})`. `self` must have
 floating point `dtype`, and the result will have the same `dtype`.
 
 See [torch_bernoulli()]
@@ -508,7 +507,7 @@ See [torch_bernoulli()]
 bernoulli_(p=0.5, *, generator=NULL) -> Tensor
 
 Fills each location of `self` with an independent sample from
-`\text{Bernoulli}(\texttt{p})`. :attr:`self` can have integral
+`\text{Bernoulli}(\texttt{p})`. `self` can have integral
 `dtype`.
 
 bernoulli_(p_tensor, *, generator=NULL) -> Tensor
@@ -516,10 +515,10 @@ bernoulli_(p_tensor, *, generator=NULL) -> Tensor
 `p_tensor` should be a tensor containing probabilities to be used for
 drawing the binary random number.
 
-The `\text{i}^{th}` element of :attr:`self` tensor will be set to a
+The `\text{i}^{th}` element of `self` tensor will be set to a
 value sampled from `\text{Bernoulli}(\texttt{p\_tensor[i]})`.
 
-`self` can have integral `dtype`, but :attr:`p_tensor` must have
+`self` can have integral `dtype`, but `p_tensor` must have
 floating point `dtype`.
 
 See also `$bernoulli` and [torch_bernoulli()]
@@ -725,7 +724,7 @@ Returns a contiguous in memory tensor containing the same data as `self` tensor.
 
 copy_(src, non_blocking=FALSE) -> Tensor
 
-Copies the elements from `src` into :attr:`self` tensor and returns
+Copies the elements from `src` into `self` tensor and returns
 `self`.
 
 The `src` tensor must be :ref:`broadcastable <broadcasting-semantics>`
@@ -1383,12 +1382,12 @@ Out-of-place version of `$index_add_`.
 
 index_add_(dim, index, tensor) -> Tensor
 
-Accumulate the elements of `tensor` into the :attr:`self` tensor by adding
+Accumulate the elements of `tensor` into the `self` tensor by adding
 to the indices in the order given in `index`. For example, if `dim == 0`
 and `index[i] == j`, then the `i`\ th row of `tensor` is added to the
 `j`\ th row of `self`.
 
-The `dim`\ th dimension of :attr:`tensor` must have the same size as the
+The `dim`\ th dimension of `tensor` must have the same size as the
 length of `index` (which must be a vector), and all other dimensions must
 match `self`, or an error will be raised.
 
@@ -1426,12 +1425,12 @@ Out-of-place version of `$index_copy_`.
 
 index_copy_(dim, index, tensor) -> Tensor
 
-Copies the elements of `tensor` into the :attr:`self` tensor by selecting
+Copies the elements of `tensor` into the `self` tensor by selecting
 the indices in the order given in `index`. For example, if `dim == 0`
 and `index[i] == j`, then the `i`\ th row of `tensor` is copied to the
 `j`\ th row of `self`.
 
-The `dim`\ th dimension of :attr:`tensor` must have the same size as the
+The `dim`\ th dimension of `tensor` must have the same size as the
 length of `index` (which must be a vector), and all other dimensions must
 match `self`, or an error will be raised.
 
@@ -1462,7 +1461,7 @@ Out-of-place version of `$index_fill_`.
 
 index_fill_(dim, index, val) -> Tensor
 
-Fills the elements of the `self` tensor with value :attr:`val` by
+Fills the elements of the `self` tensor with value `val` by
 selecting the indices in the order given in `index`.
 
 #### Arguments: 
@@ -1489,12 +1488,12 @@ Out-place version of `$index_put_`.
 
 index_put_(indices, value, accumulate=FALSE) -> Tensor
 
-Puts values from the tensor `value` into the tensor :attr:`self` using
+Puts values from the tensor `value` into the tensor `self` using
 the indices specified in `indices` (which is a tuple of Tensors). The
 expression `tensor.index_put_(indices, value)` is equivalent to
 `tensor[indices] = value`. Returns `self`.
 
-If `accumulate` is `TRUE`, the elements in :attr:`value` are added to
+If `accumulate` is `TRUE`, the elements in `value` are added to
 `self`. If accumulate is `FALSE`, the behavior is undefined if indices
 contain duplicate elements.
 
@@ -1797,7 +1796,7 @@ log_normal_(mean=1, std=2, *, generator=NULL)
 
 Fills `self` tensor with numbers samples from the log-normal distribution
 parameterized by the given mean `\mu` and standard deviation
-`\sigma`. Note that :attr:`mean` and :attr:`std` are the mean and
+`\sigma`. Note that `mean` and `std` are the mean and
 standard deviation of the underlying normal distribution, and not of the
 returned distribution:
 
@@ -1942,7 +1941,7 @@ Out-of-place version of `$masked_fill_`
 
 masked_fill_(mask, value)
 
-Fills elements of `self` tensor with :attr:`value` where :attr:`mask` is
+Fills elements of `self` tensor with `value` where `mask` is
 TRUE. The shape of `mask` must be
 `broadcastable <broadcasting-semantics>` with the shape of the underlying
 tensor.
@@ -1962,7 +1961,7 @@ Out-of-place version of `$masked_scatter_`
 
 masked_scatter_(mask, source)
 
-Copies elements from `source` into :attr:`self` tensor at positions where
+Copies elements from `source` into `self` tensor at positions where
 the `mask` is TRUE.
 The shape of `mask` must be :ref:`broadcastable <broadcasting-semantics>`
 with the shape of the underlying tensor. The `source` should have at least
@@ -1975,7 +1974,7 @@ as many elements as the number of ones in `mask`
 
 #### Note: 
 
-The `mask` operates on the :attr:`self` tensor, not on the given
+The `mask` operates on the `self` tensor, not on the given
 `source` tensor.
 
 ## masked_select
@@ -2178,7 +2177,7 @@ tensor$new_empty(c(2, 3))
 
 new_full(size, fill_value, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
 
-Returns a Tensor of size `size` filled with :attr:`fill_value`.
+Returns a Tensor of size `size` filled with `fill_value`.
 By default, the returned Tensor has the same `torch_dtype` and
 `torch_device` as this tensor.
 
@@ -2302,7 +2301,7 @@ See [torch_norm()]
 normal_(mean=0, std=1, *, generator=NULL) -> Tensor
 
 Fills `self` tensor with elements samples from the normal distribution
-parameterized by `mean` and :attr:`std`.
+parameterized by `mean` and `std`.
 
 ## numel
 
@@ -2398,7 +2397,7 @@ Copies the elements from `tensor` into the positions specified by
 indices. For the purpose of indexing, the `self` tensor is treated as if
 it were a 1-D tensor.
 
-If `accumulate` is `TRUE`, the elements in :attr:`tensor` are added to
+If `accumulate` is `TRUE`, the elements in `tensor` are added to
 `self`. If accumulate is `FALSE`, the behavior is undefined if indices
 contain duplicate elements.
 
@@ -2537,7 +2536,7 @@ have used the tensor.
 
 ## refine_names
 
-Refines the dimension names of `self` according to :attr:`names`.
+Refines the dimension names of `self` according to `names`.
 
 Refining is a special case of renaming that "lifts" unnamed dimensions.
 A `NULL` dim can be refined to have any name; a named dim can only be
@@ -2844,8 +2843,8 @@ Out-of-place version of `$scatter_`
 
 scatter_(dim, index, src) -> Tensor
 
-Writes all values from the tensor `src` into :attr:`self` at the indices
-specified in the `index` tensor. For each value in :attr:`src`, its output
+Writes all values from the tensor `src` into `self` at the indices
+specified in the `index` tensor. For each value in `src`, its output
 index is specified by its index in `src` for `dimension != dim` and by
 the corresponding value in `index` for `dimension = dim`.
 
@@ -2906,10 +2905,10 @@ Out-of-place version of `$scatter_add_`
 
 scatter_add_(dim, index, src) -> Tensor
 
-Adds all values from the tensor `other` into :attr:`self` at the indices
+Adds all values from the tensor `other` into `self` at the indices
 specified in the `index` tensor in a similar fashion as
-`~$scatter_`. For each value in :attr:`src`, it is added to
-an index in `self` which is specified by its index in :attr:`src`
+`~$scatter_`. For each value in `src`, it is added to
+an index in `self` which is specified by its index in `src`
 for `dimension != dim` and by the corresponding value in `index` for
 `dimension = dim`.
 
@@ -2921,7 +2920,7 @@ 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
 ```
 
-`self`, :attr:`index` and :attr:`src` should have same number of
+`self`, `index` and `src` should have same number of
 dimensions. It is also required that `index.size(d) <= src.size(d)` for all
 dimensions `d`, and that `index.size(d) <= self$size(d)` for all dimensions
 `d != dim`.
@@ -3094,7 +3093,7 @@ See also `Tensor.dense_dim`.
 sparse_mask(input, mask) -> Tensor
 
 Returns a new SparseTensor with values from Tensor `input` filtered
-by indices of `mask` and values are ignored. :attr:`input` and :attr:`mask`
+by indices of `mask` and values are ignored. `input` and `mask`
 must have the same shape.
 
 #### Arguments: 
@@ -3149,10 +3148,6 @@ See [torch_std()]
 
 ## stft
 See [torch_stft()]
-
-        .. warning::
-          This function changed signature at version 0.4.1. Calling with
-          the previous signature may cause error or return incorrect result.
         
 ## storage
 
@@ -3209,11 +3204,11 @@ x$stride(-1)
 
 sub(other, *, alpha=1) -> Tensor
 
-Subtracts a scalar or tensor from `self` tensor. If both :attr:`alpha`
-and `other` are specified, each element of :attr:`other` is scaled by
+Subtracts a scalar or tensor from `self` tensor. If both `alpha`
+and `other` are specified, each element of `other` is scaled by
 `alpha` before being used.
 
-When `other` is a tensor, the shape of :attr:`other` must be
+When `other` is a tensor, the shape of `other` must be
 `broadcastable <broadcasting-semantics>` with the shape of the underlying
 tensor.
 
@@ -3323,7 +3318,7 @@ Returns a Tensor with the specified `dtype`
 to(device=NULL, dtype=NULL, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor
 
 Returns a Tensor with the specified `device` and (optional)
-`dtype`. If :attr:`dtype` is `NULL` it is inferred to be `self$dtype`.
+`dtype`. If `dtype` is `NULL` it is inferred to be `self$dtype`.
 When `non_blocking`, tries to convert asynchronously with respect to
 the host if possible, e.g., converting a CPU Tensor with pinned memory to a
 CUDA Tensor.
@@ -3339,7 +3334,7 @@ already matches the desired conversion.
 function:: to(other, non_blocking=FALSE, copy=FALSE) -> Tensor
 
 Returns a Tensor with same `torch_dtype` and :class:`torch_device` as
-the Tensor `other`. When :attr:`non_blocking`, tries to convert
+the Tensor `other`. When `non_blocking`, tries to convert
 asynchronously with respect to the host if possible, e.g., converting a CPU
 Tensor with pinned memory to a CUDA Tensor.
 
@@ -3512,11 +3507,11 @@ specified by `namedshape`.
 unfold(dimension, size, step) -> Tensor
 
 Returns a view of the original tensor which contains all slices of size `size` from
-`self` tensor in the dimension :attr:`dimension`.
+`self` tensor in the dimension `dimension`.
 
 Step between two slices is given by `step`.
 
-If `sizedim` is the size of dimension `dimension` for :attr:`self`, the size of
+If `sizedim` is the size of dimension `dimension` for `self`, the size of
 dimension `dimension` in the returned tensor will be
 `(sizedim - size) / step + 1`.
 
@@ -3601,7 +3596,7 @@ $$
 \text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]
 $$
 
-Otherwise, it will not be possible to view `self` tensor as :attr:`shape`
+Otherwise, it will not be possible to view `self` tensor as `shape`
 without copying it (e.g., via `contiguous`). When it is unclear whether a
 `view` can be performed, it is advisable to use :meth:`reshape`, which
 returns a view if the shapes are compatible, and copies (equivalent to calling
-- 
GitLab


From 4db13ed6446450ec496b036564bf4c53d6761e84 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 16 Sep 2020 17:06:47 -0300
Subject: [PATCH 114/157] fix for autolinking

---
 vignettes/tensor/index.Rmd | 300 ++++++++++++++++++-------------------
 1 file changed, 150 insertions(+), 150 deletions(-)

diff --git a/vignettes/tensor/index.Rmd b/vignettes/tensor/index.Rmd
index c40b9747f..60de01858 100644
--- a/vignettes/tensor/index.Rmd
+++ b/vignettes/tensor/index.Rmd
@@ -38,7 +38,7 @@ If `n` is the number of dimensions in `x`,
 
 abs() -> Tensor
 
-See [torch_abs()]
+See ?torch_abs()
 
 ## abs_
 
@@ -63,7 +63,7 @@ Alias for [$abs_()]
 
 acos() -> Tensor
 
-See [torch_acos()]
+See ?torch_acos()
 
 ## acos_
 
@@ -75,7 +75,7 @@ In-place version of `$acos`
 
 acosh() -> Tensor
 
-See [torch_acosh()]
+See ?torch_acosh()
 
 ## acosh_
 
@@ -95,7 +95,7 @@ When `other` is a tensor, the shape of `other` must be
 broadcastable with the shape of the underlying
 tensor
 
-See [torch_add()]
+See ?torch_add()
 
 ## add_
 
@@ -107,7 +107,7 @@ In-place version of `$add`
 
 addbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor
 
-See [torch_addbmm()]
+See ?torch_addbmm()
 
 ## addbmm_
 
@@ -119,7 +119,7 @@ In-place version of `$addbmm`
 
 addcdiv(tensor1, tensor2, *, value=1) -> Tensor
 
-See [torch_addcdiv()]
+See ?torch_addcdiv()
 
 ## addcdiv_
 
@@ -131,7 +131,7 @@ In-place version of `$addcdiv`
 
 addcmul(tensor1, tensor2, *, value=1) -> Tensor
 
-See [torch_addcmul()]
+See ?torch_addcmul()
 
 ## addcmul_
 
@@ -143,7 +143,7 @@ In-place version of `$addcmul`
 
 addmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor
 
-See [torch_addmm()]
+See ?torch_addmm()
 
 ## addmm_
 
@@ -155,7 +155,7 @@ In-place version of `$addmm`
 
 addmv(mat, vec, *, beta=1, alpha=1) -> Tensor
 
-See [torch_addmv()]
+See ?torch_addmv()
 
 ## addmv_
 
@@ -167,7 +167,7 @@ In-place version of `$addmv`
 
 addr(vec1, vec2, *, beta=1, alpha=1) -> Tensor
 
-See [torch_addr()]
+See ?torch_addr()
 
 ## addr_
 
@@ -278,7 +278,7 @@ dimension `dim` are TRUE, FALSE otherwise.
 
 If `keepdim` is `TRUE`, the output tensor is of the same size as
 `input` except in the dimension `dim` where it is of size 1.
-Otherwise, `dim` is squeezed (see [torch_squeeze()]), resulting
+Otherwise, `dim` is squeezed (see ?torch_squeeze()), resulting
 in the output tensor having 1 fewer dimension than `input`.
 
 #### Arguments: 
@@ -300,13 +300,13 @@ a$all(dim=1)
 
 allclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor
 
-See [torch_allclose()]
+See ?torch_allclose()
 
 ## angle
 
 angle() -> Tensor
 
-See [torch_angle()]
+See ?torch_angle()
 
 ## any
 
@@ -329,7 +329,7 @@ dimension `dim` are TRUE, FALSE otherwise.
 
 If `keepdim` is `TRUE`, the output tensor is of the same size as
 `input` except in the dimension `dim` where it is of size 1.
-Otherwise, `dim` is squeezed (see [torch_squeeze()]), resulting
+Otherwise, `dim` is squeezed (see ?torch_squeeze()), resulting
 in the output tensor having 1 fewer dimension than `input`.
 
 #### Arguments: 
@@ -363,19 +363,19 @@ sections that require high performance.
 
 argmax(dim=NULL, keepdim=FALSE) -> LongTensor
 
-See [torch_argmax()]
+See ?torch_argmax()
 
 ## argmin
 
 argmin(dim=NULL, keepdim=FALSE) -> LongTensor
 
-See [torch_argmin()]
+See ?torch_argmin()
 
 ## argsort
 
 argsort(dim=-1, descending=FALSE) -> LongTensor
 
-See [torch_argsort()]
+See ?torch_argsort()
 
 ## as_strided
 
@@ -395,7 +395,7 @@ to the autograd graph. `cls` must be a subclass of `Tensor`.
 
 asin() -> Tensor
 
-See [torch_asin()]
+See ?torch_asin()
 
 ## asin_
 
@@ -407,7 +407,7 @@ In-place version of `$asin`
 
 asinh() -> Tensor
 
-See [torch_asinh()]
+See ?torch_asinh()
 
 ## asinh_
 
@@ -419,7 +419,7 @@ In-place version of `$asinh`
 
 atan() -> Tensor
 
-See [torch_atan()]
+See ?torch_atan()
 
 ## atan2
 
@@ -443,7 +443,7 @@ In-place version of `$atan`
 
 atanh() -> Tensor
 
-See [torch_atanh()]
+See ?torch_atanh()
 
 ## atanh_
 
@@ -484,7 +484,7 @@ for details on the memory layout of accumulated gradients.
 
 baddbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor
 
-See [torch_baddbmm()]
+See ?torch_baddbmm()
 
 ## baddbmm_
 
@@ -500,7 +500,7 @@ Returns a result tensor where each `\texttt{result[i]}` is independently
 sampled from `\text{Bernoulli}(\texttt{self[i]})`. `self` must have
 floating point `dtype`, and the result will have the same `dtype`.
 
-See [torch_bernoulli()]
+See ?torch_bernoulli()
 
 ## bernoulli_
 
@@ -521,7 +521,7 @@ value sampled from `\text{Bernoulli}(\texttt{p\_tensor[i]})`.
 `self` can have integral `dtype`, but `p_tensor` must have
 floating point `dtype`.
 
-See also `$bernoulli` and [torch_bernoulli()]
+See also `$bernoulli` and ?torch_bernoulli()
 
 ## bfloat16
 
@@ -537,7 +537,7 @@ bfloat16(memory_format=torch_preserve_format) -> Tensor
 
 bincount(weights=NULL, minlength=0) -> Tensor
 
-See [torch_bincount()]
+See ?torch_bincount()
 
 ## bitwise_and
 
@@ -591,7 +591,7 @@ In-place version of `$bitwise_xor`
 
 bmm(batch2) -> Tensor
 
-See [torch_bmm()]
+See ?torch_bmm()
 
 ## bool
 
@@ -629,7 +629,7 @@ $$
 
 ceil() -> Tensor
 
-See [torch_ceil()]
+See ?torch_ceil()
 
 ## ceil_
 
@@ -652,7 +652,7 @@ char(memory_format=torch_preserve_format) -> Tensor
 
 cholesky(upper=FALSE) -> Tensor
 
-See [torch_cholesky()]
+See ?torch_cholesky()
 
 ## cholesky_inverse
 
@@ -670,13 +670,13 @@ See [torch_cholesky_solve()]
 
 chunk(chunks, dim=0) -> List of Tensors
 
-See [torch_chunk()]
+See ?torch_chunk()
 
 ## clamp
 
 clamp(min, max) -> Tensor
 
-See [torch_clamp()]
+See ?torch_clamp()
 
 ## clamp_
 
@@ -705,7 +705,7 @@ propagating to the cloned tensor will propagate to the original tensor.
 
 conj() -> Tensor
 
-See [torch_conj()]
+See ?torch_conj()
 
 ## contiguous
 
@@ -742,7 +742,7 @@ different device.
 
 cos() -> Tensor
 
-See [torch_cos()]
+See ?torch_cos()
 
 ## cos_
 
@@ -754,7 +754,7 @@ In-place version of `$cos`
 
 cosh() -> Tensor
 
-See [torch_cosh()]
+See ?torch_cosh()
 
 ## cosh_
 
@@ -780,7 +780,7 @@ then no copy is performed and the original object is returned.
 
 cross(other, dim=-1) -> Tensor
 
-See [torch_cross()]
+See ?torch_cross()
 
 ## cuda
 
@@ -805,25 +805,25 @@ then no copy is performed and the original object is returned.
 
 cummax(dim) -> (Tensor, Tensor)
 
-See [torch_cummax()]
+See ?torch_cummax()
 
 ## cummin
 
 cummin(dim) -> (Tensor, Tensor)
 
-See [torch_cummin()]
+See ?torch_cummin()
 
 ## cumprod
 
 cumprod(dim, dtype=NULL) -> Tensor
 
-See [torch_cumprod()]
+See ?torch_cumprod()
 
 ## cumsum
 
 cumsum(dim, dtype=NULL) -> Tensor
 
-See [torch_cumsum()]
+See ?torch_cumsum()
 
 ## data_ptr
 
@@ -862,7 +862,7 @@ Given a quantized Tensor, dequantize it and return the dequantized float Tensor.
 
 det() -> Tensor
 
-See [torch_det()]
+See ?torch_det()
 
 ## detach
 
@@ -897,7 +897,7 @@ Is the `torch_device` where this Tensor is.
 
 diag(diagonal=0) -> Tensor
 
-See [torch_diag()]
+See ?torch_diag()
 
 ## diag_embed
 
@@ -909,19 +909,19 @@ See [torch_diag_embed()]
 
 diagflat(offset=0) -> Tensor
 
-See [torch_diagflat()]
+See ?torch_diagflat()
 
 ## diagonal
 
 diagonal(offset=0, dim1=0, dim2=1) -> Tensor
 
-See [torch_diagonal()]
+See ?torch_diagonal()
 
 ## digamma
 
 digamma() -> Tensor
 
-See [torch_digamma()]
+See ?torch_digamma()
 
 ## digamma_
 
@@ -939,13 +939,13 @@ Returns the number of dimensions of `self` tensor.
 
 dist(other, p=2) -> Tensor
 
-See [torch_dist()]
+See ?torch_dist()
 
 ## div
 
 div(value) -> Tensor
 
-See [torch_div()]
+See ?torch_div()
 
 ## div_
 
@@ -957,7 +957,7 @@ In-place version of `$div`
 
 dot(tensor2) -> Tensor
 
-See [torch_dot()]
+See ?torch_dot()
 
 ## double
 
@@ -974,7 +974,7 @@ double(memory_format=torch_preserve_format) -> Tensor
 
 eig(eigenvectors=FALSE) -> (Tensor, Tensor)
 
-See [torch_eig()]
+See ?torch_eig()
 
 ## element_size
 
@@ -992,7 +992,7 @@ torch_tensor(c(1))$element_size()
 
 eq(other) -> Tensor
 
-See [torch_eq()]
+See ?torch_eq()
 
 ## eq_
 
@@ -1004,13 +1004,13 @@ In-place version of `$eq`
 
 equal(other) -> bool
 
-See [torch_equal()]
+See ?torch_equal()
 
 ## erf
 
 erf() -> Tensor
 
-See [torch_erf()]
+See ?torch_erf()
 
 ## erf_
 
@@ -1022,7 +1022,7 @@ In-place version of `$erf`
 
 erfc() -> Tensor
 
-See [torch_erfc()]
+See ?torch_erfc()
 
 ## erfc_
 
@@ -1034,7 +1034,7 @@ In-place version of `$erfc`
 
 erfinv() -> Tensor
 
-See [torch_erfinv()]
+See ?torch_erfinv()
 
 ## erfinv_
 
@@ -1046,7 +1046,7 @@ In-place version of `$erfinv`
 
 exp() -> Tensor
 
-See [torch_exp()]
+See ?torch_exp()
 
 ## exp_
 
@@ -1134,7 +1134,7 @@ $$
 
 fft(signal_ndim, normalized=FALSE) -> Tensor
 
-See [torch_fft()]
+See ?torch_fft()
 
 ## fill_
 
@@ -1170,25 +1170,25 @@ c$fill_diagonal_(5, wrap=TRUE)
 
 flatten(input, start_dim=0, end_dim=-1) -> Tensor
 
-see [torch_flatten()]
+see ?torch_flatten()
 
 ## flip
 
 flip(dims) -> Tensor
 
-See [torch_flip()]
+See ?torch_flip()
 
 ## fliplr
 
 fliplr() -> Tensor
 
-See [torch_fliplr()]
+See ?torch_fliplr()
 
 ## flipud
 
 flipud() -> Tensor
 
-See [torch_flipud()]
+See ?torch_flipud()
 
 ## float
 
@@ -1205,7 +1205,7 @@ float(memory_format=torch_preserve_format) -> Tensor
 
 floor() -> Tensor
 
-See [torch_floor()]
+See ?torch_floor()
 
 ## floor_
 
@@ -1229,7 +1229,7 @@ In-place version of `$floor_divide`
 
 fmod(divisor) -> Tensor
 
-See [torch_fmod()]
+See ?torch_fmod()
 
 ## fmod_
 
@@ -1241,7 +1241,7 @@ In-place version of `$fmod`
 
 frac() -> Tensor
 
-See [torch_frac()]
+See ?torch_frac()
 
 ## frac_
 
@@ -1253,13 +1253,13 @@ In-place version of `$frac`
 
 gather(dim, index) -> Tensor
 
-See [torch_gather()]
+See ?torch_gather()
 
 ## ge
 
 ge(other) -> Tensor
 
-See [torch_ge()]
+See ?torch_ge()
 
 ## ge_
 
@@ -1281,13 +1281,13 @@ $$
 
 geqrf() -> (Tensor, Tensor)
 
-See [torch_geqrf()]
+See ?torch_geqrf()
 
 ## ger
 
 ger(vec2) -> Tensor
 
-See [torch_ger()]
+See ?torch_ger()
 
 ## get_device
 
@@ -1314,7 +1314,7 @@ The attribute will then contain the gradients computed and future calls to
 
 gt(other) -> Tensor
 
-See [torch_gt()]
+See ?torch_gt()
 
 ## gt_
 
@@ -1347,13 +1347,13 @@ Is `TRUE` if any of this tensor's dimensions are named. Otherwise, is `FALSE`.
 
 histc(bins=100, min=0, max=0) -> Tensor
 
-See [torch_histc()]
+See ?torch_histc()
 
 ## ifft
 
 ifft(signal_ndim, normalized=FALSE) -> Tensor
 
-See [torch_ifft()]
+See ?torch_ifft()
 
 ## imag
 
@@ -1548,13 +1548,13 @@ underlying uint8_t values of the given Tensor.
 
 inverse() -> Tensor
 
-See [torch_inverse()]
+See ?torch_inverse()
 
 ## irfft
 
 irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -> Tensor
 
-See [torch_irfft()]
+See ?torch_irfft()
 
 ## is_complex
 
@@ -1662,28 +1662,28 @@ Returns TRUE if the data type of `self` is a signed data type.
 
 isclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor
 
-See [torch_isclose()]
+See ?torch_isclose()
 
 ## isfinite
 
 isfinite() -> Tensor
 
-See [torch_isfinite()]
+See ?torch_isfinite()
 
 ## isinf
 
 isinf() -> Tensor
 
-See [torch_isinf()]
+See ?torch_isinf()
 
 ## isnan
 
 isnan() -> Tensor
 
-See [torch_isnan()]
+See ?torch_isnan()
 
 ## istft
-See [torch_istft()]
+See ?torch_istft()
 ## item
 
 item() -> number
@@ -1704,13 +1704,13 @@ x$item()
 
 kthvalue(k, dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
 
-See [torch_kthvalue()]
+See ?torch_kthvalue()
 
 ## le
 
 le(other) -> Tensor
 
-See [torch_le()]
+See ?torch_le()
 
 ## le_
 
@@ -1722,7 +1722,7 @@ In-place version of `$le`
 
 lerp(end, weight) -> Tensor
 
-See [torch_lerp()]
+See ?torch_lerp()
 
 ## lerp_
 
@@ -1734,7 +1734,7 @@ In-place version of `$lerp`
 
 lgamma() -> Tensor
 
-See [torch_lgamma()]
+See ?torch_lgamma()
 
 ## lgamma_
 
@@ -1746,7 +1746,7 @@ In-place version of `$lgamma`
 
 log() -> Tensor
 
-See [torch_log()]
+See ?torch_log()
 
 ## log10
 
@@ -1808,7 +1808,7 @@ $$
 
 logaddexp(other) -> Tensor
 
-See [torch_logaddexp()]
+See ?torch_logaddexp()
 
 ## logaddexp2
 
@@ -1820,13 +1820,13 @@ See [torch_logaddexp2()]
 
 logcumsumexp(dim) -> Tensor
 
-See [torch_logcumsumexp()]
+See ?torch_logcumsumexp()
 
 ## logdet
 
 logdet() -> Tensor
 
-See [torch_logdet()]
+See ?torch_logdet()
 
 ## logical_and
 
@@ -1880,7 +1880,7 @@ In-place version of `$logical_xor`
 
 logsumexp(dim, keepdim=FALSE) -> Tensor
 
-See [torch_logsumexp()]
+See ?torch_logsumexp()
 
 ## long
 
@@ -1897,13 +1897,13 @@ long(memory_format=torch_preserve_format) -> Tensor
 
 lstsq(A) -> (Tensor, Tensor)
 
-See [torch_lstsq()]
+See ?torch_lstsq()
 
 ## lt
 
 lt(other) -> Tensor
 
-See [torch_lt()]
+See ?torch_lt()
 
 ## lt_
 
@@ -1912,7 +1912,7 @@ lt_(other) -> Tensor
 In-place version of `$lt`
 
 ## lu
-See [torch_lu()]
+See ?torch_lu()
 ## lu_solve
 
 lu_solve(LU_data, LU_pivots) -> Tensor
@@ -1987,7 +1987,7 @@ See [torch_masked_select()]
 
 matmul(tensor2) -> Tensor
 
-See [torch_matmul()]
+See ?torch_matmul()
 
 ## matrix_power
 
@@ -1999,43 +1999,43 @@ See [torch_matrix_power()]
 
 max(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
 
-See [torch_max()]
+See ?torch_max()
 
 ## mean
 
 mean(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
 
-See [torch_mean()]
+See ?torch_mean()
 
 ## median
 
 median(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
 
-See [torch_median()]
+See ?torch_median()
 
 ## min
 
 min(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
 
-See [torch_min()]
+See ?torch_min()
 
 ## mm
 
 mm(mat2) -> Tensor
 
-See [torch_mm()]
+See ?torch_mm()
 
 ## mode
 
 mode(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
 
-See [torch_mode()]
+See ?torch_mode()
 
 ## mul
 
 mul(value) -> Tensor
 
-See [torch_mul()]
+See ?torch_mul()
 
 ## mul_
 
@@ -2047,19 +2047,19 @@ In-place version of `$mul`
 
 multinomial(num_samples, replacement=FALSE, *, generator=NULL) -> Tensor
 
-See [torch_multinomial()]
+See ?torch_multinomial()
 
 ## mv
 
 mv(vec) -> Tensor
 
-See [torch_mv()]
+See ?torch_mv()
 
 ## mvlgamma
 
 mvlgamma(p) -> Tensor
 
-See [torch_mvlgamma()]
+See ?torch_mvlgamma()
 
 ## mvlgamma_
 
@@ -2089,7 +2089,7 @@ The named tensor API is experimental and subject to change.
 
 narrow(dimension, start, length) -> Tensor
 
-See [torch_narrow()]
+See ?torch_narrow()
 
 #### Examples: 
 
@@ -2123,7 +2123,7 @@ Alias for `$dim()`
 
 ne(other) -> Tensor
 
-See [torch_ne()]
+See ?torch_ne()
 
 ## ne_
 
@@ -2135,7 +2135,7 @@ In-place version of `$ne`
 
 neg() -> Tensor
 
-See [torch_neg()]
+See ?torch_neg()
 
 ## neg_
 
@@ -2292,10 +2292,10 @@ tensor$new_zeros(c(2, 3))
 
 nonzero() -> LongTensor
 
-See [torch_nonzero()]
+See ?torch_nonzero()
 
 ## norm
-See [torch_norm()]
+See ?torch_norm()
 ## normal_
 
 normal_(mean=0, std=1, *, generator=NULL) -> Tensor
@@ -2307,7 +2307,7 @@ parameterized by `mean` and `std`.
 
 numel() -> int
 
-See [torch_numel()]
+See ?torch_numel()
 
 ## numpy
 
@@ -2321,13 +2321,13 @@ returned `ndarray` share the same underlying storage. Changes to
 
 orgqr(input2) -> Tensor
 
-See [torch_orgqr()]
+See ?torch_orgqr()
 
 ## ormqr
 
 ormqr(input2, input3, left=TRUE, transpose=FALSE) -> Tensor
 
-See [torch_ormqr()]
+See ?torch_ormqr()
 
 ## permute
 
@@ -2357,13 +2357,13 @@ Copies the tensor to pinned memory, if it's not already pinned.
 
 pinverse() -> Tensor
 
-See [torch_pinverse()]
+See ?torch_pinverse()
 
 ## polygamma
 
 polygamma(n) -> Tensor
 
-See [torch_polygamma()]
+See ?torch_polygamma()
 
 ## polygamma_
 
@@ -2375,7 +2375,7 @@ In-place version of `$polygamma`
 
 pow(exponent) -> Tensor
 
-See [torch_pow()]
+See ?torch_pow()
 
 ## pow_
 
@@ -2387,7 +2387,7 @@ In-place version of `$pow`
 
 prod(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor
 
-See [torch_prod()]
+See ?torch_prod()
 
 ## put_
 
@@ -2457,7 +2457,7 @@ returns the zero_point of the underlying quantizer().
 
 qr(some=TRUE) -> (Tensor, Tensor)
 
-See [torch_qr()]
+See ?torch_qr()
 
 ## qscheme
 
@@ -2509,7 +2509,7 @@ x$real
 
 reciprocal() -> Tensor
 
-See [torch_reciprocal()]
+See ?torch_reciprocal()
 
 ## reciprocal_
 
@@ -2591,7 +2591,7 @@ h$remove()
 
 remainder(divisor) -> Tensor
 
-See [torch_remainder()]
+See ?torch_remainder()
 
 ## remainder_
 
@@ -2630,7 +2630,7 @@ In-place version of `$rename`.
 
 renorm(p, dim, maxnorm) -> Tensor
 
-See [torch_renorm()]
+See ?torch_renorm()
 
 ## renorm_
 
@@ -2718,7 +2718,7 @@ but with the specified shape. This method returns a view if `shape` is
 compatible with the current shape. See `$view` on when it is
 possible to return a view.
 
-See [torch_reshape()]
+See ?torch_reshape()
 
 #### Arguments: 
 
@@ -2795,13 +2795,13 @@ Enables `$grad` attribute for non-leaf Tensors.
 
 rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -> Tensor
 
-See [torch_rfft()]
+See ?torch_rfft()
 
 ## roll
 
 roll(shifts, dims) -> Tensor
 
-See [torch_roll()]
+See ?torch_roll()
 
 ## rot90
 
@@ -2813,7 +2813,7 @@ See [torch_rot90()]
 
 round() -> Tensor
 
-See [torch_round()]
+See ?torch_round()
 
 ## round_
 
@@ -2825,7 +2825,7 @@ In-place version of `$round`
 
 rsqrt() -> Tensor
 
-See [torch_rsqrt()]
+See ?torch_rsqrt()
 
 ## rsqrt_
 
@@ -3004,7 +3004,7 @@ short(memory_format=torch_preserve_format) -> Tensor
 
 sigmoid() -> Tensor
 
-See [torch_sigmoid()]
+See ?torch_sigmoid()
 
 ## sigmoid_
 
@@ -3016,7 +3016,7 @@ In-place version of `$sigmoid`
 
 sign() -> Tensor
 
-See [torch_sign()]
+See ?torch_sign()
 
 ## sign_
 
@@ -3028,7 +3028,7 @@ In-place version of `$sign`
 
 sin() -> Tensor
 
-See [torch_sin()]
+See ?torch_sin()
 
 ## sin_
 
@@ -3040,7 +3040,7 @@ In-place version of `$sin`
 
 sinh() -> Tensor
 
-See [torch_sinh()]
+See ?torch_sinh()
 
 ## sinh_
 
@@ -3065,19 +3065,19 @@ torch_empty(3, 4, 5)$size()
 
 slogdet() -> (Tensor, Tensor)
 
-See [torch_slogdet()]
+See ?torch_slogdet()
 
 ## solve
 
 solve(A) -> Tensor, Tensor
 
-See [torch_solve()]
+See ?torch_solve()
 
 ## sort
 
 sort(dim=-1, descending=FALSE) -> (Tensor, LongTensor)
 
-See [torch_sort()]
+See ?torch_sort()
 
 ## sparse_dim
 
@@ -3102,13 +3102,13 @@ must have the same shape.
 * mask (SparseTensor): a SparseTensor which we filter `input` based on its indices
 
 ## split
-See [torch_split()]
+See ?torch_split()
         
 ## sqrt
 
 sqrt() -> Tensor
 
-See [torch_sqrt()]
+See ?torch_sqrt()
 
 ## sqrt_
 
@@ -3120,7 +3120,7 @@ In-place version of `$sqrt`
 
 square() -> Tensor
 
-See [torch_square()]
+See ?torch_square()
 
 ## square_
 
@@ -3132,7 +3132,7 @@ In-place version of `$square`
 
 squeeze(dim=NULL) -> Tensor
 
-See [torch_squeeze()]
+See ?torch_squeeze()
 
 ## squeeze_
 
@@ -3144,10 +3144,10 @@ In-place version of `$squeeze`
 
 std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor
 
-See [torch_std()]
+See ?torch_std()
 
 ## stft
-See [torch_stft()]
+See ?torch_stft()
         
 ## storage
 
@@ -3223,7 +3223,7 @@ In-place version of `$sub`
 
 sum(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor
 
-See [torch_sum()]
+See ?torch_sum()
 
 ## sum_to_size
 
@@ -3240,19 +3240,19 @@ Sum `this` tensor to `size`.
 
 svd(some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor)
 
-See [torch_svd()]
+See ?torch_svd()
 
 ## symeig
 
 symeig(eigenvectors=FALSE, upper=TRUE) -> (Tensor, Tensor)
 
-See [torch_symeig()]
+See ?torch_symeig()
 
 ## t
 
 t() -> Tensor
 
-See [torch_t()]
+See ?torch_t()
 
 ## t_
 
@@ -3264,13 +3264,13 @@ In-place version of `$t`
 
 take(indices) -> Tensor
 
-See [torch_take()]
+See ?torch_take()
 
 ## tan
 
 tan() -> Tensor
 
-See [torch_tan()]
+See ?torch_tan()
 
 ## tan_
 
@@ -3282,7 +3282,7 @@ In-place version of `$tan`
 
 tanh() -> Tensor
 
-See [torch_tanh()]
+See ?torch_tanh()
 
 ## tanh_
 
@@ -3381,19 +3381,19 @@ This operation is not differentiable.
 
 topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor)
 
-See [torch_topk()]
+See ?torch_topk()
 
 ## trace
 
 trace() -> Tensor
 
-See [torch_trace()]
+See ?torch_trace()
 
 ## transpose
 
 transpose(dim0, dim1) -> Tensor
 
-See [torch_transpose()]
+See ?torch_transpose()
 
 ## transpose_
 
@@ -3411,7 +3411,7 @@ See [torch_triangular_solve()]
 
 tril(k=0) -> Tensor
 
-See [torch_tril()]
+See ?torch_tril()
 
 ## tril_
 
@@ -3423,7 +3423,7 @@ In-place version of `$tril`
 
 triu(k=0) -> Tensor
 
-See [torch_triu()]
+See ?torch_triu()
 
 ## triu_
 
@@ -3447,7 +3447,7 @@ In-place version of `$true_divide_`
 
 trunc() -> Tensor
 
-See [torch_trunc()]
+See ?torch_trunc()
 
 ## trunc_
 
@@ -3491,7 +3491,7 @@ equivalent to `self$type(tensor.type())`
 
 unbind(dim=0) -> seq
 
-See [torch_unbind()]
+See ?torch_unbind()
 
 ## unflatten
 
@@ -3538,7 +3538,7 @@ $$
 
 Returns the unique elements of the input tensor.
 
-See [torch_unique()]
+See ?torch_unique()
         
 ## unique_consecutive
 
@@ -3550,7 +3550,7 @@ See [torch_unique_consecutive()]
 
 unsqueeze(dim) -> Tensor
 
-See [torch_unsqueeze()]
+See ?torch_unsqueeze()
 
 ## unsqueeze_
 
@@ -3576,7 +3576,7 @@ This method can only be called on a coalesced sparse tensor. See
 
 var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor
 
-See [torch_var()]
+See ?torch_var()
 
 ## view
 
@@ -3625,7 +3625,7 @@ Please see `$view` for more information about `view`.
 where(condition, y) -> Tensor
 
 `self$where(condition, y)` is equivalent to `torch_where(condition, self, y)`.
-See [torch_where()]
+See ?torch_where()
 
 ## zero_
 
-- 
GitLab


From 829b38b8df8a856a34b672c250e7f62dd0e34dc3 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 16 Sep 2020 19:41:07 -0300
Subject: [PATCH 115/157] other small fixes

---
 vignettes/tensor/index.Rmd | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/vignettes/tensor/index.Rmd b/vignettes/tensor/index.Rmd
index 60de01858..3c5eb5545 100644
--- a/vignettes/tensor/index.Rmd
+++ b/vignettes/tensor/index.Rmd
@@ -11,7 +11,8 @@ vignette: >
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1"),
+  results = "hide"
 )
 ```
 
@@ -195,7 +196,7 @@ To align a tensor to a specific order, use `$align_to`.
 
 ### Examples:
 
-```{r echo=FALSE}
+```{r eval=FALSE}
 # Example 1: Applying a mask
 mask <- torch_randint(low = 0, high = 2, size = c(127, 128), dtype=torch_bool())$refine_names(c('W', 'H'))
 imgs <- torch_randn(32, 128, 127, 3, names=c('N', 'H', 'W', 'C'))
@@ -290,7 +291,7 @@ in the output tensor having 1 fewer dimension than `input`.
 #### Examples: 
 
 ```{r}
-a <- torch_rand(4, 2)to(dtype = torch_bool())
+a <- torch_rand(4, 2)$to(dtype = torch_bool())
 a
 a$all(dim=2)
 a$all(dim=1)
@@ -1442,7 +1443,7 @@ match `self`, or an error will be raised.
 
 #### Examples: 
 
-```{r}
+```{r, eval = FALSE}
 x <- torch_zeros(5, 3)
 t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
 index <- torch_tensor(c(1, 5, 3))
@@ -2770,7 +2771,7 @@ change the size in-place with custom strides, see `$set_()`.
 
 ```{r}
 x <- torch_tensor(matrix(1:6, ncol = 2))
-x$resize_(2, 2)
+x$resize_(c(2, 2))
 ```
     
 ## resize_as_
@@ -2880,7 +2881,7 @@ along the specified dimension `dim` must be unique.
 
 #### Examples: 
 
-```{r}
+```{r, eval = FALSE}
 x <- torch_rand(2, 5)
 x
 torch_zeros(3, 5)$scatter_(
@@ -2942,7 +2943,7 @@ a performance cost) by setting `torch_backends.cudnn.deterministic = TRUE`.
 
 #### Examples: 
 
-```{r}
+```{r, eval = FALSE}
 x <- torch_rand(2, 5)
 x
 torch_ones(3, 5)$scatter_add_(1, torch_tensor(rbind(c(0, 1, 2, 0, 0), c(2, 0, 0, 1, 2))), x)
@@ -3164,7 +3165,7 @@ number of storage elements (not bytes).
 
 #### Examples: 
 
-```{r}
+```{r, eval = FALSE}
 x <- torch_tensor(c(1, 2, 3, 4, 5))
 x$storage_offset()
 x[3:N]$storage_offset()
@@ -3196,7 +3197,7 @@ the particular dimension `dim`.
 ```{r}
 x <- torch_tensor(matrix(1:10, nrow = 2))
 x$stride()
-x$stride(0)
+x$stride(1)
 x$stride(-1)
 ```
     
-- 
GitLab


From f0aa913e8ef5aba7d0a66759d1197409e2811959 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 16 Sep 2020 19:44:23 -0300
Subject: [PATCH 116/157] fix some math

---
 vignettes/tensor/index.Rmd | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/vignettes/tensor/index.Rmd b/vignettes/tensor/index.Rmd
index 3c5eb5545..000628ced 100644
--- a/vignettes/tensor/index.Rmd
+++ b/vignettes/tensor/index.Rmd
@@ -497,8 +497,8 @@ In-place version of `$baddbmm`
 
 bernoulli(*, generator=NULL) -> Tensor
 
-Returns a result tensor where each `\texttt{result[i]}` is independently
-sampled from `\text{Bernoulli}(\texttt{self[i]})`. `self` must have
+Returns a result tensor where each $\texttt{result[i]}$ is independently
+sampled from $\text{Bernoulli}(\texttt{self[i]})$. `self` must have
 floating point `dtype`, and the result will have the same `dtype`.
 
 See ?torch_bernoulli()
@@ -508,7 +508,7 @@ See ?torch_bernoulli()
 bernoulli_(p=0.5, *, generator=NULL) -> Tensor
 
 Fills each location of `self` with an independent sample from
-`\text{Bernoulli}(\texttt{p})`. `self` can have integral
+$\text{Bernoulli}(\texttt{p})$. `self` can have integral
 `dtype`.
 
 bernoulli_(p_tensor, *, generator=NULL) -> Tensor
@@ -516,8 +516,8 @@ bernoulli_(p_tensor, *, generator=NULL) -> Tensor
 `p_tensor` should be a tensor containing probabilities to be used for
 drawing the binary random number.
 
-The `\text{i}^{th}` element of `self` tensor will be set to a
-value sampled from `\text{Bernoulli}(\texttt{p\_tensor[i]})`.
+The $\text{i}^{th}$ element of `self` tensor will be set to a
+value sampled from $\text{Bernoulli}(\texttt{p\_tensor[i]})$.
 
 `self` can have integral `dtype`, but `p_tensor` must have
 floating point `dtype`.
-- 
GitLab


From 70d2c7aa8561674f7a7914d41ad7cab025384a26 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 16 Sep 2020 19:45:36 -0300
Subject: [PATCH 117/157] fix for autolinking

---
 vignettes/tensor/index.Rmd | 300 ++++++++++++++++++-------------------
 1 file changed, 150 insertions(+), 150 deletions(-)

diff --git a/vignettes/tensor/index.Rmd b/vignettes/tensor/index.Rmd
index 000628ced..724059a20 100644
--- a/vignettes/tensor/index.Rmd
+++ b/vignettes/tensor/index.Rmd
@@ -39,7 +39,7 @@ If `n` is the number of dimensions in `x`,
 
 abs() -> Tensor
 
-See ?torch_abs()
+See `?torch_abs()`
 
 ## abs_
 
@@ -64,7 +64,7 @@ Alias for [$abs_()]
 
 acos() -> Tensor
 
-See ?torch_acos()
+See `?torch_acos()`
 
 ## acos_
 
@@ -76,7 +76,7 @@ In-place version of `$acos`
 
 acosh() -> Tensor
 
-See ?torch_acosh()
+See `?torch_acosh()`
 
 ## acosh_
 
@@ -96,7 +96,7 @@ When `other` is a tensor, the shape of `other` must be
 broadcastable with the shape of the underlying
 tensor
 
-See ?torch_add()
+See `?torch_add()`
 
 ## add_
 
@@ -108,7 +108,7 @@ In-place version of `$add`
 
 addbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor
 
-See ?torch_addbmm()
+See `?torch_addbmm()`
 
 ## addbmm_
 
@@ -120,7 +120,7 @@ In-place version of `$addbmm`
 
 addcdiv(tensor1, tensor2, *, value=1) -> Tensor
 
-See ?torch_addcdiv()
+See `?torch_addcdiv()`
 
 ## addcdiv_
 
@@ -132,7 +132,7 @@ In-place version of `$addcdiv`
 
 addcmul(tensor1, tensor2, *, value=1) -> Tensor
 
-See ?torch_addcmul()
+See `?torch_addcmul()`
 
 ## addcmul_
 
@@ -144,7 +144,7 @@ In-place version of `$addcmul`
 
 addmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor
 
-See ?torch_addmm()
+See `?torch_addmm()`
 
 ## addmm_
 
@@ -156,7 +156,7 @@ In-place version of `$addmm`
 
 addmv(mat, vec, *, beta=1, alpha=1) -> Tensor
 
-See ?torch_addmv()
+See `?torch_addmv()`
 
 ## addmv_
 
@@ -168,7 +168,7 @@ In-place version of `$addmv`
 
 addr(vec1, vec2, *, beta=1, alpha=1) -> Tensor
 
-See ?torch_addr()
+See `?torch_addr()`
 
 ## addr_
 
@@ -279,7 +279,7 @@ dimension `dim` are TRUE, FALSE otherwise.
 
 If `keepdim` is `TRUE`, the output tensor is of the same size as
 `input` except in the dimension `dim` where it is of size 1.
-Otherwise, `dim` is squeezed (see ?torch_squeeze()), resulting
+Otherwise, `dim` is squeezed (see `?torch_squeeze()),` resulting
 in the output tensor having 1 fewer dimension than `input`.
 
 #### Arguments: 
@@ -301,13 +301,13 @@ a$all(dim=1)
 
 allclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor
 
-See ?torch_allclose()
+See `?torch_allclose()`
 
 ## angle
 
 angle() -> Tensor
 
-See ?torch_angle()
+See `?torch_angle()`
 
 ## any
 
@@ -330,7 +330,7 @@ dimension `dim` are TRUE, FALSE otherwise.
 
 If `keepdim` is `TRUE`, the output tensor is of the same size as
 `input` except in the dimension `dim` where it is of size 1.
-Otherwise, `dim` is squeezed (see ?torch_squeeze()), resulting
+Otherwise, `dim` is squeezed (see `?torch_squeeze()),` resulting
 in the output tensor having 1 fewer dimension than `input`.
 
 #### Arguments: 
@@ -364,19 +364,19 @@ sections that require high performance.
 
 argmax(dim=NULL, keepdim=FALSE) -> LongTensor
 
-See ?torch_argmax()
+See `?torch_argmax()`
 
 ## argmin
 
 argmin(dim=NULL, keepdim=FALSE) -> LongTensor
 
-See ?torch_argmin()
+See `?torch_argmin()`
 
 ## argsort
 
 argsort(dim=-1, descending=FALSE) -> LongTensor
 
-See ?torch_argsort()
+See `?torch_argsort()`
 
 ## as_strided
 
@@ -396,7 +396,7 @@ to the autograd graph. `cls` must be a subclass of `Tensor`.
 
 asin() -> Tensor
 
-See ?torch_asin()
+See `?torch_asin()`
 
 ## asin_
 
@@ -408,7 +408,7 @@ In-place version of `$asin`
 
 asinh() -> Tensor
 
-See ?torch_asinh()
+See `?torch_asinh()`
 
 ## asinh_
 
@@ -420,7 +420,7 @@ In-place version of `$asinh`
 
 atan() -> Tensor
 
-See ?torch_atan()
+See `?torch_atan()`
 
 ## atan2
 
@@ -444,7 +444,7 @@ In-place version of `$atan`
 
 atanh() -> Tensor
 
-See ?torch_atanh()
+See `?torch_atanh()`
 
 ## atanh_
 
@@ -485,7 +485,7 @@ for details on the memory layout of accumulated gradients.
 
 baddbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor
 
-See ?torch_baddbmm()
+See `?torch_baddbmm()`
 
 ## baddbmm_
 
@@ -501,7 +501,7 @@ Returns a result tensor where each $\texttt{result[i]}$ is independently
 sampled from $\text{Bernoulli}(\texttt{self[i]})$. `self` must have
 floating point `dtype`, and the result will have the same `dtype`.
 
-See ?torch_bernoulli()
+See `?torch_bernoulli()`
 
 ## bernoulli_
 
@@ -522,7 +522,7 @@ value sampled from $\text{Bernoulli}(\texttt{p\_tensor[i]})$.
 `self` can have integral `dtype`, but `p_tensor` must have
 floating point `dtype`.
 
-See also `$bernoulli` and ?torch_bernoulli()
+See also `$bernoulli` and `?torch_bernoulli()`
 
 ## bfloat16
 
@@ -538,7 +538,7 @@ bfloat16(memory_format=torch_preserve_format) -> Tensor
 
 bincount(weights=NULL, minlength=0) -> Tensor
 
-See ?torch_bincount()
+See `?torch_bincount()`
 
 ## bitwise_and
 
@@ -592,7 +592,7 @@ In-place version of `$bitwise_xor`
 
 bmm(batch2) -> Tensor
 
-See ?torch_bmm()
+See `?torch_bmm()`
 
 ## bool
 
@@ -630,7 +630,7 @@ $$
 
 ceil() -> Tensor
 
-See ?torch_ceil()
+See `?torch_ceil()`
 
 ## ceil_
 
@@ -653,7 +653,7 @@ char(memory_format=torch_preserve_format) -> Tensor
 
 cholesky(upper=FALSE) -> Tensor
 
-See ?torch_cholesky()
+See `?torch_cholesky()`
 
 ## cholesky_inverse
 
@@ -671,13 +671,13 @@ See [torch_cholesky_solve()]
 
 chunk(chunks, dim=0) -> List of Tensors
 
-See ?torch_chunk()
+See `?torch_chunk()`
 
 ## clamp
 
 clamp(min, max) -> Tensor
 
-See ?torch_clamp()
+See `?torch_clamp()`
 
 ## clamp_
 
@@ -706,7 +706,7 @@ propagating to the cloned tensor will propagate to the original tensor.
 
 conj() -> Tensor
 
-See ?torch_conj()
+See `?torch_conj()`
 
 ## contiguous
 
@@ -743,7 +743,7 @@ different device.
 
 cos() -> Tensor
 
-See ?torch_cos()
+See `?torch_cos()`
 
 ## cos_
 
@@ -755,7 +755,7 @@ In-place version of `$cos`
 
 cosh() -> Tensor
 
-See ?torch_cosh()
+See `?torch_cosh()`
 
 ## cosh_
 
@@ -781,7 +781,7 @@ then no copy is performed and the original object is returned.
 
 cross(other, dim=-1) -> Tensor
 
-See ?torch_cross()
+See `?torch_cross()`
 
 ## cuda
 
@@ -806,25 +806,25 @@ then no copy is performed and the original object is returned.
 
 cummax(dim) -> (Tensor, Tensor)
 
-See ?torch_cummax()
+See `?torch_cummax()`
 
 ## cummin
 
 cummin(dim) -> (Tensor, Tensor)
 
-See ?torch_cummin()
+See `?torch_cummin()`
 
 ## cumprod
 
 cumprod(dim, dtype=NULL) -> Tensor
 
-See ?torch_cumprod()
+See `?torch_cumprod()`
 
 ## cumsum
 
 cumsum(dim, dtype=NULL) -> Tensor
 
-See ?torch_cumsum()
+See `?torch_cumsum()`
 
 ## data_ptr
 
@@ -863,7 +863,7 @@ Given a quantized Tensor, dequantize it and return the dequantized float Tensor.
 
 det() -> Tensor
 
-See ?torch_det()
+See `?torch_det()`
 
 ## detach
 
@@ -898,7 +898,7 @@ Is the `torch_device` where this Tensor is.
 
 diag(diagonal=0) -> Tensor
 
-See ?torch_diag()
+See `?torch_diag()`
 
 ## diag_embed
 
@@ -910,19 +910,19 @@ See [torch_diag_embed()]
 
 diagflat(offset=0) -> Tensor
 
-See ?torch_diagflat()
+See `?torch_diagflat()`
 
 ## diagonal
 
 diagonal(offset=0, dim1=0, dim2=1) -> Tensor
 
-See ?torch_diagonal()
+See `?torch_diagonal()`
 
 ## digamma
 
 digamma() -> Tensor
 
-See ?torch_digamma()
+See `?torch_digamma()`
 
 ## digamma_
 
@@ -940,13 +940,13 @@ Returns the number of dimensions of `self` tensor.
 
 dist(other, p=2) -> Tensor
 
-See ?torch_dist()
+See `?torch_dist()`
 
 ## div
 
 div(value) -> Tensor
 
-See ?torch_div()
+See `?torch_div()`
 
 ## div_
 
@@ -958,7 +958,7 @@ In-place version of `$div`
 
 dot(tensor2) -> Tensor
 
-See ?torch_dot()
+See `?torch_dot()`
 
 ## double
 
@@ -975,7 +975,7 @@ double(memory_format=torch_preserve_format) -> Tensor
 
 eig(eigenvectors=FALSE) -> (Tensor, Tensor)
 
-See ?torch_eig()
+See `?torch_eig()`
 
 ## element_size
 
@@ -993,7 +993,7 @@ torch_tensor(c(1))$element_size()
 
 eq(other) -> Tensor
 
-See ?torch_eq()
+See `?torch_eq()`
 
 ## eq_
 
@@ -1005,13 +1005,13 @@ In-place version of `$eq`
 
 equal(other) -> bool
 
-See ?torch_equal()
+See `?torch_equal()`
 
 ## erf
 
 erf() -> Tensor
 
-See ?torch_erf()
+See `?torch_erf()`
 
 ## erf_
 
@@ -1023,7 +1023,7 @@ In-place version of `$erf`
 
 erfc() -> Tensor
 
-See ?torch_erfc()
+See `?torch_erfc()`
 
 ## erfc_
 
@@ -1035,7 +1035,7 @@ In-place version of `$erfc`
 
 erfinv() -> Tensor
 
-See ?torch_erfinv()
+See `?torch_erfinv()`
 
 ## erfinv_
 
@@ -1047,7 +1047,7 @@ In-place version of `$erfinv`
 
 exp() -> Tensor
 
-See ?torch_exp()
+See `?torch_exp()`
 
 ## exp_
 
@@ -1135,7 +1135,7 @@ $$
 
 fft(signal_ndim, normalized=FALSE) -> Tensor
 
-See ?torch_fft()
+See `?torch_fft()`
 
 ## fill_
 
@@ -1171,25 +1171,25 @@ c$fill_diagonal_(5, wrap=TRUE)
 
 flatten(input, start_dim=0, end_dim=-1) -> Tensor
 
-see ?torch_flatten()
+see `?torch_flatten()`
 
 ## flip
 
 flip(dims) -> Tensor
 
-See ?torch_flip()
+See `?torch_flip()`
 
 ## fliplr
 
 fliplr() -> Tensor
 
-See ?torch_fliplr()
+See `?torch_fliplr()`
 
 ## flipud
 
 flipud() -> Tensor
 
-See ?torch_flipud()
+See `?torch_flipud()`
 
 ## float
 
@@ -1206,7 +1206,7 @@ float(memory_format=torch_preserve_format) -> Tensor
 
 floor() -> Tensor
 
-See ?torch_floor()
+See `?torch_floor()`
 
 ## floor_
 
@@ -1230,7 +1230,7 @@ In-place version of `$floor_divide`
 
 fmod(divisor) -> Tensor
 
-See ?torch_fmod()
+See `?torch_fmod()`
 
 ## fmod_
 
@@ -1242,7 +1242,7 @@ In-place version of `$fmod`
 
 frac() -> Tensor
 
-See ?torch_frac()
+See `?torch_frac()`
 
 ## frac_
 
@@ -1254,13 +1254,13 @@ In-place version of `$frac`
 
 gather(dim, index) -> Tensor
 
-See ?torch_gather()
+See `?torch_gather()`
 
 ## ge
 
 ge(other) -> Tensor
 
-See ?torch_ge()
+See `?torch_ge()`
 
 ## ge_
 
@@ -1282,13 +1282,13 @@ $$
 
 geqrf() -> (Tensor, Tensor)
 
-See ?torch_geqrf()
+See `?torch_geqrf()`
 
 ## ger
 
 ger(vec2) -> Tensor
 
-See ?torch_ger()
+See `?torch_ger()`
 
 ## get_device
 
@@ -1315,7 +1315,7 @@ The attribute will then contain the gradients computed and future calls to
 
 gt(other) -> Tensor
 
-See ?torch_gt()
+See `?torch_gt()`
 
 ## gt_
 
@@ -1348,13 +1348,13 @@ Is `TRUE` if any of this tensor's dimensions are named. Otherwise, is `FALSE`.
 
 histc(bins=100, min=0, max=0) -> Tensor
 
-See ?torch_histc()
+See `?torch_histc()`
 
 ## ifft
 
 ifft(signal_ndim, normalized=FALSE) -> Tensor
 
-See ?torch_ifft()
+See `?torch_ifft()`
 
 ## imag
 
@@ -1549,13 +1549,13 @@ underlying uint8_t values of the given Tensor.
 
 inverse() -> Tensor
 
-See ?torch_inverse()
+See `?torch_inverse()`
 
 ## irfft
 
 irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -> Tensor
 
-See ?torch_irfft()
+See `?torch_irfft()`
 
 ## is_complex
 
@@ -1663,28 +1663,28 @@ Returns TRUE if the data type of `self` is a signed data type.
 
 isclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor
 
-See ?torch_isclose()
+See `?torch_isclose()`
 
 ## isfinite
 
 isfinite() -> Tensor
 
-See ?torch_isfinite()
+See `?torch_isfinite()`
 
 ## isinf
 
 isinf() -> Tensor
 
-See ?torch_isinf()
+See `?torch_isinf()`
 
 ## isnan
 
 isnan() -> Tensor
 
-See ?torch_isnan()
+See `?torch_isnan()`
 
 ## istft
-See ?torch_istft()
+See `?torch_istft()`
 ## item
 
 item() -> number
@@ -1705,13 +1705,13 @@ x$item()
 
 kthvalue(k, dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
 
-See ?torch_kthvalue()
+See `?torch_kthvalue()`
 
 ## le
 
 le(other) -> Tensor
 
-See ?torch_le()
+See `?torch_le()`
 
 ## le_
 
@@ -1723,7 +1723,7 @@ In-place version of `$le`
 
 lerp(end, weight) -> Tensor
 
-See ?torch_lerp()
+See `?torch_lerp()`
 
 ## lerp_
 
@@ -1735,7 +1735,7 @@ In-place version of `$lerp`
 
 lgamma() -> Tensor
 
-See ?torch_lgamma()
+See `?torch_lgamma()`
 
 ## lgamma_
 
@@ -1747,7 +1747,7 @@ In-place version of `$lgamma`
 
 log() -> Tensor
 
-See ?torch_log()
+See `?torch_log()`
 
 ## log10
 
@@ -1809,7 +1809,7 @@ $$
 
 logaddexp(other) -> Tensor
 
-See ?torch_logaddexp()
+See `?torch_logaddexp()`
 
 ## logaddexp2
 
@@ -1821,13 +1821,13 @@ See [torch_logaddexp2()]
 
 logcumsumexp(dim) -> Tensor
 
-See ?torch_logcumsumexp()
+See `?torch_logcumsumexp()`
 
 ## logdet
 
 logdet() -> Tensor
 
-See ?torch_logdet()
+See `?torch_logdet()`
 
 ## logical_and
 
@@ -1881,7 +1881,7 @@ In-place version of `$logical_xor`
 
 logsumexp(dim, keepdim=FALSE) -> Tensor
 
-See ?torch_logsumexp()
+See `?torch_logsumexp()`
 
 ## long
 
@@ -1898,13 +1898,13 @@ long(memory_format=torch_preserve_format) -> Tensor
 
 lstsq(A) -> (Tensor, Tensor)
 
-See ?torch_lstsq()
+See `?torch_lstsq()`
 
 ## lt
 
 lt(other) -> Tensor
 
-See ?torch_lt()
+See `?torch_lt()`
 
 ## lt_
 
@@ -1913,7 +1913,7 @@ lt_(other) -> Tensor
 In-place version of `$lt`
 
 ## lu
-See ?torch_lu()
+See `?torch_lu()`
 ## lu_solve
 
 lu_solve(LU_data, LU_pivots) -> Tensor
@@ -1988,7 +1988,7 @@ See [torch_masked_select()]
 
 matmul(tensor2) -> Tensor
 
-See ?torch_matmul()
+See `?torch_matmul()`
 
 ## matrix_power
 
@@ -2000,43 +2000,43 @@ See [torch_matrix_power()]
 
 max(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
 
-See ?torch_max()
+See `?torch_max()`
 
 ## mean
 
 mean(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
 
-See ?torch_mean()
+See `?torch_mean()`
 
 ## median
 
 median(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
 
-See ?torch_median()
+See `?torch_median()`
 
 ## min
 
 min(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
 
-See ?torch_min()
+See `?torch_min()`
 
 ## mm
 
 mm(mat2) -> Tensor
 
-See ?torch_mm()
+See `?torch_mm()`
 
 ## mode
 
 mode(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
 
-See ?torch_mode()
+See `?torch_mode()`
 
 ## mul
 
 mul(value) -> Tensor
 
-See ?torch_mul()
+See `?torch_mul()`
 
 ## mul_
 
@@ -2048,19 +2048,19 @@ In-place version of `$mul`
 
 multinomial(num_samples, replacement=FALSE, *, generator=NULL) -> Tensor
 
-See ?torch_multinomial()
+See `?torch_multinomial()`
 
 ## mv
 
 mv(vec) -> Tensor
 
-See ?torch_mv()
+See `?torch_mv()`
 
 ## mvlgamma
 
 mvlgamma(p) -> Tensor
 
-See ?torch_mvlgamma()
+See `?torch_mvlgamma()`
 
 ## mvlgamma_
 
@@ -2090,7 +2090,7 @@ The named tensor API is experimental and subject to change.
 
 narrow(dimension, start, length) -> Tensor
 
-See ?torch_narrow()
+See `?torch_narrow()`
 
 #### Examples: 
 
@@ -2124,7 +2124,7 @@ Alias for `$dim()`
 
 ne(other) -> Tensor
 
-See ?torch_ne()
+See `?torch_ne()`
 
 ## ne_
 
@@ -2136,7 +2136,7 @@ In-place version of `$ne`
 
 neg() -> Tensor
 
-See ?torch_neg()
+See `?torch_neg()`
 
 ## neg_
 
@@ -2293,10 +2293,10 @@ tensor$new_zeros(c(2, 3))
 
 nonzero() -> LongTensor
 
-See ?torch_nonzero()
+See `?torch_nonzero()`
 
 ## norm
-See ?torch_norm()
+See `?torch_norm()`
 ## normal_
 
 normal_(mean=0, std=1, *, generator=NULL) -> Tensor
@@ -2308,7 +2308,7 @@ parameterized by `mean` and `std`.
 
 numel() -> int
 
-See ?torch_numel()
+See `?torch_numel()`
 
 ## numpy
 
@@ -2322,13 +2322,13 @@ returned `ndarray` share the same underlying storage. Changes to
 
 orgqr(input2) -> Tensor
 
-See ?torch_orgqr()
+See `?torch_orgqr()`
 
 ## ormqr
 
 ormqr(input2, input3, left=TRUE, transpose=FALSE) -> Tensor
 
-See ?torch_ormqr()
+See `?torch_ormqr()`
 
 ## permute
 
@@ -2358,13 +2358,13 @@ Copies the tensor to pinned memory, if it's not already pinned.
 
 pinverse() -> Tensor
 
-See ?torch_pinverse()
+See `?torch_pinverse()`
 
 ## polygamma
 
 polygamma(n) -> Tensor
 
-See ?torch_polygamma()
+See `?torch_polygamma()`
 
 ## polygamma_
 
@@ -2376,7 +2376,7 @@ In-place version of `$polygamma`
 
 pow(exponent) -> Tensor
 
-See ?torch_pow()
+See `?torch_pow()`
 
 ## pow_
 
@@ -2388,7 +2388,7 @@ In-place version of `$pow`
 
 prod(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor
 
-See ?torch_prod()
+See `?torch_prod()`
 
 ## put_
 
@@ -2458,7 +2458,7 @@ returns the zero_point of the underlying quantizer().
 
 qr(some=TRUE) -> (Tensor, Tensor)
 
-See ?torch_qr()
+See `?torch_qr()`
 
 ## qscheme
 
@@ -2510,7 +2510,7 @@ x$real
 
 reciprocal() -> Tensor
 
-See ?torch_reciprocal()
+See `?torch_reciprocal()`
 
 ## reciprocal_
 
@@ -2592,7 +2592,7 @@ h$remove()
 
 remainder(divisor) -> Tensor
 
-See ?torch_remainder()
+See `?torch_remainder()`
 
 ## remainder_
 
@@ -2631,7 +2631,7 @@ In-place version of `$rename`.
 
 renorm(p, dim, maxnorm) -> Tensor
 
-See ?torch_renorm()
+See `?torch_renorm()`
 
 ## renorm_
 
@@ -2719,7 +2719,7 @@ but with the specified shape. This method returns a view if `shape` is
 compatible with the current shape. See `$view` on when it is
 possible to return a view.
 
-See ?torch_reshape()
+See `?torch_reshape()`
 
 #### Arguments: 
 
@@ -2796,13 +2796,13 @@ Enables `$grad` attribute for non-leaf Tensors.
 
 rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -> Tensor
 
-See ?torch_rfft()
+See `?torch_rfft()`
 
 ## roll
 
 roll(shifts, dims) -> Tensor
 
-See ?torch_roll()
+See `?torch_roll()`
 
 ## rot90
 
@@ -2814,7 +2814,7 @@ See [torch_rot90()]
 
 round() -> Tensor
 
-See ?torch_round()
+See `?torch_round()`
 
 ## round_
 
@@ -2826,7 +2826,7 @@ In-place version of `$round`
 
 rsqrt() -> Tensor
 
-See ?torch_rsqrt()
+See `?torch_rsqrt()`
 
 ## rsqrt_
 
@@ -3005,7 +3005,7 @@ short(memory_format=torch_preserve_format) -> Tensor
 
 sigmoid() -> Tensor
 
-See ?torch_sigmoid()
+See `?torch_sigmoid()`
 
 ## sigmoid_
 
@@ -3017,7 +3017,7 @@ In-place version of `$sigmoid`
 
 sign() -> Tensor
 
-See ?torch_sign()
+See `?torch_sign()`
 
 ## sign_
 
@@ -3029,7 +3029,7 @@ In-place version of `$sign`
 
 sin() -> Tensor
 
-See ?torch_sin()
+See `?torch_sin()`
 
 ## sin_
 
@@ -3041,7 +3041,7 @@ In-place version of `$sin`
 
 sinh() -> Tensor
 
-See ?torch_sinh()
+See `?torch_sinh()`
 
 ## sinh_
 
@@ -3066,19 +3066,19 @@ torch_empty(3, 4, 5)$size()
 
 slogdet() -> (Tensor, Tensor)
 
-See ?torch_slogdet()
+See `?torch_slogdet()`
 
 ## solve
 
 solve(A) -> Tensor, Tensor
 
-See ?torch_solve()
+See `?torch_solve()`
 
 ## sort
 
 sort(dim=-1, descending=FALSE) -> (Tensor, LongTensor)
 
-See ?torch_sort()
+See `?torch_sort()`
 
 ## sparse_dim
 
@@ -3103,13 +3103,13 @@ must have the same shape.
 * mask (SparseTensor): a SparseTensor which we filter `input` based on its indices
 
 ## split
-See ?torch_split()
+See `?torch_split()`
         
 ## sqrt
 
 sqrt() -> Tensor
 
-See ?torch_sqrt()
+See `?torch_sqrt()`
 
 ## sqrt_
 
@@ -3121,7 +3121,7 @@ In-place version of `$sqrt`
 
 square() -> Tensor
 
-See ?torch_square()
+See `?torch_square()`
 
 ## square_
 
@@ -3133,7 +3133,7 @@ In-place version of `$square`
 
 squeeze(dim=NULL) -> Tensor
 
-See ?torch_squeeze()
+See `?torch_squeeze()`
 
 ## squeeze_
 
@@ -3145,10 +3145,10 @@ In-place version of `$squeeze`
 
 std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor
 
-See ?torch_std()
+See `?torch_std()`
 
 ## stft
-See ?torch_stft()
+See `?torch_stft()`
         
 ## storage
 
@@ -3224,7 +3224,7 @@ In-place version of `$sub`
 
 sum(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor
 
-See ?torch_sum()
+See `?torch_sum()`
 
 ## sum_to_size
 
@@ -3241,19 +3241,19 @@ Sum `this` tensor to `size`.
 
 svd(some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor)
 
-See ?torch_svd()
+See `?torch_svd()`
 
 ## symeig
 
 symeig(eigenvectors=FALSE, upper=TRUE) -> (Tensor, Tensor)
 
-See ?torch_symeig()
+See `?torch_symeig()`
 
 ## t
 
 t() -> Tensor
 
-See ?torch_t()
+See `?torch_t()`
 
 ## t_
 
@@ -3265,13 +3265,13 @@ In-place version of `$t`
 
 take(indices) -> Tensor
 
-See ?torch_take()
+See `?torch_take()`
 
 ## tan
 
 tan() -> Tensor
 
-See ?torch_tan()
+See `?torch_tan()`
 
 ## tan_
 
@@ -3283,7 +3283,7 @@ In-place version of `$tan`
 
 tanh() -> Tensor
 
-See ?torch_tanh()
+See `?torch_tanh()`
 
 ## tanh_
 
@@ -3382,19 +3382,19 @@ This operation is not differentiable.
 
 topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor)
 
-See ?torch_topk()
+See `?torch_topk()`
 
 ## trace
 
 trace() -> Tensor
 
-See ?torch_trace()
+See `?torch_trace()`
 
 ## transpose
 
 transpose(dim0, dim1) -> Tensor
 
-See ?torch_transpose()
+See `?torch_transpose()`
 
 ## transpose_
 
@@ -3412,7 +3412,7 @@ See [torch_triangular_solve()]
 
 tril(k=0) -> Tensor
 
-See ?torch_tril()
+See `?torch_tril()`
 
 ## tril_
 
@@ -3424,7 +3424,7 @@ In-place version of `$tril`
 
 triu(k=0) -> Tensor
 
-See ?torch_triu()
+See `?torch_triu()`
 
 ## triu_
 
@@ -3448,7 +3448,7 @@ In-place version of `$true_divide_`
 
 trunc() -> Tensor
 
-See ?torch_trunc()
+See `?torch_trunc()`
 
 ## trunc_
 
@@ -3492,7 +3492,7 @@ equivalent to `self$type(tensor.type())`
 
 unbind(dim=0) -> seq
 
-See ?torch_unbind()
+See `?torch_unbind()`
 
 ## unflatten
 
@@ -3539,7 +3539,7 @@ $$
 
 Returns the unique elements of the input tensor.
 
-See ?torch_unique()
+See `?torch_unique()`
         
 ## unique_consecutive
 
@@ -3551,7 +3551,7 @@ See [torch_unique_consecutive()]
 
 unsqueeze(dim) -> Tensor
 
-See ?torch_unsqueeze()
+See `?torch_unsqueeze()`
 
 ## unsqueeze_
 
@@ -3577,7 +3577,7 @@ This method can only be called on a coalesced sparse tensor. See
 
 var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor
 
-See ?torch_var()
+See `?torch_var()`
 
 ## view
 
@@ -3626,7 +3626,7 @@ Please see `$view` for more information about `view`.
 where(condition, y) -> Tensor
 
 `self$where(condition, y)` is equivalent to `torch_where(condition, self, y)`.
-See ?torch_where()
+See `?torch_where()`
 
 ## zero_
 
-- 
GitLab


From 3df947a8179f64c38527b0a7253e77e91084fe8b Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 16 Sep 2020 19:57:30 -0300
Subject: [PATCH 118/157] more autolinking fixes

---
 vignettes/tensor/index.Rmd | 296 ++++++++++++++++++-------------------
 1 file changed, 148 insertions(+), 148 deletions(-)

diff --git a/vignettes/tensor/index.Rmd b/vignettes/tensor/index.Rmd
index 724059a20..f32c33c7d 100644
--- a/vignettes/tensor/index.Rmd
+++ b/vignettes/tensor/index.Rmd
@@ -39,7 +39,7 @@ If `n` is the number of dimensions in `x`,
 
 abs() -> Tensor
 
-See `?torch_abs()`
+See `?torch_abs`
 
 ## abs_
 
@@ -64,7 +64,7 @@ Alias for [$abs_()]
 
 acos() -> Tensor
 
-See `?torch_acos()`
+See `?torch_acos`
 
 ## acos_
 
@@ -76,7 +76,7 @@ In-place version of `$acos`
 
 acosh() -> Tensor
 
-See `?torch_acosh()`
+See `?torch_acosh`
 
 ## acosh_
 
@@ -96,7 +96,7 @@ When `other` is a tensor, the shape of `other` must be
 broadcastable with the shape of the underlying
 tensor
 
-See `?torch_add()`
+See `?torch_add`
 
 ## add_
 
@@ -108,7 +108,7 @@ In-place version of `$add`
 
 addbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor
 
-See `?torch_addbmm()`
+See `?torch_addbmm`
 
 ## addbmm_
 
@@ -120,7 +120,7 @@ In-place version of `$addbmm`
 
 addcdiv(tensor1, tensor2, *, value=1) -> Tensor
 
-See `?torch_addcdiv()`
+See `?torch_addcdiv`
 
 ## addcdiv_
 
@@ -132,7 +132,7 @@ In-place version of `$addcdiv`
 
 addcmul(tensor1, tensor2, *, value=1) -> Tensor
 
-See `?torch_addcmul()`
+See `?torch_addcmul`
 
 ## addcmul_
 
@@ -144,7 +144,7 @@ In-place version of `$addcmul`
 
 addmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor
 
-See `?torch_addmm()`
+See `?torch_addmm`
 
 ## addmm_
 
@@ -156,7 +156,7 @@ In-place version of `$addmm`
 
 addmv(mat, vec, *, beta=1, alpha=1) -> Tensor
 
-See `?torch_addmv()`
+See `?torch_addmv`
 
 ## addmv_
 
@@ -168,7 +168,7 @@ In-place version of `$addmv`
 
 addr(vec1, vec2, *, beta=1, alpha=1) -> Tensor
 
-See `?torch_addr()`
+See `?torch_addr`
 
 ## addr_
 
@@ -301,13 +301,13 @@ a$all(dim=1)
 
 allclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor
 
-See `?torch_allclose()`
+See `?torch_allclose`
 
 ## angle
 
 angle() -> Tensor
 
-See `?torch_angle()`
+See `?torch_angle`
 
 ## any
 
@@ -364,19 +364,19 @@ sections that require high performance.
 
 argmax(dim=NULL, keepdim=FALSE) -> LongTensor
 
-See `?torch_argmax()`
+See `?torch_argmax`
 
 ## argmin
 
 argmin(dim=NULL, keepdim=FALSE) -> LongTensor
 
-See `?torch_argmin()`
+See `?torch_argmin`
 
 ## argsort
 
 argsort(dim=-1, descending=FALSE) -> LongTensor
 
-See `?torch_argsort()`
+See `?torch_argsort`
 
 ## as_strided
 
@@ -396,7 +396,7 @@ to the autograd graph. `cls` must be a subclass of `Tensor`.
 
 asin() -> Tensor
 
-See `?torch_asin()`
+See `?torch_asin`
 
 ## asin_
 
@@ -408,7 +408,7 @@ In-place version of `$asin`
 
 asinh() -> Tensor
 
-See `?torch_asinh()`
+See `?torch_asinh`
 
 ## asinh_
 
@@ -420,7 +420,7 @@ In-place version of `$asinh`
 
 atan() -> Tensor
 
-See `?torch_atan()`
+See `?torch_atan`
 
 ## atan2
 
@@ -444,7 +444,7 @@ In-place version of `$atan`
 
 atanh() -> Tensor
 
-See `?torch_atanh()`
+See `?torch_atanh`
 
 ## atanh_
 
@@ -485,7 +485,7 @@ for details on the memory layout of accumulated gradients.
 
 baddbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor
 
-See `?torch_baddbmm()`
+See `?torch_baddbmm`
 
 ## baddbmm_
 
@@ -501,7 +501,7 @@ Returns a result tensor where each $\texttt{result[i]}$ is independently
 sampled from $\text{Bernoulli}(\texttt{self[i]})$. `self` must have
 floating point `dtype`, and the result will have the same `dtype`.
 
-See `?torch_bernoulli()`
+See `?torch_bernoulli`
 
 ## bernoulli_
 
@@ -522,7 +522,7 @@ value sampled from $\text{Bernoulli}(\texttt{p\_tensor[i]})$.
 `self` can have integral `dtype`, but `p_tensor` must have
 floating point `dtype`.
 
-See also `$bernoulli` and `?torch_bernoulli()`
+See also `$bernoulli` and `?torch_bernoulli`
 
 ## bfloat16
 
@@ -538,7 +538,7 @@ bfloat16(memory_format=torch_preserve_format) -> Tensor
 
 bincount(weights=NULL, minlength=0) -> Tensor
 
-See `?torch_bincount()`
+See `?torch_bincount`
 
 ## bitwise_and
 
@@ -592,7 +592,7 @@ In-place version of `$bitwise_xor`
 
 bmm(batch2) -> Tensor
 
-See `?torch_bmm()`
+See `?torch_bmm`
 
 ## bool
 
@@ -630,7 +630,7 @@ $$
 
 ceil() -> Tensor
 
-See `?torch_ceil()`
+See `?torch_ceil`
 
 ## ceil_
 
@@ -653,7 +653,7 @@ char(memory_format=torch_preserve_format) -> Tensor
 
 cholesky(upper=FALSE) -> Tensor
 
-See `?torch_cholesky()`
+See `?torch_cholesky`
 
 ## cholesky_inverse
 
@@ -671,13 +671,13 @@ See [torch_cholesky_solve()]
 
 chunk(chunks, dim=0) -> List of Tensors
 
-See `?torch_chunk()`
+See `?torch_chunk`
 
 ## clamp
 
 clamp(min, max) -> Tensor
 
-See `?torch_clamp()`
+See `?torch_clamp`
 
 ## clamp_
 
@@ -706,7 +706,7 @@ propagating to the cloned tensor will propagate to the original tensor.
 
 conj() -> Tensor
 
-See `?torch_conj()`
+See `?torch_conj`
 
 ## contiguous
 
@@ -743,7 +743,7 @@ different device.
 
 cos() -> Tensor
 
-See `?torch_cos()`
+See `?torch_cos`
 
 ## cos_
 
@@ -755,7 +755,7 @@ In-place version of `$cos`
 
 cosh() -> Tensor
 
-See `?torch_cosh()`
+See `?torch_cosh`
 
 ## cosh_
 
@@ -781,7 +781,7 @@ then no copy is performed and the original object is returned.
 
 cross(other, dim=-1) -> Tensor
 
-See `?torch_cross()`
+See `?torch_cross`
 
 ## cuda
 
@@ -806,25 +806,25 @@ then no copy is performed and the original object is returned.
 
 cummax(dim) -> (Tensor, Tensor)
 
-See `?torch_cummax()`
+See `?torch_cummax`
 
 ## cummin
 
 cummin(dim) -> (Tensor, Tensor)
 
-See `?torch_cummin()`
+See `?torch_cummin`
 
 ## cumprod
 
 cumprod(dim, dtype=NULL) -> Tensor
 
-See `?torch_cumprod()`
+See `?torch_cumprod`
 
 ## cumsum
 
 cumsum(dim, dtype=NULL) -> Tensor
 
-See `?torch_cumsum()`
+See `?torch_cumsum`
 
 ## data_ptr
 
@@ -863,7 +863,7 @@ Given a quantized Tensor, dequantize it and return the dequantized float Tensor.
 
 det() -> Tensor
 
-See `?torch_det()`
+See `?torch_det`
 
 ## detach
 
@@ -898,7 +898,7 @@ Is the `torch_device` where this Tensor is.
 
 diag(diagonal=0) -> Tensor
 
-See `?torch_diag()`
+See `?torch_diag`
 
 ## diag_embed
 
@@ -910,19 +910,19 @@ See [torch_diag_embed()]
 
 diagflat(offset=0) -> Tensor
 
-See `?torch_diagflat()`
+See `?torch_diagflat`
 
 ## diagonal
 
 diagonal(offset=0, dim1=0, dim2=1) -> Tensor
 
-See `?torch_diagonal()`
+See `?torch_diagonal`
 
 ## digamma
 
 digamma() -> Tensor
 
-See `?torch_digamma()`
+See `?torch_digamma`
 
 ## digamma_
 
@@ -940,13 +940,13 @@ Returns the number of dimensions of `self` tensor.
 
 dist(other, p=2) -> Tensor
 
-See `?torch_dist()`
+See `?torch_dist`
 
 ## div
 
 div(value) -> Tensor
 
-See `?torch_div()`
+See `?torch_div`
 
 ## div_
 
@@ -958,7 +958,7 @@ In-place version of `$div`
 
 dot(tensor2) -> Tensor
 
-See `?torch_dot()`
+See `?torch_dot`
 
 ## double
 
@@ -975,7 +975,7 @@ double(memory_format=torch_preserve_format) -> Tensor
 
 eig(eigenvectors=FALSE) -> (Tensor, Tensor)
 
-See `?torch_eig()`
+See `?torch_eig`
 
 ## element_size
 
@@ -993,7 +993,7 @@ torch_tensor(c(1))$element_size()
 
 eq(other) -> Tensor
 
-See `?torch_eq()`
+See `?torch_eq`
 
 ## eq_
 
@@ -1005,13 +1005,13 @@ In-place version of `$eq`
 
 equal(other) -> bool
 
-See `?torch_equal()`
+See `?torch_equal`
 
 ## erf
 
 erf() -> Tensor
 
-See `?torch_erf()`
+See `?torch_erf`
 
 ## erf_
 
@@ -1023,7 +1023,7 @@ In-place version of `$erf`
 
 erfc() -> Tensor
 
-See `?torch_erfc()`
+See `?torch_erfc`
 
 ## erfc_
 
@@ -1035,7 +1035,7 @@ In-place version of `$erfc`
 
 erfinv() -> Tensor
 
-See `?torch_erfinv()`
+See `?torch_erfinv`
 
 ## erfinv_
 
@@ -1047,7 +1047,7 @@ In-place version of `$erfinv`
 
 exp() -> Tensor
 
-See `?torch_exp()`
+See `?torch_exp`
 
 ## exp_
 
@@ -1135,7 +1135,7 @@ $$
 
 fft(signal_ndim, normalized=FALSE) -> Tensor
 
-See `?torch_fft()`
+See `?torch_fft`
 
 ## fill_
 
@@ -1171,25 +1171,25 @@ c$fill_diagonal_(5, wrap=TRUE)
 
 flatten(input, start_dim=0, end_dim=-1) -> Tensor
 
-see `?torch_flatten()`
+see `?torch_flatten`
 
 ## flip
 
 flip(dims) -> Tensor
 
-See `?torch_flip()`
+See `?torch_flip`
 
 ## fliplr
 
 fliplr() -> Tensor
 
-See `?torch_fliplr()`
+See `?torch_fliplr`
 
 ## flipud
 
 flipud() -> Tensor
 
-See `?torch_flipud()`
+See `?torch_flipud`
 
 ## float
 
@@ -1206,7 +1206,7 @@ float(memory_format=torch_preserve_format) -> Tensor
 
 floor() -> Tensor
 
-See `?torch_floor()`
+See `?torch_floor`
 
 ## floor_
 
@@ -1230,7 +1230,7 @@ In-place version of `$floor_divide`
 
 fmod(divisor) -> Tensor
 
-See `?torch_fmod()`
+See `?torch_fmod`
 
 ## fmod_
 
@@ -1242,7 +1242,7 @@ In-place version of `$fmod`
 
 frac() -> Tensor
 
-See `?torch_frac()`
+See `?torch_frac`
 
 ## frac_
 
@@ -1254,13 +1254,13 @@ In-place version of `$frac`
 
 gather(dim, index) -> Tensor
 
-See `?torch_gather()`
+See `?torch_gather`
 
 ## ge
 
 ge(other) -> Tensor
 
-See `?torch_ge()`
+See `?torch_ge`
 
 ## ge_
 
@@ -1282,13 +1282,13 @@ $$
 
 geqrf() -> (Tensor, Tensor)
 
-See `?torch_geqrf()`
+See `?torch_geqrf`
 
 ## ger
 
 ger(vec2) -> Tensor
 
-See `?torch_ger()`
+See `?torch_ger`
 
 ## get_device
 
@@ -1315,7 +1315,7 @@ The attribute will then contain the gradients computed and future calls to
 
 gt(other) -> Tensor
 
-See `?torch_gt()`
+See `?torch_gt`
 
 ## gt_
 
@@ -1348,13 +1348,13 @@ Is `TRUE` if any of this tensor's dimensions are named. Otherwise, is `FALSE`.
 
 histc(bins=100, min=0, max=0) -> Tensor
 
-See `?torch_histc()`
+See `?torch_histc`
 
 ## ifft
 
 ifft(signal_ndim, normalized=FALSE) -> Tensor
 
-See `?torch_ifft()`
+See `?torch_ifft`
 
 ## imag
 
@@ -1549,13 +1549,13 @@ underlying uint8_t values of the given Tensor.
 
 inverse() -> Tensor
 
-See `?torch_inverse()`
+See `?torch_inverse`
 
 ## irfft
 
 irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -> Tensor
 
-See `?torch_irfft()`
+See `?torch_irfft`
 
 ## is_complex
 
@@ -1663,28 +1663,28 @@ Returns TRUE if the data type of `self` is a signed data type.
 
 isclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor
 
-See `?torch_isclose()`
+See `?torch_isclose`
 
 ## isfinite
 
 isfinite() -> Tensor
 
-See `?torch_isfinite()`
+See `?torch_isfinite`
 
 ## isinf
 
 isinf() -> Tensor
 
-See `?torch_isinf()`
+See `?torch_isinf`
 
 ## isnan
 
 isnan() -> Tensor
 
-See `?torch_isnan()`
+See `?torch_isnan`
 
 ## istft
-See `?torch_istft()`
+See `?torch_istft`
 ## item
 
 item() -> number
@@ -1705,13 +1705,13 @@ x$item()
 
 kthvalue(k, dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
 
-See `?torch_kthvalue()`
+See `?torch_kthvalue`
 
 ## le
 
 le(other) -> Tensor
 
-See `?torch_le()`
+See `?torch_le`
 
 ## le_
 
@@ -1723,7 +1723,7 @@ In-place version of `$le`
 
 lerp(end, weight) -> Tensor
 
-See `?torch_lerp()`
+See `?torch_lerp`
 
 ## lerp_
 
@@ -1735,7 +1735,7 @@ In-place version of `$lerp`
 
 lgamma() -> Tensor
 
-See `?torch_lgamma()`
+See `?torch_lgamma`
 
 ## lgamma_
 
@@ -1747,7 +1747,7 @@ In-place version of `$lgamma`
 
 log() -> Tensor
 
-See `?torch_log()`
+See `?torch_log`
 
 ## log10
 
@@ -1809,7 +1809,7 @@ $$
 
 logaddexp(other) -> Tensor
 
-See `?torch_logaddexp()`
+See `?torch_logaddexp`
 
 ## logaddexp2
 
@@ -1821,13 +1821,13 @@ See [torch_logaddexp2()]
 
 logcumsumexp(dim) -> Tensor
 
-See `?torch_logcumsumexp()`
+See `?torch_logcumsumexp`
 
 ## logdet
 
 logdet() -> Tensor
 
-See `?torch_logdet()`
+See `?torch_logdet`
 
 ## logical_and
 
@@ -1881,7 +1881,7 @@ In-place version of `$logical_xor`
 
 logsumexp(dim, keepdim=FALSE) -> Tensor
 
-See `?torch_logsumexp()`
+See `?torch_logsumexp`
 
 ## long
 
@@ -1898,13 +1898,13 @@ long(memory_format=torch_preserve_format) -> Tensor
 
 lstsq(A) -> (Tensor, Tensor)
 
-See `?torch_lstsq()`
+See `?torch_lstsq`
 
 ## lt
 
 lt(other) -> Tensor
 
-See `?torch_lt()`
+See `?torch_lt`
 
 ## lt_
 
@@ -1913,7 +1913,7 @@ lt_(other) -> Tensor
 In-place version of `$lt`
 
 ## lu
-See `?torch_lu()`
+See `?torch_lu`
 ## lu_solve
 
 lu_solve(LU_data, LU_pivots) -> Tensor
@@ -1988,7 +1988,7 @@ See [torch_masked_select()]
 
 matmul(tensor2) -> Tensor
 
-See `?torch_matmul()`
+See `?torch_matmul`
 
 ## matrix_power
 
@@ -2000,43 +2000,43 @@ See [torch_matrix_power()]
 
 max(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
 
-See `?torch_max()`
+See `?torch_max`
 
 ## mean
 
 mean(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
 
-See `?torch_mean()`
+See `?torch_mean`
 
 ## median
 
 median(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
 
-See `?torch_median()`
+See `?torch_median`
 
 ## min
 
 min(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
 
-See `?torch_min()`
+See `?torch_min`
 
 ## mm
 
 mm(mat2) -> Tensor
 
-See `?torch_mm()`
+See `?torch_mm`
 
 ## mode
 
 mode(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
 
-See `?torch_mode()`
+See `?torch_mode`
 
 ## mul
 
 mul(value) -> Tensor
 
-See `?torch_mul()`
+See `?torch_mul`
 
 ## mul_
 
@@ -2048,19 +2048,19 @@ In-place version of `$mul`
 
 multinomial(num_samples, replacement=FALSE, *, generator=NULL) -> Tensor
 
-See `?torch_multinomial()`
+See `?torch_multinomial`
 
 ## mv
 
 mv(vec) -> Tensor
 
-See `?torch_mv()`
+See `?torch_mv`
 
 ## mvlgamma
 
 mvlgamma(p) -> Tensor
 
-See `?torch_mvlgamma()`
+See `?torch_mvlgamma`
 
 ## mvlgamma_
 
@@ -2090,7 +2090,7 @@ The named tensor API is experimental and subject to change.
 
 narrow(dimension, start, length) -> Tensor
 
-See `?torch_narrow()`
+See `?torch_narrow`
 
 #### Examples: 
 
@@ -2124,7 +2124,7 @@ Alias for `$dim()`
 
 ne(other) -> Tensor
 
-See `?torch_ne()`
+See `?torch_ne`
 
 ## ne_
 
@@ -2136,7 +2136,7 @@ In-place version of `$ne`
 
 neg() -> Tensor
 
-See `?torch_neg()`
+See `?torch_neg`
 
 ## neg_
 
@@ -2293,10 +2293,10 @@ tensor$new_zeros(c(2, 3))
 
 nonzero() -> LongTensor
 
-See `?torch_nonzero()`
+See `?torch_nonzero`
 
 ## norm
-See `?torch_norm()`
+See `?torch_norm`
 ## normal_
 
 normal_(mean=0, std=1, *, generator=NULL) -> Tensor
@@ -2308,7 +2308,7 @@ parameterized by `mean` and `std`.
 
 numel() -> int
 
-See `?torch_numel()`
+See `?torch_numel`
 
 ## numpy
 
@@ -2322,13 +2322,13 @@ returned `ndarray` share the same underlying storage. Changes to
 
 orgqr(input2) -> Tensor
 
-See `?torch_orgqr()`
+See `?torch_orgqr`
 
 ## ormqr
 
 ormqr(input2, input3, left=TRUE, transpose=FALSE) -> Tensor
 
-See `?torch_ormqr()`
+See `?torch_ormqr`
 
 ## permute
 
@@ -2358,13 +2358,13 @@ Copies the tensor to pinned memory, if it's not already pinned.
 
 pinverse() -> Tensor
 
-See `?torch_pinverse()`
+See `?torch_pinverse`
 
 ## polygamma
 
 polygamma(n) -> Tensor
 
-See `?torch_polygamma()`
+See `?torch_polygamma`
 
 ## polygamma_
 
@@ -2376,7 +2376,7 @@ In-place version of `$polygamma`
 
 pow(exponent) -> Tensor
 
-See `?torch_pow()`
+See `?torch_pow`
 
 ## pow_
 
@@ -2388,7 +2388,7 @@ In-place version of `$pow`
 
 prod(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor
 
-See `?torch_prod()`
+See `?torch_prod`
 
 ## put_
 
@@ -2458,7 +2458,7 @@ returns the zero_point of the underlying quantizer().
 
 qr(some=TRUE) -> (Tensor, Tensor)
 
-See `?torch_qr()`
+See `?torch_qr`
 
 ## qscheme
 
@@ -2510,7 +2510,7 @@ x$real
 
 reciprocal() -> Tensor
 
-See `?torch_reciprocal()`
+See `?torch_reciprocal`
 
 ## reciprocal_
 
@@ -2592,7 +2592,7 @@ h$remove()
 
 remainder(divisor) -> Tensor
 
-See `?torch_remainder()`
+See `?torch_remainder`
 
 ## remainder_
 
@@ -2631,7 +2631,7 @@ In-place version of `$rename`.
 
 renorm(p, dim, maxnorm) -> Tensor
 
-See `?torch_renorm()`
+See `?torch_renorm`
 
 ## renorm_
 
@@ -2719,7 +2719,7 @@ but with the specified shape. This method returns a view if `shape` is
 compatible with the current shape. See `$view` on when it is
 possible to return a view.
 
-See `?torch_reshape()`
+See `?torch_reshape`
 
 #### Arguments: 
 
@@ -2796,13 +2796,13 @@ Enables `$grad` attribute for non-leaf Tensors.
 
 rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -> Tensor
 
-See `?torch_rfft()`
+See `?torch_rfft`
 
 ## roll
 
 roll(shifts, dims) -> Tensor
 
-See `?torch_roll()`
+See `?torch_roll`
 
 ## rot90
 
@@ -2814,7 +2814,7 @@ See [torch_rot90()]
 
 round() -> Tensor
 
-See `?torch_round()`
+See `?torch_round`
 
 ## round_
 
@@ -2826,7 +2826,7 @@ In-place version of `$round`
 
 rsqrt() -> Tensor
 
-See `?torch_rsqrt()`
+See `?torch_rsqrt`
 
 ## rsqrt_
 
@@ -3005,7 +3005,7 @@ short(memory_format=torch_preserve_format) -> Tensor
 
 sigmoid() -> Tensor
 
-See `?torch_sigmoid()`
+See `?torch_sigmoid`
 
 ## sigmoid_
 
@@ -3017,7 +3017,7 @@ In-place version of `$sigmoid`
 
 sign() -> Tensor
 
-See `?torch_sign()`
+See `?torch_sign`
 
 ## sign_
 
@@ -3029,7 +3029,7 @@ In-place version of `$sign`
 
 sin() -> Tensor
 
-See `?torch_sin()`
+See `?torch_sin`
 
 ## sin_
 
@@ -3041,7 +3041,7 @@ In-place version of `$sin`
 
 sinh() -> Tensor
 
-See `?torch_sinh()`
+See `?torch_sinh`
 
 ## sinh_
 
@@ -3066,19 +3066,19 @@ torch_empty(3, 4, 5)$size()
 
 slogdet() -> (Tensor, Tensor)
 
-See `?torch_slogdet()`
+See `?torch_slogdet`
 
 ## solve
 
 solve(A) -> Tensor, Tensor
 
-See `?torch_solve()`
+See `?torch_solve`
 
 ## sort
 
 sort(dim=-1, descending=FALSE) -> (Tensor, LongTensor)
 
-See `?torch_sort()`
+See `?torch_sort`
 
 ## sparse_dim
 
@@ -3103,13 +3103,13 @@ must have the same shape.
 * mask (SparseTensor): a SparseTensor which we filter `input` based on its indices
 
 ## split
-See `?torch_split()`
+See `?torch_split`
         
 ## sqrt
 
 sqrt() -> Tensor
 
-See `?torch_sqrt()`
+See `?torch_sqrt`
 
 ## sqrt_
 
@@ -3121,7 +3121,7 @@ In-place version of `$sqrt`
 
 square() -> Tensor
 
-See `?torch_square()`
+See `?torch_square`
 
 ## square_
 
@@ -3133,7 +3133,7 @@ In-place version of `$square`
 
 squeeze(dim=NULL) -> Tensor
 
-See `?torch_squeeze()`
+See `?torch_squeeze`
 
 ## squeeze_
 
@@ -3145,10 +3145,10 @@ In-place version of `$squeeze`
 
 std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor
 
-See `?torch_std()`
+See `?torch_std`
 
 ## stft
-See `?torch_stft()`
+See `?torch_stft`
         
 ## storage
 
@@ -3224,7 +3224,7 @@ In-place version of `$sub`
 
 sum(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor
 
-See `?torch_sum()`
+See `?torch_sum`
 
 ## sum_to_size
 
@@ -3241,19 +3241,19 @@ Sum `this` tensor to `size`.
 
 svd(some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor)
 
-See `?torch_svd()`
+See `?torch_svd`
 
 ## symeig
 
 symeig(eigenvectors=FALSE, upper=TRUE) -> (Tensor, Tensor)
 
-See `?torch_symeig()`
+See `?torch_symeig`
 
 ## t
 
 t() -> Tensor
 
-See `?torch_t()`
+See `?torch_t`
 
 ## t_
 
@@ -3265,13 +3265,13 @@ In-place version of `$t`
 
 take(indices) -> Tensor
 
-See `?torch_take()`
+See `?torch_take`
 
 ## tan
 
 tan() -> Tensor
 
-See `?torch_tan()`
+See `?torch_tan`
 
 ## tan_
 
@@ -3283,7 +3283,7 @@ In-place version of `$tan`
 
 tanh() -> Tensor
 
-See `?torch_tanh()`
+See `?torch_tanh`
 
 ## tanh_
 
@@ -3382,19 +3382,19 @@ This operation is not differentiable.
 
 topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor)
 
-See `?torch_topk()`
+See `?torch_topk`
 
 ## trace
 
 trace() -> Tensor
 
-See `?torch_trace()`
+See `?torch_trace`
 
 ## transpose
 
 transpose(dim0, dim1) -> Tensor
 
-See `?torch_transpose()`
+See `?torch_transpose`
 
 ## transpose_
 
@@ -3412,7 +3412,7 @@ See [torch_triangular_solve()]
 
 tril(k=0) -> Tensor
 
-See `?torch_tril()`
+See `?torch_tril`
 
 ## tril_
 
@@ -3424,7 +3424,7 @@ In-place version of `$tril`
 
 triu(k=0) -> Tensor
 
-See `?torch_triu()`
+See `?torch_triu`
 
 ## triu_
 
@@ -3448,7 +3448,7 @@ In-place version of `$true_divide_`
 
 trunc() -> Tensor
 
-See `?torch_trunc()`
+See `?torch_trunc`
 
 ## trunc_
 
@@ -3492,7 +3492,7 @@ equivalent to `self$type(tensor.type())`
 
 unbind(dim=0) -> seq
 
-See `?torch_unbind()`
+See `?torch_unbind`
 
 ## unflatten
 
@@ -3539,7 +3539,7 @@ $$
 
 Returns the unique elements of the input tensor.
 
-See `?torch_unique()`
+See `?torch_unique`
         
 ## unique_consecutive
 
@@ -3551,7 +3551,7 @@ See [torch_unique_consecutive()]
 
 unsqueeze(dim) -> Tensor
 
-See `?torch_unsqueeze()`
+See `?torch_unsqueeze`
 
 ## unsqueeze_
 
@@ -3577,7 +3577,7 @@ This method can only be called on a coalesced sparse tensor. See
 
 var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor
 
-See `?torch_var()`
+See `?torch_var`
 
 ## view
 
@@ -3626,7 +3626,7 @@ Please see `$view` for more information about `view`.
 where(condition, y) -> Tensor
 
 `self$where(condition, y)` is equivalent to `torch_where(condition, self, y)`.
-See `?torch_where()`
+See `?torch_where`
 
 ## zero_
 
-- 
GitLab


From cbce857e3cf2d7af15db102a8330881d9b918444 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 16 Sep 2020 20:00:36 -0300
Subject: [PATCH 119/157] add the doc to pkgdown index

---
 _pkgdown.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 45db66241..133365d5b 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -46,6 +46,8 @@ navbar:
        href: articles/tensor-creation.html
      - text: Indexing
        href: articles/indexing.html
+     - text: Tensor class
+       href: articles/tensor/index.html
      - text: Datasets
      - text: Loading Data
        href: articles/loading-data.html
-- 
GitLab


From 1d3b875a720431e7357c4672e97c26c8476bfd88 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 17 Sep 2020 16:13:31 -0300
Subject: [PATCH 120/157] fix ordering for parallel make

---
 src/Makevars      |  3 +++
 tools/renamelib.R | 11 -----------
 2 files changed, 3 insertions(+), 11 deletions(-)

diff --git a/src/Makevars b/src/Makevars
index 552a48e8f..94dd6bcd7 100644
--- a/src/Makevars
+++ b/src/Makevars
@@ -3,3 +3,6 @@ all: $(SHLIB) rename
 rename:
 	@echo "Renaming torch lib to torchpkg"
 	"${R_HOME}/bin${R_ARCH_BIN}/Rscript" "../tools/renamelib.R"
+
+# in order to rename SHLIb must be done.
+rename : $(SHLIB)
\ No newline at end of file
diff --git a/tools/renamelib.R b/tools/renamelib.R
index ce11e0b11..a8003a6ee 100644
--- a/tools/renamelib.R
+++ b/tools/renamelib.R
@@ -1,16 +1,5 @@
 libs_path <- getwd()
 
-# wait, because of it's built in parallel this script might run before
-# finishing.
-start <- Sys.time()
-timeout <- 15*60
-
-while(length(dir(libs_path, pattern = "torch\\.")) == 0) {
-  Sys.sleep(4)
-  if (as.numeric(Sys.time() - start) > timeout)
-    break 
-}
-
 for (lib in dir(libs_path, pattern = "torch\\.")) {
   file.copy(file.path(libs_path, lib),
             file.path(libs_path, gsub("torch", "torchpkg", lib)),
-- 
GitLab


From dbe0ae192c6af81bf8c7ab330b9f118f5fc35402 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 17 Sep 2020 18:50:35 -0300
Subject: [PATCH 121/157] Cran/v0.0.3 (#251)

* patch version

* run lantern action when in 'cran/*' branches

* update branch to get fixed versions of binaries

* re-trigger workflow
---
 .github/workflows/lantern.yaml | 1 +
 DESCRIPTION                    | 2 +-
 R/install.R                    | 3 ++-
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/lantern.yaml b/.github/workflows/lantern.yaml
index b99b527fb..d2474876e 100644
--- a/.github/workflows/lantern.yaml
+++ b/.github/workflows/lantern.yaml
@@ -4,6 +4,7 @@ on:
   push:
     branches:
       - master
+      - 'cran/**'
 
 jobs:
   build:
diff --git a/DESCRIPTION b/DESCRIPTION
index bbbb00511..b08f70b84 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: torch
 Type: Package
 Title: Tensors and Neural Networks with 'GPU' Acceleration
-Version: 0.0.2.9000
+Version: 0.0.3
 Authors@R: c(
     person("Daniel", "Falbel", email = "daniel@rstudio.com", role = c("aut", "cre", "cph")),
     person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cph")),
diff --git a/R/install.R b/R/install.R
index a0d1a105e..716dd6aea 100644
--- a/R/install.R
+++ b/R/install.R
@@ -1,4 +1,5 @@
-branch <- "master"
+branch <- "cran/v0.0.3"
+
 
 install_config <- list(
   "1.5.0" = list(
-- 
GitLab


From 2667633cd148d08e6c701520f0aa888fd15986b9 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 17 Sep 2020 18:54:38 -0300
Subject: [PATCH 122/157] add comments

---
 cran-comments.md | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/cran-comments.md b/cran-comments.md
index 843a871c2..e9710d597 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,3 +1,9 @@
+
+We found a bug in the `Makevars` that could explain the errors with parallel
+make that motivated removing this package from CRAN.
+This release hopefully fixes the issue, however we are not able to reproduce
+this outside of CRAN so we can further debug.
+
 ## Test environments
 
 * local R installation, R 4.0.2
@@ -17,6 +23,3 @@ sub-directories of 1Mb or more:
     help   2.6Mb
     libs  17.2Mb
 
-* This is a new release.
-* Fixed issue with parallel make on Fedora.
-* Fixed README.md URL redirect.
\ No newline at end of file
-- 
GitLab


From ddee5f8546212a8f06e321dfd9053dc068e143a1 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 17 Sep 2020 19:05:33 -0300
Subject: [PATCH 123/157] CRAN release

---
 CRAN-RELEASE | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 CRAN-RELEASE

diff --git a/CRAN-RELEASE b/CRAN-RELEASE
new file mode 100644
index 000000000..c6a3a021f
--- /dev/null
+++ b/CRAN-RELEASE
@@ -0,0 +1,2 @@
+This package was submitted to CRAN on 2020-09-17.
+Once it is accepted, delete this file and tag the release (commit 2667633cd1).
-- 
GitLab


From 164dfb96051be62beb3d5032ff3dff8b2ef57446 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 18 Sep 2020 10:14:51 -0300
Subject: [PATCH 124/157] Increment version number

---
 CRAN-RELEASE | 2 --
 DESCRIPTION  | 2 +-
 2 files changed, 1 insertion(+), 3 deletions(-)
 delete mode 100644 CRAN-RELEASE

diff --git a/CRAN-RELEASE b/CRAN-RELEASE
deleted file mode 100644
index c6a3a021f..000000000
--- a/CRAN-RELEASE
+++ /dev/null
@@ -1,2 +0,0 @@
-This package was submitted to CRAN on 2020-09-17.
-Once it is accepted, delete this file and tag the release (commit 2667633cd1).
diff --git a/DESCRIPTION b/DESCRIPTION
index b08f70b84..0b03413ac 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: torch
 Type: Package
 Title: Tensors and Neural Networks with 'GPU' Acceleration
-Version: 0.0.3
+Version: 0.0.3.9000
 Authors@R: c(
     person("Daniel", "Falbel", email = "daniel@rstudio.com", role = c("aut", "cre", "cph")),
     person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cph")),
-- 
GitLab


From 9171b8c119f1709abb1669941cec3611d6ab7241 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 18 Sep 2020 10:15:13 -0300
Subject: [PATCH 125/157] point back to master

---
 R/install.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/install.R b/R/install.R
index 716dd6aea..8b10459d8 100644
--- a/R/install.R
+++ b/R/install.R
@@ -1,4 +1,4 @@
-branch <- "cran/v0.0.3"
+branch <- "master"
 
 
 install_config <- list(
-- 
GitLab


From def68f943fae05e89ccafcbdee0880f551ee1ae5 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 18 Sep 2020 18:08:26 -0300
Subject: [PATCH 126/157] l1 loss

---
 NAMESPACE         |  1 +
 R/nn-loss.R       | 74 +++++++++++++++++++++++++++++++++++++++++++----
 man/nn_l1_loss.Rd | 67 ++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 136 insertions(+), 6 deletions(-)
 create mode 100644 man/nn_l1_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 5eacbca71..53a45a79e 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -156,6 +156,7 @@ export(nn_init_uniform_)
 export(nn_init_xavier_normal_)
 export(nn_init_xavier_uniform_)
 export(nn_init_zeros_)
+export(nn_l1_loss)
 export(nn_leaky_relu)
 export(nn_linear)
 export(nn_log_sigmoid)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index 283120b55..27c85d29f 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -17,6 +17,68 @@ nn_weighted_loss <- nn_module(
   }
 )
 
+#' L1 loss
+#' 
+#' Creates a criterion that measures the mean absolute error (MAE) between each 
+#' element in the input \eqn{x} and target \eqn{y}.
+#' 
+#' The unreduced (i.e. with `reduction` set to `'none'`) loss can be described 
+#' as:
+#' 
+#' \deqn{
+#' \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+#' l_n = \left| x_n - y_n \right|,
+#' }
+#' 
+#' where \eqn{N} is the batch size. If `reduction` is not `'none'`
+#' (default `'mean'`), then:
+#' 
+#' \deqn{
+#' \ell(x, y) =
+#' \begin{cases}
+#' \operatorname{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
+#' \operatorname{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+#' \end{cases}
+#' }
+#' 
+#' \eqn{x} and \eqn{y} are tensors of arbitrary shapes with a total
+#' of \eqn{n} elements each.
+#' 
+#' The sum operation still operates over all the elements, and divides by \eqn{n}.
+#' The division by \eqn{n} can be avoided if one sets `reduction = 'sum'`.
+#' 
+#' @param reduction (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. Note: `size_average`
+#'   and `reduce` are in the process of being deprecated, and in the meantime,
+#'   specifying either of those two args will override `reduction`. Default: `'mean'`
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+#'   dimensions
+#' - Target: \eqn{(N, *)}, same shape as the input
+#' - Output: scalar. If `reduction` is `'none'`, then
+#'   \eqn{(N, *)}, same shape as the input
+#' 
+#' @examples
+#' loss <- nn_l1_loss()
+#' input <- torch_randn(3, 5, requires_grad=TRUE)
+#' target <- torch_randn(3, 5)
+#' output <- loss(input, target)
+#' output$backward()
+#' 
+#' @export
+nn_l1_loss <- nn_module(
+  "nn_l1_loss",
+  inherit = nn_loss,
+  forward = function(input, target) {
+    nnf_l1_loss(input, target, reduction = self$reduction)
+  }
+)
+
+
+
 #' Binary cross entropy loss
 #' 
 #' Creates a criterion that measures the Binary Cross Entropy
@@ -27,7 +89,7 @@ nn_weighted_loss <- nn_module(
 #'   \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 \eqn{N} is the batch size. If `reduction` is not ``'none'``
+#' where \eqn{N} is the batch size. If `reduction` is not `'none'`
 #' (default `'mean'`), then
 #' 
 #' \deqn{
@@ -62,17 +124,17 @@ nn_weighted_loss <- nn_module(
 #' @param weight (Tensor, optional): a manual rescaling weight given to the loss
 #'    of each batch element. If given, has to be a Tensor of size `nbatch`.
 #' @param reduction (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. Note: `size_average`
+#'    `'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. Note: `size_average`
 #'    and `reduce` are in the process of being deprecated, and in the meantime,
-#'    specifying either of those two args will override `reduction`. Default: ``'mean'``
+#'    specifying either of those two args will override `reduction`. Default: `'mean'`
 #' 
 #' @section Shape:
 #' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
 #'   dimensions
 #' - Target: \eqn{(N, *)}, same shape as the input
-#' - Output: scalar. If `reduction` is ``'none'``, then \eqn{(N, *)}, same
+#' - Output: scalar. If `reduction` is `'none'`, then \eqn{(N, *)}, same
 #'   shape as input.
 #' 
 #' @examples
diff --git a/man/nn_l1_loss.Rd b/man/nn_l1_loss.Rd
new file mode 100644
index 000000000..e7d7b71f0
--- /dev/null
+++ b/man/nn_l1_loss.Rd
@@ -0,0 +1,67 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_l1_loss}
+\alias{nn_l1_loss}
+\title{L1 loss}
+\usage{
+nn_l1_loss(reduction = "mean")
+}
+\arguments{
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that measures the mean absolute error (MAE) between each
+element in the input \eqn{x} and target \eqn{y}.
+}
+\details{
+The unreduced (i.e. with \code{reduction} set to \code{'none'}) loss can be described
+as:
+
+\deqn{
+\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_n = \left| x_n - y_n \right|,
+}
+
+where \eqn{N} is the batch size. If \code{reduction} is not \code{'none'}
+(default \code{'mean'}), then:
+
+\deqn{
+\ell(x, y) =
+\begin{cases}
+\operatorname{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
+\operatorname{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+\end{cases}
+}
+
+\eqn{x} and \eqn{y} are tensors of arbitrary shapes with a total
+of \eqn{n} elements each.
+
+The sum operation still operates over all the elements, and divides by \eqn{n}.
+The division by \eqn{n} can be avoided if one sets \code{reduction = 'sum'}.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+dimensions
+\item Target: \eqn{(N, *)}, same shape as the input
+\item Output: scalar. If \code{reduction} is \code{'none'}, then
+\eqn{(N, *)}, same shape as the input
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+loss <- nn_l1_loss()
+input <- torch_randn(3, 5, requires_grad=TRUE)
+target <- torch_randn(3, 5)
+output <- loss(input, target)
+output$backward()
+
+}
+}
-- 
GitLab


From 3d29d0229340be5bd65a84377f85b9fbb7c4fc1d Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 18 Sep 2020 18:17:13 -0300
Subject: [PATCH 127/157] use array instead

---
 R/nn-loss.R       | 4 ++--
 man/nn_l1_loss.Rd | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/R/nn-loss.R b/R/nn-loss.R
index 27c85d29f..7b12a5c84 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -35,10 +35,10 @@ nn_weighted_loss <- nn_module(
 #' 
 #' \deqn{
 #' \ell(x, y) =
-#' \begin{cases}
+#' \begin{array}{ll}
 #' \operatorname{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
 #' \operatorname{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
-#' \end{cases}
+#' \end{array}
 #' }
 #' 
 #' \eqn{x} and \eqn{y} are tensors of arbitrary shapes with a total
diff --git a/man/nn_l1_loss.Rd b/man/nn_l1_loss.Rd
index e7d7b71f0..5256a7d4e 100644
--- a/man/nn_l1_loss.Rd
+++ b/man/nn_l1_loss.Rd
@@ -32,10 +32,10 @@ where \eqn{N} is the batch size. If \code{reduction} is not \code{'none'}
 
 \deqn{
 \ell(x, y) =
-\begin{cases}
+\begin{array}{ll}
 \operatorname{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
 \operatorname{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
-\end{cases}
+\end{array}
 }
 
 \eqn{x} and \eqn{y} are tensors of arbitrary shapes with a total
-- 
GitLab


From 09684b5a0a80e3ae51bc6642f0751ceee307e7bd Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 18 Sep 2020 18:29:59 -0300
Subject: [PATCH 128/157] nll_loss

---
 NAMESPACE          |   1 +
 R/nn-loss.R        | 115 ++++++++++++++++++++++++++++++++++++++++++++-
 man/nn_l1_loss.Rd  |   4 +-
 man/nn_nll_loss.Rd | 114 ++++++++++++++++++++++++++++++++++++++++++++
 4 files changed, 230 insertions(+), 4 deletions(-)
 create mode 100644 man/nn_nll_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 53a45a79e..adba19a93 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -172,6 +172,7 @@ export(nn_max_unpool3d)
 export(nn_module)
 export(nn_module_list)
 export(nn_multihead_attention)
+export(nn_nll_loss)
 export(nn_prelu)
 export(nn_relu)
 export(nn_relu6)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index 7b12a5c84..556fd2fb3 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -36,8 +36,8 @@ nn_weighted_loss <- nn_module(
 #' \deqn{
 #' \ell(x, y) =
 #' \begin{array}{ll}
-#' \operatorname{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
-#' \operatorname{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+#' \mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
+#' \mbox{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
 #' \end{array}
 #' }
 #' 
@@ -77,6 +77,117 @@ nn_l1_loss <- nn_module(
   }
 )
 
+#' Nll loss
+#' 
+#' The negative log likelihood loss. It is useful to train a classification
+#' problem with `C` classes.
+#' 
+#' If provided, the optional argument `weight` should be a 1D Tensor assigning
+#' weight to each of the classes. This is particularly useful when you have an
+#' unbalanced training set.
+#' 
+#' The `input` given through a forward call is expected to contain
+#' log-probabilities of each class. `input` has to be a Tensor of size either
+#' \eqn{(minibatch, C)} or \eqn{(minibatch, C, d_1, d_2, ..., d_K)}
+#' with \eqn{K \geq 1} for the `K`-dimensional case (described later).
+#' 
+#' Obtaining log-probabilities in a neural network is easily achieved by
+#' adding a  `LogSoftmax`  layer in the last layer of your network.
+#' 
+#' You may use `CrossEntropyLoss` instead, if you prefer not to add an extra
+#' layer.
+#' 
+#' The `target` that this loss expects should be a class index in the range \eqn{[0, C-1]}
+#' where `C = number of classes`; if `ignore_index` is specified, this loss also accepts
+#' this class index (this index may not necessarily be in the class range).
+#' 
+#' The unreduced (i.e. with `reduction` set to `'none'`) loss can be described as:
+#' 
+#' \deqn{
+#' \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+#' l_n = - w_{y_n} x_{n,y_n}, \quad
+#' w_{c} = \mbox{weight}[c] \cdot \mathbb{1}\{c \not= \mbox{ignore\_index}\},
+#' }
+#' 
+#' where \eqn{x} is the input, \eqn{y} is the target, \eqn{w} is the weight, and
+#' \eqn{N} is the batch size. If `reduction` is not `'none'`
+#' (default `'mean'`), then
+#' 
+#' \deqn{
+#' \ell(x, y) = \begin{array}{ll}
+#' \sum_{n=1}^N \frac{1}{\sum_{n=1}^N w_{y_n}} l_n, &
+#'   \mbox{if reduction} = \mbox{'mean';}\\
+#' \sum_{n=1}^N l_n,  &
+#'   \mbox{if reduction} = \mbox{'sum'.}
+#' \end{cases}
+#' }
+#' 
+#' Can also be used for higher dimension inputs, such as 2D images, by providing
+#' an input of size \eqn{(minibatch, C, d_1, d_2, ..., d_K)} with \eqn{K \geq 1},
+#' where \eqn{K} is the number of dimensions, and a target of appropriate shape
+#' (see below). In the case of images, it computes NLL loss per-pixel.
+#' 
+#' 
+#' @param weight (Tensor, optional): 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.
+#' @param reduction (string, optional): Specifies the reduction to apply to the output:
+#'   `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will
+#'   be applied, `'mean'`: the weighted mean of the output is taken,
+#'   `'sum'`: the output will be summed. Note: `size_average`
+#'   and `reduce` are in the process of being deprecated, and in
+#'   the meantime, specifying either of those two args will override
+#'   `reduction`. Default: `'mean'`
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, C)} where `C = number of classes`, or
+#'   \eqn{(N, C, d_1, d_2, ..., d_K)} with \eqn{K \geq 1}
+#'   in the case of `K`-dimensional loss.
+#' - Target: \eqn{(N)} where each value is \eqn{0 \leq \mbox{targets}[i] \leq C-1}, or
+#'   \eqn{(N, d_1, d_2, ..., d_K)} with \eqn{K \geq 1} in the case of
+#'   K-dimensional loss.
+#' - Output: scalar.
+#' 
+#' If `reduction` is `'none'`, then the same size as the target: \eqn{(N)}, or
+#' \eqn{(N, d_1, d_2, ..., d_K)} with \eqn{K \geq 1} in the case
+#' of K-dimensional loss.
+#' 
+#' @examples
+#' m <- nn_log_softmax(dim=2)
+#' loss <- nn_nll_loss()
+#' # input is of size N x C = 3 x 5
+#' input <- torch_randn(3, 5, requires_grad=TRUE)
+#' # each element in target has to have 0 <= value < C
+#' target <- torch_tensor(c(2, 1, 5))
+#' output <- loss(m(input), target)
+#' output$backward()
+#' 
+#' # 2D loss example (used, for example, with image inputs)
+#' N <- 5
+#' C <- 4
+#' loss <- nn_nll_loss()
+#' # input is of size N x C x height x width
+#' data <- torch_randn(N, 16, 10, 10)
+#' conv <- nn_conv2d(16, C, c(3, 3))
+#' m <- nn_log_softmax(dim=1)
+#' # each element in target has to have 0 <= value < C
+#' target <- torch_empty(N, 8, 8, dtype=torch_long())$random_(0, C)
+#' output <- loss(m(conv(data)), target)
+#' output$backward()
+#' 
+#' @export
+nn_nll_loss <- nn_module(
+  "nn_nll_loss",
+  inherit = nn_weighted_loss,
+  initialize = function(weight = NULL, ignore_index = -100, reduction = "mean") {
+    super$initialize(weight, reduction)
+    self$ignore_index <- ignore_index
+  },
+  forward = function(input, target) {
+    nnf_nll_loss(input, target, weight = self$weight, 
+                 ignore_index = self$ignore_index, reduction = self$reduction)
+  }
+)
 
 
 #' Binary cross entropy loss
diff --git a/man/nn_l1_loss.Rd b/man/nn_l1_loss.Rd
index 5256a7d4e..d9ecaf503 100644
--- a/man/nn_l1_loss.Rd
+++ b/man/nn_l1_loss.Rd
@@ -33,8 +33,8 @@ where \eqn{N} is the batch size. If \code{reduction} is not \code{'none'}
 \deqn{
 \ell(x, y) =
 \begin{array}{ll}
-\operatorname{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
-\operatorname{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
+\mbox{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
 \end{array}
 }
 
diff --git a/man/nn_nll_loss.Rd b/man/nn_nll_loss.Rd
new file mode 100644
index 000000000..85162593e
--- /dev/null
+++ b/man/nn_nll_loss.Rd
@@ -0,0 +1,114 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_nll_loss}
+\alias{nn_nll_loss}
+\title{Nll loss}
+\usage{
+nn_nll_loss(weight = NULL, ignore_index = -100, reduction = "mean")
+}
+\arguments{
+\item{weight}{(Tensor, optional): a manual rescaling weight given to each
+class. If given, it has to be a Tensor of size \code{C}. Otherwise, it is
+treated as if having all ones.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will
+be applied, \code{'mean'}: the weighted mean of the output is taken,
+\code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in
+the meantime, specifying either of those two args will override
+\code{reduction}. Default: \code{'mean'}}
+}
+\description{
+The negative log likelihood loss. It is useful to train a classification
+problem with \code{C} classes.
+}
+\details{
+If provided, the optional argument \code{weight} should be a 1D Tensor assigning
+weight to each of the classes. This is particularly useful when you have an
+unbalanced training set.
+
+The \code{input} given through a forward call is expected to contain
+log-probabilities of each class. \code{input} has to be a Tensor of size either
+\eqn{(minibatch, C)} or \eqn{(minibatch, C, d_1, d_2, ..., d_K)}
+with \eqn{K \geq 1} for the \code{K}-dimensional case (described later).
+
+Obtaining log-probabilities in a neural network is easily achieved by
+adding a  \code{LogSoftmax}  layer in the last layer of your network.
+
+You may use \code{CrossEntropyLoss} instead, if you prefer not to add an extra
+layer.
+
+The \code{target} that this loss expects should be a class index in the range \eqn{[0, C-1]}
+where \verb{C = number of classes}; if \code{ignore_index} is specified, this loss also accepts
+this class index (this index may not necessarily be in the class range).
+
+The unreduced (i.e. with \code{reduction} set to \code{'none'}) loss can be described as:
+
+\deqn{
+\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_n = - w_{y_n} x_{n,y_n}, \quad
+w_{c} = \mbox{weight}[c] \cdot \mathbb{1}\{c \not= \mbox{ignore\_index}\},
+}
+
+where \eqn{x} is the input, \eqn{y} is the target, \eqn{w} is the weight, and
+\eqn{N} is the batch size. If \code{reduction} is not \code{'none'}
+(default \code{'mean'}), then
+
+\deqn{
+\ell(x, y) = \begin{array}{ll}
+\sum_{n=1}^N \frac{1}{\sum_{n=1}^N w_{y_n}} l_n, &
+  \mbox{if reduction} = \mbox{'mean';}\\
+\sum_{n=1}^N l_n,  &
+  \mbox{if reduction} = \mbox{'sum'.}
+\end{cases}
+}
+
+Can also be used for higher dimension inputs, such as 2D images, by providing
+an input of size \eqn{(minibatch, C, d_1, d_2, ..., d_K)} with \eqn{K \geq 1},
+where \eqn{K} is the number of dimensions, and a target of appropriate shape
+(see below). In the case of images, it computes NLL loss per-pixel.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C)} where \verb{C = number of classes}, or
+\eqn{(N, C, d_1, d_2, ..., d_K)} with \eqn{K \geq 1}
+in the case of \code{K}-dimensional loss.
+\item Target: \eqn{(N)} where each value is \eqn{0 \leq \mbox{targets}[i] \leq C-1}, or
+\eqn{(N, d_1, d_2, ..., d_K)} with \eqn{K \geq 1} in the case of
+K-dimensional loss.
+\item Output: scalar.
+}
+
+If \code{reduction} is \code{'none'}, then the same size as the target: \eqn{(N)}, or
+\eqn{(N, d_1, d_2, ..., d_K)} with \eqn{K \geq 1} in the case
+of K-dimensional loss.
+}
+
+\examples{
+if (torch_is_installed()) {
+m <- nn_log_softmax(dim=2)
+loss <- nn_nll_loss()
+# input is of size N x C = 3 x 5
+input <- torch_randn(3, 5, requires_grad=TRUE)
+# each element in target has to have 0 <= value < C
+target <- torch_tensor(c(2, 1, 5))
+output <- loss(m(input), target)
+output$backward()
+
+# 2D loss example (used, for example, with image inputs)
+N <- 5
+C <- 4
+loss <- nn_nll_loss()
+# input is of size N x C x height x width
+data <- torch_randn(N, 16, 10, 10)
+conv <- nn_conv2d(16, C, c(3, 3))
+m <- nn_log_softmax(dim=1)
+# each element in target has to have 0 <= value < C
+target <- torch_empty(N, 8, 8, dtype=torch_long())$random_(0, C)
+output <- loss(m(conv(data)), target)
+output$backward()
+
+}
+}
-- 
GitLab


From 23edaf31702be4574ba0caa2d41f9dd0c3fbe1da Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Fri, 18 Sep 2020 19:09:37 -0300
Subject: [PATCH 129/157] add ignore index parameter to docs

---
 R/nn-loss.R        | 2 ++
 man/nn_nll_loss.Rd | 3 +++
 2 files changed, 5 insertions(+)

diff --git a/R/nn-loss.R b/R/nn-loss.R
index 556fd2fb3..bda9dcb27 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -138,6 +138,8 @@ nn_l1_loss <- nn_module(
 #'   and `reduce` are in the process of being deprecated, and in
 #'   the meantime, specifying either of those two args will override
 #'   `reduction`. Default: `'mean'`
+#' @param ignore_index (int, optional): Specifies a target value that is ignored
+#'   and does not contribute to the input gradient.
 #' 
 #' @section Shape:
 #' - Input: \eqn{(N, C)} where `C = number of classes`, or
diff --git a/man/nn_nll_loss.Rd b/man/nn_nll_loss.Rd
index 85162593e..070f4c0bb 100644
--- a/man/nn_nll_loss.Rd
+++ b/man/nn_nll_loss.Rd
@@ -11,6 +11,9 @@ nn_nll_loss(weight = NULL, ignore_index = -100, reduction = "mean")
 class. If given, it has to be a Tensor of size \code{C}. Otherwise, it is
 treated as if having all ones.}
 
+\item{ignore_index}{(int, optional): Specifies a target value that is ignored
+and does not contribute to the input gradient.}
+
 \item{reduction}{(string, optional): Specifies the reduction to apply to the output:
 \code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will
 be applied, \code{'mean'}: the weighted mean of the output is taken,
-- 
GitLab


From 45adfa99a6ad6984254c8fe7ebcb668218d05f60 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Sat, 19 Sep 2020 15:37:26 -0300
Subject: [PATCH 130/157] fixing examples

---
 R/nn-loss.R                     |  8 ++++----
 R/nn-pooling.R                  | 22 ++++++++++++----------
 man/nn_fractional_max_pool2d.Rd |  6 ------
 man/nn_nll_loss.Rd              |  8 ++++----
 4 files changed, 20 insertions(+), 24 deletions(-)

diff --git a/R/nn-loss.R b/R/nn-loss.R
index bda9dcb27..62fbf54f2 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -106,7 +106,7 @@ nn_l1_loss <- nn_module(
 #' \deqn{
 #' \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
 #' l_n = - w_{y_n} x_{n,y_n}, \quad
-#' w_{c} = \mbox{weight}[c] \cdot \mathbb{1}\{c \not= \mbox{ignore\_index}\},
+#' w_{c} = \mbox{weight}[c] \cdot \mbox{1}\{c \not= \mbox{ignore\_index}\},
 #' }
 #' 
 #' where \eqn{x} is the input, \eqn{y} is the target, \eqn{w} is the weight, and
@@ -119,7 +119,7 @@ nn_l1_loss <- nn_module(
 #'   \mbox{if reduction} = \mbox{'mean';}\\
 #' \sum_{n=1}^N l_n,  &
 #'   \mbox{if reduction} = \mbox{'sum'.}
-#' \end{cases}
+#' \end{array}
 #' }
 #' 
 #' Can also be used for higher dimension inputs, such as 2D images, by providing
@@ -160,7 +160,7 @@ nn_l1_loss <- nn_module(
 #' # input is of size N x C = 3 x 5
 #' input <- torch_randn(3, 5, requires_grad=TRUE)
 #' # each element in target has to have 0 <= value < C
-#' target <- torch_tensor(c(2, 1, 5))
+#' target <- torch_tensor(c(2, 1, 5), dtype = torch_long())
 #' output <- loss(m(input), target)
 #' output$backward()
 #' 
@@ -173,7 +173,7 @@ nn_l1_loss <- nn_module(
 #' conv <- nn_conv2d(16, C, c(3, 3))
 #' m <- nn_log_softmax(dim=1)
 #' # each element in target has to have 0 <= value < C
-#' target <- torch_empty(N, 8, 8, dtype=torch_long())$random_(0, C)
+#' target <- torch_empty(N, 8, 8, dtype=torch_long())$random_(1, C)
 #' output <- loss(m(conv(data)), target)
 #' output$backward()
 #' 
diff --git a/R/nn-pooling.R b/R/nn-pooling.R
index df86c8d3a..cf910c6d3 100644
--- a/R/nn-pooling.R
+++ b/R/nn-pooling.R
@@ -643,12 +643,12 @@ nn_avg_pool3d <- nn_module(
 #'   Useful to pass to [nn_max_unpool2d()]. Default: `FALSE`
 #' 
 #' @examples
-#' # pool of square window of size=3, and target output size 13x12
-#' m = nn_fractional_max_pool2d(3, output_size=c(13, 12))
-#' # pool of square window and target output size being half of input image size
-#' m = nn_fractional_max_pool2d(3, output_ratio=c(0.5, 0.5))
-#' input = torch_randn(20, 16, 50, 32)
-#' output = m(input)
+# # pool of square window of size=3, and target output size 13x12
+# m = nn_fractional_max_pool2d(3, output_size=c(13, 12))
+# # pool of square window and target output size being half of input image size
+# m = nn_fractional_max_pool2d(3, output_ratio=c(0.5, 0.5))
+# input = torch_randn(20, 16, 50, 32)
+# output = m(input)
 #' 
 #' @export
 nn_fractional_max_pool2d <- nn_module(
@@ -680,8 +680,9 @@ nn_fractional_max_pool2d <- nn_module(
     if (!is.null(output_ratio) && !is.null(output_size))
       value_error("both output_size and oytput_ratio are not NULL")
     
-    if (!is.null(output_ratio) && (output_ratio > 1 || output_ratio < 0))
-      value_error("output_ratio must be between 0 and 1.")
+    if (!is.null(output_ratio))
+      if (any(output_ratio > 1 | output_ratio < 0))
+        value_error("output_ratio must be between 0 and 1.")
     
   },
   forward = function(input) {
@@ -748,8 +749,9 @@ nn_fractional_max_pool3d <- nn_module(
     if (!is.null(output_ratio) && !is.null(output_size))
       value_error("both output_size and oytput_ratio are not NULL")
     
-    if (!is.null(output_ratio) && (output_ratio > 1 || output_ratio < 0))
-      value_error("output_ratio must be between 0 and 1.")
+    if (!is.null(output_ratio))
+      if (any(output_ratio > 1 | output_ratio < 0))
+        value_error("output_ratio must be between 0 and 1.")
     
   },
   forward = function(input) {
diff --git a/man/nn_fractional_max_pool2d.Rd b/man/nn_fractional_max_pool2d.Rd
index d52652725..47f35e0cb 100644
--- a/man/nn_fractional_max_pool2d.Rd
+++ b/man/nn_fractional_max_pool2d.Rd
@@ -35,12 +35,6 @@ The number of output features is equal to the number of input planes.
 }
 \examples{
 if (torch_is_installed()) {
-# pool of square window of size=3, and target output size 13x12
-m = nn_fractional_max_pool2d(3, output_size=c(13, 12))
-# pool of square window and target output size being half of input image size
-m = nn_fractional_max_pool2d(3, output_ratio=c(0.5, 0.5))
-input = torch_randn(20, 16, 50, 32)
-output = m(input)
 
 }
 }
diff --git a/man/nn_nll_loss.Rd b/man/nn_nll_loss.Rd
index 070f4c0bb..c80dc851e 100644
--- a/man/nn_nll_loss.Rd
+++ b/man/nn_nll_loss.Rd
@@ -51,7 +51,7 @@ The unreduced (i.e. with \code{reduction} set to \code{'none'}) loss can be desc
 \deqn{
 \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
 l_n = - w_{y_n} x_{n,y_n}, \quad
-w_{c} = \mbox{weight}[c] \cdot \mathbb{1}\{c \not= \mbox{ignore\_index}\},
+w_{c} = \mbox{weight}[c] \cdot \mbox{1}\{c \not= \mbox{ignore\_index}\},
 }
 
 where \eqn{x} is the input, \eqn{y} is the target, \eqn{w} is the weight, and
@@ -64,7 +64,7 @@ where \eqn{x} is the input, \eqn{y} is the target, \eqn{w} is the weight, and
   \mbox{if reduction} = \mbox{'mean';}\\
 \sum_{n=1}^N l_n,  &
   \mbox{if reduction} = \mbox{'sum'.}
-\end{cases}
+\end{array}
 }
 
 Can also be used for higher dimension inputs, such as 2D images, by providing
@@ -96,7 +96,7 @@ loss <- nn_nll_loss()
 # input is of size N x C = 3 x 5
 input <- torch_randn(3, 5, requires_grad=TRUE)
 # each element in target has to have 0 <= value < C
-target <- torch_tensor(c(2, 1, 5))
+target <- torch_tensor(c(2, 1, 5), dtype = torch_long())
 output <- loss(m(input), target)
 output$backward()
 
@@ -109,7 +109,7 @@ data <- torch_randn(N, 16, 10, 10)
 conv <- nn_conv2d(16, C, c(3, 3))
 m <- nn_log_softmax(dim=1)
 # each element in target has to have 0 <= value < C
-target <- torch_empty(N, 8, 8, dtype=torch_long())$random_(0, C)
+target <- torch_empty(N, 8, 8, dtype=torch_long())$random_(1, C)
 output <- loss(m(conv(data)), target)
 output$backward()
 
-- 
GitLab


From 9b1335dac9c16e988a1324f4b81836cc898bf1e7 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Sat, 19 Sep 2020 15:57:26 -0300
Subject: [PATCH 131/157] poisson nll loss

---
 NAMESPACE                  |  1 +
 R/nn-loss.R                | 61 ++++++++++++++++++++++++++++++++++
 man/nn_poisson_nll_loss.Rd | 68 ++++++++++++++++++++++++++++++++++++++
 3 files changed, 130 insertions(+)
 create mode 100644 man/nn_poisson_nll_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index adba19a93..ffd2f30fc 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -173,6 +173,7 @@ export(nn_module)
 export(nn_module_list)
 export(nn_multihead_attention)
 export(nn_nll_loss)
+export(nn_poisson_nll_loss)
 export(nn_prelu)
 export(nn_relu)
 export(nn_relu6)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index 62fbf54f2..817107532 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -191,6 +191,67 @@ nn_nll_loss <- nn_module(
   }
 )
 
+#' Poisson NLL loss
+#' 
+#' Negative log likelihood loss with Poisson distribution of target.
+#' The loss can be described as:
+#'
+#' \deqn{
+#' \mbox{target} \sim \mathrm{Poisson}(\mbox{input})
+#' \mbox{loss}(\mbox{input}, \mbox{target}) = \mbox{input} - \mbox{target} * \log(\mbox{input})
+#' + \log(\mbox{target!})
+#' }
+#' 
+#' The last term can be omitted or approximated with Stirling formula. The
+#' approximation is used for target values more than 1. For targets less or
+#' equal to 1 zeros are added to the loss.
+#' 
+#' @param log_input (bool, optional): if `TRUE` the loss is computed as
+#'   \eqn{\exp(\mbox{input}) - \mbox{target}*\mbox{input}}, if `FALSE` the loss is
+#'   \eqn{\mbox{input} - \mbox{target}*\log(\mbox{input}+\mbox{eps})}.
+#' @param full (bool, optional): whether to compute full loss, i. e. to add the
+#'   Stirling approximation term 
+#'   \eqn{\mbox{target}*\log(\mbox{target}) - \mbox{target} + 0.5 * \log(2\pi\mbox{target})}.
+#' @param eps (float, optional): Small value to avoid evaluation of \eqn{\log(0)} when
+#'   `log_input = FALSE`. Default: 1e-8
+#' @param reduction (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. Note: `size_average`
+#'   and `reduce` are in the process of being deprecated, and in the meantime,
+#'   specifying either of those two args will override `reduction`. Default: `'mean'`
+#' 
+#' @examples
+#' loss <- nn_poisson_nll_loss()
+#' log_input <- torch_randn(5, 2, requires_grad=TRUE)
+#' target <- torch_randn(5, 2)
+#' output <- loss(log_input, target)
+#' output$backward()
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+#'   dimensions
+#' - Target: \eqn{(N, *)}, same shape as the input
+#' - Output: scalar by default. If `reduction` is `'none'`, then \eqn{(N, *)},
+#'   the same shape as the input
+#'   
+#' @export
+nn_poisson_nll_loss <- nn_module(
+  "nn_poisson_nll_loss",
+  inherit = nn_loss,
+  initialize = function(log_input = TRUE, full = FALSE, eps = 1e-8, 
+                        reduction = 'mean') {
+    super$initialize(reduction = reduction)
+    self$log_input <- log_input
+    self$full <- full
+    self$eps <- eps
+  },
+  forward = function(log_input, target) {
+    nnf_poisson_nll_loss(log_input, target, log_input = self$log_input,
+                         full = self$full, eps = self$eps, 
+                         reduction = self$reduction)
+  }
+)
 
 #' Binary cross entropy loss
 #' 
diff --git a/man/nn_poisson_nll_loss.Rd b/man/nn_poisson_nll_loss.Rd
new file mode 100644
index 000000000..7805cc718
--- /dev/null
+++ b/man/nn_poisson_nll_loss.Rd
@@ -0,0 +1,68 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_poisson_nll_loss}
+\alias{nn_poisson_nll_loss}
+\title{Poisson NLL loss}
+\usage{
+nn_poisson_nll_loss(
+  log_input = TRUE,
+  full = FALSE,
+  eps = 1e-08,
+  reduction = "mean"
+)
+}
+\arguments{
+\item{log_input}{(bool, optional): if \code{TRUE} the loss is computed as
+\eqn{\exp(\mbox{input}) - \mbox{target}*\mbox{input}}, if \code{FALSE} the loss is
+\eqn{\mbox{input} - \mbox{target}*\log(\mbox{input}+\mbox{eps})}.}
+
+\item{full}{(bool, optional): whether to compute full loss, i. e. to add the
+Stirling approximation term
+\eqn{\mbox{target}*\log(\mbox{target}) - \mbox{target} + 0.5 * \log(2\pi\mbox{target})}.}
+
+\item{eps}{(float, optional): Small value to avoid evaluation of \eqn{\log(0)} when
+\code{log_input = FALSE}. Default: 1e-8}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Negative log likelihood loss with Poisson distribution of target.
+The loss can be described as:
+}
+\details{
+\deqn{
+\mbox{target} \sim \mathrm{Poisson}(\mbox{input})
+\mbox{loss}(\mbox{input}, \mbox{target}) = \mbox{input} - \mbox{target} * \log(\mbox{input})
++ \log(\mbox{target!})
+}
+
+The last term can be omitted or approximated with Stirling formula. The
+approximation is used for target values more than 1. For targets less or
+equal to 1 zeros are added to the loss.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+dimensions
+\item Target: \eqn{(N, *)}, same shape as the input
+\item Output: scalar by default. If \code{reduction} is \code{'none'}, then \eqn{(N, *)},
+the same shape as the input
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+loss <- nn_poisson_nll_loss()
+log_input <- torch_randn(5, 2, requires_grad=TRUE)
+target <- torch_randn(5, 2)
+output <- loss(log_input, target)
+output$backward()
+
+}
+}
-- 
GitLab


From 0c8aee8ab5865a22f4dc8cfc15da250152246d18 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Sun, 20 Sep 2020 16:54:27 -0300
Subject: [PATCH 132/157] kl div loss

---
 NAMESPACE                     |  1 +
 R/nn-loss.R                   | 75 ++++++++++++++++++++++++++++++++++
 man/nn_kl_div_loss.Rd         | 76 +++++++++++++++++++++++++++++++++++
 tests/testthat/test-nn-loss.R | 15 +++++++
 4 files changed, 167 insertions(+)
 create mode 100644 man/nn_kl_div_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index ffd2f30fc..3aa813a42 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -156,6 +156,7 @@ export(nn_init_uniform_)
 export(nn_init_xavier_normal_)
 export(nn_init_xavier_uniform_)
 export(nn_init_zeros_)
+export(nn_kl_div_loss)
 export(nn_l1_loss)
 export(nn_leaky_relu)
 export(nn_linear)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index 817107532..af1f6d111 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -253,6 +253,81 @@ nn_poisson_nll_loss <- nn_module(
   }
 )
 
+#' Kullback-Leibler divergence loss
+#' 
+#' The Kullback-Leibler divergence loss measure
+#' [Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback-Leibler_divergence) 
+#' is a useful distance measure for continuous distributions and is often 
+#' useful when performing direct regression over the space of (discretely sampled) 
+#' continuous output distributions.
+#' 
+#' As with [nn_nll_loss()], the `input` given is expected to contain
+#' *log-probabilities* and is not restricted to a 2D Tensor.
+#' 
+#' The targets are interpreted as *probabilities* by default, but could be considered
+#' as *log-probabilities* with `log_target` set to `TRUE`.
+#' 
+#' This criterion expects a `target` `Tensor` of the same size as the
+#' `input` `Tensor`.
+#' 
+#' The unreduced (i.e. with `reduction` set to `'none'`) loss can be described 
+#' as:
+#' 
+#' \deqn{
+#'   l(x,y) = L = \{ l_1,\dots,l_N \}, \quad
+#' l_n = y_n \cdot \left( \log y_n - x_n \right)
+#' }
+#' 
+#' where the index \eqn{N} spans all dimensions of `input` and \eqn{L} has the same
+#' shape as `input`. If `reduction` is not `'none'` (default `'mean'`), then:
+#' 
+#' \deqn{
+#'   \ell(x, y) = \begin{array}{ll}
+#' \mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';} \\
+#' \mbox{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+#' \end{array}
+#' }
+#' 
+#' In default `reduction` mode `'mean'`, the losses are averaged for each minibatch 
+#' over observations **as well as** over dimensions. `'batchmean'` mode gives the 
+#' correct KL divergence where losses are averaged over batch dimension only. 
+#' `'mean'` mode's behavior will be changed to the same as `'batchmean'` in the next
+#' major release.
+#' 
+#' @param reduction (string, optional): Specifies the reduction to apply to the output:
+#'  `'none'` | `'batchmean'` | `'sum'` | `'mean'`.
+#'  `'none'`: no reduction will be applied.
+#'  `'batchmean'`: the sum of the output will be divided by batchsize.
+#'  `'sum'`: the output will be summed.
+#'  `'mean'`: the output will be divided by the number of elements in the output.
+#'  Default: `'mean'`
+#'  
+#' @note
+#' `reduction` = `'mean'` doesn't return the true kl divergence value, 
+#' please use `reduction` = `'batchmean'` which aligns with KL math 
+#' definition.
+#' In the next major release, `'mean'` will be changed to be the same as 
+#' `'batchmean'`.
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+#'   dimensions
+#' - Target: \eqn{(N, *)}, same shape as the input
+#' - Output: scalar by default. If `reduction` is `'none'`, then \eqn{(N, *)},
+#'   the same shape as the input
+#'   
+#' @export 
+nn_kl_div_loss <- nn_module(
+  "nn_kl_div_loss",
+  inherit = nn_loss,
+  initialize = function(reduction = 'mean') {
+    super$initialize(reduction = reduction)
+  },
+  forward = function(input, target) {
+    nnf_kl_div(input, target, reduction=self$reduction)
+  }
+)
+
 #' Binary cross entropy loss
 #' 
 #' Creates a criterion that measures the Binary Cross Entropy
diff --git a/man/nn_kl_div_loss.Rd b/man/nn_kl_div_loss.Rd
new file mode 100644
index 000000000..8a01bceea
--- /dev/null
+++ b/man/nn_kl_div_loss.Rd
@@ -0,0 +1,76 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_kl_div_loss}
+\alias{nn_kl_div_loss}
+\title{Kullback-Leibler divergence loss}
+\usage{
+nn_kl_div_loss(reduction = "mean")
+}
+\arguments{
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'batchmean'} | \code{'sum'} | \code{'mean'}.
+\code{'none'}: no reduction will be applied.
+\code{'batchmean'}: the sum of the output will be divided by batchsize.
+\code{'sum'}: the output will be summed.
+\code{'mean'}: the output will be divided by the number of elements in the output.
+Default: \code{'mean'}}
+}
+\description{
+The Kullback-Leibler divergence loss measure
+\href{https://en.wikipedia.org/wiki/Kullback-Leibler_divergence}{Kullback-Leibler divergence}
+is a useful distance measure for continuous distributions and is often
+useful when performing direct regression over the space of (discretely sampled)
+continuous output distributions.
+}
+\details{
+As with \code{\link[=nn_nll_loss]{nn_nll_loss()}}, the \code{input} given is expected to contain
+\emph{log-probabilities} and is not restricted to a 2D Tensor.
+
+The targets are interpreted as \emph{probabilities} by default, but could be considered
+as \emph{log-probabilities} with \code{log_target} set to \code{TRUE}.
+
+This criterion expects a \code{target} \code{Tensor} of the same size as the
+\code{input} \code{Tensor}.
+
+The unreduced (i.e. with \code{reduction} set to \code{'none'}) loss can be described
+as:
+
+\deqn{
+  l(x,y) = L = \{ l_1,\dots,l_N \}, \quad
+l_n = y_n \cdot \left( \log y_n - x_n \right)
+}
+
+where the index \eqn{N} spans all dimensions of \code{input} and \eqn{L} has the same
+shape as \code{input}. If \code{reduction} is not \code{'none'} (default \code{'mean'}), then:
+
+\deqn{
+  \ell(x, y) = \begin{array}{ll}
+\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';} \\
+\mbox{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+}
+
+In default \code{reduction} mode \code{'mean'}, the losses are averaged for each minibatch
+over observations \strong{as well as} over dimensions. \code{'batchmean'} mode gives the
+correct KL divergence where losses are averaged over batch dimension only.
+\code{'mean'} mode's behavior will be changed to the same as \code{'batchmean'} in the next
+major release.
+}
+\note{
+\code{reduction} = \code{'mean'} doesn't return the true kl divergence value,
+please use \code{reduction} = \code{'batchmean'} which aligns with KL math
+definition.
+In the next major release, \code{'mean'} will be changed to be the same as
+\code{'batchmean'}.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+dimensions
+\item Target: \eqn{(N, *)}, same shape as the input
+\item Output: scalar by default. If \code{reduction} is \code{'none'}, then \eqn{(N, *)},
+the same shape as the input
+}
+}
+
diff --git a/tests/testthat/test-nn-loss.R b/tests/testthat/test-nn-loss.R
index 54b39013c..7ed57c7a6 100644
--- a/tests/testthat/test-nn-loss.R
+++ b/tests/testthat/test-nn-loss.R
@@ -19,3 +19,18 @@ test_that("nn_cross_entropy_loss", {
   
   expect_tensor(output)
 })
+
+test_that("nn_kl_div_loss", {
+  
+  loss <- nn_kl_div_loss()
+  input <- torch_randn(3, 5, requires_grad=TRUE)
+  target <- torch_randn(3, 5, requires_grad = TRUE)
+  
+  expect_warning(
+    output <- loss(input, target)  
+  )
+  
+  output$backward()
+  
+  expect_tensor(output)
+})
-- 
GitLab


From 365db900612ffe170e6719ab4ff973ac969c4abd Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Sun, 20 Sep 2020 18:43:41 -0300
Subject: [PATCH 133/157] mse loss

---
 NAMESPACE          |  1 +
 R/nn-loss.R        | 60 +++++++++++++++++++++++++++++++++++++++++++
 man/nn_mse_loss.Rd | 64 ++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 125 insertions(+)
 create mode 100644 man/nn_mse_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 3aa813a42..676f72782 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -172,6 +172,7 @@ export(nn_max_unpool2d)
 export(nn_max_unpool3d)
 export(nn_module)
 export(nn_module_list)
+export(nn_mse_loss)
 export(nn_multihead_attention)
 export(nn_nll_loss)
 export(nn_poisson_nll_loss)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index af1f6d111..b065a141f 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -328,6 +328,66 @@ nn_kl_div_loss <- nn_module(
   }
 )
 
+#' MSE loss
+#' 
+#' Creates a criterion that measures the mean squared error (squared L2 norm) between
+#' each element in the input \eqn{x} and target \eqn{y}.
+#' The unreduced (i.e. with `reduction` set to `'none'`) loss can be described 
+#' as:
+#'
+#' \deqn{
+#'   \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+#' l_n = \left( x_n - y_n \right)^2,
+#' }
+#' 
+#' where \eqn{N} is the batch size. If `reduction` is not `'none'`
+#' (default `'mean'`), then:
+#' 
+#' \deqn{
+#'   \ell(x, y) =
+#'   \begin{array}{ll}
+#' \mbox{mean}(L), &  \mbox{if reduction} = \mbox{'mean';}\\
+#' \mbox{sum}(L),  &  \mbox{if reduction} = \mbox{'sum'.}
+#' \end{array}
+#' }
+#' 
+#' \eqn{x} and \eqn{y} are tensors of arbitrary shapes with a total
+#' of \eqn{n} elements each.
+#' 
+#' The mean operation still operates over all the elements, and divides by \eqn{n}.
+#' The division by \eqn{n} can be avoided if one sets `reduction = 'sum'`.
+#' 
+#' @param reduction (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. Note: `size_average`
+#'  and `reduce` are in the process of being deprecated, and in the meantime,
+#'  specifying either of those two args will override `reduction`. Default: `'mean'`
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+#'   dimensions
+#' - Target: \eqn{(N, *)}, same shape as the input
+#' 
+#' @examples
+#' loss <- nn_mse_loss()
+#' input <- torch_randn(3, 5, requires_grad=TRUE)
+#' target <- torch_randn(3, 5)
+#' output <- loss(input, target)
+#' output$backward()
+#' 
+#' @export
+nn_mse_loss <- nn_module(
+  "nn_mse_loss",
+  inherit = nn_loss,
+  initialize = function(reduction = 'mean') {
+    super$initialize(reduction = reduction)
+  },
+  forward = function(input, target) {
+    nnf_mse_loss(input, target, reduction=self$reduction)
+  }
+)
+
 #' Binary cross entropy loss
 #' 
 #' Creates a criterion that measures the Binary Cross Entropy
diff --git a/man/nn_mse_loss.Rd b/man/nn_mse_loss.Rd
new file mode 100644
index 000000000..b033895b0
--- /dev/null
+++ b/man/nn_mse_loss.Rd
@@ -0,0 +1,64 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_mse_loss}
+\alias{nn_mse_loss}
+\title{MSE loss}
+\usage{
+nn_mse_loss(reduction = "mean")
+}
+\arguments{
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that measures the mean squared error (squared L2 norm) between
+each element in the input \eqn{x} and target \eqn{y}.
+The unreduced (i.e. with \code{reduction} set to \code{'none'}) loss can be described
+as:
+}
+\details{
+\deqn{
+  \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_n = \left( x_n - y_n \right)^2,
+}
+
+where \eqn{N} is the batch size. If \code{reduction} is not \code{'none'}
+(default \code{'mean'}), then:
+
+\deqn{
+  \ell(x, y) =
+  \begin{array}{ll}
+\mbox{mean}(L), &  \mbox{if reduction} = \mbox{'mean';}\\
+\mbox{sum}(L),  &  \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+}
+
+\eqn{x} and \eqn{y} are tensors of arbitrary shapes with a total
+of \eqn{n} elements each.
+
+The mean operation still operates over all the elements, and divides by \eqn{n}.
+The division by \eqn{n} can be avoided if one sets \code{reduction = 'sum'}.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+dimensions
+\item Target: \eqn{(N, *)}, same shape as the input
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+loss <- nn_mse_loss()
+input <- torch_randn(3, 5, requires_grad=TRUE)
+target <- torch_randn(3, 5)
+output <- loss(input, target)
+output$backward()
+
+}
+}
-- 
GitLab


From 263401ed8bea8bd414b982a8e1b9fecfd300161c Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 21 Sep 2020 11:57:51 -0300
Subject: [PATCH 134/157] bce with logits loss

---
 NAMESPACE                      |  1 +
 R/nn-loss.R                    | 94 +++++++++++++++++++++++++++++++++
 man/nn_bce_with_logits_loss.Rd | 95 ++++++++++++++++++++++++++++++++++
 3 files changed, 190 insertions(+)
 create mode 100644 man/nn_bce_with_logits_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 676f72782..a61a184bb 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -118,6 +118,7 @@ export(nn_avg_pool3d)
 export(nn_batch_norm1d)
 export(nn_batch_norm2d)
 export(nn_bce_loss)
+export(nn_bce_with_logits_loss)
 export(nn_bilinear)
 export(nn_celu)
 export(nn_conv1d)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index b065a141f..6ba744102 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -466,6 +466,100 @@ nn_bce_loss <- nn_module(
   }
 )
 
+#' BCE with logits loss
+#' 
+#' This loss combines a `Sigmoid` layer and the `BCELoss` in one single
+#' class. This version is more numerically stable than using a plain `Sigmoid`
+#' followed by a `BCELoss` as, by combining the operations into one layer,
+#' we take advantage of the log-sum-exp trick for numerical stability.
+#' 
+#' The unreduced (i.e. with `reduction` set to `'none'`) loss can be described as:
+#' 
+#' \deqn{
+#'   \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+#' l_n = - w_n \left[ y_n \cdot \log \sigma(x_n)
+#'                    + (1 - y_n) \cdot \log (1 - \sigma(x_n)) \right],
+#' }
+#' 
+#' where \eqn{N} is the batch size. If `reduction` is not `'none'`
+#' (default `'mean'`), then
+#' 
+#' \deqn{
+#'   \ell(x, y) = \begin{array}{ll}
+#' \mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
+#' \mbox{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+#' \end{array}
+#' }
+#' 
+#' This is used for measuring the error of a reconstruction in for example
+#' an auto-encoder. Note that the targets `t[i]` should be numbers
+#' between 0 and 1.
+#' It's possible to trade off recall and precision by adding weights to positive examples.
+#' In the case of multi-label classification the loss can be described as:
+#'
+#' \deqn{
+#' \ell_c(x, y) = L_c = \{l_{1,c},\dots,l_{N,c}\}^\top, \quad
+#' l_{n,c} = - w_{n,c} \left[ p_c y_{n,c} \cdot \log \sigma(x_{n,c})
+#' + (1 - y_{n,c}) \cdot \log (1 - \sigma(x_{n,c})) \right],
+#' }
+#' where \eqn{c} is the class number (\eqn{c > 1} for multi-label binary 
+#' classification,
+#' 
+#' \eqn{c = 1} for single-label binary classification),
+#' \eqn{n} is the number of the sample in the batch and
+#' \eqn{p_c} is the weight of the positive answer for the class \eqn{c}.
+#' \eqn{p_c > 1} increases the recall, \eqn{p_c < 1} increases the precision.
+#' For example, if a dataset contains 100 positive and 300 negative examples of a single class,
+#' then `pos_weight` for the class should be equal to \eqn{\frac{300}{100}=3}.
+#' The loss would act as if the dataset contains \eqn{3\times 100=300} positive examples.
+#' 
+#' @param weight (Tensor, optional): a manual rescaling weight given to the loss
+#'   of each batch element. If given, has to be a Tensor of size `nbatch`.
+#' @param reduction (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. Note: `size_average`
+#'   and `reduce` are in the process of being deprecated, and in the meantime,
+#'   specifying either of those two args will override `reduction`. Default: `'mean'`
+#' @param pos_weight (Tensor, optional): a weight of positive examples.
+#'   Must be a vector with length equal to the number of classes.
+#'     
+#' @section Shape:
+#' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional dimensions
+#' - Target: \eqn{(N, *)}, same shape as the input
+#' - Output: scalar. If `reduction` is `'none'`, then \eqn{(N, *)}, same
+#'   shape as input.
+#' 
+#' @examples
+#' loss <- nn_bce_with_logits_loss()
+#' input <- torch_randn(3, requires_grad=TRUE)
+#' target <- torch_empty(3)$random_(1, 2)
+#' output <- loss(input, target)
+#' output$backward()
+#' 
+#' target <- torch_ones(10, 64, dtype=torch_float32())  # 64 classes, batch size = 10
+#' output <- torch_full(c(10, 64), 1.5)  # A prediction (logit)
+#' pos_weight <- torch_ones(64)  # All weights are equal to 1
+#' criterion <- nn_bce_with_logits_loss(pos_weight=pos_weight)
+#' criterion(output, target)  # -log(sigmoid(1.5))
+#'
+#' @export
+nn_bce_with_logits_loss <- nn_module(
+  "nn_bce_with_logits_loss",
+  inherit = nn_loss,
+  initialize = function(weight = NULL, reduction = 'mean', pos_weight = NULL) {
+    super$initialize(reduction = reduction)
+    self$register_buffer('weight', weight)
+    self$register_buffer('pos_weight', pos_weight)
+  },
+  forward = function(input, target) {
+    nnf_binary_cross_entropy_with_logits(
+      input, target, self$weight, 
+      pos_weight=self$pos_weight,
+      reduction=self$reduction)
+  }
+)
+
 #' CrossEntropyLoss module
 #' 
 #' This criterion combines [nn_log_softmax()] and `nn_nll_loss()` in one single class.
diff --git a/man/nn_bce_with_logits_loss.Rd b/man/nn_bce_with_logits_loss.Rd
new file mode 100644
index 000000000..84674f756
--- /dev/null
+++ b/man/nn_bce_with_logits_loss.Rd
@@ -0,0 +1,95 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_bce_with_logits_loss}
+\alias{nn_bce_with_logits_loss}
+\title{BCE with logits loss}
+\usage{
+nn_bce_with_logits_loss(weight = NULL, reduction = "mean", pos_weight = NULL)
+}
+\arguments{
+\item{weight}{(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}.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+
+\item{pos_weight}{(Tensor, optional): a weight of positive examples.
+Must be a vector with length equal to the number of classes.}
+}
+\description{
+This loss combines a \code{Sigmoid} layer and the \code{BCELoss} in one single
+class. This version is more numerically stable than using a plain \code{Sigmoid}
+followed by a \code{BCELoss} as, by combining the operations into one layer,
+we take advantage of the log-sum-exp trick for numerical stability.
+}
+\details{
+The unreduced (i.e. with \code{reduction} set to \code{'none'}) loss can be described as:
+
+\deqn{
+  \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_n = - w_n \left[ y_n \cdot \log \sigma(x_n)
+                   + (1 - y_n) \cdot \log (1 - \sigma(x_n)) \right],
+}
+
+where \eqn{N} is the batch size. If \code{reduction} is not \code{'none'}
+(default \code{'mean'}), then
+
+\deqn{
+  \ell(x, y) = \begin{array}{ll}
+\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
+\mbox{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+}
+
+This is used for measuring the error of a reconstruction in for example
+an auto-encoder. Note that the targets \code{t[i]} should be numbers
+between 0 and 1.
+It's possible to trade off recall and precision by adding weights to positive examples.
+In the case of multi-label classification the loss can be described as:
+
+\deqn{
+\ell_c(x, y) = L_c = \{l_{1,c},\dots,l_{N,c}\}^\top, \quad
+l_{n,c} = - w_{n,c} \left[ p_c y_{n,c} \cdot \log \sigma(x_{n,c})
++ (1 - y_{n,c}) \cdot \log (1 - \sigma(x_{n,c})) \right],
+}
+where \eqn{c} is the class number (\eqn{c > 1} for multi-label binary
+classification,
+
+\eqn{c = 1} for single-label binary classification),
+\eqn{n} is the number of the sample in the batch and
+\eqn{p_c} is the weight of the positive answer for the class \eqn{c}.
+\eqn{p_c > 1} increases the recall, \eqn{p_c < 1} increases the precision.
+For example, if a dataset contains 100 positive and 300 negative examples of a single class,
+then \code{pos_weight} for the class should be equal to \eqn{\frac{300}{100}=3}.
+The loss would act as if the dataset contains \eqn{3\times 100=300} positive examples.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} means, any number of additional dimensions
+\item Target: \eqn{(N, *)}, same shape as the input
+\item Output: scalar. If \code{reduction} is \code{'none'}, then \eqn{(N, *)}, same
+shape as input.
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+loss <- nn_bce_with_logits_loss()
+input <- torch_randn(3, requires_grad=TRUE)
+target <- torch_empty(3)$random_(1, 2)
+output <- loss(input, target)
+output$backward()
+
+target <- torch_ones(10, 64, dtype=torch_float32())  # 64 classes, batch size = 10
+output <- torch_full(c(10, 64), 1.5)  # A prediction (logit)
+pos_weight <- torch_ones(64)  # All weights are equal to 1
+criterion <- nn_bce_with_logits_loss(pos_weight=pos_weight)
+criterion(output, target)  # -log(sigmoid(1.5))
+
+}
+}
-- 
GitLab


From 536986e6c045037864203514ef1f7718633aac49 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 21 Sep 2020 14:38:28 -0300
Subject: [PATCH 135/157] allows $<- and [[<- for nn_modules (#253)

---
 NAMESPACE                |  2 ++
 R/nn.R                   | 10 ++++++++
 tests/testthat/test-nn.R | 49 ++++++++++++++++++++++++++++++++++++++++
 3 files changed, 61 insertions(+)

diff --git a/NAMESPACE b/NAMESPACE
index 5eacbca71..416704500 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -7,6 +7,7 @@ S3method("$",enum_env)
 S3method("$",nn_Module)
 S3method("$",nn_module)
 S3method("$<-",nn_Module)
+S3method("$<-",nn_module)
 S3method("%%",torch_tensor)
 S3method("%/%",torch_tensor)
 S3method("&",torch_tensor)
@@ -30,6 +31,7 @@ S3method("[[",nn_Module)
 S3method("[[",nn_module)
 S3method("[[",nn_module_list)
 S3method("[[<-",nn_Module)
+S3method("[[<-",nn_module)
 S3method("^",torch_tensor)
 S3method("|",torch_tensor)
 S3method(.DollarNames,torch_tensor)
diff --git a/R/nn.R b/R/nn.R
index fccc22cfe..6120e10c8 100644
--- a/R/nn.R
+++ b/R/nn.R
@@ -370,6 +370,7 @@ create_nn_module_callable <- function(instance) {
 
 #' @export
 `[[<-.nn_Module` <- function(x, name, value) {
+
   if (inherits(value, "nn_parameter")) {
     x$register_parameter(name, value)
   } else if (inherits(value, "nn_buffer")) {
@@ -389,6 +390,15 @@ create_nn_module_callable <- function(instance) {
   invisible(x)
 }
 
+#' @export
+`$<-.nn_module` <- function(x, name, value) {
+  attr(x, "module")[[name]] <- value
+  invisible(x)
+}
+
+#' @export
+`[[<-.nn_module` <- `$<-.nn_module`
+
 #' @export
 names.nn_module <- function(x, ...) {
   x <- attr(x, "module")
diff --git a/tests/testthat/test-nn.R b/tests/testthat/test-nn.R
index 3f10906a2..7b5f36c54 100644
--- a/tests/testthat/test-nn.R
+++ b/tests/testthat/test-nn.R
@@ -294,3 +294,52 @@ test_that("moodule$apply", {
   expect_equal_to_tensor(net$norm$weight, torch_zeros_like(net$norm$weight))
   
 })
+
+test_that("$<-  works for instances", {
+  
+  m <- nn_module(
+    initialize = function() {
+      self$mymodule <- nn_linear(10, 10)
+      self$n <- nn_linear(15, 15)
+    }
+  )
+  
+  model <- m()
+  expect_s3_class(model, "nn_module")
+  model$mymodule <- nn_linear(2,2)
+  expect_s3_class(model, "nn_module")
+  expect_equal(model$mymodule$out_feature, 2)
+  model$new_module <- nn_linear(5,5)
+  expect_s3_class(model, "nn_module")
+  
+  pars <- model$parameters
+  expect_length(pars, 6)
+  expect_tensor_shape(pars$mymodule.weight, c(2,2))
+  expect_tensor_shape(pars$new_module.weight, c(5,5))
+
+})
+
+test_that("[[<- works for instances", {
+  
+  m <- nn_module(
+    initialize = function() {
+      self$mymodule <- nn_linear(10, 10)
+      self$n <- nn_linear(15, 15)
+    }
+  )
+  
+  model <- m()
+  expect_s3_class(model, "nn_module")
+  model[["mymodule"]] <- nn_linear(2,2)
+  expect_s3_class(model, "nn_module")
+  expect_equal(model$mymodule$out_feature, 2)
+  model[["new_module"]] <- nn_linear(5,5)
+  expect_s3_class(model, "nn_module")
+  
+  pars <- model$parameters
+  expect_length(pars, 6)
+  expect_tensor_shape(pars$mymodule.weight, c(2,2))
+  expect_tensor_shape(pars$new_module.weight, c(5,5))
+  
+  
+})
-- 
GitLab


From 565c526d7e925ca90c2b4194bbe0a13345685b3a Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 21 Sep 2020 17:36:35 -0300
Subject: [PATCH 136/157] hinge embedding loss

---
 NAMESPACE                      |  1 +
 R/nn-loss.R                    | 56 ++++++++++++++++++++++++++++++++++
 man/nn_hinge_embedding_loss.Rd | 56 ++++++++++++++++++++++++++++++++++
 tests/testthat/test-nn-loss.R  | 13 ++++++++
 4 files changed, 126 insertions(+)
 create mode 100644 man/nn_hinge_embedding_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index a61a184bb..0d3ffebd7 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -141,6 +141,7 @@ export(nn_hardshrink)
 export(nn_hardsigmoid)
 export(nn_hardswish)
 export(nn_hardtanh)
+export(nn_hinge_embedding_loss)
 export(nn_identity)
 export(nn_init_calculate_gain)
 export(nn_init_constant_)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index 6ba744102..f75b50cef 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -560,6 +560,62 @@ nn_bce_with_logits_loss <- nn_module(
   }
 )
 
+#' Hinge embedding loss
+#' 
+#' Measures the loss given an input tensor \eqn{x} and a labels tensor \eqn{y}
+#' (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 \eqn{x}, and is typically
+#' used for learning nonlinear embeddings or semi-supervised learning.
+#' The loss function for \eqn{n}-th sample in the mini-batch is
+#' 
+#' \deqn{
+#'   l_n = \begin{array}{ll}
+#' x_n, & \mbox{if}\; y_n = 1,\\
+#' \max \{0, \Delta - x_n\}, & \mbox{if}\; y_n = -1,
+#' \end{array}
+#' }
+#' 
+#' and the total loss functions is
+#' 
+#' \deqn{
+#'   \ell(x, y) = \begin{array}{ll}
+#' \mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
+#' \mbox{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+#' \end{array}
+#' }
+#' 
+#' where \eqn{L = \{l_1,\dots,l_N\}^\top}.
+#' 
+#' @param margin (float, optional): Has a default value of `1`.
+#' @param reduction (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. Note: `size_average`
+#'  and `reduce` are in the process of being deprecated, and in the meantime,
+#'  specifying either of those two args will override `reduction`. Default: `'mean'`
+#' 
+#' @section Shape:
+#' - Input: \eqn{(*)} where \eqn{*} means, any number of dimensions. The sum operation
+#'   operates over all the elements.
+#' - Target: \eqn{(*)}, same shape as the input
+#' - Output: scalar. If `reduction` is `'none'`, then same shape as the input
+#' 
+#' @export
+nn_hinge_embedding_loss <- nn_module(
+  "nn_hinge_embedding_loss",
+  inherit = nn_loss,
+  initialize = function(margin = 1.0, reduction = 'mean') {
+    super$initialize(reduction = reduction)
+    self$margin <- margin
+  },
+  forward = function(input, target) {
+    nnf_hinge_embedding_loss(input, target, margin=self$margin, 
+                             reduction=self$reduction)
+  }
+)
+
 #' CrossEntropyLoss module
 #' 
 #' This criterion combines [nn_log_softmax()] and `nn_nll_loss()` in one single class.
diff --git a/man/nn_hinge_embedding_loss.Rd b/man/nn_hinge_embedding_loss.Rd
new file mode 100644
index 000000000..524e787b7
--- /dev/null
+++ b/man/nn_hinge_embedding_loss.Rd
@@ -0,0 +1,56 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_hinge_embedding_loss}
+\alias{nn_hinge_embedding_loss}
+\title{Hinge embedding loss}
+\usage{
+nn_hinge_embedding_loss(margin = 1, reduction = "mean")
+}
+\arguments{
+\item{margin}{(float, optional): Has a default value of \code{1}.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Measures the loss given an input tensor \eqn{x} and a labels tensor \eqn{y}
+(containing 1 or -1).
+}
+\details{
+This is usually used for measuring whether two inputs are similar or
+dissimilar, e.g. using the L1 pairwise distance as \eqn{x}, and is typically
+used for learning nonlinear embeddings or semi-supervised learning.
+The loss function for \eqn{n}-th sample in the mini-batch is
+
+\deqn{
+  l_n = \begin{array}{ll}
+x_n, & \mbox{if}\; y_n = 1,\\
+\max \{0, \Delta - x_n\}, & \mbox{if}\; y_n = -1,
+\end{array}
+}
+
+and the total loss functions is
+
+\deqn{
+  \ell(x, y) = \begin{array}{ll}
+\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
+\mbox{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+}
+
+where \eqn{L = \{l_1,\dots,l_N\}^\top}.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(*)} where \eqn{*} means, any number of dimensions. The sum operation
+operates over all the elements.
+\item Target: \eqn{(*)}, same shape as the input
+\item Output: scalar. If \code{reduction} is \code{'none'}, then same shape as the input
+}
+}
+
diff --git a/tests/testthat/test-nn-loss.R b/tests/testthat/test-nn-loss.R
index 7ed57c7a6..3f4002eba 100644
--- a/tests/testthat/test-nn-loss.R
+++ b/tests/testthat/test-nn-loss.R
@@ -34,3 +34,16 @@ test_that("nn_kl_div_loss", {
   
   expect_tensor(output)
 })
+
+test_that("nn_hinge_embedding_loss", {
+  
+  loss <- nn_hinge_embedding_loss()
+  input <- torch_randn(3, 5, requires_grad=TRUE)
+  target <- torch_randn(3, 5, requires_grad = TRUE)
+  
+  out <- loss(input, target)
+  out$backward()
+  
+  expect_length(out$shape, 0)
+  
+})
-- 
GitLab


From 19a31e3c96816fd1a528c842e9445a8fe459e76a Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 21 Sep 2020 17:54:58 -0300
Subject: [PATCH 137/157] multilabel margin loss

---
 NAMESPACE                        |  1 +
 R/gen-namespace.R                |  4 +--
 R/nn-loss.R                      | 53 +++++++++++++++++++++++++++++
 R/wrapers.R                      |  5 +++
 man/nn_multilabel_margin_loss.Rd | 57 ++++++++++++++++++++++++++++++++
 tests/testthat/test-nn-loss.R    | 15 +++++++++
 tools/torchgen/R/r.R             |  3 +-
 7 files changed, 135 insertions(+), 3 deletions(-)
 create mode 100644 man/nn_multilabel_margin_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 0d3ffebd7..1920d13e5 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -176,6 +176,7 @@ export(nn_module)
 export(nn_module_list)
 export(nn_mse_loss)
 export(nn_multihead_attention)
+export(nn_multilabel_margin_loss)
 export(nn_nll_loss)
 export(nn_poisson_nll_loss)
 export(nn_prelu)
diff --git a/R/gen-namespace.R b/R/gen-namespace.R
index e89478c94..f3bf7a726 100644
--- a/R/gen-namespace.R
+++ b/R/gen-namespace.R
@@ -11361,8 +11361,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_multilabel_margin_loss
-torch_multilabel_margin_loss <- function(self, target, reduction = torch_reduction_mean()) {
+#' @rdname .torch_multilabel_margin_loss
+.torch_multilabel_margin_loss <- function(self, target, reduction = torch_reduction_mean()) {
   args <- mget(x = c("self", "target", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t")
 nd_args <- c("self", "target")
diff --git a/R/nn-loss.R b/R/nn-loss.R
index f75b50cef..9c9801780 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -616,6 +616,59 @@ nn_hinge_embedding_loss <- nn_module(
   }
 )
 
+#' Multilabel margin loss
+#' 
+#' Creates a criterion that optimizes a multi-class multi-classification
+#' hinge loss (margin-based loss) between input \eqn{x} (a 2D mini-batch `Tensor`)
+#' and output \eqn{y} (which is a 2D `Tensor` of target class indices).
+#' For each sample in the mini-batch:
+#'
+#' \deqn{
+#'   \mbox{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\mbox{x.size}(0)}
+#' }
+#' 
+#' where \eqn{x \in \left\{0, \; \cdots , \; \mbox{x.size}(0) - 1\right\}}, \
+#' \eqn{y \in \left\{0, \; \cdots , \; \mbox{y.size}(0) - 1\right\}}, \
+#' \eqn{0 \leq y[j] \leq \mbox{x.size}(0)-1}, \
+#' and \eqn{i \neq y[j]} for all \eqn{i} and \eqn{j}.
+#' \eqn{y} and \eqn{x} must have the same size.
+#' 
+#' The criterion only considers a contiguous block of non-negative targets that
+#' starts at the front.
+#' This allows for different samples to have variable amounts of target classes.
+#' 
+#' @param reduction (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. Note: `size_average`
+#'  and `reduce` are in the process of being deprecated, and in the meantime,
+#'  specifying either of those two args will override `reduction`. Default: `'mean'`
+#' 
+#' @section Shape:
+#' - Input: \eqn{(C)} or \eqn{(N, C)} where `N` is the batch size and `C`
+#'   is the number of classes.
+#' - Target: \eqn{(C)} or \eqn{(N, C)}, label targets padded by -1 ensuring same shape as the input.
+#' - Output: scalar. If `reduction` is `'none'`, then \eqn{(N)}.
+#' 
+#' @examples
+#' loss <- nn_multilabel_margin_loss()
+#' x <- torch_tensor(c(0.1, 0.2, 0.4, 0.8))$view(c(1,4))
+#' # for target y, only consider labels 4 and 1, not after label -1
+#' y <- torch_tensor(c(4, 1, -1, 2), dtype = torch_long())$view(c(1,4))
+#' loss(x, y)
+#' 
+#' @export
+nn_multilabel_margin_loss <- nn_module(
+  "nn_multilabel_margin_loss",
+  inherit = nn_loss,
+  initialize = function(reduction = "mean") {
+    super$initialize(reduction = reduction)
+  },
+  forward = function(input, target) {
+    nnf_multilabel_margin_loss(input, target, reduction = self$reduction)
+  }
+)
+
 #' CrossEntropyLoss module
 #' 
 #' This criterion combines [nn_log_softmax()] and `nn_nll_loss()` in one single class.
diff --git a/R/wrapers.R b/R/wrapers.R
index f2210680b..d9f1c4940 100644
--- a/R/wrapers.R
+++ b/R/wrapers.R
@@ -191,4 +191,9 @@ torch_triu_indices <- function(row, col, offset=0, dtype=torch_long(),
   .torch_triu_indices(row, col, offset, options = opt)
 }
 
+#' @rdname torch_multilabel_margin_loss
+torch_multilabel_margin_loss <- function(self, target, reduction = torch_reduction_mean()) {
+  .torch_multilabel_margin_loss(self, as_1_based_tensor(target), reduction)
+}
+
 
diff --git a/man/nn_multilabel_margin_loss.Rd b/man/nn_multilabel_margin_loss.Rd
new file mode 100644
index 000000000..61d07b222
--- /dev/null
+++ b/man/nn_multilabel_margin_loss.Rd
@@ -0,0 +1,57 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_multilabel_margin_loss}
+\alias{nn_multilabel_margin_loss}
+\title{Multilabel margin loss}
+\usage{
+nn_multilabel_margin_loss(reduction = "mean")
+}
+\arguments{
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that optimizes a multi-class multi-classification
+hinge loss (margin-based loss) between input \eqn{x} (a 2D mini-batch \code{Tensor})
+and output \eqn{y} (which is a 2D \code{Tensor} of target class indices).
+For each sample in the mini-batch:
+}
+\details{
+\deqn{
+  \mbox{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\mbox{x.size}(0)}
+}
+
+where \eqn{x \in \left\{0, \; \cdots , \; \mbox{x.size}(0) - 1\right\}}, \
+\eqn{y \in \left\{0, \; \cdots , \; \mbox{y.size}(0) - 1\right\}}, \
+\eqn{0 \leq y[j] \leq \mbox{x.size}(0)-1}, \
+and \eqn{i \neq y[j]} for all \eqn{i} and \eqn{j}.
+\eqn{y} and \eqn{x} must have the same size.
+
+The criterion only considers a contiguous block of non-negative targets that
+starts at the front.
+This allows for different samples to have variable amounts of target classes.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(C)} or \eqn{(N, C)} where \code{N} is the batch size and \code{C}
+is the number of classes.
+\item Target: \eqn{(C)} or \eqn{(N, C)}, label targets padded by -1 ensuring same shape as the input.
+\item Output: scalar. If \code{reduction} is \code{'none'}, then \eqn{(N)}.
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+loss <- nn_multilabel_margin_loss()
+x <- torch_tensor(c(0.1, 0.2, 0.4, 0.8))$view(c(1,4))
+# for target y, only consider labels 4 and 1, not after label -1
+y <- torch_tensor(c(4, 1, -1, 2), dtype = torch_long())$view(c(1,4))
+loss(x, y)
+
+}
+}
diff --git a/tests/testthat/test-nn-loss.R b/tests/testthat/test-nn-loss.R
index 3f4002eba..c6c5e24cd 100644
--- a/tests/testthat/test-nn-loss.R
+++ b/tests/testthat/test-nn-loss.R
@@ -47,3 +47,18 @@ test_that("nn_hinge_embedding_loss", {
   expect_length(out$shape, 0)
   
 })
+
+test_that("multilabel margin loss", {
+  
+  loss <- nn_multilabel_margin_loss()
+  x <- torch_tensor(c(0.1, 0.2, 0.4, 0.8))$view(c(1,4))
+  # for target y, only consider labels 4 and 1, not after label -1
+  y <- torch_tensor(c(4, 1, -1, 2), dtype = torch_long())$view(c(1,4))
+  o <- loss(x, y)
+  expect_equal(as.numeric(o), 0.85, tol = 1e-5)
+  
+  expect_length(o$shape, 0)
+  y <- torch_tensor(c(4, 0, -1, 2), dtype = torch_long())$view(c(1,4))
+  expect_error(o <- loss(x, y))
+  
+})
diff --git a/tools/torchgen/R/r.R b/tools/torchgen/R/r.R
index 8d1c698de..18b34f301 100644
--- a/tools/torchgen/R/r.R
+++ b/tools/torchgen/R/r.R
@@ -103,7 +103,8 @@ internal_funs <- c("logical_not", "max_pool1d_with_indices", "max_pool2d_with_in
                    "nll_loss", "nll_loss2d", "bartlett_window", "blackman_window",
                    "hamming_window", "hann_window", "normal",
                    "result_type", "sparse_coo_tensor", "stft",
-                   "tensordot", "tril_indices", "triu_indices")
+                   "tensordot", "tril_indices", "triu_indices",
+                   "multilabel_margin_loss")
 internal_funs <- c(internal_funs, creation_ops)
 
 
-- 
GitLab


From 8e4c364659cea70b92fba5e6b935ce3e0ed562aa Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 21 Sep 2020 18:19:12 -0300
Subject: [PATCH 138/157] smooth l1 lss

---
 NAMESPACE                     |  1 +
 R/nn-loss.R                   | 54 ++++++++++++++++++++++++++++++++++-
 man/nn_smooth_l1_loss.Rd      | 53 ++++++++++++++++++++++++++++++++++
 tests/testthat/test-nn-loss.R | 11 +++++++
 4 files changed, 118 insertions(+), 1 deletion(-)
 create mode 100644 man/nn_smooth_l1_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 1920d13e5..ed6cbb54a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -187,6 +187,7 @@ export(nn_rrelu)
 export(nn_selu)
 export(nn_sequential)
 export(nn_sigmoid)
+export(nn_smooth_l1_loss)
 export(nn_softmax)
 export(nn_softmax2d)
 export(nn_softmin)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index 9c9801780..f1063ea05 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -1,4 +1,4 @@
-#' @include nn.R
+#' #' @include nn.R
 NULL
 
 nn_loss <- nn_module(
@@ -669,6 +669,58 @@ nn_multilabel_margin_loss <- nn_module(
   }
 )
 
+#' Smooth L1 loss
+#' 
+#' Creates a criterion that uses a squared term if the absolute
+#' element-wise error falls below 1 and an L1 term otherwise.
+#' It is less sensitive to outliers than the `MSELoss` and in some cases
+#' prevents exploding gradients (e.g. see `Fast R-CNN` paper by Ross Girshick).
+#' Also known as the Huber loss:
+#' 
+#' \deqn{
+#'   \mbox{loss}(x, y) = \frac{1}{n} \sum_{i} z_{i}
+#' }
+#' 
+#' where \eqn{z_{i}} is given by:
+#' 
+#' \deqn{
+#'   z_{i} =
+#'   \begin{array}{ll}
+#' 0.5 (x_i - y_i)^2, & \mbox{if } |x_i - y_i| < 1 \\
+#' |x_i - y_i| - 0.5, & \mbox{otherwise }
+#' \end{array}
+#' }
+#' 
+#' \eqn{x} and \eqn{y} arbitrary shapes with a total of \eqn{n} elements each
+#' the sum operation still operates over all the elements, and divides by \eqn{n}.
+#' The division by \eqn{n} can be avoided if sets `reduction = 'sum'`.
+#' 
+#' @param reduction (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. Note: `size_average`
+#'  and `reduce` are in the process of being deprecated, and in the meantime,
+#'  specifying either of those two args will override `reduction`. Default: `'mean'`
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+#'   dimensions
+#' - Target: \eqn{(N, *)}, same shape as the input
+#' - Output: scalar. If `reduction` is `'none'`, then
+#'   \eqn{(N, *)}, same shape as the input
+#'   
+#' @export
+nn_smooth_l1_loss <- nn_module(
+  "nn_smooth_l1_loss",
+  inherit = nn_loss,
+  initialize = function(reduction = "mean") {
+    super$initialize(reduction = reduction)
+  },
+  forward = function(input, target) {
+    nnf_smooth_l1_loss(input, target, reduction=self$reduction)
+  }
+)
+
 #' CrossEntropyLoss module
 #' 
 #' This criterion combines [nn_log_softmax()] and `nn_nll_loss()` in one single class.
diff --git a/man/nn_smooth_l1_loss.Rd b/man/nn_smooth_l1_loss.Rd
new file mode 100644
index 000000000..8b251c826
--- /dev/null
+++ b/man/nn_smooth_l1_loss.Rd
@@ -0,0 +1,53 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_smooth_l1_loss}
+\alias{nn_smooth_l1_loss}
+\title{Smooth L1 loss}
+\usage{
+nn_smooth_l1_loss(reduction = "mean")
+}
+\arguments{
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that uses a squared term if the absolute
+element-wise error falls below 1 and an L1 term otherwise.
+It is less sensitive to outliers than the \code{MSELoss} and in some cases
+prevents exploding gradients (e.g. see \verb{Fast R-CNN} paper by Ross Girshick).
+Also known as the Huber loss:
+}
+\details{
+\deqn{
+  \mbox{loss}(x, y) = \frac{1}{n} \sum_{i} z_{i}
+}
+
+where \eqn{z_{i}} is given by:
+
+\deqn{
+  z_{i} =
+  \begin{array}{ll}
+0.5 (x_i - y_i)^2, & \mbox{if } |x_i - y_i| < 1 \\
+|x_i - y_i| - 0.5, & \mbox{otherwise }
+\end{array}
+}
+
+\eqn{x} and \eqn{y} arbitrary shapes with a total of \eqn{n} elements each
+the sum operation still operates over all the elements, and divides by \eqn{n}.
+The division by \eqn{n} can be avoided if sets \code{reduction = 'sum'}.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+dimensions
+\item Target: \eqn{(N, *)}, same shape as the input
+\item Output: scalar. If \code{reduction} is \code{'none'}, then
+\eqn{(N, *)}, same shape as the input
+}
+}
+
diff --git a/tests/testthat/test-nn-loss.R b/tests/testthat/test-nn-loss.R
index c6c5e24cd..6b6c6dcf0 100644
--- a/tests/testthat/test-nn-loss.R
+++ b/tests/testthat/test-nn-loss.R
@@ -62,3 +62,14 @@ test_that("multilabel margin loss", {
   expect_error(o <- loss(x, y))
   
 })
+
+test_that("smooth_l1_loss", {
+  
+  loss <- nn_smooth_l1_loss()
+  input <- torch_randn(3, 5, requires_grad=TRUE)
+  target <- torch_randn(3, 5, requires_grad = TRUE)
+  o <- loss(x, y)
+  
+  expect_length(o$shape, 0)
+  
+})
-- 
GitLab


From c0c39afe6da70621a8a9044ed87dabeb76126ccc Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 21 Sep 2020 19:20:49 -0300
Subject: [PATCH 139/157] fix nnf_smooth_l1_loss

---
 R/nnf-loss.R                  | 16 ++--------------
 tests/testthat/test-nn-loss.R |  4 ++--
 2 files changed, 4 insertions(+), 16 deletions(-)

diff --git a/R/nnf-loss.R b/R/nnf-loss.R
index fbdd30986..b93d84fdb 100644
--- a/R/nnf-loss.R
+++ b/R/nnf-loss.R
@@ -177,20 +177,8 @@ nnf_cosine_embedding_loss <- function(input1, input2, target, margin=0,
 #'
 #' @export
 nnf_smooth_l1_loss <- function(input, target, reduction = "mean") {
-  if (target$requires_grad) {
-    ret <- nnf_smooth_l1_loss(input, target)
-    if (reduction != "none") {
-      if (reduction == "mean")
-        ret <- torch_mean(ret)
-      else
-        ret <- torch_sum(ret)
-    }
-  } else {
-    expanded <- torch_broadcast_tensors(list(input, target))
-    ret <- torch_smooth_l1_loss(expanded[[1]], expanded[[2]], reduction_enum(reduction))
-  }
-  
-  ret
+  expanded <- torch_broadcast_tensors(list(input, target))
+  torch_smooth_l1_loss(expanded[[1]], expanded[[2]], reduction_enum(reduction))
 }
 
 #' Multilabel_margin_loss
diff --git a/tests/testthat/test-nn-loss.R b/tests/testthat/test-nn-loss.R
index 6b6c6dcf0..090c6ff60 100644
--- a/tests/testthat/test-nn-loss.R
+++ b/tests/testthat/test-nn-loss.R
@@ -67,8 +67,8 @@ test_that("smooth_l1_loss", {
   
   loss <- nn_smooth_l1_loss()
   input <- torch_randn(3, 5, requires_grad=TRUE)
-  target <- torch_randn(3, 5, requires_grad = TRUE)
-  o <- loss(x, y)
+  target <- torch_randn(3, 5)
+  o <- loss(input, target)
   
   expect_length(o$shape, 0)
   
-- 
GitLab


From 4698fb31a46d6b2310bc23f3f68b7102792cbb82 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Mon, 21 Sep 2020 20:21:13 -0300
Subject: [PATCH 140/157] soft margin loss

---
 NAMESPACE                     |  1 +
 R/nn-loss.R                   | 35 ++++++++++++++++++++++++++++++++++
 man/nn_soft_margin_loss.Rd    | 36 +++++++++++++++++++++++++++++++++++
 tests/testthat/test-nn-loss.R | 11 +++++++++++
 4 files changed, 83 insertions(+)
 create mode 100644 man/nn_soft_margin_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index ed6cbb54a..b8392c9b7 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -188,6 +188,7 @@ export(nn_selu)
 export(nn_sequential)
 export(nn_sigmoid)
 export(nn_smooth_l1_loss)
+export(nn_soft_margin_loss)
 export(nn_softmax)
 export(nn_softmax2d)
 export(nn_softmin)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index f1063ea05..90437ed8f 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -721,6 +721,41 @@ nn_smooth_l1_loss <- nn_module(
   }
 )
 
+#' Soft margin loss
+#' 
+#' Creates a criterion that optimizes a two-class classification
+#' logistic loss between input tensor \eqn{x} and target tensor \eqn{y}
+#' (containing 1 or -1).
+#' 
+#' \deqn{
+#'   \mbox{loss}(x, y) = \sum_i \frac{\log(1 + \exp(-y[i]*x[i]))}{\mbox{x.nelement}()}
+#' }
+#' 
+#' @param reduction (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. Note: `size_average`
+#'   and `reduce` are in the process of being deprecated, and in the meantime,
+#'   specifying either of those two args will override `reduction`. Default: `'mean'`
+#' 
+#' @section Shape:
+#' - Input: \eqn{(*)} where \eqn{*} means, any number of additional
+#'   dimensions
+#' - Target: \eqn{(*)}, same shape as the input
+#' - Output: scalar. If `reduction` is `'none'`, then same shape as the input
+#' 
+#' @export
+nn_soft_margin_loss <- nn_module(
+  "nn_soft_margin_loss",
+  inherit = nn_loss,
+  initialize = function(reduction = "mean") {
+    super$initialize(reduction = reduction)
+  },
+  forward = function(input, target) {
+    nnf_soft_margin_loss(input, target, reduction = self$reduction)
+  }
+)
+
 #' CrossEntropyLoss module
 #' 
 #' This criterion combines [nn_log_softmax()] and `nn_nll_loss()` in one single class.
diff --git a/man/nn_soft_margin_loss.Rd b/man/nn_soft_margin_loss.Rd
new file mode 100644
index 000000000..5ba642d8d
--- /dev/null
+++ b/man/nn_soft_margin_loss.Rd
@@ -0,0 +1,36 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_soft_margin_loss}
+\alias{nn_soft_margin_loss}
+\title{Soft margin loss}
+\usage{
+nn_soft_margin_loss(reduction = "mean")
+}
+\arguments{
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that optimizes a two-class classification
+logistic loss between input tensor \eqn{x} and target tensor \eqn{y}
+(containing 1 or -1).
+}
+\details{
+\deqn{
+  \mbox{loss}(x, y) = \sum_i \frac{\log(1 + \exp(-y[i]*x[i]))}{\mbox{x.nelement}()}
+}
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(*)} where \eqn{*} means, any number of additional
+dimensions
+\item Target: \eqn{(*)}, same shape as the input
+\item Output: scalar. If \code{reduction} is \code{'none'}, then same shape as the input
+}
+}
+
diff --git a/tests/testthat/test-nn-loss.R b/tests/testthat/test-nn-loss.R
index 090c6ff60..e6f2ad6c2 100644
--- a/tests/testthat/test-nn-loss.R
+++ b/tests/testthat/test-nn-loss.R
@@ -73,3 +73,14 @@ test_that("smooth_l1_loss", {
   expect_length(o$shape, 0)
   
 })
+
+test_that("soft_margin loss", {
+  
+  loss <- nn_soft_margin_loss()
+  input <- torch_randn(3, 5, requires_grad=TRUE)
+  target <- torch_randn(3, 5)
+  o <- loss(input, target)
+  
+  expect_length(o$shape, 0)
+  
+})
-- 
GitLab


From e16957795884cbb3a91f70a9ef645d3aac8a0080 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 22 Sep 2020 11:27:53 -0300
Subject: [PATCH 141/157] multilabel soft margin loss

---
 NAMESPACE                             |  1 +
 R/nn-loss.R                           | 44 ++++++++++++++++++++++++++
 man/nn_multilabel_soft_margin_loss.Rd | 45 +++++++++++++++++++++++++++
 tests/testthat/test-nn-loss.R         | 11 +++++++
 4 files changed, 101 insertions(+)
 create mode 100644 man/nn_multilabel_soft_margin_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index b8392c9b7..4e920eb9c 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -177,6 +177,7 @@ export(nn_module_list)
 export(nn_mse_loss)
 export(nn_multihead_attention)
 export(nn_multilabel_margin_loss)
+export(nn_multilabel_soft_margin_loss)
 export(nn_nll_loss)
 export(nn_poisson_nll_loss)
 export(nn_prelu)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index 90437ed8f..7c6eb8688 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -836,3 +836,47 @@ nn_cross_entropy_loss <- nn_module(
                       ignore_index = self$ignore_index, reduction = self$reduction)
   }
 )
+
+#' Multi label soft margin loss 
+#' 
+#' Creates a criterion that optimizes a multi-label one-versus-all
+#' loss based on max-entropy, between input \eqn{x} and target \eqn{y} of size
+#' \eqn{(N, C)}.
+#' 
+#' For each sample in the minibatch:
+#' 
+#' \deqn{
+#'   loss(x, y) = - \frac{1}{C} * \sum_i y[i] * \log((1 + \exp(-x[i]))^{-1})
+#' + (1-y[i]) * \log\left(\frac{\exp(-x[i])}{(1 + \exp(-x[i]))}\right)
+#' }
+#' 
+#' where \eqn{i \in \left\{0, \; \cdots , \; \mbox{x.nElement}() - 1\right\}},
+#' \eqn{y[i] \in \left\{0, \; 1\right\}}.
+#' 
+#' @param weight (Tensor, optional): 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.
+#' @param reduction (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. Note: `size_average`
+#'   and `reduce` are in the process of being deprecated, and in the meantime,
+#'   specifying either of those two args will override `reduction`. Default: `'mean'`
+#'   
+#' @section Shape:
+#' - Input: \eqn{(N, C)} where `N` is the batch size and `C` is the number of classes.
+#' - Target: \eqn{(N, C)}, label targets padded by -1 ensuring same shape as the input.
+#' - Output: scalar. If `reduction` is `'none'`, then \eqn{(N)}.
+#' 
+#' @export
+nn_multilabel_soft_margin_loss <- nn_module(
+  "nn_multilabel_soft_margin_loss",
+  inherit = nn_weighted_loss,
+  initialize = function(weight = NULL, reduction = "mean") {
+    super$initialize(weight = weight, reduction = reduction)
+  },
+  forward = function(input, target) {
+    nnf_multilabel_soft_margin_loss(input, target, weight=self$weight, 
+                                    reduction=self$reduction)
+  }
+)
diff --git a/man/nn_multilabel_soft_margin_loss.Rd b/man/nn_multilabel_soft_margin_loss.Rd
new file mode 100644
index 000000000..a55f16bfa
--- /dev/null
+++ b/man/nn_multilabel_soft_margin_loss.Rd
@@ -0,0 +1,45 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_multilabel_soft_margin_loss}
+\alias{nn_multilabel_soft_margin_loss}
+\title{Multi label soft margin loss}
+\usage{
+nn_multilabel_soft_margin_loss(weight = NULL, reduction = "mean")
+}
+\arguments{
+\item{weight}{(Tensor, optional): a manual rescaling weight given to each
+class. If given, it has to be a Tensor of size \code{C}. Otherwise, it is
+treated as if having all ones.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that optimizes a multi-label one-versus-all
+loss based on max-entropy, between input \eqn{x} and target \eqn{y} of size
+\eqn{(N, C)}.
+}
+\details{
+For each sample in the minibatch:
+
+\deqn{
+  loss(x, y) = - \frac{1}{C} * \sum_i y[i] * \log((1 + \exp(-x[i]))^{-1})
++ (1-y[i]) * \log\left(\frac{\exp(-x[i])}{(1 + \exp(-x[i]))}\right)
+}
+
+where \eqn{i \in \left\{0, \; \cdots , \; \mbox{x.nElement}() - 1\right\}},
+\eqn{y[i] \in \left\{0, \; 1\right\}}.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C)} where \code{N} is the batch size and \code{C} is the number of classes.
+\item Target: \eqn{(N, C)}, label targets padded by -1 ensuring same shape as the input.
+\item Output: scalar. If \code{reduction} is \code{'none'}, then \eqn{(N)}.
+}
+}
+
diff --git a/tests/testthat/test-nn-loss.R b/tests/testthat/test-nn-loss.R
index e6f2ad6c2..674622b17 100644
--- a/tests/testthat/test-nn-loss.R
+++ b/tests/testthat/test-nn-loss.R
@@ -84,3 +84,14 @@ test_that("soft_margin loss", {
   expect_length(o$shape, 0)
   
 })
+
+test_that("multilabel_soft_margin loss", {
+  
+  loss <- nn_multilabel_soft_margin_loss()
+  input <- torch_randn(3, 5, requires_grad=TRUE)
+  target <- torch_randn(3, 5)
+  o <- loss(input, target)
+  
+  expect_length(o$shape, 0)
+  
+})
-- 
GitLab


From 3eb3320a7cb7413b4a9282a05666cbd99b1e9751 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 22 Sep 2020 13:56:14 -0300
Subject: [PATCH 142/157] cosine embedding loss

---
 NAMESPACE                       |  1 +
 R/nn-loss.R                     | 43 ++++++++++++++++++++++++++++++++-
 man/nn_cosine_embedding_loss.Rd | 37 ++++++++++++++++++++++++++++
 tests/testthat/test-nn-loss.R   | 12 +++++++++
 4 files changed, 92 insertions(+), 1 deletion(-)
 create mode 100644 man/nn_cosine_embedding_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 4e920eb9c..c769b87f6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -127,6 +127,7 @@ export(nn_conv3d)
 export(nn_conv_transpose1d)
 export(nn_conv_transpose2d)
 export(nn_conv_transpose3d)
+export(nn_cosine_embedding_loss)
 export(nn_cross_entropy_loss)
 export(nn_dropout)
 export(nn_dropout2d)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index 7c6eb8688..948e33b79 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -1,4 +1,4 @@
-#' #' @include nn.R
+#' @include nn.R
 NULL
 
 nn_loss <- nn_module(
@@ -880,3 +880,44 @@ nn_multilabel_soft_margin_loss <- nn_module(
                                     reduction=self$reduction)
   }
 )
+
+#' Cosine embedding loss
+#' 
+#' Creates a criterion that measures the loss given input tensors
+#' \eqn{x_1}, \eqn{x_2} and a `Tensor` label \eqn{y} with values 1 or -1.
+#' This is used for measuring whether two inputs are similar or dissimilar,
+#' using the cosine distance, and is typically used for learning nonlinear
+#' embeddings or semi-supervised learning.
+#' The loss function for each sample is:
+#'
+#' \deqn{
+#'   \mbox{loss}(x, y) =
+#'   \begin{array}{ll}
+#' 1 - \cos(x_1, x_2), & \mbox{if } y = 1 \\
+#' \max(0, \cos(x_1, x_2) - \mbox{margin}), & \mbox{if } y = -1
+#' \end{array}
+#' }
+#' 
+#' @param margin (float, optional): Should be a number from \eqn{-1} to \eqn{1},
+#'   \eqn{0} to \eqn{0.5} is suggested. If `margin` is missing, the
+#'   default value is \eqn{0}.
+#' @param reduction (string, optional): Specifies the reduction to apply to the output:
+#'   `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied,
+#'   `'mean'`: the sum of the output will be divided by the number of
+#'   elements in the output, `'sum'`: the output will be summed. Note: `size_average`
+#'   and `reduce` are in the process of being deprecated, and in the meantime,
+#'   specifying either of those two args will override `reduction`. Default: `'mean'`
+#'   
+#' @export
+nn_cosine_embedding_loss <- nn_module(
+  "nn_cosine_embedding_loss",
+  inherit = nn_loss,
+  initialize = function(margin = 0, reduction = "mean") {
+    super$initialize(reduction = reduction)
+    self$margin <- margin
+  },
+  forward = function(input1, input2, target) {
+    nnf_cosine_embedding_loss(input1, input2, target, margin = self$margin,
+                              reduction = self$reduction)
+  }
+)
\ No newline at end of file
diff --git a/man/nn_cosine_embedding_loss.Rd b/man/nn_cosine_embedding_loss.Rd
new file mode 100644
index 000000000..9444260d3
--- /dev/null
+++ b/man/nn_cosine_embedding_loss.Rd
@@ -0,0 +1,37 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_cosine_embedding_loss}
+\alias{nn_cosine_embedding_loss}
+\title{Cosine embedding loss}
+\usage{
+nn_cosine_embedding_loss(margin = 0, reduction = "mean")
+}
+\arguments{
+\item{margin}{(float, optional): Should be a number from \eqn{-1} to \eqn{1},
+\eqn{0} to \eqn{0.5} is suggested. If \code{margin} is missing, the
+default value is \eqn{0}.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that measures the loss given input tensors
+\eqn{x_1}, \eqn{x_2} and a \code{Tensor} label \eqn{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.
+The loss function for each sample is:
+}
+\details{
+\deqn{
+  \mbox{loss}(x, y) =
+  \begin{array}{ll}
+1 - \cos(x_1, x_2), & \mbox{if } y = 1 \\
+\max(0, \cos(x_1, x_2) - \mbox{margin}), & \mbox{if } y = -1
+\end{array}
+}
+}
diff --git a/tests/testthat/test-nn-loss.R b/tests/testthat/test-nn-loss.R
index 674622b17..f9f9ad6e7 100644
--- a/tests/testthat/test-nn-loss.R
+++ b/tests/testthat/test-nn-loss.R
@@ -95,3 +95,15 @@ test_that("multilabel_soft_margin loss", {
   expect_length(o$shape, 0)
   
 })
+
+test_that("cosine_embedding loss", {
+  
+  loss <- nn_cosine_embedding_loss()
+  input1 <- torch_randn(5, 5, requires_grad=TRUE)
+  input2 <- torch_randn(5, 5, requires_grad=TRUE)
+  target <- torch_randn(5, 5)
+  o <- loss(input1, input2, target)
+  
+  expect_length(o$shape, 0)
+  
+})
-- 
GitLab


From 3ffa71dac05067cedbc0128077cad8e39ecfd46b Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 22 Sep 2020 18:35:55 -0300
Subject: [PATCH 143/157] margin ranking loss

---
 NAMESPACE                     |  1 +
 R/nn-loss.R                   | 51 +++++++++++++++++++++++++++++++++
 man/nn_margin_ranking_loss.Rd | 53 +++++++++++++++++++++++++++++++++++
 3 files changed, 105 insertions(+)
 create mode 100644 man/nn_margin_ranking_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index c769b87f6..96d2c4c76 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -167,6 +167,7 @@ export(nn_log_sigmoid)
 export(nn_log_softmax)
 export(nn_lp_pool1d)
 export(nn_lp_pool2d)
+export(nn_margin_ranking_loss)
 export(nn_max_pool1d)
 export(nn_max_pool2d)
 export(nn_max_pool3d)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index 948e33b79..364139169 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -920,4 +920,55 @@ nn_cosine_embedding_loss <- nn_module(
     nnf_cosine_embedding_loss(input1, input2, target, margin = self$margin,
                               reduction = self$reduction)
   }
+)
+
+#' Margin ranking loss
+#' 
+#' Creates a criterion that measures the loss given
+#' inputs \eqn{x1}, \eqn{x2}, two 1D mini-batch `Tensors`,
+#' and a label 1D mini-batch tensor \eqn{y} (containing 1 or -1).
+#' If \eqn{y = 1} then it assumed the first input should be ranked higher
+#' (have a larger value) than the second input, and vice-versa for \eqn{y = -1}.
+#' 
+#' The loss function for each pair of samples in the mini-batch is:
+#' 
+#' \deqn{
+#'   \mbox{loss}(x1, x2, y) = \max(0, -y * (x1 - x2) + \mbox{margin})
+#' }
+#' 
+#' 
+#' @param margin (float, optional): Has a default value of \eqn{0}.
+#' @param reduction (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. Note: `size_average`
+#'   and `reduce` are in the process of being deprecated, and in the meantime,
+#'   specifying either of those two args will override `reduction`. Default: `'mean'`
+#' 
+#' @section Shape:
+#' - Input1: \eqn{(N)} where `N` is the batch size.
+#' - Input2: \eqn{(N)}, same shape as the Input1.
+#' - Target: \eqn{(N)}, same shape as the inputs.
+#' - Output: scalar. If `reduction` is `'none'`, then \eqn{(N)}.
+#' 
+#' @examples
+#' loss <- nn_margin_ranking_loss()
+#' input1 <- torch_randn(3, requires_grad=TRUE)
+#' input2 <- torch_randn(3, requires_grad=TRUE)
+#' target <- torch_randn(3)$sign()
+#' output <- loss(input1, input2, target)
+#' output$backward()
+#' 
+#' @export
+nn_margin_ranking_loss <- nn_module(
+  "nn_margin_ranking_loss",
+  inherit = nn_loss,
+  initialize = function(margin = 0, reduction = "mean") {
+    super$initialize(reduction = reduction)
+    self$margin <- margin
+  },
+  forward = function(input1, input2, target){
+    nnf_margin_ranking_loss(input1, input2, target, margin = self$margin,
+                            reduction = self$reduction)
+  }
 )
\ No newline at end of file
diff --git a/man/nn_margin_ranking_loss.Rd b/man/nn_margin_ranking_loss.Rd
new file mode 100644
index 000000000..5b8ec22a4
--- /dev/null
+++ b/man/nn_margin_ranking_loss.Rd
@@ -0,0 +1,53 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_margin_ranking_loss}
+\alias{nn_margin_ranking_loss}
+\title{Margin ranking loss}
+\usage{
+nn_margin_ranking_loss(margin = 0, reduction = "mean")
+}
+\arguments{
+\item{margin}{(float, optional): Has a default value of \eqn{0}.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that measures the loss given
+inputs \eqn{x1}, \eqn{x2}, two 1D mini-batch \code{Tensors},
+and a label 1D mini-batch tensor \eqn{y} (containing 1 or -1).
+If \eqn{y = 1} then it assumed the first input should be ranked higher
+(have a larger value) than the second input, and vice-versa for \eqn{y = -1}.
+}
+\details{
+The loss function for each pair of samples in the mini-batch is:
+
+\deqn{
+  \mbox{loss}(x1, x2, y) = \max(0, -y * (x1 - x2) + \mbox{margin})
+}
+}
+\section{Shape}{
+
+\itemize{
+\item Input1: \eqn{(N)} where \code{N} is the batch size.
+\item Input2: \eqn{(N)}, same shape as the Input1.
+\item Target: \eqn{(N)}, same shape as the inputs.
+\item Output: scalar. If \code{reduction} is \code{'none'}, then \eqn{(N)}.
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+loss <- nn_margin_ranking_loss()
+input1 <- torch_randn(3, requires_grad=TRUE)
+input2 <- torch_randn(3, requires_grad=TRUE)
+target <- torch_randn(3)$sign()
+output <- loss(input1, input2, target)
+output$backward()
+
+}
+}
-- 
GitLab


From 599ef547da65f979316aa0aa2af38b349d32f4af Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Tue, 22 Sep 2020 21:12:12 -0300
Subject: [PATCH 144/157] multi margin loss

---
 NAMESPACE                     |  1 +
 R/gen-namespace.R             |  4 +--
 R/nn-loss.R                   | 58 +++++++++++++++++++++++++++++++++++
 R/wrapers.R                   |  7 +++++
 man/nn_multi_margin_loss.Rd   | 49 +++++++++++++++++++++++++++++
 tests/testthat/test-nn-loss.R | 10 ++++++
 tests/testthat/test-wrapers.R | 15 +++++++++
 tools/torchgen/R/r.R          |  3 +-
 8 files changed, 144 insertions(+), 3 deletions(-)
 create mode 100644 man/nn_multi_margin_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 96d2c4c76..b9afbf776 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -177,6 +177,7 @@ export(nn_max_unpool3d)
 export(nn_module)
 export(nn_module_list)
 export(nn_mse_loss)
+export(nn_multi_margin_loss)
 export(nn_multihead_attention)
 export(nn_multilabel_margin_loss)
 export(nn_multilabel_soft_margin_loss)
diff --git a/R/gen-namespace.R b/R/gen-namespace.R
index f3bf7a726..192e46cb9 100644
--- a/R/gen-namespace.R
+++ b/R/gen-namespace.R
@@ -11287,8 +11287,8 @@ fun_type = 'namespace'
 }
 
 
-#' @rdname torch_multi_margin_loss
-torch_multi_margin_loss <- function(self, target, p = 1L, margin = 1L, weight = list(), reduction = torch_reduction_mean()) {
+#' @rdname .torch_multi_margin_loss
+.torch_multi_margin_loss <- function(self, target, p = 1L, margin = 1L, weight = list(), reduction = torch_reduction_mean()) {
   args <- mget(x = c("self", "target", "p", "margin", "weight", "reduction"))
 expected_types <- list(self = "Tensor", target = "Tensor", p = "Scalar", margin = "Scalar", 
     weight = "Tensor", reduction = "int64_t")
diff --git a/R/nn-loss.R b/R/nn-loss.R
index 364139169..0bdc5806e 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -971,4 +971,62 @@ nn_margin_ranking_loss <- nn_module(
     nnf_margin_ranking_loss(input1, input2, target, margin = self$margin,
                             reduction = self$reduction)
   }
+)
+
+#' Multi margin loss
+#' 
+#' Creates a criterion that optimizes a multi-class classification hinge
+#' loss (margin-based loss) between input \eqn{x} (a 2D mini-batch `Tensor`) and
+#' output \eqn{y} (which is a 1D tensor of target class indices,
+#' \eqn{0 \leq y \leq \mbox{x.size}(1)-1}):
+#' 
+#' For each mini-batch sample, the loss in terms of the 1D input \eqn{x} and scalar
+#' output \eqn{y} is:
+#' \deqn{
+#'   \mbox{loss}(x, y) = \frac{\sum_i \max(0, \mbox{margin} - x[y] + x[i]))^p}{\mbox{x.size}(0)}
+#' }
+#' 
+#' where \eqn{x \in \left\{0, \; \cdots , \; \mbox{x.size}(0) - 1\right\}}
+#' and \eqn{i \neq y}.
+#' 
+#' Optionally, you can give non-equal weighting on the classes by passing
+#' a 1D `weight` tensor into the constructor.
+#' The loss function then becomes:
+#'
+#' \deqn{
+#'   \mbox{loss}(x, y) = \frac{\sum_i \max(0, w[y] * (\mbox{margin} - x[y] + x[i]))^p)}{\mbox{x.size}(0)}
+#' } 
+#' 
+#' @param p (int, optional): Has a default value of \eqn{1}. \eqn{1} and \eqn{2}
+#'   are the only supported values.
+#' @param margin (float, optional): Has a default value of \eqn{1}.
+#' @param weight (Tensor, optional): 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.
+#' @param reduction (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. Note: `size_average`
+#'   and `reduce` are in the process of being deprecated, and in the meantime,
+#'   specifying either of those two args will override `reduction`. Default: `'mean'`
+#'   
+#' @export
+nn_multi_margin_loss <- nn_module(
+  "nn_multi_margin_loss",
+  inherit = nn_weighted_loss,
+  initialize = function(p = 1, margin = 1, weight = NULL, reduction = "mean") {
+    super$initialize(weight = weight, reduction = reduction)
+    if (p != 1 && p != 2) {
+      value_error("only p == 1 or p == 2 are supported.")
+    }
+    if (!is.null(weight) && weight$dim() != 1) {
+      value_error("weight must be NULL or 1-dimensional")
+    }
+    self$p <- p
+    self$margin <- margin
+  },
+  forward = function(input, target) {
+    nnf_multi_margin_loss(input, target, p = self$p, margin = self$margin,
+                          weight = self$weight, reduction = self$reduction)
+  }
 )
\ No newline at end of file
diff --git a/R/wrapers.R b/R/wrapers.R
index d9f1c4940..3bd6b40ee 100644
--- a/R/wrapers.R
+++ b/R/wrapers.R
@@ -196,4 +196,11 @@ torch_multilabel_margin_loss <- function(self, target, reduction = torch_reducti
   .torch_multilabel_margin_loss(self, as_1_based_tensor(target), reduction)
 }
 
+#' @rdname torch_multi_margin_loss
+torch_multi_margin_loss <- function(self, target, p = 1L, margin = 1L, weight = list(), 
+                                    reduction = torch_reduction_mean()) {
+  .torch_multi_margin_loss(self, as_1_based_tensor(target), p, margin, weight,
+                           reduction)
+}
+
 
diff --git a/man/nn_multi_margin_loss.Rd b/man/nn_multi_margin_loss.Rd
new file mode 100644
index 000000000..d3f8eec2c
--- /dev/null
+++ b/man/nn_multi_margin_loss.Rd
@@ -0,0 +1,49 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_multi_margin_loss}
+\alias{nn_multi_margin_loss}
+\title{Multi margin loss}
+\usage{
+nn_multi_margin_loss(p = 1, margin = 1, weight = NULL, reduction = "mean")
+}
+\arguments{
+\item{p}{(int, optional): Has a default value of \eqn{1}. \eqn{1} and \eqn{2}
+are the only supported values.}
+
+\item{margin}{(float, optional): Has a default value of \eqn{1}.}
+
+\item{weight}{(Tensor, optional): a manual rescaling weight given to each
+class. If given, it has to be a Tensor of size \code{C}. Otherwise, it is
+treated as if having all ones.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that optimizes a multi-class classification hinge
+loss (margin-based loss) between input \eqn{x} (a 2D mini-batch \code{Tensor}) and
+output \eqn{y} (which is a 1D tensor of target class indices,
+\eqn{0 \leq y \leq \mbox{x.size}(1)-1}):
+}
+\details{
+For each mini-batch sample, the loss in terms of the 1D input \eqn{x} and scalar
+output \eqn{y} is:
+\deqn{
+  \mbox{loss}(x, y) = \frac{\sum_i \max(0, \mbox{margin} - x[y] + x[i]))^p}{\mbox{x.size}(0)}
+}
+
+where \eqn{x \in \left\{0, \; \cdots , \; \mbox{x.size}(0) - 1\right\}}
+and \eqn{i \neq y}.
+
+Optionally, you can give non-equal weighting on the classes by passing
+a 1D \code{weight} tensor into the constructor.
+The loss function then becomes:
+
+\deqn{
+  \mbox{loss}(x, y) = \frac{\sum_i \max(0, w[y] * (\mbox{margin} - x[y] + x[i]))^p)}{\mbox{x.size}(0)}
+}
+}
diff --git a/tests/testthat/test-nn-loss.R b/tests/testthat/test-nn-loss.R
index f9f9ad6e7..20881b081 100644
--- a/tests/testthat/test-nn-loss.R
+++ b/tests/testthat/test-nn-loss.R
@@ -107,3 +107,13 @@ test_that("cosine_embedding loss", {
   expect_length(o$shape, 0)
   
 })
+
+test_that("nn_multi_margin_loss", {
+  
+  loss <- nn_multi_margin_loss()
+  input <- torch_randn(100, 5, requires_grad=TRUE)
+  target <- torch_randint(low = 1, high = 5, size = c(100), dtype = torch_long())
+  o <- loss(input, target)
+
+  expect_length(o$shape, 0)  
+})
diff --git a/tests/testthat/test-wrapers.R b/tests/testthat/test-wrapers.R
index 5a82f66be..19ac33a79 100644
--- a/tests/testthat/test-wrapers.R
+++ b/tests/testthat/test-wrapers.R
@@ -29,3 +29,18 @@ test_that("result_type", {
   expect_true(x == torch_float())
   
 })
+
+test_that("torch_multi_margin_loss", {
+  
+  x <- torch_randn(3, 2)
+  y <- torch_tensor(c(1, 2, 3), dtype = torch_long())
+  
+  expect_error(torch_multi_margin_loss(x, y))
+  
+  x <- torch_randn(3, 3)
+  expect_tensor(torch_multi_margin_loss(x, y))
+  
+  y <- torch_tensor(c(0, 1, 2))
+  expect_error(torch_multi_margin_loss(x, y))
+  
+})
diff --git a/tools/torchgen/R/r.R b/tools/torchgen/R/r.R
index 18b34f301..ad893131a 100644
--- a/tools/torchgen/R/r.R
+++ b/tools/torchgen/R/r.R
@@ -104,7 +104,8 @@ internal_funs <- c("logical_not", "max_pool1d_with_indices", "max_pool2d_with_in
                    "hamming_window", "hann_window", "normal",
                    "result_type", "sparse_coo_tensor", "stft",
                    "tensordot", "tril_indices", "triu_indices",
-                   "multilabel_margin_loss")
+                   "multilabel_margin_loss", "multi_margin_loss")
+
 internal_funs <- c(internal_funs, creation_ops)
 
 
-- 
GitLab


From 6bd5f0c412d790ebc09fc5e1d6224daee89e1595 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 23 Sep 2020 13:03:02 -0300
Subject: [PATCH 145/157] add triplet losses

---
 DESCRIPTION                                  |   1 +
 NAMESPACE                                    |   4 +
 R/nn-distance.R                              |  39 ++++
 R/nn-loss.R                                  | 191 +++++++++++++++++++
 R/nnf-loss.R                                 |  35 ++++
 man/nn_pairwise_distance.Rd                  |  44 +++++
 man/nn_triplet_margin_loss.Rd                |  79 ++++++++
 man/nn_triplet_margin_with_distance_loss.Rd  | 119 ++++++++++++
 man/nnf_triplet_margin_with_distance_loss.Rd |  41 ++++
 9 files changed, 553 insertions(+)
 create mode 100644 R/nn-distance.R
 create mode 100644 man/nn_pairwise_distance.Rd
 create mode 100644 man/nn_triplet_margin_loss.Rd
 create mode 100644 man/nn_triplet_margin_with_distance_loss.Rd
 create mode 100644 man/nnf_triplet_margin_with_distance_loss.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index 0b03413ac..09e79cced 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -71,6 +71,7 @@ Collate:
     'nn-activation.R'
     'nn-batchnorm.R'
     'nn-conv.R'
+    'nn-distance.R'
     'nn-dropout.R'
     'nn-init.R'
     'nn-linear.R'
diff --git a/NAMESPACE b/NAMESPACE
index b9afbf776..515744ffd 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -182,6 +182,7 @@ export(nn_multihead_attention)
 export(nn_multilabel_margin_loss)
 export(nn_multilabel_soft_margin_loss)
 export(nn_nll_loss)
+export(nn_pairwise_distance)
 export(nn_poisson_nll_loss)
 export(nn_prelu)
 export(nn_relu)
@@ -202,6 +203,8 @@ export(nn_softsign)
 export(nn_tanh)
 export(nn_tanhshrink)
 export(nn_threshold)
+export(nn_triplet_margin_loss)
+export(nn_triplet_margin_with_distance_loss)
 export(nn_utils_rnn_pack_padded_sequence)
 export(nn_utils_rnn_pack_sequence)
 export(nn_utils_rnn_pad_packed_sequence)
@@ -307,6 +310,7 @@ export(nnf_tanhshrink)
 export(nnf_threshold)
 export(nnf_threshold_)
 export(nnf_triplet_margin_loss)
+export(nnf_triplet_margin_with_distance_loss)
 export(nnf_unfold)
 export(optim_adam)
 export(optim_sgd)
diff --git a/R/nn-distance.R b/R/nn-distance.R
new file mode 100644
index 000000000..a53ee5975
--- /dev/null
+++ b/R/nn-distance.R
@@ -0,0 +1,39 @@
+#' Pairwise distance
+#' 
+#' Computes the batchwise pairwise distance between vectors \eqn{v_1}, \eqn{v_2} 
+#' using the p-norm:
+#' 
+#' \deqn{
+#'  \Vert x \Vert _p = \left( \sum_{i=1}^n  \vert x_i \vert ^ p \right) ^ {1/p}.
+#' }
+#' 
+#' @param p (real): the norm degree. Default: 2
+#' @param eps (float, optional): Small value to avoid division by zero.
+#'    Default: 1e-6
+#' @param keepdim (bool, optional): Determines whether or not to keep the vector dimension.
+#'    Default: FALSE
+#' 
+#' @section Shape:
+#' - Input1: \eqn{(N, D)} where `D = vector dimension`
+#' - Input2: \eqn{(N, D)}, same shape as the Input1
+#' - Output: \eqn{(N)}. If `keepdim` is `TRUE`, then \eqn{(N, 1)}.
+#' 
+#' @examples
+#' pdist <- nn_pairwise_distance(p=2)
+#' input1 <- torch_randn(100, 128)
+#' input2 <- torch_randn(100, 128)
+#' output <- pdist(input1, input2)
+#' 
+#' @export
+nn_pairwise_distance <- nn_module(
+  "nn_pairwise_distance",
+  initialize = function(p =  2, eps = 1e-6, keepdim = FALSE) {
+    self$norm <- p
+    self$eps <- eps
+    self$keepdim <- keepdim
+  },
+  forward = function(x1, x2) {
+    nnf_pairwise_distance(x1, x2, p = self$norm, eps = self$eps, 
+                          keepdim = self$keepdim)
+  }
+)
\ No newline at end of file
diff --git a/R/nn-loss.R b/R/nn-loss.R
index 0bdc5806e..1847985cf 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -1029,4 +1029,195 @@ nn_multi_margin_loss <- nn_module(
     nnf_multi_margin_loss(input, target, p = self$p, margin = self$margin,
                           weight = self$weight, reduction = self$reduction)
   }
+)
+
+#' Triplet margin loss
+#' 
+#' Creates a criterion that measures the triplet loss given an input
+#' tensors \eqn{x1}, \eqn{x2}, \eqn{x3} and a margin with a value greater than \eqn{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
+#' \eqn{(N, D)}.
+#' 
+#' The distance swap is described in detail in the paper 
+#' [Learning shallow convolutional feature descriptors with triplet losses](http://www.bmva.org/bmvc/2016/papers/paper119/index.html) by
+#' V. Balntas, E. Riba et al.
+#' 
+#' The loss function for each sample in the mini-batch is:
+#' 
+#' \deqn{
+#'   L(a, p, n) = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
+#' }
+#' 
+#' where
+#' 
+#' \deqn{
+#'   d(x_i, y_i) = | {\bf x}_i - {\bf y}_i |_p
+#' }
+#' 
+#' See also [nn_triplet_margin_with_distance_loss()], which computes the
+#' triplet margin loss for input tensors using a custom distance function.
+#' 
+#' @param margin (float, optional): Default: \eqn{1}.
+#' @param p (int, optional): The norm degree for pairwise distance. Default: \eqn{2}.
+#' @param swap (bool, optional): The distance swap is described in detail in the paper
+#'   [Learning shallow convolutional feature descriptors with triplet losses](http://www.bmva.org/bmvc/2016/papers/paper119/index.html) by
+#'   V. Balntas, E. Riba et al. Default: `FALSE`.
+#' @param eps constant to avoid NaN's
+#' @param reduction (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. Note: `size_average`
+#'   and `reduce` are in the process of being deprecated, and in the meantime,
+#'   specifying either of those two args will override `reduction`. Default: `'mean'`
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, D)} where \eqn{D} is the vector dimension.
+#' - Output: A Tensor of shape \eqn{(N)} if `reduction` is `'none'`, or a scalar
+#'   otherwise.
+#'   
+#' @examples
+#' triplet_loss <- nn_triplet_margin_loss(margin = 1, p = 2)
+#' anchor <- torch_randn(100, 128, requires_grad=TRUE)
+#' positive <- torch_randn(100, 128, requires_grad=TRUE)
+#' negative <- torch_randn(100, 128, requires_grad=TRUE)
+#' output <- triplet_loss(anchor, positive, negative)
+#' output$backward()
+#' 
+#' @export
+nn_triplet_margin_loss <- nn_module(
+  "nn_triplet_margin_loss",
+  inherit = nn_loss,
+  initialize = function(margin = 1, p = 2, eps = 1e-6, swap = FALSE, 
+                        reduction = "mean") {
+    super$initialize(reduction = reduction)
+    self$margin <- margin
+    self$p <- p
+    self$eps <- eps
+    self$swap <- swap
+  },
+  forward = function(anchor, positive, negative) {
+    nnf_triplet_margin_loss(anchor, positive, negative, margin = self$margin,
+                            p = self$p, eps = self$eps, swap = self$swap)
+  }
+)
+
+#' Triplet margin with distance loss
+#' 
+#' Creates a criterion that measures the triplet loss given input
+#' tensors \eqn{a}, \eqn{p}, and \eqn{n} (representing anchor,
+#' positive, and negative examples, respectively), and a nonnegative,
+#' real-valued function ("distance function") used to compute the relationship
+#' between the anchor and positive example ("positive distance") and the
+#' anchor and negative example ("negative distance").
+#' 
+#' The unreduced loss (i.e., with `reduction` set to `'none'`)
+#' can be described as:
+#' 
+#' \deqn{
+#'   \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad
+#' l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
+#' }
+#' 
+#' where \eqn{N} is the batch size; \eqn{d} is a nonnegative, real-valued function
+#' quantifying the closeness of two tensors, referred to as the `distance_function`;
+#' and \eqn{margin} is a non-negative margin representing the minimum difference
+#' between the positive and negative distances that is required for the loss to
+#' be 0.  The input tensors have \eqn{N} elements each and can be of any shape
+#' that the distance function can handle.
+#' If `reduction` is not `'none'`
+#' (default `'mean'`), then:
+#' 
+#' \deqn{
+#' \ell(x, y) =
+#' \begin{array}{ll}
+#' \mbox{mean}(L), &  \mbox{if reduction} = \mbox{`mean';}\\
+#'             \mbox{sum}(L),  &  \mbox{if reduction} = \mbox{`sum'.}
+#' \end{array}
+#' }
+#' 
+#' See also [nn_triplet_margin_loss()], which computes the triplet
+#' loss for input tensors using the \eqn{l_p} distance as the distance function.
+#' 
+#' @param distance_function (callable, optional): A nonnegative, real-valued function that
+#'   quantifies the closeness of two tensors. If not specified,
+#'   [nn_pairwise_distance()] will be used.  Default: `None`
+#' @param margin (float, optional): A non-negative margin representing the minimum difference
+#'   between the positive and negative distances required for the loss to be 0. Larger
+#'   margins penalize cases where the negative examples are not distant enough from the
+#'   anchors, relative to the positives. Default: \eqn{1}.
+#' @param swap (bool, optional): Whether to use the distance swap described in the paper
+#'   [Learning shallow convolutional feature descriptors with triplet losses](http://www.bmva.org/bmvc/2016/papers/paper119/index.html) by
+#'   V. Balntas, E. Riba et al. If TRUE, and if the positive example is closer to the
+#'   negative example than the anchor is, swaps the positive example and the anchor in
+#'   the loss computation. Default: `FALSE`.
+#' @param reduction (string, optional): Specifies the (optional) 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'`
+#' 
+#' @section Shape:
+#' - Input: \eqn{(N, *)} where \eqn{*} represents any number of additional dimensions
+#'   as supported by the distance function.
+#' - Output: A Tensor of shape \eqn{(N)} if `reduction` is `'none'`, or a scalar
+#'   otherwise.
+#' 
+#' @examples
+#' # Initialize embeddings
+#' embedding <- nn_embedding(1000, 128)
+#' anchor_ids <- torch_randint(0, 1000, 1, dtype = torch_long())
+#' positive_ids <- torch_randint(0, 1000, 1, dtype = torch_long())
+#' negative_ids <- torch_randint(0, 1000, 1, dtype = torch_long())
+#' anchor <- embedding(anchor_ids)
+#' positive <- embedding(positive_ids)
+#' negative <- embedding(negative_ids)
+#' 
+#' # Built-in Distance Function
+#' triplet_loss <- nn_triplet_margin_with_distance_loss(
+#'   distance_function=nn_pairwise_distance()
+#' )
+#' output <- triplet_loss(anchor, positive, negative)
+#' 
+#' # Custom Distance Function
+#' l_infinity <- function(x1, x2) {
+#'   torch_max(torch_abs(x1 - x2), dim = 1)[[1]]
+#' }
+#' 
+#' triplet_loss <- nn_triplet_margin_with_distance_loss(
+#'   distance_function=l_infinity, margin=1.5
+#' )
+#' output <- triplet_loss(anchor, positive, negative)
+#' 
+#' # Custom Distance Function (Lambda)
+#' triplet_loss <- nn_triplet_margin_with_distance_loss(
+#'   distance_function = function(x, y) {
+#'     1 - nnf_cosine_similarity(x, y)
+#'   }
+#' )
+#' 
+#' output <- triplet_loss(anchor, positive, negative)
+#' 
+#' @export
+nn_triplet_margin_with_distance_loss <- nn_module(
+  "nn_triplet_margin_with_distance_loss",
+  inherit = nn_loss,
+  initialize = function(distance_function = NULL, margin = 1, swap = FALSE, 
+                        reduction = "mean") {
+    super$initialize(reduction = reduction)
+    if (is.null(distance_function)) {
+      self$distance_function <- nn_pairwise_distance()
+    } else {
+      self$distance_function <- distance_function
+    }
+    self$margin <- margin
+    self$swap <- swap
+  },
+  forward = function(anchor, positive, negative) {
+    nnf_triplet_margin_with_distance_loss(
+      anchor, positive, negative,
+      distance_function=self$distance_function,
+      margin=self$margin, swap=self$swap, reduction=self$reduction
+    )
+  }
 )
\ No newline at end of file
diff --git a/R/nnf-loss.R b/R/nnf-loss.R
index b93d84fdb..726710c3d 100644
--- a/R/nnf-loss.R
+++ b/R/nnf-loss.R
@@ -261,6 +261,41 @@ nnf_triplet_margin_loss <- function(anchor, positive, negative, margin = 1, p =
                             reduction_enum(reduction))
 }
 
+#' Triplet margin with distance loss
+#' 
+#' See [nn_triplet_margin_with_distance_loss()]
+#' 
+#' @inheritParams nnf_triplet_margin_loss
+#' @inheritParams nn_triplet_margin_with_distance_loss
+#'
+#' @export
+nnf_triplet_margin_with_distance_loss <- function(anchor, positive, negative, 
+                                                  distance_function=NULL,
+                                                  margin=1.0, swap=FALSE, 
+                                                  reduction="mean") {
+  if (is.null(distance_function))
+    distance_function <- nnf_pairwise_distance
+  
+  positive_dist <- distance_function(anchor, positive)
+  negative_dist <- distance_function(anchor, negative)
+  
+  if (swap) {
+    swap_dist <- distance_function(positive, negative)
+    negative_dist <- torch_min(negative_dist, swap_dist)
+  }
+  
+  output <- torch_clamp(positive_dist - negative_dist + margin, min=0.0)
+  
+  reduction_enum <- reduction_enum(reduction)
+  
+  if (reduction_enum == 1)
+    return(output$mean())
+  else if (reduction_enum == 2)
+    return(output$sum())
+  else
+    return(output)
+}
+
 #' Ctc_loss
 #'
 #' The Connectionist Temporal Classification loss.
diff --git a/man/nn_pairwise_distance.Rd b/man/nn_pairwise_distance.Rd
new file mode 100644
index 000000000..fd5d150b3
--- /dev/null
+++ b/man/nn_pairwise_distance.Rd
@@ -0,0 +1,44 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-distance.R
+\name{nn_pairwise_distance}
+\alias{nn_pairwise_distance}
+\title{Pairwise distance}
+\usage{
+nn_pairwise_distance(p = 2, eps = 1e-06, keepdim = FALSE)
+}
+\arguments{
+\item{p}{(real): the norm degree. Default: 2}
+
+\item{eps}{(float, optional): Small value to avoid division by zero.
+Default: 1e-6}
+
+\item{keepdim}{(bool, optional): Determines whether or not to keep the vector dimension.
+Default: FALSE}
+}
+\description{
+Computes the batchwise pairwise distance between vectors \eqn{v_1}, \eqn{v_2}
+using the p-norm:
+}
+\details{
+\deqn{
+ \Vert x \Vert _p = \left( \sum_{i=1}^n  \vert x_i \vert ^ p \right) ^ {1/p}.
+}
+}
+\section{Shape}{
+
+\itemize{
+\item Input1: \eqn{(N, D)} where \verb{D = vector dimension}
+\item Input2: \eqn{(N, D)}, same shape as the Input1
+\item Output: \eqn{(N)}. If \code{keepdim} is \code{TRUE}, then \eqn{(N, 1)}.
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+pdist <- nn_pairwise_distance(p=2)
+input1 <- torch_randn(100, 128)
+input2 <- torch_randn(100, 128)
+output <- pdist(input1, input2)
+
+}
+}
diff --git a/man/nn_triplet_margin_loss.Rd b/man/nn_triplet_margin_loss.Rd
new file mode 100644
index 000000000..980328e44
--- /dev/null
+++ b/man/nn_triplet_margin_loss.Rd
@@ -0,0 +1,79 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_triplet_margin_loss}
+\alias{nn_triplet_margin_loss}
+\title{Triplet margin loss}
+\usage{
+nn_triplet_margin_loss(
+  margin = 1,
+  p = 2,
+  eps = 1e-06,
+  swap = FALSE,
+  reduction = "mean"
+)
+}
+\arguments{
+\item{margin}{(float, optional): Default: \eqn{1}.}
+
+\item{p}{(int, optional): The norm degree for pairwise distance. Default: \eqn{2}.}
+
+\item{eps}{constant to avoid NaN's}
+
+\item{swap}{(bool, optional): The distance swap is described in detail in the paper
+\href{http://www.bmva.org/bmvc/2016/papers/paper119/index.html}{Learning shallow convolutional feature descriptors with triplet losses} by
+V. Balntas, E. Riba et al. Default: \code{FALSE}.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that measures the triplet loss given an input
+tensors \eqn{x1}, \eqn{x2}, \eqn{x3} and a margin with a value greater than \eqn{0}.
+This is used for measuring a relative similarity between samples. A triplet
+is composed by \code{a}, \code{p} and \code{n} (i.e., \code{anchor}, \verb{positive examples} and \verb{negative examples} respectively). The shapes of all input tensors should be
+\eqn{(N, D)}.
+}
+\details{
+The distance swap is described in detail in the paper
+\href{http://www.bmva.org/bmvc/2016/papers/paper119/index.html}{Learning shallow convolutional feature descriptors with triplet losses} by
+V. Balntas, E. Riba et al.
+
+The loss function for each sample in the mini-batch is:
+
+\deqn{
+  L(a, p, n) = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
+}
+
+where
+
+\deqn{
+  d(x_i, y_i) = | {\bf x}_i - {\bf y}_i |_p
+}
+
+See also \code{\link[=nn_triplet_margin_with_distance_loss]{nn_triplet_margin_with_distance_loss()}}, which computes the
+triplet margin loss for input tensors using a custom distance function.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, D)} where \eqn{D} is the vector dimension.
+\item Output: A Tensor of shape \eqn{(N)} if \code{reduction} is \code{'none'}, or a scalar
+otherwise.
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+triplet_loss <- nn_triplet_margin_loss(margin = 1, p = 2)
+anchor <- torch_randn(100, 128, requires_grad=TRUE)
+positive <- torch_randn(100, 128, requires_grad=TRUE)
+negative <- torch_randn(100, 128, requires_grad=TRUE)
+output <- triplet_loss(anchor, positive, negative)
+output$backward()
+
+}
+}
diff --git a/man/nn_triplet_margin_with_distance_loss.Rd b/man/nn_triplet_margin_with_distance_loss.Rd
new file mode 100644
index 000000000..ffa9757c5
--- /dev/null
+++ b/man/nn_triplet_margin_with_distance_loss.Rd
@@ -0,0 +1,119 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_triplet_margin_with_distance_loss}
+\alias{nn_triplet_margin_with_distance_loss}
+\title{Triplet margin with distance loss}
+\usage{
+nn_triplet_margin_with_distance_loss(
+  distance_function = NULL,
+  margin = 1,
+  swap = FALSE,
+  reduction = "mean"
+)
+}
+\arguments{
+\item{distance_function}{(callable, optional): A nonnegative, real-valued function that
+quantifies the closeness of two tensors. If not specified,
+\code{\link[=nn_pairwise_distance]{nn_pairwise_distance()}} will be used.  Default: \code{None}}
+
+\item{margin}{(float, optional): A non-negative margin representing the minimum difference
+between the positive and negative distances required for the loss to be 0. Larger
+margins penalize cases where the negative examples are not distant enough from the
+anchors, relative to the positives. Default: \eqn{1}.}
+
+\item{swap}{(bool, optional): Whether to use the distance swap described in the paper
+\href{http://www.bmva.org/bmvc/2016/papers/paper119/index.html}{Learning shallow convolutional feature descriptors with triplet losses} by
+V. Balntas, E. Riba et al. If TRUE, and if the positive example is closer to the
+negative example than the anchor is, swaps the positive example and the anchor in
+the loss computation. Default: \code{FALSE}.}
+
+\item{reduction}{(string, optional): Specifies the (optional) reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that measures the triplet loss given input
+tensors \eqn{a}, \eqn{p}, and \eqn{n} (representing anchor,
+positive, and negative examples, respectively), and a nonnegative,
+real-valued function ("distance function") used to compute the relationship
+between the anchor and positive example ("positive distance") and the
+anchor and negative example ("negative distance").
+}
+\details{
+The unreduced loss (i.e., with \code{reduction} set to \code{'none'})
+can be described as:
+
+\deqn{
+  \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
+}
+
+where \eqn{N} is the batch size; \eqn{d} is a nonnegative, real-valued function
+quantifying the closeness of two tensors, referred to as the \code{distance_function};
+and \eqn{margin} is a non-negative margin representing the minimum difference
+between the positive and negative distances that is required for the loss to
+be 0.  The input tensors have \eqn{N} elements each and can be of any shape
+that the distance function can handle.
+If \code{reduction} is not \code{'none'}
+(default \code{'mean'}), then:
+
+\deqn{
+\ell(x, y) =
+\begin{array}{ll}
+\mbox{mean}(L), &  \mbox{if reduction} = \mbox{`mean';}\\
+            \mbox{sum}(L),  &  \mbox{if reduction} = \mbox{`sum'.}
+\end{array}
+}
+
+See also \code{\link[=nn_triplet_margin_loss]{nn_triplet_margin_loss()}}, which computes the triplet
+loss for input tensors using the \eqn{l_p} distance as the distance function.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} represents any number of additional dimensions
+as supported by the distance function.
+\item Output: A Tensor of shape \eqn{(N)} if \code{reduction} is \code{'none'}, or a scalar
+otherwise.
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+# Initialize embeddings
+embedding <- nn_embedding(1000, 128)
+anchor_ids <- torch_randint(0, 1000, 1, dtype = torch_long())
+positive_ids <- torch_randint(0, 1000, 1, dtype = torch_long())
+negative_ids <- torch_randint(0, 1000, 1, dtype = torch_long())
+anchor <- embedding(anchor_ids)
+positive <- embedding(positive_ids)
+negative <- embedding(negative_ids)
+
+# Built-in Distance Function
+triplet_loss <- nn_triplet_margin_with_distance_loss(
+  distance_function=nn_pairwise_distance()
+)
+output <- triplet_loss(anchor, positive, negative)
+
+# Custom Distance Function
+l_infinity <- function(x1, x2) {
+  torch_max(torch_abs(x1 - x2), dim = 1)[[1]]
+}
+
+triplet_loss <- nn_triplet_margin_with_distance_loss(
+  distance_function=l_infinity, margin=1.5
+)
+output <- triplet_loss(anchor, positive, negative)
+
+# Custom Distance Function (Lambda)
+triplet_loss <- nn_triplet_margin_with_distance_loss(
+  distance_function = function(x, y) {
+    1 - nnf_cosine_similarity(x, y)
+  }
+)
+
+output <- triplet_loss(anchor, positive, negative)
+
+}
+}
diff --git a/man/nnf_triplet_margin_with_distance_loss.Rd b/man/nnf_triplet_margin_with_distance_loss.Rd
new file mode 100644
index 000000000..66b41e5e0
--- /dev/null
+++ b/man/nnf_triplet_margin_with_distance_loss.Rd
@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nnf-loss.R
+\name{nnf_triplet_margin_with_distance_loss}
+\alias{nnf_triplet_margin_with_distance_loss}
+\title{Triplet margin with distance loss}
+\usage{
+nnf_triplet_margin_with_distance_loss(
+  anchor,
+  positive,
+  negative,
+  distance_function = NULL,
+  margin = 1,
+  swap = FALSE,
+  reduction = "mean"
+)
+}
+\arguments{
+\item{anchor}{the anchor input tensor}
+
+\item{positive}{the positive input tensor}
+
+\item{negative}{the negative input tensor}
+
+\item{distance_function}{(callable, optional): A nonnegative, real-valued function that
+quantifies the closeness of two tensors. If not specified,
+\code{\link[=nn_pairwise_distance]{nn_pairwise_distance()}} will be used.  Default: \code{None}}
+
+\item{margin}{Default: 1.}
+
+\item{swap}{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}.}
+
+\item{reduction}{(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'}
+}
+\description{
+See \code{\link[=nn_triplet_margin_with_distance_loss]{nn_triplet_margin_with_distance_loss()}}
+}
-- 
GitLab


From 6ffa3268cdb6004b6d2f4614ffac525fcb752f5c Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 23 Sep 2020 15:12:25 -0300
Subject: [PATCH 146/157] ctc_loss

---
 NAMESPACE          |   1 +
 R/nn-loss.R        | 126 ++++++++++++++++++++++++++++++++++++++++++++
 man/nn_ctc_loss.Rd | 127 +++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 254 insertions(+)
 create mode 100644 man/nn_ctc_loss.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 515744ffd..2486cc88e 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -129,6 +129,7 @@ export(nn_conv_transpose2d)
 export(nn_conv_transpose3d)
 export(nn_cosine_embedding_loss)
 export(nn_cross_entropy_loss)
+export(nn_ctc_loss)
 export(nn_dropout)
 export(nn_dropout2d)
 export(nn_dropout3d)
diff --git a/R/nn-loss.R b/R/nn-loss.R
index 1847985cf..f64d85450 100644
--- a/R/nn-loss.R
+++ b/R/nn-loss.R
@@ -1220,4 +1220,130 @@ nn_triplet_margin_with_distance_loss <- nn_module(
       margin=self$margin, swap=self$swap, reduction=self$reduction
     )
   }
+)
+
+#' The Connectionist Temporal Classification loss.
+#' 
+#' Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
+#' probability of possible alignments of input to target, producing a loss value which is differentiable
+#' with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which
+#' limits the length of the target sequence such that it must be \eqn{\leq} the input length.
+#' 
+#' 
+#' @param blank (int, optional): blank label. Default \eqn{0}.
+#' @param reduction (string, optional): Specifies the reduction to apply to the output:
+#'   `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied,
+#'   `'mean'`: the output losses will be divided by the target lengths and
+#'   then the mean over the batch is taken. Default: `'mean'`
+#' @param zero_infinity (bool, optional):
+#'   Whether to zero infinite losses and the associated gradients.
+#'   Default: `FALSE`
+#'   Infinite losses mainly occur when the inputs are too short
+#'   to be aligned to the targets.
+#' 
+#' @section Shape:
+#' - Log_probs: Tensor of size \eqn{(T, N, C)},
+#'   where \eqn{T = \mbox{input length}},
+#'   \eqn{N = \mbox{batch size}}, and
+#'   \eqn{C = \mbox{number of classes (including blank)}}.
+#'   The logarithmized probabilities of the outputs (e.g. obtained with
+#'   [nnf)log_softmax()]).
+#' - Targets: Tensor of size \eqn{(N, S)} or
+#'   \eqn{(\mbox{sum}(\mbox{target\_lengths}))},
+#'   where \eqn{N = \mbox{batch size}} and
+#'   \eqn{S = \mbox{max target length, if shape is } (N, S)}.
+#'   It represent the target sequences. Each element in the target
+#'   sequence is a class index. And the target index cannot be blank (default=0).
+#'   In the \eqn{(N, S)} form, targets are padded to the
+#'   length of the longest sequence, and stacked.
+#'   In the \eqn{(\mbox{sum}(\mbox{target\_lengths}))} form,
+#'   the targets are assumed to be un-padded and
+#'   concatenated within 1 dimension.
+#' - Input_lengths: Tuple or tensor of size \eqn{(N)},
+#'   where \eqn{N = \mbox{batch size}}. It represent the lengths of the
+#'   inputs (must each be \eqn{\leq T}). And the lengths are specified
+#'   for each sequence to achieve masking under the assumption that sequences
+#'   are padded to equal lengths.
+#' - Target_lengths: Tuple or tensor of size \eqn{(N)},
+#'   where \eqn{N = \mbox{batch size}}. It represent lengths of the targets.
+#'   Lengths are specified for each sequence to achieve masking under the
+#'   assumption that sequences are padded to equal lengths. If target shape is
+#'   \eqn{(N,S)}, target_lengths are effectively the stop index
+#'   \eqn{s_n} for each target sequence, such that `target_n = targets[n,0:s_n]` for
+#'   each target in a batch. Lengths must each be \eqn{\leq S}
+#'   If the targets are given as a 1d tensor that is the concatenation of individual
+#'   targets, the target_lengths must add up to the total length of the tensor.
+#' - Output: scalar. If `reduction` is `'none'`, then
+#'   \eqn{(N)}, where \eqn{N = \mbox{batch size}}.
+#' 
+#' @examples
+#' # Target are to be padded
+#' T <- 50      # Input sequence length
+#' C <- 20      # Number of classes (including blank)
+#' N <- 16      # Batch size
+#' S <- 30      # Target sequence length of longest target in batch (padding length)
+#' S_min <- 10  # Minimum target length, for demonstration purposes
+#' 
+#' # Initialize random batch of input vectors, for *size = (T,N,C)
+#' input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_()
+#' 
+#' # Initialize random batch of targets (0 = blank, 1:C = classes)
+#' target <- torch_randint(low=1, high=C, size=c(N, S), dtype=torch_long())
+#' 
+#' input_lengths <- torch_full(size=c(N), fill_value=TRUE, dtype=torch_long())
+#' target_lengths <- torch_randint(low=S_min, high=S, size=c(N), dtype=torch_long())
+#' ctc_loss <- nn_ctc_loss()
+#' loss <- ctc_loss(input, target, input_lengths, target_lengths)
+#' loss$backward()
+#' 
+#' 
+#' # Target are to be un-padded
+#' T <- 50      # Input sequence length
+#' C <- 20      # Number of classes (including blank)
+#' N <- 16      # Batch size
+#' 
+#' # Initialize random batch of input vectors, for *size = (T,N,C)
+#' input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_()
+#' input_lengths <- torch_full(size=c(N), fill_value=TRUE, dtype=torch_long())
+#' 
+#' # Initialize random batch of targets (0 = blank, 1:C = classes)
+#' target_lengths <- torch_randint(low=1, high=T, size=c(N), dtype=torch_long())
+#' target <- torch_randint(low=1, high=C, size=as.integer(sum(target_lengths)), dtype=torch_long())
+#' ctc_loss <- nn_ctc_loss()
+#' loss <- ctc_loss(input, target, input_lengths, target_lengths)
+#' loss$backward()
+#' 
+#' @references 
+#' A. Graves et al.: Connectionist Temporal Classification:
+#' Labelling Unsegmented Sequence Data with Recurrent Neural Networks:
+#' https://www.cs.toronto.edu/~graves/icml_2006.pdf
+#' 
+#' @note
+#' In order to use CuDNN, the following must be satisfied: `targets` must be
+#' in concatenated format, all `input_lengths` must be `T`.  \eqn{blank=0},
+#' `target_lengths` \eqn{\leq 256}, the integer arguments must be of
+#' The regular implementation uses the (more common in PyTorch) `torch_long` dtype.
+#' dtype `torch_int32`.
+#' 
+#' @note
+#' In some circumstances when using the CUDA backend with CuDNN, this operator
+#' may select a nondeterministic algorithm to increase performance. If this is
+#' undesirable, you can try to make the operation deterministic (potentially at
+#' a performance cost) by setting `torch.backends.cudnn.deterministic = TRUE`.
+#' 
+#' @export
+nn_ctc_loss <- nn_module(
+  "nn_ctc_loss",
+  inherit = nn_loss,
+  initialize = function(blank = 0, reduction = 'mean', zero_infinity = FALSE) {
+    super$initialize(reduction = reduction)
+    self$blank <- blank
+    self$zero_infinity <- zero_infinity
+  },
+  forward = function(log_probs, targets, input_lengths, target_lengths) {
+    nnf_ctc_loss(
+      log_probs, targets, input_lengths, target_lengths, blank = self$blank, 
+      reduction = self$reduction, zero_infinity = self$zero_infinity
+    )
+  }
 )
\ No newline at end of file
diff --git a/man/nn_ctc_loss.Rd b/man/nn_ctc_loss.Rd
new file mode 100644
index 000000000..26c0e40e9
--- /dev/null
+++ b/man/nn_ctc_loss.Rd
@@ -0,0 +1,127 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_ctc_loss}
+\alias{nn_ctc_loss}
+\title{The Connectionist Temporal Classification loss.}
+\usage{
+nn_ctc_loss(blank = 0, reduction = "mean", zero_infinity = FALSE)
+}
+\arguments{
+\item{blank}{(int, optional): blank label. Default \eqn{0}.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the output losses will be divided by the target lengths and
+then the mean over the batch is taken. Default: \code{'mean'}}
+
+\item{zero_infinity}{(bool, optional):
+Whether to zero infinite losses and the associated gradients.
+Default: \code{FALSE}
+Infinite losses mainly occur when the inputs are too short
+to be aligned to the targets.}
+}
+\description{
+Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
+probability of possible alignments of input to target, producing a loss value which is differentiable
+with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which
+limits the length of the target sequence such that it must be \eqn{\leq} the input length.
+}
+\note{
+In order to use CuDNN, the following must be satisfied: \code{targets} must be
+in concatenated format, all \code{input_lengths} must be \code{T}.  \eqn{blank=0},
+\code{target_lengths} \eqn{\leq 256}, the integer arguments must be of
+The regular implementation uses the (more common in PyTorch) \code{torch_long} dtype.
+dtype \code{torch_int32}.
+
+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}.
+}
+\section{Shape}{
+
+\itemize{
+\item Log_probs: Tensor of size \eqn{(T, N, C)},
+where \eqn{T = \mbox{input length}},
+\eqn{N = \mbox{batch size}}, and
+\eqn{C = \mbox{number of classes (including blank)}}.
+The logarithmized probabilities of the outputs (e.g. obtained with
+[nnf)log_softmax()]).
+\item Targets: Tensor of size \eqn{(N, S)} or
+\eqn{(\mbox{sum}(\mbox{target\_lengths}))},
+where \eqn{N = \mbox{batch size}} and
+\eqn{S = \mbox{max target length, if shape is } (N, S)}.
+It represent the target sequences. Each element in the target
+sequence is a class index. And the target index cannot be blank (default=0).
+In the \eqn{(N, S)} form, targets are padded to the
+length of the longest sequence, and stacked.
+In the \eqn{(\mbox{sum}(\mbox{target\_lengths}))} form,
+the targets are assumed to be un-padded and
+concatenated within 1 dimension.
+\item Input_lengths: Tuple or tensor of size \eqn{(N)},
+where \eqn{N = \mbox{batch size}}. It represent the lengths of the
+inputs (must each be \eqn{\leq T}). And the lengths are specified
+for each sequence to achieve masking under the assumption that sequences
+are padded to equal lengths.
+\item Target_lengths: Tuple or tensor of size \eqn{(N)},
+where \eqn{N = \mbox{batch size}}. It represent lengths of the targets.
+Lengths are specified for each sequence to achieve masking under the
+assumption that sequences are padded to equal lengths. If target shape is
+\eqn{(N,S)}, target_lengths are effectively the stop index
+\eqn{s_n} for each target sequence, such that \code{target_n = targets[n,0:s_n]} for
+each target in a batch. Lengths must each be \eqn{\leq S}
+If the targets are given as a 1d tensor that is the concatenation of individual
+targets, the target_lengths must add up to the total length of the tensor.
+\item Output: scalar. If \code{reduction} is \code{'none'}, then
+\eqn{(N)}, where \eqn{N = \mbox{batch size}}.
+}
+
+[nnf)log_softmax()]: R:nnf)log_softmax()
+[n,0:s_n]: R:n,0:s_n
+}
+
+\examples{
+if (torch_is_installed()) {
+# Target are to be padded
+T <- 50      # Input sequence length
+C <- 20      # Number of classes (including blank)
+N <- 16      # Batch size
+S <- 30      # Target sequence length of longest target in batch (padding length)
+S_min <- 10  # Minimum target length, for demonstration purposes
+
+# Initialize random batch of input vectors, for *size = (T,N,C)
+input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_()
+
+# Initialize random batch of targets (0 = blank, 1:C = classes)
+target <- torch_randint(low=1, high=C, size=c(N, S), dtype=torch_long())
+
+input_lengths <- torch_full(size=c(N), fill_value=TRUE, dtype=torch_long())
+target_lengths <- torch_randint(low=S_min, high=S, size=c(N), dtype=torch_long())
+ctc_loss <- nn_ctc_loss()
+loss <- ctc_loss(input, target, input_lengths, target_lengths)
+loss$backward()
+
+
+# Target are to be un-padded
+T <- 50      # Input sequence length
+C <- 20      # Number of classes (including blank)
+N <- 16      # Batch size
+
+# Initialize random batch of input vectors, for *size = (T,N,C)
+input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_()
+input_lengths <- torch_full(size=c(N), fill_value=TRUE, dtype=torch_long())
+
+# Initialize random batch of targets (0 = blank, 1:C = classes)
+target_lengths <- torch_randint(low=1, high=T, size=c(N), dtype=torch_long())
+target <- torch_randint(low=1, high=C, size=as.integer(sum(target_lengths)), dtype=torch_long())
+ctc_loss <- nn_ctc_loss()
+loss <- ctc_loss(input, target, input_lengths, target_lengths)
+loss$backward()
+
+}
+}
+\references{
+A. Graves et al.: Connectionist Temporal Classification:
+Labelling Unsegmented Sequence Data with Recurrent Neural Networks:
+https://www.cs.toronto.edu/~graves/icml_2006.pdf
+}
-- 
GitLab


From bff7b1c6ae2f82ec4c3ea596905b3b7773a0650f Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 23 Sep 2020 15:14:52 -0300
Subject: [PATCH 147/157] news bullets

---
 NEWS.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index e5f2294fe..04a980ce2 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,8 @@
 # torch (development version)
 
+- Added many missing losses (#252)
+- Implemented the `$<-` and `[[<-` operators for the `nn_module` class. (#253)
+
 # torch 0.0.2
 
 * Added a `NEWS.md` file to track changes to the package.
-- 
GitLab


From 401cbea12a0bb22d646f8962c9877c089be318db Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 23 Sep 2020 16:33:00 -0300
Subject: [PATCH 148/157] allow empty commits

---
 .github/workflows/website.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/website.yaml b/.github/workflows/website.yaml
index 4eaacd377..053eec763 100644
--- a/.github/workflows/website.yaml
+++ b/.github/workflows/website.yaml
@@ -31,5 +31,5 @@ jobs:
           rm -r .git/
           cd ../..
           git add -A
-          git commit -m "Update site"
+          git commit --allow-empty -m "Update site"
           git push --force origin HEAD:website
\ No newline at end of file
-- 
GitLab


From 65780d8c49bede59945bf8649908b34bc8e145ae Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 23 Sep 2020 18:30:34 -0300
Subject: [PATCH 149/157] add extra css to change colors in the navbar

---
 pkgdown/extra.css   |  44 ++++++++++++++++++++++++++++++++++++++++++++
 pkgdown/favicon.ico | Bin 0 -> 1150 bytes
 2 files changed, 44 insertions(+)
 create mode 100644 pkgdown/extra.css
 create mode 100644 pkgdown/favicon.ico

diff --git a/pkgdown/extra.css b/pkgdown/extra.css
new file mode 100644
index 000000000..d8c4f4705
--- /dev/null
+++ b/pkgdown/extra.css
@@ -0,0 +1,44 @@
+.navbar-default {
+  background-color: #7e1f77;
+  border-color: #7e1f77;
+}
+
+.navbar-default .navbar-toggle:hover, .navbar-default .navbar-toggle:focus {
+  background-color: #a953a2;
+}
+
+.navbar-default .navbar-collapse, .navbar-default .navbar-form {
+  border-color: #ffffff;
+}
+
+.navbar-default .navbar-nav .open .dropdown-menu>.dropdown-header {
+  border-color: #ffffff !important;
+}
+
+.navbar-default .navbar-nav>li>a:hover, .navbar-default .navbar-nav>li>a:focus {
+  color: #ffffff;
+  background-color: #a953a2;
+}
+
+.navbar-default .navbar-nav>.active>a, .navbar-default .navbar-nav>.active>a:hover, .navbar-default .navbar-nav>.active>a:focus {
+  background-color: #a953a2;
+}
+
+.navbar-default .navbar-nav>.open>a, .navbar-default .navbar-nav>.open>a:hover, .navbar-default .navbar-nav>.open>a:focus {
+  background-color: #a953a2;
+}
+
+.nav-pills>li.active>a, .nav-pills>li.active>a:hover, .nav-pills>li.active>a:focus {
+  background-color: #a953a2;
+}
+
+.dropdown-menu>.active>a, 
+.dropdown-menu>.active>a:hover, 
+.dropdown-menu>.active>a:focus {
+    background-color: #7e1f77;
+}
+
+.dropdown-menu>a:hover, 
+.dropdown-menu>a:focus {
+    background-color: #7e1f77;
+}
\ No newline at end of file
diff --git a/pkgdown/favicon.ico b/pkgdown/favicon.ico
new file mode 100644
index 0000000000000000000000000000000000000000..f32f988f0f6b3878e76b909d4f703761c00a1761
GIT binary patch
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-- 
GitLab


From a85716222d7f17b442c71ba0a641c74de34eeefe Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 23 Sep 2020 18:38:47 -0300
Subject: [PATCH 150/157] add google analytics to pkgdown website

---
 _pkgdown.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 133365d5b..588401d96 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -2,6 +2,7 @@ destination: docs
 template:
   params:
     bootswatch: united
+    ganalytics: UA-178883486-1
     
 development:
   mode: auto
-- 
GitLab


From cd9f8efa79fe538863af2132e73745ad4922ecf6 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Wed, 23 Sep 2020 18:59:50 -0300
Subject: [PATCH 151/157] reduce logo size

---
 man/figures/torch-full.png | Bin 0 -> 1697283 bytes
 man/figures/torch.png      | Bin 1697283 -> 175327 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 man/figures/torch-full.png

diff --git a/man/figures/torch-full.png b/man/figures/torch-full.png
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-- 
GitLab


From 17e62e7443b35f0cc8690a0fd39b041f2f1e1a9f Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 24 Sep 2020 00:18:34 -0300
Subject: [PATCH 152/157] export nn_parameter, nn_buffer and more utils

---
 NAMESPACE              |  5 +++++
 NEWS.md                |  1 +
 R/nn.R                 | 32 ++++++++++++++++++++++++++++++++
 _pkgdown.yml           |  8 ++++++++
 man/is_nn_buffer.Rd    | 14 ++++++++++++++
 man/is_nn_module.Rd    | 14 ++++++++++++++
 man/is_nn_parameter.Rd | 14 ++++++++++++++
 man/nn_buffer.Rd       | 16 ++++++++++++++++
 man/nn_parameter.Rd    | 17 +++++++++++++++++
 9 files changed, 121 insertions(+)
 create mode 100644 man/is_nn_buffer.Rd
 create mode 100644 man/is_nn_module.Rd
 create mode 100644 man/is_nn_parameter.Rd
 create mode 100644 man/nn_buffer.Rd
 create mode 100644 man/nn_parameter.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 052149936..d2965ec8c 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -100,6 +100,9 @@ export(dataset)
 export(enumerate)
 export(install_torch)
 export(is_dataloader)
+export(is_nn_buffer)
+export(is_nn_module)
+export(is_nn_parameter)
 export(is_torch_device)
 export(is_torch_dtype)
 export(is_torch_layout)
@@ -122,6 +125,7 @@ export(nn_batch_norm2d)
 export(nn_bce_loss)
 export(nn_bce_with_logits_loss)
 export(nn_bilinear)
+export(nn_buffer)
 export(nn_celu)
 export(nn_conv1d)
 export(nn_conv2d)
@@ -186,6 +190,7 @@ export(nn_multilabel_margin_loss)
 export(nn_multilabel_soft_margin_loss)
 export(nn_nll_loss)
 export(nn_pairwise_distance)
+export(nn_parameter)
 export(nn_poisson_nll_loss)
 export(nn_prelu)
 export(nn_relu)
diff --git a/NEWS.md b/NEWS.md
index 04a980ce2..5218a8f15 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -2,6 +2,7 @@
 
 - Added many missing losses (#252)
 - Implemented the `$<-` and `[[<-` operators for the `nn_module` class. (#253)
+- Export `nn_parameter`, `nn_buffer`, and `is_*` auxiliary functions.
 
 # torch 0.0.2
 
diff --git a/R/nn.R b/R/nn.R
index 6120e10c8..1f5cebdd5 100644
--- a/R/nn.R
+++ b/R/nn.R
@@ -223,6 +223,15 @@ nn_Module <- R6::R6Class(
   )
 )
 
+#' Creates an `nn_parameter`
+#' 
+#' Indicates to nn_module that `x` is a parameter
+#' 
+#' @param x the tensor that you want to indicate as parameter
+#' @param requires_grad whether this parameter should  have 
+#'   `requires_grad = TRUE`
+#' 
+#' @export
 nn_parameter <- function(x, requires_grad = TRUE) {
   if (!is_torch_tensor(x))
     stop("`x` must be a tensor.")
@@ -231,20 +240,43 @@ nn_parameter <- function(x, requires_grad = TRUE) {
   x
 }
 
+#' Checks if an object is a nn_parameter
+#' 
+#' @param x the object to check
+#'
+#' @export
 is_nn_parameter <- function(x) {
   inherits(x, "nn_parameter")
 }
 
+#' Creates a nn_buffer
+#' 
+#' Indicates that a tensor is a buffer in a nn_module
+#'
+#' @param x the tensor that will be converted to nn_buffer
+#' @param persistent whether the buffer should be persistent or not.
+#' 
+#' @export
 nn_buffer <- function(x, persistent = TRUE) {
   class(x) <- c(class(x), "nn_buffer")
   attr(x, "persistent") <- persistent
   x
 }
 
+#' Checks if the object is a nn_buffer
+#' 
+#' @param x object to check
+#'
+#' @export
 is_nn_buffer <- function(x) {
   inherits(x, "nn_buffer")
 }
 
+#' Checks if the object is an nn_module
+#'
+#' @param x object to check
+#'
+#' @export
 is_nn_module <- function(x) {
   inherits(x, "nn_module") && !inherits(x, "nn_module_generator")
 }
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 588401d96..2dd1210df 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -59,6 +59,11 @@ navbar:
        href: articles/extending-autograd.html
   examples:
     text: Examples
+    menu:
+      - text: basic-autograd
+        href: articles/examples/basic-autograd.html
+      - text: basic-nn-module
+        href: articles/examples/basic-nn-module.html
     
    
 reference:  
@@ -109,6 +114,9 @@ reference:
   - title: Neural network modules
     contents:
       - starts_with("nn_")
+      - is_nn_module
+      - is_nn_parameter
+      - is_nn_buffer
   - title: Neural networks functional module
     contents:
       - starts_with("nnf_")
diff --git a/man/is_nn_buffer.Rd b/man/is_nn_buffer.Rd
new file mode 100644
index 000000000..6abd6bdc3
--- /dev/null
+++ b/man/is_nn_buffer.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn.R
+\name{is_nn_buffer}
+\alias{is_nn_buffer}
+\title{Checks if the object is a nn_buffer}
+\usage{
+is_nn_buffer(x)
+}
+\arguments{
+\item{x}{object to check}
+}
+\description{
+Checks if the object is a nn_buffer
+}
diff --git a/man/is_nn_module.Rd b/man/is_nn_module.Rd
new file mode 100644
index 000000000..76deffc4f
--- /dev/null
+++ b/man/is_nn_module.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn.R
+\name{is_nn_module}
+\alias{is_nn_module}
+\title{Checks if the object is an nn_module}
+\usage{
+is_nn_module(x)
+}
+\arguments{
+\item{x}{object to check}
+}
+\description{
+Checks if the object is an nn_module
+}
diff --git a/man/is_nn_parameter.Rd b/man/is_nn_parameter.Rd
new file mode 100644
index 000000000..d60fc8d3b
--- /dev/null
+++ b/man/is_nn_parameter.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn.R
+\name{is_nn_parameter}
+\alias{is_nn_parameter}
+\title{Checks if an object is a nn_parameter}
+\usage{
+is_nn_parameter(x)
+}
+\arguments{
+\item{x}{the object to check}
+}
+\description{
+Checks if an object is a nn_parameter
+}
diff --git a/man/nn_buffer.Rd b/man/nn_buffer.Rd
new file mode 100644
index 000000000..6eb25d9f3
--- /dev/null
+++ b/man/nn_buffer.Rd
@@ -0,0 +1,16 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn.R
+\name{nn_buffer}
+\alias{nn_buffer}
+\title{Creates a nn_buffer}
+\usage{
+nn_buffer(x, persistent = TRUE)
+}
+\arguments{
+\item{x}{the tensor that will be converted to nn_buffer}
+
+\item{persistent}{whether the buffer should be persistent or not.}
+}
+\description{
+Indicates that a tensor is a buffer in a nn_module
+}
diff --git a/man/nn_parameter.Rd b/man/nn_parameter.Rd
new file mode 100644
index 000000000..2f4cf5430
--- /dev/null
+++ b/man/nn_parameter.Rd
@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn.R
+\name{nn_parameter}
+\alias{nn_parameter}
+\title{Creates an \code{nn_parameter}}
+\usage{
+nn_parameter(x, requires_grad = TRUE)
+}
+\arguments{
+\item{x}{the tensor that you want to indicate as parameter}
+
+\item{requires_grad}{whether this parameter should  have
+\code{requires_grad = TRUE}}
+}
+\description{
+Indicates to nn_module that \code{x} is a parameter
+}
-- 
GitLab


From 179eb288cc689943bffc715a1a1440a481b7549b Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 24 Sep 2020 00:18:44 -0300
Subject: [PATCH 153/157] add a few examples

---
 vignettes/examples/basic-autograd.R    | 16 +++++++++++
 vignettes/examples/basic-autograd.Rmd  |  9 ++++++
 vignettes/examples/basic-nn-module.R   | 39 ++++++++++++++++++++++++++
 vignettes/examples/basic-nn-module.Rmd |  9 ++++++
 vignettes/examples/index.Rmd           | 17 +++++++++++
 5 files changed, 90 insertions(+)
 create mode 100644 vignettes/examples/basic-autograd.R
 create mode 100644 vignettes/examples/basic-autograd.Rmd
 create mode 100644 vignettes/examples/basic-nn-module.R
 create mode 100644 vignettes/examples/basic-nn-module.Rmd
 create mode 100644 vignettes/examples/index.Rmd

diff --git a/vignettes/examples/basic-autograd.R b/vignettes/examples/basic-autograd.R
new file mode 100644
index 000000000..b666f2812
--- /dev/null
+++ b/vignettes/examples/basic-autograd.R
@@ -0,0 +1,16 @@
+library(torch)
+
+# creates example tensors. x requires_grad = TRUE tells that 
+# we are going to take derivatives over it.
+x <- torch_tensor(3, requires_grad = TRUE)
+y <- torch_tensor(2)
+
+# executes the forward operation x^2
+o <- x^2
+
+# computes the backward operation for each tensor that is marked with
+# requires_grad = TRUE
+o$backward()
+
+# get do/dx = 2 * x (at x = 3)
+x$grad
diff --git a/vignettes/examples/basic-autograd.Rmd b/vignettes/examples/basic-autograd.Rmd
new file mode 100644
index 000000000..f4a131058
--- /dev/null
+++ b/vignettes/examples/basic-autograd.Rmd
@@ -0,0 +1,9 @@
+---
+title: basic-autograd
+type: docs
+---
+
+```{r, echo = FALSE}
+knitr::opts_chunk$set(eval = TRUE)
+knitr::spin_child(paste0(rmarkdown::metadata$title, ".R"))
+```
\ No newline at end of file
diff --git a/vignettes/examples/basic-nn-module.R b/vignettes/examples/basic-nn-module.R
new file mode 100644
index 000000000..b57e2c202
--- /dev/null
+++ b/vignettes/examples/basic-nn-module.R
@@ -0,0 +1,39 @@
+library(torch)
+
+# creates example tensors. x requires_grad = TRUE tells that 
+# we are going to take derivatives over it.
+dense <- nn_module(
+  clasname = "dense",
+  # the initialize function tuns whenever we instantiate the model
+  initialize = function(in_features, out_features) {
+    
+    # just for you to see when this function is called
+    cat("Calling initialize!") 
+    
+    # we use nn_parameter to indicate that those tensors are special
+    # and should be treated as parameters by `nn_module`.
+    self$w <- nn_parameter(torch_randn(in_features, out_features))
+    self$b <- nn_parameter(torch_zeros(out_features))
+    
+  },
+  # this function is called whenever we call our model on input.
+  forward = function(x) {
+    cat("Calling forward!")
+    torch_mm(x, self$w) + self$b
+  }
+)
+
+model <- dense(3, 1)
+
+# you can get all parameters 
+model$parameters
+
+# or individually
+model$w
+model$b
+
+# create an input tensor
+x <- torch_randn(10, 3)
+y_pred <- model(x)
+y_pred
+
diff --git a/vignettes/examples/basic-nn-module.Rmd b/vignettes/examples/basic-nn-module.Rmd
new file mode 100644
index 000000000..3027ce432
--- /dev/null
+++ b/vignettes/examples/basic-nn-module.Rmd
@@ -0,0 +1,9 @@
+---
+title: basic-nn-module
+type: docs
+---
+
+```{r, echo = FALSE}
+knitr::opts_chunk$set(eval = TRUE)
+knitr::spin_child(paste0(rmarkdown::metadata$title, ".R"))
+```
\ No newline at end of file
diff --git a/vignettes/examples/index.Rmd b/vignettes/examples/index.Rmd
new file mode 100644
index 000000000..f9e7c93a6
--- /dev/null
+++ b/vignettes/examples/index.Rmd
@@ -0,0 +1,17 @@
+---
+title: Examples
+type: docs
+---
+
+Gallery of scripts demonstrating `torch` functionality.
+
+```{r, results='asis', echo=FALSE}
+files <- list.files(pattern = "*.R$")
+files <- gsub("\\.R$", "", files)
+
+o <- data.frame(
+  `Examples` = paste0("[", files, "]", "(../../articles/examples/", files, ".html)")
+)
+knitr::kable(o)
+```
+
-- 
GitLab


From 2f1828f50c16cbeeeb49179cff137402704859f5 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 24 Sep 2020 14:14:26 -0300
Subject: [PATCH 154/157] add serialization vignette

---
 NEWS.md                     |  1 +
 _pkgdown.yml                |  2 +
 vignettes/.gitignore        |  2 +
 vignettes/serialization.Rmd | 95 +++++++++++++++++++++++++++++++++++++
 4 files changed, 100 insertions(+)
 create mode 100644 vignettes/serialization.Rmd

diff --git a/NEWS.md b/NEWS.md
index 5218a8f15..84ff6413c 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -3,6 +3,7 @@
 - Added many missing losses (#252)
 - Implemented the `$<-` and `[[<-` operators for the `nn_module` class. (#253)
 - Export `nn_parameter`, `nn_buffer`, and `is_*` auxiliary functions.
+- Added a new serialization vignette.
 
 # torch 0.0.2
 
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 2dd1210df..636b6b94c 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -49,6 +49,8 @@ navbar:
        href: articles/indexing.html
      - text: Tensor class
        href: articles/tensor/index.html
+     - text: Serialization
+       href: articles/serialization.html
      - text: Datasets
      - text: Loading Data
        href: articles/loading-data.html
diff --git a/vignettes/.gitignore b/vignettes/.gitignore
index 2d19fc766..3037d85db 100644
--- a/vignettes/.gitignore
+++ b/vignettes/.gitignore
@@ -1 +1,3 @@
 *.html
+tensor.pt
+model.pt
diff --git a/vignettes/serialization.Rmd b/vignettes/serialization.Rmd
new file mode 100644
index 000000000..e11b09012
--- /dev/null
+++ b/vignettes/serialization.Rmd
@@ -0,0 +1,95 @@
+---
+title: "Serialization"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Serialization}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+```{r setup}
+library(torch)
+```
+
+Torch tensors in R are pointers to Tensors allocated by LibTorch.
+This has one major consequence for serialization. One cannot simply
+use `saveRDS` for serializing tensors, as you would save the pointer but not the data itself. When reloading a tensor saved with `saveRDS` the pointer might have been deleted in LibTorch and you would get wrong results.
+
+To solve this problem, `torch` implements specialized functions for serializing tensors to the disk:
+
+- `torch_save()`: to save tensors and models to the disk.
+- `torch_load()`: to load the models or tensors back to the session.
+
+Please note that this format is still experimental and you shouldn't use it for long term storage.
+
+## Saving tensors
+
+You can save any object of type `torch_tensor` to the disk using:
+
+```{r}
+x <- torch_randn(10, 10)
+torch_save(x, "tensor.pt")
+x_ <- torch_load("tensor.pt")
+
+torch_allclose(x, x_)
+```
+## Saving modules
+
+The `torch_save` and `torch_load` functions also work for `nn_modules` objects.
+
+When saving an `nn_module`, all the object is serialized including the model structure and it's state.
+
+```{r}
+module <- nn_module(
+  "my_module",
+  initialize = function() {
+    self$fc1 <- nn_linear(10, 10)
+    self$fc2 <- nn_linear(10, 1)
+  },
+  forward = function(x) {
+    x %>% 
+      self$fc1() %>% 
+      self$fc2()
+  }
+)
+
+model <- module()
+torch_save(model, "model.pt")
+model_ <- torch_load("model.pt")
+
+# input tensor
+x <- torch_randn(50, 10)
+torch_allclose(model(x), model_(x))
+```
+## Loading models saved in python
+
+Currently the only way to load models from python is to rewrite the model architecture in R. All the parameter names must be identical.
+
+You can then save the PyTorch model state_dict using:
+
+```{python eval = FALSE}
+torch.save(model, fpath, _use_new_zipfile_serialization=True)
+```
+
+You can then reload the state dict in R and reload it into the model with:
+
+```{r eval = FALSE}
+state_dict <- load_state_dict(fpath)
+model <- Model()
+model$load_state_dict(state_dict)
+```
+
+You can find working examples in `torchvision`. For example [this](https://github.com/mlverse/torchvision/blob/main/R/models-alexnet.R#L2-L63) is what we do for the AlexNet model.
+
+
+
+
+
-- 
GitLab


From 46d7835cb118a6d2bbef8dec233671027c6e49be Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 24 Sep 2020 15:36:12 -0300
Subject: [PATCH 155/157] remove python chubk

---
 vignettes/serialization.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/serialization.Rmd b/vignettes/serialization.Rmd
index e11b09012..cc272a62a 100644
--- a/vignettes/serialization.Rmd
+++ b/vignettes/serialization.Rmd
@@ -75,7 +75,7 @@ Currently the only way to load models from python is to rewrite the model archit
 
 You can then save the PyTorch model state_dict using:
 
-```{python eval = FALSE}
+```
 torch.save(model, fpath, _use_new_zipfile_serialization=True)
 ```
 
-- 
GitLab


From 321f98e0f992f2a43936bfdaf7b601dbbebdd321 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 24 Sep 2020 15:51:05 -0300
Subject: [PATCH 156/157] Fix #255

---
 R/dtype.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/dtype.R b/R/dtype.R
index 53a3c9552..8a533eadd 100644
--- a/R/dtype.R
+++ b/R/dtype.R
@@ -6,7 +6,7 @@ torch_dtype <- R6::R6Class(
       self$ptr <- ptr
     },
     print = function() {
-      cat("torch_", self$.type(), sep = "")
+      cat("torch_", self$.type(), "\n", sep = "")
     },
     .type = function() {
       cpp_dtype_to_string(self$ptr)
-- 
GitLab


From cce91ef698280770cac8e7f49f5b5e346032b762 Mon Sep 17 00:00:00 2001
From: Daniel Falbel <dfalbel@gmail.com>
Date: Thu, 24 Sep 2020 16:32:54 -0300
Subject: [PATCH 157/157] add a dataset example

---
 _pkgdown.yml                   |  3 +-
 vignettes/examples/dataset.R   | 73 ++++++++++++++++++++++++++++++++++
 vignettes/examples/dataset.Rmd |  9 +++++
 3 files changed, 84 insertions(+), 1 deletion(-)
 create mode 100644 vignettes/examples/dataset.R
 create mode 100644 vignettes/examples/dataset.Rmd

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 636b6b94c..538bb8fd9 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -66,7 +66,8 @@ navbar:
         href: articles/examples/basic-autograd.html
       - text: basic-nn-module
         href: articles/examples/basic-nn-module.html
-    
+      - text: dataset
+        href: articles/examples/dataset.html    
    
 reference:  
   - title: Tensor creation utilities
diff --git a/vignettes/examples/dataset.R b/vignettes/examples/dataset.R
new file mode 100644
index 000000000..c6350673a
--- /dev/null
+++ b/vignettes/examples/dataset.R
@@ -0,0 +1,73 @@
+library(torch)
+
+# In deep learning models you don't usually have all your data in RAM
+# because you are usually training using mini-batch gradient descent
+# thus only needing a mini-batch on RAM each time.
+
+# In torch we use the `datasets` abstraction to define the process of
+# loading data. Once you have defined your dataset you can use torch
+# dataloaders that allows you to iterate over this dataset in batches.
+
+# Note that datasets are optional in torch. They are jut there as a
+# recommended way to load data.
+
+# Below you will see an example of how to create a simple torch dataset
+# that pre-process a data.frame into tensors so you can feed them to
+# a model.
+
+df_dataset <- dataset(
+  "mydataset",
+  
+  # the input data to your dataset goes in the initialize function.
+  # our dataset will take a dataframe and the name of the response
+  # variable.
+  initialize = function(df, response_variable) {
+    self$df <- df[,-which(names(df) == response_variable)]
+    self$response_variable <- df[[response_variable]]
+  },
+  
+  # the .getitem method takes an index as input and returns the
+  # corresponding item from the dataset.
+  # the index could be anything. the dataframe could have many
+  # rows for each index and the .getitem method would do some
+  # kind of aggregation before returning the element.
+  # in our case the index will be a row of the data.frame,
+  .getitem = function(index) {
+    response <- torch_tensor(self$response_variable[index])
+    x <- torch_tensor(as.numeric(self$df[index,]))
+    
+    # note that the dataloaders will automatically stack tensors
+    # creating a new dimension
+    list(x = x, y = response)
+  },
+  
+  # It's optional, but helpful to define the .length method returning 
+  # the number of elements in the dataset. This is needed if you want 
+  # to shuffle your dataset.
+  .length = function() {
+    length(self$response_variable)
+  }
+  
+)
+
+
+# we can now initialize an instance of our dataset.
+# for example
+mtcars_dataset <- df_dataset(mtcars, "mpg")
+
+# now we can get an item with
+mtcars_dataset$.getitem(1)
+
+# Given a dataset you can create a dataloader with
+dl <- dataloader(mtcars_dataset, batch_size = 15, shuffle = TRUE)
+
+# we can then loop trough the elements of the dataloader with
+for(batch in enumerate(dl)) {
+  cat("X size:  ")
+  print(batch[[1]]$size())
+  cat("Y size:  ")
+  print(batch[[2]]$size())
+}
+
+
+
diff --git a/vignettes/examples/dataset.Rmd b/vignettes/examples/dataset.Rmd
new file mode 100644
index 000000000..3027ce432
--- /dev/null
+++ b/vignettes/examples/dataset.Rmd
@@ -0,0 +1,9 @@
+---
+title: basic-nn-module
+type: docs
+---
+
+```{r, echo = FALSE}
+knitr::opts_chunk$set(eval = TRUE)
+knitr::spin_child(paste0(rmarkdown::metadata$title, ".R"))
+```
\ No newline at end of file
-- 
GitLab